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BMC Public Health

Research article Open Access

Factors influencing the spatial extent of mobile source air pollution 

impacts: a meta-analysis

Ying Zhou* and Jonathan I Levy

Address: Department of Environmental Health, Harvard School of Public Health, Landmark Center 4th Floor West, P.O. Box 15677, Boston, MA, 

02215, USA

Email: Ying Zhou* - yzhou@hsph.harvard.edu; Jonathan I Levy - jilevy@hsph.harvard.edu

* Corresponding author 

Abstract

Background: There has been growing interest among exposure assessors, epidemiologists, and policymakers in the

concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review

attempts to quantitatively synthesize findings about the spatial extent under various circumstances.

Methods: We include both the peer-reviewed literature and government reports, and focus on four significant air

pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts

and fine particle mass). From the identified studies, we extracted information about significant factors that would be

hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion

modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background

concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial

extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling.

Results: We found that pollutant characteristics and background concentrations best explained variability in previously

published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on

health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background

concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in nearsource chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source

chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our

illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background

concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings

indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources

reviewed in this study is on the order of 100–400 m for elemental carbon or particulate matter mass concentration

(excluding background concentration), 200–500 m for nitrogen dioxide and 100–300 m for ultrafine particle counts.

Conclusion: First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial

extent explicitly, including the comparison method, threshold values, and whether background concentration is included.

Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads

demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers

should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify

the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to

utilize.

Published: 22 May 2007

BMC Public Health 2007, 7:89 doi:10.1186/1471-2458-7-89

Received: 14 December 2006

Accepted: 22 May 2007

This article is available from: http://www.biomedcentral.com/1471-2458/7/89

© 2007 Zhou and Levy; licensee BioMed Central Ltd. 

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Background

Given growing evidence of the health effects of trafficrelated air pollution as well as of proximity to major roads

[1-3], there has been growing interest in the concept of

"hot spots", or more broadly, spatial gradients in exposures to and health risks from traffic-related air pollutants.

Hot spots have been defined as locations where emissions

from specific sources may expose individuals and population groups to elevated risks of adverse health effects –

including but not limited to cancer – and contribute to the

cumulative health risks of emissions from other sources in

the area [4]. We more broadly consider the concept of the

distance from a source at which such impacts would be

observed, which we refer to as the "spatial extent" of the

impacts.

Studies in the literature have used a variety of methods

considering different pollutants in numerous settings to

draw inferences about the spatial extent of mobile source

air pollution. For example, GIS-based epidemiological

investigations establish buffers around roadways and

measure the cumulative traffic volume within a defined

radius to link with a variety of health outcomes [5-8]. The

radius most strongly associated with the health outcomes

in question would be presumed to be the spatial extent of

the impacts of the source, although the GIS measures are

only occasionally able to be compared with measured

pollutant concentrations (and then with only a subset of

traffic-related pollutants) [9,10], and the radius of impact

would theoretically differ by pollutant.

Other studies have monitored concentrations of a

number of pollutants at varying distances from roadways

[11,12] and compared these concentrations with background levels of the pollutant, often with the implicit

assumption that the spatial extent of the source extends

until the point at which the concentrations are not significantly different from background. Yet another approach

for understanding the spatial extent involves modeling

the health risks associated with the source in question. In

the case of cancer risk assessment, studies have estimated

the incremental health risks at different distances from the

mobile source of interest [13,14], which were then compared with a predetermined risk threshold (i.e., a one in a

million lifetime risk) to determine the spatial extent of

influence. This approach will clearly have the same meteorological and site influences as the monitoring or GISbased approaches, but the spatial extent in this case will

also be dependent on assumptions in the risk assessment

(i.e., the dose-response function) as well as on the threshold value selected.

Clearly, conclusions about the spatial extent of impact

will be influenced by both the methodology used and factors more generally related to pollutant fate and transport,

such as source strength, pollutant characteristics (i.e., reactivity, particle diameter), meteorological factors (i.e.,

wind speed and direction), or site characteristics (i.e.,

presence/absence of a street canyon). The combination of

these factors has led to substantial variability in conclusions across studies from within tens of meters [15] to tens

of kilometers or more [13].

At present, it is unclear which of the above factors contributes most significantly to the variability in spatial extent

estimates, leading to some confusion and limiting generalizable insights from a public policy or planning perspective. For example, a stakeholder may wish to have

preliminary insight about whether a roadway expansion

would have deleterious effects on nearby residential populations or whether more intervention is needed on an

existing port. The variability in the literature makes it difficult to determine whether a defined radius of concern is

adequately protective, too restrictive, or not protective

enough.

In this study, we conduct a quantitative meta-analysis of

the published literature on the spatial extent of mobile

source air pollution, with the objective of determining

which factors best explain variability in spatial extent estimates. We supplement and provide further context for the

interpretation of this meta-analysis by estimating spatial

extent using a variety of definitions and parametric values

within a simplified dispersion model. We use this information to draw conclusions about the current knowledge

base and the spatial extent under various circumstances,

with the overarching aim to determine whether it is plausible to construct representative spatial extent values for

specific traffic-related air pollutants in unstudied settings.

Methods

Search criteria

In January 2006, we searched the Science Citation Index

database for publications between 1997 and 2005 with

keywords related to the spatial extent of mobile source

related air pollution. We considered as relevant studies

about on-road vehicles, construction engines/equipment,

ports, and locomotives. We focused our search on four

significant air pollutants related to mobile sources: particulate matter (PM), carbon monoxide (CO), benzene, and

nitrogen oxides (NOx). CO and benzene represent nonreactive gaseous pollutants, while NO and NO2 represent

gaseous pollutants that undergo rapid chemical reactions

after being emitted. We include PM and particularly fine

particles (PM2.5) because they pose a health risk [16,17]

and because spatial patterns may differ in important ways

for particles of different size fractions (i.e., ultrafine particles vs. particle mass).

Keywords used include
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• Air pollution related terms such as "air pollution",

"automobile exhaust", "particulate matter", "elemental

carbon", "carbon monoxide", "benzene", "nitrogen

oxides", and "hydrocarbons" as well as any variations of

these terms (e.g., PM instead of particulate matter, NOx or

oxides of nitrogen instead of nitrogen oxides);

• Spatial extent-related terms: e.g., "distance", "spatial

extent", "spatial variability" or "spatial variation";

• Mobile source-related terms such as "highway", "traffic",

"mobile source", "ferry", "marine vessel", "locomotive" as

well as any variations of these terms

Among articles that met the search criteria, we excluded

those studies that did not focus on air pollution dispersion from mobile sources (e.g., studies on mobile source

emission factors, point sources). We also excluded studies

with distance or spatial variation used in a general sense

or as a continuous regression variable without conclusions about the spatial extent. We included a few earlier

publications (before 1997), if they themselves satisfy the

above criteria (except for the year of publication) and

were cited by the publications which satisfied the above

criteria.

In addition to the peer-reviewed literature, we collected

government reports that focused on the spatial extent of

mobile source (e.g., automobile, ferry, locomotive) air

pollution. These studies provide either data on the spatial

extent or an indication of how the literature has been

interpreted in a policy context. Since there is no search

database for reports that span a variety of government

agencies (including federal and state agencies), we collected information through a targeted internet search on

specific agency websites and from a previous report by

Environmental Defense [18]. The reports reviewed here

were not meant to be inclusive of all governmental reports

on this topic, but rather provide a representative sample.

Data gathering

From the identified studies, we extracted information

about significant factors that would be hypothesized to

influence the spatial extent within the study. This

included the pollutant, background concentration, emission rate, and meteorological factors, as well as the site

and season of the study, the methodology of the study,

and its implicit or explicit definition of spatial extent. It is

worth noting that most of the studies reviewed do not

directly focus on spatial extent. Instead, information on

spatial extent is provided as an ancillary part of the study

findings. Therefore, not all information is available or

meaningful for all studies. We either contacted the

authors of these studies for clarification/additional information or used our judgments to determine proxies of

these factors in cases where direct information was not

available. Below, we briefly describe the rationale for each

factor and our data extraction methodology.

Study type

Five types of studies potentially satisfy the search criteria:

monitoring, air dispersion modeling, land use regression,

epidemiology, and biomonitoring studies. In addition,

some studies combine more than one of these study types

(hybrid studies). Monitoring and air dispersion modeling

studies directly provide information on the pollutant concentration gradient and the resulting spatial extent. Biomonitoring studies, which use organisms to assess or

monitor environmental conditions (often analysis of

trace elements in plants or plant species composition near

the source of interest), are also directly informative, albeit

with an indirect measurement methodology.

Land use regression model studies predict pollutant concentrations at a given site based on surrounding traffic and

land use characteristics within various radii (or buffers)

around the site [19]. This type of study is receptor-based

rather than source-based, so is not directly informative

about the spatial extent of mobile source impact, but the

variables chosen for the final regression model can provide some insight on this issue. Similarly, epidemiological

studies provide information on the distance from a roadway most strongly associated with health outcomes, an

indirect measure of spatial extent. However, epidemiological studies which find no association between increased

health risk and mobile source proximity provide little

insight about the spatial extent of air pollution, as a null

association could be related to many other factors (i.e., a

lack of a causal association between the pollutant and

health outcome in question).

For the quantitative meta-analysis, we created a categorical variable that indicated whether a study used monitoring/biomonitoring, dispersion modeling, epidemiology,

or land-use regression.

Definition of spatial extent

The various spatial extent definitions used by different

studies are generally based on absolute or relative comparisons. For relative comparisons, downwind concentrations as a percentage of a defined reference point

concentration are compared with a cutoff value to determine spatial extent. The most commonly used reference

point is the maximum concentration measured at the

location nearest to the mobile source under study. The

cutoff values used in the studies reviewed range from 50

percent of maximum concentration [15] to 10 percent

[20,21]. For absolute comparisons, downwind pollutant

concentrations or health risks are compared directly with

threshold values or measurements upwind or at other dis-
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tances to determine whether there are significant differences. The threshold values include health risk values

such as 1 in a million [13,22] or incremental concentrations such as 0.01 ppm for NO2 [23]. Other approaches

include selection of significant covariates in land-use

regression models or epidemiological studies.

For the meta-analysis, we created a categorical variable

indicating whether the study estimated the percentage

decrease from a maximum concentration, a significant

absolute difference from background levels, or all other

approaches. These are broad categories with significant

variability within each category, but we lacked the statistical power to address the nuances in these spatial extent

definitions within our meta-analysis. This issue is

addressed within our dispersion modeling case study.

Pollutant characteristics

Pollutant type will clearly influence the spatial extent,

based both on chemical properties and background concentrations. For relatively inert pollutants such as CO,

downwind concentrations mainly decrease through dilution with ambient air. For more reactive pollutants, their

concentration profiles can also be influenced by the rate

of chemical reactions. For example, NO reacts with ambient ozone to form NO2 near the emission source. For NO,

the combination of the reaction and the dilution in the

surrounding air mass results in a rapid decrease in concentration with downwind distance. For NO2, on the other

hand, the dominant formation process slows down its

dilution and the concentrations decrease at a gradual rate.

Both the intrinsic reaction rate and the abundance of substances involved in the reactions such as O3 can play an

important part in the concentration distribution. These

factors would imply that there could be important seasonal influences on the spatial extent of reactive pollutants, so we gathered information about season of study

where available.

Particulate matter is involved in different processes

depending on particle size. For particles larger than about

1 μm, turbulent diffusion and gravitational settling are the

dominant processes, whereas, for particles smaller than

0.1 μm, Brownian diffusion becomes increasingly important [24]. Due to coagulation processes wherein aerosol

particles collide with one another and adhere to form

larger particles [25], there will be a continuous decrease in

number concentration coupled with an increase in particle size. For smaller particles (e.g., particles smaller than

0.1 μm), the combination of coagulation and dilution

results in a rapid decrease in concentration with downwind distance. For larger particles, the formation through

coagulation slows down the dilution and the concentrations decrease at a gradual rate. Thus, particle size and

consideration of number vs. mass are key factors to consider.

Related to pollutant type is the magnitude of background

concentrations relative to concentrations attributable to

the source in question. For monitoring studies, the concentrations measured at different distances from the roadway include both background concentrations and the

incremental contribution from the mobile source of interest. Therefore, the resulting concentration profile would

be very different for two pollutants with and without significant background concentrations, even if the same

amount of them are emitted from the source under study

and they have similar dispersion characteristics. For example, suppose that the emissions from the source of interest

result in a concentration increase of 1 μg/m3 at 10 m and

0.1 μg/m3 at 100 m for both pollutant A and B. If A has no

background concentration while B has a uniform background concentration of 10 μg/m3, then it appears that

the measured concentration of A at 100 m is only 10% of

that at 10 m (90% decrease) while for B, the concentration

at 100 m is still 92% of that at 10 m (less than 10%

decrease), even though the source contribution is the

same. This will have an influence on statistical significance tests and any "spatial extent" definitions based on

concentration ratios.

Among the pollutants we focus on, CO, benzene and NOx

have relatively low background concentrations. Mobile

sources (both on-road and nonroad) are responsible for

about 80 percent of benzene and CO, and more than half

of all NOx emissions in the United States [26]. PM2.5 is primarily related to regional transport with the majority contributed by non-mobile sources [26], but ultrafine

particles (smaller than 0.1 μm) and elemental carbon/

black smoke are more closely related to local sources. For

the meta-analysis, we develop a categorical variable combining pollutant type and background concentrations. We

categorize each study as either "inert pollutant, high background" (PM mass without background removed in the

analysis), "inert pollutant, low background or background removed" (CO, benzene, EC/black smoke, PM

mass with background removed in the analysis), "reactive

pollutant, near-source removal" (NO, ultrafine particles),

"reactive pollutant, near-source formation" (NO2). Of

note, background concentrations are generally not

addressed in air dispersion modeling studies, but are

explicitly reported in monitoring studies in which the

comparison between the downwind concentrations and

that of the background is used to define spatial extent.

Emission rate

If we assume a linear relationship between concentration

and emission rate, an increased emission rate will increase

the spatial extent of the impacts, if the spatial extent is
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defined as the distance at which a risk threshold is

reached, the distance before which the concentration

change is greater than a predefined value, or statistical significance tests are being used. Few studies directly report

emission rates, but there are multiple useful proxies. Most

monitoring studies use higher traffic counts to indicate

higher emission rates, so we created a categorical variable

for traffic count above/below the median across studies

reporting traffic counts.

Meteorological factors

The importance of meteorological factors is apparent

when considering a simple Gaussian equation [27] for

estimating the concentrations downwind of a continuously emitting infinite line source for relatively inert pollutants: , in which C is the downwind

concentration (μg/m3), Q is the source strength per unit

distance (μg/(m•s)), U is the average wind speed (m/s),

and σz is the vertical dispersion coefficient (m).

Wind speed determines the extent to which pollutants are

initially diluted, with the inverse relationship between the

wind speed and concentration given in the Gaussian

equation. Wind speed also plays an important role in the

dispersion parameter computations. At lower wind

speeds, both initial vertical dispersion and vehicleinduced thermal effects lead to higher estimates of the vertical dispersion parameter and, hence, lower concentration estimates. In addition, wind speed will affect travel

time to the measurement location, which can have an

influence on (for example) the amount of coagulation for

ultrafine particles. Wind speed can therefore have somewhat complex relationships with the spatial extent, due to

sometimes competing effects on initial dilution, vertical

dispersion, and for ultrafine particles, coagulation. Wind

direction will also be influential, as whether the wind is

parallel or perpendicular to the road (in upwind or downwind directions) will influence concentration patterns

substantially.

In addition, according to the Gaussian equation above, a

higher vertical dispersion coefficient (σz) corresponds to

lower downwind concentrations. Dispersion coefficients

are functions of downwind distance and atmospheric stability. At the same downwind distance, unstable conditions correspond to higher dispersion coefficients than

neutral conditions, followed by stable conditions. Stability classification in turn depends on measures of mechanical turbulence (such as surface roughness), measures of

convective turbulence during daytime (such as mixing

depth) and wind speed and wind direction fluctuations

[24]. Therefore, if all other factors are the same, the spatial

extent of influence for the same pollutant is smaller under

unstable conditions.

To incorporate meteorological factors, we gathered information where possible on wind speed and direction. We

coded each measurement as either being downwind from

the road or upwind/parallel (with multiple estimates

often available from an individual study as a function of

wind direction). Wind speed was coded as above or below

2 m/s (the median value across studies). Few studies

directly presented information on atmospheric stability or

other meteorological factors, so we considered these factors only within our dispersion model application.

Statistical analysis

To pool the evidence across selected studies, we considered two different dependent variables. The first is the

reported spatial extent extracted from each of the studies.

We evaluated predictors of the spatial extent in one-way

and factorial ANOVAs. However, a number of studies

reported no significant concentration gradient. Rather

than omitting those studies from the analysis, we assigned

each of these studies to have the maximum spatial extent

reported across all studies of the same type (i.e., monitoring studies). Because of the potential that this approach

could influence our findings, we also considered predictors of a dummy variable for above/below 500 meters.

Case study

To corroborate the findings from the meta-analysis and to

provide insight about the influence of multiple factors in

a controlled setting, we conducted an illustrative case

study. Assuming a flat terrain, we calculate the downwind

incremental concentration (Ci) of a relatively inert pollutant A from a continuously emitting infinite line source,

when the wind direction is perpendicular to the line, by

 (as described above). The total concentration of this pollutant is the sum of the background (Cb)

and incremental concentration (Ci):

For the base case, we assume a source strength per unit distance of 5 μg/(m·s), wind speed of 4 m/s, neutral stability

(Pasquill stability class D), and background concentration

of zero. The vertical dispersion coefficient (σz) can be calculated as , combining the CALINE4

mixing zone calculation [28] (σz, M) with a formula

reflecting dispersion outside of the mixing zone [24] (σz,

C Q

U z

= 2

2π σ

Ci Q

U z

= 2

2π σ

Ctotal Ci Cb Q

U

Cb

z

=+ = +

2

2π σ

σσ σ z zM zB

22 2 = + , ,
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B). The mixing zone is defined as the region over the traffic

lanes plus three meters on either side. When wind is perpendicular to the line source, CALINE4 models the mixing zone vertical dispersion coefficient as σz, M= 1.5+ 0.05

× W/U, in which W is the width of the mixing zone and U

is the wind speed. Assuming a mixing zone width of 24 m

and our base case wind speed, σz, M= 1.8 m. Under stability class D, σz, B = 0.06x(1+0.0015x)-0.5, in which × is the

downwind distance from the edge of the mixing zone in

meters.

With the above information, we calculate the downwind

concentration at different distances from the source for

different values of parameters deemed important in our

meta-analysis (for inert pollutants only).

Results

Meta-analysis

In our literature review, we found 33 studies that met our

selection criteria —18 monitoring studies

[11,12,15,20,21,23,29-40], 4 air dispersion modeling

studies[13,14,22,41], 1 land use regression study [9], 3

biomonitoring studies [42-44], and 7 epidemiology studies [1,5-8,45,46]. Three of these studies are regulatory

reports [13,14,22], and the rest are from the peer-reviewed

literature. All monitoring studies targeted automobile

related air pollution from major highways or city roads,

while air dispersion modeling studies investigated a wider

range of ground level pollution sources including construction equipment, ports, locomotives and roadways. In

addition, three of the four air dispersion modeling studies

were regulatory reports, which also used a health risk

framework in evaluating spatial extent. Additional files 1,

2, 3, 4, 5, 6 provide in detail the information extracted

from the studies for particulate matter (PM) mass concentration, (ultrafine) particle number count, black smoke/

black carbon/elemental carbon, CO and benzene, NOx,

and all other pollutants/endpoints, respectively.

In total, these 33 studies yielded 67 estimates of spatial

extent potentially suitable for quantitative meta-analysis.

Estimates of spatial extent based on an "elevated cancer

risk" definition were highly variable, based on the risk

threshold used, and included some extreme values (8.5

km and 32 km, where the maximum for all other studies

was 1000 m). As these values would significantly influence any meta-analyses, we excluded this category from

future statistical analysis, while noting the substantive

influence of this definition on spatial extent values. Of the

remaining 63 estimates, three were based on dispersion

modeling and eight on epidemiological studies, with the

other 52 derived from monitoring studies. There was a

moderately significant difference in spatial extent estimates by study type (p = 0.05), with a median spatial

extent of 300 m in monitoring studies, versus 125 m in

epidemiological studies and 400 m in dispersion modeling studies.

There was a significant difference in spatial extent estimates by pollutant type (p < 0.0001, Figure 1), with a

median value of 1000 m (the maximum value in the dataset) for inert pollutants with high background, versus 140

m for inert pollutants with low background, 350 m for

reactive pollutants formed in the atmosphere, and 175 m

for reactive pollutants removed from the atmosphere. The

lack of a significant spatial gradient for inert pollutants

with high background is driven in large part by studies of

PM mass [11,15,31,32,34,36], in which the high background implies that spatial extent criteria based on percentage differences are never met (see additional file 1 –

Particulate matter (PM) mass concentration related studies). The only studies in this category that did demonstrate

significant spatial gradients were either land use regressions or monitoring studies using less traditional definitions of spatial extent (i.e., distance at which consecutive

measurements were no longer significantly different from

one another, distance after which representative sites for

the average concentration are found). Of note, studies of

PM mass that excluded background concentrations, e.g.

monitoring results with background values subtracted

[21] or air dispersion modeling studies focusing only on

Spatial extent estimates from studies in meta-analysis, strati- fied by pollutant type Figure 1

Spatial extent estimates from studies in meta-analysis, stratified by pollutant type. The dashed line represents the mean, the solid line the median, the box the 25th–

75th percentile range, and the whiskers the 5th–95th percentage range.
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the source of interest [13,14,41], find significant concentration gradients.

In univariate ANOVA, the spatial extent definition was

also a statistically significant predictor (p = 0.04), with significantly higher values among studies considering absolute differences from background levels (median of 500

m) rather than percentage differences from baseline levels

or other approaches (medians of 180 m and 200 m,

respectively). In addition, although the sample size was

limited (n = 27), the spatial extent was significantly lower

when the receptor was downwind from the road (p =

0.03). Wind speed was not a statistically significant predictor (p = 0.18), but the median spatial extent was greater

under high wind conditions (425 m) than under low

wind conditions (150 m). Traffic volume, as a proxy for

emission rate, was a highly insignificant predictor (p =

0.60).

For our multivariate models, given the limited non-monitoring studies and the extent of missing information in

those studies (no meteorological information and no pollutant characterization in epidemiological studies), we

restrict our analysis to the 52 monitoring-based estimates,

and focus on two-variable models given statistical power

issues. In factorial ANOVA, pollutant type remains significant in all models, while definition of spatial extent

becomes insignificant (p = 0.46) when controlling for

pollutant type. Upwind/downwind conditions remained

statistically significant even after controlling for pollutant

type (p = 0.01). Conclusions were generally similar in

logistic regression analyses considering spatial extent

above/below 500 m.

Case study

The meta-analysis clearly demonstrates the importance of

pollutant type and background conditions, as well as

meteorological factors to a lesser extent, but the literature

is not sufficiently robust to capture the interactions

among covariates (e.g., wind speed and stability class) or

the effect of subtle variations in spatial extent definitions.

In addition, the number of dispersion modeling studies

found that satisfy all criteria is limited, therefore, running

our simple dispersion model under a variety of conditions, we estimate the spatial extent using multiple definitions, the results of which are summarized in Table 1.

The spatial extent increases with the decrease in the

threshold value for an absolute comparison. For example,

if we define the concentration threshold value as 0.25,

0.1, or 0.05 μg/m3, the corresponding spatial extent

increases from 70 to 190 to 430 m. Similarly, using a relative comparison to define the spatial extent, when we

define the percentage threshold as 50, 20 and 10 percent

of the reference concentration, the spatial extent increases

from 60 to 170 to 380 m.

In addition, the emission rate can influence the spatial

extent for absolute comparisons, with the spatial extent

increasing from 90 m to 430 m when the emission rate

increases from 2.5 to 10 μg/(m·s). Relative spatial extent

definitions are unaffected by emission rates, at least with

zero background concentrations. As the background concentration increases, the spatial extent based on a relative

comparison increases correspondingly (Table 1). In an

extreme case, when the background concentration is 1 μg/

m3, the concentration never drops below 50% of the reference. Changing meteorology also clearly influences the

spatial extent, with more unstable conditions (e.g., class

B, D and F are moderately unstable, neutral and moderately stable respectively) resulting in lower spatial extents,

although with an important modifying effect of wind

speed (Table 1).

Finally, basing the spatial extent on cancer risk thresholds

rather than concentrations significantly influences the

spatial extent (Table 1). If we assume the pollutant under

study is diesel PM, according to California EPA [47], the

cancer risk potency factor is 300 per million per μg/m3

over 70 years lifetime. The lifetime cancer risk would

range from 166 to 13 per million from the edge of the

mixing zone to 500 m downwind under the base case. The

spatial extent corresponding to a threshold of 20 per million in cancer risk is about 300 m from the source, and the

spatial extent for a threshold of 1 per million would be

well beyond our modeling region. Of note, this definition

corresponds directly with absolute concentration definitions, although with lower concentrations allowed (i.e., a

1 per million risk threshold corresponds with a 0.003 μg/

m3 concentration threshold).

Discussion and conclusion

In this meta-analysis and case study, we have demonstrated that the substantial range in spatial extent estimates in the literature can be largely explained by three

key factors – the definition of spatial extent used, the characteristics of the pollutant of interest, and the local meteorology. Extremely large spatial extent values are seen

when using a risk threshold definition, especially when

the threshold is relatively low (i.e., a 1 in a million cancer

risk). Setting that approach aside, the remainder of the literature is reasonably consistent. Non-reactive pollutants

with high background concentrations (i.e., PM mass)

demonstrate little discernable spatial gradient, and there

is then a general hierarchy based on whether a pollutant

is formed or removed from the atmosphere in the short

term (Figure 1). The smallest spatial extent estimates

occur for pollutants like ultrafine particles and NO and

when wind speeds are lower. These meta-analysis findings
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agree with our dispersion modeling results, which further

demonstrated lower spatial extent estimates for spatial

extent definitions with high thresholds, for low emission

rates, and for more unstable meteorological conditions.

While it is apparent based on first principles that many

factors can affect the spatial extent of influence for mobile

sources, the relative contributions of these factors and

importance of definitions as well as pollutant type and

site/meteorological characteristics have not been previously considered. The spatial extent definition was not

statistically significant in multivariate models, but it was

clearly influential in our case study, especially when the

risk-based estimates are considered or subtleties in the

definitions are evaluated. There is no single commonly

accepted definition regarding the comparison method,

threshold value, or inclusion of background concentration; the appropriate choices will be driven in part by the

question under study. For example, when a regulatory

threshold is involved, an absolute comparison is normally

used, in which concentrations/health risks at different distances are compared with the regulatory related threshold,

e.g., 1 in a million health risk. Other studies, e.g., those

focusing on the behavior of specific pollutants, utilize relative comparisons more often, as it helps to remove the

influence of factors such as meteorology and emissions

when combining monitoring results on different days. In

addition to comparison method, a wide range of threshold values are used (ranging from 10 to 50 percent of the

maximum concentration in relative comparisons in the

studies reviewed), and studies address background concentrations in different ways. Many of these decisions

were somewhat arbitrary within published studies, yet

these factors (especially treatment of background concentrations) explain a substantial portion of the variability in

findings.

In spite of the above intricacies, the literature allows us to

develop some first-order rules of thumb for policy makers

and other stakeholders. Omitting the health risk threshold perspective or circumstances with high background

concentrations and no significant gradients, the spatial

extent of impact for mobile sources reviewed in this study

is generally on the order of 100–400 m for elemental carbon or particulate matter mass concentration (excluding

background concentration), 200–500 m for NO2, and

100–300 m for ultrafine particle count. From a policy perspective, this might indicate that a 500 meter buffer

around a roadway would be appropriately protective

under most circumstances. However, policy makers may

be concerned about risk thresholds, which could imply

quite large spatial extents of impact. While these distances

could be implausibly large for offsets/buffers, this alternative framing emphasizes that there are circumstances in

which exposure increments that are difficult to detect and

well below maximum impacts may still be relevant for

public health, and studies with an individual health risk

framing should not restrict their focus to a 500 meter

radius.

In addition, it should be noted that all studies within our

meta-analysis defined spatial extent based on either concentration or individual health risk. However, within benefit-cost analyses and related applications, total

Table 1: Calculated spatial extent from illustrative case study under differing assumptions and definitions

Factor under 

study

Emission rate 

(μg/m·s)

Background 

concentration 

(μg/m3)

Meteorological conditions Definition of spatial extent Estimated spatial 

extent (m)

Wind speed (m/

s)

Stability class Comparison 

method

Threshold

Spatial extent 

definition

5 0 4 D Absolute 0.25 μg/m3 70

5 0 4 D Absolute 0.1 μg/m3 190

5 0 4 D Absolute 0.05 μg/m3 430

5 0 4 D Relative 50% of reference 60

5 0 4 D Relative 20% of reference 170

5 0 4 D Relative 10% of reference 380

Emission rate 2.5 0 4 D Absolute 0.1 μg/m3 90

10 0 4 D Absolute 0.1 μg/m3 430

Background 

concentration

5 0.25 4 D Relative 50% of reference 120

5 1 4 D Relative 50% of reference N/A

Meteorological 

factor

5 0 3 F Absolute 0.25 μg/m3 350

5 0 2 B Absolute 0.25 μg/m3 70

5 0 6 D Absolute 0.25 μg/m3 40

Health risk 

threshold

5 0 4 D Absolute 20 per million 

cancer risk

300

5 0 4 D Absolute 1 per million 

cancer risk

> 500 (beyond domain)
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population exposure (the product of population and

incremental concentration change) would be the endpoint of interest, and an alternative definition of spatial

extent would consider the distance from the source by

which a substantial fraction of the total population exposure was achieved. This definition of spatial extent has

been used in previous studies of power plant health risks

[48-51] and has been developed for mobile sources as

well [52]. Under this definition, the distance of concern

will depend on population patterns, and could be significantly greater than under other definitions (i.e., if few

people lived close to the roadway but population density

was high at longer range).

There are some limitations in the conclusions that can be

drawn from our meta-analysis and case study. As mentioned before, one limitation is that many of the papers

reviewed do not directly focus on spatial extent. In gathering necessary information we either contacted the authors

of these studies for clarification/additional information or

used our judgments to determine proxies of spatial extent

in cases where direct information was not available. More

broadly, many important factors were not reported in

these publications; fewer than half of estimates included

information on wind speed or direction, limiting our ability to conduct robust multivariate analyses. The studies

also varied substantially in the pollutants measured,

methods used, and site characteristics, and our simple categorical variables may have omitted some important

nuances. The number of studies was insufficient to look in

more detail at, for example, differences across different

particulate matter size fractions and definitions. We were

also constrained to the configurations previously evaluated in the literature, along with a simple configuration in

our case study. Other circumstances (i.e., an elevated

highway or street canyon) could yield somewhat different

conclusions about the spatial extent, so our findings are

not generalizable to all circumstances.

In spite of these limitations, we can draw some useful conclusions both for future research on the spatial extent of

mobile source air pollution and for policymakers and

stakeholders interested in utilizing this concept. First, to

allow for meaningful comparisons across studies, it is

important to state the definition of spatial extent explicitly, including in particular the comparison method,

threshold values, and whether background concentration

is included. While many monitoring studies are not conducted explicitly for this purpose, the authors should at a

minimum take care to define terms before making statements about the distance over which impacts were

observed. Second, the observation that the spatial extent

is generally within a few hundred meters for highway or

city roads demonstrates the need for high resolution modeling near the source for studies focused on exposure patterns or individual risk thresholds. The question regarding

the appropriate resolution and scope for benefit-cost analysis was not addressed in our study, but our findings confirm the expectation that important dynamics within

hundreds of meters of the roadway may be influential for

population exposure as well, at least in dense urban areas.

Finally, our findings emphasize that policymakers should

be able to develop reasonable estimates of the "zone of

influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that

they wish to utilize.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

YZ carried out the literature search and review, coded the

relevant information for the meta-analysis, conducted initial statistical analysis and drafted the manuscript. JIL conceived of the study, refined the meta-analysis and revised

the manuscript. All authors read and approved the final

manuscript.

Additional material

Additional file 1

Particulate matter (PM) mass concentration related studies

Click here for file

[http://www.biomedcentral.com/content/supplementary/1471-

2458-7-89-S1.doc]

Additional file 2

Particle number count/ultrafine particle count related studies

Click here for file

[http://www.biomedcentral.com/content/supplementary/1471-

2458-7-89-S2.doc]

Additional file 3

Black smoke/Black carbon/Elemental carbon related studies

Click here for file

[http://www.biomedcentral.com/content/supplementary/1471-

2458-7-89-S3.doc]

Additional file 4

CO and benzene related studies

Click here for file

[http://www.biomedcentral.com/content/supplementary/1471-

2458-7-89-S4.doc]

Additional file 5

NO2, NO, NOx related studies

Click here for file

[http://www.biomedcentral.com/content/supplementary/1471-

2458-7-89-S5.doc]
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Acknowledgements

This study was funded by the Gilbert and Ildiko Butler Foundation. We 

thank John Balbus from Environmental Defense, and Steven Hanna and 

Yang Liu from Harvard School of Public Health for valuable input and comments. The contents of this manuscript reflect the views of the authors 

alone and do not necessarily reflect the views of the reviewers or the 

funder.

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