Bridging Research Gaps in Air Pollution Analysis: Methods, Models, and Low-Cost Solutions

Abstract

To address ambient and household air pollution, countries have implemented diverse strategies suchas emissions regulations, fuel bans, and the adoption of cleaner cooking technologies. Although these efforts have markedly improved air quality, significant research gaps persist in quantifying the effects of these interventions and in forecasting outcomes to determine the most beneficial policies. This gap is largely due in part to the scarcity of accessible, user-friendly tools that can accurately measure and link changes in air pollution with specific initiatives. This dissertation is structured around three main projects that employ innovative methods to advance our understanding of changes in air pollution exposure resulting from various interventions across different countries. It seeks to bridge these research gaps by enhancing data analysis techniques and developing cost-effective, low-maintenance air pollution sensors that can inform effective policy and technological solutions. Chapter 1 provides an overview of current accountability analysis methods, outlining the motivation, objectives, and approaches of the dissertation. Chapter 2 introduces a method for estimating “expected” or baseline concentrations to determine what air quality levels would have been if a specific event or intervention had not occurred. This methodology was used to evaluate changes in pollutant levels in the US during the initial months of the Covid-19 pandemic when state governments issued stay-at-home orders. Comparing “expected” concentrations—calculated using this method— with the actual observed concentrations during the stay-at-home period, the analysis found that while PM2.5 levels were slightly higher than anticipated, the levels of O3, CO, NO2, and PM10 were lower (though amounts varied by pollutant). This chapter highlights the significant impact of reduced human activity on air quality and emphasizes the critical role of methodological choices in shaping research findings. Chapter 3 unveils the updated InMAP Source Receptor Matrix (ISRM), a comprehensive library of pre-run simulations using the reduced complexity model, InMAP. This innovative tool allows for rapid analysis of emission reduction scenarios and swift calculation of health outcomes; model runs that would require days or weeks with conventional approaches can require only a few minutes with the ISRM. The chapter uses the new ISRM to attribute air pollution-related mortality from 2002 to 2019 to various economic sectors. The findings indicate significant reductions in mortality attributable to decreased emissions primarily in the transportation and electricity sectors, yet with an emissions increase in the food and agriculture sector. This chapter corroborates the ISRM against conventional modeling and against measured concentrations and illustrates the practical utility of the ISRM. Chapter 4 explores the development of the Washington Passive Sampler (WPS), an ultra-lowcost measurement tool designed to assess black carbon levels in resource-constrained, high-pollution settings. This innovative method involves image-based processing, wherein a cellulose fiber filter is photographed before and after deployment. The subsequent analysis of changes in pixel intensity on these images is used to estimate the amount of light-absorbing carbon. Field tests have quantified the method’s precision and accuracy: it achieved an Intraclass Correlation Coefficient (ICC) of 90% for duplicate measurements, indicating good precision, and, indicating accuracy, a Root Mean Square Error (RMSE) of 21%, compared to 10% for gold-standard reference measurements. The chapter discusses how the WPS provides household air pollution researchers with an accessible, user-friendly tool for use in intervention studies, potentially transforming research approaches in low-resource highpollution environments. Overall, this body of work contributes valuable methodologies and tools to the field of air pollution research, supporting more effective policy-making and global health improvements. Chapter 5 summarizes the dissertation and suggests the next steps for this research.