Mortality and hospital admissions risks from fine particulate matter by source sector and fuel: A national analysis

Yale University, New Haven CT

Investigators

Abstract

ABSTRACT There is an urgent need to understand the health impacts of alternative climate change mitigation policies. Such policies can impact various source sectors (e.g., transportation), fuel type (e.g., coal) and chemical composition of fine particulate matter (PM2.5). Detailed characterization of the health consequences of exposure to PM2.5 sources and chemical composition will provide timely guidance to develop the most effective interventions for both climate change and air pollution mitigation in the present day. The National Academy of Sciences, Health Effects Institute, US EPA, and Global Burden of Disease have all identified understanding health outcomes of air pollution sources as a critical research need. Prior studies suffer from the inability to identify relative contributions of sources and fuel types to pollutant mixtures, quantify uncertainties, or apply rigorous cross- validation, often due to lack of appropriate data and approaches. Most past analyses have inherently assumed that all PM2.5 mass is equally toxic, regardless of source or composition; in fact, PM2.5 is the only air pollutant regulated without regard to chemical form. To address this knowledge gap, we will conduct a nationwide analysis to determine which PM2.5 source sectors and fuel types are most associated with mortality and cardiovascular and respiratory hospital admissions. We will employ 2 large, well-characterized datasets (Medicare: >60 million >200,000 participants with detailed data on >100 individual-level variables). Using multiple approaches, including causal inference, we will test the hypothesis that health impacts of PM2.5 vary by pollution source and/or combustion type for total mortality and cardiovascular and respiratory hospital admissions. We will estimate and validate PM2.5 exposure by chemical components (e.g., sulfate, organics, black carbon), source sectors (e.g., transportation, wildfires), and fuel types, including fossil fuels (e.g., liquid oil and natural gas, solid biofuel), for the continental US for 2000-2023 at monthly, ZIP code-level resolution by harmonizing data from satellites, air quality models, emissions inventories, and monitors (Aim 1). We will evaluate how associations between PM2.5 and health vary by subpopulation (i.e., race, age, sex, socioeconomic status), addressing critical environmental justice concerns. We will apply well- established approaches, modified for our analysis, to estimate exposure- interpretation, for PM2.5 components, source sectors, and fuel types (Aim 2). We will ensure computational scalability and account for uncertainty in estimated exposures, unmeasured confounding bias, and co-pollutants. -specific (cardiovascular, neurological, asthma, other respiratory) hospital admissions by PM2.5 constituent, sector/fuel type, and subpopulation (Aim 3). This work will help air quality and climate policymakers prioritize mitigation efforts for specific PM2.5 sources and fuel types to maximize health benefits. Findings will inform assessments of health effects of PM2.5 exposure and co-benefits of climate change policies. We will disseminate new exposure data, causal inference applications, and statistical code.