0,5
100
50
%
0
2,5 3,5
HEALTH EFFECTS OF
BLACK CARBON
Health effects of
black carbon
By:
Nicole AH Janssen, Miriam E Gerlofs-Nijland, Timo Lanki,
Raimo O Salonen, Flemming Cassee, Gerard Hoek,
Paul Fischer, Bert Brunekreef, Michal Krzyzanowski
The WHO European Centre for Environment and Health, Bonn, WHO Regional Office for Europe,
coordinated the development of this publication.
ABSTRACT
This report presents the results of a systematic review of evidence of the health effects of black carbon
(BC). Short-term epidemiological studies provide sufficient evidence of an association of daily variations in
BC concentrations with short-term changes in health (all-cause and cardiovascular mortality, and
cardiopulmonary hospital admissions). Cohort studies provide sufficient evidence of associations of allcause
and cardiopulmonary mortality with long-term average BC exposure. Studies of short-term health
effects suggest that BC is a better indicator of harmful particulate substances from combustion sources
(especially traffic) than undifferentiated particulate matter (PM) mass, but the evidence for the relative
strength of association from long-term studies is inconclusive. The review of the results of all available
toxicological studies suggested that BC may not be a major directly toxic component of fine PM, but it may
operate as a universal carrier of a wide variety of chemicals of varying toxicity to the lungs, the body’s
major defence cells and possibly the systemic blood circulation. A reduction in exposure to PM2.5 containing
BC and other combustion-related PM material for which BC is an indirect indicator should lead to a
reduction in the health effects associated with PM.
Keywords
AIR POLLUTION – adverse effects
SOOT – toxicity
INHALATION EXPOSURE – adverse effects
PARTICULATE MATTER – analysis
RISK ASSESSMENT
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CONTENTS
Page
Acknowledgements............................................................................................................ iv
Abbreviations..................................................................................................................... v
Executive summary ...........................................................................................................vii
Introduction....................................................................................................................... 1
References ................................................................................................................ 3
1. Metrics used to estimate the exposure to BC in health studies:
strengths and weaknesses........................................................................................ 4
Introduction .............................................................................................................. 4
Measurement methods of the dark component of PM.................................................... 5
Comparison of the optical measurement methods with each other
and with more sophisticated methods........................................................................ 6
Conclusions ............................................................................................................. 10
References .............................................................................................................. 11
2. Assessment of exposure to BC in epidemiological studies ............................................ 13
Short-term exposures............................................................................................... 13
Long-term exposures ............................................................................................... 16
Conclusions ............................................................................................................. 19
References .............................................................................................................. 20
3. Effects of BC exposure observed in epidemiological studies ......................................... 23
Results.................................................................................................................... 24
Discussion............................................................................................................... 30
References .............................................................................................................. 33
4. Evidence from toxicology, including human clinical studies .......................................... 37
Introduction ............................................................................................................ 37
Adverse health effects of BC in the controlled human exposure experiments................. 41
Mechanisms of toxicity ............................................................................................. 45
Conclusions ............................................................................................................. 46
References .............................................................................................................. 46
Annex 1. Literature search criteria ..................................................................................... 51
Annex 2. Contributors to the report.................................................................................... 55
Annex 3. Supplementary material to the review of epidemiological studies ............................ 57
Health effects of black carbon
page iv
Acknowledgements
This report was prepared by the Joint World Health Organization (WHO)/Convention Task
Force on Health Aspects of Air Pollution according to the Memorandum of Understanding
between the United Nations Economic Commission for Europe and the WHO Regional Office
for Europe. The Regional Office thanks the Swiss Federal Office for the Environment for its
financial support of the work of the Task Force. The Task Force on Health work is coordinated
by the WHO European Centre for Environment and Health, Bonn.
Convention on Long-range Transboundary Air Pollution
Health effects of black carbon
page v
Abbreviations
Abs absorbance
BC black carbon
BCP black carbon particles
BS black smoke
CVD cardiovascular disease
DE diesel engine exhaust
EC elemental carbon
IQR inter-quartile range
NIOSH National Institute for Occupational Safety and Health
OC organic carbon
PAH polycyclic aromatic hydrocarbons
PM particulate matter
POM particulate organic matter
RSS rice-straw smoke
RR relative risk
TOR thermal optical reflectance
TOT thermal optical transmittance
UFP ultrafine particles
Health effects of black carbon
page vii
Executive summary1
Following decision 2010/2 of the Executive Body for the Convention on Long-range
Transboundary Air Pollution (ECE/EB.AIR/106/Add.1, para 8(b)(i)), the Task Force on Health
Aspects of Air Pollution working under the Convention conducted an assessment of the health
effects of black carbon (BC) as a component of fine particulate matter (PM2.5). The Task Force’s
discussion focused on formulating the conclusions presented below, on the basis of the working
papers prepared for it and comments received from external reviewers.
BC is an operationally defined term which describes carbon as measured by light absorption. As
such, it is not the same as elemental carbon (EC), which is usually monitored with thermaloptical
methods. Current measurement methods for BC and EC need to be standardized so as to
facilitate comparison between the results of various studies. The main sources of BC are
combustion engines (especially diesel), residential burning of wood and coal, power stations
using heavy oil or coal, field burning of agricultural wastes, as well as forest and vegetation fires.
Consequently, BC is a universal indicator of a variable mixture of particulate material from a
large variety of combustion sources and, when measured in the atmosphere, it is always
associated with other substances from combustion sources, such as organic compounds. The
spatial variation of BC is greater than that of PM2.5. Although, in general, ambient measurements
or model estimates of BC reflect personal exposures reasonably well and with similar precision
as for PM2.5, the differences in exposure assessment errors may vary between studies and
possibly affect estimates of risk.
The systematic review of the available time-series studies, as well as information from panel
studies, provides sufficient evidence of an association of short-term (daily) variations in BC
concentrations with short-term changes in health (all-cause and cardiovascular mortality, and
cardiopulmonary hospital admissions). Cohort studies provide sufficient evidence of associations
of all-cause and cardiopulmonary mortality with long-term average BC exposure.
Health outcomes associated with exposure to PM2.5 or thoracic particles (PM10) are usually also
associated with BC (and vice versa) in the epidemiological studies reviewed. Effects estimates
(from both short- and long-term studies) are much higher for BC compared to PM10 and PM2.5
when the particulate measures are expressed per unit of mass concentration (µg/m3
). Effect
estimates are, however, generally similar per inter-quartile range in pollutant levels. Studies of
short-term health effects show that the associations with BC are more robust than those with
PM2.5 or PM10, suggesting that BC is a better indicator of harmful particulate substances from
combustion sources (especially traffic) than undifferentiated PM mass. In multi-pollutant models
used in these studies, the BC effect estimates are robust to adjustment for PM mass, whereas PM
mass effect estimates decreased considerably after adjustment for BC. The evidence from longterm
studies is inconclusive: in one of the two available cohort studies, using multi-pollutant
models in the analysis, the effect estimates for BC are stronger than those for sulfates, but an
opposite order in the strength of relationship is suggested in the other study.
1
Also published as part of Effects of air pollution on health. Report of the Joint Task Force on Health Aspects of Air
Pollution (2011). Geneva, United Nations Economic and Social Council (ECE/EB.AIR/WG.1/2011/11) (http://
www.unece.org/fileadmin/DAM/env/ documents/2011/eb/wge/ece.eb.air.wg.1.2011.11.pdf, accessed 12 December
2011).
Health effects of black carbon
page viii
There are not enough clinical or toxicological studies to allow an evaluation of the qualitative
differences between the health effects of exposure to BC or to PM mass (for example, different
health outcomes), of quantitative comparison of the strength of the associations or of
identification of any distinctive mechanism of BC effects. The review of the results of all
available toxicological studies suggested that BC (measured as EC) may not be a major directly
toxic component of fine PM, but it may operate as a universal carrier of a wide variety of,
especially, combustion-derived chemical constituents of varying toxicity to sensitive targets in
the human body such as the lungs, the body’s major defence cells and possibly the systemic
blood circulation.
The Task Force on Health agreed that a reduction in exposure to PM2.5 containing BC and other
combustion-related PM material for which BC is an indirect indicator should lead to a reduction
in the health effects associated with PM. The Task Force recommended that PM2.5 should
continue to be used as the primary metric in quantifying human exposure to PM and the health
effects of such exposure, and for predicting the benefits of exposure reduction measures. The use
of BC as an additional indicator may be useful in evaluating local action aimed at reducing the
population’s exposure to combustion PM (for example, from motorized traffic).
Health effects of black carbon
page 1
Introduction
The health effects of combustion-related air pollution indicated by black particles were identified
decades ago, when the monitoring of black smoke (or “British smoke” – BS) was a widespread
method for air quality assessment in Europe. The evidence about the health effects of this
pollution was used to recommend the first guidelines for exposure limits (then) consistent with
the protection of public health (WHO, 1979). In the 1990s, BS was one of the indicators of air
quality used, for example, in European time-series studies linking mortality with pollution
(Katsouyanni et al., 2001). A recognition of the difficulties in standardizing BS measurements
and an appreciation of the health effects of the non-black components of particulate matter (PM)
attracted the attention of researchers and regulators to the mass concentration of inhalable or
respirable fractions of suspended PM such as PM10 and PM2.5 (WHO Regional Office for
Europe, 2000). BS is not addressed by air quality regulations and the intensity of BS monitoring
has decreased.
New scientific evidence has led to a recognition of the significant role of black particles (black
carbon – BC) as one of the short-lived climate forcers. Measures focused on BC and methane are
expected to achieve a significant short-term reduction in global warming. If they were to be
implemented immediately, together with measures to reduce CO2 emissions, the chances of
keeping the earth’s temperature increase to less than 2 °C relative to pre-industrial levels would
be greatly improved (UNEP, 2011). The same measures would also directly benefit global health
and food security.
The synergy between action to address global warming and air quality has been noted by the
parties to the Convention on Long-range Transboundary Air Pollution. Taking into account the
conclusions of the report of the Ad Hoc Expert Group on Black Carbon (UNECE, 2010a), the
Executive Body of the Convention decided to include consideration of BC, as a component of
PM, in the revision process of the 1999 Gothenburg Protocol to Abate Acidification,
Eutrophication and Ground-level Ozone (Gothenburg Protocol) (UNECE, 2010b). The Executive
Body also requested the Joint Task Force on the Health Aspects of Air Pollution (the Task Force
on Health) to look at the adverse effects on human health of black carbon as a component of
PM2.5.
There is still no systematic comparison of health effects estimated using PM versus BC
indicators. A WHO working group has acknowledged that the evidence on the hazardous nature
of combustion-related PM (from both mobile and stationary sources) was more consistent than
that for PM from other sources (WHO Regional Office for Europe, 2007). Grahame &
Schlesinger (2010) reviewed the evidence of the effects of BC on cardiovascular health
endpoints and concluded that it may be desirable to promulgate a BC PM2.5 standard.
Conversely, Smith et al. (2009) noted that although the results of their time-series meta-analysis
suggest greater effects per unit mass of sulfate than BS, this distinction was less clear in the few
studies that directly compared the estimated effects of both indicators. This indicates the need for
a critical comparison of studies that have measured PM mass as well as BC particles.
In response to the request from the Executive Body of the Convention, and in view of the lack of
a systematic review of the accumulated evidence on the health effects of BC, the Task Force on
Health launched the review by addressing the following specific questions.
Health effects of black carbon
page 2
1. What metrics have been used to estimate the health effects of exposure to BC?
a. What are their respective strengths and weaknesses?
b. How is personal exposure related to ambient levels?
2. What are the effects of BC exposure observed in epidemiological studies (health outcomes,
exposure/response function)?
a. What are the effects of short-term exposure?
b. What are the effects of long-term exposure?
c. Are they different qualitatively (for example, different health outcomes) and/or
quantitatively from the effects of:
i. PM2.5 mass concentration
ii. other measured components of PM2.5?
3. What are the effects of BC in the human controlled exposure experiments? Are they
different qualitatively (for example, different health outcomes) and/or quantitatively from
the effects of:
a. PM2.5 mass concentration
b. other measured components of PM2.5?
4. What are the mechanisms of the effects of BC indicated by toxicological studies?
a. Are they different from the mechanisms of effects attributed to undifferentiated PM2.5
mass concentrations or other measured components of PM2.5?
b. Is there evidence supporting the thesis that (some of) the mechanisms are specific for
BC?
Leading the Task Force on Health, WHO invited selected experts to prepare concise background
papers summarizing evidence corresponding to each of the above questions. The experts signed
the WHO declaration of interest, assuring the absence of any conflicts of interests related to their
contributions to the assessment. The papers were based on a systematic review of the literature,
with relevant documentation of the protocol of the review and of the evidence reviewed (see
Annex 1).
The conclusions of the review were prepared by WHO and the authors of the background papers
based on the papers. The summary also rated the quality of the evidence supporting each
conclusion based on the approach used in the WHO Indoor air quality guidelines (WHO
Regional Office for Europe, 2010, p 6). Both the papers and the summary were subject to review
by another group of experts, and their comments were made available to all members of the Task
Force on Health in advance of the 14th Task Force Meeting, held in Bonn on 12–13 May 2011
(list of participants in Annex 2). The discussion at the Meeting focused on finalizing the
summary assessment, which has been published in the Task Force Report (UNECE, 2011). This
summary also forms the Executive Summary of this report.
The background papers presented in this report were revised after the Task Force Meeting, based
on the comments of the reviewers before and at the Meeting.
Health effects of black carbon
page 3
References
Grahame TJ, Schlesinger RB (2010). Cardiovascular health and particulate vehicular emissions: a critical
evaluation of the evidence. Air Quality, Atmosphere and Health, 1:3–27.
Katsouyanni K et al. (2001). Confounding and effect modification in the short-term effects of ambient
particles on total mortality: results from 29 European cities within APHEA2 project. Epidemiology, 12:
521–531.
Smith KR et al. (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: health
implications of short-lived greenhouse pollutants. Lancet, 74:2091–2103.
UNECE (2010a). Black carbon. Report by the co-chairs of the ad-hoc expert group on black carbon.
Geneva, United Nations Economic and Social Council (ECE/EB.AIR/2010/7Corr.1) (http://www.unece.
org/fileadmin/DAM/env/documents/2010/eb/eb/ece.eb.air.2010.7.corr.1.e.pdf, accessed 5 January 2012).
UNECE (2010b). Report of the Executive Body on its twenty-eighth session. Addendum. Decisions
adopted at the twenty-eighth session. Geneva, United Nations Economic and Social Council
(ECE/EB.AIR/106/Add.1) (http://www.unece.org/fileadmin/DAM/env/documents/2010/eb/eb/ece.eb.air.
106.add.1.e.pdf, accessed 5 January 2012).
UNECE (2011). Effects of air pollution on health. Report of the Joint Task Force on Health Aspects of Air
Pollution. Geneva, United Nations Economic and Social Council (ECE/EB.AIR/WG.1/2011/11)
(http://www.unece.org/fileadmin/DAM/env/documents/2011/eb/wge/ece.eb.air.wg.1.2011.11.pdf, accessed
12 December 2011).
UNEP (2011). Integrated assessment of black carbon and tropospheric ozone. Summary for decision
makers. Nairobi, United Nations Environment Programme (http://www.unep.org/dewa/Portals/67/pdf/
BlackCarbon_SDM.pdf, accessed 5 January 2012).
WHO (1979). Sulfur oxides and suspended particulate matter. Geneva, World Health Organization
(Environmental Health Criteria, No. 8).
WHO Regional Office for Europe (2000). Chapter 7.3. Particulate matter. In: Air quality guidelines for
Europe, 2nd ed. Copenhagen, WHO Regional Office for Europe (CD ROM version) (http://www.euro.
who.int/__data/assets/pdf_file/0019/123085/AQG2ndEd_7_3Particulate–matter.pdf, accessed 5 January
2012).
WHO Regional Office for Europe (2007). Health relevance of particulate matter from various sources.
Report on a WHO workshop. Copenhagen, WHO Regional Office for Europe (http://www.euro.who.
int/__data/assets/pdf_file/0007/78658/E90672.pdf, accessed 5 January 2012).
WHO Regional Office for Europe (2010). Indoor air quality guidelines: selected pollutants. Copenhagen,
WHO Regional Office for Europe (http://www.euro.who.int/__data/assets/pdf_file/0009/128169/e94535.
pdf, accessed 5 January 2012).
Health effects of black carbon
page 4
1. Metrics used to estimate the exposure to BC in health
studies: strengths and weaknesses
Raimo O Salonen
Introduction
There are several types of measurement method and commercial instrument available for
continuous, semi-continuous and integrated filter sample-based optical and thermal-optical
measurements of aerosol parameters reflecting combustion-derived char, soot, black carbon or
elemental carbon contents in PM. The concentrations of these carbonaceous material are low or
moderate (close to source) in atmospheric PM, and much higher in emissions from common
combustion sources (diesel engines, power plants or ship engines using heavy oil, or small
residential heaters using wood or other biomass).
The following are explanations of the bolded terms in common language according to Han et al.
(2007; 2010).
 Char is defined as carbonaceous material obtained by heating organic substances and
formed directly from pyrolysis, or as an impure form of graphitic carbon obtained as a
residue when carbonaceous material is partially burned or heated with limited access of air
(typical of burning vegetation and wood in small residential heaters).
 Soot is defined as only those carbon particles that form at high temperature via gas-phase
processes (typical of diesel engines).
 Black carbon (BC) refers to the dark, light-absorbing components of aerosols that contain
two forms of elemental carbon.
 Elemental carbon (EC) in atmospheric PM derived from a variety of combustion sources
contains the two forms “char-EC” (the original graphite-like structure of natural carbon
partly preserved, brownish colour) and “soot-EC” (the original structure of natural carbon
not preserved, black colour) with different chemical and physical properties and different
optical light-absorbing properties.
A thermal optical reflectance method can be applied to differentiate between char-EC and soot-EC
according to a stepwise thermal evolutional oxidation of different proportions of carbon under
different temperatures and atmosphere (more details under Measurement methods of the dark
component of PM, below). The health significance of the separate char-EC and soot-EC is not
known. In general, EC or BC are regarded as having negligible toxic effects on human and animal
lungs in controlled studies and on airway cells such as macrophages and respiratory epithelial cells.
Instead, it has been suggested that they exert an indirect key role in toxicity as a universal carrier of
toxic semi-volatile organics and other compounds co-released in combustion processes or attached
to their surface during regional and long-range transport (see Chapter 4).
The optimal combustion of fuel at high temperature, such as the current low-sulfur fossil diesel
fuel in modern diesel engines, results in the emission of large numbers of very small soot
particles (aerodynamic diameter 1–5 nm) that rapidly grow in size (10–100 nm) in the tailpipe by
coagulation to form aggregated chains, and further by condensation of the simultaneously
released semi-volatile organic substances on their surfaces in the atmosphere. The speed of
growth depends on air temperature, sunlight, concomitant oxidants, etc. (D’Anna, 2009).
Health effects of black carbon
page 5
The burning of solid fuels, such as wood and coal, is usually not optimal, especially in small
residential heaters, since there is, to a varying degree, incomplete smouldering combustion due
to the relative shortage of oxygen. Subsequently, the aerodynamic diameter of emitted PM in
flue gas becomes larger (150–600 nm) than in the case of diesel oil combustion in car engines,
because in addition to thermochemically-formed EC there are incompletely burnt tar-like
organics attached to it. As with diesel car PM, these emitted PM continue to grow in the
atmosphere by condensation of semi-volatile organics on their surface. The combustion of solid
fuels, such as wood and coal, tends to produce much larger amounts of semi-volatile organics
than combustion of low-sulfur diesel oil (Naeher et al., 2007; Kocbach Bolling et al., 2009).
While ageing in the atmosphere for several hours or days, the combustion-derived particles become
even larger (up to 1 µm in diameter) because inorganic salts originating from both NO2 and SO2,
together with atmospheric water, attach to the surfaces of hygroscopic carbonaceous particles.
Taking into account the wide variations in the formation and composition of combustion-derived
PM, and the fact that some of its chemical composition is known to exert not only light-absorbing
(soot/BC/EC) but also considerable light-scattering (organics, inorganics) properties, it is no
surprise that many indirect optical measurement techniques and thermal optical analysis methods,
which have been used for many years in air quality measurements by aerosol and by health
scientists, have proved to give only a rough proxy of the BC or EC concentration in ambient air
without instrument-specific corrective measures. Some methods have also had instrument-specific
technical problems during operation in large methodological inter-comparison studies conducted
by the leading aerosol scientists in Asia, Europe and the United States (Müller et al., 2011; Chow
et al., 2009; Reisinger et al., 2008; Kanaya et al., 2008; Hitzenberger et al., 2006).
Measurement methods of the dark component of PM
Combustion-derived soot and char (in practice, their dark components) have been determined in
epidemiological studies by the following techniques:
 light reflectance from (absorbance (Abs), BS) or light transmission through (basis of
measurement of BC) integrated PM samples usually collected at 24-hour intervals on thin
cellulose fibre filter or other filter material, followed by conversion of the optical
measurement units to mass-based units;
 real-time photometers measuring light absorption of PM sample spots (BC) at 1–5 minute
intervals and automatically giving readings in mass-based units;
 chemical determination of EC and organic carbon (OC) using thermal optical analysis
methods either semi-continuously with mass-based readings given every 30 minutes to
3 hours, or from integrated PM samples collected at 24-hour intervals on quartz filters
(Müller et al., 2011; Janssen et al., 2011; Chow et al., 2009).
The absorption coefficient of PM and BS measured with a reflectometer and BC measured with
an optical transmissometer are metrics that are based on the blackness of aerosol material
collected on a filter. Light is focused on the filter sample and the amount of light reflected or
transmitted is measured. For BS and Abs, the amount of reflected light is converted into PM
mass units (OECD standard) (OECD, 1964) or the black smoke index ISO standard 9835:1993
(ISO, 1993), whereas in the BC method the light transmitted is converted to represent the mass
of EC. BS measurement has been used in Europe since the 1920s, when urban air pollution was
dominated in many places by smoke from coal combustion. Although BS and Abs
determinations are expressed in µg/m3
, there is no clear relationship to PM mass, as conversion
Health effects of black carbon
page 6
of the optical measurement results into mass units depends on location, season and type of
combustion particle.
Absorption photometers for real-time application have been available since the 1980s. These are
filter-based instruments that measure at intervals of one to five minutes the changes in
transmittance through a fibrous filter tape as particles are deposited. The complex relationship
between changes in light transmission and aerosol absorption and scattering on the filter requires
an adequate calibration of these methods, including the selection of an effective wavelength for a
valid absorption co-efficient, determination of filter spot size and characterization of the aerosol
flow (Müller et al., 2011). Algorithms have been published for correcting artefactual
enhancement of light absorption by filter-loading, back-scattering, and multiple scattering caused
by PM and the filter matrix in connection with aethalometers and particle soot absorption
photometers. The multi-angle absorption photometer is the only real-time absorption photometer
that corrects for these artefacts by design (Müller et al., 2011; Chow et al., 2009) (Table 1).
Thermal optical methods are based on OC and EC removed from sampling substrates (such as
quartz-fibre filter) by volatilization and/or combustion at selected temperatures, and by
conversion of the released gases to carbon dioxide (CO2) or methane (CH4). This is followed
by infrared absorption (CO2) or flame ionization (CH4) detection. EC is not volatile and is only
released by oxidation. Most of the atmospheric OC tends to evolve at temperatures ≤550 C in
pure helium atmosphere and, thus, it can be separated from EC that needs to be oxidized in
helium 98%/oxygen 2% at temperatures 550 C. Heating in an inert helium atmosphere,
however, causes certain OC compounds to pyrolyse or char, thereby exaggerating the
atmospheric EC in the sample. In thermal optical carbon analysis, this can be corrected by
simultaneous measurement of thermal optical reflectance (TOR) or thermal optical
transmittance (TOT). Although the principles of thermal methods appear to be similar, they
contain variations with respect to: location of the temperature monitor (thermocouple) relative
to the sample, analysis atmospheres and temperature ramping rates; temperature plateaus;
residence time at each plateau; optical pyrolysis monitoring configuration; carrier gas flow
through or across the sample; and oven flushing conditions. Chow et al. (2005; 2009) and Han
et al. (2007; 2010) have done a lot of development and comparisons of thermal optical
methods. Currently, their Interagency Monitoring of Protected Visual Environments
(IMPROVE_A) thermal optical reflectance protocol (IMPROVE_A_TOR) seems the best
thermal optical method for separating various OC fractions from each other as well as for
separating char-EC from soot-EC (Table 1).
Comparison of the optical measurement methods with each other
and with more sophisticated methods
BS/PM10 ratios measured with the reflectometer have varied widely in Europe and many times
exceeded one in some locations (Hoek et al., 1997), as the Abs units are converted to BS values
in µg/m3
by using a constant conversion factor. This is a major source of bias, because the
greatly varying OC/EC ratio in PM affects Abs due to scattering of light from combustion-type
organic material. A typical OC/EC ratio in urban traffic environments is two, while the OC/EC
ratio can be five in rural background areas with more prevalent biomass combustion. Thus, BS
data from different types of site or from different seasons or from decade-long time-series at the
same site are not comparable. BS measurement should always be accompanied by local
calibration of the conversion factor from Abs units to BS values in µg/m3
on the basis of the
OC/EC ratio in PM (Schaap & Denier van der Gon, 2007).
Health effects of black carbon
page 7
Table 1. Summary of methodological aspects in relation to measurement of light Abs,
BS, BC or EC in atmospheric PM
PM metrics General information Methodological principle Strengths and limitations
PM Abs, BS,
BC. Cheap and
simple measurements
from
integrated filter
samples
Reflectometer. Collection
of usually 24-hour
PM samples on
Whatman paper filter at
sampling flow volume
of 2±0.2 m
3
/day,
absorption coefficient
measured from PM on
filter using simple
reflectometer consisting
of a light source
and a detector (ISO
9835:1993 (E)). Originally,
there was an
OECD standard (1964)
for BS measurement
from total suspended
particulate samples.
Optical transmissometer.
This portable instrument
can perform
rapid, non-destructive
BC determination from
PM material collected
on different types of
filter (diameter 25 mm,
37 mm or 47mm). The
instrument has a
movable tray with two
filter-holder slots, one
inside and the other
outside. The outside
holder is used to
measure light attenuation
through the
sample filter, while a
simultaneous measurement
is made through
the reference (blank)
filter placed in the
inside holder. The
analysis time for an
individual measurement
is less than
one minute.
Reflectometer. Analogue or digital
readout of either percentage
reflectance (linear scale,
recommended range 35–95%) or
absorption coefficient (logarithmic
scale, recommended range 0.64–
13.13 × 10
-5
) that can be transformed
into a BS index (ISO 9835,
1993). According to the OECD
standard (1964), there is a
conversion of the reflectance data
into gravimetric units (µg/m
3
). The
same has been done with absorption
by using a fixed conversion factor:
1 unit of Abs equals an increase of
10 µg/m
3
BS (Roorda-Knape et al.,
1998).
Optical transmissometer. The OT-21
is based on the optics used in some
aethalometer models. It measures
the transmission intensity of light at
880 nm and 370 nm passing through
a particle-loaded filter and
determines the attenuation of light
compared to the intensity of a blank
filter.
Reflectometer. Standardized,
traditional and cheap method;
long time-series in several
central European countries
according to the OECD (1964)
specifications.
Baseline reflectance of unused
filters may vary from batch to
batch. Scattering of light from
PM sample rich in organics or
due to some inorganics results in
biased reflectance values.
BS (R
2
=0.82–0.93) and absorption
(R
2
=0.85-0.98) methods
have had high correlations with
thermal optical EC, but the
slopes of the association show
wide variations (BS 10 µg/m
3
equals EC 0.5–1.8 µg/m
3
)
(Janssen et al., 2011).
A study in the Netherlands
showed that BS readings
depended on the OC/EC ratio in
ambient air (r
2
=0.85 for urban
sites and r
2
=0.75 for rural sites)
and the slopes of association
varied with the type of
measurement site and local
combustion sources (Schaap &
Denier van der Gon, 2007).
Optical transmissometer. The
results obtained from three
different types of site in the
United States (New York State)
and one site in Turkey showed
that the relationships between
BC values obtained from the OT-
21 and thermal optical BC
values from a semi-continuous
carbon analyser were linear. The
slopes for the data from the sites
varied from 0.75 to 1.02 (r
2
=
0.44 to 0.88), which was mainly
attributed to the different chemical
composition of aerosols as
well as their age in the
atmosphere. When the data
were combined and plotted as
monthly average BC, the two
methods showed excellent
agreement (slope 0.91, r
2
=0.84)
(Ahmed et al., 2009).
BC. Absorption
photometers for
real-time application
(averageing
time 1–5
minutes).
Real-time absorption
photometers. Filterbased
instruments
measure the change of
transmittance through a
fibrous filter tape as
Aethalometer (Hansen, Rosen &
Novakov, 1984). Offered in different
configurations. Multispectral (370–
950 nm) absorption coefficients
provide insight into chemical
composition in PM sample. PM
Unit-to-unit variability between
similar instruments. Up to 30%
for PSAPs and aethalometers,
while less than 5% for multiangle
absorption photometers.
Reasons for the high variability
Health effects of black carbon
page 8
PM metrics General information Methodological principle Strengths and limitations
particles are deposited.
The complex relationship
between change in
light transmission and
aerosol absorption and
scattering on the filter
requires a calibration of
these methods (effecttive
wavelength for
valid absorption coefficient,
determination
of filter spot size,
aerosol flow characterization)
(Muller et al.,
2011). Published algorithms
for correction of
artefactual enhancement
of light absorption
by filter-loading, backscattering
and multiple
scattering by PM and
the filter matrix in
connection with aethalometers
and particle
soot absorption photometers.
Multi-angle absorption
photometers
correct by design for
these artefacts (Muller
et al., 2011; Chow et
al., 2009; Kanaya et al.,
2008).
collection on quartz-fibre filter tape,
flow rate 6.7 litres/ minute and
averaging time 5 minutes.
Particle soot absorption photometers
(Bond, Anderson & Campbell, 1999).
Absorption coefficients measured at
variable wavelengths (467–660 nm).
Dependence of response on PM
size and cross-sensitivity to particle
scattering that can be controlled by
simultaneously measured nephelometer
data. PM collection on glassfibre
filter tape, typical flow rate 0.5–
1 litre/minute and averaging time 3
seconds.
Multi-angle absorption photometers
(Petzold & Schonlinner, 2004).
Measures radiation transmitted
through and scattered back from a
PM-loaded filter. A two-stream
radiative transfer model used to
minimize the cross-sensitivity to
particle scattering. Usual emission at
wavelength 670 nm. PM collection
on glass-fibre filter tape, flow rate
16.7 litres/minute. Minimum detection
limit as specified by the
manufacturer is BC<0.1 μg/m
3
with
an averaging time of 2 minutes
(Chow et al., 2009).
were identified as variations in
sample flow and spot size and
as cross-sensitivity to PM
scattering (Müller et al., 2011).
Correlations in absorption coefficients
between different
instruments. Particle soot absorption
photometers versus multiangle
absorption photometers
(R2=0.96–0.99), aethalometers
versus multi-angle absorption
photometers (R2=0.96) (Muller
et al., 2011). In a campaign in
the United States (Fresno supersite),
agreement in BC between
corrected aethalometers (660 nm)
and multi-angle absorption photometers
(670 nm) was within 1%.
BC concentrations determined
with the semi-continuous carbon
analyser were highly correlated
(R≥0.93) but were 47% and 49%
lower than BC measured with
aethalometers and multi-angle
absorption photometers, respectively
(Chow et al., 2009). Elevated
BC-to-EC ratios with multiangle
absorption photometers
possibly connected to biomassderived
abundant OC fraction
volatilizing at high temperatures
(Reisinger et al., 2008, Kanaya et
al., 2008) and to aged BC with
coating by transparent materials
causing a lensing effect in optical
measurements (Kanaya et al.,
2008).
Comparison of
absorption photometers
with
more advanced
measurement
techniques
Photoacoustic instrument.
This is regarded
as an unofficial reference
or benchmark
method for BC.
Photoacoustic instrument (Arnott et
al., 1999). PM are drawn into a
cavity and illuminated by a laser with
the desired wavelength modulated at
the resonant frequency of the cavity.
The heating and cooling of the
particle in response to the absorbed
light creates a sound wave that is
detected by a microphone. The
intensity of the acoustic wave is
related to PM light absorption by
calibration with NO2 absorption.
Typical flow rate 1 litre/minute and
averaging time 3–4 seconds.
Comparison with photoacoustic
instrument. In the Fresno
supersite campaign, uncorrected
PM light absorptions with
aethalometers were 4.7–7.2
times, and with PSAP 3.7–4.1
times, higher than those with a
photoacoustic instrument. After
applying published algorithms to
correct for the artefacts, the
adjusted values for aethalometers
were 24–69% higher,
and for PSAP 17–28% higher,
than those for the photoacoustic
instrument. The greater differences
were at higher wavelengths.
Multi-angle absorption
photometers gave 51% higher
PM light absorption than the
photoacoustic instrument. However,
all uncorrected and corrected
aethalometer, particle soot
absorption photometer and
multi-angle absorption photometer
data were highly corre-
Health effects of black carbon
page 9
PM metrics General information Methodological principle Strengths and limitations
lated (R≥0.95) with photoacoustic
instrument data (Chow et al.,
2009).
Comparison with thermal optical
methods. The average differences
between BC
concentration by adjusted 7-AE
(660 nm) and multi-angle absorption
photometers (670 nm)
versus EC concentration by
IMPROVE_A_TOR were 0 and
6%, respectively. The BC
analysed by semi-continuous
carbon analyser using the
National Institute for Occupational
Safety and Health
(NIOSH) 5040_TOT protocol
(660 nm) was 47% lower than
the EC analysed by
IMPROVE_A_TOR. In all
comparisons, correlations were
r≥0.87 (Chow et al., 2009).
Comparisons
between
EC/OC thermal
optical methods
IMPROVE_A_TOR/TOT
protocol. PM collected
on a quartz-fibre filter
at ambient temperature
and pressure is subject
to thermal carbon
analysis following the
IMPROVE_A protocol
using the DRI Model
2001 thermal/optical
carbon analyser. The
correction for pyrolysed
OC is done by monitoring
laser reflectance
(TOR) or laser
transmittance (TOT).
STN TOR/TOT protocol.
PM collection the same
as above. Thermal/optical
transmission/reflectance
analysis applied
to the US PM2.5.
Speciation Trends Network
(STN). Filter
transmittance is monitored
to split OC and
EC (STN_TOT). With
the DRI Model 2001
thermal/optical carbon
analyser, reflectance
can also be recorded
during the analyses
(STN_TOR).
Semi-continuous carbon
analyser_TOT. PM
collected on the quartz
fibre filter tape is
IMPROVE_A_TOR/TOT. The evolved
carbon is converted to CO2 and
reduced to CH4 that is detected using
a flame ionization detector. Pure
helium is used as the carrier gas in
stepwise rising temperatures from
30C to 550 C or 580 C to separate
various OC fractions from each other.
The separation of various EC
fractions from each other is done in
helium 98%/oxygen 2% at temperatures
from 550 C or 580 C to
800 C or 840 C: char-EC separated
from soot-EC at around 700 C
or 740 C (Chow et al., 2009; Han et
al., 2007; Chow et al., 2005). Reports
24-hour concentrations of EC and OC
(including their sub-fractions), total
carbon and PM light absorption.
STN TOR/TOT. Pure helium is used
as the carrier gas in stepwise rising
temperatures from 30 C to 900 C
to separate various OC fractions.
Helium 98%/oxygen 2% is applied to
EC fractions at temperatures from
600 C to 920 C. Reports 24-hour
concentrations of EC, OC and total
carbon.
Semi-continuous carbon analyser_
TOT. Evolved CO2 is analysed by
the non-dispersive infrared sensor.
In the NIOSH 5040 protocol. Pure
helium is used as the carrier gas in
stepwise rising temperatures from
30 C to 840 C for various OC fractions.
Helium 98%/oxygen 2% is
applied to EC fractions at tempeIMPROVE_A_TOR/TOT.
The
residence time (150–580
seconds) at each temperature
plateau in the IMPROVE_A
protocol is flexible to achieve
well-defined carbon fractions,
and depends on when the flame
ionization detector signal returns
to the baseline (Chow et al.,
2005; 2009).
STN TOR/TOT and NIOSH
5040_TOT. The STN protocol
has short and fixed residence
times (45–120 seconds), as
does the NIOSH 5040 protocol
(30–120 seconds) for each
temperature plateau. They
cannot, therefore, report distinguishable
carbon fractions.
Comparison between thermal
optical protocols. In the Fresno
supersite study, 24-hour EC
concentration by TOR was 23%
higher than EC by TOT following
the IMPROVE_A protocol, and
29% higher following the STN
protocol. These differences were
smaller when TOR was used to
determine the OC/EC split. EC
by STN_TOR was 10% lower
than by IMPROVE_A_TOR.
NIOSH 5040_TOT of the semicontinuous
carbon analyser
gave 45% lower integrated
24-hour EC concentration than
that by IMPROVE_A_TOR. In all
cases, the pairwise correlations
were r≥0.87 (Chow et al., 2009).
Health effects of black carbon
page 10
PM metrics General information Methodological principle Strengths and limitations
subjected to thermal
optical analysis following
the NIOSH
5040_TOT protocol.
Typical flow rate 8.5
litres/minute and averaging
time 1 hour.
Used as a field instrument
for air quality and
health studies.
ratures from 550 C to 850 C. Laser
transmittance (TOT) is used to
correct for pyrolysis. During the PM
collection phase, light transmission
through the filter is monitored to
quantify BC similarly to aethalometers.
All measurements at
660 nm. Reports 1-hour concentrations
of BC, EC, OC and total
carbon for ambient conditions.
The variability in the chemical composition of BC aerosol at different locations also biases the
BC data of optical transmissometers. It has been suggested that these should be calibrated with
the help of more sophisticated and reliable measurement techniques using statistically significant
numbers of samples for the specific sites (Ahmed et al., 2009). As with reflectometers, however,
controlling the measurement bias by local calibrations may not be easy, because the OC/EC ratio
in PM can also vary with the season and with day-to-day temperatures at the same site due to
variations in biomass combustion for residential heating.
Aerosol scientists have produced valuable information about the type and quantity of sources of
measurement error in relation to absorption photometers for real-time application (Müller et al.,
2011; Chow et al., 2009; Reisinger et al., 2008; Kanaya et al., 2008; Hitzenberger et al., 2006).
In fact, the use of filter-based instruments to derive information on aerosol light Abs and BC is a
matter of debate (Müller et al., 2011), as is the use of older optical measurements of BS and Abs
(see Janssen et al., 2011). Currently, there is no generally accepted standard method to measure
BC or EC. It has, however, been possible to make comparisons of several filter-based
instruments of aerosol light Abs with more sophisticated instruments such as the photoacoustic
analyser (Chow et al., 2009).
Several workshops have been conducted to investigate the performance of individual
instruments, for example, two workshops with large sets of aerosol absorption photometers in
2005 and 2007. The data from these instruments have been corrected using existing methods, but
still the most recent inter-comparison has shown relatively broad variations in responses to PM
light absorption in connection with some instruments (Müller et al., 2011). Significant biases
associated with filter-based measurements of PM light absorption, BC and EC are methodspecific.
Correction of these biases must take into account the variations in aerosol
concentration, composition and sources (Chow et al., 2009).
The key results from the comparisons of the real-time optical measurement methods with each
other and with more sophisticated methods of measuring BC and EC, and from the comparisons
of BS and Abs with EC (Janssen et al., 2011) are summarized in Table 1. The literature search
and the criteria for selection of the literature cited are described in Annex 1.
Conclusions
BC is an operationally defined term, which describes carbon as measured by light absorption. As
such, it is not the same as EC, which is usually monitored with thermal-optical methods. Despite
intensive efforts during the past 20 years, there are no generally accepted standard methods to
measure BC or EC in atmospheric aerosol. While most of the measurement methods of BC or
Health effects of black carbon
page 11
EC seem to be well-correlated, biases in filter-based light absorption and thermal optical carbon
measurements need to be identified and corrected for accurate determination of aerosol light
absorption, BC and EC in different environments. Variations in the OC/EC ratio bias filter-based
PM light absorption in addition to other artefacts. The multi-angle absorption photometer is
currently the only type of real-time absorption photometer that corrects for these biases and
artefacts of BC measurement by design. However, aethalometer data can be corrected using
published algorithms. The IMPROVE_A protocol in thermal optical carbon analyser, equipped
with laser reflectance (TOR) to correct for pyrolysed OC, currently seems to be the most reliable
method to measure OC and EC concentrations from atmospheric PM in integrated filter samples.
The flexible residence time (150–580 seconds) at each temperature plateau also enables the
measurement of well-defined OC and EC sub-fractions, which may be useful in PM source
analysis. At their best in a field campaign, the 24-hour concentrations of BC by multi-angle
absorption photometer and from corrected aethalometer data have been nearly equal to the
24-hour EC concentration measured by IMPROVE_A_TOR. Current methods of measuring BC
and EC need standardization to facilitate comparison between various study results.
References
Ahmed T et al. (2009). Measurement of black carbon (BC) by an optical method and a thermal-optical
method: intercomparison for four sites. Atmospheric Environment, 43(40):6305–6311.
Arnott WP et al. (1999). Photoacoustic spectrometer for measuring light absorption by aerosol:
instrument description. Atmospheric Environment, 33:2845–2852.
Bond TC, Anderson TL, Campbell D (1999). Calibration and intercomparison of filter-based
measurements of visible light absorption by aerosols. Aerosol Science and Technology, 30:582–600.
Chow JC et al. (2005). Refining temperature measures in thermal/optical carbon analysis. Atmospheric
Chemistry and Physics, 5(4):2961–2972.
Chow JC et al. (2009). Aerosol light absorption, black carbon, and elemental carbon at the Fresno
Supersite, California. Atmospheric Research, 93:874–887.
D’Anna A (2009). Combustion-formed nanoparticles. Proceedings of the Combustion Institute, 32:593–
613.
Han YM et al. (2007). Evaluation of the thermal/optical reflectance method for discrimination between
char- and soot-EC. Chemosphere, 69:569–574.
Han YM et al. (2010). Different characteristics of char and soot in the atmosphere and their ratio as an
indicator for source identification in Xi’an, China. Atmospheric Chemistry and Physics, 10:595–607.
Hansen ADA, Rosen H, Novakov T (1984). The aethalometer – an instrument for the real-time
measurement of optical absorption by aerosol particles. Science of the Total Environment, 36:191–196.
Hitzenberger RA et al. (2006). Intercomparison of thermal and optical measurement methods for
elemental carbon and black carbon at an urban location. Environmental Science & Technology, 40:6377–
6383.
Hoek G et al. (1997). Wintertime PM10 and black smoke concentrations across Europe: results from the
PEACE study. Atmospheric Environment, 31:3609–3622.
ISO (1993). ISO standard 9835:1993 (E). Ambient air – determination of a black smoke index. Geneva,
International Organization for Standardization.
Janssen NAH et al. (2011). Black carbon as an additional indicator of the adverse health of airborne
particles compared to PM10 and PM2.5. Environmental Health Perspectives, 119:1691–1699.
Kanaya Y et al. (2008). Mass concentrations of black carbon measured by four instruments in the middle
of Central East China in June 2006. Atmospheric Chemistry and Physics, 8:7637–7649.
Health effects of black carbon
page 12
Kocbach Bolling A et al. (2009). Health effects of residential wood smoke particles: the importance of
combustion conditions and physicochemical particle properties. Particle and Fibre Toxicology, 6:29.
Müller TM et al. (2011). Characterization and intercomparison of aerosol absorption photometers: result
of two intercomparison workshops. Atmospheric Measurements Techniques, 4: 245–268.
Naeher LP et al. (2007). Woodsmoke health effects: a review. Inhalation Toxicology, 19:67–106.
OECD (1964). Methods of measuring air pollution. Report of the working party on methods of measuring
air pollution and survey techniques. Paris, Organisation for Economic Co-operation and Development.
Petzold A, Schonlinner M (2004). Multi-angle absorption photometry – a new method for the measurement
of aerosol light absorption and atmospheric black carbon. Journal of Aerosol Science, 35:421–441.
Reisinger P et al. (2008). Intercomparison of measurement techniques for black or elemental carbon
under urban background conditions in wintertime: influence of biomass combustion. Environmental
Science & Technology, 42:884–889.
Roorda-Knape MC et al. (1998). Air pollution from traffic in city districts near major roadways.
Atmospheric Environment, 32:1921–1930.
Schaap M, Denier van der Gon HAC (2007). On the variability of black smoke and carbonaceous
aerosols in the Netherlands. Atmospheric Environment, 41:5908–5920.
Health effects of black carbon
page 13
2. Assessment of exposure to BC in epidemiological
studies
Timo Lanki
The light Abs of PM2.5 filter samples has been used in most European epidemiological studies as
a measure of exposure to black carbon particles (BCP), whereas in studies in the United States,
the EC content of the samples has mostly been used for the purpose. In some studies, Abs has
been further converted into BS, which was widely used in the past in Europe for air quality
monitoring. However, the conversion factor found in ISO standard 9835:1993 (ISO, 1993) is not
suitable for present-day particulate air pollution mixture, but local calibration factors should be
used. In earlier studies, the coefficient of haze may have been used as a measure of BCP.
Because of similar measurement principles, the method gives results that are highly correlated
with BC concentrations obtained with more modern methods such as aethalometers. Real-time
BC measurement methods will undoubtedly increase in popularity with time, especially in
settings where filter sampling is not needed for other purposes.
Short-term exposures
Time-series study design has been the most frequently used method to evaluate the acute effects of
BC exposure on population health. The design is based on comparing short-term (typically daily)
variations in exposure with short-term variations in population health, for example, mortality or
hospitalization. In the setting, population exposure is assessed by measuring BCP at one or more
centrally located outdoor monitoring stations. The accuracy of estimates of the effects on health
eventually depends on how well daily BCP levels measured at the central outdoor monitoring site
(ambient BCP) reflect daily changes in personal exposure to BCP (personal BCP) in the study area.
It should be noted that ambient concentration is a valid proxy for personal exposure even when an
individual’s exposure on a given day may not be predicted very accurately because of random
error (Zeger et al., 2000). In contrast, an inability by outdoor monitoring to reflect daily mean
exposure in the study population leads to biased health effect estimates. Panel studies with repeated
clinical and air pollution measurements similarly rely on accurate assessment of day-to-day
variability in exposure.
Only a few studies have evaluated longitudinal relationships between daily ambient and personal
BCP concentrations (Table 2). Considering the large proportion of the 24-hour cycle typically
spent in the home, the observation of a high correlation between repeated daily measurements of
personal BCP and BCP indoors (indoor BCP) (median Pearson’s r >0.9 for individual regression
results) is not surprising (Janssen et al., 2000). One study linking ambient BCP with indoor BCP
has, therefore, been included in Table 2.
In European studies, the Abs of PM2.5 filters has been used as a measure of BCP (Table 2).
Ambient Abs was found to be more strongly associated with respective personal and indoor
levels than PM2.5 in these studies. It is noteworthy that indoor EC was reasonably correlated with
indoor Abs (R=0.57–0.85) in the Dutch-Finnish study (Janssen et al., 2000), but the slope was
different for homes with and without environmental tobacco smoke. In the studies included in
this report, Abs has been measured from PM2.5 filters, and thus size-fraction is not mentioned
any more. PM2.5 Abs has been reported as capturing most of the particulate Abs in ambient air
(Cyrys et al., 2003).
Health effects of black carbon
page 14
Table 2. Relationships of ambient BCP and PM2.5 with respective indoor and
personal concentrations in longitudinal studies with repeated 24-hour measurements
Study
population/
locations
Abs
or EC
(n)
Study area Relationships between: Reference
ambient,
personal
BC
ambient,
personal
PM2.5
ambient,
indoor
BC
ambient,
indoor
PM2.5
Individual regression results, median Pearson’s r (slope)
82 cardio-
vascular
patients
Abs
(463)
Amsterdam,
Netherlands
0.93 (0.92) 0.79 (0.43) 0.96 (0.84) 0.84 (0.47) Janssen et
al., 2000
Helsinki, Finland 0.81 (0.62) 0.73 (0.45) 0.74 (0.49) 0.70 (0.51)
152 homes Abs
(one
week/
home)
Helsinki, Finland 0.79 0.70 Hoek et
al., 2008bAthens, Greece 0.64 0.40
Amsterdam,
Netherlands
0.92 0.80
Birmingham,
United Kingdom
0.90 0.55
Mixed models, r
2
(slope)
15 senior
adults
EC
(335)
Steubenville, United
States (summer)
0.08 (0.33) 0.60 (0.73) Sarnat et
al., 2006b
Steubenville, United
States (autumn)
0.44 (0.70) 0.47 (0.63)
25 homes EC
(167)
Boston, United
States (winter)
0.30 (0.60) 0.17 (0.37) 0.30
a
(0.91) 0.17 (0.29) Brown et
al., 2008
Boston, United
States (summer)
0.41 (0.08) 0.55 (0.75) 0.05 (0.29) 0.55 (0.89)
Cross-sectional correlation, Pearson’s r (slope)
38 cardio-
vascular
patients
Abs
(162)
Barcelona, Spain 0.69
b
(0.67) 0.14
b
(0.04) Jacquemin
et al., 2007
a
Excluding one home with candle burning.
b
Excluding days with ETS exposure.
Longitudinal studies conducted in the United States have evaluated BCP exposure as EC, and
have found associations of ambient EC with personal EC to be similar or stronger than those of
PM2.5 during the winter (Table 2). It was speculated that the weak link between ambient and
personal EC during the summer may be due to more measurement error and less variability in
concentrations. Similarly in another study in the United States conducted mainly during the
warm season (Delfino et al., 2006), no (cross-sectional) correlation was found between personal
EC and ambient EC. Personal and indoor sampling of EC may be especially affected by errors in
measurement because of lower concentrations.
A point estimate of an individual’s exposure is dependent on long-term (for example, annual
concentration at the residential area) and short-term (daily variations in concentration)
components of exposure. Thus, correlations observed in cross-sectional exposure studies
between daily ambient concentrations and personal exposures are of lesser value when
interpreting time-series studies (that rely on within-person variability in exposure). In any case,
cross-sectional studies also suggest that with the use of ambient measurements, exposure can be
estimated at least as accurately for BCP as for PM2.5 (Johannesson et al., 2007).
Health effects of black carbon
page 15
In Table 3, repeated “front-door” outdoor measurements of BCP (known as outdoor BCP: an
outdoor measurement site as close as possible to the indoor measurements, on a balcony, in a
garden or courtyard, etc.) have been linked with daily variations in indoor BCP. In most of the
studies, outdoor concentrations of BCP were highly correlated with indoor concentrations. The
slope of the corresponding regression equation can be interpreted as an infiltration factor, thus
the results suggest that infiltration for BCP is somewhat more efficient than for PM2.5. An
exception is the German study conducted in a hospital building (Cyrys et al., 2004), where
infiltration was less efficient for BCP than for PM2.5. The authors hypothesized that BCP in the
study area fell into the smallest size categories, for which penetration is less efficient but the
deposition rate higher than for larger particles (included in PM2.5). Indeed, the size of ambient
BCP is not constant, but near emission sources (such as major roads) they are at their smallest
and include ultrafine (aerodynamic diameter <100 nm) particles.
Table 3. Relation of daily outdoor BCP and PM2.5 with respective indoor concentrations
in studies with repeated measurements
Study
locations
Study area Abs/
EC
R BC R PM2.5 Infiltration
BC
Infiltration
PM2.5
Reference
152 homes Helsinki, Finland Abs 0.96 0.74 0.63 0.48 Hoek et
al., 2008bAthens, Greece 0.88 0.63 0.84 0.42
Amsterdam,
Netherlands
0.96 0.85 0.78 0.39
Birmingham,
United Kingdom
0.93 0.35 0.71 0.34
25 homes Boston, United States EC 0.53
a,b
0.55
a,b
0.47
b
0.53
b
Brown et
al., 2008
2 retirement
communities
in the Los
Angeles basin
c
Site A, summer EC 0.73 0.71 Polidori et
al., 2007Site B, autumn 0.71 0.60
Site A, winter 0.77 0.59
Site B, winter 0.64 0.45
Hospital
building
Erfurt, Germany Abs 0.91 0.88 0.53 0.79 Cyrys et
al., 2004
18 homes,
16 (pre)schools
Stockholm, Sweden Abs 0.46 0.25 Wichman
et al., 2010
28 homes Huddersfield,
United Kingdom
Abs 0.83 0.59 Kingham
et al., 2000
a
The paper reported R2
for a mixed model.
b
Excluding one home with candle burning.
c
Outdoor measurement sites at a distance of 300 m from the buildings.
Outdoor BCP has also been found to be more strongly associated with respective indoor levels
than PM2.5 in cross-sectional studies (Gotschi et al., 2002). It should be noted that there are
substantial differences in infiltration rates between geographical areas due to differences in
building codes and human behaviour, and thus generalizability of the results from single (-city)
studies is limited.
In the absence of indoor sources, indoor/outdoor concentration ratios can be interpreted to reflect
infiltration directly. The effect of indoor sources can be eliminated by taking measurements at
night or in an uninhabited building. Such studies have also reported higher infiltration for BCP
than for PM2.5: 0.84 versus 0.48 in Los Angeles homes (Sarnat et al., 2006a), and 0.61 versus
0.41 for a home in Clovis, California (Lunden et al., 2008).
Health effects of black carbon
page 16
Concentrations measured at a central outdoor site have been found to reflect well temporal
variability in 24-hour concentrations of both PM2.5 and BC across urban areas (Puustinen et al.,
2007). Considering that BCP and PM2.5 do not seem to differ in that respect, the higher infiltration
rate for BCP may be the main reason for the observed higher ambient‒personal correlation for
BCP. Overall, measurement errors for BCP and PM2.5 seem comparable, which means that effect
estimates obtained in epidemiological studies for the two can be directly compared.
Even hourly peak exposures may be relevant to health as potential triggers of cardiorespiratory
events. Although ambient 24-hour levels of BCP seem to reflect personal exposures well, it can
be assumed that the correlation is lower on shorter time-scales due to short-term changes in
ventilation (for example, opening windows at home or in a car) and the microenvironment (such
as an office or in a public transport vehicle). Short-term BCP exposures are noticeably elevated
during commuting (Adams et al., 2002), and the differences between background concentrations
and concentrations measured in traffic by cyclists and passengers in vehicles seem to be even
greater for BCP than for PM2.5 (Zuurbier et al., 2010).
BCP also has significant indoor sources, such as cooking and environmental tobacco smoke, which
may lead to peaks in exposure (Lanki et al., 2007; Raaschou-Nielsen et al., 2010). A reasonable
assumption is that these indoor sources do not confound the association between ambient BCP and
health outcomes because the strength of the source is not related to ambient levels.
Distinguishing between the effects of highly correlated air pollutants is always challenging
because of potential problems caused by multi-collinearity in statistical models. The extent of
correlation between ambient BCP and PM2.5 does not rule out a calculation of reliable effect
estimates in two-pollutant models (Table 4). It should, however, be noted that there is no single
still acceptable value for a correlation coefficient (often limit of R<0.7 is used), but the
robustness of the models should always be tested. Because of comparable infiltration factors,
inter-correlations outdoors can be assumed also to reflect correlations between personal BCP and
personal PM2.5. Because BCP acts as an indicator for combustion particles and is measured from
PM2.5, two-pollutant models separate in practice between the health effects of combustion and
non-combustion PM2.5.
Daily variations in BCP in urban areas are most strongly associated with local traffic emissions
(Vallius et al., 2005), although factors such as long-range transported air pollution, local industry,
open biomass burning, and residential wood and coal combustion may also affect the
concentrations (Larson et al., 2004). The considerable correlation between EC and OC suggests
that the health effects associated in epidemiological studies with BCP may be at least partly due to
organic compounds, which are typically not measured. Even if inter-correlations at some study
areas allow two-pollutant models of BCP and OC, their interpretation is challenging because of
common emission sources. The EC/OC ratio is location-specific and varies in time (Jeong et al.,
2003; Schaap & Denier van der Gon, 2007), which further complicates reasoning on causal factors.
Long-term exposures
In the calculation of effect estimates in long-term epidemiological studies, contrasts in long-term
exposure between persons are used. Consequently, the aim of exposure assessment is to accurately
predict spatial variability in outdoor concentrations and further in personal exposures. For BCP,
within-city variability in concentrations is larger than for PM2.5 owing to the considerable effect of
local combustion sources, especially traffic, on concentrations (Hoek et al., 2002; Janssen et al.,
2008). Within-city variability may exceed between-city variability, which underlines the
Health effects of black carbon
page 17
Table 4. Correlations between daily outdoor PM2.5, BCP and OC
in longitudinal epidemiological studies
Study areas and years BC/ EC Correlation coefficients
between outdoor
Reference
BCP–
PM2.5
BCP–
NO2
BCP–
OC
Time-series/case-crossover studies
Phoenix, United States (1995–1997) EC 0.84 0.82 0.91 Mar et al., 2000
6 counties in California, United States
(2000–2003)
EC 0.53 0.61 Ostro et al., 2007
119 counties in the United States
(2000–2006)
EC 0.46 0.64 Peng et al., 2009
Boston, United States (1995–1999) BC 0.66 0.40 Zanobetti & Schwartz, 2006
Vancouver, Canada (2000) EC 0.17 0.24 0.92 Rich et al., 2004
Panel studies
Steubenville, United States (2000) EC 0.51 0.65 Sarnat et al., 2006c
St. Louis, United States (2001) EC 0.53 0.62 Rich et al., 2006
Fresno, United States (2000–2005) EC 0.76 0.68 Mann et al., 2010
Southern California, United States (2003) EC 0.55 0.70 0.87 Delfino et al., 2006
Atlanta, United States (1999–2000) EC 0.59 0.58 Suh & Zanobetti, 2010
Helsinki, Finland (1998‒1999)
Amsterdam, Netherlands (1998–1999)
BC
BC
0.70
0.73
Jacquemin et al., 2009
importance of taking into account small-scale variations in BCP in epidemiological studies.
Vehicular traffic leads to marked BCP concentration gradients along busy roads (Roorda-Knape
et al., 1998), and residential wood combustion, harbours, and point sources such as power
stations may lead to lasting elevations in local BCP concentrations (Lu et al., 2006; Polidori et
al., 2010; Snyder et al., 2010).
Some epidemiological studies on the long-term effects of BCP have relied on a crude estimation
of exposure: BCP concentrations measured at a single outdoor monitoring site have been
assumed to reflect exposure within a city or even over a whole county. In others, the monitoring
network has been dense enough to allow interpolation of exposures over an urban area. Neither
method is able sufficiently to take into account small-scale variations in BCP concentrations,
which may lead to an underestimation of the effects of BCP. In contrast, land-use regression
models have proved their efficiency in a number of recent studies (Table 5). Physico(chemical)
dispersion models are another possibility, but they need substantial computational power and
detailed information on emissions, which may not be available.
Land-use regression models are stochastic models that typically use predictor variables obtained
through geographic information systems. These rather simple regression models can explain
similar proportions of variability in long-term outdoor concentrations as can dispersion models
(Hoek et al., 2008a). The explained proportion of BC variability has reached 80% in some
studies, but a considerable variability in R2
is also evident in Table 5. The difference between
models in R2
for the same location (Munich) shows that geography is not the sole reason for the
differences in the performance of the models, but that the selection of variables is most
important, as suggested by Hoek et al. (2008a). In Vancouver, R2
for BCP was apparently later
improved to 0.52 (from 0.41 in Table 5), but model validation results have not been presented
(Brauer et al., 2008). In any case, based on the still limited number of studies available, it is
possible to construct for BCP land-use regression models that perform even better than the
Health effects of black carbon
page 18
Table 5. Comparison of performance of land-use regression models for long-term PM2.5 and BCP
Study area Predictor variables for BC Model R
2
RMSE
a
Reference
BCP PM2.5 BCP
[*10
-5
m
-1
]
PM2.5
[µg/m
3
]
Netherlands High traffic roads 250 m, address
density 300 m, distance to major
road, region
0.81 0.73 0.31 1.59 Brauer et al.,
2003
Munich,
Germany
Traffic intensity 50 m and 50–250 m,
population density 300 m and
300–5000 m
0.67 0.56 0.31 1.35
Stockholm
county,
Sweden
Traffic intensity on nearest road,
population density 1000–5000 m
0.66 0.50 0.22 1.10
North Rhine-
Westphalia,
Germany
Heavy vehicle traffic 100–10 000 m,
total traffic 100 m, distance to
highway
0.82 0.17 0.16 2.3 Hochadel et
al., 2006
Munich,
Germany
Household density 2500–5000 m,
distance federal road, land cover
factor 100–250 m, length to country
roads 1000 m
0.42 0.36 0.46 1.48 Morgenstern
et al., 2007
Vancouver,
Canada
Length to expressway 1000 m, length
to major roads 100 m, distance to
highway, open area 500 m
0.41 0.52 0.4 1.5 Henderson et
al., 2007
a
Root mean squared error of model validation.
models for PM2.5. Naturally, the models predict best the BCP concentrations for the periods
closest in time to the collection of air pollution and geographic information systems data. The
success of interpolation of concentrations back in time depends on how much the emission
landscape has changed during the previous years.
As can be seen in Table 5, various indicators of traffic always end up as predictors in the
regression models for BCP. Indeed, land-use regression models typically work best for the
traffic-originating particles, whereas the effect of, for example, point sources and residential
wood combustion on air quality may not be fully taken into account owing to the lack of reliable
indicators of emissions.
According to the available evidence, no studies have evaluated the ability of land-use regressionmodelled
outdoor BCP levels to predict annual BCP exposures, which is an obvious research
gap. However, a number of studies have shown that traffic intensity near the home is an
important determinant of long-term BCP exposure (for example, Raaschou-Nielsen et al., 2010;
van Roosbroeck et al., 2006), and probably a more important determinant of BCP than of PM2.5
(Fischer et al., 2000; Lanki et al., 2007). Thus, it is plausible that reasonably modelled outdoor
BCP levels also predict long-term personal exposures in traffic-impacted communities.
Not too many studies were available to judge spatial correlations between long-term PM2.5 and
BCP (Table 6), but it seems that at least an occasionally high correlation will make the
separation of the long-term health effects of PM2.5 and BCP very difficult. It is not clear how
much of the high correlation is due to the modelling itself (for example, a large number of
predictors that are identical for BCP and PM2.5), and consequently whether improved modelling,
including finer resolution, would lower the correlation. Further, the correlation of BCP with NO2
Health effects of black carbon
page 19
may be even higher, which implies that the long-term health effects of ultrafine particles, which
are highly correlated with NO2, are also hard to separate from the BCP effects. Estimation of the
effects of long-term exposure to BCP on health is further complicated by the fact that traffic
noise, another correlate, has been suggested as playing a role in the exacerbation of
cardiovascular diseases (CVD).
Table 6. Correlations between long-term outdoor concentrations of BCP and PM2.5
Study area BC/
EC
R
BCP-PM2.5
R
BCP-NO2
R
BCP-OC
Reference
Modelled with land-use regression models
North Rhine-Westphalia, Germany BC 0.52 0.93 Hochadel et al., 2006
Munich, Germany BC 0.49 0.59 Morgenstern et al., 2007
Netherlands BC 0.99 0.96 Brauer et al., 2002
Netherlands (only pregnancy time) BC 0.75 0.84 Gehring et al., 2011
South-west British Columbia, Canada BC 0.56 MacIntyre et al., 2011
Measured (representing large areas)
California, United States EC 0.84 0.51 Ostro et al., 2007
Southern California, United States EC 0.91 0.82 Gauderman et al., 2004
Conclusions
Light Abs of PM2.5 filter samples has been used in most European epidemiological studies as a
measure of exposure to BCP, whereas studies in the United States have mainly used the EC
content of the samples for this purpose. In some studies, Abs has been further converted into BS,
which was widely used in the past in Europe for air quality monitoring. The conversion factor
found in ISO standard 9835:1993 (ISO, 1993) is not, however, suitable for the present-day
particulate air pollution mixture.
Vehicular traffic, especially diesel-powered, is a major source of BCP in urban areas. However,
in some areas residential burning of wood or coal, and at least periodically open biomassburning,
may be even more important sources of BCP. More locally, harbours and industrial
facilities may have a pronounced effect on BCP concentrations. Altogether, when interpreting
effect estimates for BCP in epidemiological studies, information on the main sources of BCP in
the area should be used.
Based on the studies reviewed, daily BCP concentrations measured at a central outdoor site well
reflect daily personal exposures to BCP. The correlation between ambient levels and exposure
seems to be slightly higher for BCP than for PM2.5, possibly because of the higher infiltration
rate of BCP. Measurement errors being comparable for PM2.5 and BCP, exposure assessment is
not likely to create significant differences between effect estimates for BCP and PM2.5 in
epidemiological studies on the acute health effects of air pollution.
Within-city variability is clearly greater for long-term outdoor BCP than for PM2.5, which is a
challenge for exposure assessment in epidemiological studies on the long-term effects of air
pollution. A high proportion of spatial variation in ambient BCP can, however, be explained with
the use of land-use regression models with carefully selected predictors. In the few available
studies including both BCP and PM2.5, the performance of the model has typically been at least
as good for BCP as for PM2.5.
Health effects of black carbon
page 20
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Health effects of black carbon
page 23
3. Effects of BC exposure observed in epidemiological
studies2
Nicole AH Janssen, Gerard Hoek, Paul Fischer, Bert Brunekreef, Flemming Cassee3
As in the Janssen et al. (2011) paper, this chapter systematically reviews the effects of the
combustion particle metrics BS, BC, EC and Abs compared to PM mass for time-series studies of
daily mortality and hospital admissions and cohort studies of mortality and morbidity. For acute
health effects, only time-series studies on daily mortality and hospital admissions or emergency
department visits were considered, as these are generally more similar in design and are therefore
more likely to allow meta-analyses than studies of, for example, symptoms or biomarkers. For this
chapter, panel (diary) studies of respiratory symptoms in symptomatic children were added, as
these were also expected to provide sufficient comparable studies based on a recent systematic
review and meta-analysis of the short-term effects of PM10 and NO2 on respiratory health among
children with asthma or asthma-like symptoms by Weinmayr et al. (2010).
Only studies that provided information on PM mass as well as combustion particle metrics were
included. As described in the previous chapters of this report, the terms BS, EC, soot, BC, Abs
(absorption coefficient) and light-absorbing carbon are used in different studies referring to
different methods to measure or express concentrations of BCP. In this chapter, the abbreviation
BCP is used as a generic term for any of the different metrics (BS, EC, BC or Abs) in general,
but the study-specific terms are used when individual studies are described.
The different optical measurements for BCP (BS, BC and Abs) are highly correlated (Quincey
2007; Roorda-Knape et al., 1998). However, the quantitative relation between thermally
determined EC and optical measures of BC varies between countries, cities and types of location
(for example, regional, urban, traffic), highlighting the need for site-specific calibrations (Cyrys
et al., 2003; Schaap & Denier van der Gon, 2007). Differences between EC measurement
methods add to this variation. To facilitate comparisons among studies that used different
measures of BCP, the BS to EC conversion factor was used, based on the average increase in EC
associated with a 10 µg/m3
increase in BS reported by 11 studies with information on both
measures (Janssen et al., 2011). On the basis of this analysis, it is assumed by default that 10 µg/m3
BS is equivalent to 1.1 µg/m3
EC. In addition, sensitivity analyses were conducted using
conversion factors over the range of the estimates from the individual studies (0.5–1.8 µg/m3
EC
per 10 µg/m3
BS; see Annex 3A for details).
For the time-series studies, a meta-analysis was performed. Pooled fixed and random effects
relative risk (RR) estimates were calculated for all health endpoints for which estimates from at
least three different studies were available for the same age group and for different cities. Random
effect estimates are reported as significant heterogeneity was observed (P<0.05) among individual
estimates for some endpoints. In cases of no heterogeneity, fixed and random effect estimates are
similar, so random effect estimates are reported for all endpoints for reasons of consistency. If
estimates for multiple lags were reported, the estimate discussed by the author was used and
indicated as “selected” lag in the Air Pollution Epidemiology Database maintained by St George’s,
University of London, and used to identify suitable studies for this analysis. If multiple risk
estimates were available from the same city, only the most recent estimate was included, and if the
2
This chapter is based on a paper by Janssen NAH et al., 2011.
3
Additional contributions by Milena Simic-Lawson.
Health effects of black carbon
page 24
study area was part of a larger administrative area included in another paper (for example, the
Netherlands rather than Amsterdam), only the results for the larger area were included. Finally,
city-specific estimates for which PM10 was partly derived from BS were excluded.
For the panel studies, the methods used by Weinmayr et al. (2010) were followed for definition
of asthmatic or symptomatic children as well as definition of the evaluated outcomes asthma
symptoms and cough.
Summary fixed and random effects estimates were calculated using the metan procedure in Stata,
as described by Harris et al. (2008). In order to calculate pooled estimates and compare estimated
effects for BS and PM per mass unit, RRs for BS were converted to RRs for EC using the
average conversion factor (10 µg/m3
BS equals 1.1 µg/m3
EC) or the range of conversion factors
from individual studies (0.5–1.8) for sensitivity analysis.
Pooled effect estimates were expressed per 10 µg/m3
(for BS and PM10) or 1 µg/m3
(for PM2.5
and EC). To compare the effects based on comparable contrasts, the average ratio was calculated
of the inter-quartile ranges (IQR) for PM mass/BCP and compared to the ratio of the RRs for
BCP/PM mass. It was not possible to use study-specific IQRs to estimate pooled effects as IQRs
were not available for all studies.
Results
Time-series studies of daily mortality and hospital admissions
Studies of BCP and PM10
The majority of the papers concerned time-series studies of PM10 and BS (as a measure of BCP)
conducted in Europe. Random effects estimates for the percentage change in each outcome with
a 10 µg/m3
increase in PM10 or BS are presented in Table 7. Information and effect estimates for
all individual studies, and tests of heterogeneity and fixed effects estimates for studies included
in meta-analyses, are reported separately for each outcome in Annex 3B (Tables 3B 1–10).
Single-city estimates for the percentage change in all-cause mortality with a 10µg/m3
increase in
BCP and PM10 are also presented in Fig. 1. The available data were dominated by estimates from
the Air Pollution and Health: a European Approach (APHEA) study (Katsouyanni et al., 2001;
Analitis et al., 2006; Atkinson et al., 2001; Le Tertre et al., 2002).
For most outcomes, pooled effects estimates for a 10 µg/m3
increase in exposure are greater for
BS than PM10, especially for mortality and hospital admissions for cardiovascular causes
(Table 7). However, the average ratio of the IQRs for PM10/BS (1.7, see Annex 3B, Tables
3B 1–10) was consistent with the ratios of RR for BS/PM10 (for example, 0.90/0.6=1.5 for
cardiovascular mortality in Table 7), which suggests that effects estimates expressed for a similar
increase in concentration (IQR) would be more or less equivalent. When a 10 to 1.1 conversion
factor was used to transform the estimated effect of a 10-µg/m3
increase in BS to the estimated
effect for a 1 µg/m3
increase in EC, the pooled random effect estimate for all-cause mortality
changed from 0.68% (95% confidence interval (CI) 0.31–1.06) to 0.62% (that is, 0.68/1.1; CI
0.26–0.96). When study-specific conversion factors were used, the estimated effects for a
1µg/m3
increase in EC ranged from 0.38% to 1.36% (for conversion factors of 1.8 and 0.5,
respectively), which suggests that the effect of a 1 µg/m3
increase in EC on all-cause mortality is
at least eight times larger than the estimated effect of a 1 µg/m3
increase in PM10 (0.05%).
Health effects of black carbon
page 25
Table 7. Summary of comparison of pooled effects for PM10 and BS
from time-series studies
Health outcomes No. of
estimates
Percentage change per 10 µg/m3 increase
(95% CI)
PM10 BS
Mortality
All causes 7 0.48
*
(0.18–0.79) 0.68
*
(0.31–1.06)
CVD 7 0.60
*
(0.23–0.97) 0.90
*
(0.40–1.41)
Respiratory diseases 7 0.31 (-0.23–0.86) 0.95 (-0.31–2.22)
Hospital admissions
All respiratory diseases, elderly people 6 0.70
*
(0.00–1.40) -0.06 (-0.53–0.44)
Asthma + chronic obstructive pulmonary
disease, elderly people
5 0.86
*
(0.03–1.70) 0.22 (-0.73–1.18)
Asthma, children 5 0.69 (-0.74–2.14) 1.64
*
(0.28–3.02)
Asthma, young adults 5 0.77 (-0.05–1.61) 0.52 (-0.51–1.55)
Cardiac, all ages 4 0.51
*
(0.04–0.98) 1.07
*
(0.27–1.89)
Cardiac, elderly people 4 0.67
*
(0.28–1.06) 1.32
*
(0.28–2.38)
Ischaemic heart disease, elderly people 5 0.68
*
(0.01–1.36) 1.13
*
(0.72–1.54)
*
P <0.05.
Source: Janssen et al., 2011.
Fig. 1. Single-city, single-pollutant estimates for PM10 and BS
and all-cause mortality
*Cities or areas included in the pooled estimate (year indicates year of publication).
Source: Janssen et al., 2011.
Health effects of black carbon
page 26
Studies of BCP and PM2.5
Less, but more recent, information was available from studies in which both PM2.5 and BCP were
measured. Three studies provided estimates of PM2.5 and EC, both for all-cause mortality and for
cardiovascular mortality. Only two studies provided estimates for respiratory mortality (Klemm et
al., 2004; Ostro et al., 2007; Cakmak, Dales & Blanco Vida, 2009; Mar et al., 2000) (see Annex 3C,
Tables 3C 1–3). In pooled analyses, a 1 µg/m3
increase in PM2.5 was associated with a 0.19% (0.03–
0.35%) increase in all-cause mortality and a 0.29% (0.07–0.50%) increase in cardiovascular
mortality. For EC, a 1 µg/m3
increase was associated with a 1.45% (1.32–1.57%) increase in allcause
mortality and a 1.77% (1.08–3.08%) increase in cardiovascular mortality. Thus, expressed per
mass unit, effect estimates are much larger (7–8 times) for EC than for PM2.5. However, if the ratio
of the IQR for PM2.5/EC (~11) is taken into account, effect estimates were similar.
Information on the effect of PM and EC on hospital admissions or emergency department visits
was even more limited than for mortality, and no pooled estimates could be calculated (Zanobetti
& Schwartz, 2006; Ostro et al., 2009; Peng et al., 2009; Tolbert et al., 2007; Cakmak et al., 2009;
see Annex 3D, Table 3D 1). When expressed per 1 µg/m3
increase, effect estimates were generally
10–30 times higher for EC compared to PM2.5. However, IQRs for EC were lower by a similar
factor (for example, the ratio of the IQRs for PM2.5/EC from Zanobetti & Schwartz (2006)
(8.9/1.0) was similar to the ratio of the effect estimates for pneumonia with a 1 µg/m3
increase in
EC/PM2.5 (0.054/0.0037), suggesting comparable effects with a comparable change in exposure.
Two-pollutant models of PM mass and BCP
In total, six papers included results of two-pollutant models that included a measure of PM mass
as well as BCP. These included studies of mortality as well as hospital admissions and
emergency department visits (Table 8). With one exception, effect estimates for BCP either
increased or they decreased by  33% after adjustment for PM mass. In contrast, adjusting for
BCP substantially reduced most effect estimates for PM mass (effect estimates became negative
in three out of nine studies and decreased by >50% in five of the six other studies), suggesting
that the effect of BCP is more robust than the effect of PM mass.
Studies of BCP and other PM components
In addition to the effects of BCP compared to PM mass, the relative health effects of BCP
compared to other PM components are of interest. Specifically, it was interesting to evaluate
whether the effects of BCP remained significant after adjustment for other potentially relevant
components such as metals. Eight studies that reported effect estimates for EC and PM mass also
reported estimates for PM components, including OC, sulfate and metals (Cakmak, Dales &
Blanco Vida, 2009; Cakmak et al., 2009; Klemm et al., 2004; Mar et al., 2000; Ostro et al., 2007;
Peng et al., 2009; Sarnat et al., 2008). In general, effects per IQR increase in exposure were greater
for EC than for most of the six other frequently reported components (Annex 3E, Table 3E 1). For
cardiovascular mortality and morbidity, four out of five studies reported significant associations
with an IQR increase in OC, four out of four reported significant associations with potassium, and
three out of four reported significant associations with zinc. Estimated effects of an IQR increase in
EC on cardiovascular mortality and morbidity were significant in all five studies. For respiratory
mortality and morbidity, the results were more diverse, with the strongest effects observed in two
EC studies (Cakmak, Dales & Blanco Vida, 2009; Cakmak et al., 2009), with OC and/or sulfate in
three studies (Ostro et al., 2009; Peng et al., 2009; Sarnat et al., 2008), and no significant (P<0.05)
effects for any of the measured components in a sixth study (Ostro et al., 2007).
Health effects of black carbon
page 27
Table 8. Results from single- and two-pollutant models of time-series studies
including PM10 or PM2.5
a
and BCP (measured as BS in all studies shown)
Reference/
location
Health endpoint R
b
PM-BS
Percentage change (per 10 µg/m
3
)
PM single-
pollutant
PM two-
pollutant
c
BS single-
pollutant
BS two-
pollutant
c
Mortality
Bremner et al.,
1999, London,
United Kingdom
Respiratory
mortality
CVD mortality
NA 1.3 (0.3–2.3) 0.4 (-1.0–1.8) 1.9 (0.2–3.7) 2.0 (-0.4–4.4)
0.6 (-0.1–1.2) 0.2 (-0.6–1.0) 1.2 (0.1–2.2) 0.8 (-0.6–2.2)
Hoek et al., 2000
Netherlands
Total mortality
CVD mortality
0.77 0.3 (0.0–0.5) 0.1 (-0.3–0.6) 0.7 (0.4–0.9) 0.4 (-0.6–1.4)
0.2 (-0.2–0.5) -0.6 (-1.3–0.1) 0.8 (0.4–1.2) 2.1 (0.5–3.7)
Admissions
Anderson et al.,
2001, West
Midlands,
United Kingdom
Respiratory
admissions,
all ages
0.64 0.6 (-0.5–1.7) Considerably
reduced
d
1.1 (-0.1–2.2) 2.0 (0.3–2.8)
Atkinson et al.,
1999a,
c
London,
United Kingdom
Accident and
emergency visits for
asthma by children
NA 2.4 (0.7–4.1) 2.0 (-0.1–4.2) 2.8 (-0.0–5.7) 0.9 (-3.0–5.1)
Atkinson et al.,
1999b, London,
United Kingdom
e
Cardiovascular
admissions,
>65 years
0.6–0.7 0.5 (-0.0–1.0) -0.1 (-0.8–0.5) 1.9 (0.9–3.0) 2.3 (0.8–3.8)
Le Tertre et al.,
2002, APHEA
study
Cardiac admissions:
cardiac disease,
>65 years
ischaemic heart
disease, >65 years
0.5–0.8 0.5 (0.2–0.8)
0.7 (0.4–1.0)
0.8 ( 0.3–1.2)
-0.2 (-1.2–0.8)
0.1 (-0.4–0.7)
0.2 (-0.9–1.4)
1.1 (0.4–1.8)
1.3 (0.4–2.2)
1.1 (0.7–1.5)
1.6 (-0.3–3.5)
1.5 (0.3–2.7)
0.8 (-1.1–2.7)
a
PM2.5 for Anderson, 2001; PM10 for all other studies.
b
Coefficient of the correlation (R) between PM and BS.
c
PM two-pollutant=PM from model with both PM and BS. BS two-pollutant=BS from model with both BS and PM.
d
Quantitative information not available. Paper states that the effect of PM2.5 was considerably reduced when BS was included in
the model.
e
Results only described qualitatively in the paper. Quantitative estimates provided by the authors on request.
Source: Janssen et al., 2011.
Three studies also reported effects estimates based on multi-pollutant models that included a
variety of PM components (see Annex 3E, Table 3E 2). Two studies conducted in Santiago,
Chile, reported significant associations with mortality (total, cardiac and respiratory) and hospital
admissions (all non-accidental and respiratory) for EC, OC and 10–15 of 16 individual elements
based on single-pollutant models, but effects estimates for only EC and OC remained significant
after adjustment for all other pollutants measured (Cakmak, Dales & Blanco Vida, 2009;
Cakmak et al., 2009). In a study on emergency department visits for cardiovascular and
respiratory disease in 119 urban communities in the United States (Peng et al., 2009), seven
major PM components were considered (sulfate, nitrate, silicon, EC, OC, sodium ion and
ammonium ion). These seven components in aggregate constituted 83% of the total PM2.5 mass,
whereas all other components contributed <1% individually. In single-pollutant models,
cardiovascular admissions were significantly associated with same-day concentrations of five out
of seven major PM components, including EC. In multi-pollutant models with all seven
components, only EC remained significant. For respiratory admissions, only same-day OC
concentrations were significant, in both single- and multi-pollutant models. In a study of
associations between hospital admissions for cardiovascular and respiratory disease in 106
Health effects of black carbon
page 28
counties in the United States that related admissions to the fraction of 20 elements to the total
PM2.5 mass rather than the concentration, RRs for cardiovascular and respiratory hospitalizations
were highest in counties with a high PM2.5 content of nickel, vanadium and EC (Bell et al.,
2009). Here, nickel was the most robust component in multi-pollutant analyses, especially for
cardiovascular admissions. Peng et al. (2009) reported statistically significant heterogeneity
among effect estimates for different PM components, with the strongest estimated risk of
cardiovascular admissions associated with EC concentrations. Cakmak et al. (2009) and
Cakmak, Dales & Blanco Vida (2009) also reported that the 95% CI of the estimated effect of an
IQR increase in EC did not overlap the 95% CIs of the other elements, with the exception of OC
and 2–3 of the other 16 elements, indicating that the effect per IQR for EC was significantly
greater than the estimated effects of most other single elements.
Panel studies of asthma symptoms and cough among children with asthma or
asthma-like symptoms
The 9 papers on panel studies included 8 papers that provided single-city estimates and 1 paper
that provided pooled effect estimates for the 28 panels from 14 countries in the Pollution Effects
on Asthmatic Children in Europe (PEACE) study (Roemer et al., 1998). Table 9 summarizes
effects estimates for the PEACE study and random pooled effects for the other eight studies.
Information and effect estimates for all individual studies, and tests of heterogeneity and fixed
effects estimates for studies included in meta-analyses, are reported separately for each outcome
in Annex 3F (Tables 3F 1–4).
Table 9. Summary of comparison of pooled effects for PM10 and BS from panel studies
among children with asthma or asthma-like symptoms
Study No. of panels
and children
Lag Percentage change per 1 µg/m
3
increase
(95% CI)
PM10 BCP
Asthma
PEACE study 28 panels;
2010 children
a
Lag 0 -0.07 (-0.16 to 0.01) -0.66 (-1.42 to 0.11)
Lag 1 0.00 (-0.10 to 0.10) -0.76 (-1.88 to 0.38)
Other studies 8 panels;
791 children
b
Lag 0 0.27 ( 0.03 to 0.51)
c
4.27 ( 0.19 to 8.52)
c
Lag 1 0.19 (-0.13 to 0.51) 2.85 (-1.01 to 6.86)
Cough
PEACE study 28 panels;
2010 children
a
Lag 0 -0.04 (-0.08 to 0.02) -0.37 (-0.80 to 0.06)
Lag 1 -0.03 (-0.08 to -0.01)
*
0.18 (-0.44 to 0.81)
Other studies 7 panels;
734 children
b
Lag 0 0.03 (-0.02 to 0.09) 1.33 (-0.28 to 2.96)
Lag 1 0.04 (-0.03 to 0.12) 1.07 (-0.86 to 3.04)
* P <0.05.
a
All children studied in the winter of 1993/1994.
b
Children studied in different periods between 1990 and 2004.
Pooled effect estimates for the PEACE study were generally negative, and significantly negative
(P <0.05) for cough at lag 1. Random pooled effect estimates for the other studies were all
positive, but only significant (P <0.05) for asthma at lag 1 (both for PM10 and BCP). Random
pooled effect estimates for all studies, including the PEACE as a single study, were also
Health effects of black carbon
page 29
generally positive. Effects estimates for BCP per µg/m3
were an order of magnitude higher for
BCP compared to PM10, but none of the pooled estimates were significant (Annex 3F).
Significant pooled effect estimates for PM10 were found in the review by Weinmayr et al. (2010).
Compared to the studies presented in Table 9, a total of 42 single-panel estimates for asthma
were included. These included the 28 panel-specific estimates from the PEACE study as well as
16 single-panel estimates from other studies. The percentage change per 1 µg/m3
increase from
random pooled effect estimates for asthma at lag1 were 0.15% (95% CI, 0.04–0.26%) for all 42
panels and 0.25% (95% CI, 0.10–0.39%) after excluding the PEACE study (Weinmayr et al.,
2010). The non-significant effects found in this analysis are, therefore, possibly caused by the
lower number of studies and the relatively larger influence of the PEACE study in the selection
of studies that provided effect estimates for PM mass as well as BCP.
Of the studies summarized in Table 9, only one provided information on two-pollutant models
for PM mass and BCP (Delfino et al., 2003). In this study, both PM10 and EC were significantly
associated with asthma symptoms in single-pollutant models. In a two-pollutant model that
included both PM10 and EC, the OR for PM10 was reduced to 1.0 while the OR for EC remained
stable (Delfino et al., 2003).
Cohort studies of long-term exposure to BCP and PM and mortality and
morbidity
Cohort studies of mortality
Seven papers were identified that presented results from four different cohort studies, two of
which included effect estimates for BS and PM and two for EC and PM (Table 10). Table 10
shows a recalculation of effects estimates for BS to effects estimates for EC on the assumption
that EC=0.11 BS.
Table 10. RR for mortality related to long-term exposure to PM2.5 and EC per 1 µg/m
3
Reference Cohort R
PM-BCP
Cause RR PM2.5 RR EC
Smith et al.,
2009
500 000 adults, aged
20–87 years, United
States
NA All causes 1.006 (1.002–1.010) 1.06 (1.01–1.11)
Cardiopulmonary 1.012 (1.008–1.018) 1.11 (1.03–1.19)
Lipfert et al.,
2006
70 000 male veterans,
United States
0.54 All causes 1.006 (0.993–1.020) 1.18 (1.05–1.33)
Beelen et
al., 2008
a
120 852 adults; aged
55–69 years,
Netherlands
>0.8
b
Natural causes 1.006 (0.997–1.015) 1.05 (1.00–1.10)
Respiratory 1.007 (0.972–1.043) 1.20 (0.99–1.45)
Cardiovascular 1.004 (0.990–1.019) 1.04 (0.95–1.12)
Lung cancer 1.006 (0.980–1.033) 1.03 (0.89–1.18)
Other 1.008 (0.996–1.021) 1.04 (0.97–1.11)
Filleul et al.,
2005
a,c
14 284 adults, aged
25–59 years, France
0.87
d
Natural causes 1.010 (1.004–1.016) 1.06 (1.03–1.09)
Cardiopulmonary 1.012 (1.002–1.023) 1.05 (0.98–1.11)
Lung cancer 1.000 (0.983–1.019) 1.03 (0.93–1.14)
Pooled effect (fixed)
e
All causes 1.007 (1.004–1.009) 1.06 (1.04–1.09)
a
RR for EC in European studies estimated from BS as EC 0.11 BS.
b
For regional and urban component, NA for total.
c
RR for PM2.5 estimated from total suspended particles (TSP) as PM2.5=0.5 × TSP.
d
For all 24 sites, whereas RR presented for 18 sites (non-traffic).
e
Pooled effect when using EC=0.18BS: 1.04 (1.02–1.06); when using EC=0.05BS: 1.10 (1.06–1.14).
Health effects of black carbon
page 30
When using the average conversion factor of 10 µg/m3
BS=1.1 µg/m3
EC, RRs for all-cause or
natural cause mortality per 1 µg/m3
EC in the two European studies and in the study by Smith et al.
(2009) range from 1.05 to 1.06. RRs for EC and all-cause mortality in the veterans study were
about three times larger than RRs for the same outcomes from the other studies, but as the standard
error in the veterans study was two to four times higher compared to the other studies, this study
contributes less to the pooled estimate (1.06[95% CI 1.04–1.09] per µg/m3
EC). Pooled estimates
for a 1 µg/m3
increase in EC derived using high- and low-end conversion factors of 1.8 and
0.5 µg/m3
per 10 µg/m3
BS were 1.05 and 1.11, respectively. When expressed per 1 µg/m3
, the RR
for EC is, therefore, 7–16 times higher than that for PM2.5 mass (pooled estimate 1.007 per
1 µg/m3
). However, ratios of IQRs for PM2.5/EC for the studies by Smith et al. (2009) and Beelen
et al. (2008) were 14 and 9, respectively, and it was estimated that there was a ratio of about 5
based on graphical data presented for the study by Filleul et al. (2005). For the study by Lipfert et
al. (2006), IQRs were not available but RRs expressed for the difference between the mean
concentration and the minimum were 1.06 per 9.5 µg/m3
for PM2.5 and 1.09 per 0.5 µg/m3
for EC.
Hence, it appears that effects estimates for PM2.5 and EC from cohort studies would also be similar
if expressed for an IQR increase in exposure instead of a 1µg/m3
exposure contrast.
Multi-pollutant modelling was applied in the studies by Lipfert et al. (2006) and Smith et al.
(2009). Based on four-pollutant models that included EC, OC, sulfate and nitrate, Lipfert et al.
(2006) concluded that EC had the greatest estimated impact on all-cause mortality, and that
nitrate was the next most important constituent. In the analysis of data from the American
Cancer Society study by Smith et al. (2009), the EC estimate for all-cause mortality was reduced
by about 50% and lost statistical significance after adjustment for sulfate and/or ozone. For
cardiopulmonary mortality, EC fell by about 33% and remained significantly associated after
adjustment for sulfate, but fell by about 80% and lost significance after additional adjustment for
ozone.
Cohort studies on morbidity
The eight papers on respiratory health outcomes in children included six papers describing
results from one Dutch and two German birth cohorts, analysed using the same exposure
assessment strategy, and two papers on lung function growth in two cohorts of children in
southern California (Brauer et al., 2002; 2006; 2007; Clark et al., 2010; Gauderman et al., 2002;
2004; Gehring et al., 2002; 2010; Morgenstern et al., 2007; 2008; see Annex 3G, Tables 3G 1
and 2). For most of the studies, PM2.5 and BCP were highly correlated (R>0.9). Overall,
consistent with other types of study, estimated effects of a 1 µg/m3
increase in BCP were greater
than estimated effects of a 1 µg/m3
increase in PM2.5, whereas effects estimated for IQR
increases were similar for BCP and PM2.5.
Discussion
Single-pollutant effects estimates for daily mortality or hospital admissions generally were an
order of magnitude higher for BCP compared to PM10 and PM2.5 when expressed per µg/m3
.
When differences in IQRs were accounted for, effects estimates were generally similar. It should
be noted that there was a moderate to moderately high correlation between PM10 and BS
measurements reported by the individual studies included in the pooled estimates (Pearson
correlations of 0.5 to 0.8), consistent with correlations between daily wintertime PM10 and BS
concentrations from a study in 14 European study areas (Hoek et al., 1997). Although this raises
concerns about the ability to distinguish effects due to PM10 versus BS, there is at least some
variation in the temporal patterns of these exposures.
Health effects of black carbon
page 31
In studies examining a variety of different PM components, BCP generally showed significant
associations, especially with cardiovascular health endpoints, both before and after adjusting for
other components. For cohort studies, pooled estimates for all-cause mortality per 1 µg/m3
were
5 to 14 times higher for BCP compared to PM2.5, but IQRs for PM 2.5 were higher than those for
BCP by a similar factor.
The available evidence from two-pollutant models for time-series studies suggests that the effect
of BCP is more robust than the effect of PM mass. Two-pollutant models with BCP and PM
mass were not, however, conducted in any of the cohort studies. Although, overall, the results of
multi-pollutant analysis including BCP, sulfate and ozone in the ACS study suggest that sulfate
has the most robust association with all-cause and cardiopulmonary mortality, Smith et al. (2009)
indicate that this can also be caused by differential amounts of measurement error. In the ACS
study, where exposure was assessed at the metropolitan area level, estimates of the spatial
distribution of EC probably contain more measurement errors than the assigned sulfate
exposures as EC is more locally generated, as opposed to sulfate, which is a secondary pollutant
with little spatial variation. When there are errors in measurement, variables measured with high
precision will tend to dominate model-based predictions relative to variables measured with less
precision (Smith et al., 2009). For time-series studies, there are no great differences in temporal
relationships between central-site ambient concentrations and personal exposure for BCP and
PM2.5 (Janssen et al., 2005). In addition, issues related to the correlation between different
pollutants and the extent to which they can act as surrogates for the etiological agent(s)
complicate the interpretation of results from multi-pollutant models (Tolbert et al., 2007). This
report’s interpretation that the results from two-pollutant models for the time-series studies
suggest that BCP is a more health-relevant indicator in these studies than PM mass is supported
by Roemer & van Wijnen (2001; 2002), who calculated separate effects estimates with separate
exposure estimates using background and traffic-influenced measurement stations for the total
population and for people living along busy roads. Effects estimates for urban background BS
were greater in the population living along busy roads than for the total population, suggesting
that this subpopulation is more highly exposed. Indeed, effects estimates for the population
living along busy roads using BS measured at traffic stations were more or less equivalent to
effects estimates for the total population using BS measured at urban background stations.
It is also important that the spatial variation of BCP is much larger than that of PM mass (Hoek
et al., 2002; Puustinen et al., 2007). This has in particular been demonstrated in relation to
traffic. In a review of studies that simultaneously measured PM mass and BCP concentrations
<50 m from busy roads and at background concentrations, Janssen et al. (2011) found that, on
average, BCP concentrations near busy roads were twice as high as urban background BCP
concentrations, whereas PM concentrations near busy roads were only about 20% higher than
background. The one-time series study that explicitly took this phenomenon into account was
conducted in Amsterdam (Roemer & van Wijnen, 2001; 2002) and showed that mortality effects
were more closely associated with BS than with PM10.
Based on the absolute differences in concentrations between street and background locations, it
is estimated that, on average, 55% of the roadside increment in PM2.5 was comprised of EC
(Annex 3H). Given this relatively large proportion of carbon particles in the roadside increment
of PM2.5 mass, it can be expected that traffic abatement measures will result in greater reductions
in BCP relative to reductions in PM mass. An illustration of an evaluation of the health benefits
of a hypothetical traffic abatement policy measure using reported effect estimates for PM2.5 mass
and BCP, respectively, is included in Annex 3H. This calculation can be interpreted as an
indication of the potential difference in a health impact assessment based upon PM2.5 or BCP for
Health effects of black carbon
page 32
populations living along major roads. It can also be interpreted as the potential health gain for
policies that reduce concentrations in approximately the same ratio as the current roadside
increment, for example, a limitation of overall traffic intensity. This evaluation illustrates that the
health effects of such policies may be seriously underestimated when based on effects estimates
for PM2.5 or PM10. The implication of similar effects per IQR is that for policies that reduce all
relevant components of PM proportionate to current levels, the estimated health benefits would
be similar based on either indicator.
In the studies reviewed, ambient measurements of various BCP metrics were used. Although
motorized traffic was an important source of BC in most of these studies, they included the
impact of all combustion sources on BCP concentrations, including coal- and wood-burning,
shipping emissions and industrial sources. In a review of source apportionment studies for fine
particle EC, Schauer (2003) found that the combined contribution of diesel- and gasolinepowered
vehicles ranged from 74% to 98%; the contribution from biomass-burning ranged from
0.7% to 25% and the contribution from other sources ranged from 0.5% to 17%. The derived
risks therefore represent those for BCP as a general indicator of combustion particles, not
exclusively traffic. Issues remain when these risk estimates are applied to specific combustion
sources such as traffic or wood-burning. This report, however, holds that BCP more closely
resembles the harmful components in these air pollution mixtures than general PM2.5 does.
The systematic review was conducted for studies published up to January 2010 (Janssen et al.,
2011). Although this review was not systematically updated with studies published after that
date, the authors are aware of some recent studies that also provided effects estimates for PM
mass as well as BCP. For cohort studies on mortality, Ostro et al. (2010) reported RRs for allcause,
cardiopulmonary and ischaemic heart disease mortality for a cohort of 45 000 women in
California (United States). When expressed per 1 µg/m3
, the RR for all-cause mortality for the
population living within 30 km of a monitor was 5 times higher for EC (RR 1.03) compared to
PM2.5 (RR 1.006). Effects per IQR were similar.4
Gan et al. (2011) conducted a population-based
cohort study on coronary heart disease mortality and hospitalizations for all 450 000 residents in
metropolitan Vancouver (Canada). When expressed per 1 µg/m3
, the sex, age, co-morbidity and
socioeconomic status-adjusted RR for coronary heart disease mortality was 10 times higher for
BC (RR 1.06) compared to PM2.5 (RR 1.006). The effects per IQR were 6 times higher (RR 1.06
for BC and 1.01 for PM2.5). In three-pollutant models, including BC, PM2.5 and NO2, the effect
for BC was unaffected (RR 1.06; 95% CI, 1.03–1.09), whereas the RR for PM2.5 was reduced to
1.00. In two recent birth cohort studies on respiratory health in children, both PM and BCP were
significantly associated with adult onset-incident asthma, with similar effects per IQR and higher
effects for BCP per 1 µg/m3
(Gehring et al., 2010; Clark et al., 2010). In a third recent birth
cohort study, PM2.5 was significantly associated with incident asthma, whereas BC was not
(Carlsten et al., 2011). In two recent panel studies, neither PM2.5 nor EC were significantly
associated with wheeze at lag 0 or lag 1 in one study (Mann et al., 2010), whereas only BC (and
not PM2.5) was significantly associated with wheeze at lag 0 and lag 1 in the second study (Patel
et al., 2010). Overall, these more recent studies do not change the conclusions based on the
studies published up to January 2010.
Is it now possible to answer the question as to whether the effects of BCP are different from
effects of PM2.5 mass or other components in airborne PM? In general, the appropriate
comparison would be the one based on IQR effect estimates, and those do not provide
unequivocal support for BCP being more important than the remainder of the PM mass.
4
Effects estimates reported in the original paper were recently adjusted in an erratum.
Health effects of black carbon
page 33
However, for assessments of exposure conditions dominated by combustion sources, or policies
directed towards specific combustion sources, the comparison of RRs expressed per one unit
change in mass is relevant. Additional evidence comes from the quoted two- and multi-pollutant
studies which predominantly show that BCP effects estimates are robust to adjustment for PM
mass, whereas PM mass effects estimates decrease considerably after adjustment for BCP.
Limited evidence from the one study comparing effects estimates based on traffic-exposed and
urban background monitoring stations also supports this view. For the cohort studies, the
database is small and, especially for the birth cohort studies, characterized by often very high
spatial correlations between PM mass and measures of BCP. Multi-pollutant models with BCP
and PM mass were only conducted in one recent cohort study on coronary heart disease mortality
(Gan et al., 2011); this showed that the significant effect observed with BC was robust for
adjustment for PM2.5, whereas no significant effect of PM2.5 on coronary heart disease mortality
was observed.
Collectively, these studies show that BCP is associated with health effects that are not reflected
quantitatively in the same way by the PM mass concentration. Based on the considerations
mentioned, the conclusion is that BCP represents one of the more health-relevant components of
PM, especially for cardiovascular effects. BCP could, therefore, be a valuable additional air
quality indicator to evaluate the health risks of air quality dominated by primary combustion
particles.
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Health effects of black carbon
page 37
4. Evidence from toxicology, including human clinical
studies
Miriam E Gerlofs-Nijland, Flemming R Cassee, Raimo O Salonen
Introduction
BC or any other alternative (optical) methods such as reflection on filters are seldom assessed (or
reported) in toxicity studies on ambient air, either in controlled human exposure studies or in
studies using experimental animals or in vitro systems. For this reason, this chapter will also
consider exposure to carbon-rich test atmospheres such as (diluted) combustion-derived PM as
the next best measure to compare with measures in the air quality area and epidemiology. There
has been broad discrimination between combustion of fossil fuels in engines and combustion of
biofuels such as wood in residential heaters. In addition, studies in which EC has been used were
selected for this review.
Reflectance (a metric for BC) does not solely result from combustion-derived aerosols. Windblown
dust, iron oxide from railways and non-tailpipe emissions such as brake or tyre wear from
vehicles can also contribute to BC. These sources have not, however, been considered for the
assessment described below, largely because of a lack of relevant information.
This review focused on the following two points.
 What are the effects of BC in the human controlled exposure experiments? Are they
differently qualitatively (for example, different health outcomes) and/or quantitatively
from the effects of PM2.5 mass concentration or other measured components?
 What are the mechanisms of the BC effects indicated by toxicological studies?
A literature search (Annex 1) focused on human clinical studies in which both PM and some
measure for BC were recorded, or inhalation studies that have used diluted diesel engine exhaust,
woodsmoke or EC. Studies assessing the hazard of coal dust (not relevant for the general
population and PM largely >2.5 um in aerodynamic size) and other non-combustion PM sources
(for example, brake wear, or rail or tyre dust) that may contribute to the blackness of ambient
PM material were excluded.
This selection resulted in seven studies, in which both BC and PM mass were measured
(Table 11). From these, no single study provided information on the concentration–dose–
response relationships. This hampers comparison of PM mass with some measure of BC. In
addition, all studies were limited by the duration of exposure (only a few hours) and no
supporting evidence for longer exposure duration can be provided. Subsequently, biological
responses related to the inhalation exposure were summarized and an assessment was made to
establish if the effects were more strongly related to BC than to PM mass. In addition, a search
was performed to identify specific mechanisms and biological pathways for BC leading to
toxicity and adverse health effects as identified in epidemiological studies.
Health effects of black carbon
page 38
Table 11. Summary of clinical findings in human subjects exposed to ambient PM, (diluted) diesel
engine exhaust, pure carbon particles in air, or combustion particles of wood and other biomass
Subjects Exposure Outcome Main findings Reference
Ambient PM
Healthy (n=12)
and asthmatic
(12) subjects
2-hour exposure with
intermittent exercise to
on average 174 µg/m3
PM0.1– 2.5 , 10% EC.
Respiratory function and
blood. Spirometry and
routine haematological
measurements. Columnar
cells in postexposure-induced
sputum,
mediators of blood
coagulation, systemic
inflammation, heart rate
variability, systolic blood
pressure.
Cardiovascular (but not respiratory)
symptoms increased
slightly in both groups.
Gong et al.,
2003
Age-matched
volunteers
(n=12), middleaged
healthy
volunteers or
patients with
coronary heart
disease
2-hour exposure with
intermittent exercise to
on average 190 µg/m3
PM2.5, low or no EC.
Vascular and blood
parameters, respiratory
inflammation.
No effect on vasomotor or
fibrinolytic function; slight
increase in local airway
inflammation.
Mills et al.,
2008
Diesel engine exhaust (DE)
Healthy nonsmoking
male
volunteers
(n=18), mean
age 27 years
1-hour exposure with
intermittent moderate
exercise to 250 µg/m3
DE, European transient
cycle engine running
conditions.
Blood and arterial
function. Vascular vasomotor,
fibrinolytic function
6 hours after
exposure.
Impaired vascular function:
attenuated vasodilatation,
reduced capacity to remove
blood clots (tissue plasminogen
activator reduction).
Impaired calcium channeldependent
vasomotor function
under European transient
cycle condition.
Barath et al.,
2010
Healthy nonsmokers
aged
21–44 years
2-hour exposures to DE
350 µg/m3
from two
different engines and
fuels (one in Edinburgh
(United Kingdom) and
the other in Umeå
(Sweden)), idling engine,
intermittent moderate
exercise.
Blood and thrombosis.
Ex vivo thrombus formation
(Badimon), in vivo
platelet activation (flow
cytometry), haematology,
coagulation and
inflammation markers at
2 and 6 hours after
exposure.
Increased thrombogenicity at
2 and 6 hours, when using two
different diesel engines and
fuels.
Increased platelet-neutrophil
(52%) and platelet-monocyte
(30%) aggregates.
Lucking et al.,
2008
Healthy nonsmoking
males
aged 20–38
years
1-hour exposure to DE
300 µg/m3
, idling engine,
intermittent moderate
exercise.
Vascular and blood.
Forearm blood flow by
venous occlusion plethysmography,
tPA,
PAI-1, haematology at 2
and 6 hours postexposure,
IL-6, TNF-α,
ET-1, CRP.
No differences in resting
forearm blood flow or in
inflammation markers.
Impairment of vascular tone
and endogenous fibrinolysis.
Mills et al.,
2005
Healthy (n=4)
and stable asthmatics
(n=7)
2-hour exposure to DE
100 µg/m3
, idling diesel
truck.
Respiratory function.
Lung function (FEV1).
No changes in lung function. Sawant et al.,
2008
Male/female
healthy
subjects
(n=15)
1-hour exposure to DE
300 µg/m3
, European
transient cycle engine
running conditions,
moderate intermittent
exercise.
Lung. Airway inflammation,
lung function,
bronchoscopy (endobronchial
mucosal biopsy)
and airway lavage
at 6 hours post-
exposure.
Increased bronchial adhesion
molecule expression.
Bronchoalveolar eosinophilia,
which has not been shown
after exposure to DE at idling
(Stenfors et al., 2004).
Sehlstedt et
al., 2010a
Health effects of black carbon
page 39
Subjects Exposure Outcome Main findings Reference
Pure EC
Healthy
subjects
Subjects with
asthma
Freshly generated EC
particles (mean diameter
25 nm, geometric standard
deviation 1.6 nm).
Exposure for 2 hours:
10 µg/m3
at rest; 10 and
25 µg/m3
with exercise;
50 µg/m3
with exercise.
2-hour exposure to
10 µg/m3
with exercise.
Blood. Collection of peripheral
venous blood.
Flow cytometry to
quantify leukocyte expression
of surface
markers.
Increased retention of
leukocytes in pulmonary
vascular bed as shown by
altered peripheral blood
leukocyte distribution and
expression of adhesion
molecules.
Frampton et
al., 2006
Healthy subjects
and patients
with
stable angina
(20/group)
1-hour exposure to
carbon particles
50 µg/m3
.
Cardiovascular and
blood. Heart rate variability,
inflammation and
coagulation markers.
No effects on heart rate
variability or systemic inflam-
mation.
Routledge et
al., 2006
Male/female
healthy subjects
(n=16)
aged 18–40
years
2-hour exposure to
carbon ultrafine particles
(UFP) 50 µg/m3
, intermittent
exercise.
Vascular and blood.
Vital signs, venous
occlusion plethysmography,
hypaeremia of
the forearm, venous
plasma nitrate and
nitrite levels.
Impaired peak forearm blood
flow during reactive hypaer-
emia.
Shah et al.,
2008
Subjects with
type 2 diabetes
(n=16)
2-hour exposure at rest
to 50 µg/m3
of elemental
carbon UFP, median
diameter 32 nm.
Vascular and blood.
Vascular activation,
coagulation, systemic
inflammation at 0.5, 3.5,
21 or 45 hours post-
exposure.
Platelet activation with
possible associated activation
of blood leukocytes and vascular
endothelium.
Stewart et al.,
2010
Male/female
healthy subjects
aged 18–
40 years
Two separate
studies, n=12
in each
2-hour exposure to
elemental carbon UFP
10 µg/m3
at rest.
2-hour exposure to
elemental carbon UFP
10 and 25 µg/m3
with
repeated exercise.
Cardiovascular. Electrocardiogram,
parameters
of heart rate variability,
repolarization duration,
morphology and variability,
changes in the ST
segment (distance from
segments S to T of the
electrocardiogram).
No marked changes in
electrocardiogram-derived
parameters.
Zareba et al.,
2009
Combustion of wood and other biomass
Healthy individuals,
subjects
with mild to
moderate asthma,
subjects
with allergic
rhinitis (15 persons
in each
group; a total
of 18 men and
27 women
aged 22–54
years)
Rice straw smoke (RSS)
aerosol rich in inorganic
and organic carbonaceous
material by
purpose-built, controlled
burner to simulate open
burning. Of all PM, 85%
smaller than 4.2 µm in
diameter.
Four 30-minute exposure
patterns in environmental
chamber for
each subject group:
single filtered air, single
RSS-PM10 200 µg/m3
,
single RSS-PM10 600
µg/m3
, and RSS-PM10
200 µg/m3
repeated on
three successive days.
Spirometric lung functions.
Pre-exposure, and
immediately and 6
hours after controlled
exposure.
Bronchial washing (BW)
and bronchoalveolar
lavage. 6 hours after
controlled exposure.
Airway inflammation (increased
neutrophils in BW and
macrophages or lymphocytes
in bronchoalveolar lavage
fluid) in healthy individuals
and subjects with asthma by
RSS-PM10 600 µg/m3
and 3 ×
RSS-PM10 200 µg/m3
. Asthmatics
had higher neutrophil
concentration and percentage
in bronchoalveolar lavage fluid
compared to healthy individuals
and subjects with
allergic rhinitis. In healthy
individuals only, increased
IL-8 in BW by 3 × RSS-PM10
200 µg/m3
. No changes in
lung functions by any
exposure pattern in any
subject group.
Several indices of inflammation,
including increases in
neutrophils, epithelial cells
and IL-8 in bronchoalveolar
Solomon et
al., 2003
Health effects of black carbon
page 40
Subjects Exposure Outcome Main findings Reference
lavage fluid of all subject
groups indicated that 3 ×
RSS-PM10 200 µg/m3
caused
a more powerful inflammatory
stimulus to the respiratory
tract than the single exposure
to RSS-PM10 600 µg/m3
.
Healthy subjects
(6 men
and 7 women,
aged 20–56
years)
Woodsmoke aerosol in a
130 m3
environmental
chamber generated by
logwood heating of a
small cast-iron wood
stove.
Two 4-hour exposure
patterns: clean air or
woodsmoke aerosol in
two similar sessions of 6
and 7 subjects, two 25minute
periods of light
exercise.
Median PM2.5 279 µg/m3
and 243 µg/m3
in the
two sessions (clean air
13 and 11 µg/m3
).
Median total PM number
concentration
180000#/cm3
and
95000#/cm3
(clean air
4400#/cm3
and
7500#/cm3
).
Geometric mean diameters
of PM 42 nm and
117 nm, standard deviations
1.7 nm and 1.4 nm.
Median BS 72 and
91 × 10-5
/m (clean air
1.6 × 10-5
/m).
Benzo[a]pyrene 19 and
21 ng/m3
(no data for
clean air).
Blood samples. Collection
of peripheral venous
blood immediately
before, and 3 and 20
hours after controlled
exposure.
Exhaled breath measurements/samples.
Immediately
before, and 3
and 20 hours after
controlled exposure.
Urine samples. Immediately
before and 20
hours after controlled
exposure.
Increased serum amyloid A
(cardiovascular risk factor),
increased tendency towards
blood coagulation (factor VIII
in plasma and factor VIII/von
Willebrand factor ratio) and
temporary increase in free
radical-mediated lipid peroxidation
(urinary excretion of
free 8-iso-prostaglandin2).
Inflammatory response and
signs of increased oxidative
stress in the alveolar region of
the respiratory tract (exhaled
nitric oxide, malondialdehyde
in breath condensate, serum
Clara cell protein).
DNA strand breaks decreased
and significant up-regulation
of the repair gene hOGG1 in
human peripheral blood
mononuclear cells 20 hours
after exposure, but no
evidence of direct genotoxic
effects (timing of sampling,
enhanced repair).
Barregård et
al., 2006
Barregård et
al., 2008
Danielsen et
al., 2008
Healthy subjects
(10 men
and 9 women,
aged 21–31
years)
Woodsmoke aerosol in
an 18 m3
environmental
chamber generated by
15kW residential pellet
burner connected to a
boiler.
Two 3-hour exposure
patterns: filtered air or
woodsmoke aerosol
from low-temperature
incomplete combustion;
alternating 15-minute
periods of light exercise
and rest.
Mean PM2.5 224 µg/m3
(no data for clean air).
Mean total PM number
concentration
67000#/cm3
(filtered air
1–2#/cm3
).
Spirometric lung functions.
Pre-exposure and
immediately, 4 and 24
hours after controlled
exposure.
Symptom recording. Preexposure,
every 30
minutes during exposure
and immediately after
controlled exposure.
Exhaled air nitric oxide.
Immediately before and
3 and 20 hours after
controlled exposure.
Bronchial washing (BW)
bronchoalveolar lavage
and endobronchial biopsies.
24 hours after
controlled exposure.
No significant changes in
lung functions or exhaled air
nitric oxide. Mild but
progressive irritation in the
nose and throat and
unpleasant smell during the
woodsmoke exposure (peaking
between 1.5 and 2.5
hours from the beginning of
exposure), but a subset (7–
10 of the 19 subjects)
experienced no symptoms.
No significant changes in the
differential cell counts in BW
and bronchoalveolar lavage
fluid. Antioxidant response
(increased glutathione) in
bronchoalveolar lavage fluid at
24 hours after woodsmoke
exposure.
No changes in low molecular
weight antioxidant concenSehlstedt
et
al. 2010b
Health effects of black carbon
page 41
Subjects Exposure Outcome Main findings Reference
Geometric mean diameter
of PM 120 nm
(two modes with peaks
at 100 nm and 190 nm).
Chemical composition of
diluted woodsmoke: PM:
60% OC, 25% EC, 13%
alkali salts and 2% trace
elements.
Total polycyclic aromatic
hydrocarbons (PAH)
760 ng/m3
(12–26% in
PM fraction).
trations in the endobronchial
mucosal biopsy samples.
Atopic subjects
(10 men and
10 women,
aged 19–55
years)
Woodsmoke aerosol in a
79 m3
environmental
chamber generated by
logwood heating in a
small cast-iron wood
stove or filtered air. PM
diameters in chamber
aerosol not measured.
Three patterns of 3.5hour
exposures: filtered
air, single low exposure
aiming at PM2.5 200
µg/m3
and single high
exposure aiming at
PM2.5 400 µg/m3
.
Mean PM2.5 during low
exposure 221 µg/m3
and
high exposure 354 µg/m3
(filtered air 14 µg/m3
).
Median total PM number
concentration
29000#/cm3
and
71000#/cm3
(clean air
222#/cm3
).
Benzo[a]pyrene 329 and
325 ng/m3
(clean air
0.2 ng/m3
).
Recording of symptoms.
Self-evaluation of 29
symptoms or perceptions
with standard
visual analogue scales
before and every 30
minutes during the
exposure.
Weak significant effects for
categorized symptom indices:
environmental perception,
irritative body perceptions,
psychological/neurological
effects and weak inflammatory
responses. Significant increase
in self-reported general
mucosal irritation. Nearly
significant difference in
psychological/neurological
effects between high and low
exposure.
Riddervold et
al., 2011
Adverse health effects of BC in the controlled human exposure
experiments
The vast majority of controlled human exposure studies set up to identify adverse responses took
diesel engines as a source for BC emissions. These studies were limited in the duration of
exposure, which did not usually last much longer than a single two-hour period. The studies used
PM exposure concentrations in the range 100–350 µg/m3
. Effects related with carbonaceous
emissions included oxidative stress, inflammation, lipid peroxidation and atherosclerosis, change
in heart rate variability, arrhythmias, ST-segment depression (heart function), and changes in
vascular function (such as blood pressure) (Grahame & Schlesinger, 2010).
For this review, studies were selected that evaluated PM mixtures relevant for the questions,
mainly diesel engine exhaust, woodsmoke and concentrated ambient PM2.5. Controlled exposure
Health effects of black carbon
page 42
studies compared health responses to the selected exposure with those to filtered air in order to
adjust for potential effects of the experimental setup. Usually, each subject served as his/her own
control. Typically, the evaluated PM exposures (100–350 μg/m3
) were higher than those
encountered in ambient air, although not excessively so. Because of ethical concerns, healthy
subjects or subjects with mild disease were commonly selected for the controlled exposure
studies that focused only on mild, reversible adverse effects.
Ambient PM
The importance of the composition of particles is illustrated by the study of Mills et al. (2008),
which saw little effect in a two-hour exposure to high PM2.5 concentrations taken in Edinburgh
(Table 11). This was attributed to the high sea salt content (90%) providing evidence that mass
per se is not the best predictor for cardiovascular symptoms. It should be noted that the BC : PM
mass ratio was rather low in this study. Gong et al. (2003) exposed healthy and asthmatic
subjects to increased levels of fine PM in Los Angeles (United States) and presented a detailed
chemical specification. Relatively high levels of BC were reported (~75 µg BC/mg PM). Both
respiratory and cardiovascular effects were examined (Table 11) and only a slight increase in
cardiovascular symptoms (parasympathetic stimulation of heart rate variability, blood
coagulation and systemic inflammation) were observed for both groups. A higher EC
concentration predicted a more positive change in the composite index of ST voltage (ST-AMD)
post-exposure and a more negative change two days after exposure. On the other hand, total
mass was related to the low frequency/high frequency power ratio (Gong et al., 2003). Other
than this, there are no data to support the idea that EC/BC is specifically associated with subtle
short-term health effects. More controlled volunteer studies have attempted to link health effects
with several PM components, but the problem is that it will require a lot more subjects exposed
than the 20 or so that have been included in most human controlled exposure studies. So far no
component, including BC, has been successfully linked to effects from a concentrated PM2.5
study, with the exception of a link of soluble sulfate and metals with increased bronchiolar
lavaged neutrophils or increased blood fibrinogen (Huang et al., 2003). The designs of most
controlled studies have not, for the most part, been suitable to test the BC-related health effects,
and in any case, the statistical power (due to a lack of BC exposure contrasts between or within
subjects) would be too low to give strong evidence.
Diesel engine exhaust
Several studies have documented airway and systemic inflammation following exposure to diesel
engine exhaust, although a measure of BC was only taken in the study of Sehlstedt et al. (2010a).
No changes in respiratory function were observed after a two-hour exposure to diesel engine
exhaust in both healthy subjects and stable asthmatics (Sawant et al., 2008). Most studies that
included PM mass and a measure for BC examined changes in the cardiovascular system. Barath
et al. (2010) compared the cardiovascular effects of exhaust from an idling diesel engine with
that of an engine running according to an urban transient cycle. Similar results were reported for
both protocols. Even though the PM mass concentrations were not that different (255 versus
220 µg/m3
for urban transient and idling, respectively), there was a 16-fold difference in the EC
content, with the transient cycle giving the highest EC levels (88 µg/m3
). The opposite contrast
was measured for OC. Unfortunately, none of these studies provided concentration–dose–
response relationships, which militates against a full comparison of the predictive value of both
PM mass and EC or OC concentration in the diesel engine exhaust studies. Different operating
conditions, such as transient cycle and idling (as mentioned in the study by Barath et al., 2010),
will result in variations in the EC : PM and EC : OC ratios, which allows comparison of the
effects for PM and EC. At present, however, insufficient data have been published to allow a
Health effects of black carbon
page 43
thorough analysis of the role of EC in these complex mixtures, despite the fact that there has
been much carbonaceous material in the test atmospheres.
All diesel engine exhaust studies included the gaseous fraction of the engine exhausts, and gases
and vapours are also known to contribute to the total health effect. Yet the findings of Lucking et
al. (2011) provided evidence that removing the particulates with a modern diesel particle trap
(and incidentally increasing the NO2 levels) resulted in a complete absence of the cardiovascular
effects seen for the whole mixture. Similar findings were seen in a study in which the
particulates were removed by filtration (Mills et al., 2011). Interestingly, these authors also
exposed the volunteers to pure EC at even higher particle number counts (although slightly lower
mass concentrations) than that measured in the engine exhausts. Adverse responses were only
observed for whole diesel engine exhaust (increased arterial stiffness and lowered capacity to
reduce blood clots) but not for EC-free exhaust or EC-only atmospheres, suggesting that EC acts
as a carrier of harmful constituents to the target tissue.
The effects of diluted diesel engine exhaust on other targets, such as the brain, have also recently
been described. Crüts et al. (2008a) demonstrated an immediate impact on brain activity during
and shortly after a one-hour exposure to 300 µg/m3
of diesel engine exhaust, suggesting a stress
response. No BC measure was included in this study, but in a follow-up study using artificially
generated carbon nanoparticles, similar responses could not be demonstrated (unpublished
findings). The clinical relevance of the brain activity measured by quantitative
electroencephalography is, however, questionable. No other controlled human exposure studies
are known in which effects on the central nervous system have been studied.
Pure EC
Most studies that focused on the black fraction of PM were carried out at Rochester University
(United States). Frampton et al. (2006) reported that inhalation of carbon particles (10–50 µg/m3
)
did not result two hours after exposure in lung inflammation or in an acute phase response in the
blood. The observed subtle changes in leukocyte subsets and adhesion molecule expression are,
however, consistent with effects on vascular endothelial function. In a subsequent study,
Frampton and colleagues exposed subjects with type 2 diabetes to 50 µg/m3
of ultrafine EC PM
(count median diameter, 32 nm) for two hours at rest, and reported slightly increased activated
platelets in the blood (Stewart at al., 2010).
Routledge et al. (2006) were not able to detect any adverse effects on the cardiovascular system
after a one-hour exposure to 50 µg/m3
of EC. This was confirmed by Mills et al. (2011). From
both studies, it was concluded that the adverse effects of vehicle-derived particulates are likely to
be caused by more reactive species found on the particle surface rather than BC itself. Indeed,
Biswas et al. (2009) demonstrated that the organic fraction absorbed on the surface of BC
contributed to the most part of the redox activity of the PM, suggesting that this also represents a
fraction of tailpipe exhaust that can cause acute toxicity.
In summary, acute exposure to carbon particles at levels higher than those found in ambient air
do not result in clinically relevant adverse effects. The representativeness of these particles for
combustion-derived BC is, however, not known.
Biomass, including wood
Many fewer controlled exposure studies in humans have been conducted on emission aerosols
from residential wood combustion devices than on diesel engine exhausts (Table 11). The
Health effects of black carbon
page 44
commonly used PM2.5 or PM10 concentrations (200–400 µg/m3
) in these studies correspond to
the highest ambient air peak concentrations measured during wintertime temperature inversions
in older residential areas of western countries where wood is prevalent as the primary domestic
heating fuel. In developing countries, biomass combustion may increase ambient PM2.5 or PM10
concentrations to exceed 1 mg/m3
and, without a chimney, the indoor peak concentrations can be
even higher. Unfortunately, it is impossible to assess the role of BC in the measured responses,
because BS has been measured in one exposure set-up (Sällsten et al., 2006), and EC and OC in
only one other study (Sehlstedt et al., 2010b); both of these studies had only a single exposure
level in healthy subjects. Only one peer-reviewed journal paper (Riddervold et al., 2011) and one
research report on the internet (Solomon et al., 2003) provide data on PM2.5 or PM10 at more than
one exposure level. The overall interpretation of the results from these few studies is also
hampered by their highly varying protocols in both exposure duration and response recording.
Sällsten et al. (2006) and Barregård et al. (2006; 2008) from the University of Gothenburg
(Sweden) used a conventional stove to generate woodsmoke. Blood and urine measurements
after four-hour exposures in healthy volunteers suggested that woodsmoke was associated with
systemic inflammation and an increased tendency towards blood coagulation and lipid
peroxidation (Barregard et al., 2006). In addition, exposure to woodsmoke increased markers of
inflammation in distal airways (Barregard et al., 2008). The exposure was also followed by
significant up-regulation of the repair gene hOGG1 in human peripheral blood mononuclear
cells, but no direct genotoxic effects were observed, possibly due to the timing of sampling or
enhanced cellular repair (Danielsen et al., 2008).
Sehlstedt et al. (2010b) from the University of Umeå (Sweden) exposed healthy volunteers for
three hours to woodsmoke aerosol from incomplete combustion dominated by carbonaceous PM
composition. They found that the antioxidant glutathione concentrations were significantly
increased in bronchoalveolar lavage fluid at 24 hours after exposure to woodsmoke, together
with an increase in upper airway symptoms. Lung function, exhaled NO concentration or
parameters of systemic or airway inflammation were not significantly altered.
Riddervold et al. (2011) from Aarhus University (Denmark) conducted two levels of three-hour
exposure to woodsmoke aerosol (PM2.5 220 µg/m3
and 354 µg/m3
) among non-smoking atopic
human participants with normal lung function and normal bronchial reactivity. Statistically
significant increases in the subjectively rated mucosal irritation were observed by woodsmoke
aerosol without any major difference between the two exposure levels.
Solomon et al. (2003) from the University of California (United States) have published results in a
public research report on controlled exposures among three different subjects groups to rice straw
combustion aerosol adjusted to share features with poor combustion in agricultural open fires. The
aerosol was physico-chemically somewhat different from conventional wood combustion aerosols;
for example, about 25% of the emitted and diluted PM were larger than 1 µm in diameter (up to
30 µm), while in wood combustion practically all PM material is smaller than 1 µm in diameter.
Even though the rice straw combustion aerosol was dominated by carbonaceous material, the types
of organic may have differed from those of wood combustion aerosol. The results indicated that
airway inflammation, as measured by neutrophils, macrophages, lymphocytes and IL-8 measured
in bronchoalveolar lavage fluid at 6 hours after a 30-minute exposure, increased as a function of
smoke concentration and of the total dose of smoke aerosol in all subject groups. However, none of
the exposures caused significant changes in spirometric pulmonary functions measured during the
exposures. Asthmatic subjects exhibited greater overall inflammatory responses than healthy
individuals and subjects with allergic rhinitis. The authors suggested that the anti-inflammatory
Health effects of black carbon
page 45
mechanisms involved in controlling the inflammatory response to single-smoke exposure were
probably exceeded in responses to the serial-day exposures.
Mechanisms of toxicity
There were only five experimental studies on urban air fine PM (three in macrophages, one in
mice, two using a cell-free assay of oxidative potential) that fulfilled the criteria for selection of
original science articles: animal, cell or other in vitro studies on urban air fine PM with in-depth
chemical characterization of the investigated PM samples (EC, OC/particulate organic matter
(POM), major inorganic ions, transition metals and insoluble soil mineral components). Three
European studies from the Chemical and Biological Characterisation of Ambient Air Coarse,
Fine and Ultra Fine Particles for Human Health Risk Assessment in Europe (PAMCHAR)
project (Happo et al., 2008; Jalava et al., 2008; 2009) have systematically compared the
toxicological associations of EC with relevant organic constituents (PAHs, dicarboxylic acids,
endotoxin) and inorganic constituents (ions, water-soluble and water-insoluble elements) of the
same urban air fine PM samples collected from six different source environments characterized
with chemical markers. EC was not significantly associated with any toxicological responses,
while dicarboxylic acids (for example, oxalate, malonate and succinate, which are
atmospherically transformed organics from semi-volatiles of combustion sources) and transition
metals from heavy oil combustion (vanadium, nickel) and soil-derived mineral compositions
were positively associated with the inflammatory activity of fine PM. PAHs from residential
biomass and coal combustion were positively associated with cytotoxicity in macrophages and
immunotoxicity in the mouse lungs. The findings in these in vivo and in vitro studies agreed well
with each other. Moreover, they agree very well with the findings of Hu et al. (2008), who
showed that the redox activity in rat alveolar macrophages and in cell-free dithiothreitol assay by
quasi-ultrafine PM0.25 samples collected in the communities of the Los Angeles-Long Beach
harbour (United States) was most strongly associated with the water-soluble and water-insoluble
organics, PAHs and vanadium. In continuation, Verma et al. (2011) demonstrated that the
oxidative potential of Los Angeles quasi-ultrafine PM0.25 in the dithiothreitol assay was
progressively lost by removing semi-volatile organics by heating the PM samples at 50–200 C,
which does not affect EC or metals. In a regression analysis, the cellular generation of reactive
oxygen species was associated especially with OC and PAHs.
Some recent toxicological studies on wood combustion emission PM (Jalava et al., 2010) and on
fossil diesel emission PM (Biswas et al., 2009) also stressed the role of organic constituents, for
example, PAHs or various water-soluble organics, including organic acids, in toxicity and
suggested a carrier role for EC. Given the large number of exposure variables compared to the
number of observations in animals or cell cultures, the causality of certain chemical
compositions cannot be assessed for certain.
So far, very few studies have aimed to distinguish between the effects due to BC and those of
other constituents of fine PM, or have really focused on the mechanisms of action of BC in PM
mixtures. There are also suggestions that different chemical compositions may share similar
biological mechanisms, leading to the adverse effects seen in experimental studies. Thus, on the
basis of present knowledge, there is no convincing evidence supporting the hypothesis that some
of the mechanisms of action of ambient air PM are specific for BC.
Inhalation toxicity studies using pure carbon particles at concentrations of environmental relevance
have uniformly shown no adverse effects, even when sensitive animal models have been used
(Lippmann & Chen, 2009). Because PM mass equals total EC mass in these studies, they will not
Health effects of black carbon
page 46
expand an understanding of the role of EC or BC in the complex toxicity of ambient air PM mass
in any way other than suggesting that pure EC may not possess strong toxic properties.
Conclusions
At present, it is not possible to say definitively whether health effects due to exposure to BC or
PM mass are different qualitatively (for example, different health outcomes) and/or
quantitatively from each other. This is partly due to the fact that an insufficient number of
controlled health studies have been implemented which involve human subjects with
simultaneous BC or EC measurements and other PM speciation.
The present very few toxicological in vivo and in vitro studies of urban air and combustion
emission fine PM with in-depth chemical characterization of the investigated PM samples
suggest that EC (or BC) may not be a directly toxic component of fine PM. It may, however,
operate as a universal carrier of a wide variety of chemical constituents of varying toxicity, such
as semi-volatile organics, to sensitive pulmonary and cardiovascular targets. Because of this role,
EC may very well act as a good indicator for combustion-derived and potentially very harmful
parts of PM. More information is, however, needed on the mechanisms of the potential toxicity
of BC (or EC) both alone and as a component of urban air fine and ultrafine PM. The
comparison of toxicity should be done with simultaneously released, combustion-derived and
subsequently transformed organic constituents (for example, PAHs, quinones, dicarboxylic
acids) and metals (for example, nickel, vanadium, arsenic), and with other transition metals and
soil minerals released from other combustion sources such as the metal industry and noncombustion
mechanics in traffic (brakes, tyres, etc.).
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exhaust in human subjects. Occupational and Environmental Medicine, 56(4):222–231.
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Health effects of black carbon
page 51
Annex 1
LITERATURE SEARCH CRITERIA
Metrics used to estimate the exposure to BC in health studies: strengths and
weaknesses
The literature search was made in the Information Sciences Institute Web of Science database,
which was considered to give the largest coverage of relevant literature to the present topic.
There were two reasons to limit the literature search to 2000‒2010. First, it was discovered that
the biases associated with the wide variety of commonly used optical and thermal optical
measurement techniques had not been systematically investigated and revealed before the large
inter-comparison workshops or campaigns conducted since 2002. Second, the review of
literature performed for Chapter 3 (Effects of BC exposure observed in epidemiological
studies) summarized the older comparisons of optical BS and Abs determinations reflecting
soot with more specific thermal optical methods than for EC.
The literature search was performed on 6 April 2011 as described below.
Topic 1 = (partic*) AND Topic 2 (black carbon AND elemental carbon AND light absorption)
AND Topic 3: (intercomp* OR inter-comp* OR correl* OR intercorrel* OR inter-correl*). This
search resulted in 33 records. The criteria for the selected articles were: review article; and
original science articles with inter-comparison of at least three methods in one urban source
environment, or comparison of at least two methods in more than one urban source environment
or in one environment during different seasons.
Assessment of exposure to BCP in epidemiological studies
The literature search was conducted within the Web of Science database, which provides the
most complete coverage of papers dealing with exposure assessment. The search syntaxes used
were:
 search 1 (absorbance OR “black smoke” OR “elemental carbon” or “black carbon”) AND
“air pollution” AND exposure;
 search 2 (absorbance OR “black smoke” OR “elemental carbon” or “black carbon”) AND
personal AND indoor.
In addition, the reference lists of the papers identified were used to find additional literature.
Since the overall goal of the review was to evaluate whether BCP has any health effects that are
independent of those of PM2.5, papers that included assessments of exposure to both PM2.5 and
BCP were preferred when compiling the tables for this working paper. Further, studies in
northern America and Europe were preferred in view of the focus area of the Task Force. Studies
with central monitoring sites close to traffic, as well as studies where ambient PM2.5 and BCP
were not measured at the same location, were excluded. To avoid repetition, only one study per
study area was usually included in each table, unless the studies were conducted at clearly
different times.
Health effects of black carbon
page 52
Effects of BC exposure observed in epidemiological studies
For time-series studies on daily mortality and hospital admissions and cohort studies, the
literature search was used as described in Janssen et al. (2011), for which a search was conducted
for peer-reviewed literature in Medline (January 2010) for epidemiological studies that evaluated
the health effects of (a measure of) PM mass as well as health effects of (a measure of) BCP.
Here the following keywords were used: (British smoke or black smoke or black carbon or
elemental carbon or EC or soot or absorbance or absorption coefficient) AND (Particles or
particulate matter or particulates or particulate air pollution or fine partic* or “PM10” or
“PM2.5” or “PM(2.5)” or “PM(10)” or sulfate* or sulphate*) AND (mortality or cohort or
hospital or emergency).
Panel studies among children were identified by adding (panel or children or subjects or
participants) to the keywords. For health effects due to long-term exposure, only cohort studies
were considered as these have been the most relevant for standard-setting.
All abstracts were scanned and papers retrieved that potentially included effects estimates for
PM mass as well as for BCP. For the time-series studies, APED at St George’s, University of
London, was also used to identify suitable studies. This database comprises standardized
estimates extracted from ecological time-series studies identified by systematic review that meet
certain quality criteria, with the last retrieval performed on 15 May, 2009 (Smith et al., 2009).
APED was searched for estimates related to the effects of PM10, PM2.5, PM13, TSP or sulfate as
well as BS, BC or EC. For the panel studies, an evaluation was made of all papers included in
the Weinmayr et al. (2010) review that provided effects estimates for PM10 to see whether they
reported effects estimates of BCP as well.
Forty papers were identified on time-series studies on daily mortality or hospital admissions
that included area-specific estimates for both PM and BCP, 11 papers on panel studies among
symptomatic children and 17 papers on cohort studies. The APED search identified six papers
that were not identified in the Medline search, but four were excluded because more recent
estimates from the same city were available. The Medline search identified four papers
published after the last APED systematic review in May 2009. For the panel studies, the
Medline search only identified the paper on the combined effects estimates of all 28 panels
from 14 countries in the PEACE study, whereas the Weinmayr et al. (2010) review included 14
papers that provided country-specific estimates. All other papers included in the Weinmayr et
al. (2010) paper that included effects estimates for PM mass as well as BCP were also
identified in the Medline search. The Medline search identified one panel study not included in
the Weinmayr et al. (2010) paper that was published in 2009 (after the retrieval date for the
Weinmayr review).
For the time-series studies on daily mortality and hospital admissions, five papers on total
suspended particulates were excluded as more recent data, including effects estimates for PM10,
were available for most of the cities. Also, one paper was excluded on a rare health endpoint
(hospital admissions for headache), resulting in a total of 34 papers included in the review. All
studies included were adjusted for major confounders, specifically seasonality and non-linear
function of temperature and relative humidity. For the panel studies, two papers were excluded
that focused on heterogeneity/subgroup analysis of effect presented in other papers. For the
cohort studies, two papers on birth outcomes were excluded.
Health effects of black carbon
page 53
Evidence from toxicology, including human clinical studies
After the initial search in Medline and Pubmed (21 March 2011) according to the search protocol
described below, additional searches (April 2011) for human clinical studies were performed in
Pubmed with similar search terms (black smoke, black carbon, elemental carbon, soot, EC,
particulate matter, carbonaceous component, human clinical, toxicity, inhalation exposure) to
supplement the database. References published in epidemiological journals were removed
manually to reduce noise, which resulted in a database of 619 papers. Thereafter, articles in the
database with some term of BC were assigned to a group (epi, mechanisms, method, tox animal,
tox human, tox in vitro). A total of 359 papers were assigned to specific groups, and 260 papers
remained unfiled. This resulted in 76 articles on human clinical studies and 50 articles filed
under mechanisms; 110 papers presented in vitro toxicology studies and 52 papers presented
animal toxicology studies.
Strategy for literature search on controlled human studies
Initial start in Medline (1990‒2011) using the following search protocol:
1 exp Inhalation Exposure/ (4675)
2 exp Toxicity Tests/ (73653)
3 (toxic* or exposure* or inflammation or cardiovascular* or pulmonar* or lung or inhal* or
respirat* or vascular* or allergic* or airway*).mp. [mp=protocol supplementary concept, rare
disease supplementary concept, title, original title, abstract, name of substance word, subject
heading word, unique identifier] (2733444)
4 1 or 2 or 3 (2774555)
5 *Smoke/ (3137)
6 exp Soot/ (290)
7 *Vehicle Emissions/ae, po, to (1503)
8 ((diesel adj3 exhaust) or wood smoke or woodsmoke or black smoke or combustion or black
carbon or (elemental adj3 carbon) or carbon black or carbonaceous nanopartic* or soot).ti,ab.
(11112)
9 (((air or airborne or atmospheric or ambient or carbon or ultrafine) adj6 (particle* or
particulate*)) or (diesel adj3 exhaust) or wood smoke or woodsmoke or black smoke or black
carbon or (elemental adj3 carbon) or carbon black or carbonaceous nanopartic* or soot or
(combustion adj3 (smoke or carbon))).ti. (5086)
10 5 or 6 or 7 or 8 (14664)
11 4 and 10 (7427)
12 (human* or man or men or volunteer*).mp. [mp=protocol supplementary concept, rare
disease supplementary concept, title, original title, abstract, name of substance word, subject
heading word, unique identifier] (11940675)
13 11 and 12 (4460)
14 13 and 9 (744)
15 limit 14 to year=1990-2011 (665)
Combined with a search in PubMed (only last year to catch e-publications not yet published in
Medline) according to the following protocol (111 records):
#1 “diesel exhaust”[ti] OR “wood smoke”[ti] OR woodsmoke[ti] OR “black smoke”[ti] OR
combustion[ti] OR “black carbon”[ti] OR “elemental carbon”[ti] OR “carbon black”[ti] OR
“carbonaceous nanoparticle”[ti] OR “carbonaceous nanoparticles”[ti] OR soot[Title]
05:54:24 3540
Health effects of black carbon
page 54
#2 toxic*[tiab] or exposure*[tiab] or inflammation[tiab] or cardiovascular*[tiab] or
pulmonar*[tiab] or lung[tiab] or inhal*[tiab] or respirat*[tiab] or vascular*[tiab] or
allergic*[tiab] or airway*[tiab]; limits: published in the last 1 year 06:01:29 135983
#3 #1 AND #2 limits: published in the last 1 year 06:04:31 111.
An additional literature search was made in the Web of Science specifically addressing the
mechanisms of toxicity. The literature search covered 1990–2010 (and, to some extent, early
2011).
LITERATURE SEARCH (18 April 2011): Topic 1 = (urban air AND partic*) AND Topic 2 =
(EC OR OC OR POM OR elemental carbon OR organic carbon OR partic* organic matter)
AND Topic 3 = (toxic* OR inflammat* OR genotoxic* OR cytotoxic* OR blood coagul* OR
immunotoxic*) AND Topic 4 = (mouse OR rat OR macrophage* OR epithel*) AND Topic 5 =
(experimental OR exposure OR CAP* OR lung OR respir* OR heart OR blood OR brain). This
search resulted in identification of 46 papers.
The criteria for selection of the original science articles reviewed were: animal, cell and cell-free
in vitro studies on urban air fine PM with in-depth chemical characterization of the investigated
PM samples (EC, OC/POM, major ions, water-soluble and/or water-insoluble elements,
including transition metals and soil mineral components). The search results were complemented
by papers cited in the selected studies and by relevant new literature appearing as linked to the
identified papers in the Web of Science.
References
Janssen NAH et al. (2011). Black carbon as an additional indicator of the adverse health effects of
airborne particles compared to PM10 and PM2.5. Environmental Health Perspectives, 119:1691–1699
(http://dx.doi.org/10.1289/ehp. 1003369, accessed 4 January 2012).
Smith KR et al. (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: health
implications of short-lived greenhouse pollutants. Lancet, 74:2091–2103.
Weinmayr G et al. (2010). Short-term effects of PM10 and NO2 on respiratory health among children with
asthma or asthma-like symptoms: a systematic review and meta-analysis. Environmental Health
Perspectives, 118:449–457.
Health effects of black carbon
page 55
Annex 2
CONTRIBUTORS TO THE REPORT
Participants in the 14th
Meeting of the Task Force on Health Aspects of Air Pollution,
Bonn, 12–13 May 2011
Ismayil Afandiyev Centre of Toxicology, Ministry of Health, Azerbaijan
Markus Amann International Institute for Applied Systems Analysis, Austria
Dana Berdymbayeva Ministry of Health, Kazakhstan
Hans Berkhout National Institute for Public Health and the Environment,
Netherlands
Flemming Casseea
National Institute for Public Health and the Environment,
Netherlands
Aaron Cohenb
Health Effects Institute, United States of America
Christophe Declercq French Institute for Public Health Surveillance, France
Olga Doronina AN Sysin Research Institute of Human Ecology and
Environmental Health, Russian Federation
Marie Fiori Ministry of Health, France
Nino Giuashvili Ministry of Labour, Health and Social Affairs, Georgia
Patrick Goodman Dublin Institute of Technology, Ireland
Alison Gowers Health Protection Agency, United Kingdom
Gerard Hoeka
Utrecht University, Netherlands
Mike Holland European Medical Research Council, United Kingdom
Fintan Hurleyb
Institute of Occupational Medicine, United Kingdom
Nicole AH Janssena
National Institute for Public Health and the Environment,
Netherlands
Klea Katsouyannib
University of Athens Medical School, Greece
Mihail Kochubovski Institute of Public Health of the Republic of Macedonia, The
former Yugoslav Republic of Macedonia
Michal Krzyzanowskia,c
WHO European Centre for Environment and Health, Bonn
Titus Kyearklund Swedish Environmental Protection Agency, Sweden
Timo Lankia
National Institute for Health and Welfare, Finland
Tom Lubenb
US Environmental Protection Agency, United States of
America
Arben Luzati National Institute of Public Health, Albania
Silvia Medina French Institute for Public Health Surveillance, France
Brian Miller Institute of Occupational Medicine, United Kingdom
Hans-Guido Mücke WHO collaborating centre for air quality management &
air pollution control, Federal Environment Agency, Germany
Christian Nagl Environment Agency, Austria
Rifat Pamuk Ministry of Health, Turkey
Tomasz Pecka Institute of Environmental Protection, Poland
Aida Petikyan State Hygiene and Antiepidemic Inspectorate, Ministry of
Health, Armenia
Andrei Pilipchuk Department of Regulation Impacts on Air and Water
Resources, Ministry of Natural Resources and Environmental
Protection, Belarus
Health effects of black carbon
page 56
Yorghos Remvikos Université de Versailles, France
Raimo O Salonena
National Institute for Health and Welfare, Finland
Wolfgang Schoepp International Institute for Applied Systems Analysis, Austria
Per E. Schwarze Norwegian Institute of Public Health, Norway
Anara Shakhmatova Scientific and Production Centre for Preventive Medicine,
Kyrgyzstan
Krzysztof Skotak National Institute of Public Health, Poland
Peter Straehl Air Pollution Control and NIR Division, Federal Office for the
Environment, Switzerland
Olena Turos OM Marzeiev Institute for Hygiene and Medical Ecology of
NAMSUF, Ukraine
Leendert Van Breeb
Netherlands Environmental Assessment Agency, Netherlands
Andre Zuber European Commission, Belgium
Reviewers not participating in the Task Force Meeting
Ross H Andersonb
St George’s, University of London, United Kingdom
Richard Atkinsonb
St George’s, University of London, United Kingdom
Bert Brunekreefa
Utrecht University, Netherlands
Maria Constantinib
Health Effects Institute, United States of America
Miriam E Gerlofs-Nijlanda
National Institute for Public Health and the Environment,
Netherlands
Roy Harrisonb
University of Birmingham, United Kingdom
Paul Fischera
National Institute for Public Health and the Environment,
Netherlands
Frank Kellyb
King’s College London, United Kingdom
Nino Kuenzlib
Swiss Tropical and Public Health Institute, Basle, Switzerland
Regula Rappb
Basle University, Switzerland
Milena Simic-Lawsond
St George’s, University of London, United Kingdom
Tobias Stoegerb
Helmholtz Zentrum, Munich, Germany
Katherine Walkerb
Health Effects Institute, United States of America
a
Co-authors of the text.
b
Invited reviewers.
c
Coordinator of the project.
d
Contributor to Chapter 3.
Health effects of black carbon
page 57
Annex 3
SUPPLEMENTARY MATERIAL TO THE REVIEW OF EPIDEMIOLOGICAL STUDIES
Annex 3A. Estimation of EC from BS or Abs of PM2.5 filters
Annex 3B. Single-city estimates for mortality and hospital admissions in studies that include
both PM10 and BS
Annex 3C. Study-specific effects estimates for mortality in studies that include both PM2.5
and EC
Annex 3D. Study-specific effects estimates for hospital admissions and emergency
department visits in studies that include both PM2.5 and EC
Annex 3E. Effects estimates for EC and other particle components
Annex 3F. Study-specific estimates for asthma and cough in panel studies among asthmatic
and symptomatic children
Annex 3G. Effects of PM2.5 and BC in cohort studies of respiratory health in children
Annex 3H. Comparison of calculated health benefits of traffic abatement measures using
PM2.5 or BCP
Healtheffectsofblackcarbon
page58
Annex 3A. Estimation of EC from BS or Abs of PM2.5 filters
Table 3A 1. Estimation of EC from BS or Abs of PM2.5 filters
Reference Study
period
Locations Method R
a
Regression equation Increase in EC per
10 µg/m
3
increase
in BS
b
EC BS/Abs Intercept Slope
Edwards et al., 1983 NA Washington, United States; urban + traffic Thermal optical
c
BS 0.82 -0.1 0.13 1.3
Erdman, Israel &
Ulrich, 1993
1989/1990 Berlin, Germany; urban VDI 3481 BS 0.93 0 0.23 1.8
Schaap & Denier
van der Gon, 2007
1998/1999;
2001/2002
Netherlands; urban Sunset BS 0.92 0.32 0.09 0.9
Netherlands; rural Sunset BS 0.87 0.16 0.06 0.6
Kinney et al., 2000 1996 New York, United States; urban + traffic Sunset Abs 0.95 0 0.83 0.8
Janssen et al., 2001
d
1997/1998 Netherlands; urban + traffic VDI 2465-part 1 Abs 0.92 0 2.11 1.7
Lena et al., 2002 1999 New York, United States; urban + traffic Sunset Abs 0.90 0 0.52 0.5
Adams et al., 2002 1999/2000 London, United Kingdom; urban + traffic NIOSH Abs 0.98 0 1.21 1.2
Cyrys et al., 2003 1999/2000 Munich, Germany; urban + traffic VDI 2465-part 1 Abs 0.97 -1.19 2.02 1.6
Cyrys et al., 2003 1999/2000 Netherlands; rural, urban, traffic VDI 2465-part 1 Abs 0.97 -0.26 1.61 1.3
Cyrys et al., 2003 1999/2000 Sweden; rural, urban, traffic VDI 2465-part 1 Abs 0.85 0.36 0.90 0.7
Mean
e
1.1
Minimum 0.5
Maximum 1.8
a
Coefficient of the correlation between EC and BCP concentrations.
b
Results from studies that have used the VideoOverIP (VDI) protocol were divided by 1.25, as this method has been shown to overestimate EC by, on average, 25% (Schmid et al., 2001).
An increase in 1 unit of Abs is considered to equal an increase of 10 µg/m3
BS, according to Roorda-Knape et al. (1998).
c
Measurement method not further specified.
d
Paper presents regression equation as Abs=EC; inverse equation, forced through zero, calculated using the original data from the study.
e
Mean, minimum and maximum of all 11 values; mean for BS only=1.16 (range: 0.6–1.8; n=4); mean for Abs only=1.12 (range 0.5–1.7; n=7).
Source: Janssen et al., 2011 (supplemental material, Table A1).
Healtheffectsofblackcarbon
page59
Annex 3B. Single-city estimates for mortality and hospital admissions in studies that include both PM10 and BS
Table 3B 1. All-cause mortality, all ages
a
Reference Location Estimate PM10 Estimate BS IQR
(µg/m
3
)
Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Verhoeff et al., 1996 Amsterdam 0.00060 0.00038 0.00171 0.00077 22 10 38 12 0.51 1986–1992 Lag 0
Roemer & van Wijnen,
2001a
Amsterdam 0.00027 0.00020 0.00324 0.00093 18 7 39 10 NA 1987–1994 Lag 1
Katsouyanni et al., 2001 Athens
d
0.00153 0.00028 0.00065 0.00012 NA NA 40 64 NA 1992–1996 Lag 0–1
Katsouyanni et al., 2001 Barcelona 0.00093 0.00018 0.00157 0.00027 24
f
18
f
60 39 NA 1991–1996 Lag 0–1
Katsouyanni et al., 2001 Birmingham 0.00028 0.00026 0.00034 0.00047 15
f
7
f
21 11 NA 1992–1996 Lag 0–1
Katsouyanni et al., 2001 Cracow
e
0.00013 0.00035 -0.00021 0.00021 NA NA 54 36 NA 1990–1996 Lag 0–1
Zeghnoun et al., 2001a Le Havre 0.00079 0.00057 0.00026 0.00085 24 12 36 16 0.70 1990–1995 PM lag 1/
BS lag 0–1
Katsouyanni et al., 2001 London 0.00069 0.00017 0.00093 0.00030 14
f
8
f
25 11 NA 1992–1996 Lag 0–1
Bremner et al., 1999 London 0.00026 0.00023 0.00074 0.00038 NA NA 28 13 NA 1992–1994 Lag 1
Hoek et al., 2000 Netherlands 0.00018 0.00008 0.00040 0.00010 23
f
9
f
34 10 0.77 1986–1994 Lag 1
Zeghnoun et al., 2001b Paris 0.00066 0.00020 0.00043 0.00015 15 14 22 16 NA 1990–1995 Lag 1
Katsouyanni et al., 2001 Paris 0.00043 0.00023 0.00038 0.00015 13
f
15
f
22 21 NA 1991–1996 Lag 0–1
Zeghnoun et al., 2001a Rouen 0.00024 0.00040 0.00035 0.00083 21 14 33 19 0.73 1990–1995 Lag 1
Anderson et al., 2001 West Midlands 0.00008 0.00042 0.00036 0.00064 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.34 (0.23–0.47) 0.52 (0.37–0.66)
Pooled random effects 0.48 (0.18–0.79) 0.68 (0.31–1.06)
Heterogeneity chi-squared (df=6) Q=19.9 P=0.003 Q=19.2 P=0.004
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Excluded from meta-analyses because PM10 was partly derived from BS.
f
Taken from APHEA II paper on hospital admissions (Le Tertre et al., 2002).
Source: Janssen et al., 2011 (supplemental material, Tables B1–10).
Healtheffectsofblackcarbon
page60
Table 3B 2. CVD mortality, all ages
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Analitis et al., 2006 Athens
e
0.00167 0.00045 0.00069 0.00018 NA NA 40 64 NA 1992–1996 Lag 0–1
Analitis et al., 2006 Barcelona 0.00055 0.00032 0.00137 0.00050 24
c
18
f
60 39 NA 1991–1996 Lag 0–1
Analitis et al., 2006 Birmingham 0.00021 0.00040 0.00039 0.00071 15
c
7
f
21 11 NA 1992–1996 Lag 0–1
Analitis et al., 2006 Cracow
e
0.00032 0.00052 -0.00007 0.00031 NA NA 54 36 NA 1990–1996 Lag 0–1
Zeghnoun et al., 2001a Le Havre 0.00252 0.00126 0.00164 0.00155 24 12 36 16 0.70 1990–1995 PM lag1;
BS lag 0–3
Analitis et al., 2006 London 0.00091 0.00028 0.00156 0.00046 14
f
8
f
25 11 NA 1992–1996 Lag 0–1
Bremner et al., 1999 London 0.00055 0.00031 0.00117 0.00066 NA NA 28 13 NA 1992–1994 Lag 1
Hoek et al., 2000 Netherlands 0.00019 0.00018 0.00079 0.00020 23
f
9
f
34 10 0.77 1986–1994 lag 0–6
Analitis et al., 2006 Netherlands 0.00017 0.00016 0.00026 0.00027 23
f
9
f
33 9 NA 1990–1995 Lag 0–1
Hoek et al., 2001 Netherlands 0.00015 0.00018 0.00071 0.00020 23
f
9
f
34 10 0.77 1992/1986–
1994
g
Lag 0–6
Zeghnoun et al., 2001b Paris 0.00086 0.00037 0.00036 0.00029 15 14 22 16 NA 1990–1995 PM lag 2;
BS lag 1
Analitis et al., 2006 Paris 0.00081 0.00047 0.00063 0.00029 13
f
15
f
22 21 NA 1991–1996 Lag 0–1
Zeghnoun et al., 2001a Rouen 0.00106 0.00069 0.00276 0.00155 21 14 33 19 0.73 1990–1995 Lag 1
Anderson et al., 2001 West Midlands 0.00041 0.00061 0.00089 0.00092 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.45 (0.22–0.68) 0.73 (0.41–1.06)
Pooled random effects 0.60 (0.23–0.97) 0.90 (0.40–1.41)
Heterogeneity chi-squared (df=6) Q=9.9 P=0.127 Q=10.2 P=0.116
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Excluded from meta-analyses because PM10 was partly derived from BS.
f
Taken from APHEA II paper on hospital admissions (Le Tertre et al., 2002).
g
1992–1994 for PM10 ; 1986–1994 for BS.
Healtheffectsofblackcarbon
page61
Table 3B 3. Respiratory diseases mortality, all ages
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Analitis et al., 2006 Athens
e
0.00101 0.00122 0.00006 0.00048 NA NA 40 64 NA 1992–1996 Lag 0–1
Analitis et al., 2006 Barcelona 0.00117 0.00075 0.00374 0.00100 24
f
18
f
60 39 NA 1991–1996 Lag 0–1
Analitis et al., 2006 Birmingham 0.00003 0.00078 0.00069 0.00130 15
f
7
f
21 11 NA 1992–1996 Lag 0–1
Analitis et al., 2006 Cracow
e
0.00529 0.00216 0.00357 0.00132 NA NA 54 36 NA 1990–1996 Lag 0–1
Zeghnoun et al.,
2001a
Le Havre 0.00200 0.00196 0.00249 0.00294 24 12 36 16 0.70 1990–1995 PM lag 2;
BS lag 0–1
Analitis et al., 2006 London 0.00022 0.00044 -0.00034 0.00073 14
f
8
f
25 11 NA 1992–1996 Lag 0–1
Bremner et al., 1999 London 0.00128 0.00050 0.00190 0.00084 NA NA 28 13 NA 1992–1994 Lag 3
Analitis et al., 2006 Netherlands 0.00031 0.00036 0.00029 0.00061 23
f
9
f
33 9 NA 1990–1995 Lag 0–1
Dab et al., 1996 Paris 0.00155 0.00059 0.00069 0.00048 NA NA 51 32 NA 1987–1992 PM lag 0–1;
BS lag1
Analitis et al., 2006 Paris -0.00121 0.00095 0.00063 0.00029 13
f
15
f
22 21 NA 1991–1996 Lag 0–1
Zeghnoun et al.,
2001a
Rouen 0.00176 0.00120 0.00201 0.00310 21 14 33 19 0.73 1990–1995 Lag 0–1
Anderson et al., 2001 West Midlands -0.00058 0.00100 0.00006 0.00153 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.31 (-0.16–0.78) 0.70 (-0.05–1.45)
Pooled random effects 0.31 (-0.23–0.86) 0.95 (-0.31–2.22)
Heterogeneity chi-squared (df=6) Q=6.9 P=0.329 Q=12.5 P=0.051
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Excluded from meta-analyses because PM10 was partly derived from BS.
f
Taken from APHEA II paper on hospital admissions (Le Tertre et al., 2002).
Healtheffectsofblackcarbon
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Table 3B 4. Hospital admissions, respiratory diseases, elderly people
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Atkinson et al., 2001 Barcelona 0.00198 0.00060 -0.00070 0.00083 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Atkinson et al., 2001 Birmingham 0.00090 0.00061 0.00286 0.00115 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Prescott et al., 1998 Edinburgh 0.00208 0.00304 0.00305 0.00338 NA NA 21 9 0.4 1992–1995 Lag 1–3
Atkinson et al., 2001 London 0.00040 0.00036 -0.00111 0.00068 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 1999b London 0.00096 0.00041 0.00082 0.00063 NA NA 29 13 0.6–0.7 1992–1994 Lag 3
Atkinson et al., 2001 Netherlands 0.00119 0.00025 0.00000 0.00036 23 9 40 13 0.5–0.8
e
1992/1989–1995 f
Lag 0–1
Atkinson et al., 2001 Paris -0.00010 0.00062 0.00050 0.00046 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Anderson et al., 2001 West Midlands -0.00045 0.00069 -0.00018 0.00100 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.85 (0.49–1.20) –0.07 (–0.58–0.44)
Pooled random effects 0.70 (0.00–1.40) –0.06 (–0.53–0.41)
Heterogeneity chi-squared (df=5) Q=13.1 P=0.023 Q=5.4 P=0.372
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Atkinson et al. (2001) (three cities were not included in this review as no data were available on BS).
f
1992–1995 for PM10; 1989–1995 for BS.
Healtheffectsofblackcarbon
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Table 3B 5. Hospital admissions, respiratory diseases – asthma and chronic obstructive pulmonary disease, ages 65 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Atkinson et al., 2001 Barcelona 0.00257 0.00080 -0.00212 0.00116 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Atkinson et al., 2001 Birmingham 0.00050 0.00097 0.00218 0.00199 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 2001 London 0.00030 0.00056 0.00040 0.00103 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 1999 London 0.00227 0.00137 -0.00091 0.00099 NA NA 29 13 0.6–0.7 1992–1994 Lag 3
Atkinson et al., 2001 Netherlands 0.00109 0.00030 0.00070 0.00046 23 9 40 13 0.5–0.8
e
1992/1989–1995
f
Lag 0–1
Atkinson et al., 2001 Paris -0.00060 0.00098 0.00020 0.00077 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.95 (0.48–1.42) 0.36 (–0.81–1.54)
Pooled random effects 0.86 (0.03–1.70) 0.22 (–0.73–1.18)
Heterogeneity chi-squared (df=4) Q=8.3 P=0.08 Q=6.0 P=0.199
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Atkinson et al. (2001) (three cities were not included in this review as no data were available on BS).
f
1992–1995 for PM10; 1989–1995 for BS.
Healtheffectsofblackcarbon
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Table 3B 6. Hospital admissions, respiratory diseases, asthma, ages 0–14 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Atkinson et al., 2001 Barcelona 0.00266 0.00392 0.00989 0.00484 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Atkinson et al., 2001 Birmingham 0.00276 0.00100 0.00198 0.00199 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 2001 London 0.00060 0.00072 0.00109 0.00123 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 1999 London 0.00324 0.00203 0.00245 0.00179 NA NA 29 13 0.6–0.7 1992–1994 Lag 3
Atkinson et al., 2001 Netherlands -0.00090 0.00062 0.00139 0.00091 23 9 40 13 0.5–0.8
e
1992/1989–1995
f
Lag 0–1
Atkinson et al., 2001 Paris 0.00070 0.00113 0.00090 0.00087 13 15 23 23 0.5–0.8e
d
1992–1996 Lag 0–1
Anderson et al., 2001 West Midlands 0.00797 0.00321 0.00714 0.00329 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.24 (–0.56–1.05) 1.47 (0.41–2.54)
Pooled random effects 0.69 (–0.74–2.14) 1.64 (0.28–3.02)
Heterogeneity chi-squared (df=4) Q=9.5 P=0.050 Q=5.6 P=0.231
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Atkinson et al. (2001) (three cities were not included in this review as no data were available on BS).
f
1992–1995 for PM10; 1989–1995 for BS.
Healtheffectsofblackcarbon
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Table 3B 7. Hospital admissions, respiratory diseases, asthma, ages 15–64 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Atkinson, 2001 Barcelona 0.00040 0.00202 0.00208 0.00121 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Atkinson, 2001 Birmingham 0.00247 0.00121 0.00276 0.00239 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson, 2001 London 0.00139 0.00076 0.00178 0.00137 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson, 1999 London 0.00555 0.00249 0.00234 0.00224 NA NA 29 13 0.6–0.7 1992–1994 PM lag 3;
BS lag2
Atkinson, 2001 Netherlands 0.00040 0.0006 -0.00040 0.00093 23 9 40 13 0.5–0.8
e
1992/89–1995
f
Lag 0–1
Atkinson, 2001 Paris 0.00119 0.00097 0.00080 0.00076 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Anderson, 2001 West Midlands -0.00233 0.00419 -0.00284 0.00432 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.77 (-0.05–1.61) 0.52 (-0.50–1.55)
Pooled random effects 0.77 (-0.05–1.61) 0.52 (-0.50–1.55)
Heterogeneity chi-squared (df=4) Q=2.2 P=0.697 Q=3.1 P=0.549
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Atkinson et al. (2001) (three cities were not included in this review as no data were available on BS).
f
1992–1995 for PM10; 1989–1995 for BS.
Healtheffectsofblackcarbon
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Table 3B 8. Hospital admissions, cardiac diseases, ages 65 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Le Tertre et al., 2002 Barcelona 0.00050 0.00046 0.00066 0.00064 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Le Tertre et al., 2002 Birmingham -0.00014 0.00039 0.00114 0.00078 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Le Tertre et al., 2002 London 0.00104 0.00027 0.00214 0.00049 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Le Tertre et al., 2002 Paris 0.00020 0.00028 0.00057 0.00022 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Anderson et al., 2001 West Midlands 0.00030 0.00108 0.00169 0.00117 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.54 (0.21–0.87) 0.83 (0.47–1.19)
Pooled random effects 0.51 (0.04–0.98) 1.07 (0.27–1.89)
Heterogeneity chi-squared (df=3) Q=5.7 P=0.129 Q=8.8 P=0.032
a
Locations in bold included in meta-analysis
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Le Tertre et al. (2002). No information on cardiac admissions is available for the Netherlands. Three other cities are not included in
this review as no data were available on BS.
Healtheffectsofblackcarbon
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Table 3B 9. Hospital admissions, cardiac diseases, ages 65 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–BS
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Le Tertre et al., 2002 Barcelona 0.00068 0.00055 0.00130 0.00075 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Le Tertre et al., 2002 Birmingham 0.00031 0.00047 0.00168 0.00094 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Le Tertre et al., 2002 London 0.00096 0.00032 0.00227 0.00057 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Le Tertre et al., 2002 Paris 0.00053 0.00035 0.00042 0.00027 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.67 (0.28–1.06) 0.86 (0.41–1.30)
Pooled random effects 0.67 (0.28–1.06) 1.32 (0.28–2.38)
Heterogeneity chi-squared (df=3) Q=1.5 P=0.673 Q=9.9 P=0.019
a
Locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Le Tertre et al. (2002). No information on cardiac admissions is available for the Netherlands. Three other cities are not
included in this review as no data were available on BS.
Healtheffectsofblackcarbon
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Table 3B 10. Hospital admissions, ischaemic heart disease, ages 65 years
a
Reference Location Estimate PM10 Estimate BS IQR (µg/m
3
) Concentration
b
Correlation
(R) PM–
BSc
c
Period Selected
lag
d
Beta Standard
error
Beta Standard
error
PM10 BS PM10 BS
Le Tertre et al., 2002 Barcelona -0.00087 0.00087 0.00061 0.00120 24 18 56 39 0.5–0.8
e
1994–1996 Lag 0–1
Le Tertre et al., 2002 Birmingham 0.00033 0.00076 -0.00073 0.00150 15 7 25 13 0.5–0.8
e
1992–1994 Lag 0–1
Le Tertre et al., 2002 London 0.00104 0.00049 0.00265 0.00086 14 8 28 13 0.5–0.8
e
1992–1994 Lag 0–1
Atkinson et al., 1999 London 0.00298 0.00128 0.00288 0.00119 NA NA 29 13 0.6–0.7 1992–1994 PM lag 0;
BS lag 3
Le Tertre et al., 2002 Netherlands 0.00036 0.00018 0.00100 0.00026 23 9 40 13 0.5–0.8
e
1992/89–1995
c
Lag 0–1
Le Tertre et al., 2002 Paris 0.00168 0.00057 0.00116 0.00043 13 15 23 23 0.5–0.8
e
1992–1996 Lag 0–1
Anderson et al., 2001 West Midlands 0.00208 0.00209 0.00198 0.00220 NA NA 23 13 0.64 1994–1996 Lag 0–1
Percentage change per 10 µg/m
3
increase
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.50 (0.20–0.81) 1.13 (0.72–1.54)
Pooled random effects 0.68 (0.01–1.36) 1.13 (0.72–1.54)
Heterogeneity chi-squared (df=4) Q=8.8 P=0.066 Q=3.6 P=0.463
a
Locations in italics occur more than once; locations in bold included in meta-analysis.
b
Mean or median (µg/m3
).
c
Coefficient of the correlation between PM10 and BS concentrations.
d
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
e
Range in correlation coefficient for all eight cities described in Le Tertre et al. (2002). No information on cardiac admissions is available for the Netherlands. Three other cities were not included in
this review as no data were available on BS.
Healtheffectsofblackcarbon
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Annex 3C. Study-specific effects estimates for mortality in studies that include both PM2.5 and EC
Table 3C 1. Effects estimates for PM2.5 and EC for all-cause mortality
Reference Location Estimate PM2.5 Estimate EC IQR (µg/m
3
) Concentration
a
Correlation
(R) PM–EC
b
Period Selected
lag
c
Beta Standard
error
Beta Standard
error
PM2.5 EC PM2.5 EC
Klemm et al., 2004
d
Atlanta, United States 0.00544 0.00184 0.01343 0.01072 11.6 1.1 19.6 2.0 NA 1998–2000 Lag 01
Ostro et al., 2007
d
6 counties in California,
United States
0.00056 0.00037 0.00829 0.00776 14.6 0.8 19.3 1.0 0.53 2000–2003 Lag 3
Cakmak, Dales &
Blanco Vida, 2009
Santiago, Chile 0.00212 0.00025 0.01440 0.00063 35.8 5.3 NA 3.3 NA 1998–2006 PM NA;
EC lag 1
Percentage change per 1 µg/m
3
increase
e
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.17 (0.13–0.21) 1.45 (1.32–1.57)
Pooled random effects 0.19 (0.03–0.35) 1.45 (1.32–1.57)
a
Mean or median.
b
Coefficient of the correlation between PM2.5 and EC concentrations.
c
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
d
Age >65 years ; not all ages.
e
In supplement B of Janssen et al. (2011), the percentage change was calculated per 10 µg/m3
.
Source: Janssen et al., 2011 (supplemental material, Tables C1–3).
Healtheffectsofblackcarbon
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Table 3C 2. Effects estimates for PM2.5 and EC for cardiovascular mortality
Reference Location Estimate PM2.5 Estimate EC IQR (µg/m
3
) Concentration
a
Correlation
(R) PM–EC
b
Period Selected
lag
c
Beta Standard
error
Beta Standard
error
PM2.5 EC PM2.5 EC
Mar et al., 2000 Phoenix, United
States
0.00685 0.00236 0.04400 0.01820 8.5 1.2 12.0 1.3 0.84 1995–1997 Lag 1
Ostro et al., 2007
d
6 counties in
California, United
States
0.00105 0.00054 0.02574 0.01129 14.6 0.8 19.3 1.0 0.53 2000–2003 Lag 3
Cakmak, Dales &
Blanco Vida, 2009
Santiago, Chile 0.00327 0.00037 0.01736 0.00097 35.8 5.3 NA 3.3 NA 1998–2006 PM NA;
EC lag1
Percentage change per 1 µg/m
3
increase
e
Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.26 (0.20–0.32) 1.76 (1.57–1.96)
Pooled random effects 0.29 (0.07–0.50) 1.77 (1.08–3.08)
a
Mean or median.
b
Coefficient of the correlation between PM2.5 and EC concentrations.
c
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
d
Age >65 years, not all ages.
e
In supplement B of Janssen et al. (2011), the percentage change was calculated per 10 µg/m3
.
Table 3C 3. Effects estimates for PM2.5 and EC on respiratory mortality
Reference Location Estimate PM2.5 Estimate EC IQR (µg/m
3
) Concentration
a
Correlation
(R) PM–EC
b
Period Selected
lag
c
Beta Standard
error
Beta Standard
error
PM2.5 EC PM2.5 EC
Ostro et al., 2007
d
6 counties in California,
United States
0.00098 0.00103 -0.03298 0.02219 14.6 0.8 19.3 1.0 0.53 2000–2003 Lag 3
Cakmak, Dales &
Blanco Vida, 2009
Santiago, Chile 0.00648 0.00058 0.03453 0.00146 35.8 5.3 NA 3.3 NA 1998–2006 PM NA;
EC lag 2
a
Mean or median.
b
Coefficient of the correlation between PM2.5 and EC concentrations.
c
In cases where multiple lags are reported in Chapter 3, the estimate discussed by the author was used, as indicated in the APED as “selected lag”.
d
Age >65 years, not all ages.
Healtheffectsofblackcarbon
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Annex 3D. Study-specific effects estimates for hospital admissions and emergency department visits in studies that include both
PM2.5 and EC
Table 3D 1. Effects estimates for PM2.5, EC and sulfate on hospital admissions and emergency department visits
a
Reference Location Endpoint PM2.5 EC Sulfate IQR (µg/m
3
) Concentration (µg/m
3
)
Beta Standard
error
Beta Standard
error
Beta Standard
error
PM2.5 EC Sulfate PM2.5 EC Sulfate
Hospital admissions
Zanobetti &
Schwartz,
2006
Boston, United
States; elderly
people
Pneumonia
Acute myocardial
infarction
0.0037
0.0048
0.0015
0.0021
0.0540
0.0391
0.0159
0.0194
8.9 1.0 11.1 1.2
Ostro et al.,
2009
6 counties in
California,
United States;
children
All respiratory
diseases
Asthma
Bronchitis
Pneumonia
0.0027
0.0023
0.0034
0.0022
0.0008
0.0015
0.0015
0.0014
0.0640
0.0628
0.0525
0.0623
0.0277
0.0339
0.0341
0.0476
0.0199
0.0026
0.0449
0.0182
0.0089
0.0199
0.0211
0.0224
14.6 0.8 1.5 19.4 1.0 2.0
Peng et al.,
2009
119 counties in
United States;
elderly people
CVD
Respiratory diseases
0.00068
0.00031
0.00021
0.00035
0.01794
0.00998
0.00375
0.00599
0.00140
0.00266
0.00075
0.00140
9.5 0.4 3.1 12.2 0.6 2.6
Emergency department visits
Tolbert et al.,
2007
Atlanta, United
States; all ages
b
CVD
Respiratory
diseases
0.00046
0.00046
0.00056
0.00047
0.01295
-0.00349
0.00439
0.00313
-0.00026
0.00183
0.00161
0.00147
11.0 1.2 3.8 17.1 1.6 4.9
Cakmak et
al., 2009
c
Santiago, Chile;
all ages
All non-accidental
Respiratory diseases
0.00152
0.00241
0.00018
0.00028
0.02287
0.03531
0.00184
0.00247
0.02232
0.03185
0.00804
0.01092
40.3 4.8 2.3 NA 2.8 2.6
a
Significant effects (P<0.05) in bold.
b
Also estimates from additional endpoints available from three older papers that included a shorter study period (Metzger et al., 2004; Sarnat et al., 2008; Tolbert et al., 2000).
c
Sulfate estimated from sulfur.
Source: Janssen et al., 2011 (supplemental material, Table D1).
Healtheffectsofblackcarbon
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Annex 3E. Effects estimates for EC and other particle components
Table 3E 1. Effects estimates for EC and other particle components
a
Reference Location Endpoint Percentage increase per IQR IQR (µg/m
3
)
EC OC Sulfate Nitrate Zinc Potassium Silicon EC OC Sulfate Nitrate Zinc Potassium Silicon
Mortality
Mar et al.,
2000
b
Phoenix,
United States
All causes NS NS -3.0 NS NS 1.2 3.0 0.8 NA 0.06
Cardiovascular 5.2 4.4 NS NS 3.2
Klemm
et al., 2004
Atlanta,
United States
All causes 1.5 1.3 3.4 -0.1 1.1 2.4 3.9 1.3
Ostro et al.,
2007
6 counties in
California,
United States
All causes 0.7 0.6 0.2 0.1 0.6 0.2 0.0 0.8 4.6 1.5 5.5 0.01 0.08 0.15
Cardiovascular 2.1 1.6 0.6 1.5 2.2 0.5 0.6
Respiratory -2.6 -2.9 1.1 1.0 -0.5 0.5 1.5
Maynard
et al., 2007
Boston,
United States
All causes 2.3 1.1 0.2 2.3
Respiratory 3.7 2.1
Cardiovascular 1.5 -0.2
Stroke 4.4 2.0
Diabetes 5.7 2.9
Cakmak, Dales
& Blanco Vida,
2009
b
Santiago, Chile All causes 7.9 6.6 3.2 5.3 3.5 1.7 5.3 7.4 2.8 0.08 0.23 0.20
Cardiac 9.6 8.3 5.1 5.9 5.1 4.2
Respiratory 20.0 17.9 6.9 13.6 11.7 8.1
Hospital admission
Ostro et al.,
2009
6 counties in
California,
United States;
children
All respiratory 5.4 3.4 3.0 3.3 1.6 0.8 2.8
Asthma 5.3 4.0 0.4 2.4 1.8 0.3 2.9 0.8 4.5 1.5 5.6 0.01 0.08 0.15
Bronchitis 4.4 4.8 6.9 3.9 1.7 2.1 6.1
Pneumonia 5.3 4.5 2.8 2.2 2.0 0.7 4.3
Peng et al.,
2009
119 counties,
United States;
elderly people
Cardiovascular 0.7 0.7 0.4 0.5 0.2 0.4 3.2 3.1 1.6 0.07
Respiratory 0.4 0.8 -0.3 0.0 0.1
Emergency department visits
Sarnat et al.,
2008
c
Atlanta, United
States; all ages
CVD 2.5 2.4 0.7 0.2 1.3 3.0 0.8 NA NA NA NA NA NA NA
Respiratory -0.4 -0.3 2.0 -0.1 -0.3 0.2 -0.4
Cakmak
et al., 2009
d
Santiago, Chile;
all ages
All non-accidental 11.5 9.3 5.2 5.2 5.8 5.8 4.8 8.5 2.3 0.07 0.21 0.18
Respiratory 18.3 14.3 7.5 7.5 9.8 11.4
a
Percentage increase per IQR (significant effects (P<0.05) in bold).
b
NS=non-significant (effect estimates not reported in Chapter 3).
c
Estimates from Sarnat et al. (2008) used instead of Tolbert et al. (2007), despite shorter period (four instead of six years), as the Sarnat paper included more elements.
d
Sulfate estimated from sulfur.
Source: Janssen et al., 2011 (supplemental material, Table E1).
Healtheffectsofblackcarbon
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Table 3E 2. Results from single- and multipollutant models including BC and sulfate
Reference/location Health endpoint BCP
metric
R Sulfate-
BCP
a
Percentage change in RR
b
Sulfate single-
pollutant
Sulfate
multipollutant
BCP single-
pollutant
BCP
multipollutant
Hoek et al., 2000 Total mortality
CVD mortality
BS 0.65 3.2 (0.6–5.9)
2.1 (-1.9–6.3)
2.7 (-0.3–5.8)
0.8 (-3.7–5.4)
2.8 (1.7–3.8)
3.2 (1.6–4.8)
1.2 (-1.5–4.1)
2.9 (-1.3–7.4)
Anderson et al., 2001;
West Midlands,
United Kingdom
Respiratory admissions BS 0.30 0.8 (-1.3–2.9) NA 2.1 (-0.1–4.2) 2.4 (0.1–4.7)
Maynard et al., 2007 Total mortality BC 0.44 1.1 (0.01–2.0) 0.5 (-0.45–1.6) 2.3 (1.2–3.4) 2.2 (0.2–4.2)
Peng et al., 2009;
119 counties,
United States
c
Respiratory admissions
Cardiovascular admissions
EC 0.18 -0.3 (-1.1–0.5)
0.4 (-0.0–0.9)
-0.6 (-1.1–0.3)
0.0 (-0.5–0.6)
0.4 (-0.1–0.9)
0.7 (0.4–1.0)
0.0 (-0.1–0.8)
0.8 (0.3–1.3)
Cakmak, Dales &
Blanco Vida, 2009;
Santiago, Chile
d
Total mortality
Cardiac mortality
Respiratory mortality
EC 0.33 3.2 (1.4–5.0)
5.1 (2.4–8.0)
6.9 (1.9–12.1)
Lost significance 7.9 (7.2–8.6)
9.6 (8.5–10.8)
20.0 (18.2–21.9)
Remained
significantly
associated
Cakmak et al., 2009
Santiago, Chile
c
All non-accidental admissions
Respiratory admissions
EC 0.20 5.2 (1.5–9.1)
7.5 (2.4–12.8)
Lost significance 11.5 (9.6–13.5)
18.3 (15.6–21.2)
Remained
significant
a
Coefficient of the correlation between sulfate and BCP concentrations.
b
RRs expressed as reported in Chapter 3: IQR for Maynard (2007); Peng (2009); Cakmak, Dales & Blanco Vida (2009) and Cakmak et al. (2009); 1st–199th percentile for Hoek (2000);
10–1990th percentile for Anderson (2001).
c
Multipollutant estimates also adjusted for OC matter, nitrate, silicon, and sodium and ammonium ions.
d
Multipollutant estimates also adjusted for 16 other PM components and 3 gases; quantitative estimates for multipollutant models requested from the authors but not received.
Source: Janssen et al., 2011 (supplemental material, Table E2).
Healtheffectsofblackcarbon
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Annex 3F. Study-specific estimates for asthma and cough in panel studies among asthmatic and symptomatic children
Table 3F 1. Effects estimates for asthma, lag 1
Reference Location Population
(N, age, asthmatic/
symptomatic)
Year;
duration
Estimate PM10 Estimate BCP Concentration
a
Correlation
(R) PM–
BCPBeta Standard
error
Beta Standard
error
PM10 BCP
Single-location estimates
Roemer, Hoek &
Brunekreef, 1993
Rural, Netherlands N=73; 6–12 years;
symptomatic
1990;
3 months
0.033 0.010 0.382 0.127 NA NA 0.81
Gielen et al.,
1997
Amsterdam,
Netherlands
N=61; 7–13 years;
asthmatic
1995;
2.5 months
0.000 0.004 0.018 0.056 30.5 1.5
b
NA
Segala et al.,
1998
Paris, France N=84; 7–15 years;
asthmatic
1992;
6 months
0.005 0.008 0.055 0.073 34.2 3.5
b
0.82
van der Zee
et al., 1999
Urban, Netherlands N=142; 7–11 years;
symptomatic
1993–1995;
3 months
0.004 0.001 0.034 0.029 38 1.2
b
NA
Rural, Netherlands N=178; 7–11 years;
symptomatic
1993–1995;
3 months
0.000 0.001 0.005 0.023 31 0.9
b
NA
Just et al.,
2002
Paris, France N=82; 7–15 years;
asthmatic
1996;
3 months
0.006 0.028 0.196 0.155 23.5 1.8
b
0.59
Delfino et al.,
2003
Los Angeles,
United States
N=22; 10–16 years;
asthmatic
1999;
3 months
0.002 0.007 0.003 0.075 59.9 5.1 0.82
Gent et al.,
2009
New Haven,
United States
N=149; 4–12 years;
asthmatic
2000–2004;
12 months
-0.0020 0.0020 0.010 0.026 17.0 1.9 NA
Multicentre studies
Roemer et al.,
1998
PEACE study,
Europe; 14 urban
and 14 rural panels
N=2010; 6–12 years;
symptomatic
1993–1994;
2 months
0.0000 0.0005 -0.008 0.006 11.2–1998.8 0.5–12.0
b
Percentage change per 1 µg/m
3
increase; single-location estimates Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.13 ( 0.00–0.26) 2.11 (-0.58–4.86)
Pooled random effects 0.19 (-0.13–0.51) 2.85 (-1.01–6.86)
Heterogeneity chi-squared (df=7) Q=18.0 P=0.012 Q=10.5 P=0.16
Percentage change per 1 µg/m
3
increase; including PEACE study
Pooled fixed effects 0.05 (-0.03–0.12) -0.32 (-1.36–0.73)
Pooled random effects 0.12 (-0.09–0.32) 1.53 (-1.39–4.53)
Heterogeneity chi-squared (df=8) Q=20.3 P=0.009 Q=14.2 P=0.077
a
Mean or median (µg/m3
).
b
Derived from BS as 10 BS=1.1 EC.
Healtheffectsofblackcarbon
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Table 3F 2. Effects estimates for asthma, lag 0
Reference Location Population
(N, age, asthmatic/
symptomatic)
Year;
duration
Estimate PM10 Estimate BCP Concentration
a
Correlation
(R) PM–
BCPBeta Standard
error
Beta Standard
error
PM10 BCP
Single-location estimates
Roemer, Hoek
& Brunekreef,
1993
Rural, Netherlands N=73; 6–12 years;
symptomatic
1990;
3 months
0.027 0.010 0.273 0.145 NA NA 0.81
Gielen et al.,
1997
Amsterdam,
Netherlands
N=61; 7–13 years;
asthmatic
1995;
2.5 months
0.005 0.003 0.005 0.055 30.5 1.5
b
NA
Segala et al.,
1998
Paris, France N=84; 7–15 years;
asthmatic
1992;
6 months
0.013 0.008 0.082 0.064 34.2 3.5
b
0.82
van der Zee
et al., 1999
Urban, Netherlands N=142; 7–11 years;
symptomatic
1993–1995;
3 months
0.003 0.001 0.036 0.032 38 1.2
b
NA
Rural, Netherlands N=178; 7–11 years;
symptomatic
1993–1995;
3 months
0.000 0.001 -0.009 0.026 31 0.9
b
NA
Just et al.,
2002
Paris, France N=82; 7–15 years;
asthmatic
1996;
3 months
0.006 0.028 0.196 0.155 23.5 1.8
b
0.59
Delfino et al.,
2003
Los Angeles,
United States
N=22; 10–16 years;
asthmatic
1999;
3 months
0.010 0.004 0.211 0.089 59.9 5.1 0.82
Gent et al.,
2009
New Haven,
United States
N=149; 4–12 years;
asthmatic
2000–2004;
12 months
0.0001 0.0004 0.039 0.019 17.0 1.9 NA
Multicentre studies
Roemer et al.,
1998
PEACE study,
Europe; 14 urban
and 14 rural panels
N=2010; 6–12 years;
symptomatic
1993–1994;
2 months
-0.0007 0.0004 -0.007 0.004 11.2–1998.8 0.5–12.0
b
Percentage change per 1 µg/m
3
increase; single-location estimates Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.05 (-0.02–0.12) 3.40 (0.81–6.05)
Pooled random effects 0.27 ( 0.03–0.51) 4.27 (0.19–8.52)
Heterogeneity chi-squared (df=7) Q=22.4 P=0.002 Q=11.4 P=0.123
Percentage change per 1 µg/m
3
increase; including PEACE study
Pooled fixed effects 0.00 (-0.05–0.06) -0.32 (-1.05–0.41)
Pooled random effects 0.13 (-0.03–0.29) 2.89 (-0.56–6.46)
Heterogeneity chi-squared (df=8) Q=27.2 P=0.001 Q=20.2 P=0.010
a
Mean or median (µg/m3
).
b
Derived from BS as 10 BS=1.1 EC.
Healtheffectsofblackcarbon
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Table 3F 3. Effects estimates for cough, lag 1
Reference Location Population
(N, age, asthmatic/
symptomatic)
Year(s),
duration
Estimate PM10 Estimate BCP Concentration
a
Correlation
(R) PM–
BCPBeta Standard
error
Beta Standard
error
PM10 BCP
Single-location estimates
Roemer, Hoek
& Brunekreef,
1993
Rural, Netherlands N=73; 6–12 years;
symptomatic
1990;
3 months
0.005 0.018 0.182 0.227 NA NA 0.81
Gielen et al.,
1997
Amsterdam,
Netherlands
N=61; 7–13 years;
asthmatic
1995;
2.5 months
0.004 0.002 -0.008 0.043 30.5 1.5
b
NA
Tiitanen et al.,
1999
Kuopio, Finland N=49; 8–13 years;
symptomatic
1995;
6 weeks
-0.001 0.002 0.054 0.082 28 0.8 0.74
van der Zee
et al., 1999
Urban, Netherlands N=142; 7–11 years;
symptomatic
1993–1995;
3 months
-0.001 0.001 -0.016 0.017 38 1.2
b
NA
Rural, Netherlands N=178; 7–11 years;
symptomatic
1993–1995;
3 months
0.001 0.001 0.024 0.012 31 0.9
b
NA
Just et al.,
2002
Paris, France N=82; 7–15 years;
asthmatic
1996;
3 months
0.010 0.011 0.181 0.097 23.5 1.8
b
0.59
Gent et al.,
2009
New Haven,
United States
N=149; 4–12 years;
asthmatic
2000–2004;
12 months
0.0001 0.0010 0.010 0.011 17.0 1.9 Na
Multicentre studies
Roemer et al.,
1998
PEACE study,
Europe; 14 urban
and 14 rural panels
N=2010; 6–12 years;
symptomatic
1993–1994;
2 months
-0.0003 0.0003 0.002 0.003 11.2–1998.8 0.5–12.0
b
Percentage change per 1 µg/m
3
increase; single-location estimates Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.04 (-0.03–0.12) 1.15 (-0.28–2.66)
Pooled random effects 0.04 (-0.03–0.12) 1.07 (-1.01–3.04)
Heterogeneity chi-squared (df=6) Q=5.8 P=0.44 Q=7.8 P=0.25
Percentage change per 1 µg/m
3
increase; including PEACE study
Pooled fixed effects -0.00 (-0.05–0.04) 0.34 (-0.24–0.91)
Pooled random effects 0.01 (-0.06–0.07) 0.64 (-0.56–1.86)
Heterogeneity chi-squared (df=7) Q=8.4 P=0.30 Q=9.3 P=0.23
a
Mean or median (µg/m3
).
b
Derived from BS as 10 B=1.1 EC.
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Table 3F 4. Effects estimates for cough, lag 0
Reference Location Population
(N, age, asthmatic/
symptomatic)
Year(s),
duration
Estimate PM10 Estimate BCP Concentration
a
Correlation
(R) PM–
BCPBeta Standard
error
Beta Standard
error
PM10 BCP
Single-location estimates
Roemer, Hoek
& Brunekreef,
1993
Rural, Netherlands N=73; 6–12 years;
symptomatic
1990;
3 months
0.000 0.019 0.127 0.245 NA NA 0.81
Gielen et al.,
1997
Amsterdam,
Netherlands
N=61; 7–13 years;
asthmatic
1995;
2.5 months
-0.001 0.003 -0.003 0.055 30.5 1.5
b
NA
Tiitanen et al.,
1999
Kuopio, Finland N=49; 8–13 years;
symptomatic
1995;
6 weeks
0.000 0.002 -0.018 0.082 28 0.8 0.74
van der Zee
et al., 1999
Urban, Netherlands N=142; 7–11 years;
symptomatic
1993–1995;
3 months
0.000 0.001 0.024 0.018 38 1.2
b
NA
Rural, Netherlands N=178; 7–11 years;
symptomatic
1993–1995;
3 months
0.001 0.001 0.009 0.015 31 0.9
b
NA
Just et al.,
2002
Paris, France N=82; 7–15 years;
asthmatic
1996;
3 months
0.010 0.011 0.181 0.097 23.5 1.8
b
0.59
Gent et al.,
2009
New Haven,
United States
N=149; 4–12 years;
asthmatic
2000–2004;
12 months
0.0001 0.0004 0.010 0.012 17.0 1.9 NA
Multicentre studies
Roemer et al.,
1998
PEACE study,
Europe; 14 urban
and 14 rural panels
N=2010; 6–12 years;
symptomatic
1993–1994;
2 months
-0.0004 0.0002 -0.004 0.002 11.2–1998.8 0.5–12.0
b
Percentage change per 1 µg/m
3
increase; single-location estimates Percentage 95% CI Percentage 95% CI
Pooled fixed effects 0.03 (-0.02–0.09) 1.33 (-0.28–2.96)
Pooled random effects 0.03 (-0.02–0.09) 1.33 (-0.28–2.96)
Heterogeneity chi-squared (df=6) Q=1.6 P=0.26 Q=3.9 P=0.69
Percentage change per 1 µg/m
3
increase; including PEACE study
Pooled fixed effects -0.02 (-0.05–0.01) -0.26 (-0.67–0.15)
Pooled random effects -0.02 (-0.05–0.01) 0.17 (-0.79–1.15)
Heterogeneity chi-squared (df=7) Q=6.5 P=0.48 Q=7.9 P=0.34
a
Mean or median (µg/m3
).
b
Derived from BS as 10 BS=1.1 EC.
Healtheffectsofblackcarbon
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Annex 3G. Effects of PM2.5 and BC in cohort studies of respiratory health in children
Table 3G 1. Effects of PM2.5 and BCP in birth cohort studies
Reference Cohort R PM–
BCP
a
RR expressed
per IQR
Health endpoint
b
RR PM RR BCP
Gehring et
al., 2002
Birth cohort (GINI/LISA);
1756 children born in the
Munich metropolitan area,
Germany; age 2 years
0.96 PM2.5: 1.5 µg/m
3
Abs: 0.4 m
–1
×10
–5
Wheeze 0.96 (0.83–1.12) 0.98 (0.84–1.14)
Dry cough at night 1.20 (1.02–1.42) 1.16 (0.98–1.37)
DD
b
asthmoid/spastic/obstructive
bronchitis
0.92 (0.78–1.09) 0.94 (0.79–1.12)
Respiratory infections 0.98 (0.80–1.20) 0.99 (0.80–1.22)
Sneezing/runny stuffed nose 0.96 (0.82–1.12) 0.92 (0.78–1.09)
Brauer et al.,
2002
Piama cohort;
3000 children throughout
the Netherlands;
symptoms at age 2 years
0.99 PM2.5: 3.2 µg/m
3
Abs: 0.54 m
–1
×10
–5
Wheeze 1.14 (0.98–1.34) 1.11 (0.97–1.26)
DD asthma 1.12 (0.84–1.50) 1.12 (0.88–1.43)
Dry cough at night 1.04 (0.88–1.23) 1.02 (0.88–1.17)
DD bronchitis 1.04 (0.85–1.26) 0.99 (0.84–1.17)
Ear, nose, throat infections 1.20 (1.01–1.42) 1.15 (1.00–1.33)
DD influenza/serious colds 1.12 (1.00–1.27) 1.09 (0.98–1.21)
Itchy rash 1.01 (0.88–1.16) 1.02 (0.91–1.15)
DD eczema 0.95 (0.83–1.10) 0.96 (0.85–1.08)
Brauer et al.,
2006
Birth cohort (Piama);
3000 children throughout
the Netherlands
0.99 PM2.5: 3 µg/m
3
EC: 0.5 µg/m
3
Otitis media at age:
1 year
2 years
1.13
1.13
(0.98– 1.32)
(1.00–1.27)
1.11
1.10
(0.98–1.26)
(1.00–1.22
Birth cohort (LISA);
600 children from Munich,
Germany
PM2.5: 3 µg/m
3
EC: 0.5 µg/m
3
Otitis media at age:
1 year
2 years
1.19
1.24
(0.73– 1.92)
(0.84–1.83)
1.12
1.10
(0.83–1.51)
(0.86–1.41)
Brauer et al.,
2007
PIAMA cohort;
3000 children throughout
the Netherlands;
symptoms at age 4 years
0.99 PM2.5: 3.3 µg/m
3
Abs: 0.58 m
–1
×10
–5
Wheeze 1.20 (0.99–1.46) 1.18 (1.00–1.40)
DD asthma 1.32 (0.98–1.71) 1.30 (0.98–1.71)
Dry cough at night 1.14 (0.98–1.33) 1.14 (1.00–1.31)
DD bronchitis 0.86 (0.66–1.11) 0.88 (0.69–1.11)
Ear, nose, throat infections 1.17 (1.02–1.34) 1.16 (1.03–1.31)
DD influenza/serious colds 1.25 (1.07–1.46) 1.19 (1.04–1.37)
Itchy rash 0.98 (0.85–1.14) 0.97 (0.85–1.10)
DD eczema 0.98 (0.82–1.17) 0.97 (0.83–1.14)
Morgenstern
et al., 2007
c
GINI/LISA cohort;
3577 children living in the
Munich metropolitan area,
Germany; age 2 years
0.49 PM2.5: 1.0 µg/m
3
Abs: 0.22 m
–1
×10
–5
Wheeze 1.10 (0.96–1.25) 1.09 (0.90–1.33)
Dry cough at night 1.03 (0.89–1.19) 1.18 (0.93–1.50)
DD asthmoid/spastic/obstructive
bronchitis
1.05 (0.92–1.20) 0.85 (0.31–2.34)
Respiratory infections 1.09 (0.94–1.27) 1.05 (0.79–1.39)
Sneezing/runny stuffed nose 1.19 (1.04–1.36) 1.27 (1.04–1.56)
Healtheffectsofblackcarbon
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Reference Cohort R PM–
BCP
a
RR expressed
per IQR
Health endpoint
b
RR PM RR BCP
Morgenstern
et al., 2008
c
Gehring et
al., 2010
GINI/LISA cohort;
±3000 children living in the
Munich metropolitan area,
Germany; ages 4 and 6
years
GINI/LISA cohort;
±3000 children living in the
Munich metropolitan area,
Germany; age 8 years
0.49 PM2.5: 1.0 µg/m
3
Abs: 0.22 m
–1
×10
–5
DD asthmoid/spastic/obstructive
bronchitis
1.12 (0.94–1.29) 1.56 (1.03–2.37)
DD hayfever 1.01 (0.91–1.12) 1.59 (1.11–2.27)
DD eczema 1.00 (0.86–1.24) 1.03 (0.86–1.24)
PR
b
asthmoid/spastic/obstructive
bronchitis
0.97 (0.91–1.02) 0.96 (0.83–1.11)
DD hayfever 1.02 (0.96–1.08) 1.11 (0.93–1.31)
DD eczema 1.05 (0.90–1.37) 1.05 (0.93–1.47)
Prevalent asthma 1.26 (1.04–1.51) 1.20 (1.02–1.42)
Incident asthma 1.28 (1.10–1.49) 1.21 (1.06–1.38)
Asthma symptoms 1.15 (1.02–1.28) 1.12 (1.01–1.24)
Wheeze 1.20 (1.08–1.33) 1.16 (1.06–1.27)
Sneezing, runny/blocked nose 1.12 (1.01–1.24) 1.11 (1.01–1.21)
Hayfever 1.05 (0.83–1.32) 1.04 (0.85–1.27)
Atopic eczema 1.00 (0.90–1.11) 1.00 (0.91–1.10)
Wheeze 1.29 (1.04–1.62) 1.22 (1.00–1.48)
Bronchial hyperactivity 0.98 (0.76–1.24) 1.04 (0.84–1.29)
Allergic sensitization: 1.16 (0.96–1.39) 1.12 (0.95–1.32)
– in utero exposure 1.02 (1.00–1.03) 1.08 (1.02–1.15)
– first year exposure 1.01 (0.99–1.03) 1.14 (1.01–1.29)
a
Coefficient of the correlation between PM2.5 and BCP concentrations.
b
DD=diagnosed by doctor; PR=parental report.
c
Further analyses by Gehring et al. (2002). Here, the study population was expanded by including subjects who lived outside the Munich area. Although this resulted in a lower correlation
between PM2.5 and BCP (R=0.49), the performance of the land use regression model used to assign exposure to individual participants was poorer than that of the smaller population
(Morgenstern et al., 2007).
Source: Janssen et al., 2011 (supplemental material, Tables F1, F2).
Healtheffectsofblackcarbon
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Table 3G 2. Effects of PM2.5 and BCP in cohort studies on lung function growth
a
Coefficient of the correlation between PM2.5 and BCP concentrations.
Reference Cohort R PM–BCP
a
RR expressed for
concentration range
(maximum–minimum)
Health endpoint RR PM RR BCP
Gauderman et al., 2002 Results from 2 cohorts:
(1) 1457 children
recruited in 1993;
4-year follow-up
0.91 PM2.5: 22.2 µg/m
3
EC: 1.1 µg/m
3
Growth rate FVC (%)
Growth rate FEV1 (%)
Growth rate MMEF (%)
-0.42 (-0.86 – 0.03)
-0.63 (-1.28 – 0.02)
-0.94 (-1.88 – 0.01)
-0.49 (-0.88 – -0.09)
-0.71 (-1.30 – -0.12)
-1.07 (-1.94 – -0.19)
(2) 1678 children
recruited in 1996;
4-year follow-up
0.93 Growth rate FVC (%)
Growth rate FEV1 (%)
Growth rate MMEF (%)
-0.14 (-0.67 – 0.40)
-0.39 (-1.06 – 0.28)
-0.94 (-1.87 – 0.00)
-0.17 (-0.67 – 0.33)
-0.40 (-1.02 – 0.23)
-0.92 (-1.78 – -0.05)
Gauderman et al., 2004 Cohort (1);
8-year follow-up
0.91 PM2.5: 22.8 µg/m
3
EC: 1.1 µg/m
3
Growth rate FVC (ml)
Growth rate FEV1 (ml)
Growth rate MMEF (ml)
-60.1 (-166.1 – 45.9)
-79.7 (-153.0 – -6.4)
-168.9 (-345.5 – 7.8)
-77.7 (-166.7 – 11.3)
-87.9 (-146.4 – -29.4)
-165.5 (-323.4 – -7.6)
Health effects of black carbon
page 81
Annex 3H. Comparison of calculated health benefits of traffic abatement measures using
PM2.5 or BCP
As an illustration of the potential implications of using BCP as an air quality indicator, the health
benefits of a traffic abatement measure for the population living along busy roads were
calculated using PM2.5 mass and BCP. Given the relatively large proportion of BCP in the
roadside increment of PM2.5 mass, it can be expected that traffic abatement measures will result
in larger reductions in BCP, relative to reductions in PM mass. There are, however, few
empirical data to support larger impacts on BCP than on PM2.5 mass. In an evaluation of the
effects on air quality of retrofitting trucks in southern California with diesel engine particle
filters, Millstein & Harley (2010), using an Eulerian photochemical air quality model, estimated
a decrease in EC concentrations in 2014 of 12–14%. The estimated effect on PM2.5 mass
concentrations was much smaller (<1%). In a modelling study of the effect of a speed limit
reduction (from 120 to 90 km/hour) on air quality in Flanders, the EC concentrations decreased
up to 30% just next to the busiest highways, compared to an estimated reduction of at most 8.5%
for PM2.5. For buffer zones of 0–100m distance from the highways, EC concentrations decreased
by 9–10% (Lefebvre et al., 2011). A small monitoring study of the effects of road closures
associated with the 2004 Democratic National Convention in Boston suggested slightly lower
concentrations of EC and NO2 during the road closure periods at monitoring sites proximate to
the closed highway segments. This decrease was not observed for PM2.5 or further from major
highways (Levy, Baxter & Clougherty, 2006).
Based on a review of studies that have looked at roadside increments of PM mass compared to
BCP, it is estimated that the percentage of EC in the roadside increment in PM2.5 will be on
average 55% (Table 3H 1). This result is in line with two modelling studies that estimated the
percentage of EC in traffic emissions mentioned above (Millstein & Harley, 2010; Lefebvre et
al., 2011) and also in the range provided by the European emission inventory database
COPERT4 for the EC fraction in PM2.5 in exhaust emissions for different vehicle categories
(such as passenger cars, vans and trucks) (Ntziachristos & Samaras, 2009). The estimated
percentage of EC in the roadside increment in PM2.5 of 0.4–0.7 µg/m3
implies that every 1 µg/m3
reduction in traffic-related PM2.5 will result in a 0.4–0.7 µg/m3
reduction in EC. This range was
used to estimate the health benefits of a hypothetical traffic abatement policy measure resulting
in a 1 µg/m3
reduction in PM2.5 mass. This approach assumes that the reduction in BCP resulting
from traffic abatement will be proportional to the decrease in PM mass by the percentage of EC
in the roadside increment for PM mass, an assumption that will not hold for all policies. A
comparison of the effect on life expectancy of the population living along major roads, using the
calculated RRs for PM2.5 mass and EC (Table 4), is given in Table 3H 2. When the average
conversion factor of 10 µg/m3
BS=1.1 µg/m3
EC is used to derive the RR for a 1 µg/m3
increase
in EC (Table 4) and the percentage of EC in a roadside increment of PM2.5 (Table 3H 1), the
increase in life expectancy per person is five times higher for EC compared to PM2.5 (3.6 months
compared with 21 days, Table 3H 2). When the maximum and minimum conversion factors of
1.8 and 0.5 µg/m3
EC per 10 µg/m3
BS are used, the increase in estimated life expectancy is four
to nine times higher. The estimated health benefits are, therefore, much greater when expressed
in EC compared to an equivalent change in PM mass.
Health effects of black carbon
page 82
Table 3H 1. Estimated percentage of EC in roadside increment in PM2.5
Reference Location, period Measurement
method of
BCP
Difference traffic–
background (µg/m
3
)
Percentage of EC in roadside
increment
PM BCP
Kinney et al.,
2000
New York, United States;
sidewalk; 1996
EC; sunset 4.4 2.6 58
Funasaka et
al., 2000
Osaka, Japan; outside
homes; period N/A
EC 6.0 3.6 60
Janssen et al.,
2001; 2008
Netherlands; 50 m of
highway; 1997/1998
EC from Abs;
a
VDI 2465
2.1 2.0 76
b
Lena et al.,
2002
New York, United States;
sidewalk; 1999
EC from Abs;
a
sunset
6.2 3.1 50
Cyrys et al.,
2003
Munich, Germany;
1999/2000
EC; VDI 2465 1.0 1.0 80
b
Netherlands; 1999/2000 EC; VDI 2465 2.1 1.8 69
b
Sweden; 1999 /2000 EC; VDI 2465 3.6 1.1 24
b
Harrison,
Jones &
Lawrence,
2004
London & Birmingham,
United Kingdom;
roadside; 2000/2002
EC 8.4 6.1 69
10 µg/m
3
BS =
1.1µg/m
3
EC
10 µg/m
3
BS =
0.5µg/m
3
EC
10 µg/m
3
BS =
1.8µg/m
3
EC
Janssen et al.,
1997
Arnhem, Netherlands;
curbside; October–
November 1994
BS 7.9 28.3 39 18 64
Roorda-Knape
et al., 1998
Netherlands;
15–32 m of highways;
May–August 1995
BSBS 1.4 5.5 43 20 71
Roemer & van
Wijnen,
2001b
Amsterdam, Netherlands;
7 m of busy street;
1998/1999
BS 2.0 11.0 61 28 99
Fischer et al.,
2000
Amsterdam, Netherlands;
outside homes; 1995
Abs of PM2.5
filters
c
3.0 1.3 47 22 77
Smargiassi et
al., 2005
Montreal, Canada;
curbside or on collector
Abs of PM2.5
filters
c
1.8 0.7 42 19 68
Janssen et al.,
2008
Netherlands; 50 m of
highway; 2001/2002
Abs of PM2.5
filters
c
2.4 0.9 43 19 70
Janssen et al.,
2008
Munich, Germany; along
highway; 2002
Abs of PM2.5
filters
c
3.6 1.2 38 17 62
Boogaard
et al., 2010
Netherlands; 9–15 m of
busy roads in large
cities; 2008/2009
Abs of PM2.5
filters
c
2.2 1.6 77 35 127
Average
d
55 41 70
95% CI 46–63 29–54 59–82
a
Calculated using a study-specific calibration derived from co-located samples (see Table 3A 1).
b
Results from studies that have used the VDI protocol were divided by 1.25, as this method has been shown to overestimate EC by,
on average, 25% (Schmid et al., 2001).
c
An increase in 1 unit of Abs is considered to equal an increase of 10 µg/m3
BS, according to Roorda-Knape et al (1998).
d
Average includes all studies; average of studies that directly measured EC was 61%.
Source: Janssen et al., 2011 (supplemental material, Table G2).
Health effects of black carbon
page 83
Table 3H 2. Comparison of the estimated effect on life expectancy of reductions
in PM2.5 mass and EC resulting from a traffic management plan
Component Conversion BS to EC
a
RR
b
Reduction (µg/m
3
)
c
Increase in life expectancy
per person
d
PM2.5 1.007 1.00 21 days
EC 10 µg/m
3
BS=1.1 µg/m
3
EC 1.06 0.55 (0.46–0.63) 3.6 months (3.0–4.1 months)
10 µg/m
3
BS=1.8 µg/m
3
EC 1.04 0.70 (0.59–0.82) 3.1 months (2.6–3.6 months)
10 µg/m
3
BS=0.5 µg/m
3
EC 1.10 0.41 (0.29–0.54) 4.5 months (3.2–5.9 months)
a
BS was converted to EC for 2 of the 4 studies that were used to calculate the RR for EC, and for 8 of 16 studies that were used
to calculate the percentage EC in the roadside increment of PM2.5 over background.
b
RR per 1 µg/m3
.
c
A traffic abatement measure is evaluated that reduces EC proportional to the percentage EC in the roadside increment of PM2.5
over background. Values in brackets for the reduction correspond to the 95% CI of the percentage EC in the roadside increment of
PM2.5 (Janssen et al., 2011, supplemental material, Table G2).
d
Values in brackets for the increase in life expectancy are based on the 95% CI of the reduction.
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Black carbon (BC) is an indicator of combustion-related air pollution and was recently recognized as one of
the short-lived climate-forcers. This report presents the results of a systematic review of evidence of the
health effects of BC in ambient air. It concludes that epidemiological studies provide sufficient evidence of
the association of cardiopulmonary morbidity and mortality with BC exposure.The review of the toxicological
studies suggested that BC may not be a major directly toxic component of fine particulate matter (PM2.5),
but it may operate as a universal carrier of a wide variety of chemicals of varying toxicity to the human body.
A reduction in exposure to PM2.5 containing BC and other combustion-related particulate material for which
BC is an indirect indicator should lead to a reduction in the health effects associated with PM and simultaneously
contribute to the mitigation of climate change.
World Health Organization
Regional Office for Europe
Scherfigsvej 8 | DK-2100 Copenhagen Ø | Denmark
Tel.: +45 39 17 17 17 | Fax: +45 39 17 18 18
Email: contact@euro.who.int | Web site: www.euro.who.int