The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010–2016) county-level exposures to NO₂, PM_2.5, and O₃ and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology. From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO₂ concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO₂ (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%–18.2%) and 16.2% (95% CI 8.7%–24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM_2.5 or O₃, although per IQR increase in PM_2.5 (2.6 μg/m³) was marginally associated, with a 14.9% (95% CI 0.0%–31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants. Long-term exposure to NO₂, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM_2.5 and O₃ exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO₂ levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.

Exposure to air pollution during pregnancy has been suggested to be a risk factor for preterm birth; however, epidemiologic evidence remains mixed and limited. The authors examined the association between ambient levels of particulate matter <2.5 μm in aerodynamic diameter (PM_2.5) and the risk of preterm birth in North Carolina during the period 2001–2005. They estimated the risks of cumulative and lagged average exposures to PM_2.5 during pregnancy via a 2-stage discrete-time survival model. The authors also considered exposure metrics derived from 1) ambient concentrations measured by the Air Quality System (AQS) monitoring network and 2) concentrations predicted by statistically fusing AQS data with process-based numerical model output (the Statistically Fused Air and Deposition Surfaces (FSD) database). Using the AQS measurements, an interquartile-range (1.73 μg/m³) increase in cumulative PM_2.5 exposure was associated with a 6.8% (95% posterior interval: 0.5, 13.6) increase in the risk of preterm birth. Using the FSD-predicted levels and accounting for prediction error, the authors also found significant adverse associations between trimester 1, trimester 2, and cumulative PM_2.5 exposure and preterm birth. These findings suggest that exposure to ambient PM_2.5 during pregnancy is associated with increased risk of preterm birth, even in a region characterized by relatively good air quality.

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

There is increasing evidence linking long-term fine particulate matter (PM_2.5) exposure to negative health effects. However, the relative influence of each component of PM_2.5 on health risk is poorly understood. In a cohort study in the contiguous United States between 2000 and 2017, we examined the effect of long-term exposure to PM_2.5 main components and all-cause mortality in older adults who had to be at least 65 years old and enrolled in Medicare. We estimated the yearly mean concentrations of six key PM_2.5 compounds, including black carbon (BC), organic matter (OM), soil dust (DUST), nitrate (NO₃^-), sulfate (SO₄^2-), and ammonium (NH₄⁺), using two independently sourced well-validated prediction models. We applied Cox proportional hazard models to evaluate the hazard ratios for mortality and penalized splines for assessing potential nonlinear concentration-response associations. Results suggested that increased exposure to PM_2.5 mass and its six main constituents were significantly linked to elevated all-cause mortality. All components showed linear concentration-response relationships in the low exposure concentration ranges. Our research indicates that long-term exposure to PM_2.5 mass and its essential compounds are strongly connected to increased mortality risk. Reductions of fossil fuel burning may yield significant air quality and public health benefit.

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB). One important research objective is to identify critical periods of exposure and estimate the associated effects at different stages of pregnancy. However, population studies have reported inconsistent findings. This may be due to limitations from the standard analytic approach of treating PTB as a binary outcome without considering time-varying exposures together over the course of pregnancy. To address this research gap, we present a Bayesian hierarchical model for conducting a comprehensive examination of gestational air pollution exposure by estimating the joint effects of weekly exposures during different vulnerable periods. Our model also treats PTB as a time-to-event outcome to address the challenge of different exposure lengths among ongoing pregnancies. The proposed model is applied to a dataset of geocoded birth records in the Atlanta metropolitan area between 1999–2005 to examine the risk of PTB associated with gestational exposure to ambient fine particulate matter |$\lt 2.5\,{\rm \mu}$|m in aerodynamic diameter (PM|$_{2.5}$|⁠). We find positive associations between PM|$_{2.5}$| exposure during early and mid-pregnancy, and evidence that associations are stronger for PTBs occurring around week 30.