Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states

Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data

A critical question in environmental epidemiology is whether air pollution exposures of large populations can be refined using individual mobile-device-based mobility patterns. Cellular network data has become an essential tool for understanding the movements of human populations. As such, through inferring the daily home and work locations of 407,435 mobile phone users whose positions are determined, we assess exposure to PM_2.5. Spatiotemporal PM_2.5 concentrations are predicted using an Aerosol Optical Depth- and Land Use Regression-combined model. Air pollution exposures of subjects are assigned considering modeled PM_2.5 levels at both their home and work locations. These exposures are then compared to residence-only exposure metric, which does not consider daily mobility. In our study, we demonstrate that individual air pollution exposures can be quantified using mobile device data, for populations of unprecedented size. In examining mean annual PM_2.5 exposures determined, bias for the residence-based exposures was 0.91, relative to the exposure metric considering the work location. Thus, we find that ignoring daily mobility potentially contributes to misclassification in health effect estimates. Our framework for understanding population exposure to environmental pollution could play a key role in prospective environmental epidemiological studies.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

The sensitivity of satellite-based PM2.5 estimates to its inputs: Implications to model development in data-poor regions

Exposure to fine particulate matter (PM_2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air pollution health effects research in the data-poor regions, where pollution levels are often the highest, is still very limited due to the lack of high-quality exposure estimates. To improve our understanding of the desired input datasets for the application of satellite-based PM_2.5 exposure models in data-poor areas, we applied a Bayesian ensemble model in the southeast U.S. that was selected as a representative data-rich region. We designed four groups of sensitivity tests to simulate various data-poor scenarios. The factors considered that would influence the model performance included the temporal sampling frequency of the monitors, the number of ground monitors, the accuracy of the chemical transport model simulation of PM_2.5 concentrations, and different combinations of the additional predictors. While our full model achieved a 10-fold cross-validated (CV) R² of 0.82, we found that when reducing the sampling frequency from the current 1-in-3 day to 1-in-9 day, the CV R² decreased to 0.58, and the predictions could not capture the daily variations of PM_2.5. Half of the current stations (i.e., 30 monitors) could still support a robust model with a CV R² of 0.79. With 20 monitors, the CV R² decreased from 0.71 to 0.55 when 100% additional random errors were added to the original CMAQ simulations. However, with a sufficient number of ground monitors (e.g., 30 monitors), our Bayesian ensemble model had the ability to tolerate CMAQ errors with only a slight decrease in CV R² (from 0.79 to 0.75). With fewer than 15 monitors, our full model collapsed and failed to fit any covariates, while the models with only time-varying variables could still converge even with only five monitors left. A model without the land use parameters lacked fine spatial details in the prediction maps, but could still capture the daily variability of PM_2.5 (CV R² ≥ 0.67) and might support a study of the acute health effects of PM_2.5 exposure.

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data

The long satellite aerosol data record enables assessments of historical PM_2.5 level in regions where routine PM_2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM_2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM_2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM_2.5 well with the cross-validation (CV) R² (RMSE) of 0.79 (21 μg/m³). The cluster-based subregion models outperformed national models and improved the CV R² by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R² = 0.58, RMSE = 29 μg/m³) and monthly level ( R² = 0.76, RMSE = 16 μg/m³). Our hindcast modeling system allows for the construction of unbiased historical PM_2.5 levels.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM_2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM_2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM_2.5 ("SAT-PM_2.5"). We aimed to determine the validity of such satellite-based LUR models for PM_2.5 in Australia. We used global SAT-PM_2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM_2.5 predictor variable (and six others) explained the most spatial variability in PM_2.5 (adjusted R² = 0.63, RMSE (μg/m³ [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R² = 0.52, RMSE: 1.15 [16%]). The evaluation R² of the SAT-PM_2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM_2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM_2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM_2.5 exposure assessment in Australia.

A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information

BACKGROUND

Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM_2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level.

OBJECTIVES

To estimate daily concentrations of PM_2.5 across China during 2005-2016.

METHODS

Daily ground-level PM_2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM_2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM_2.5 across China with a resolution of 0.1° (≈10 km) during 2005-2016.

RESULTS

The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM_2.5 [10-fold cross-validation (CV) R² = 83%, root mean squared prediction error (RMSE) = 28.1 μg/m³]. At the monthly and annual time-scale, the explained variability of average PM_2.5 increased up to 86% (RMSE = 10.7 μg/m³ and 6.9 μg/m³, respectively).

CONCLUSIONS

Taking advantage of a novel application of modeling framework and the most recent ground-level PM_2.5 observations, the machine learning method showed higher predictive ability than previous studies.

CAPSULE

Random forests approach can be used to estimate historical exposure to PM_2.5 in China with high accuracy.

Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment

Air pollution is a leading global disease risk factor. Tracking progress (e.g., for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian hierarchical model was developed to estimate annual average fine particle (PM_2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010-2016. The model incorporated spatially varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates, and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R² from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 μg/m³ to 12 μg/m³). In 2016, 95% of the world's population lived in areas where ambient PM_2.5 levels exceeded the World Health Organization 10 μg/m³ (annual average) guideline; 58% resided in areas above the 35 μg/m³ Interim Target-1. Global population-weighted PM_2.5 concentrations were 18% higher in 2016 (51.1 μg/m³) than in 2010 (43.2 μg/m³), reflecting in particular increases in populous South Asian countries and from Saharan dust transported to West Africa. Concentrations in China were high (2016 population-weighted mean: 56.4 μg/m³) but stable during this period.

Satellite-Based Daily PM2.5 Estimates During Fire Seasons in Colorado

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high-resolution PM2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011-2014. Our model had a 10-fold cross-validated R 2 of 0.66 and root-mean-squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM2.5.

Short period PM2.5 prediction based on multivariate linear regression model

A multivariate linear regression model was proposed to achieve short period prediction of PM_2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO₂, NO₂, CO, and O₃). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R² = 0.766) goodness-of-fit and (R² = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study.

Estimating the Causal Effect of Low Levels of Fine Particulate Matter on Hospitalization

BACKGROUND

In 2012, the EPA enacted more stringent National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5). Few studies have characterized the health effects of air pollution levels lower than the most recent NAAQS for long-term exposure to PM2.5 (now 12 μg/m).

METHODS

We constructed a cohort of 32,119 Medicare beneficiaries residing in 5138 US ZIP codes who were interviewed as part of the Medicare Current Beneficiary Survey (MCBS) between 2002 and 2010 and had 1 year of follow-up. We considered four outcomes: all-cause hospitalizations, hospitalizations for circulatory diseases and respiratory diseases, and death.

RESULTS

We found that increasing exposure to PM2.5 from levels lower than 12 μg/m to levels higher than 12 μg/m is associated with increases in all-cause admission rates of 7% (95% CI = 3%, 10%) and in circulatory admission hazard rates of 6% (95% CI = 2%, 9%). When we restricted analysis to enrollees with exposure always lower than 12 μg/m, we found that increasing exposure from levels lower than 8 μg/m to levels higher than 8 μg/m increased all-cause admission hazard rates by 15% (95% CI = 8%, 23%), circulatory by 18% (95% CI = 10%, 27%), and respiratory by 21% (95% CI = 9%, 34%).

CONCLUSIONS

In a nationally representative sample of Medicare enrollees, changes in exposure to PM2.5, even at levels consistently below standards, are associated with increases in hospital admissions for all causes and cardiovascular and respiratory diseases. The robustness of our results to inclusion of many additional individual level potential confounders adds validity to studies of air pollution that rely entirely on administrative data.

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

The technological ability to make personal measurements of toxicant exposures is growing rapidly. While this can decrease measurement error and therefore help reduce attenuation of effect estimates, we argue that as measures of exposure or dose become more personal, threats to validity of study findings can increase in ways that more proxy measures may avoid. We use directed acyclic graphs (DAGs) to describe conditions where confounding is introduced by use of more personal measures of exposure and avoided via more proxy measures of personal exposure or target tissue dose. As exposure or dose estimates are more removed from the individual, they become less susceptible to biases from confounding by personal factors that can often be hard to control, such as personal behaviors. Similarly, more proxy exposure estimates are less susceptible to reverse causation. We provide examples from the literature where adjustment for personal factors in analyses that use more proxy exposure estimates have little effect on study results. In conclusion, increased personalized exposure assessment has important advantages for measurement accuracy, but it can increase the possibility of biases from personal factors and reverse causation compared with more proxy exposure estimates. Understanding the relation between more and less proxy exposures, and variables that could introduce confounding are critical components to study design.

Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model

The short-term and acute health effects of fine particulate matter less than 2.5 μm (PM_2.5) have highlighted the need for exposure assessment models with high spatiotemporal resolution. Here, we utilize satellite, meteorologic, atmospheric, and land-use data to train a random forest model capable of accurately predicting daily PM_2.5 concentrations at a resolution of 1 × 1 km throughout an urban area encompassing seven counties. Unlike previous models based on aerosol optical density (AOD), we show that the missingness of AOD is an effective predictor of ground-level PM_2.5 and create an ensemble model that explicitly deals with AOD missingness and is capable of predicting with complete spatial and temporal coverage of the study domain. Our model performed well with an overall cross-validated root mean squared error (RMSE) of 2.22 μg/m³ and a cross-validated R² of 0.91. We illustrate the daily changing spatial patterns of PM_2.5 concentrations across our urban study area made possible by our accurate, high-resolution model. The model will facilitate high-resolution assessment of both long-term and acute PM_2.5 exposures in order to quantify their associations with related health outcomes.

Examining the effects of socioeconomic development on fine particulate matter (PM2.5) in China's cities using spatial regression and the geographical detector technique

The frequent occurrence of extreme smog episodes in recent years has begun to present a serious threat to human health. In addition to pollutant emissions and meteorological conditions, fine particulate matter (PM_2.5) is also influenced by socioeconomic development. Thus, identifying the potential effects of socioeconomic development on PM_2.5 variations can provide insights into particulate pollution control. This study applied spatial regression and the geographical detector technique for assessing the directions and strength of association between socioeconomic factors and PM_2.5 concentrations, using data collected from 945 monitoring stations in 190 Chinese cities in 2014. The results indicated that the annual average PM_2.5 concentrations is 61±20μg/m³, and cites with more than 75μg/m³ were mainly located in North China, especially in Tianjin and Hebei province. We also identified a marked seasonal variation in concentrations levels, with the highest level in winter due to coal consumption, lower temperatures, and less rainfall than in summer. Monthly variations followed a "U-shaped" pattern, with a down trend from January and an inflection point in September and then an increasing trend from October. The results of spatial regression indicated that population density, industrial structure, industrial soot (dust) emissions, and road density have a significantly positive effect on PM_2.5 concentrations, with a significantly negative influence exerted only by economic growth. In addition, trade openness and electricity consumption were found to have no significant impact on PM_2.5 concentrations. Using the geographical detector technique, the strength of association between the five significant drivers and PM_2.5 concentrations was further analyzed. We found notable differences among the variables, with industrial soot (dust) emissions playing a greater role in the PM_2.5 concentrations than the other variables. These results will be helpful in understanding the dynamics and the underlying mechanisms at work in PM_2.5 concentrations in China at the city level, and thereby assisting the Chinese government in employing effective strategies to tackle pollution.

Influences of socioeconomic vulnerability and intra-urban air pollution exposure on short-term mortality during extreme dust events

Air pollution has been shown to be significantly associated with morbidity and mortality in urban areas, but there is lack of studies focused on extreme pollution events such as extreme dust episodes in high-density Asian cities. However, such cities have had extreme climate episodes that could have adverse health implications for downwind areas. More importantly, few studies have comprehensively investigated the mortality risks of extreme dust events for socioeconomically vulnerable populations. This paper examined the association between air pollutants and mortality risk in Hong Kong from 2006 to 2010, with a case-crossover analysis, to determine the elevated risk after an extreme dust event in a high-density city. The results indicate that PM_10-2.5 dominated the all-cause mortality effect at the lag 0 day (OR: 1.074 [1.051, 1.098]). This study also found that people who were aged ≥ 65, economically inactive, or non-married had higher risks of all-cause mortality and cardiorespiratory mortality during days with extreme dust events. In addition, people who were in areas with higher air pollution had significantly higher risks of all-cause mortality and cardiorespiratory mortality. In conclusion, the results of this study can be used to target the vulnerable among a population or an area and the day(s) at risk to assist in health protocol development and emergency planning, as well as to develop early warnings for the general public in order to mitigate potential mortality risk for vulnerable population groups caused by extreme dust events.

Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data

Extremely high fine particulate matter (PM_2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM_2.5 exposure assessments have practical limitations, due to the assumption that population distribution or air pollution levels are spatially stationary and temporally constant and people move within regions of generally the same air quality throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM_2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM_2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM_2.5 concentrations and cumulative inhaled PM_2.5 masses with a 3-h updating frequency. Compared with the census-based method, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to better assess the population exposure to PM_2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollutant exposures such as PM₁₀, O₃, SO₂, and NO₂, and may hold potential utilities in supporting the environmental exposure assessment and related policy-driven environmental actions.

Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity

Urban heat island (UHI) effect significantly raises the health burden and building energy consumption in the high-density urban environment of Hong Kong. A better understanding of the spatiotemporal pattern of UHI is essential to health risk assessments and energy consumption management but challenging in a high-density environment due to the sparsely distributed meteorological stations and the highly diverse urban features. In this study, we modelled the spatiotemporal pattern of UHI effect using the land use regression (LUR) approach in geographic information system with meteorological records of the recent 4years (2013-2016), sounding data and geographic predictors in Hong Kong. A total of 224 predictor variables were calculated and involved in model development. As a result, a total of 10 models were developed (daytime and nighttime, four seasons and annual average). As expected, meteorological records (CLD, Spd, MSLP) and sounding indices (KINX, CAPV and SHOW) are temporally correlated with UHI at high significance levels. On the top of the resultant LUR models, the influential spatial predictors of UHI with regression coefficients and their critical buffer width were also identified for the high-density urban scenario of Hong Kong. The study results indicate that the spatial pattern of UHI is largely determined by the LU/LC (RES1500, FVC500) and urban geomorphometry (h¯, BVD, λ¯_F, Ψ_sky and z₀) in a high-density built environment, especially during nighttime. The resultant models could be adopted to enrich the current urban design guideline and help with the UHI mitigation.

Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis

BACKGROUND

Associations between ambient particulate matter < 2.5 μm (PM2.5) and asthma morbidity have been suggested in previous epidemiologic studies but results are inconsistent for areas with lower PM2.5 levels. We estimated the associations between early-life short-term PM2.5 exposure and the risk of asthma or wheeze clinical encounters among Massachusetts children in the innovative Pregnancy to Early Life Longitudinal (PELL) cohort data linkage system.

METHODS

We used a semi-bidirectional case-crossover study design with short-term exposure lags for asthma exacerbation using data from the PELL system. Cases included children up to 9 years of age who had a hospitalization, observational stay, or emergency department visit for asthma or wheeze between January 2001 and September 2009 (n = 33,387). Daily PM2.5 concentrations were estimated at a 4-km resolution using satellite remote sensing, land use, and meteorological data. We applied conditional logistic regression models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI). We also stratified by potential effect modifiers.

RESULTS

The median PM2.5 concentration among participants was 7.8 μg/m3 with an interquartile range of 5.9 μg/m3. Overall, associations between PM2.5 exposure and asthma clinical encounters among children at lags 0, 1 and 2 were close to the null value of OR = 1.0. Evidence of effect modification was observed by birthweight for lags 0, 1 and 2 (p < 0.05), and season of clinical encounter for lags 0 and 1 (p < 0.05). Children with low birthweight (LBW) (< 2500 g) had increased odds of having an asthma clinical encounter due to higher PM2.5 exposure for lag 1 (OR: 1.08 per interquartile range (IQR) increase in PM2.5; 95% CI: 1.01, 1.15).

CONCLUSION

Asthma or wheeze exacerbations among LBW children were associated with short-term increases in PM2.5 concentrations at low levels in Massachusetts.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

PURPOSE OF REVIEW

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.

RECENT FINDINGS

We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

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.

Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM_2.5) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM_2.5 monitoring data was supplemented by imputing PM_2.5 concentrations at PM₁₀ sites, using PM_2.5/PM₁₀ ratios at co-located sites. Daily PM_2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM_2.5 in cells with AOD but without PM_2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM_2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM_2.5 concentrations.

Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information

BACKGROUND

PM₁ might be more hazardous than PM_2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM₁ concentrations and its health effects are limited due to a lack of PM₁ monitoring data.

OBJECTIVES

To estimate spatial and temporal variations of PM₁ concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information.

METHODS

Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM₁ data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability.

RESULTS

The results of 10-fold cross-validation showed R² and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m³, respectively. For seasonal prediction, the R² and RMSE were 77% and 11.4 μg/m³, respectively. The predicted annual mean concentration of PM₁ across China was 26.9 μg/m³. The PM₁ level was highest in winter while lowest in summer. Generally, the PM₁ levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM₁ levels increased substantially in the South-Western Hebei and Beijing-Tianjin region.

CONCLUSIONS

GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM₁. Ambient PM₁ reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM₁.

Fine Particulate Matter, Residential Proximity to Major Roads, and Markers of Small Vessel Disease in a Memory Study Population

BACKGROUND

Long-term exposure to ambient air pollution has been associated with impaired cognitive function and vascular disease in older adults, but little is known about these associations among people with concerns about memory loss.

OBJECTIVE

To examine associations between exposures to fine particulate matter and residential proximity to major roads and markers of small vessel disease.

METHODS

From 2004-2010, 236 participants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort participated in neuroimaging studies. Residential proximity to major roads and estimated 2003 residential annual average of fine particulate air pollution (PM2.5) were linked to measures of brain parenchymal fraction (BPF), white matter hyperintensities (WMH), and cerebral microbleeds. Associations were modeled using linear and logistic regression and adjusted for clinical and lifestyle factors.

RESULTS

In this population (median age [interquartile range] = 74 [12], 57% female) living in a region with median 2003 PM2.5 annual average below the current Environmental Protection Agency (EPA) standard, there were no associations between living closer to a major roadway or for a 2μg/m3 increment in PM2.5 and smaller BPF, greater WMH volume, or a higher odds of microbleeds. However, a 2μg/m3 increment in PM2.5 was associated with -0.19 (95% Confidence Interval (CI): -0.37, -0.005) lower natural log-transformed WMH volume. Other associations had wide confidence intervals.

CONCLUSIONS

In this population, where median 2003 estimated PM2.5 levels were below the current EPA standard, we observed no pattern of association between residential proximity to major roads or 2003 average PM2.5 and greater burden of small vessel disease or neurodegeneration.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

Visibility-Based PM2.5 Concentrations in China: 1957-1964 and 1973-2014

China established ground PM_2.5 monitoring network in late 2012 and hence the long-term and large-scale PM_2.5 data were lacking before 2013. In this work, we developed a national-scale spatiotemporal linear mixed effects model to estimate the long-term PM_2.5 concentrations in China from 1957 to 1964 and from 1973 to 2014 using ground visibility monitoring data as the primary predictor. The overall model-fitting and cross-validation R² is 0.72 and 0.71, suggesting that the model is not overfitted. Validation beyond the model year (2014) indicated that the model could accurately estimate historical PM_2.5 concentrations at the monthly (R² = 0.71) level. The historical PM_2.5 estimates suggest that air pollution is not a new environmental issue that occurs in the recent decades but a problem existing in a longer time before 1980. The PM_2.5 concentrations have reached 60-80 μg/m³ in the north part of North China Plain during 1950s-1960s and increased to generally higher than 90 μg/m³ during 1970s. The results also show that the entire China experienced an overall increasing trend (0.19 μg/m³/yr, P < 0.001) in PM_2.5 concentrations from 1957 to 2014 with fluctuations among different periods. This paper demonstrated visibility data allow us to understand the spatiotemporal characteristics of PM_2.5 pollution in China in a long-term.

Spatial Variability of Geriatric Depression Risk in a High-Density City: A Data-Driven Socio-Environmental Vulnerability Mapping Approach

Previous studies found a relationship between geriatric depression and social deprivation. However, most studies did not include environmental factors in the statistical models, introducing a bias to estimate geriatric depression risk because the urban environment was found to have significant associations with mental health. We developed a cross-sectional study with a binomial logistic regression to examine the geriatric depression risk of a high-density city based on five social vulnerability factors and four environmental measures. We constructed a socio-environmental vulnerability index by including the significant variables to map the geriatric depression risk in Hong Kong, a high-density city characterized by compact urban environment and high-rise buildings. Crude and adjusted odds ratios (ORs) of the variables were significantly different, indicating that both social and environmental variables should be included as confounding factors. For the comprehensive model controlled by all confounding factors, older adults who were of lower education had the highest geriatric depression risks (OR: 1.60 (1.21, 2.12)). Higher percentage of residential area and greater variation in building height within the neighborhood also contributed to geriatric depression risk in Hong Kong, while average building height had negative association with geriatric depression risk. In addition, the socio-environmental vulnerability index showed that higher scores were associated with higher geriatric depression risk at neighborhood scale. The results of mapping and cross-section model suggested that geriatric depression risk was associated with a compact living environment with low socio-economic conditions in historical urban areas in Hong Kong. In conclusion, our study found a significant difference in geriatric depression risk between unadjusted and adjusted models, suggesting the importance of including environmental factors in estimating geriatric depression risk. We also developed a framework to map geriatric depression risk across a city, which can be used for identifying neighborhoods with higher risk for public health surveillance and sustainable urban planning.

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM_2.5 is a promising way to fill the areas that are not covered by ground PM_2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM_2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM_2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R² = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM_2.5 estimates.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

To estimate PM_2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM_2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R² value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m³, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM_2.5 measurements increase CV R² by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM_2.5 measurements and AOD values are among the most-important predictor variables for the training process.

Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

BACKGROUND

Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality.

OBJECTIVES

We compared relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information.

METHODS

We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, and one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RRs) for the association of PM2.5 with circulatory mortality and ischemic heart disease.

RESULTS

Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths [RR = 1.02, 95% confidence interval (CI): 1.00, 1.04 per 10 μg/m3 increment in PM2.5]. The largest relative risk was observed for the land-use regression model that included traffic information (RR = 1.14, 95% CI: 1.11, 1.17 per 10 μg/m3 increment in PM2.5).

CONCLUSIONS

We found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated using RS alone.

Estimation of daily PM10 concentrations in Italy (2006-2012) using finely resolved satellite data, land use variables and meteorology

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM₁₀ measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM₁₀ concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM₁₀=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM₁₀ levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM₁₀ concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data.

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. The authors propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. The authors used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. The authors trained the model with the Air Quality System (AQS) 8-hr daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R² on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km × 1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

IMPLICATIONS

Ozone monitors do not provide full data coverage over the United States, which is an obstacle to assess the health effect of ozone when monitoring data are not available. This paper used a hybrid approach to combine satellite-based ozone measurements, chemical transport model simulations, land-use terms, and other auxiliary variables to obtain spatially and temporally resolved ground-level ozone estimation.

A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan

In order to study effects of ambient particulate matter (PM) it was previously necessary to have access to a comprehensive air monitoring network. However, there are locations in the world where PM levels are above generally accepted exposure standards but lack a monitoring infrastructure. This is true in Iraq and other locations in Southwest Asia and Afghanistan where U.S. and other coalition troops were deployed beginning in 2001. Since aerosol optical depth (AOD), determined by satellite, and visibility are both highly related to atmospheric PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) concentrations, we employed a novel approach that took advantage of historic airport visibility measurements to calibrate the AOD-visibility relationship and determine visibility spatially and temporally (2006-2007) over an approximately 17,000 km2 region of Iraq. We obtained daily visibility predictions that were highly associated with satellite-based 1x1 km AOD daily observations (R2=0.87). Based on a previously derived calibration between PM2.5 and visibility, we were able to predict spatially and temporally resolved PM2.5 concentrations. Variability of PM2.5 among sites was high, with daily concentrations differing by as much as ~30 μg/m3. This study demonstrates the feasibility of characterizing historic PM2.5 exposures in Iraq and other locations in Southwest Asia and Afghanistan with similar climate characteristics. This is of utility for epidemiologists seeking to assess the potential health effects related to PM2.5 exposures among previously deployed military personnel and of the population of the region.

IMPLICATIONS

This study demonstrates the ability to utilize aerosol optical depth to successfully estimate visibility spatially and temporally in Southwest Asia and Afghanistan. This enables for the estimation of spatially resolved PM2.5 concentrations in the region. The ability to caracterize PM2.5 concentrations in Southwest Asia and Afghanistan is highly important for epidemiologists investigating the relationship between chronic exposure to PM2.5 and respiratory diseases among military personnel deployed to the region. This information will better position policy makers to draft meaningful legislation relating to military health.

Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome

Ambient air pollution and temperature have been linked with cardiovascular morbidity and mortality. Metabolic syndrome and its components-abdominal obesity, elevated fasting blood glucose concentration, low high-density lipoprotein cholesterol concentration, hypertension, and hypertriglyceridemia-predict cardiovascular disease, but the environmental causes are understudied. In this study, we prospectively examined the long-term associations of air pollution, defined as particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM_2.5), and temperature with the development of metabolic syndrome and its components. Using covariate-adjustment Cox proportional hazards models, we estimated associations of mean annual PM_2.5 concentration and temperature with risk of incident metabolic dysfunctions between 1993 and 2011 in 587 elderly (mean = 70 (standard deviation, 7) years of age) male participants in the Normative Aging Study. A 1-μg/m³ increase in mean annual PM_2.5 concentration was associated with a higher risk of developing metabolic syndrome (hazard ratio (HR) = 1.27, 95% confidence interval (CI): 1.06, 1.52), an elevated fasting blood glucose level (HR = 1.20, 95% CI: 1.03, 1.39), and hypertriglyceridemia (HR = 1.14, 95% CI: 1.00, 1.30). Our findings for metabolic syndrome and high fasting blood glucose remained significant for PM_2.5 levels below the Environmental Protection Agency's health-safety limit (12 μg/m³). A 1°C increase in mean annual temperature was associated with a higher risk of developing elevated fasting blood glucose (HR = 1.33, 95% CI: 1.14, 1.56). Men living in neighborhoods with worse air quality-with higher PM_2.5 levels and/or temperatures than average-showed increased risk of developing metabolic dysfunctions.

Particulate Air Pollution and Fasting Blood Glucose in Nondiabetic Individuals: Associations and Epigenetic Mediation in the Normative Aging Study, 2000-2011

BACKGROUND

Among nondiabetic individuals, higher fasting blood glucose (FBG) independently predicts diabetes risk, cardiovascular disease, and dementia. Ambient PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) is an emerging determinant of glucose dysregulation. PM2.5 effects and mechanisms are understudied among nondiabetic individuals.

OBJECTIVES

Our goals were to investigate whether PM2.5 is associated with an increase in FBG and to explore potential mediating roles of epigenetic gene regulation.

METHODS

In 551 nondiabetic participants in the Normative Aging Study, we measured FBG, and DNA methylation of four inflammatory genes (IFN-γ, IL-6, ICAM-1, and TLR-2), up to four times between 2000 and 2011 (median = 2). We estimated short- and medium-term (1-, 7-, and 28-day preceding each clinical visit) ambient PM2.5 at each participant's address using a validated hybrid land-use regression satellite-based model. We fitted covariate-adjusted regression models accounting for repeated measures.

RESULTS

Mean FBG was 99.8 mg/dL (SD = 10.7), 18% of the participants had impaired fasting glucose (IFG; i.e., 100-125 mg/dL FBG) at first visit. Interquartile increases in 1-, 7-, and 28-day PM2.5 were associated with 0.57 mg/dL (95% CI: 0.02, 1.11, p = 0.04), 1.02 mg/dL (95% CI: 0.41, 1.63, p = 0.001), and 0.89 mg/dL (95% CI: 0.32, 1.47, p = 0.003) higher FBG, respectively. The same PM2.5 metrics were associated with 13% (95% CI: -3%, 33%, p = 0.12), 27% (95% CI: 6%, 52%, p = 0.01) and 32% (95% CI: 10%, 58%, p = 0.003) higher odds of IFG, respectively. PM2.5 was negatively correlated with ICAM-1 methylation (p = 0.01), but not with other genes. Mediation analysis estimated that ICAM-1 methylation mediated 9% of the association of 28-day PM2.5 with FBG.

CONCLUSIONS

Among nondiabetics, short- and medium-term PM2.5 were associated with higher FBG. Mediation analysis indicated that part of this association was mediated by ICAM-1 promoter methylation. Citation: Peng C, Bind MA, Colicino E, Kloog I, Byun HM, Cantone L, Trevisi L, Zhong J, Brennan K, Dereix AE, Vokonas PS, Coull BA, Schwartz JD, Baccarelli AA. 2016. Particulate air pollution and fasting blood glucose in nondiabetic individuals: associations and epigenetic mediation in the Normative Aging Study, 2000-2011. Environ Health Perspect 124:1715-1721; http://dx.doi.org/10.1289/EHP183.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM_2.5 and NO₂ are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM_2.5 and NO₂ models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM_2.5 and NO₂ ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM_2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM_2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR²: 0.33-0.38). For NO₂ CTM improved prediction modestly (adjR²: 0.58) compared to models without SAT and CTM (adjR²: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM_2.5 and NO₂ significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

Air pollution, epigenetics, and asthma

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.

Fine particulate matter (PM2.5) association with peripheral artery disease admissions in northeastern United States

BACKGROUND

Current evidence, on the association of PM2.5 and peripheral artery disease (PAD) is very sparse.

METHODS

We use novel PM2.5 prediction models to investigate associations between chronic and acute PM2.5 exposures and hospital PAD admissions across the northeast USA. Poisson regression analysis was preformed where daily admission counts in each zip code are regressed against both chronic and acute PM2.5 exposure, temperature, socio-economic characteristics and time to control for seasonal patterns.

RESULTS

Positive significant associations were observed between both chronic and acute exposure to PM2.5 and PAD hospitalizations. Every 10-μg/m(3) increase in acute PM2.5 exposure was associated with a 0.26 % increase in admissions (CI = 0.08 - 0.45 %) and every 10-μg/m(3) increase in chronic PM 2.5 exposure was associated with a 4.4 % increase in admissions (CI = 3.50 - 5.35 %).

CONCLUSIONS

The study supports the hypothesis that acute and chronic exposure to PM2.5 can increase the risk of PAD.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Prenatal and childhood traffic-related air pollution exposure and childhood executive function and behavior

BACKGROUND

Traffic-related air pollution exposure may influence brain development and function and thus be related to neurobehavioral problems in children, but little is known about windows of susceptibility.

AIMS

Examine associations of gestational and childhood exposure to traffic-related pollution with executive function and behavior problems in children.

METHODS

We studied associations of pre- and postnatal pollution exposures with neurobehavioral outcomes in 1212 children in the Project Viva pre-birth cohort followed to mid-childhood (median age 7.7years). Parents and classroom teachers completed the Behavior Rating Inventory of Executive Function (BRIEF) and the Strengths and Difficulties Questionnaire (SDQ). Using validated spatiotemporal models, we estimated exposure to black carbon (BC) and fine particulate matter (PM_2.5) in the third trimester of pregnancy, from birth to 3years, from birth to 6years, and in the year before behavioral ratings. We also measured residential distance to major roadways and near-residence traffic density at birth and in mid-childhood. We estimated associations of BC, PM_2.5, and other traffic exposure measures with BRIEF and SDQ scores, adjusted for potential confounders.

RESULTS

Higher childhood BC exposure was associated with higher teacher-rated BRIEF Behavioral Regulation Index (BRI) scores, indicating greater problems: 1.0 points (95% confidence interval (CI): 0.0, 2.1) per interquartile range (IQR) increase in birth-age 6BC, and 1.7 points (95% CI: 0.6, 2.8) for BC in the year prior to behavioral ratings. Mid-childhood residential traffic density was also associated with BRI score (0.6, 95% CI: 0.1, 1.1). Birth-age 3BC was not associated with BRIEF or SDQ scores. Third trimester BC exposure was not associated with teacher-rated BRI scores (-0.2, 95% CI: -1.1, 0.8), and predicted lower scores (fewer problems) on the BRIEF Metacognition Index (-1.2, 95% CI: -2.2, -0.2) and SDQ total difficulties (-0.9, 95% CI: -1.4, -0.4). PM_2.5 exposure was associated with teacher-rated BRIEF and SDQ scores in minimally adjusted models but associations attenuated with covariate adjustment. None of the parent-rated outcomes suggested adverse effects of greater pollution exposure at any time point.

CONCLUSIONS

Children with higher mid-childhood exposure to BC and greater near-residence traffic density in mid-childhood had greater problems with behavioral regulation as assessed by classroom teachers, but not as assessed by parents. Prenatal and early childhood exposure to traffic-related pollution did not predict greater executive function or behavior problems; third trimester BC was associated with lower scores (representing fewer problems) on measures of metacognition and behavioral problems.

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5

Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R(2) of 0.84 on the left out monitors. Regional R(2) could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily predictions of PM2.5 at 1 km × 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term.

Pediatric Emergency Visits and Short-Term Changes in PM2.5 Concentrations in the U.S. State of Georgia

BACKGROUND

Associations between pediatric emergency department (ED) visits and ambient concentrations of particulate matter ≤ 2.5 μm in diameter (PM2.5) have been reported in previous studies, although few were performed in nonmetropolitan areas.

OBJECTIVE

We estimated associations between daily PM2.5 concentrations, using a two-stage model that included land use parameters and satellite aerosol optical depth measurements at 1-km resolution, and ED visits for six pediatric conditions in the U.S. state of Georgia by urbanicity classification.

METHODS

We obtained pediatric ED visits geocoded to residential ZIP codes for visits with nonmissing PM2.5 estimates and admission dates during 1 January 2002-30 June 2010 for 2- to 18-year-olds for asthma or wheeze (n = 189,816), and for 0- to 18-year-olds for bronchitis (n = 76,243), chronic sinusitis (n = 15,745), otitis media (n = 237,833), pneumonia (n = 52,946), and upper respiratory infections (n = 414,556). Daily ZIP code-level estimates of 24-hr average PM2.5 were calculated by averaging concentrations within ZIP code boundaries. We used time-stratified case-crossover models stratified on ZIP code, year, and month to estimate odds ratios (ORs) between ED visits and same-day and previous-day PM2.5 concentrations at the ZIP code level, and we investigated effect modification by county-level urbanicity.

RESULTS

A 10-μg/m3 increase in same-day PM2.5 concentrations was associated with ED visits for asthma or wheeze (OR = 1.013; 95% CI: 1.003, 1.023) and upper respiratory infections (OR = 1.015; 95% CI: 1.008, 1.022); associations with previous-day PM2.5 concentrations were lower. Differences in the association estimates across levels of urbanicity were not statistically significant.

CONCLUSION

Pediatric ED visits for asthma or wheeze and for upper respiratory infections were associated with PM2.5 concentrations in Georgia.

CITATION

Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, Liu Y. 2016. Pediatric emergency visits and short-term changes in PM2.5 concentrations in the U.S. state of Georgia. Environ Health Perspect 124:690-696; http://dx.doi.org/10.1289/ehp.1509856.

Satellite remote sensing in epidemiological studies

PURPOSE OF REVIEW

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies.

RECENT FINDINGS

Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates.

SUMMARY

It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities

BACKGROUND

The majority of people live in cities and urbanization is continuing worldwide. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also a main source of pollution and disease.

METHODS

We conducted a review around the topic urban and transport planning, environmental exposures and health and describe the findings.

RESULTS

Within cities there is considerable variation in the levels of environmental exposures such as air pollution, noise, temperature and green space. Emerging evidence suggests that urban and transport planning indicators such as road network, distance to major roads, and traffic density, household density, industry and natural and green space explain a large proportion of the variability. Personal behavior including mobility adds further variability to personal exposures, determines variability in green space and UV exposure, and can provide increased levels of physical activity. Air pollution, noise and temperature have been associated with adverse health effects including increased morbidity and premature mortality, UV and green space with both positive and negative health effects and physical activity with many health benefits. In many cities there is still scope for further improvement in environmental quality through targeted policies. Making cities 'green and healthy' goes far beyond simply reducing CO2 emissions. Environmental factors are highly modifiable, and environmental interventions at the community level, such as urban and transport planning, have been shown to be promising and more cost effective than interventions at the individual level. However, the urban environment is a complex interlinked system. Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also enhanced understanding of the linkages to be able to know at which level to target their actions. New research tools, methods and paradigms such as geographical information systems, smartphones, and other GPS devices, small sensors to measure environmental exposures, remote sensing and the exposome paradigm together with citizens observatories and science and health impact assessment can now provide this information.

CONCLUSION

While in cities there are often silos of urban planning, mobility and transport, parks and green space, environmental department, (public) health department that do not work together well enough, multi-sectorial approaches are needed to tackle the environmental problems. The city of the future needs to be a green city, a social city, an active city, a healthy city.