The World Health Organization estimates that air pollution causes 7 million premature deaths a year. Emerging technologies, such as low-cost air quality sensors have been widely used in communities in the past few years. However, air pollution values differ substantially even between neighboring blocks,2 and the global average distance between a population and its nearest fine particulate matter monitor is about 20 kilometers3. To visualize air quality information at a community level, Ramboll developed the Shair air quality monitoring system, which synthesizes sensor data, regulatory-grade air quality models and monitors, street-level, regional physical and chemical transport models, and real-time traffic models. This system allows us to identify geographic and temporal air pollution trends on a hyperlocal scale.
In addition to helping citizens understand daily potential exposures from outdoor activities in real-time, historical data produced by the Shair system also allows us to evaluate chronic local scale air pollution exposures. Long-term exposure to PM2.5 has been linked to an increase in emergency department visits, particularity in sensitive individuals with pre-existing health conditions such as asthma, and chronic obstructive pulmonary disease, and premature death. In this study, we use the Shair system to evaluate weighted annual mean PM2.5 concentrations at a 10m-by-10m scale in select census tracts within the Richmond, California community, a designated disadvantaged community identified by the California Air Resources Board. The variation between these values is graphically compared with the annual mean PM2.5 concentrations measured within the census tracts from the CalEnviroScreen 4.0 (CES) tool, which uses an air monitoring network, and satellite remote sensing data to calculate PM2.5 to visualize the variability. We will calculate mean concentrations from Shair data within each census tract to statistically compare the Shair calculated mean with the average from each CES census tract. We also use US EPA risk assessment methodologies to evaluate PM2.5 chronic exposures at various sensitive receptors, focusing on schools. The variation in localized air quality data will provide insight into community blocks that may have elevated PM2.5 levels, which may contribute to long-term adverse health effects. The Ramboll Shair system’s high-resolution air quality model demonstrates community-scale variations in air quality exposure, emphasizing the importance of tool development that help scientists better understand local exposure. This project demonstrates the variability in PM2.5 concentrations even within a small spatial scale like a census tract, and the need for higher resolution data that the Shair system can provide.
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