Determining the correlation between particulate matter emissions, greenhouse gas emissions and respiratory disease risk in large cities

The emission and distribution of particulate matter (PM) among numerous atmospheric gases is a growing problem in large cities. Particulate matter is correlated with declining air quality and increasing respiratory disease risk. An accurate and efficient measurement strategy to estimate PM emissions from large cities does not currently exist. However, particulate matter tends to be co-emitted with other common pollutants such as carbon dioxide and methane. Existing sensor networks and modeling techniques are capable of quantifying and pinpointing urban CO2 sources. In this project, I discuss the correlation between PM emissions and CO2 emissions. The existing sensor network and dispersion models are used to predict total PM emissions from urban sources in Washington DC. Results are validated through comparison with state-of-the-art instruments. Together, we construct the tools for comprehensive prediction of PM emissions via correlation with abundant greenhouse gas emissions. PM emission levels have established associations with emergency hospital visits for respiratory disease and negative health impacts for those with existing respiratory and cardiovascular risk factors. Quantification of PM sources is a crucial first step to designing effective mitigation strategies and improving city-wide air quality.