Monitoring Neighborhood Concentrations of PM2.5 and Black Carbon: When Using Citywide Averages Underestimates Impacts in a Community with Environmental Justice Issues

Monitoring networks are commonly designed to measure the air quality of regional air sheds and may not be representative of neighborhood air quality. This study presents data collected in a neighborhood in Washington, District of Columbia (DC), through a partnership between academics, residents, and a grassroots organization. It contributes to a growing body of studies that demonstrate the extent of differences in air quality experienced by some local communities compared with concentrations from monitoring networks. Ambient concentrations of fine particulate matter (PM2.5) and black carbon (BC) were measured in Ivy City, North East Washington, DC, for more than 60 days over the winter, spring, and fall of 2013. These neighborhood concentrations were compared with those from DCs District Department of Energy and the Environment (DOEE) monitoring network. During the monitoring campaigns, the maximum difference in daily PM2.5 concentrations between Ivy City and the nearest DOEE site was 21.7 mu g/m(3), and the mean daily difference between the two monitoring sites was 5.7 mu g/m(3). Average daily BC concentrations in Ivy City were also statistically significantly (p < 0.001) higher, ranging from 0.3 to 2.6 mu g/m(3) for Ivy City as opposed to 0.2-0.92 mu g/m(3) for the DOEE site. Multivariate linear regression was used to develop an annual estimate of PM2.5 and BC concentrations for Ivy City. Using this annual estimate of local PM2.5 concentration could have led to a different conclusion in the DOEE environmental assessment (EA) for a proposed bus parking lot in Ivy City. The actual EA concluded that the additional bus parking lot would have no significant impact, after considering bus emissions on top of DOEE monitor levels of PM2.5. Using concentrations more representative for the neighborhood could have shown that additional bus parking would further contribute to PM2.5 levels that exceeded the Environmental Protection Agency standards. This case study illustrates how using network data to make citing decisions about local emission sources can contribute to environmental injustice.