Air Quality Citizen Science Research Project in NYC

Traditional approaches to air quality monitoring typically involve regulatory agencies that utilize expensive and complex stationary equipment, maintained by trained staff, to provide the type of highly accurate data needed to demonstrate attainment with federal air quality standards. While this type of monitoring is a vital component to air quality management, in urban areas these monitors are often deployed at, only, a limited number of rooftop locations. Though intended to track urban scale trends in pollution levels, the placement of these monitors is not spatially dense enough to characterize intra-urban spatial variation in air quality, due to local emissions sources such as traffic. To address this limitation, this project explored the feasibility of using stationary low-cost monitoring networks for spatial and temporal estimation of ambient fine particulate concentrations in two environmental justice communities in New York City - El Puente (Brooklyn) and Youth Ministries for Peace and Justice (the Bronx). The data from the community-based low-cost stationary monitoring networks were compared to FEM/FRM data and the findings land use regression (LUR) analysis of the New York City Community Air Survey (NYCCAS). The stationary networks in both neighborhoods consisted of a total of 22 monitoring locations. The data collection started in January 2019 and lasted until November 2019. In collaboration with the New York State Department of Environment Conservation (DEC), the low-cost air quality monitors (AirBeam2) were surveyed and assessed through field colocation and integrated into a cellular data acquisition system. QC/QA data were collected both, before and after the deployment for a duration of 3 weeks. Based on the r2-value a strong agreement was observed between FEM and AirBeam2 low-cost sensors. Personal monitoring and stationary network (2019) PM2.5 averages were found to be around 6 g/m3, while NYCCAS LUR (2017) PM2.5 averaged 8.50 g/m3. Based on the time series analysis of the collected data it can be concluded that the Stationary Network provided us more precise information on diurnal patterns by going into the details of hourly measurements while NYCCAS study was more focused on the identification of seasonal patterns.