Air Pollution in High Definition: Building Low-Cost Sensor Networks & Community Partnerships - Livestream

Measuring atmospheric pollutants at high spatiotemporal resolution has the potential to help identify problematic sources as well as pinpoint communities facing disproportionate risks. Most traditional air quality monitoring campaigns, however, have been necessarily sparse in their resolution owing to the significant upfront and operational costs of high-precision and high-accuracy instrumentation. We explore the intersection of this measurement challenge with the issue of environmental justice in the United States and make an argument for the benefits of tracking air pollution at the neighborhood scale using low-cost monitoring techniques. We also present initial results from community air quality studies in West Oakland and Richmond, two San Francisco Bay Area communities that are burdened by diesel particulate matter pollution. In these studies, we deployed custom-built, low-cost black carbon (BC) - or soot - sensors outside of community members’ homes and businesses. These dense networks captured seasonal trends in ambient BC on a block-by-block basis and found that the spatiotemporal patterns in BC concentrations were driven by truck activity. Through meaningful partnerships between researchers and key community stakeholders, these collaborations created actionable datasets that advance both science and advocacy goals as part of broader Community Air Protection Program monitoring efforts (AB 617).

Speakers: Dr. Alexis Shusterman, UC Berkeley; Dr. Chelsea Preble, UC Berkeley

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