Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000

PM_2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM_2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM_2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM_2.5 data. PM_2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM_2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM_2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM 2.5 Transition Metals and Oxidative Potential

BACKGROUND

Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O 3 and NO 2 ) and fine particulate air pollution (PM 2.5 ). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM 2.5 is more capable of causing oxidative stress.

METHODS

We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM 2.5 components and oxidative potential. O x exposures (i.e., the redox-weighted average of O 3 and NO 2 ) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average O x and mortality outcomes across strata of transition metals and sulfur in PM 2.5 and three measures of PM 2.5 oxidative potential adjusting for possible confounding factors.

RESULTS

Associations between O x and mortality were consistently stronger in regions with elevated PM 2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in O x was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OP GSH ) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OP GSH levels below the median (interaction P value <0.001).

CONCLUSION

Spatial variations in PM 2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.

Prenatal exposure to fine particulate matter and the risk of spontaneous preterm birth: A population-based cohort study of twins

Background

Studies in singletons have suggested that prenatal exposure to fine particulate matter (PM_2.5) and some of its chemical components is associated with an increased risk of preterm birth (PTB). However, no study has been conducted in twins.

Purpose

To examine the associations of maternal exposure to total PM_2.5 mass and its carbonaceous components with PTB in twin pregnancies.

Methods

A total of 1,515 pairs of twins and their mothers were enrolled from a previous twin birth cohort that had been conducted at the Shanghai First Maternity and Infant Hospital School of Medicine of Tongji University in China. Participants who had iatrogenic PTBs were excluded. Maternal exposure to total PM_2.5 mass and two carbonaceous components, namely, organic carbon (OC) and black carbon (BC), was estimated by a satellite-based model. The associations between PM_2.5 exposure and the risk of spontaneous PTB were evaluated by logistic regression analysis.

Results

This study found that exposure to total PM_2.5 mass and OC during the second trimester of pregnancy was significantly associated with an increased risk of spontaneous PTB. An interquartile range (IQR) increase in total PM_2.5 mass and OC exposure during the second trimester was associated with 48% (OR = 1.48, 95% CI, 1.06, 2.05) and 50% (OR = 1.50, 95% CI, 1.00, 2.25) increases in the odds of PTB, respectively. However, no significant association was found between BC exposure during any exposure window and the risk of PTB.

Conclusion

The findings suggest that exposure to ambient air pollution with fine particles may be a risk factor for spontaneous PTB in twin pregnancies. The middle stage of pregnancy seems to be a critical window for the impacts of PM_2.5 exposure on PTB in twin pregnancies.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R² 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R²: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM_2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Long-term exposure to PM2.5 constituents in relation to glucose levels and diabetes in middle-aged and older Chinese

BACKGROUND

Previous studies have indicated the associations between fine particulate matter (PM_2.5) exposure and diabetes or glucose levels. However, evidence linking PM_2.5 constituents and diabetes or glucose levels was extensively scarce, particularly in developing countries. This study aimed to investigate the associations of exposure to PM_2.5 and its five constituents (black carbon [BC], organic matter [OM], nitrate [NO₃^-], sulfate [SO₄^2-], and ammonium [NH₄⁺]) with diabetes and glucose levels among the middle-aged and elderly Chinese populations.

METHODS

A national cross-sectional sample of participants aged 45+ years was enrolled from 28 provinces across China's mainland. Health examination and questionnaire survey for each respondent were performed during 2011-2012. Diabetes was determined by alternative definitions, and the main definition (MD) was self-report diabetes or antidiabetic medicine use or HbA_1c ≥6.5 or fasting glucose ≥7 mmol/L or random glucose ≥11.1 mmol/L. Monthly exposure to PM_2.5 mass and its five constituents (BC, OM, NO₃^-, SO₄^2-, and NH₄⁺) for each participant at residence were estimated using satellite-based spatiotemporal prediction models. Generalized linear models and linear mixed-effects models were used to assess the effects of exposure to PM_2.5 and its constituents on diabetes or glucose levels, respectively. Stratification analyses were done by sex and age.

RESULTS

We included a total of 17,326 adults over 45 years in this study. The 3-year mean (interquartile range [IQR]) concentrations of PM_2.5, BC, OM, NO₃^-, SO₄^2-, and NH₄⁺ were 47.9 (27.4) µg/m³, 2.9 (2.2) µg/m³, 9.2 (6.6) µg/m³, 10.2 (9.4) µg/m³, 11.0 (5.2) µg/m³, and 7.1 (4.4) µg/m³, respectively. Per IQR rise in exposure to PM_2.5 was significantly associated with an increase of 0.133 mmol/L (95% confidence interval, 0.048-0.219) in glucose concentrations. Similar positive associations were observed for BC (0.097 mmol/L [0.012-0.181]), OM (0.160 mmol/L [0.065-0.256]), NO₃^- (0.145 mmol/L [0.039-0.251]), SO₄^2- (0.111 mmol/L [0.026-0.196]), and NH₄⁺ (0.135 mmol/L [0.041-0.230]). Under different diabetes definitions, PM_2.5 mass and selected constituents with the exception of SO₄^2- were all associated with a higher risk of prevalent diabetes. In MD-based analysis, similar positive associations were observed for four constituents, with corresponding odds ratios of 1.180 (1.097-1.270) for PM_2.5, 1.154 (1.079-1.235) for BC, 1.170 (1.079-1.270) for OM, 1.200 (1.098-1.312) for NO₃^-, and 1.123 (1.037-1.215) for NH₄⁺. Stratified analyses showed a significantly higher risk of diabetes in males (1.225 [1.064-1.411]) than females (1.024 [0.923-1.136]) when exposed to PM_2.5. Participants under 65 years were generally more vulnerable to diabetes hazards related to PM_2.5 constituents exposure.

CONCLUSIONS

Exposures to PM_2.5 and its constituents (i.e., BC, OM, NO₃^-, and NH₄⁺) were positively associated with increased risks of prevalent diabetes and elevated glucose levels in middle-aged and older adults.

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM 2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Maternal PM2.5 exposure during gestation and offspring neurodevelopment: Findings from a prospective birth cohort study

Emerging data have suggested the potential role of prenatal PM_2.5 exposure as a neurotoxin for offspring. However, the existing results are equivocal, and no study has examined the effects of complex chemical constituents of the particular matter on offspring neurodevelopment. Therefore, in a prospective birth cohort study conducted in Jiangsu, China, we aimed to investigate the association between prenatal exposure to PM_2.5 and the neurodevelopment in infants, and further assess the effects of specific chemical constituents of PM_2.5. A total of 1531 children who had available data on daily prenatal PM_2.5 exposure and completed assessment on neurodevelopment at 1 year old were enrolled. We used the high-performance machine-learning model to estimate daily PM_2.5 exposure concentrations at 1 km × 1 km spatial resolution. The combined geospatial-statistical model was applied to evaluate average concentrations of six chemical constituents [organic matter (OM), black carbon (BC), sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), and soil dust (Dust)]. The neurodevelopment of children was assessed using Bayley-III Screening Test. After adjusting for confounding factors, the risk of non-optimal gross motor development increased by 31 % for every 10 μg/m³ increase in average PM_2.5 exposure during gestation (aRR: 1.31; 95 % CI: 1.04, 1.64). Further analysis of PM_2.5 constituents showed that prenatally exposed to high SO₄^2- was associated with the risk of non-optimal gross motor development (aRR: 1.40; 95 % CI: 1.08, 1.81). Null associations were observed for the rest four neurodevelopment domains. Collectively, our study suggested that prenatal exposure to PM_2.5, particularly with high SO₄^2- concentration, was associated with children's non-optimal gross motor development at 1 year old. The short- and long-term influences of perinatal PM_2.5 exposure on children's neurodevelopment warrant further investigation.

Coordinated effects of energy transition on air pollution mitigation and CO2 emission control in China

China has made progress in energy transition to improve air quality, but still confronts challenges including further ambient PM_2.5 reduction, O₃ pollution mitigation, and CO₂ emission control. To explore the coordinated effects of energy transition on air quality and carbon emission in the near term in China, we designed 4 scenarios in 2025 based on different projections of energy transition progress with varying end-of-pipe control level, in each of which we calculated emissions of major air pollutants and CO₂, and simulated ambient PM_2.5 and O₃ concentrations. Results show that energy transition has disparate effects on emission reduction of different air pollutants and sectors, which largely depends on their current end-of-pipe control levels. The different effects on emission reduction may result in opposite variation tendencies of ambient PM_2.5 and O₃ concentration in a future scenario with aggressive energy transition policies and end-of-pipe control level in 2018. With the end-of-pipe control level strengthened in 2025, PM_2.5 and O₃ concentration could both reduce on the national scale, but the reduction of ambient O₃ lags behind PM_2.5, indicating the difficulty of O₃ pollution control. As to CO₂, national emission would go up in 2025 either implementing current or aggressive energy transition policies due to growing needs of electricity and on-road transportation, but emissions in most provinces could decline to below the 2018 level with aggressive energy transition policies because of substitution of clean energy in industrial, residential and off-road transportation sectors. The study results suggest strictly implementing restrictive end-of-pipe control measures along with energy transition to simultaneously reduce ambient PM_2.5 and O₃ concentration, and accelerating substitution of renewable energy in power sectors where electricity generation grows rapidly to synergistically control air pollution and CO₂ emissions. Furthermore, the projection of CO₂ emissions could provide references for short-term emission control targets from the perspective of air quality improvement.

Association between long-term exposure to particulate air pollution with SARS-CoV-2 infections and COVID-19 deaths in California, U.S.A

Previous studies have reported associations between air pollution and COVID-19 morbidity and mortality, but most have limited their exposure assessment to a large area, have not used individual-level variables, nor studied infections. We examined 3.1 million SARS-CoV-2 infections and 49,691 COVID-19 deaths that occurred in California from February 2020 to February 2021 to evaluate risks associated with long-term neighborhood concentrations of particulate matter less than 2.5 μm in diameter (PM2.5). We obtained individual address data on SARS-CoV-2 infections and COVID-19 deaths and assigned 2000-2018 1km-1km gridded PM2.5 surfaces to census block groups. We included individual covariate data on age and sex, and census block data on race/ethnicity, air basin, Area Deprivation Index, and relevant comorbidities. Our analyses were based on generalized linear mixed models utilizing a Poisson distribution. Those living in the highest quintile of long-term PM2.5 exposure had risks of SARS-CoV-2 infections 20% higher and risks of COVID-19 mortality 51% higher, compared to those living in the lowest quintile of long-term PM2.5 exposure. Those living in the areas of highest long-term PM2.5 exposure were more likely to be Hispanic and more vulnerable, based on the Area Deprivation Index. The increased risks for SARS-CoV-2 Infections and COVID-19 mortality associated with highest long-term PM2.5 concentrations at the neighborhood-level in California were consistent with a growing body of literature from studies worldwide, and further highlight the importance of reducing levels of air pollution to protect public health.

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.

Excess Mortality during the COVID-19 Pandemic in Cities of Chile: Magnitude, Inequalities, and Urban Determinants

We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or "comunas", grouped in 4 macroregions. We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016-2019, using a generalized additive model. We estimated a total of 21,699 (95%CI 21,693 to 21,704) excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions (28.9% and 22.2%, respectively), followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.

How low can you go? Air pollution affects mortality at very low levels

The World Health Organization (WHO) recently released new guidelines for outdoor fine particulate air pollution (PM_2.5) recommending an annual average concentration of 5 μg/m³. Yet, our understanding of the concentration-response relationship between outdoor PM_2.5 and mortality in this range of near-background concentrations remains incomplete. To address this uncertainty, we conducted a population-based cohort study of 7.1 million adults in one of the world's lowest exposure environments. Our findings reveal a supralinear concentration-response relationship between outdoor PM_2.5 and mortality at very low (<5 μg/m³) concentrations. Our updated global concentration-response function incorporating this new information suggests an additional 1.5 million deaths globally attributable to outdoor PM_2.5 annually compared to previous estimates. The global health benefits of meeting the new WHO guideline for outdoor PM_2.5 are greater than previously assumed and indicate a need for continued reductions in outdoor air pollution around the world.

Air Pollution, Oxidative Stress, and the Risk of Development of Type 1 Diabetes

Despite multiple studies focusing on environmental factors conducive to the development of type 1 diabetes mellitus (T1DM), knowledge about the involvement of long-term exposure to air pollution seems insufficient. The main focus of epidemiological studies is placed on the relationship between exposure to various concentrations of particulate matter (PM): PM1, PM2.5, PM10, and sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (O3), versus the risk of T1DM development. Although the specific molecular mechanism(s) behind the link between increased air pollution exposure and a higher risk of diabetes and metabolic dysfunction is yet unknown, available data indicate air pollution-induced inflammation and oxidative stress as a significant pathway. The purpose of this paper is to assess recent research examining the association between inhalation exposure to PM and associated metals and the increasing rates of T1DM worldwide. The development of modern and more adequate methods for air quality monitoring is also introduced. A particular emphasis on microsensors, mobile and autonomous measuring platforms, satellites, and innovative approaches of IoT, 5G connections, and Block chain technologies are also presented. Reputable databases, including PubMed, Scopus, and Web of Science, were used to search for relevant literature. Eligibility criteria involved recent publication years, particularly publications within the last five years (except for papers presenting a certain novelty or mechanism for the first time). Population, toxicological and epidemiological studies that focused particularly on fine and ultra-fine PM and associated ambient metals, were preferred, as well as full-text publications.

Effects of long-term exposure to PM2.5 and chemical constituents on blood lipids in an essential hypertensive population: A multi-city study in China

Previous studies on the effects of fine particulate matter (PM_2.5) and chemical constituents on lipid disorder among hypertension populations, particularly in China, are very limited. We aimed to examine the effects of long-term exposure to PM_2.5 and chemical constituents on dyslipidemias in China. Finally, we included 34,841 participants with essential hypertension from 19 regions in China during 2010-2011. Data were modeled using the generalized additive mixed model. We found that PM_2.5 and chemical constituents exposure were positively associated with the increased risk of dyslipidemias and increased levels of total cholesterol (TC) and triglyceride (TG). The odds ratio for hypercholesterolemia was 1.356 [95% confidence interval (CI): 1.246, 1.477] for PM_2.5, and the strongest association with PM_2.5 constituents was found for nitrate. Each 10 μg/m³ increase in PM_2.5 showed a significant increase of TC by 2.60% (95% CI: 2.03, 3.17) and TG by 2.91% (95% CI: 1.60, 4.24), respectively. Meanwhile, an interquartile range increase in nitrate, ammonium and organic matter had stronger associations with TC and TG parameters than black carbon, sulfate, and mineral dust. Our findings may contribute to a better understanding of the chronic effects of PM_2.5 and chemical constituents on lipid disorder in an essential hypertensive population.

Chemical characteristics and regional transport of submicron particulate matter at a suburban site near Lanzhou, China

Lanzhou, which is a valley city on the Loess Plateau, frequently suffered from aerosol pollution in recent years, especially in winter. However, the lack of understanding of factors governing aerosol pollution limits the implementation of effective emission policies in and around Lanzhou. To help solve this problem, an intensive field campaign was conducted at the SACOL site, which is a suburban site near Lanzhou, in winter 2018. The chemical characteristics and sources of submicron particulate matter (PM₁) were investigated, and the influence of the topography around Lanzhou on aerosol pollution was examined. In the present study, the average PM₁ mass concentration reached 25.6 ± 12.8 μg m^-3, with 41.0% organics, 16.1% sulfate, 19.7% nitrate, 10.7% ammonium, 3.1% chloride, and 9.4% black carbon (BC). Three organic aerosol (OA) factors were identified with the positive matrix factorization (PMF) algorithm, including a biomass burning OA (BBOA, 13.6%), a coal combustion OA (CCOA, 34.2%), and an oxygenated OA (OOA, 52.2%). The significant relationships between organics, BC, and chloride and wind pattern suggested that the SACOL site was strongly influenced by regionally transported aerosols. Further analysis suggested that these aerosol regional transport events were caused by topography. Due to the limitation of the valley, aerosols accumulated in the valley. These accumulated aerosols were then transported to the SACOL site along the valley by prevailing winds. Our study highlights enhanced aerosol regional transport in valleys, which provides a new perspective for future studies on aerosol pollution in basins and valleys.

A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA

The world is experiencing a pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The USA is also suffering from a catastrophic death toll from COVID-19. Several studies are providing preliminary evidence that short- and long-term exposure to air pollution might increase the severity of COVID-19 outcomes, including a higher risk of death. In this study, we develop a spatiotemporal model to estimate the association between exposure to fine particulate matter PM2.5 and mortality accounting for several social and environmental factors. More specifically, we implement a Bayesian zero-inflated negative binomial regression model with random effects that vary in time and space. Our goal is to estimate the association between air pollution and mortality accounting for the spatiotemporal variability that remained unexplained by the measured confounders. We applied our model to four regions of the USA with weekly data available for each county within each region. We analyze the data separately for each region because each region shows a different disease spread pattern. We found a positive association between long-term exposure to PM2.5 and the mortality from the COVID-19 disease for all four regions with three of four being statistically significant. Data and code are available at our GitHub repository. Supplementary materials accompanying this paper appear on-line.

COVID-19, green space exposure, and mask mandates

INTRODUCTION

Mask-wearing and social distancing are critical prevention measures that have been implemented to stem the spread of COVID-19. The degree to which these measures are adhered to in the US, however, may be influenced by access to outdoor resources such as green space, as well as mask mandates that may vary by state.

PURPOSE

To examine the association between the presence or absence of statewide mask mandates and green space exposure with COVID-19 cumulative incidence in the US.

METHODS

In October 2020, COVID-19 case data for each US county was downloaded from USA Facts, in addition to statewide mask mandates from a database maintained by the American Association of Retired Persons. The Normalized Difference Vegetation Index from the US Geological Survey (USGS), was used as a measure of greenspace, while the 2016 National Land Cover Database was used to assess tree canopy exposure as an alternative measure of greenspace. We performed generalized linear regression to evaluate associations with COVID-19 incidence, adjusting for potential confounders such as other environmental factors (i.e., air pollution and climate) and socio-economic factors derived from the CDC social vulnerability index. In addition, we also performed spatial regression analyses to account for spatial autocorrelation across counties.

RESULTS

Counties with mandatory mask-wearing policies had a lower cumulative incidence of COVID-19 (B = -0.299, SE = 0.038). Among environmental factors, precipitation (B = 0.005, SE = 0.001) and PM 2.5 (B = 0.072, SE = 0.012) were associated with a higher incidence of COVID-19, while tree canopy (B = -0.501, SE = 0.129) was associated with a lower risk of COVID-19. COVID-19 incidence was higher in counties with socially vulnerable populations regarding socioeconomic status, minority status, and housing and transportation.

CONCLUSION

Mandatory mask regulation, exposure to green space, and reduced exposure to air pollution may reduce COVID-19 incidence in the US. Additional public health policies should consider ways to mitigate environmental conditions that may contribute to the risk of COVID-19, especially for vulnerable populations.

Associations among drinking water quality, dyslipidemia, and cognitive function for older adults in China: evidence from CHARLS

BACKGROUND

The current study aimed to examine the association between drinking water quality and cognitive function and to identify the direct and indirect effects of drinking water quality and dyslipidemia on cognitive function among older adults in China.

METHODS

Primary data for the study were selected from China Health and Retirement Longitudinal Study (CHARLS, 2015) and 4,951 respondents aged 60 and above were included. Data on drinking water quality were selected from the 2015 prefectural water quality data from the Institute of Public and Environment Affairs in China and measured by the Blue City Water Quality Index. Dyslipidemia was measured by self-reported dyslipidemia diagnosis and lipid panel. Three composite measures of cognitive function included mental status, episodic memory, and global cognition. Mixed effects models were conducted to assess the associations between drinking water quality or dyslipidemia and cognitive function. The mediation effects of dyslipidemia were examined by path analyses.

RESULTS

Exposure to high quality drinking water was significantly associated with higher scores in mental status, episodic memory, and global cognition (β = 0.34, p < 0.001 for mental status; β = 0.24, p < 0.05 for episodic memory; β = 0.58, p < 0.01 for global cognition). Respondents who reported dyslipidemia diagnosis had higher scores in the three composite measures of cognitive function (β = 0.39, p < 0.001 for mental status; β = 0.27 p < 0.05 for episodic memory; β = 0.66, p < 0.001 for global cognition). An elevated blood triglycerides was only associated with higher scores in mental status (β = 0.21, p < 0.05). Self-reported dyslipidemia diagnosis was a suppressor, which increased the magnitude of the direct effect of drinking water quality on mental status, episodic memory, and global cognition.

CONCLUSION

Drinking water quality was associated with cognitive function in older Chinese and the relationship was independent of natural or socioeconomic variations in neighborhood environments. Improving drinking water quality could be a potential public health effort to delay the onset of cognitive impairment and prevent the dementia pandemic in older people.

Prenatal Exposure to PM2.5 and Its Specific Components and Risk of Hypertensive Disorders in Pregnancy: A Nationwide Cohort Study in China

Hypertensive disorders in pregnancy (HDP) are a leading cause of maternal mortality and adverse birth outcomes. Fine particulate matter (PM_2.5) has been linked to HDP risk; however, limited studies have explored the relationships between specific chemical constituents of PM_2.5 and HDP risk. Based on maternal data from the China Labor and Delivery Survey (CLDS), this study included a total of 67,659 participants from 95 participant hospitals in 25 provinces of China between March 1, 2015, and December 31, 2016. Maternal exposure to total PM_2.5 mass and six main components during pregestation and pregnancy were estimated using the Combined Geoscience-Statistical Method. Multilevel logistic regression models were applied to quantify the associations, controlling for sociodemographic characteristics. We found that an interquartile range (IQR) increase in PM_2.5 exposure during the second trimester was associated with a 14% increase in HDP risk (95% CI: 2%, 29%). We observed that black carbon (BC) and SO₄^2- had larger or comparable estimates of the effect than total PM_2.5 mass. The association estimates were greater in the gestational hypertension group than in the group of pre-eclampsia and eclampsia. Our findings suggest that PM_2.5 exposure and specific chemical components (particularly BC and SO₄^2-) were associated with an increased HDP risk in China.

Impacts of Sugarcane Fires on Air Quality and Public Health in South Florida

BACKGROUND

Preharvest burning of sugarcane is a common agricultural practice in Florida, which produces fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] that is associated with higher mortality.

OBJECTIVES

We estimated premature mortality associated with exposure to PM2.5 from sugarcane burning in people age 25 y and above for 20 counties in South Florida.

METHODS

We combined information from an atmospheric dispersion model, satellites, and surface measurements to quantify PM2.5 concentrations in South Florida and the fraction of PM2.5 from sugarcane fires. From these concentrations, estimated mortalities attributable to PM2.5 from sugarcane fires were calculated by census tract using health impact functions derived from literature for six causes of death linked to PM2.5. Confidence intervals (CI) are provided based on Monte Carlo simulations that propagate uncertainty in the emissions, dispersion model, health impact functions, and demographic data.

RESULTS

Sugarcane fires emitted an amount of primary PM2.5 similar to that of motor vehicles in Florida. PM2.5 from sugarcane fires is estimated to contribute to mortality rates within the Florida Sugarcane Growing Region (SGR) by 0.4 death per 100,000 people per year (95% CI: 0.3, 1.6 per 100,000). These estimates imply 2.5 deaths per year across South Florida were associated with PM2.5 from sugarcane fires (95% CI: 1.2, 6.1), with 0.16 in the SGR (95% CI: 0.09, 0.6) and 0.72 in Palm Beach County (95% CI: 0.17, 2.2).

DISCUSSION

PM2.5 from sugarcane fires was estimated to contribute to mortality risk across South Florida, particularly in the SGR. This is consistent with prior studies that documented impacts of sugarcane fire on air quality but did not quantify mortality. Additional health impacts of sugarcane fires, which were not quantified here, include exacerbating nonfatal health conditions such as asthma and cardiovascular problems. Harvesting sugarcane without field burning would likely reduce PM2.5 and health burdens in this region. https://doi.org/10.1289/EHP9957.

Associations between nighttime light and COVID-19 incidence and mortality in the United States

COVID-19 has caused almost 770,000 deaths in the United States by November 2021. The nighttime light (NTL), representing the intensity of human activities, may reflect the degree of human contacts and therefore the intensity of COVID-19 transmission. This study intended to assess the associations between NTL differences and COVID-19 incidence and mortality among U.S. counties. The COVID-19 data of U.S. counties as of 31 December 2020 were collected. The average NTL values for each county in 2019 and 2020 were derived from satellite data. A negative binomial mixed model was adopted to assess the relationships between NTL intensity and COVID-19 incidence and mortality. Compared to the counties with the lowest NTL level (0.14-0.37 nW/cm²/sr), those with the highest NTL level (1.78-59.61 nW/cm²/sr) were related with 15% higher mortality rates (mortality rate ratio:1.15, 95 %CI: 1.02-1.30, p-value: 0.02) and 23% higher incidence rates (incidence rate ratio:1.23, 95 %CI: 1.13-1.34, p-value < 0.0001). Our study suggested that more intensive NTL was related with higher incidence and mortality rates of COVID-19, and NTL had a stronger correlation with the COVID-19 incidence rate than mortality rate. Our findings have contributed solid epidemiological evidence to the existing COVID-19 knowledge pool, and would help policymakers develop interventions when faced with the potential risk of the following outbreaks.

Linking the Fasting Blood Glucose Level to Short-Term-Exposed Particulate Constituents and Pollution Sources: Results from a Multicenter Cross-Sectional Study in China

Ambient PM_2.5 (fine particulate matter with aerodynamic diameters ≤ 2.5 μm) is thought to be associated with the development of diabetes, but few studies traced the effects of PM_2.5 components and pollution sources on the change in the fasting blood glucose (FBG). In the present study, we assessed the associations of PM_2.5 constituents and their sources with the FBG in a general Chinese population aged over 40 years. Exposure to PM_2.5 was positively associated with the FBG level, and each interquartile range (IQR) increase in a lag period of 30 days (18.4 μg/m³) showed the strongest association with an elevated FBG of 0.16 mmol/L (95% confidence interval: 0.04, 0.28). Among various constituents, increases in exposed elemental carbon, organic matter, arsenic, and heavy metals such as silver, cadmium, lead, and zinc were associated with higher FBG, whereas barium and chromium were associated with lower FBG levels. The elevated FBG level was closely associated with the PM_2.5 from coal combustion, industrial sources, and vehicle emissions, while the association with secondary sources was statistically insignificant. Improving air quality by tracing back to the pollution sources would help to develop well-directed policies to protect human health.

Ambient Air Pollution and Dysanapsis: Associations with Lung Function and Chronic Obstructive Pulmonary Disease in the Canadian Cohort Obstructive Lung Disease Study

Rationale: Outdoor air pollution is a potential risk factor for lower lung function and chronic obstructive pulmonary disease (COPD). Little is known about how airway abnormalities and lung growth might modify this relationship. Objectives: To evaluate the associations of ambient air pollution exposure with lung function and COPD and examine possible interactions with dysanapsis. Methods: We made use of cross-sectional postbronchodilator spirometry data from 1,452 individuals enrolled in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study with linked ambient fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) air pollution estimates. Dysanapsis, or the ratio of the airway-to-lung volume calculated from thoracic computed tomography images, was used to examine possible interactions. Measurements and Main Results: In adjusted models, 101.7 ml (95% confidence interval [CI], -166.2 to -37.2) and 115.0 ml (95% CI, -196.5 to -33.4) lower FEV₁ were demonstrated per increase of 2.4 ug/m³ PM_2.5 and 9.2 ppb NO₂, respectively. Interaction between air pollution and dysanapsis was not statistically significant when modeling the airway-to-lung ratio as a continuous variable. However, a 109.8 ml (95% CI, -209.0 to -10.5] lower FEV₁ and an 87% (95% CI, 12% to 213%) higher odds of COPD were observed among individuals in the lowest, relative to highest, airway-to-lung ratio, per 2.4 μg/m³ increment of PM_2.5. Conclusions: Ambient air pollution exposure was associated with lower lung function, even at relatively low concentrations. Individuals with dysanaptic lung growth might be particularly susceptible to inhaled ambient air pollutants, especially those at the extremes of dysanapsis.

Maternal exposure to PM2.5/BC during pregnancy predisposes children to allergic rhinitis which varies by regions and exclusive breastfeeding

BACKGROUND

Increasing prevalence of childhood allergic rhinitis(AR) needs a deeper understanding on the potential adverse effects of early life exposure to air pollution.

OBJECTIVES

The main aim was to evaluate the effects of maternal exposure to PM_2.5 and chemical constituents during pregnancy on preschool children's AR, and further to explore the modification effects of regions and exclusive breastfeeding.

METHODS

A multi-center population-based study was performed in 6 cities from 3 regions of China in 2011-2012. Maternal exposure to ambient PM_2.5 and main chemical constituents(BC, OM, SO₄^2-, NO₃^-, NH₄⁺) during pregnancy was assessed and a longitudinal prospective analysis was applied on preschool children's AR. The modification effects of regions and exclusive breastfeeding were investigated.

RESULTS

A total of 8.8% and 9.8% of children reported doctor-diagnosed allergic rhinitis(DDAR) and current hay fever, respectively, and 48.6% had less than 6 months of exclusive breastfeeding. The means of PM_2.5 during pregnancy were 52.7 μg/m³, 70.3 μg/m³ and 76.4 μg/m³ in the east, north and central south of China, respectively. Multilevel log-binomial model regression showed that each interquartile range(IQR) increase of PM_2.5 during pregnancy was associated with an average increase in prevalence ratio (PR) of DDAR by 1.43(95% confidence interval(CI): 1.11, 1.84) and current hay fever by 1.79(95% CI: 1.26, 2.55), respectively. Among chemical constituents, black carbon (BC) had the strongest associations. Across 3 regions, the eastern cities had the highest associations, followed by those in the central south and the north. For those equal to or longer than 6 months of exclusive breastfeeding, the associations were significantly reduced.

CONCLUSIONS

Children in east of China had the highest risks of developing AR per unit increase of maternal exposure to PM_2.5 during pregnancy, especially BC constituent. Remarkable decline was found in association with an increase in breastfeeding for ≥6 months, in particular in east of China.

Early-life exposure to PM2.5 constituents and childhood asthma and wheezing: Findings from China, Children, Homes, Health study

BACKGROUND

Emerging evidence suggests that early-life (in-utero and first-year since birth) exposure to ambient PM_2.5 is a risk factor for asthma onset and exacerbation among children, while the hazards caused by PM_2.5 compositions remain largely unknown.

OBJECTIVE

To examine potential associations of early-life exposures to PM_2.5 mass and its major chemical constituents with childhood asthma and wheezing.

METHODS

By conducting the Phase II of the China, Children, Homes, Health study, we investigated 30,325 preschool children aged 3-6 years during 2019-2020 in mainland China. Early-life exposure to PM_2.5 mass and its constituents (i.e., black carbon [BC], organic matter [OM], nitrate, ammonium, sulfate) were calculated based on monthly estimates at a 1 km × 1 km resolution from satellite-based models. We adopted a novel quantile-based g-computation approach to assess the effect of a mixture of PM_2.5 constituents on childhood asthma/wheezing.

RESULTS

The average PM_2.5 concentrations during in-utero and the first year since birth were 64.7 ± 10.6 and 61.8 ± 10.5 µg/m³, respectively. Early-life exposures to a mixture of major PM_2.5 constituents were significantly associated with increased risks of asthma and wheezing, while no evident compositions-wheezing associations were found in the first year. Each quintile increases in all five PM_2.5 components exposures in utero was accordingly associated with an odds ratio of 1.18 [95% confidence interval: 1.07-1.29] for asthma and 1.08 [1.01-1.16] for wheezing. BC, OM and SO₄^2- contributed more to risks of asthma and wheezing than the other PM_2.5 constituents during early life, wherein the effects of BC were only observed during pregnancy. Sex subgroup analyses suggested stronger associations among girls of first-year exposures to PM_2.5 components with childhood asthma.

CONCLUSION

Early-life exposures to ambient PM_2.5, particularly compositions of BC, OM and SO₄^2-, are associated with an increased risk of childhood asthma.

Association of PM2.5 and its components with lengths of hospital stay for hand foot and mouth disease in children

Hand foot and mouth disease (HFMD) is a widespread public health concern but the studies on air pollution and the lengths of hospital stay (LOS) of HFMD are scarce nevertheless. Clinic demographic features among 5135 hospitalized HFMD cases in Nanjing, China, had been characterized from 2012 to 2017. Then, we had analyzed the association between PM2.5 short-term exposure as well as its components (OM, BC, SO42-, NH4+, NIT, SOIL, and SS) and the LOS of HFMD. Among these cases that were involved in our study, 98.62% were aged 0-6 years old, and 3772 (73.46%) were hospitalized within 1 week or less. The LOS of HFMD patients was different in various age ranges, illness onset years, and illness onset seasons (P < 0.01). For per IQR increase in PM2.5 concentrations, LOS of HFMD increased by 0.52 (0.33, 0.71), 0.50 (95% CI, 0.31-0.69) and 0.46 (95% CI, 0.28-0.65) day in adjusted models at lag 3 days, lag 7 days, and lag 14 days, respectively. In addition, per IQR increase of BC, SO42-, NH4+, NIT, and SOIL was also significantly associated with the LOS of HFMD. Our findings corroborated that short-term PM2.5 exposure was associated with the increased LOS of HFMD, and its components (BC, SO42-, NH4+, NIT, and SOIL) of PM2.5 might play a key role in prolonged LOS of HFMD.

Associations of Ambient Fine Particulate Matter and Its Chemical Constituents with Birth Weight for Gestational Age in China: A Nationwide Survey

This study examined the associations of fine particulate matter (PM_2.5) and its chemical constituents with risks of small for gestational age (SGA) and large for gestational age (LGA). Based on the China Labor and Delivery Survey, we included 70,206 birth records from 24 provinces in China. Concentrations of PM_2.5 mass and six main constituents were estimated using satellite-based models. Logistic regression analysis was used to examine the associations, adjusted for sociodemographic characteristics and time trends. We found that an interquartile range increase in PM_2.5 exposure during pregnancy was associated with 16% (95% confidence interval [CI]: 3-30%) and 11% (95% CI: 1-22%) higher risk of SGA and LGA, respectively. Elevated risk of SGA was associated with exposure to black carbon [odds ratio (OR) = 1.15, 95% CI: 1.00-1.32], ammonium (OR = 1.12, 95% CI: 1.01-1.25), and sulfate (OR = 1.12, 95% CI: 1.04-1.21); while increased risk of LGA was associated with exposure to black carbon (OR = 1.13, 95% CI: 1.02-1.26), ammonium (OR = 1.13, 95% CI: 1.03-1.24), sulfate (OR = 1.08, 95% CI: 1.01-1.15), and nitrate (OR = 1.14, 95% CI: 1.03-1.27). Our findings provide evidence that PM_2.5 exposure was associated with increased risks of SGA and LGA, and constituents related to emissions from anthropogenic sources may play important roles in these associations.

Longitudinal Impacts of PM2.5 Constituents on Adult Mortality in China

Limited evidence exists for long-term effects of PM_2.5 constituents on mortality. Hence, we aimed to assess associations between all-cause mortality and long-term exposure to PM_2.5 constituents in China. We designed a nationwide cohort study of 30524 adults from 162 prefectural areas across mainland China with follow-ups through years 2010-2017. Cox proportional hazards models with time-varying exposures were employed to quantify associations between all-cause mortality and long-term exposure to PM_2.5 and constituents. A total of 1210 deaths occurred during 172297.7 person-years. A multiadjusted Cox model estimated an hazard ratio (HR) of 1.125 (95% confidence interval: 1.058-1.197) for all-cause mortality, associated with an interquartile range (IQR = 26.7 μg/m³) rise in exposure to PM_2.5. Comparable or stronger associations were found among PM_2.5 constituents with the mortality risk increased by 11.3-14.1% per IQR increase in exposure concentrations. After adjustment for the collinearity between total PM_2.5 and constituents, effect estimates for nitrate, ammonium, and sulfate remained significant and became larger. Urban residents, alcohol drinkers, smokers, and men were more susceptible to chronic impacts from ambient PM_2.5 constituents. This cohort study added the novel longitudinal evidence for elevated mortality linked with long-term exposure to PM_2.5 constituents among Chinese adults.

The Effect of Transportation and Wildfires on the Spatiotemporal Heterogeneity of PM2.5 Mass in the New York-New Jersey Metropolitan Statistical Area

Declining ambient PM_2.5 concentrations have been attributed to fuel consumption standards and emission controls of secondary sulfate and nitrate aerosol precursors from transportation and industrial sectors. As a result, the relative contribution of PM_2.5 sources is modified, shifting PM_2.5 trends, physicochemical characteristics, and health effects. Carbonaceous fine aerosol account for most of PM_2.5 mass in the US. This study aims to examine the spatiotemporal trends of ambient PM_2.5 levels and their association with primary PM_2.5 emissions from anthropogenic activities and fires in the New York/New Jersey metropolitan statistical area (NYNJ MSA) airshed. PM_2.5 mass concentrations were obtained from the U.S. Environmental Protection Agency (USEPA) Air Data. Ambient PM_2.5 mass levels declined on average by 47%, at a rate of -0.61 ± 0.01 μg/m³/year in urban locations and -0.25 ± 0.01 μg/m³/year in upwind and peri-urban locations over the 2007 to 2017 period. The strong spatial gradient in 2007, with high PM_2.5 levels in urban locations and low PM_2.5 levels in peri-urban locations gradually weakened by 2013 but re-appeared in 2017. Over the same period, primary PM_2.5 emissions declined by 52% from transportation, 15% from industrial, and 8% from other anthropogenic sources corresponding to a decrease of 0.8, 0.9, and 0.6 μg/m³ on ambient PM_2.5 mass, respectively. Wildland and prescribed fires emissions increased more than 3 times adding 0.8 μg/m³ to ambient PM_2.5 mass. These results indicate that (i) fire emissions may impede the effectiveness of existing policies to improve air quality and (ii) the chemical content of PM_2.5 may be changing to an evolving mixture of aromatic and oxygenated organic species with differential toxicological responses as compared to inert ammonium sulfate and nitrate salts.

Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM_2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO₄), ammonium (NH₄), nitrate (NO₃)) and five sources of PM_2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM_2.5 (total PM_2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM_2.5 (per unit μg/m³). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM_2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Research on the spatial effects of haze pollution on public health: spatial-temporal evidence from the Yangtze River Delta urban agglomerations, China

Haze pollution poses a serious threat to residents' health. In this study, a spatial econometric model of environmental health was established to investigate the direction, intensity, and spatial-temporal heterogeneity of the impact of haze pollution and its spillover effects on public health in 26 cities of the Yangtze River Delta urban agglomerations from 2005 to 2018. The study found that (1) PM_2.5 pollution and public health level all show the characteristic of positive spatial correlation and spatial clustering. (2) Haze pollution is the main influencing factor of residents' public health level, with significant negative effects and obvious spillover effects. The urbanization rate, the number of health technicians, and the green area per capita have significant positive impacts on public health. (3) The spatial and temporal heterogeneity of the impact of haze pollution and other factors on public health is obvious. The negative correlation between PM_2.5 pollution and public health in eastern cities is higher than that in other cities. Both urbanization rate and green area per capita have a greater positive impact on public health in the northeast of the Yangtze River Delta region. The improvement effect of the number of health technicians on the public health is stronger in the cities of Anhui Province. The research results of this paper provide certain support for the city governments to formulate targeted policies.

Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China

Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

Air pollution, service development and innovation: Evidence from China

The environment is crucial in economic development. However, the impact of the environment on cities, especially, on urban innovation still needs more attention. Based on city-level and PM2.5 data from 280 prefecture-level cities and municipalities in China from 2003 to 2016, we empirically studied the influence of haze on urban innovation and its mechanism. The results show that (1) haze has a significant negative impact on urban innovation, which varies with regions, resource-dependent cities, provincial capital cities, and sub-provincial cities. (2) This result appears to be driven by industrial structure. By influencing the development of the service industry, haze affects the level of urban innovation. (3) In particular, advanced producer services and basic producer services are heterogeneous. Advanced producer services have a stronger impact on innovation, while basic producer services are more sensitive to haze. We used the Instrumental Variable (IV) regression to address endogeneity, the conclusion remains robust.

Association between long-term exposure to ambient air pollution and COVID-19 severity: a prospective cohort study

Background:

The tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.

Methods:

We used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and ground-level ozone (O₃), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals’ long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.

Results:

Among the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM_2.5 (1.70 μg/m³), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01–1.12), 1.09 (95% CI 0.98–1.21) and 1.00 (95% CI 0.90–1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO₂. We also estimated odds ratios of 1.15 (95% CI 1.06–1.23), 1.30 (95% CI 1.12–1.50) and 1.18 (95% CI 1.02–1.36) per interquartile range increase of 5.14 ppb in O₃ for hospital admission, ICU admission and death, respectively.

Interpretation:

Chronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O₃.

Prediction of Fine Particulate Matter Concentration near the Ground in North China from Multivariable Remote Sensing Data Based on MIV-BP Neural Network

Rapid urbanization and industrialization lead to severe air pollution in China, threatening public health. However, it is challenging to understand the pollutants’ spatial distributions by relying on a network of ground-based monitoring instruments, considering the incomplete dataset. To predict the spatial distribution of fine-mode particulate matter (PM2.5) pollution near the surface, we established models based on the back propagation (BP) neural network for PM2.5 mass concentration in North China using remote sensing products. According to our predictions, PM2.5 mass concentrations are affected by changes in surface reflectance and the dominant particle size for different seasons. The PM2.5 mass concentration predicted by the seasonal model shows a similar spatial pattern (high in the east but low in the west) influenced by the terrain, but shows high value in winter and low in summer. Compared to the ground-based data, our predictions agree with the spatial distribution of PM2.5 mass concentrations, with a mean bias of +17% in the North China Plain in 2017. Furthermore, the correlation coefficients (R) of the four seasons’ instantaneous measurements are always above 0.7, indicating that the seasonal models primarily improve the PM2.5 mass concentration prediction.

Fine Particulate Air Pollution, Early Life Stress, and Their Interactive Effects on Adolescent Structural Brain Development: A Longitudinal Tensor-Based Morphometry Study

Air pollution is a major environmental threat to public health; we know little, however, about its effects on adolescent brain development. Exposure to air pollution co-occurs, and may interact, with social factors that also affect brain development, such as early life stress (ELS). Here, we show that severity of ELS and fine particulate air pollution (PM2.5) are associated with volumetric changes in distinct brain regions, but also uncover regions in which ELS moderates the effects of PM2.5. We interviewed adolescents about ELS events, used satellite-derived estimates of ambient PM2.5 concentrations, and conducted longitudinal tensor-based morphometry to assess regional changes in brain volume over an approximately 2-year period (N = 115, ages 9-13 years at Time 1). For adolescents who had experienced less severe ELS, PM2.5 was associated with volumetric changes across several gray and white matter regions. Fewer effects of PM2.5 were observed for adolescents who had experienced more severe ELS, although occasionally they were in the opposite direction. This pattern of results suggests that for many brain regions, moderate to severe ELS largely constrains the effects of PM2.5 on structural development. Further theory and research is needed on the joint effects of ELS and air pollution on the brain.

Green Space, Air Pollution, Weather, and Cognitive Function in Middle and Old Age in China

Prior research has shown that environmental hazards, such as limited green space, air pollution, and harmful weather, have the strong adverse impact on older adults' cognitive function; however, most of the studies were conducted in developed countries and limited to cross-sectional analyses. China has the largest aging population in the world so the research evidence from it can offer an insight to the study in other developing countries facing similar issues and inform future public health policy and disease control. This study examined the long-term impact of environmental factors, namely, green space coverage, air pollution, and weather conditions on cognitive function using a nationally representative sample consisting of adults aged 45 years and older selected from the China Health and Retirement Longitudinal Study (CHARLS 2011–2018), the China City Statistical Yearbook, and other sources. Multilevel growth curve models were utilized for analysis and the mediator effects of physical activity and social engagement on the relationship between environmental factors and cognitive function were examined. Findings of this study showed that after controlling for sociodemographic characteristics, annual precipitation of 80 cm or more, living in areas with July temperature of 28°C or higher, urban community, and green space coverage were positively associated with cognition score at the baseline and lower precipitation, urban community, and greater green space coverage were associated with slower cognitive decline over a 7-year period. The impact of gross domestic product (GDP) seemed to take into effect more and more over time. These effects did not substantially change after weekly total hours of physical activities and levels of social engagement were added. More research on the mechanisms of the effect of environmental factors on cognition is needed such as the subgroup analyses and/or with more aspects of environmental measures.

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.

Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃^-/SO₄^2- ratio and the increasing of NO₃^-/SO₄^2- ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl^-/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.

Effects of PM2.5 and its constituents on hemoglobin during the third trimester in pregnant women

Anemia has been a public health issue evoking global concern, and the low hemoglobin (Hb) concentration links to adverse pregnancy outcomes. However, the associations of PM_2.5 and its constituents with Hb and anemia in pregnant women remain unclear. In this retrospective birth cohort study, 7932 pregnant women who delivered in the First Affiliated Hospital of Xi'an Jiaotong University from 2015 to 2018 were included. The Hb during the third trimester in pregnant women was assessed before delivery. PM_2.5 and its constituents (BC, NH₄⁺, NO₃^-, OM, SO₄^2-, and Dust) during pregnancy were retrieved from the V4.CH.03 product constructed by the Atmospheric Composition Analysis Group. Generalized linear regression model was applied to investigate the effects of PM_2.5 and its constituents on Hb and anemia during the third trimester in pregnant women. The means and standard deviations of PM_2.5, BC, NH₄⁺, NO₃^-, OM, SO₄^2-, and Dust were 69.56 (15.24), 10.02 (2.72), 8.11 (1.77), 14.96 (5.42), 15.36 (4.11), 10.08 (1.20), and 10.98 (1.85) μg/m³, respectively. Per IQR increase (μg/m³) of PM_2.5, BC, NO₃^-, and OM linked to - 0.75 (- 1.50, - 0.01), - 0.85 (- 1.65, - 0.04), - 0.79 (- 1.56, - 0.03), and - 0.73 (- 1.44, - 0.03) g/L decrease of Hb during the third trimester in multiparous pregnant women, but not for NH₄⁺, SO₄^2-, Dust, and primiparous pregnant women. PM_2.5 and its constituents had no significant association with anemia, except for Dust (OR: 0.90, 95% CI: 0.82, 0.99, per IQR increase) in primiparous pregnant women. Besides, SO4^2- was of lag effects on Hb and anemia in multiparous pregnant women. Moreover, non-linear associations were found among PM_2.5 and its constituents, Hb, and anemia. Therefore, exposure to PM_2.5 and some constituents of PM_2.5 was associated with reduced Hb level during the third trimester in multiparous pregnant women. Related departments and pregnant women should take targeted actions to eliminate the detrimental effects of PM_2.5 and its constituents on pregnancy outcomes.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Associations of long-term exposure to fine particulate matter and its constituents with cardiovascular mortality: A prospective cohort study in China

BACKGROUND

Few studies have evaluated long-term cardiovascular effects of fine particulate matter (PM_2.5) and its constituents in countries with high air pollution levels. We aimed to investigate the associations of long-term exposure to PM_2.5 and constituents with cardiovascular mortality in China.

METHODS

We conducted a prospective cohort study of 90,672 adults ≥ 18 years from 2010 to 2017 in 161 districts/counties across China. The residential annual-average exposure to PM_2.5 and 6 main components from 2011 to 2017 were estimated by satellite-based and chemical transport models. Associations of PM_2.5 and constituents with cardiovascular mortality were analyzed by competing-risk Cox proportional hazards regression.

RESULTS

The average PM_2.5 exposure throughout the whole period was 46 ± 22 μg/m³. The hazard ratios of mortality (95% confidence intervals) per 10 μg/m³ increase in PM_2.5 concentrations were 1.02 (1.00, 1.05) for overall cardiovascular disease, 1.05 (1.01, 1.09) for ischemic heart disease, 1.03 (1.00, 1.06) for overall stroke, 0.99 (0.94, 1.04) for hemorrhagic stroke, and 1.11 (1.04, 1.19) for ischemic stroke. PM_2.5 constituents from fossil fuel combustion (i.e., black carbon, organic matter, nitrate, ammonium, and sulfate) showed larger hazard ratios than PM_2.5 total mass, while soil dust showed no risks.

CONCLUSIONS

This nationwide cohort study demonstrated associations of long-term exposure to PM_2.5 and its constituents with increased risks of cardiovascular mortality in the general population of China. Our study highlighted the importance of PM_2.5 constituents from fossil fuel combustion in the long-term cardiovascular effects of PM_2.5 in China.

Association of long-term exposure to PM2.5 with hypertension and diabetes among the middle-aged and elderly people in Chinese mainland: a spatial study

Background

Epidemiological evidence has shown an association between long-term exposure to fine particulate matter (PM_2.5) and hypertension and diabetes, but few studies have considered the spatial properties of the samples. This study aimed to investigate the long-term effect of PM_2.5 exposure on hypertension and diabetes among middle-aged and elderly people in China based on a spatial study.

Methods

We conducted a national cross-sectional study of the most recently launched wave 4 2018 data of the China Health and Retirement Longitudinal Study (CHARLS) to calculate the prevalence of hypertension and diabetes. The exposure data of annual average PM_2.5 concentrations were estimated combined with satellite observations, chemical transport modeling, and ground-based monitoring. A shared component model (SCM) was used to explore the association of PM_2.5 with hypertension and diabetes, in which these two diseases borrowed information on spatial variations from each other. Then, we evaluated the effect variations in PM_2.5 in different periods and smoking status on changes in outcomes.

Results

The prevalence of hypertension and diabetes was 44.27% and 18.44%, respectively, among 19,529 participants. The annual average PM_2.5 concentration in 31 provinces ranged from 4.4 μg/m³ to 51.3 μg/m³ with an average of 27.86 μg/m³ in 2018. Spatial auto-correlations of the prevalence of hypertension and diabetes and PM_2.5 concentrations were seen (Moran’s I = 0.336, p = 0.01; Moran’s I = 0.288, p = 0.03; Moran’s I = 0.490, p = 0.01). An interquartile range (IQR: 16.2 μg/m³) increase in PM_2.5 concentrations was significantly associated with a higher prevalence of hypertension and diabetes with odds ratios (ORs) of 1.070 [95% credible interval (95% CrI): 1.034, 1.108] and 1.149 (95% CrI: 1.100, 1.200), respectively. Notably, the effect of PM_2.5 on both hypertension and diabetes was relatively stronger among non-smokers than smokers.

Conclusion

Our nationwide study demonstrated that long-term exposure to PM_2.5 might increase the risk of hypertension and diabetes, and could provide guidance to public policymakers to prevent and control hypertension and diabetes according to the spatial distribution patterns of the above effects in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12984-6.

Composition of fine particulate matter and risk of preterm birth: A nationwide birth cohort study in 336 Chinese cities

BACKGROUND

Potential hazards of fine particulate matter (PM_2.5) constituents on preterm birth (PTB) have rarely been explored in China.

OBJECTIVE

To quantify the associations of PM_2.5 constituents with PTB.

METHODS

This study was based on a nationwide cohort of 3,723,169 live singleton births delivered between January 2010 and December 2015 in China. We applied satellite-based estimates of 5 PM_2.5 constituents (organic carbon; black carbon; sulfate; ammonium; and nitrate). We used Cox proportional hazards regression models adjusted for individual covariates, temperature, humidity, and seasonality to evaluate the associations.

RESULTS

During the entire pregnancy, each interquartile range (29 μg/m³) increase in PM_2.5 concentrations was associated with a 7% increase in PTB risk [hazard ratio (HR): 1.07; 95% confidence interval (CI): 1.07-1.08). We observed the largest effect estimates on carbonaceous components (HR: 1.09; 95% CI: 1.08-1.10 for organic carbon and black carbon). Early pregnancy appeared to be the critical exposure window for most constituents. Women who were older, exposed to second-hand smoke, overweight or obese before pregnancy, conceived during winter, and living in northern China or rural areas were more susceptible.

CONCLUSIONS

Carbonaceous components of PM_2.5 were associated with higher PTB risk. Findings on characteristics of vulnerability underlined targeted protections on susceptible subgroups.

Long-term effects of PM2.5 components on blood pressure and hypertension in Chinese children and adolescents

Growing evidence has linked fine particulate matter (PM_2.5) exposure to elevated blood pressure, but the effects of PM_2.5 components are unclear, particularly in children and adolescents. Based on a cross-sectional investigation in China, we analyzed the associations between long-term exposure to PM_2.5 and its major components with elevated blood pressure in children and adolescents. A representative sample (N = 37,610) of children and adolescents with age 7-18 years was collected in seven Chinese provinces. Exposures to PM_2.5 and five of its major components, including black carbon (BC), organic matter (OM), inorganic nitrate (NO₃^-), sulfate (SO₄^2-), and soil particles (SOIL), were estimated using satellite-based spatiotemporal models. The associations between long-term exposures to PM_2.5 and its components and diastolic blood pressure (DBP), systolic blood pressure (SBP), and hypertension were investigated using mixed-effects logistic and linear regression models. Within the populations, 11.5 % were classified as hypertension. After adjusting for a variety of covariates, per interquartile range (IQR) increment in PM_2.5 mass and BC levels were significantly associated with a higher hypertension prevalence with odds ratios (ORs) of 1.56 (95% confidence interval (CI): 1.08, 2.25) for PM_2.5 and 1.19 (95% CI: 1.04, 1.35) for BC. Long-term exposures to PM_2.5 and BC have also been associated with elevated SBP and DBP. Additionally, OM and NO₃^- were significantly associated with increased SBP, while SOIL was significantly associated with increased DBP. In the subgroup analysis, the associations between long-term exposures to BC and blood pressure vary significantly by urbanicity of residential area and diet habits. Our study suggests that long-term exposure to PM_2.5 mass and specific PM_2.5 components, especially for BC, are significantly associated with elevated blood pressure and a higher hypertension prevalence in Chinese children and adolescents.

Differential Mortality Risks Associated With PM2.5 Components: A Multi-Country, Multi-City Study

BACKGROUND

The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality.

METHODS

We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators.

RESULTS

We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk.

CONCLUSIONS

These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.

COVID-19 in New York state: Effects of demographics and air quality on infection and fatality

The coronavirus disease 2019 (COVID-19) has had a global impact that has been unevenly distributed among and even within countries. Multiple demographic and environmental factors have been associated with the risk of COVID-19 spread and fatality, including age, gender, ethnicity, poverty, and air quality among others. However, specific contributions of these factors are yet to be understood. Here, we attempted to explain the variability in infection, death, and fatality rates by understanding the contributions of a few selected factors. We compared the incidence of COVID-19 in New York State (NYS) counties during the first wave of infection and analyzed how different demographic and environmental variables associate with the variation observed across the counties. We observed that infection and death rates, two important COVID-19 metrics, to be highly correlated with both being highest in counties located near New York City, considered as one of the epicenters of the infection in the US. In contrast, disease fatality was found to be highest in a different set of counties despite registering a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on COVID-19 infection, death, or fatality, and compared the differences in the demographic and environmental variables such as ethnicity, age, population density, poverty, temperature, and air quality in each of these clusters. Furthermore, a regression model built on this data reveals PM_2.5 and distance from the epicenter are significant risk factors for infection, while disease fatality has a strong association with age and PM_2.5. Our results demonstrate that for the NYS, demographic components distinctly associate with specific aspects of COVID-19 burden and also highlight the detrimental impact of poor air quality. These results could help design and direct location-specific control and mitigation strategies.