National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010

DNA damage and its association with early-life exposome: Gene-environment analysis in Colombian children under five years old

Environmental exposures and gene-exposure interactions are the major causes of some diseases. Early-life exposome studies are needed to elucidate the role of environmental exposures and their complex interactions with biological mechanisms involved in childhood health. This study aimed to determine the contribution of early-life exposome to DNA damage and the modifying effect of genetic polymorphisms involved in air pollutants metabolism, antioxidant defense, and DNA repair. We conducted a cohort study in 416 Colombian children under five years. Blood samples at baseline were collected to measure DNA damage by the Comet assay and to determine GSTT1, GSTM1, CYP1A1, H2AX, OGG1, and SOD2 genetic polymorphisms. The exposome was estimated using geographic information systems, remote sensing, LUR models, and questionnaires. The association exposome-DNA damage was estimated using the Elastic Net linear regression with log link. Our results suggest that exposure to PM2.5 one year before the blood draw (BBD) (0.83, 95 %CI: 0.76; 0.91), soft drinks consumption (0.94, 0.89; 0.98), and GSTM1 null genotype (0.05, 0.01; 0.36) diminished the DNA damage, whereas exposure to PM2.5 one-week BBD (1.18, 1.06; 1.32), NO2 lag-5 days BBD (1.27, 1.18; 1.36), in-house cockroaches (1.10, 1.00; 1.21) at the recruitment, crowding at home (1.34, 1.08; 1.67) at the recruitment, cereal consumption (1.11, 1.04; 1.19) and H2AX (AG/GG vs. AA) (1.44, 1.11; 1.88) increased the DNA damage. The interactions between H2AX (AG/GG vs. AA) genotypes with crowding and PM2.5 one week BBD, GSTM1 (null vs. present) with humidity at the first year of life, and OGG1 (SC/CC vs. SS) with walkability at the first year of life were significant. The early-life exposome contributes to elucidating the effect of environmental exposures on DNA damage in Colombian children under five years old. The exposome-DNA damage effect appears to be modulated by genetic variants in DNA repair and antioxidant defense enzymes.

Early-life external exposome in children 2-5 years old in Colombia

Exposome studies are advancing in high-income countries to understand how multiple environmental exposures impact health. However, there is a significant research gap in low- and middle-income and tropical countries. We aimed to describe the spatiotemporal variation of the external exposome, its correlation structure between and within exposure groups, and its dimensionality. A one-year follow-up cohort study of 506 children under 5 in two cities in Colombia was conducted to evaluate asthma, acute respiratory infections, and DNA damage. We examined 48 environmental exposures during pregnancy and 168 during childhood in eight exposure groups, including atmospheric pollutants, natural spaces, meteorology, built environment, traffic, indoor exposure, and socioeconomic capital. The exposome was estimated using geographic information systems, remote sensing, spatiotemporal modeling, and questionnaires. The median age of children at study entry was 3.7 years (interquartile range: 2.9-4.3). Air pollution and natural spaces exposure decreased from pregnancy to childhood, while socioeconomic capital increased. The highest median correlations within exposure groups were observed in meteorology (r = 0.85), traffic (r = 0.83), and atmospheric pollutants (r = 0.64). Important correlations between variables from different exposure groups were found, such as atmospheric pollutants and meteorology (r = 0.76), natural spaces (r = -0.34), and the built environment (r = 0.53). Twenty principal components explained 70%, and 57 explained 95% of the total variance in the childhood exposome. Our findings show that there is an important spatiotemporal variation in the exposome of children under 5. This is the first characterization of the external exposome in urban areas of Latin America and highlights its complexity, but also the need to better characterize and understand the exposome in order to optimize its analysis and applications in local interventions aimed at improving the health conditions and well-being of the child population and contributing to environmental health decision-making.

Prenatal Exposure to Ambient Air Pollution and Cerebral Palsy

Importance

Air pollution is associated with structural brain changes, disruption of neurogenesis, and neurodevelopmental disorders. The association between prenatal exposure to ambient air pollution and risk of cerebral palsy (CP), which is the most common motor disability in childhood, has not been thoroughly investigated.

Objective

To evaluate the associations between prenatal residential exposure to ambient air pollution and risk of CP among children born at term gestation in a population cohort in Ontario, Canada.

Design, Setting, and Participants

Population-based cohort study in Ontario, Canada using linked, province-wide health administrative databases. Participants were singleton full term births (≥37 gestational weeks) born in Ontario hospitals between April 1, 2002, and March 31, 2017. Data were analyzed from January to December 2022.

Exposures

Weekly average concentrations of ambient fine particulate matter with a diameter 2.5 μm (PM2.5) or smaller, nitrogen dioxide (NO2), and ozone (O3) during pregnancy assigned by maternal residence reported at delivery from satellite-based estimates and ground-level monitoring data.

Main outcome and measures

CP cases were ascertained by a single inpatient hospitalization diagnosis or at least 2 outpatient diagnoses for children from birth to age 18 years.

Results

The present study included 1 587 935 mother-child pairs who reached term gestation, among whom 3170 (0.2%) children were diagnosed with CP. The study population had a mean (SD) maternal age of 30.1 (5.6) years and 811 745 infants (51.1%) were male. A per IQR increase (2.7 μg/m3) in prenatal ambient PM2.5 concentration was associated with a cumulative hazard ratio (CHR) of 1.12 (95% CI, 1.03-1.21) for CP. The CHR in male infants (1.14; 95% CI, 1.02-1.26) was higher compared with the CHR in female infants (1.08; 95% CI, 0.96-1.22). No specific window of susceptibility was found for prenatal PM2.5 exposure and CP in the study population. No associations or windows of susceptibility were found for prenatal NO2 or O3 exposure and CP risk.

Conclusions and relevance

In this large cohort study of singleton full term births in Canada, prenatal ambient PM2.5 exposure was associated with an increased risk of CP in offspring. Further studies are needed to explore this association and its potential biological pathways, which could advance the identification of environmental risk factors of CP in early life.

Outdoor air pollution exposure and uterine cancer incidence in the Sister Study

BACKGROUND

Outdoor air pollution is a ubiquitous exposure that includes endocrine-disrupting and carcinogenic compounds that may contribute to the risk of hormone-sensitive outcomes such as uterine cancer. However, there is limited evidence about the relationship between outdoor air pollution and uterine cancer incidence.

METHODS

We investigated the associations of residential exposure to particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2) with uterine cancer among 33 417 Sister Study participants with an intact uterus at baseline (2003-2009). Annual average air pollutant concentrations were estimated at participants' geocoded primary residential addresses using validated spatiotemporal models. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for the association between time-varying 12-month PM2.5 (µg/m3) and NO2 (parts per billion; ppb) averages and uterine cancer incidence.

RESULTS

Over a median follow-up period of 9.8 years, 319 incident uterine cancer cases were identified. A 5-ppb increase in NO2 was associated with a 23% higher incidence of uterine cancer (hazard ratio = 1.23, 95% confidence interval = 1.04 to 1.46), especially among participants living in urban areas (hazard ratio = 1.53, 95% confidence interval = 1.13 to 2.07), but  PM2.5 was not associated with increased uterine cancer incidence.

CONCLUSION

In this large US cohort, NO2, a marker of vehicular traffic exposure, was associated with a higher incidence of uterine cancer. These findings expand the scope of health effects associated with air pollution, supporting the need for policy and other interventions designed to reduce air pollutant exposure.

Ambient air pollution and rate of spontaneous abortion

Spontaneous abortion (SAB), defined as a pregnancy loss before 20 weeks of gestation, affects up to 30% of conceptions, yet few modifiable risk factors have been identified. We estimated the effect of ambient air pollution exposure on SAB incidence in Pregnancy Study Online (PRESTO), a preconception cohort study of North American couples who were trying to conceive. Participants completed questionnaires at baseline, every 8 weeks during preconception follow-up, and in early and late pregnancy. We analyzed data on 4643 United States (U.S.) participants and 851 Canadian participants who enrolled during 2013-2019 and conceived during 12 months of follow-up. We used country-specific national spatiotemporal models to estimate concentrations of particulate matter <2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) during the preconception and prenatal periods at each participant's residential address. On follow-up and pregnancy questionnaires, participants reported information on pregnancy status, including SAB incidence and timing. We fit Cox proportional hazards regression models with gestational weeks as the time scale to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of time-varying prenatal concentrations of PM2.5, NO2, and O3 with rate of SAB, adjusting for individual- and neighborhood-level factors. Nineteen percent of pregnancies ended in SAB. Greater PM2.5 concentrations were associated with a higher incidence of SAB in Canada, but not in the U.S. (HRs for a 5 μg/m3 increase = 1.29, 95% CI: 0.99, 1.68 and 0.94, 95% CI: 0.83, 1.08, respectively). NO2 and O3 concentrations were not appreciably associated with SAB incidence. Results did not vary substantially by gestational weeks or season at risk. In summary, we found little evidence for an effect of residential ambient PM2.5, NO2, and O3 concentrations on SAB incidence in the U.S., but a moderate positive association of PM2.5 with SAB incidence in Canada.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Air pollution and fecundability in a North American preconception cohort study

BACKGROUND

Animal and epidemiologic studies indicate that air pollution may adversely affect fertility. However, the level of evidence is limited and specific pollutants driving the association are inconsistent across studies.

METHODS

We used data from a web-based preconception cohort study of pregnancy planners enrolled during 2013-2019 (Pregnancy Study Online; PRESTO). Eligible participants self-identified as female, were aged 21-45 years, resided in the United States (U.S.) or Canada, and were trying to conceive without fertility treatments. Participants completed a baseline questionnaire and bi-monthly follow-up questionnaires until conception or 12 months. We analyzed data from 8,747 participants (U.S.: 7,304; Canada: 1,443) who had been trying to conceive for < 12 cycles at enrollment. We estimated residential ambient concentrations of particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) using validated spatiotemporal models specific to each country. We fit country-specific proportional probabilities regression models to estimate the association between annual average, menstrual cycle-specific, and preconception average pollutant concentrations with fecundability, the per-cycle probability of conception. We calculated fecundability ratios (FRs) and 95% confidence intervals (CIs) and adjusted for individual- and neighborhood-level confounders.

RESULTS

In the U.S., the FRs for a 5-µg/m3 increase in annual average, cycle-specific, and preconception average PM2.5 concentrations were 0.94 (95% CI: 0.83, 1.08), 1.00 (95% CI: 0.93, 1.07), and 1.00 (95% CI: 0.93, 1.09), respectively. In Canada, the corresponding FRs were 0.92 (95% CI: 0.74, 1.16), 0.97 (95% CI: 0.87, 1.09), and 0.94 (95% CI: 0.80, 1.09), respectively. Likewise, NO2 and O3 concentrations were not strongly associated with fecundability in either country.

CONCLUSIONS

Neither annual average, menstrual cycle-specific, nor preconception average exposure to ambient PM2.5, NO2, and O3 were appreciably associated with reduced fecundability in this cohort of pregnancy planners.

Ambient NO2 Air Pollution and Public Schools in the United States: Relationships with Urbanicity, Race-Ethnicity, and Income

Schools may have important impacts on children's exposure to ambient air pollution, yet ambient air quality at schools is not consistently tracked. We characterize ambient air quality at home and school locations in the United States using satellite-based empirical model (i.e., land use regression) estimates of outdoor annual nitrogen dioxide (NO2). We report disparities by race-ethnicity and impoverishment status, and investigate differences by level of urbanicity. Average NO2 levels at home and school for racial-ethnic minoritized students are 18-22% higher than average (and 37-39% higher than for non-Hispanic, white students). Minoritized students are less likely than their white peers to live (0.55 times) and attend school (0.58 times) in areas below the World Health Organization's NO2 guideline. Predominantly minoritized schools (i.e., >50% minoritized students) are less likely than predominantly white schools (0.43 times) to be in locations below the guideline. Income and race-ethnicity impacts are intertwined, yet in large cities, racial disparities persist after controlling for income.

A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506-307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275-207,826] premature deaths, a ~19% increase (22,576 [11,288 - 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

The Impact of Long-Term Air Pollution Exposure on Type 1 Diabetes Mellitus-Related Mortality among U.S. Medicare Beneficiaries

BACKGROUND

Little of the previous literature has investigated associations between air pollution exposure and type 1 diabetes mellitus (T1DM)-related mortality, despite a well-established link between air pollution exposure and other autoimmune diseases.

METHODS

In a cohort of 53 million Medicare beneficiaries living across the conterminous United States, we used Cox proportional hazard models to assess the association of long-term PM_2.5 and NO₂ exposures on T1DM-related mortality from 2000 to 2008. Models included strata for age, sex, race, and ZIP code and controlled for neighborhood socioeconomic status (SES); we additionally investigated associations in two-pollutant models, and whether associations were modified by participant demographics.

RESULTS

A 10 μg/m³ increase in 12-month average PM_2.5 (HR: 1.183; 95% CI: 1.037-1.349) and a 10 ppb increase in NO₂ (HR: 1.248; 95% CI: 1.089-1.431) was associated with an increased risk of T1DM-related mortality in age-, sex-, race-, ZIP code-, and SES-adjusted models. Associations for both pollutants were consistently stronger among Black (PM_2.5: HR:1.877, 95% CI: 1.386-2.542; NO₂: HR: 1.586, 95% CI: 1.258-2.001) and female (PM_2.5: HR:1.297, 95% CI: 1.101-1.529; NO₂: HR: 1.390, 95% CI: 1.187-1.627) beneficiaries.

CONCLUSIONS

Long-term NO₂ and, to a lesser extent, PM_2.5 exposure is associated with statistically significant elevations in T1DM-related mortality risk.

Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England

Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.

Neighborhood Emission Mapping Operation (NEMO): A 1-km anthropogenic emission dataset in the United States

We present an unprecedented effort to map anthropogenic emissions of air pollutants at 1 km spatial resolution in the contiguous United States (CONUS). This new dataset, Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on the United States Environmental Protection Agency (US EPA) National Emission Inventories 2017. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1 km grid. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 sectors and stored in NetCDF format for air quality models, and in shapefile format for GIS users and air quality managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies.

Measurement(s)	anthropogenic emission in United States
Technology Type(s)	Census survey and computer models

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO₂ models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO₂. Our results suggest that street view imagery alone may provide sufficient information to explain NO₂ variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R² of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO₂ pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

What you see is what you breathe? Estimating air pollution spatial variation using street level imagery

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.

Long-term PM2.5 exposure and sepsis mortality in a US medicare cohort

Background

Risk factors contributing to sepsis-related mortality include clinical conditions such as cardiovascular disease, chronic lung disease, and diabetes, all of which have also been shown to be associated with air pollution exposure. However, the impact of chronic exposure to air pollution on sepsis-related mortality has been little studied. 

Methods

In a cohort of 53 million Medicare beneficiaries (228,439 sepsis-related deaths) living across the conterminous United States between 2000 and 2008, we examined the association of long-term PM_2.5 exposure and sepsis-related mortality. For each Medicare beneficiary (ages 65–120), we estimated the 12-month moving average PM_2.5 concentration for the 12 month before death, for their ZIP code of residence using well validated GIS-based spatio-temporal models. Deaths were categorized as sepsis-related if they have ICD-10 codes for bacterial or other sepsis. We used Cox proportional hazard models to assess the association of long-term PM_2.5 exposure on sepsis-related mortality. Models included strata for age, sex, race, and ZIP code and controlled for neighborhood socio-economic status (SES). We also evaluated confounding through adjustment of neighborhood behavioral covariates.

Results

A 10 μg/m³ increase in 12-month moving average PM_2.5 was associated with a 9.1% increased risk of sepsis mortality (95% CI: 3.6–14.9) in models adjusted for age, sex, race, ZIP code, and SES. HRs for PM_2.5 were higher and statistically significant for older (> 75), Black, and urban beneficiaries. In stratified analyses, null associations were found for younger beneficiaries (65–75), beneficiaries who lived in non-urban ZIP codes, and those residing in low-SES urban ZIP codes.

Conclusions

Long-term PM_2.5 exposure is associated with elevated risks of sepsis-related mortality.

A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents

Although PM_2.5 mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM_2.5 mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM_2.5 mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM_2.5 characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM_2.5 sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM_2.5 impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM_2.5 levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R² ranging from 0.41 to 0.95. For PM_2.5 sources, the highest CV R² was attained for traffic PM_2.5 (CV R² = 0.73), followed by coal (CV R² = 0.65), oil (CV R² = 0.62), soil (CV R² = 0.60), and biomass (CV R² = 0.41). Among constituents, the CV was highest for sulfur (CV R² = 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM_2.5 concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM_2.5 mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.

Long-term nitrogen dioxide exposure and cause-specific mortality in the U.S. Medicare population

BACKGROUND

Since 1971, the annual National Ambient Air Quality Standard (NAAQS) for nitrogen dioxide (NO₂) has remained at 53 ppb, the impact of long-term NO₂ exposure on mortality is poorly understood.

OBJECTIVES

We examined associations between long-term NO₂ exposure (12-month moving average of NO₂) below the annual NAAQS and cause-specific mortality among the older adults in the U.S.

METHODS

Cox proportional-hazard models were used to estimate Hazard Ratio (HR) for cause-specific mortality associated with long-term NO₂ exposures among about 50 million Medicare beneficiaries living within the conterminous U.S. from 2001 to 2008.

RESULTS

A 10 ppb increase in NO₂ was associated with increased mortality from all-cause (HR: 1.06; 95% CI: 1.05-1.06), cardiovascular (HR: 1.10; 95% CI: 1.10-1.11), respiratory disease (HR: 1.09; 95% CI: 1.08-1.11), and cancer (HR: 1.01; 95% CI: 1.00-1.02) adjusting for age, sex, race, ZIP code as strata ZIP code- and state-level socio-economic status (SES) as covariates, and PM_2.5 exposure using a 2-stage approach. NO₂ was also associated with elevated mortality from ischemic heart disease, cerebrovascular disease, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, and lung cancer. We found no evidence of a threshold, with positive and significant HRs across the range of NO₂ exposures for all causes of death examined. Exposure-response curves were linear for all-cause, supra-linear for cardiovascular-, and sub-linear for respiratory-related mortality. HRs were highest consistently among Black beneficiaries.

CONCLUSIONS

Long-term NO₂ exposure is associated with elevated risks of death by multiple causes, without evidence of a threshold response. Our findings raise concerns about the sufficiency of the annual NAAQS for NO₂.

A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, like satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimation. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data is limited.

Estimating 2013-2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO2). Current studies in China at the national scale were less focused on NO2 exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO2 predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO2, TROPOspheric Monitoring Instrument NO2, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO2 concentrations from 2013 to 2019 across China at 1×1 km2 resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R2 = 0.72) and the spatial (R2 = 0.85) variations of the NO2 predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R2 > 0.68) or regions far away from monitors (CV R2 > 0.63). We identified a clear decreasing trend of NO2 exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%-14% in some megacities and captured substantial NO2 variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets

BACKGROUND

Combustion-related nitrogen dioxide (NO₂) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO₂ concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019.

METHODS

We scaled an existing annual average NO₂ concentration dataset for 2010-12 from a land use regression model (based on 5220 NO₂ monitors in 58 countries and land use variables) to other years using NO₂ column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO₂-attributable paediatric asthma incidence.

FINDINGS

We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO₂ globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO₂ in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO₂ concentrations, paediatric population size, and asthma incidence rates to the change in NO₂-attributable paediatric asthma incidence differed regionally.

INTERPRETATION

Despite improvements in some regions, combustion-related NO₂ pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children.

FUNDING

Health Effects Institute, NASA.

Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010

BACKGROUND

Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time.

OBJECTIVE

The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010.

METHODS

We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ; excluding year-1990), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), and sulfur dioxide (SO 2 ). We used census data for demographic information and a national empirical model for ambient air pollution levels.

RESULTS

For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO 2 [54% (4.6  ppb )], smallest for O 3 [3.6% (1.6  ppb )], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM 2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.6 6 μ g / m 3 ; PM 2.5 ) and 88% (0.35  ppm ; CO); relative disparities declined to between 0.99 × (PM 2.5 ; i.e., nearly zero change) and 0.71 × (CO; i.e., a ∼ 29 % reduction).

DISCUSSION

As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.

Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe

The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have affected air quality in Europe, we developed Bayesian spatio-temporal (BST) models that assess changes in the surface nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentration across the continent. We fitted BST models to measurements of the two pollutants in 2020 using a lockdown indicator covariate, while accounting for the spatial and temporal correlation present in the data. Since other factors, such as weather conditions, local combustion sources and/or land surface characteristics may contribute to the variation of pollutant concentrations, we proposed two model formulations that allowed the differentiation between the variations in pollutant concentrations due to seasonality from the variations associated to the lockdown policies. The first model compares the changes in 2020, with the ones during the same period in the previous five years, by introducing an offset term, which controls for the long-term average concentrations of each pollutant during 2014–2019. The second approach models only the 2020 data, but adjusts for confounding factors. The results indicated that the latter can better capture the lockdown effect. The measures taken to tackle the virus in Europe reduced the average surface concentrations of NO₂ and PM_2.5 by 29.5% (95% Bayesian credible interval: 28.1%, 30.9%) and 25.9% (23.6%, 28.1%), respectively. To our knowledge, this research is the first to account for the spatio-temporal correlation present in the monitoring data during the pandemic and to assess how it affects estimation of the lockdown effect while accounting for confounding. The proposed methodology improves our understanding of the effect of COVID-19 lockdown policies on the air pollution burden across the continent.

Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P

Nitrogen dioxide (NO2) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO2 distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO2 data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO2 vertical column density (NO2 VCD) in the YREB from 2019 to 2020. The results showed that the NO2 VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO2 VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO2 VCD.

Estimating monthly concentrations of ambient key air pollutants in Japan during 2010-2015 for a national-scale birth cohort

Exposure to ambient air pollution is associated with maternal and child health. Some air pollutants exhibit similar behavior in the atmosphere, and some interact with each other; thus, comprehensive assessments of individual air pollutants are required. In this study, we developed national-scale monthly models for six air pollutants (NO, NO₂, SO₂, O₃, PM_2.5, and suspended particulate matter (SPM)) to obtain accurate estimates of pollutant concentrations at 1 km × 1 km resolution from 2010 through 2015 for application to the Japan Environment and Children's Study (JECS), which is a large-scale birth cohort study. We developed our models in the land use regression framework using random forests in conjunction with kriging. We evaluated the model performance via 5-fold location-based cross-validation. We successfully predicted monthly NO (r² = 0.65), NO₂ (r² = 0.84), O₃ (r² = 0.86), PM_2.5 (r² = 0.79), and SPM (r² = 0.64) concentrations. For SO₂, a satisfactory model could not be developed (r² = 0.45) because of the low SO₂ concentrations in Japan. The performance of our models is comparable to those reported in previous studies at similar temporal and spatial scales. The model predictions in conjunction with the JECS will reveal the critical windows of prenatal and infancy exposure to ambient air pollutants, thus contributing to the development of environmental policies on air pollution.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

BACKGROUND

Long-term surface NO₂ data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO₂ observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift.

OBJECTIVE

This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO₂ levels across China during 2005-2018.

METHODS

On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO₂ through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO₂ levels.

RESULTS

The validation against Taiwan's NO₂ observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m³, 7.1 to 4.3 µg/m³, and 6.1 to 2.9 µg/m³ in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO₂ ([NO₂]_pw) during 2005-2012 was estimated as 40.2 and 50.9 µg/m³ by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO₂]_pw increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO₂ > 40 µg/m³ were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively.

CONCLUSION

With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO₂ across China during 2005-2018, which is valuable for environmental management and epidemiological research.

A prospective cohort study of ambient air pollution exposure and risk of uterine leiomyomata

STUDY QUESTION

To what extent are ambient concentrations of particulate matter <2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) associated with risk of self-reported physician-diagnosed uterine leiomyomata (UL)?

SUMMARY ANSWER

In this large prospective cohort study of Black women, ambient concentrations of O3, but not PM2.5 or NO2, were associated with increased risk of UL.

WHAT IS KNOWN ALREADY

UL are benign tumors of the myometrium that are the leading cause of gynecologic inpatient care among reproductive-aged women. Black women are clinically diagnosed at two to three times the rate of white women and tend to exhibit earlier onset and more severe disease. Two epidemiologic studies have found positive associations between air pollution exposure and UL risk, but neither included large numbers of Black women.

STUDY DESIGN, SIZE, DURATION

We conducted a prospective cohort study of 21 998 premenopausal Black women residing in 56 US metropolitan areas from 1997 to 2011.

PARTICIPANTS/MATERIAL, SETTING, METHODS

Women reported incident UL diagnosis and method of confirmation (i.e. ultrasound, surgery) on biennial follow-up questionnaires. We modeled annual residential concentrations of PM2.5, NO2 and O3 throughout the study period. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for a one-interquartile range (IQR) increase in air pollutant concentrations, adjusting for confounders and co-pollutants.

MAIN RESULTS AND THE ROLE OF CHANCE

During 196 685 person-years of follow-up, 6238 participants (28.4%) reported physician-diagnosed UL confirmed by ultrasound or surgery. Although concentrations of PM2.5 and NO2 were not appreciably associated with UL (HRs for a one-IQR increase: 1.01 (95% CI: 0.93, 1.10) and 1.05 (95% CI: 0.95, 1.16), respectively), O3 concentrations were associated with increased UL risk (HR for a one-IQR increase: 1.19, 95% CI: 1.07, 1.32). The association was stronger among women age <35 years (HR: 1.26, 95% CI: 0.98, 1.62) and parous women (HR: 1.28, 95% CI: 1.11, 1.48).

LIMITATIONS, REASONS FOR CAUTION

Our measurement of air pollution is subject to misclassification, as monitoring data are not equally spatially distributed and we did not account for time-activity patterns. Our outcome measure was based on self-report of a physician diagnosis, likely resulting in under-ascertainment of UL. Although we controlled for several individual- and neighborhood-level confounding variables, residual confounding remains a possibility.

WIDER IMPLICATIONS OF THE FINDINGS

Inequitable burden of air pollution exposure has important implications for racial health disparities, and may be related to disparities in UL. Our results emphasize the need for additional research focused on environmental causes of UL.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by the National Cancer Institute (U01-CAA164974) and the National Institute of Environmental Health Sciences (R01-ES019573). L.A.W. is a fibroid consultant for AbbVie, Inc. and accepts in-kind donations from Swiss Precision Diagnostics, Sandstone Diagnostics, FertilityFriend.com and Kindara.com for primary data collection in Pregnancy Study Online (PRESTO). M.J. declares consultancy fees from the Health Effects Institute (as a member of the review committee). The remaining authors declare they have no actual or potential competing financial interests.

TRIAL REGISTRATION NUMBER

N/A.

Monetizing the Burden of Childhood Asthma Due to Traffic Related Air Pollution in the Contiguous United States in 2010

BACKGROUND

Traffic-related air pollution (TRAP) refers to the wide range of air pollutants emitted by traffic that are dispersed into the ambient air. Emerging evidence shows that TRAP can increase asthma incidence in children. Living with asthma can carry a huge financial burden for individuals and families due to direct and indirect medical expenses, which can include costs of hospitalization, medical visits, medication, missed school days, and loss of wages from missed workdays for caregivers.

OBJECTIVE

The objective of this paper is to estimate the economic impact of childhood asthma incident cases attributable to nitrogen dioxide (NO₂), a common traffic-related air pollutant in urban areas, in the United States at the state level.

METHODS

We calculate the direct and indirect costs of childhood asthma incident cases attributable to NO₂ using previously published burden of disease estimates and per person asthma cost estimates. By multiplying the per person indirect and direct costs for each state with the NO₂-attributable asthma incident cases in each state, we were able to estimate the total cost of childhood asthma cases attributable to NO₂ in the United States.

RESULTS

The cost calculation estimates the total direct and indirect annual cost of childhood asthma cases attributable to NO₂ in the year 2010 to be $178,900,138.989 (95% CI: $101,019,728.20-$256,980,126.65). The state with the highest cost burden is California with $24,501,859.84 (95% CI: $10,020,182.62-$38,982,261.250), and the state with the lowest cost burden is Montana with $88,880.12 (95% CI: $33,491.06-$144,269.18).

CONCLUSION

This study estimates the annual costs of childhood asthma incident cases attributable to NO₂ and demonstrates the importance of conducting economic impacts studies of TRAP. It is important for policy-making institutions to focus on this problem by advocating and supporting more studies on TRAP's impact on the national economy and health, including these economic impact estimates in the decision-making process, and devising mitigation strategies to reduce TRAP and the population's exposure.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Changes in criteria air pollution levels in the US before, during, and after Covid-19 stay-at-home orders: Evidence from regulatory monitors

The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental Protection Agency for criteria pollutants (PM_2.5, ozone, NO₂, CO, PM₁₀) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM_2.5 levels were slightly higher (~10% of its multi-year interquartile range [IQR]) than expected; average ozone, NO₂, CO, and PM₁₀ levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO₂, CO: 3 weeks after; PM₁₀: 2 weeks after); but, by 5-6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM_2.5, ozone, CO, and PM₁₀, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO₂, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM_2.5 levels across the US beyond their normal range of variability; for ozone, NO₂, CO, and PM₁₀ concentrations were lowered but the reduction was modest and transient.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

Air pollution and breast cancer risk in the Black Women's Health Study

BACKGROUND

Air pollution contains numerous carcinogens and endocrine disruptors which may be relevant for breast cancer. Previous research has predominantly been conducted in White women; however, Black women may have higher air pollution exposure due to geographic and residential factors.

OBJECTIVE

We evaluated the association between air pollution and breast cancer risk in a large prospective population of Black women.

METHODS

We estimated annual average ambient levels of particulate matter <2.5 μm (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃) at the 1995 residence of 41,317 participants in the Black Women's Health Study who resided in 56 metropolitan areas across the United States. Cox proportional hazards regression was used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for an interquartile range (IQR) increase in each pollutant. We evaluated whether the association varied by menopausal status, estrogen receptor (ER) status of the tumor and geographic region of residence.

RESULTS

With follow-up through 2015 (mean = 18.3 years), 2146 incident cases of breast cancer were confirmed. Higher exposure to NO₂ or O₃ was not associated with a higher risk of breast cancer. For PM_2.5, although we observed no association overall, there was evidence of modification by geographic region for both ER- (p for heterogeneity = 0.01) and premenopausal breast cancer (p for heterogeneity = 0.01). Among women living in the Midwest, an IQR increase in PM_2.5 (2.87 μg/m³), was associated with a higher risk of ER- (HR = 1.53, 95% CI: 1.07-2.19) and premenopausal breast cancer (HR = 1.32, 95% CI: 1.03-1.71). In contrast, among women living in the South, PM_2.5 was inversely associated with both ER- (HR = 0.74, 95% CI: 0.56-0.97) and premenopausal breast cancer risk (HR = 0.75, 95% CI: 0.62-0.91).

DISCUSSION

Overall, we observed no association between air pollution and increased breast cancer risk among Black women, except perhaps among women living in the Midwestern US.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

BACKGROUND

Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.

OBJECTIVES

We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.

METHODS

We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.

RESULTS

Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell-level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)].

DISCUSSION

Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Ambient ultrafine particle concentrations and incidence of childhood cancers

BACKGROUND

Ambient air pollution has been associated with childhood cancer. However, little is known about the possible impact of ambient ultrafine particles (<0.1 μm) (UFPs) on childhood cancer incidence.

OBJECTIVE

This study aimed to evaluate the association between prenatal and childhood exposure to UFPs and development of childhood cancer.

METHODS

We conducted a population-based cohort study of within-city spatiotemporal variations in ambient UFPs across the City of Toronto, Canada using 653,702 singleton live births occurring between April 1, 1998 and March 31, 2017. Incident cases of 13 subtypes of paediatric cancers among children up to age 14 were ascertained using a cancer registry. Associations between ambient air pollutant concentrations and childhood cancer incidence were estimated using random-effects Cox proportional hazards models. We investigated both single- and multi-pollutant models accounting for co-exposures to PM_2.5 and NO₂.

RESULTS

A total of 1,066 childhood cancers were identified. We found that first trimester exposure to UFPs (Hazard Ratio (HR) per 10,000/cm³ increase = 1.13, 95% CI: 1.03-1.22) was associated with overall cancer incidence diagnosed before 6 years of age after adjusting for PM_2.5, NO₂, and for personal and neighborhood-level covariates. Association between UFPs and overall cancer incidence exhibited a linear shape. No statistically significant associations were found for specific cancer subtypes.

CONCLUSION

Ambient UFPs may represent a previously unrecognized risk factor in the aetiology of cancers in children. Our findings reinforce the importance of conducting further research on the effects of UFPs given their high prevalence of exposure in urban areas.

The Associations of Ambient Fine Particulate Matter Exposure During Pregnancy With Blood Glucose Levels and Gestational Diabetes Mellitus Risk: A Prospective Cohort Study in Wuhan, China

Investigators in previous studies have drawn inconsistent conclusions regarding the relationship between relatively low exposure to fine particulate matter (particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5)) and risk of gestational diabetes mellitus (GDM), while the association between high PM2.5 exposure and GDM risk has not been well studied. We investigated the association of high PM2.5 exposure during pregnancy with blood glucose levels and GDM risk in Chinese women. The present study was conducted from August 2013 to May 2016 among 3,967 pregnant women in the Tongji Maternal and Child Health Cohort in Wuhan, China. PM2.5 exposure during pregnancy for each participant was estimated by means of land-use regression models. An interquartile-range increase in PM2.5 exposure (33.84 μg/m3 for trimester 1 and 33.23 μg/m3 for trimester 2) was associated with 36% (95% confidence interval (CI): 1.15, 1.61) and 23% (95% CI: 1.01, 1.50) increased odds of GDM during trimester 1 and trimester 2, respectively. An interquartile-range increment of PM2.5 exposure during trimester 1 increased 1-hour and 2-hour blood glucose levels by 1.40% (95% CI: 0.42, 2.37) and 1.82% (95% CI: 0.98, 2.66), respectively. The same increment of PM2.5 exposure during trimester 2 increased fasting glucose level by 0.85% (95% CI: 0.41, 1.29). Our findings suggest that high PM2.5 exposure during pregnancy increases blood glucose levels and GDM risk in Chinese women.

The impact of baseline incidence rates on burden of disease assessment of air pollution and onset childhood asthma: analysis of data from the contiguous United States

PURPOSE

Burden of disease (BoD) assessments typically rely on national-level incidence rates for the health outcomes of interest. The impact of using a constant national-level incidence rate, versus a more granular spatially varying rate, remains unknown and understudied in the literature. There has been an increasing number of publications estimating the BoD of childhood asthma attributable to air pollution, as emerging evidence demonstrates that traffic-related air pollution (TRAP) leads to onset of the disease. In this study, we estimated the burden of incident childhood asthma cases which may be attributable to nitrogen dioxide (NO₂), a criteria pollutant and a good marker of TRAP, in the contiguous United States. We used both a national-level and newly generated state-specific asthma incidence rates and compared results from the two approaches.

METHODS

We estimated incident childhood asthma cases which may be attributable to NO₂ using standard BoD assessment methods. We combined child (<18 years) counts with 2010 NO₂ exposures at the census block level, concentration-response function, and state-specific asthma incidence rates. NO₂ concentrations were obtained from a previously validated land-use regression model. We sourced the concentration-response function from a meta-analysis on TRAP and risk of childhood asthma. We estimated incidence rates using raw data collected in the 2006-2010 Behavioral Risk Factor Surveillance System and Asthma Call-back Surveys. We stratified the estimated BoD by urban versus rural status and by median household income, explored trends in BoD across 48 states and the District of Columbia, and compared our results with a published BoD analysis which used a constant national-level incidence rate across all states.

RESULTS

The overall mean (min-max) NO₂ concentration(s) was 13.2 (1.5-58.3) ug/m³ and was highest in urbanized areas. The estimated national aggregate asthma incidence rate was 11.6 per 1000 at-risk children and ranged from 4.3 (Montana) to 17.7 (District of Columbia) per 1000 at-risk children. The 17 states that did not have data to estimate an incidence rate were assigned the national aggregate asthma incidence rate. Using the state-specific incidence rates, we estimated a total of 134,166 (95% confidence interval: 75,177-193,327) childhood asthma incident cases attributable to NO₂, accounting for 17.6% of all childhood asthma incident cases. Using the national-level incidence rate, we estimated a total of 141,931 (95% confidence interval: 119,222-163,505) incident cases attributable to NO₂, accounting for 17.9% of all childhood asthma incident cases. Using the state-specific incidence rates therefore reduced the attributable number of cases by 7,765 (5.5% relative reduction), compared with estimates using the national-level incidence rate. Across states, the change in the attributable number of cases ranged from -64.1% (Montana) to +33.8% (Texas). California had the largest absolute decrease (-6,190) in attributable cases, whereas Texas had the largest increase (+3,615). Stratifying by socioeconomic status and urban versus rural status produced new trends compared with the previously published BoD analysis showing high heterogeneity across the states.

CONCLUSIONS

We estimated new state-specific asthma incidence rates for the contiguous United States. Using state-specific incidence rates versus a constant national incidence rate resulted in a small change in the NO₂ attributable BoD at the national level, but had a more prominent impact at the state level.

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.

Estimating historical PM2.5 exposures for three decades (1987-2016) in Japan using measurements of associated air pollutants and land use regression

Accurate estimation of historical PM_2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM_2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM_2.5 estimates for years prior to implementation of extensive PM_2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000-2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM_2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.

TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations

This work presents a regridding procedure applied to the nitrogen dioxide (NO2) tropospheric column data, derived from the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI). The regridding has been performed to provide a better comparison with punctual surface observations. It will be demonstrated that TROPOMI NO2 tropospheric column data show improved consistency with in situ surface measurements once the satellite retrievals are scaled to 1 km spatial sampling. A geostatistical technique, i.e., the ordinary kriging, has been applied to improve the spatial distribution of Level 2 TROPOMI NO2 data, which is originally sparse and uneven because of gaps introduced by clouds, to a final spatial, regular, sampling of 1 km × 1 km. The analysis has been performed for two study areas, one in the North and the other in the South of Italy, and for May 2018-April 2020, which also covers the period January 2020-April 2020 of COVID-19 diffusion over the Po Valley. The higher spatial sampling NO2 dataset indicated as Level 3 data, allowed us to explore spatial and seasonal data variability, obtaining better information on NO2 sources. In this respect, it will be shown that NO2 concentrations in March 2020 have likely decreased as a consequence of the lockdown because of COVID-19, although the far warmest winter season ever recorded over Europe in 2020 has favored a general NO2 decrease in comparison to the 2019 winter. Moreover, the comparison between NO2 concentrations related to weekdays and weekend days allowed us to show the strong correlation of NO2 emissions with traffic and industrial activities. To assess the quality and capability of TROPOMI NO2 observations, we have studied their relationship and correlation with in situ NO2 concentrations measured at air quality monitoring stations. We have found that the correlation increases when we pass from Level 2 to Level 3 data, showing the importance of regridding the satellite data. In particular, correlation coefficients of Level 3 data, which range between 0.50–0.90 have been found with higher correlation applying to urban, polluted locations and/or cities.

Long-term ozone exposures and cause-specific mortality in a US Medicare cohort

We examined the association of long-term, daily 1-h maximum O₃ (ozone) exposures on cause-specific mortality for 22.2 million US Medicare beneficiaries between 2000-2008. We modeled the association between O₃ and mortality using age-gender-race stratified log-linear regression models, adjusted for state of residence. We examined confounding by (1) adjusting for PM_2.5 (particles with aerodynamic diameters <2.5 μm) and NO₂ (nitrogen dioxide) exposures, temperature, and neighborhood-level characteristics and behaviors, and (2) decomposing O₃ into its temporal and spatio-temporal components and comparing estimated risk ratios. We also examined sensitivity of our results to alternate exposure measures based on warm-season 8-h daily maximum and 24-h average exposures. We found increased risks from long-term O₃ exposures to be strongest and most consistent for mortality from respiratory disease (1.030, 95% CI: 1.027, 1.034) (including COPD (chronic obstructive pulmonary disease)), CHF (congestive heart failure), and lung cancer (1.015, 95% CI: 1.010, 1.020), with no evidence of confounding by PM_2.5, NO₂, and temperature and with results similar across O₃ exposure measures. While significant, associations between long-term O₃ exposures and CVD (cardiovascular)-related mortality (1.005, 95% CI: 1.003, 1.007) were confounded by PM_2.5 and varied with the exposure measure, with associations no longer significantly positive when warm-season 8-h maximum or 24-h average O₃ was used to assess exposures. In this large study, we provide strong evidence that O₃ exposure is associated with mortality from respiratory-related causes and for the first-time, lung cancer, but raise questions regarding O₃-related impacts on CVD mortality. Our findings demonstrate the need to further identify potential confounders.

Ambient air pollution and incidence of early-onset paediatric type 1 diabetes: A retrospective population-based cohort study

BACKGROUND

Studies have reported increasing incidence rates of paediatric diabetes, especially among those aged 0-5 years. Epidemiological evidence linking ambient air pollution to paediatric diabetes remains mixed.

OBJECTIVE

This study investigated the association between maternal and early-life exposures to common air pollutants (NO₂, PM_2.5, O₃, and oxidant capacity [Ox; the redox-weighted average of O₃ and NO₂]) and the incidence of paediatric diabetes in children up to 6 years of age.

METHODS

All registered singleton births in Ontario, Ca nada occurring between April 1st^, 2006 and March 31st^, 2012 were included through linkage from health administrative data. Monthly exposures to NO₂, PM_2.5, O₃, and O_x were estimated across trimesters, the entire pregnancy period and during childhood. Random effects Cox proportional hazards models were used to assess the relationships with paediatric diabetes incidence while controlling for important covariates. We also modelled the shape of concentration-response (CR) relationships.

RESULTS

There were 1094 children out of a cohort of 754,698 diagnosed with diabetes before the age of six. O₃ exposures during the first trimester of pregnancy were associated with paediatric diabetes incidence (hazard ratio (HR) per interquartile (IQR) increase = 2.00, 95% CI: 1.04-3.86). The CR relationship between O₃ during the first trimester and paediatric diabetes incidence appeared to have a risk threshold, in which there was little-to-no risk below 25 ppb of O₃, while above this level risk increased sigmoidally. No other associations were observed.

CONCLUSION

O₃ exposures during a critical period of development were associated with an increased risk of paediatric diabetes incidence.

Bayesian geostatistical modelling of high-resolution NO2 exposure in Europe combining data from monitors, satellites and chemical transport models

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO₂) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO₂ concentrations at 1 km² spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO₂ derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO₂ concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO₂ ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO₂ values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO₂ estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO₂. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO₂ in 2016.

Ambient air pollution and the risk of pediatric-onset inflammatory bowel disease: A population-based cohort study

BACKGROUND

High-income nations have the highest rates of inflammatory bowel disease (IBD). The incidence of pediatric-onset IBD is increasing faster than IBD diagnosed in older individuals. Previous epidemiological studies have shown that air pollution might be a risk factor for development of earlier-onset IBD, but results remain mixed.

OBJECTIVES

The objective of this study was to evaluate the associations between maternal and early-life exposures to nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), ozone (O₃,) and oxidant capacity (O_x) and risk of pediatric-onset IBD diagnosis.

METHODS

We conducted a retrospective cohort study using linked population-based health administrative data. Singleton livebirths in Ontario, Canada between April 1st, 1991 and March 31st, 2014 were included. We investigated the association between weekly exposures during pregnancy and annual exposures from birth until the age of 18 years, and IBD diagnosed <18 years of age using Cox proportional hazards models. We reported hazard ratios (HR) and 95% confidence intervals (CI) for an associated increase in the interquartile range (IQR) of each pollutant. Models were mutually adjusted for exposures in both prenatal and postnatal periods, as well as for sex, rurality of residence at birth, maternal IBD, and neighborhood income.

RESULTS

2,218,789 newborns were included in this study, of whom 2491 developed IBD during follow-up. Increased associations with pediatric-onset IBD were noted for childhood exposure to O_x (HR 1.08, 95% CI 1.01-1.16). IBD development was also associated with O_x during the second trimester (HR 1.21, 95% CI 1.03-1.42), but not the overall pregnancy period (HR 1.12, 95% CI 0.79-1.59). There were no associations of IBD with exposure to NO₂, PM_2.5, or O₃.

DISCUSSION

Exposure to O_x during childhood was associated with IBD < 18 years. This suggests that air pollution may impact the developing child physiology in such a way that leads to early onset of IBD.

Spatiotemporal Variations in Ambient Ultrafine Particles and the Incidence of Childhood Asthma

Rationale: Little is known regarding the impact of ambient ultrafine particles (UFPs; <0.1 μm) on childhood asthma development. Objectives: To examine the association between prenatal and early postnatal life exposure to UFPs and development of childhood asthma. Methods: A total of 160,641 singleton live births occurring in the City of Toronto, Canada between April 1, 2006, and March 31, 2012, were identified from a birth registry. Associations between exposure to ambient air pollutants and childhood asthma incidence (up to age 6) were estimated using random effects Cox proportional hazards models, adjusting for personal- and neighborhood-level covariates. We investigated both single-pollutant and multipollutant models accounting for coexposures to particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5) and NO₂. Measurements and Main Results: We identified 27,062 children with incident asthma diagnosis during the follow-up. In adjusted models, second-trimester exposure to UFPs (hazard ratio per interquartile range increase, 1.09; 95% confidence interval, 1.06-1.12) was associated with asthma incidence. In models additionally adjusted for PM_2.5 and nitrogen dioxide, UFPs exposure during the second trimester of pregnancy remained positively associated with childhood asthma incidence (hazard ratio per interquartile range increase, 1.05; 95% confidence interval, 1.01-1.09). Conclusions: This is the first study to evaluate the association between perinatal exposure to UFPs and the incidence of childhood asthma. Exposure to UFPs during a critical period of lung development was linked to the onset of asthma in children, independent of PM_2.5 and NO₂.

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.