Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

BACKGROUND

Reported associations between particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity.

OBJECTIVES

To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes.

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors.

DISCUSSION

PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

The relationship between particulate matter and lung function of children: A systematic review and meta-analysis

There have been many studies on the relationship between fine particulate matter (PM_2.5) and lung function. However, the impact of short-term or long-term PM_2.5 exposures on lung function in children is still inconsistent globally, and the reasons for the inconsistency of the research results are not clear. Therefore, we searched the PubMed, Embase and Web of Science databases up to May 2022, and a total of 653 studies about PM_2.5 exposures on children's lung function were identified. Random effects meta-analysis was used to estimate the combined effects of the 25 articles included. PM_2.5 concentrations in short-term exposure studies mainly come from individual and site monitoring. And for every 10 μg/m³ increase, forced vital capacity (FVC), forced expiratory volume in the first second (FEV₁) and peak expiratory flow (PEF) decreased by 21.39 ml (95% CI: 13.87, 28.92), 25.66 ml (95% CI: 14.85, 36.47) and 1.76 L/min (95% CI: 1.04, 2.49), respectively. The effect of PM_2.5 on lung function has a lag effect. For every 10 μg/m³ increase in the 1-day moving average PM_2.5 concentration, FEV₁, FVC and PEF decreased by 14.81 ml, 15.40 ml and 1.18 L/min, respectively. PM_2.5 concentrations in long-term exposure studies mainly obtained via ground monitoring stations. And for every 10 μg/m³ increase, FEV₁, FVC and PEF decreased by 61.00 ml (95% CI: 25.80, 96.21), 54.47 ml (95% CI: 7.29, 101.64) and 10.02 L/min (95% CI: 7.07, 12.98), respectively. The sex, body mass index (BMI), relative humidity (RH), temperature (Temp) and the average PM_2.5 exposure level modify the relationship between short-term PM_2.5 exposure and lung function. Our study provides further scientific evidence for the deleterious effects of PM_2.5 exposures on children's lung function, suggesting that exposure to PM_2.5 is detrimental to children's respiratory health. Appropriate protective measures should be taken to reduce the adverse impact of air pollution on children's health.

Impact of air pollution exposure on lung function and exhaled breath biomarkers in children and adolescents

A growing number of scientific papers focus on the description and quantification of the detrimental effects of pollution exposure on human health. The respiratory system is one of the main targets of these effects and children are potentially a vulnerable population. Many studies analyzed the effects of short- and long-term exposure to air pollutants on children's respiratory function. Aim of the present narrative review is to summarize the results of the available cohort studies which investigated how children's lung function is affected by exposure to air pollution. In addition, an overview is provided on the association, in children, between pollution exposure and exhaled breath biomarkers, as possible indicators of the pathogenetic mechanisms involved in pollution-related lung damages. The identified cohort studies suggest that, beside the possible impact of recent exposure, early and lifetime exposure are the variables most consistently associated with a reduction in lung function parameters in both children and adolescents. As for the effect of air pollution exposure on exhaled breath biomarkers, the available studies show an association with increased exhaled nitric oxide, with increased concentrations of malondialdehyde and 8-isoprostane in exhaled breath condensate (EBC), and with EBC acidification. These studies, therefore, suggest lung inflammation and oxidative stress as possible pathogenetic mechanisms involved in pollution related lung damages. Taken together, the available data underscore the importance of the development and application of policies aimed at reducing air pollutant concentration, since the protection of children's lung function can have a beneficial impact on adults' respiratory health in the future.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.

Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model

INTRODUCTION

To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality.

METHODS

We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models.

RESULTS

Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61-0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2.

CONCLUSION

Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

Impact of different exposure models and spatial resolution on the long-term effects of air pollution

Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM₁₀, and nitrogen dioxide, NO₂) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO₂ and PM₁₀ were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km²) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO₂ and PM₁₀ concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO₂ and PM₁₀ regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO₂ estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM₁₀, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m³ is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Changes in the relationship between ambient fine particle concentrations and childhood lung function over 5 years

BACKGROUND

Exposure to ambient fine particles, particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5), is a public health concern. Concentrations of ambient PM_2.5 have changed temporally in the past 10 years after a series of action policies for improving air quality were implemented in Taiwan. In this study, temporal changes in the relationship between PM_2.5 and lung function among children were investigated.

METHODS

A nationwide respiratory health survey was conducted among Taiwanese elementary and middle school students in 2011 and again in 2016-2017. A questionnaire was administered to students, for whom forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV₁) were measured using spirometry. During the study period, monthly concentrations of ambient PM_2.5 were obtained from the monitoring stations of the Environmental Protection Administration. Lung function measurements were compared with ambient PM_2.5 exposure using mixed-effects models.

RESULTS

In the 2011 survey (mean PM_2.5: 40.6 μg/m³), exposure to PM_2.5 in the preceding 1-2 months was associated with a 2.2% decrease (95% confidence interval [CI]: -4.1%, -0.3%) in FVC and a 2.3% decrease (95% CI: -4.0%, -0.5%) in FEV₁. By contrast, a significant relationship between PM_2.5 concentrations and lung function was not observed in the 2016-2017 survey (mean PM_2.5: 30.0 μg/m³).

CONCLUSIONS

As improvement in air quality over time, the negative relationship between PM_2.5 and childhood lung function tend to be not significant.

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

BACKGROUND

Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO₂) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model.

METHODS

Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO₂ and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO₂ concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC).

RESULTS

The NO₂ LUR model predicted 84% of spatial variability in annual mean NO₂ (RMSE: 1.2 ppb; cross-validated R²: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R²: 0.90). The annual average NO₂ concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO₂ cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO₂ cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69.

CONCLUSIONS

Our LUR models explained a high degree of spatial variability in annual mean NO₂ and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO₂ for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

Remote Sensing in Environmental Justice Research—A Review

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.

Associations of greenness, greyness and air pollution exposure with children's health: a cross-sectional study in Southern Italy

BACKGROUND

Due to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of "green", "grey" and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren.

METHODS

This study involved 219 schoolchildren (8-10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely "discontinuous urban fabric - DUF" - and "continuous urban fabric - CUF" - areas were found. Exposure to traffic-related nitrogen dioxide (NO₂) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms.

RESULTS

A very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07-2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10-2.03]) and general symptoms (OR = 1.18, 95% CI [1.00-1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31-2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12-1.98]). A very high exposure to NO₂ ≥ 60 μg/m³ (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10-1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02-1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03-1.19]) than children living in DUF areas.

CONCLUSIONS

Multiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.

The Australian Child Health and Air Pollution Study (ACHAPS): A national population-based cross-sectional study of long-term exposure to outdoor air pollution, asthma, and lung function

Most studies of long-term air pollution exposure and children's respiratory health have been performed in urban locations with moderate pollution levels. We assessed the effect of outdoor nitrogen dioxide (NO₂), as a proxy for urban air pollution, on current asthma and lung function in Australia, a low-pollution setting. We undertook a national population-based cross-sectional study of children aged 7-11 years living in 12 Australian cities. We collected information on asthma symptoms from parents via questionnaire and measured children's lung function (forced expiratory volume in 1 s [FEV₁], forced vital capacity [FVC]) and fractional exhaled nitric oxide [Fe_NO]). We estimated recent NO₂ exposure (last 12 months) using monitors near each child's school, and used a satellite-based land-use regression (LUR) model to estimate NO₂ at each child's school and home. Our analysis comprised 2630 children, among whom the prevalence of current asthma was 14.9%. Mean (±SD) NO₂ exposure was 8.8 ppb (±3.2) and 8.8 ppb (±2.3) for monitor- and LUR-based estimates, respectively. Mean percent predicted post-bronchodilator FEV₁ and FVC were 101.7% (±10.5) and 98.8% (±10.5), respectively. The geometric mean Fe_NO concentration was 9.4 ppb (±7.1). An IQR increase in NO₂ (4.0 ppb) was significantly associated with increased odds of having current asthma; odds ratios (ORs) were 1.24 (95% CI: 1.08, 1.43) and 1.54 (95% CI: 1.26, 1.87) for monitor- and LUR-based estimates, respectively. Increased NO₂ exposure was significantly associated with decreased percent predicted FEV₁ (-1.35 percentage points [95% CI: -2.21, -0.49]) and FVC (-1.19 percentage points [95% CI: -2.04, -0.35], and an increase in Fe_NO of 71% (95% CI: 38%, 112%). Exposure to outdoor NO₂ was associated with adverse respiratory health effects in this population-based sample of Australian children. The relatively low NO₂ levels at which these effects were observed highlight the potential benefits of continuous exposure reduction.

Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NO_x predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

Purpose of Review

Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments.

Recent Findings

New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO₂). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted.

Summary

Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.

Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure

BACKGROUND

Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods.

METHODS

We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002-2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status.

RESULTS

We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12km-CMAQ models.

CONCLUSIONS

Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data.

Effects of Long-Term Exposure to Traffic-Related Air Pollution on Lung Function in Children

Lung function in early life has been shown to be an important predictor for peak lung function in adults and later decline. Reduced lung function per se is associated with increased morbidity and mortality. With this review, we aim to summarize the current epidemiological evidence on the effect of traffic-related air pollution on lung function in children and adolescents. We focus in particular on time windows of exposure, small airway involvement, and vulnerable sub-groups in the population. Findings from studies published to date support the notion that exposure over the entire childhood age range seems to be of importance for lung function development. We could not find any conclusive data to support evidence of sup-group effects considering gender, sensitization status, and asthma status, although a possibly stronger effect may be present for children with asthma. The long-term effects into adulthood of exposure to air pollution during childhood remains unknown, but current studies suggest that these deficits may be propagated into later life. In addition, further research on the effect of exposure on small airway function is warranted.

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Background: Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods: We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and “Transport database”. We included studies which examined the association between children’s exposure to TRAP metrics and their risk of “asthma” incidence or lifetime prevalence, from birth to the age of 18 years old. Results: We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO₂) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants’ mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions: Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity.

Comparison of diverse estimation methods for personal exposure to air pollutants and associations with allergic symptoms: The Allergy & Gene-Environment Link (ANGEL) study

We estimated the exposure to ambient air pollutants and analyzed the associations with allergic diseases. We enrolled 177 children with atopic dermatitis (AD) and 70 asthmatic adults living in Seoul Metropolitan Area, Korea, and followed for 17months between August 2013 and December 2014. Parents or patients recorded symptom scores on a daily basis. Exposure to particulate matter with a diameter <10μm (PM₁₀) and nitrogen dioxide (NO₂) was estimated in four different ways in each individual, using the AQ1 (measurements from the nearest air quality monitoring station to residential houses), AQ2 (measurements modified from AQ1 with the indoor level of air pollutants and time activity of each individual), AQ1-DI, and AQ2-DI (measurements modified from AQ1 and AQ2, respectively, with daily inhalation intakes of air pollutants). A generalized linear mixed model (GLMM) was used to analyze the associations between exposure metrics and clinical symptoms after adjusting for ambient temperature and humidity, age, season, gender, and time trend. The exposure metrics for PM₁₀ and NO₂ showed different distributions. Symptoms of AD and asthma were positively associated with exposure to PM₁₀, but not NO₂, in all exposure metrics. The effect size of PM₁₀ exposure on asthma symptoms was slightly greater in metrics with inhalation capacity (AQ-DIs) than in those without (AQs). This pattern was not observed in AD. Exposure to PM₁₀ is associated with symptom aggravation in childhood AD and adult asthma. Different exposure estimates may be used to evaluate the impact of air pollution on different allergic diseases.

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies-termed accountability studies-contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed "counterfactual"-the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

IMPLICATIONS

The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.

Associations between maternal exposure to air pollution and traffic noise and newborn's size at birth: A cohort study

BACKGROUND

Maternal exposure to air pollution and traffic noise has been suggested to impair fetal growth, but studies have reported inconsistent findings. Objective To investigate associations between residential air pollution and traffic noise during pregnancy and newborn's size at birth.

METHODS

From a national birth cohort we identified 75,166 live-born singletons born at term with information on the children's size at birth. Residential address history from conception until birth was collected and air pollution (NO2 and NOx) and road traffic noise was modeled at all addresses. Associations between exposures and indicators of newborn's size at birth: birth weight, placental weight and head and abdominal circumference were analyzed by linear and logistic regression, and adjusted for potential confounders.

RESULTS

In mutually adjusted models we found a 10μg/m(3) higher time-weighted mean exposure to NO2 during pregnancy to be associated with a 0.35mm smaller head circumference (95% confidence interval (CI): 95% CI: -0.57; -0.12); a 0.50mm smaller abdominal circumference (95% CI: -0.80; -0.20) and a 5.02g higher placental weight (95% CI: 2.93; 7.11). No associations were found between air pollution and birth weight. Exposure to residential road traffic noise was weakly associated with reduced head circumference, whereas none of the other newborn's size indicators were associated with noise, neither before nor after adjustment for air pollution.

CONCLUSIONS

This study indicates that air pollution may result in a small reduction in offspring's birth head and abdominal circumference, but not birth weight, whereas traffic noise seems not to affect newborn's size at birth.

Association by Spatial Interpolation between Ozone Levels and Lung Function of Residents at an Industrial Complex in South Korea

Spatial interpolation is employed to improve exposure estimates and to assess adverse health effects associated with environmental risk factors. Since various studies have reported that high ozone (O₃) concentrations can give rise to adverse effects on respiratory symptoms and lung function, we investigated the association between O₃ levels and lung function using a variety of spatial interpolation techniques and evaluated how different methods for estimating exposure may influence health results for a cohort from an industrial complex (Gwangyang Bay) in South Korea in 2009. To estimate daily concentrations of O₃ in each subject, four different methods were used, which include simple averaging, nearest neighbor, inverse distance weighting, and kriging. Also, to compare the association between O₃ levels and lung function by age-groups, we explored ozone's impacts on three age-related groups: children (9-14 years), adults (15-64 years), and the elderly (≥65 years). The overall change of effect size on lung function in each age group tended to show similar patterns for lag and methods for estimating exposure. A significant negative association was only observed between O₃ levels and FVC and FEV₁ for most of the lag and methods in children. The largest effect of O₃ levels was found at the average for the lung function test day and last 2 days (0-2 days). In conclusions, the spatial interpolation methods may benefit in providing individual-level exposure with appropriate temporal resolution from ambient monitors. However, time-activity patterns of residents, monitoring site locations, methodological choices, and other factors should be considered to minimize exposure misclassification.

Assessment of indoor and outdoor PM species at schools and residences in a high-altitude Ecuadorian urban center

An air monitoring campaign to assess children's environmental exposures in schools and residences, both indoors and outdoors, was conducted in 2010 in three low-income neighborhoods in Z1 (north), Z2 (central), and Z3 (southeast) zones of Quito, Ecuador - a major urban center of 2.2 million inhabitants situated 2850 m above sea level in a narrow mountainous basin. Z1 zone, located in northern Quito, historically experienced emissions from quarries and moderate traffic. Z2 zone was influenced by heavy traffic in contrast to Z3 zone which experienced low traffic densities. Weekly averages of PM samples were collected at schools (one in each zone) and residences (Z1 = 47, Z2 = 45, and Z3 = 41) every month, over a twelve-month period at the three zones. Indoor PM2.5 concentrations ranged from 10.6 ± 4.9 μg/m(3) (Z1 school) to 29.0 ± 30.5 μg/m(3) (Z1 residences) and outdoor PM2.5 concentrations varied from 10.9 ± 3.2 μg/m(3) (Z1 school) to 14.3 ± 10.1 μg/m(3) (Z2 residences), across the three zones. The lowest values for PM10-2.5 for indoor and outdoor microenvironments were recorded at Z2 school, 5.7 ± 2.8 μg/m(3) and 7.9 ± 2.2 μg/m(3), respectively. Outdoor school PM concentrations exhibited stronger associations with corresponding indoor values making them robust proxies for indoor exposures in naturally ventilated Quito public schools. Correlation analysis between the school and residential PM size fractions and the various pollutant and meteorological parameters from central ambient monitoring (CAM) sites suggested varying degrees of temporal relationship. Strong positive correlation was observed for outdoor PM2.5 at Z2 school and its corresponding CAM site (r = 0.77) suggesting common traffic related emissions. Spatial heterogeneity in PM2.5 concentrations between CAM network and sampled sites was assessed using Coefficient of Divergence (COD) analysis. COD values were lower when CAM sites were paired with outdoor measurements (<0.2) and higher when CAM and indoor values were compared (>0.2), suggesting that CAM network in Quito may not represent actual indoor exposures.

Respiratory effects of air pollution on children

A substantial proportion of the global burden of disease is directly or indirectly attributable to exposure to air pollution. Exposures occurring during the periods of organogenesis and rapid lung growth during fetal development and early post-natal life are especially damaging. In this State of the Art review, we discuss air toxicants impacting on children's respiratory health, routes of exposure with an emphasis on unique pathways relevant to young children, methods of exposure assessment and their limitations and the adverse health consequences of exposures. Finally, we point out gaps in knowledge and research needs in this area. A greater understanding of the adverse health consequences of exposure to air pollution in early life is required to encourage policy makers to reduce such exposures and improve human health.

Monte Carlo optimization for site selection of new chemical plants

Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.