Multi-pollutant Modeling Research Articles

Using triangulation to assess confounding by co-exposures to multiple per- and polyfluoroalkyl substances (PFAS)

Background and Aim Evaluation of health effects of highly correlated chemicals like per- and polyfluoroalkyl substances (PFAS) is complicated by the potential for confounding. We used a triangulation approach to determine the likelihood that observed associations could be explained by confounding across PFAS. Methods As part of a systematic review of the hepatic effects of perfluorooctanoic acid (PFOA), we considered the sources and direction of bias in studies with varying exposure scenarios. In addition, we examined PFAS correlations and multipollutant modeling results within and across studies. Results Twenty-five studies reported on the association between PFOA exposure and hepatic effects. Fourteen were in the general population using biomarkers in blood, in which individuals are exposed to a mixture of PFAS and it can be challenging to disentangle individual effects. Eight studies were in workers exposed to PFOA via inhalation and two were in communities with significant PFOA drinking water contamination. In these studies, PFOA exposure is predominant so there is less confounding by other PFAS but there may be potential for selection bias (e.g., healthy worker effect in occupational studies and heightened awareness of PFAS exposure and toxicity in contamination communities) and other confounding (occupational). For all exposure scenarios, the majority of studies reported associations between PFOA exposure and increased alanine aminotransferase in blood. Conclusions Consistency in the direction of association combined with differences in the sources of bias across studies with different exposure sources reduces the likelihood that the observed effects can be fully explained by confounding across PFAS. Keywords Per- and polyfluoroalkyl substances, systematic review, evidence synthesis Disclaimer: The views expressed in this abstract are those of the author and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency.

Effects of ambient air pollution on childhood asthma exacerbation in the Philadelphia metropolitan Region, 2011–2014

Fine particulate matter (PM2.5) and ozone (O3) air pollutants are known risk factors for asthma exacerbation. We studied the association of these air pollutants with pediatric asthma exacerbation in the Philadelphia metropolitan region, and evaluated potential effect modification by children's characteristics (e.g., race/ethnicity, atopic conditions) and environmental factors (e.g., neighborhood tree canopy, meteorological factors, aeroallergens). We conducted a time-stratified case-crossover study of 54,632 pediatric (age ≤18 years) asthma exacerbation cases occurring from 2011 to 2014, identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system. We applied conditional logistic regression to estimate associations between air pollution and asthma exacerbation, using daily census-tract level pollutant concentrations estimated from the EPA Fused Air Quality Surface Using Downscaling (FAQSD) files. The associations were estimated within warm (Apr–Sep) and cold (Oct–Mar) months for unlagged exposure and for cumulative effects up to 5 days after exposure, with adjustment for temperature, relative humidity, and holidays. We found small increases in odds of asthma exacerbation with higher pollutant concentrations, with positive associations (OR, comparing concentrations of 75th to 25th percentile) observed for PM2.5 during both warm (1.03, 95% CI: 0.98–1.08) and cold months (1.05, 95% CI: 1.02–1.07), and for O3 during cold months (1.08, 95% CI: 1.02–1.14). The exposure-response relationship with PM2.5 during the cold months was essentially linear, whereas thresholds of effect were observed for the other associations at low-medium pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates. We found no effect modification by most children's characteristics, while effect sizes were higher on days with detected tree and grass pollens during warm months. Our results suggest that even small decreases in pollutant concentrations could potentially reduce risk of childhood asthma exacerbation – an important finding, given the high burden of childhood asthma and known disparities in asthma control.

Exposure to Polychlorinated Biphenyls and Organochlorine Pesticides and Risk of Thyroid Cancer in Women

Background: Both polychlorinated biphenyls (PCB) and organochlorine pesticides (OCP) have been associated with thyroid hormone disruption, but their relationship to thyroid cancer is not known. Methods: We investigated the relationship between serum PCB and OCP concentrations and papillary thyroid cancer (PTC) in 250 incident female PTC cases and 250 female controls frequency-matched on age, all residing in Connecticut. Interviews and serum samples were collected from 2010-2013. Samples were analyzed for 32 different chemicals using gas chromatography with isotope dilution high resolution mass spectrometry. We calculated odds ratios (OR) and 95% confidence intervals (CI) using single pollutant logistic regression models for concentrations (per interquartile range) of individual PCB/OCP and summed groups of structurally or biologically similar PCB/OCP, adjusted for education, family history of cancer, alcohol consumption, and body mass index. Sub-analyses included stratification by tumor size (<=1 cm and >1 cm) and birth cohort (born before or in 1960 and born after 1960). We also applied three multi-pollutant approaches (standard multi-pollutant regression, hierarchical Bayesian modeling, principal components regression analysis) to investigate associations with exposures to multiple PCB/OCPs. Results: No PCB/OCPs were positively associated with PTC risk in primary analyses. Statistically significant associations were observed for 9 of the 32 chemicals and 3 summed groups of similar chemicals in the younger birth cohort based on single-pollutant models. Multi-pollutant analyses suggested null associations overall. Conclusions: Our results using single and multi-pollutant modeling do not generally support an association between PCB or OCP exposure and PTC risk, but some associations in the younger birth cohort suggest that additional investigation into early-life exposures and subsequent thyroid cancer risk may be warranted.

Effects of Fine Particulate Matter on Childhood Asthma Exacerbation in the Philadelphia Metropolitan Region

Background: Fine particulate matter air pollution (PM2.5) is a known risk factor for asthma exacerbation. We aimed to describe the local association between PM2.5 and asthma exacerbation in the Philadelphia region, and to evaluate whether the effect is modified by children’s characteristics or other environmental exposures.Methods: We conducted a time-stratified case-crossover study of pediatric asthma exacerbation (age ≤18 years) occurring from 2011-2014, identified through electronic health records (EHR) of the Children’s Hospital of Philadelphia (CHOP) Care Network. Modeled, census-tract level estimates of daily PM2.5 concentrations were acquired from the EPA’s Downscaler model. We applied conditional logistic regression to estimate the association within warm (Apr-Sep) and cold (Oct-Mar) months, with PM2.5 level modeled as a natural cubic spline (3 degrees of freedom), and adjusting for temperature, relative humidity, and holidays. We evaluated unlagged effects (i.e., PM2.5 levels on the same day as the exacerbation) and effects lagged up to 5 days.Results: There were 54,632 asthma exacerbation events during the study period, with the majority occurring among male (60.75%) and black, non-Hispanic (58.99%) children. We found a positive association between PM2.5 and asthma exacerbation in both warm months (odds ratio [OR] comparing 90th to 25th percentile = 1.09, 95% confidence interval [CI]: 1.01-1.18, lag05) and cold months (OR, 90th vs. 25th percentile = 1.08, 95% CI: 1.04-1.12, lag0). During cold months, risk increases started from the lowest pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates, and we found no effect modification by children’s characteristics (e.g., age, race/ethnicity, allergic rhinitis, eczema). During warm months, the PM2.5 effect was higher on days with detected tree pollen.Conclusion: We found a positive association between PM2.5 and asthma exacerbation. Our results suggest that even small reductions in fine particulate matter air pollution may reduce clinical visits for asthma exacerbation.

Exposure to polychlorinated biphenyls and organochlorine pesticides and thyroid cancer in connecticut women

BackgroundThyroid cancer incidence has increased substantially over the past decades, and environmental risk factors have been suggested to play a role. Polychlorinated biphenyls (PCB) and organochlorine pesticides (OCP) are established thyroid hormone disruptors, but their relationship to thyroid cancer is not known. MethodsWe investigated the relationship between serum PCB and OCP concentrations and papillary thyroid cancer (PTC) in 250 incident female PTC cases and 250 female controls frequency-matched on age, all residing in Connecticut. Interviews and serum samples were collected from 2010 to 2013. Samples were analyzed for 32 different chemicals using gas chromatography with isotope dilution high resolution mass spectrometry. We calculated odds ratios (OR) and 95% confidence intervals (CI) using single pollutant logistic regression models for concentrations (per interquartile range) of individual PCB/OCP and summed groups of structurally or biologically similar PCB/OCP, adjusted for education, family history of cancer, alcohol consumption, age, and body mass index. Sub-analyses included stratification by tumor size (≤ and >1 cm) and birth before or during peak PCB production (born in 1960 or earlier and born after 1960), as exposures during early life may be important. We also applied three multi-pollutant approaches (standard multi-pollutant regression, hierarchical Bayesian modeling, principal components regression analysis) to investigate associations with co-exposures to multiple PCB/OCPs. ResultsNo PCB/OCPs were positively associated with PTC in primary analyses. Statistically significant associations were observed for 9 of the 32 chemicals and 3 summed groups of similar chemicals in the those born during peak production based on single-pollutant models. Multi-pollutant analyses suggested null associations overall. ConclusionsOur results using single and multi-pollutant modeling do not generally support an association between PCB or OCP exposure and PTC, but some associations in those born during peak production suggest that additional investigation into early-life exposures and subsequent thyroid cancer risk may be warranted.

Exposure to Polybrominated Diphenyl Ethers and a Polybrominated Biphenyl and Risk of Thyroid Cancer in Women: Single and Multi-Pollutant Approaches.

Thyroid cancer incidence is the most rapidly increasing malignancy; rates are three times higher in women than men. Thyroid hormone-disrupting flame-retardant chemicals, including polybrominated diphenyl ethers (PBDE) and polybrominated biphenyls (PBB), may contribute to this trend. We investigated the relationship between PBDE/PBB exposure and papillary thyroid cancer (PTC) in 250 incident female papillary thyroid cancer cases and 250 female controls frequency-matched on age. Interviews and postdiagnostic serum samples were collected from 2010 to 2013. Serum samples were analyzed for 11 congeners. We calculated ORs and 95% confidence intervals (95% CI) using single-pollutant logistic regression models for continuous and categorical lipid-adjusted serum concentrations of PBDE/PBB, adjusted for age, alcohol consumption, and education. We applied three multi-pollutant approaches [standard multipollutant regression models, hierarchical Bayesian logistic regression modeling (HBLR), principal components analysis (PCA)] to investigate associations with PBDE/PBB mixtures. In single-pollutant models, a decreased risk was observed at the highest (>90th percentile) versus lowest (<median) category of BDE-209 concentrations (OR, 0.47; 95% CI, 0.23-0.98); an elevated PTC risk was observed at the highest versus lowest category of BB-153 concentrations (OR, 1.81; 95% CI, 0.96-3.39). In standard multi-pollutant models, an interquartile range increase in BDE-100 concentrations was associated with increased PTC risk (OR, 1.18; 95% CI, 1.01-1.38). HBLR and PCA yielded no statistically significant results. Our results using single- and multi-pollutant modeling do not generally support a positive association with PBDE/PBB and PTC risk. Prospective studies with more advanced statistical approaches to analyze mixtures and populations with higher exposures could reveal new insights.

Effects of different size fractions of ultrafine particles from short-term exposure to indoor sources on arterial stiffness using single and multipollutant modeling

Effects of different size fractions of ultrafine particles from short-term exposure to indoor sources on arterial stiffness using single and multipollutant modeling

Multipollutant modeling of ozone, reactive nitrogen and HAPs across the continental US with CMAQ-CB6

The accuracy of atmospheric chemical mechanisms used in air quality models is critical for robustly predicting the production and decay of air pollutants and developing strategies to reduce concentrations that are above levels harmful to humans and ecosystems. In this study we document, evaluate and analyze an application of the CB6r3 chemical mechanism in a 3-D air quality model, the Community Multiscale Air Quality (CMAQ) model, and demonstrate the impact of this updated chemical mechanism on predictions of ozone, oxidized nitrogen and formaldehyde. In general, CMAQ-CB6 slightly improves the predictions of ozone over much of the dynamic range, while providing updates that are more consistent with current scientific understanding. For these reasons, CMAQ-CB6 is recommended in place of earlier versions available in CMAQ. Nitric acid is generally overpredicted in both winter and summer, and ongoing work continues to address this overprediction and update other aspects of the mechanism.

Short-term effects of airport-associated ultrafine particle exposure on lung function and inflammation in adults with asthma

BackgroundExposure to ultrafine particles (UFP, particles with aerodynamic diameter < 100 nm) is associated with reduced lung function and airway inflammation in individuals with asthma. Recently, elevated UFP number concentrations (PN) from aircraft landing and takeoff activity were identified downwind of the Los Angeles International Airport (LAX) but little is known about the health impacts of airport-related UFP exposure. MethodsWe conducted a randomized crossover study of 22 non-smoking adults with mild to moderate asthma in Nov-Dec 2014 and May-Jul 2015 to investigate short-term effects of exposure to LAX airport-related UFPs. Participants conducted scripted, mild walking activity on two occasions in public parks inside (exposure) and outside (control) of the high UFP zone. Spirometry, multiple flow exhaled nitric oxide, and circulating inflammatory cytokines were measured before and after exposure. Personal UFP PN and lung deposited surface area (LDSA) and stationary UFP PN, black carbon (BC), particle-bound PAHs (PB-PAH), ozone (O3), carbon dioxide (CO2) and particulate matter (PM2.5) mass were measured. Source apportionment analysis was conducted to distinguish aircraft from roadway traffic related UFP sources. Health models investigated within-subject changes in outcomes as a function of pollutants and source factors. ResultsA high two-hour walking period average contrast of ~34,000 particles·cm−3 was achieved with mean (std) PN concentrations of 53,342 (25,529) and 19,557 (11,131) particles·cm−3 and mean (std) particle size of 28.7 (9.5) and 33.2 (11.5) at the exposure and control site, respectively. Principal components analysis differentiated airport UFPs (PN), roadway traffic (BC, PB-PAH), PM mass (PM2.5, PM10), and secondary photochemistry (O3) sources. A standard deviation increase in the ‘Airport UFPs’ factor was significantly associated with IL-6, a circulating marker of inflammation (single-pollutant model: 0.21, 95% CI = 0.08–0.34; multi-pollutant model: 0.18, 0.04–0.32). The ‘Traffic’ factor was significantly associated with lower Forced Expiratory Volume in 1 s (FEV1) (single-pollutant model: −1.52, −2.28 to −0.77) and elevated sTNFrII (single-pollutant model: 36.47; 6.03–66.91; multi-pollutant model: 64.38; 6.30–122.46). No consistent associations were observed with exhaled nitric oxide. ConclusionsTo our knowledge, our study is the first to demonstrate increased acute systemic inflammation following exposure to airport-related UFPs. Health effects associated with roadway traffic exposure were distinct. This study emphasizes the importance of multi-pollutant measurements and modeling techniques to disentangle sources of UFPs contributing to the complex urban air pollution mixture and to evaluate population health risks.

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the "bad actors" most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures. One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1.

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency’s (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

Multi-Pollutant Modeling On The Impact Of Particulate Matter On Mortality In Seven Major Cities, South Korea, 2003-2011: Is There An Interaction With Gaseous Pollutants?

Background: in air pollution epidemiology, multi-pollutant modelling have been discussed for its importance to detect multiple exposure effect including interaction. An interaction of particles wit...

Performance Of Variable Selection Methods For Correlated Exposures And Logistic Models

Introduction: There is growing recognition that simultaneously assessing multiple exposures (multipollutant modeling) may better estimate independent and mutually adjusted epidemiological effect es...

Framing Air Pollution Epidemiology in Terms of Population Interventions, with Applications to Multipollutant Modeling

Air pollution epidemiology continues moving toward the study of mixtures and multipollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multipollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5 μm) related to one another within 2 seasons (summer and winter), within 3 cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city season, we examined how realistically IQR scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.

Using community level strategies to reduce asthma attacks triggered by outdoor air pollution: a case crossover analysis

BackgroundEvidence indicates that asthma attacks can be triggered by exposure to ambient air pollutants, however, detailed pollution information is missing from asthma action plans. Asthma is commonly associated with four criteria pollutants with standards derived by the United States Environmental Protection Agency. Since multiple pollutants trigger attacks and risks depend upon city-specific mixtures of pollutants, there is lack of specific guidance to reduce exposure. Until multi-pollutant statistical modeling fully addresses this gap, some guidance on pollutant attack risk is required. This study examines the risks from exposure to the asthma-related pollutants in a large metropolitan city and defines the city-specific association between attacks and pollutant mixtures. Our goal is that city-specific pollution risks be incorporated into individual asthma action plans as additional guidance to prevent attacks.MethodsCase-crossover analysis and conditional logistic regression were used to measure the association between ozone, fine particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide pollution and 11,754 emergency medical service ambulance treated asthma attacks in Houston, Texas from 2004-2011. Both single and multi-pollutant models are presented.ResultsIn Houston, ozone and nitrogen dioxide are important triggers (RR = 1.05; 95% CI: 1.00, 1.09), (RR = 1.10; 95% CI: 1.05, 1.15) with 20 and 8 ppb increase in ozone and nitrogen dioxide, respectively, in a multi-pollutant model. Both pollutants are simultaneously high at certain times of the year. The risk attributed to these pollutants differs when they are considered together, especially as concentrations increase. Cumulative exposure for ozone (0-2 day lag) is of concern, whereas for nitrogen dioxide the concern is with single day exposure. Persons at highest risk are aged 46-66, African Americans, and males.ConclusionsAccounting for cumulative and concomitant outdoor pollutant exposure is important to effectively attribute risk for triggering of an asthma attack, especially as concentrations increase. Improved asthma action plans for Houston individuals should warn of these pollutants, their trends, correlation and cumulative effects. Our Houston based study identifies nitrogen dioxide levels and the three-day exposure to ozone to be of concern whereas current single pollutant based national standards do not.

Comment on Slama R, Cyrys J, Herbarth O, Wichmann H-E, Heinrich J. (2009) A further plea for rigorous science and explicit disclosure of potential conflicts of interest. Morfeld P. (2009) A plea for rigorous and honest science—false positive findings and biased presentations in epidemiological studies. Archives of Toxicology 83:105–106

Slama et al. (2009) criticized my guest editorial in this journal (Morfeld 2009). In the editorial, I used the publication of Slama et al. 2007 as an example to demonstrate a biased analysis and presentation of an epidemiological study. Slama et al. (2007) investigated the possible link between traYc-related atmospheric pollutants and birth weight in oVsprings. They presented results (signiWcantly elevated odds ratios) from a binary analysis (birth weight dichotomized at 3,000 g) whereas their a priori choice was a continuous analysis (Slama et al. 2007, p. 1284). They changed the analysis strategy because in the continuous analysis birth weight “...did not turn out to be associated with air pollution (data not shown)” (Slama et al. 2007, p. 1284). Slama et al. (2007) mentioned this important fact of no association in only one single sentence within the methods section. It was neither picked up again in the results section or discussion section nor in the abstract of the paper. Furthermore, they reported in the abstract only results on the eVect of PM 2.5 levels and on PM 2.5 absorbance from models that did not adjust for other pollutants simultaneously, thus exaggerating the odds ratios and their statistical signiWcance additionally in comparison to the multipollutant modeling results shown in Table 3 (Slama et al. 2007). Therefore, I argued in the guest editorial that the reporting of results in Slama et al. (2007) was biased. Slama et al. (2009) conWrmed that the continuous outcome analysis (which was their a priori choice of analysis) did not show an association between maternal exposure and atmospheric pollution. They justiWed the severe underreporting of this important Wnding by limitations in word counts and number of tables. However, it is the duty of authors to present their Wndings in a balanced way. Only one sentence in the methods section among eight full pages of text clearly means a distortion. When reading Slama et al. (2009), it becomes obvious that this strange presentation was not an accident but intentional. Slama et al. (2009) argued that due to high correlations between the pollutants it is better to report the results of single pollutant models in the abstract of a paper. I clearly disagree because the opposite is true. Due to these strong correlations, the emphasis of results from a single pollutant model is misleading. The reader of the abstract is seduced to believe that the authors present results that are valid for the one pollutant—after an appropriate adjustment. If the correlations are very high, near to collinearity, such a study is even unable to identify the relative impact of a single pollutant among the collinear pollutants studied. This must be reported and not masked by a prominent presentation of single pollutant models. In case of Slama et al. (2007) the multiple regression analysis of the pollutants was feasible (no collinearity) and the results of these multi-pollutant models (Table 3, Slama et al. 2007) should be primarily considered and presented, not the Wndings of the single pollutant models emphasized by Slama and co-workers in the abstract. However, such a correction would not overcome the main presentation bias of a severe underreporting of the results from the continuous analysis. P. Morfeld Institute for Occupational Medicine of Cologne University, Cologne, Germany

Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants

BackgroundThere is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques.MethodsWe measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations.ResultsPM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56).ConclusionEach pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods.

Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta

Multipollutant models are frequently used to differentiate roles of multiple pollutants in epidemiologic studies of ambient air pollution. In the presence of differing levels of measurement error across pollutants under consideration, however, they can be biased and as misleading as single-pollutant models. Their appropriate interpretation depends on the relationships among the pollutant measurements and the outcomes in question. In situations where two or more pollutant variables may be acting as surrogates for the etiologic agent(s), multipollutant models can help identify the best surrogate, but the risk estimates may be influenced by inclusion of a second variable that is not itself an independent risk factor for the outcome in question. In this paper, these issues will be illustrated in the context of an ongoing study of emergency visits in Atlanta. Emergency department visits from 41 of 42 hospitals serving the 20-county Atlanta metropolitan area for the period 1993-2004 (n=10,206,389 visits) were studied in relation to ambient pollutant levels, including speciated particle measurements from an intensive monitoring campaign at a downtown station starting in 1998. Relative to our earlier publications, reporting results through 2000, the period for which the speciated data are available is now tripled (6 years in length). Poisson generalized linear models were used to examine outcome counts in relation to 3-day moving average concentrations of pollutants of a priori interest (ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide, oxygenated hydrocarbons, PM10, coarse PM, PM2.5, and the following components of PM2.5: elemental carbon, organic carbon, sulfate, and water-soluble transition metals). In the present analysis, we report results for two outcome groups: a respiratory outcomes group and a cardiovascular outcomes group. For cardiovascular visits, associations were observed with CO, NO2, and PM2.5 elemental carbon and organic carbon. In multipollutant models, CO was the strongest predictor. For respiratory visits, associations were observed with ozone, PM10, CO, and NO2 in single-pollutant models. In multipollutant models, PM10 and ozone persisted as predictors, with ozone the stronger predictor. Caveats and considerations in interpreting the multipollutant model results are discussed.