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. 2015 Apr 14;123(11):1107–1112. doi: 10.1289/ehp.1408882

Lung Cancer and Exposure to Nitrogen Dioxide and Traffic: A Systematic Review and Meta-Analysis

Ghassan B Hamra 1,✉, Francine Laden 2,3,4, Aaron J Cohen 5, Ole Raaschou-Nielsen 6, Michael Brauer 7, Dana Loomis 8
PMCID: PMC4629738  PMID: 25870974

Abstract

Background and objective

Exposure to traffic-related air pollutants is an important public health issue. Here, we present a systematic review and meta-analysis of research examining the relationship of measures of nitrogen oxides (NOx) and of various measures of traffic-related air pollution exposure with lung cancer.

Methods

We conducted random-effects meta-analyses of studies examining exposure to nitrogen dioxide (NO2) and NOx and its association with lung cancer. We identified 20 studies that met inclusion criteria and provided information necessary to estimate the change in lung cancer per 10-μg/m3 increase in exposure to measured NO2. Further, we qualitatively assessed the evidence of association between distance to roadways and traffic volume associated with lung cancer.

Results

The meta-estimate for the change in lung cancer associated with a 10-μg/m3 increase in exposure to NO2 was 4% (95% CI: 1%, 8%). The meta-estimate for change in lung cancer associated with a 10-μg/m3 increase in NOx was similar and slightly more precise, 3% (95% CI: 1%, 5%). The NO2 meta-estimate was robust to different confounding adjustment sets as well as the exposure assessment techniques used. Trim-and-fill analyses suggest that if publication bias exists, the overall meta-estimate is biased away from the null. Forest plots for measures of traffic volume and distance to roadways largely suggest a modest increase in lung cancer risk.

Conclusion

We found consistent evidence of a relationship between NO2, as a proxy for traffic-sourced air pollution exposure, with lung cancer. Studies of lung cancer related to residential proximity to roadways and NOx also suggest increased risk, which may be attributable partly to air pollution exposure. The International Agency for Research on Cancer recently classified outdoor air pollution and particulate matter as carcinogenic (Group 1). These meta-analyses support this conclusion, drawing particular attention to traffic-sourced air pollution.

Citation

Hamra GB, Laden F, Cohen AJ, Raaschou-Nielsen O, Brauer M, Loomis D. 2015. Lung cancer and exposure to nitrogen dioxide and traffic: a systematic review and meta-analysis. Environ Health Perspect 123:1107–1112; http://dx.doi.org/10.1289/ehp.1408882

Introduction

Exposure to air pollution from high-density urban traffic is an important public health issue. Traffic-related air pollutants are associated with acute health outcomes as well as chronic respiratory and cardiovascular outcomes [Brunekreef et al. 1997; Health Effects Institute (HEI) 2010; Hoek et al. 2002; Tonne et al. 2007]. Associations of traffic-related air pollution with allergies, asthma, and respiratory infections have also been demonstrated in children (Brauer et al. 2002) and in both developed and developing nations (Brunekreef et al. 2009). Evaluation of carcinogenicity of traffic-based pollutants is complicated by the fact that the diseases are rare and often develop after a long latency period. Thus, multiple large, long-term observational studies are required to demonstrate evidence of carcinogenicity from traffic-related air pollution. A recent systematic review and meta-analysis evaluated the evidence from epidemiology of lung cancer associated with particulate matter exposure (Hamra et al. 2014); importantly, road traffic is an important contributor to urban particulate matter. The evidence supported the recent classification of ambient air pollution and particulate matter as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC).

Here, we present a systematic review and meta-analysis of research examining the relationship of measures of nitrogen oxides [measured as nitrogen dioxide (NO2) or nitrogen oxides (NOx)] and of various measures of traffic exposure and lung cancer. In urban settings, NO2 is often used as a marker for traffic-based air pollution, which is a complex mixture of many diverse carcinogens, such as volatile organic compounds, metals, and carbonyls (Brook et al. 2007; Valavanidis et al. 2008). We conducted analyses of studies focused on NOx and NO2; the latter is a component of the former, but both have been used as markers of traffic exposure in epidemiological research. We also evaluated studies that used distance from roadways or volume of traffic on nearby roadways as indicators of exposure to traffic-related air pollution (Brook et al. 2007).

Methods

Literature search. We began with a systematic review of the PubMed database using the following search criteria: (traffic[Title/Abstract] OR Nitrogen dioxide[Title/Abstract] OR NO2[Title/Abstract] OR NOx[Title/Abstract] OR Nitrogen oxide[Title/Abstract]) AND (lung cancer[Title/Abstract]). Studies were further required to be human-based epidemiologic studies written in English. A previous, similar search conducted by Hamra et al. (2014) did not retrieve relevant studies in non-English languages; therefore, we do not believe the language restriction led to exclusion of informative studies. This search was conducted in January 2014, and yielded 179 records. Abstracts from each paper were then reviewed for relevance to the topic of traffic or nitrogen oxide exposure and lung cancer. This review led to 30 studies for initial consideration.

We required abstracts to explicitly provide a quantitative value for change in lung cancer incidence or mortality associated with exposure to nitrogen oxides or other measure of traffic-related air pollution or to suggest that such an estimate was provided in the text. Of the initial 30 abstracts examined, 26 provided this information. Further, we considered only studies that used a case–control or cohort design; thus, ecological studies were not included. Finally, where there were multiple studies that considered the same, or highly overlapping, cohorts, we chose the study that had the longest follow-up time and/or the greater number of lung cancer events.

We abstracted estimates of lung cancer associated with three exposure metrics: NO2, NOx (NO + NO2), and volume of traffic and distance to nearest traffic source. Further, we abstracted information on confounders for which the authors adjusted in their analyses. All studies adjusted for age and sex.

Statistical analyses. For consistency, all study estimates were converted to represent the change in lung cancer incidence or mortality per 10-μg/m3 exposure to NO2 or NOx. All studies that considered NOx used units of micrograms per cubic meter. Studies of NO2 measured exposure in units of micrograms per cubic meter or parts per billion. For studies measuring NO2 in parts per billion, we used a conversion factor of 1 ppb = 1.88 μg/m3, which is based on ambient pressure of 1 atmosphere and a temperature of 25°C (Vrijheid et al. 2011). We abstracted the effect estimates that were believed to most effectively adjust for confounding (i.e., the least biased effect estimates), which were largely the “main models” discussed by the authors. There did not appear to be issues of overadjustment or potential adjustment for causal intermediates or colliders in any studies. Most studies used a single-pollutant model; when authors used both a single- and a multi-pollutant model, we chose estimates from the former.

We used random-effects estimation for our meta-analysis. Unlike fixed-effects estimation, random-effects methods include an estimate of the percent of the total variance attributable to between study inconsistency, referred to as the I2 value (DerSimonian and Laird 1986; Higgins and Green 2008; Higgins et al. 2003). One study (Abbey et al. 1999) presented estimates by sex; these estimates were combined with fixed-effects estimation and then included in analyses of the overall meta-estimates. This assumes that the stratum-specific estimates are collapsible across strata of sex.

Forest and funnel plots were created to provide a visual representation of the distribution of study-specific effect estimates. In addition, we conducted trim-and-fill analyses, which, in the presence of funnel plot asymmetry, recalculate the meta-estimate based on hypothetical, unobserved studies that would have been necessary to create a symmetrical funnel plot (Duval and Tweedie 2000; Higgins and Green 2008). Finally, we conducted influence analyses, where the meta-estimate is recalculated excluding one study at a time to test if the overall meta-estimate is robust to exclusion of any single study. We did not include formal assessments of study quality. Analyses were conducted using STATA software (version 12.1; StataCorp).

Results

Studies included. We identified 20 studies that met our initial inclusion criteria through PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and three additional studies through discussion among co-authors or by searching article references (Cao et al. 2011; Hart et al. 2011; Puett et al. 2014). Six of the 23 studies had at least partially overlapping populations; of these, the three judged to be less informative were excluded (Beeson et al. 1998; Jerrett et al. 2013; Vineis et al. 2006). More informative studies are those that have either better information on estimated individual exposure or a greater number of observed lung cancers. Of particular note, although the exposure assessment for an updated American Cancer Society Cancer Prevention II Study was more accurate (Jerrett et al. 2013), we considered a slightly older study that benefits from a much larger sample size (Krewski et al. 2009). The final data set included 20 studies that met all required criteria for inclusion in our meta-analyses (Abbey et al. 1999; Beelen et al. 2008; Cao et al. 2011; Carey et al. 2013; Cesaroni et al. 2013; Filleul et al. 2005; Hart et al. 2011; Heinrich et al. 2013; Hystad et al. 2013; Katanoda et al. 2011; Krewski et al. 2009; Lipsett et al. 2011; Nafstad et al. 2003; Nyberg et al. 2000; Puett et al. 2014; Raaschou-Nielsen et al. 2010, 2011, 2013; Villeneuve et al. 2014; Yorifuji et al. 2013). Table 1 summarizes the 20 studies included in our analyses, and reports mean (± SD) of annual, individual exposure to NO2 or NOx in units of micrograms per cubic meter or parts per billion, depending on what the authors originally reported. Of these studies, 15 and 4 provide estimates of the change in lung cancer incidence or mortality associated with NO2 and NOx, respectively; Puett et al. (2014) provide information only for distance from roadways. Because the ratio of NO2 to NOx is complex and location-specific, and depends on the time-varying levels of atmospheric oxides, there is no simple conversion factor to estimate the percent of NOx represented by NO2. We therefore summarize meta-estimates from studies of NOx separately.

Table 1.

Description of cohorts included.

Region Study IDa Reference No. of events Total population Study period Exposure assessment methodb Mean (± SD) annual, individual exposurec Study
North America
California, USA 1 Abbey et al. 1999 29 (mortality) 5,652 1977–1992 Fixed-site monitor Adventist Health Study on Smog (AHSMOG)
USA 2 Krewski et al. 2009 16,615 (mortality) 499,968 1982–2000 Fixed-site monitor 27.9 (85.25) ppb American Cancer Society Cancer Prevention Study II
USA 3 Hart et al. 2011 800 (mortality) 53,814 1985–2000 Spatiotemporal model 14.2 (7.1) ppb Trucking Industry Particle Study (TrIPS)
California, USA 4 Lipsett et al. 2011 67 (mortality) 12,366 1997–2005 Inverse distance weighting 33.6 (9.6) ppb California Teachers Study (women only)
Canada 5 Hystad et al. 2013d,e 2,390 (incidence) 5,897 1994–1997 Spatiotemporal model 15.4 (9.0) ppb National Enhanced Cancer Surveillance System case–control study
Toronto, Canada 6 Villeneuve et al. 2014d 327 (incidence) 752 1997–2002 Land use regression 22.3 (NR) ppb Study of four tertiary care hospitals in Toronto
USA NA Puett et al. 2014e 1,648 (incidence) 97,865 1998-2010 Spatiotemporal model NA Nurses’ Health Study
Europe
Stockholm, Sweden 7 Nyberg et al. 2000d 1,042 (incidence) 3,406 1985–1990 Air dispersion NR Residents of Stockholm County (1950–1990)
Oslo, Norway NA Nafstad et al. 2003a,f 418 (incidence) 16,209 1972–1998 Air dispersion 10.7 (NR) μg/m3 Norwegian Cancer Registry (Oslo, men only)
France 8 Filleul et al. 2005 178 (mortality) 14,284 1974–1998 Fixed-site monitor NR Air pollution and chronic respiratory diseases
Netherlands 9 Beelen et al. 2008e 1,940 (incidence) 120,852 1986–1997 Land use regression 36.9 (NR) μg/m3 Netherlands Cohort Study of Diet and Cancer.
United Kingdom 10 Carey et al. 2013 5,241 (mortality) 830,429 2003–2007 Air dispersion 22.5 (7.4) μg/m3 Clinical Practice Research Datalink
Italy 11 Cesaroni et al. 2013e 12,208 (mortality) 1,265,058 2001–2010 Air dispersion 43.6 (8.4) μg/m3 Rome Longitudinal Study
Germany 12 Heinreich et al. 2013e 41 (mortality) 4,752 1980–2008 Fixed-site monitor 39 (NR) μg/m3 German Women’s Health Study
Denmark NA Raaschou-Nielsen et al. 2010f 679 (incidence) 4,160 1970–2001 Spatiotemporal model 37.6 (NR) μg/m3 Copenhagen City Heart Study; Copenhagen male study
Denmark NA Raaschou-Nielsen et al. 2011d,e 592 (incidence) 52,970 1993–2006 Spatiotemporal model 28.3 (NR) μg/m3 Danish Diet Cancer Health cohort
European Union 13 Raaschou-Nielsen et al. 2013d,e,f 2,095 (incidence) 312,944 1990s Land use regression NO2: 5.2 (2.5)–59.8 (10.6) μg/m3; NOx: 8.7 (5.7)–107.3 (24.3) μg/m3g European Study of Cohorts for Air Pollution Effects (ESCAPE)
Asia
China NA Cao et al. 2011f 624 (mortality) 70,947 1991–2000 Fixed site monitor 50 (NR) μg/m3 China National Hypertension Epidemiology follow-up study
Japan 14 Katanoda et al. 2011 421 (mortality) 63,520 1983–1995 Fixed site monitor 1.2–33.7 ppbg Three Prefecture Cohort Study
Japan 15 Yorifuji et al. 2013 116 (mortality) 13,412 1999–2009 Land use regression 22.0 (15.0) μg/m3 Shizuoka elderly cohort study
aOnly studies of NO2 receive a study ID to indicate those studies that contributed to the subgroup analyses in Table 2; others are not applicable (NA). bStudies using land use regression, spatiotemporal, inverse distance–weighted, or air dispersion models are more capable of addressing intra-city comparisons of air pollution exposure because they more closely estimate residential exposure to air pollutants. Each technique achieves this goal in a similar but distinct manner: Inverse distance weighting accounts for the distance from monitoring sites to the individual’s residence; land use regression includes relevant environmental GIS–derived variables in regression analyses; spatiotemporal models create air pollutant surfaces that vary by space and time using relevant environmental GIS–derived variables; and air dispersion models use relevant environmental GIS–derived variables, but assume that air pollutant transport follows a deterministic process based on Gaussian plume equations. In contrast, fixed-site monitors apply single exposure values obtained at monitoring sites to the populations surrounding the monitors, and are most useful for intercity comparisons. cNR indicates that exposure means (± SDs) were not reported. Nafstad et al. (2003) report the median value of NOx. dStudies that use a case–control design. Unless otherwise noted, all studies are cohort designs. eStudies that examine lung cancer risk associated with distance to roadways or traffic density. These measures are explicitly described in Figure 2. fStudies of NOx. All other studies examine NO2 exposure. gWe report the highest and lowest cohort specific mean (± SD) of measured air pollution from Raaschou-Nielsen et al. (2013) among the cohorts pooled for the ESCAPE study. Katanoda et al. (2011) report the range of observed NO2 values.
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NO2/NOx and lung cancer. Figure 1 and Table 2 summarize the studies examining the change in lung cancer incidence and mortality associated with traffic exposure as measured by NO2. The overall meta-estimate of the change in lung cancer incidence or mortality per 10-μg/m3 increase in exposure is 4% [95% confidence interval (CI): 1%, 8%], with an I2 estimate of 72.8%. Region-specific estimates vary; for Europe, North America, and Japan, the meta-estimates are 2% (95% CI: –1%, 6%), 7% (95% CI: 0%, 14%), and 11% (95% CI: 3%, 20%), respectively. The funnel plot is visually asymmetrical (Figure 2). Trim-and-fill analyses indicate that five hypothetical studies would need to have been observed to create a symmetrical funnel plot; the meta-estimate for the change in lung cancer per 10-μg/m3 increase in NO2 recalculated based on inclusion of these unobserved studies was attenuated toward the null: 1% (95% CI: –2%, 5%). A cohort study by Naess et al. (2007) of all residents in Oslo, Norway, who were 51–90 years of age met all inclusion criteria, but could not be included in our analyses because risk estimates from this study were based on quartiles of exposure. However, the results of that work support an increase in lung cancer risk associated with NO2; percent change in lung cancer mortality associated with a 1-quartile increase in NO2 for men and women age 51–70 years were 7% (95% CI: –3%, 18%) and 23% (95% CI: 10%, 38%), and for men and women age 71–90 years were 9% (95% CI: –2%, 20%) and 12% (95% CI: –2%, 27%), respectively (Naess et al. 2007).

Figure 1.

Figure 1

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Forest plot of study-specific estimates of relative risk (RR) of lung cancer associated with a 10-μg/m3 increase in exposure to NO2. The meta-estimate and weights in the forest plot are estimated from random effects meta-analyses.

Table 2.

Meta-estimates of the association between lung cancer and a 10-μg/m3 increase in NO2.

Estimate Meta-estimate (95% CI) I2 (%) Studies included (by ID)a
Full meta-estimate 1.04 (1.01, 1.08) 72.8 All
Continent
Europe 1.02 (0.99, 1.06) 41.1 7, 8–13
North America 1.07 (1.01, 1.14) 77.1 1–6
Asia 1.11 (1.03, 1.20) 32.5 14, 15
Exposure assessment method
Fixed-site monitor 1.05 (0.98, 1.13) 80.7 1, 2, 4, 8, 12, 14
Other 1.04 (1.00, 1.08) 60.5 3, 6, 7, 9–11, 13, 15
Confounder adjustment
Smoking status 1.04 (1.00, 1.08) 71.8 1, 2, 4, 5, 7, 8, 9, 12–15
Socioeconomic status/income 1.04 (0.98, 1.11) 68.8 4, 6, 7, 9, 11, 13, 15
Education 1.03 (1.00, 1.07) 68.2 1, 2, 5, 8, 10–13
Occupation 1.02 (0.99, 1.04) 40.9 2–5, 7, 8, 11, 13
aStudy IDs are listed in Table 1.
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Figure 2.

Figure 2

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Funnel plot of study-specific estimates of the relative risk of lung cancer associated with a 10-μg/m3 increase in exposure to NO2. The meta-estimate represented by the vertical solid line of the funnel plot is based on a fixed-effects meta-analysis. Fixed-effects meta-analyses are required to assess the potential for publication bias, and assume that there is no between-study variation.

The meta-estimate for the change in lung cancer associated with a 10-μg/m3 increase in measured NOx is 3% (95% CI: 1%, 5%), based on five relative risk estimates for lung cancer associated with a 10-μg/m3 increase in NOx derived from four studies: 1.03 (0.99, 1.07) (Cao et al. 2011), 1.08 (1.02, 1.15) (Nafstad et al. 2003), 1.00 (0.98, 1.03) (Raaschou-Nielsen et al. 2013), and 1.04 (1.01, 1.08) and 1.00 (0.93, 1.08) (Raaschou-Nielsen et al. 2010). Figure 3 provides a visual summary of these study-specific relative risk estimates. The smaller number of risk estimates available does not allow for detailed analyses of sensitivity to confounder adjustment or other subgroup-specific analyses. However, it is notable that the meta-estimate for lung cancer associated with NOx is similar to that of NO2, but slightly more precise.

Figure 3.

Figure 3

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Forest plot of study specific estimates of relative risk (RR) of lung cancer associated with a 10-μg/m3 increase in exposure to NOx.

Table 2 summarizes sensitivity and subgroup analyses for lung cancer associated with measured NO2. When restricted to studies that adjusted for confounding due to smoking status, socioeconomic status/income, education, or occupation, meta-estimates are largely unchanged; point estimates are similar, and confidence intervals largely overlap with that of the overall meta-estimate. Further, when we divide studies by the exposure assessment technique used, the meta-estimates are identical, or nearly identical, to the overall meta-estimate, but with slightly different confidence intervals. Influence analyses suggested that no single study influenced the overall meta-estimate (see Supplemental Material, Table S1).

Evidence from studies that consider distance from traffic sources or volume of traffic at roads nearest to residence is summarized in Figure 4. Each study uses a unique exposure contrast; thus, we do not provide a meta-estimate of these study specific measures of lung cancer associated with traffic exposure. We only note that many of the estimates reported are either close to or include a null association within the bounds of 95% confidence intervals reported.

Figure 4.

Figure 4

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Relative risk of lung cancer associated with measures of traffic exposure. Abbreviations: k, 1,000; km, kilometers; m, meters; v, vehicles; vkm, vehicle kilometers.

Discussion

We provide a systematic review and meta-analyses of the evidence regarding the association of NO2, NOx, and distance to roadways or volume of vehicles as markers of exposure to traffic-based air pollution. The studies we considered represent many regions of the world and vary by the level of exposure measured, exposure assessment technique, and potential confounders considered. The results of the overall meta-analyses support a relationship between lung cancer and exposure to air pollution from traffic sources.

Analyses stratified by continent suggest the possibility of geographic variability in the magnitude of the relative risk. However, confidence intervals largely overlap, which limits the ability to draw conclusions about heterogeneity. In addition, exposure lags varied in studies from 0 years to 20 years, but were not reported in some studies; Raaschou-Nielsen et al. (2010) found no difference in risk estimates when using a 0-year versus a 10-year exposure lag. Trim-and-fill analyses for NO2 meta-estimates indicated the potential for publication bias, which, if present, would shift results away from the null. To obtain the null result suggested by the trim-and-fill analyses, we would need to assess five hypothetical (i.e., unobserved) studies that obtained results that are the inverse of five studies with the largest and least precise risk estimates. Otherwise, the meta-estimate for NO2 was robust to confounders considered and methods of exposure assessment used by the authors of each study. No formal assessment of study quality was conducted as part of this meta-analysis.

Epidemiologic evidence alone does not allow us to assess nitrogen oxides as a causative agent in development of lung cancer. Nitrogen oxides are not thought to be carcinogenic agents (Valavanidis et al. 2008). Rather, they serve as a marker for other pollutants formed in high-temperature combustion of fossil fuels, a mixture that also includes fine particles. The role of nitrogen oxides as a marker for traffic-related air pollution is most plausible in urban settings, where traffic is often the primary source of NOx in the atmosphere and the main source of variability in NOx levels. NOx is the sum of NO (nitrogen oxide) and NO2; because NO2 is a secondary oxidation product of atmospheric reactions involving NO, the latter of which is directly emitted in combustion, NOx is considered a better indicator of traffic pollution than is NO2 alone. Because NO2 is a component of NOx and will, therefore, always be present at a lower concentration, one would expect a lower effect estimate for NOx (per 1-μg/m3) compared with that for NO2, assuming that either was the true carcinogenic agent in the traffic pollution mixture. Recent studies have shown that NOx is highly correlated with combustion by-products, such as volatile organic compounds and carbonyls (Brook et al. 2007; Curren et al. 2006), many of which are known carcinogens. Combustion of fuel from motor vehicles can contribute a significant portion of outdoor air pollution, particularly in urban areas.

Because cancer is an outcome that is believed to develop over many years, exposure modeling that better approximates long-term, individual-level exposures may provide more valid estimates of the carcinogenic potential of traffic, or other, air pollution. This is distinct from studies of outcomes with sudden onset; the latter can rely on aggregate measures, such as fixed-site monitors and short-term exposure information, because these measures adequately describe relevant, short-term exposure episodes such as high traffic or temperature days (Gram et al. 2003). Even when modeling techniques are used, the residential-level exposures assigned to an individual are, at best, an approximation of individual-level exposure. Further, each modeling technique will vary in its ability to provide individual-level estimates of exposure to pollution. Land use regression, used in four studies considered here, can provide better prediction around major highways where traffic patterns are better known, but not in areas without dense monitoring systems (Jerrett et al. 2005). Alternately, air dispersion models can account for spatiotemporal differences in pollution concentrations, but rely on assumptions that dispersion patterns are Gaussian, which may not be valid (Bellander et al. 2001). Both of these techniques, as well as other spatiotemporal models, attempt to assess within-city contrasts that cannot be accounted for with fixed-site monitors. In short, all exposure-modeling techniques provide, at best, an approximation of individual exposure. A detailed comparison of the most commonly used exposure assessment techniques was provided by Jerrett et al. (2005). Results of our meta-analyses for NO2 were robust to stratification by exposure assessment technique; that is, results were nearly identical regardless of whether or not an exposure modeling technique was used in place of fixed site monitors. It is noteworthy that the meta-estimate restricted to exposure modeling techniques was slightly more precise than that obtained from studies using fixed-site monitors.

Similarly, risk estimates for distance to traffic or roadways, summarized in Figure 2, are crude in that they do not postulate what an individual’s exposure at a residence would be from traffic sources. Distance to roadways can be a marker of socioeconomic status, the influence of which can be difficult to fully take into account. Also, the exposure measures for which the relative risks are estimated are inconsistent across studies; thus, it is impossible to summarize them in any quantitatively meaningful way.

A recent meta-analysis (Hamra et al. 2014) showed a strong and consistent association between exposure to particulate matter and lung cancer; the results, like those presented here, were robust to different confounder sets considered, as well as variation in exposure assessment techniques and study locations (Hamra et al. 2014). In the present review we considered indicators of exposure to motor vehicle traffic (nitrogen oxides and distance). Vehicle traffic also generates particulate matter, which can also arise from numerous anthropogenic and natural processes outside of traffic. By considering markers of traffic exposure, we hope to provide a better understanding of the association of lung cancer with pollutant mixtures that are characteristic of urban and highly populated environments. The results for NOx and NO2 are compatible with the hypothesis that traffic-related air pollution increases the risk of lung cancer. Further, the IARC has classified exposure to diesel exhaust as a Group I carcinogen (Benbrahim-Tallaa et al. 2012). The results of our meta-analyses are consistent with this previous determination, and provide further support for the carcinogenicity of air pollution.

Supplemental Material

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Footnotes

A.J.C. is employed by the Health Effects Institute (HEI). The HEI receives about half of its core funds from the U.S. Environmental Protection Agency and half from the worldwide motor vehicle industry, although other public and private organizations periodically support special projects or certain research programs.

The views expressed in this article do not represent those of the HEI or its sponsors.

The authors declare they have no actual or potential competing financial interests.

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