UC Berkeley

The rise of urban areas as the predominant residence for the world’s population has positioned cities as leading contributors to global greenhouse gas and air pollutant emissions. In order to mitigate emissions and their effects efficiently, it is crucial to have a thorough understanding of the urban emission landscape, which remains a challenge given the heterogeneity of emission sources within cities. The atmospheric observation network continues to expand as more and more measurements from satellites, mobile monitoring and aircraft campaigns, and sparse stationary monitoring sites are being made. These observation techniques provide useful information at a wide variety of spatial and temporal scales, but gaps in our understanding of urban air quality still persist. Dense sensor networks fill a niche measurement gap in our current observing system by providing long-term, high frequency measurements of air pollutants at high spatial density across target areas. These measurement networks provide a unique opportunity to study details of the urban emission landscape, including local spatial variability, transient events, and long-term trends of neighborhood-level emission patterns. In this work, we use the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) to explore the multitude of ways at which dense sensor networks can provide unique insights to both air pollutant and CO2 emissions within cities.We start by outlining a calibration methodology for the Plantower PMS5003 sensor to generate robust measurements of ambient PM2.5. We adopt a physics-based model to account for changes in particle size due to hygroscopic growth–the uptake of water onto particles based on ambient humidity and the hygroscopicity of the particle. Using BEACO2N data in the San Francisco Bay Area and in Los Angeles, CA, we find unique seasonal cycles of hygroscopicity that match with particle composition measurements and employ these empirical measures of hygroscopicity to calibrate the Plantower sensor. We then use the BEACO2N network to quantify emissions of air pollutants and CO2 from different point sources. We start with a transient pollution event, a small urban fire in Albany, CA, where the spatial density of sensors is used to constrain the shape of the pollutant plume over time using a 2-D Gaussian model. We calculate the total emissions of PM2.5, CO, NOx, and CO2. Using this case study, we explore the extent to which dense sensor networks can quantify point source emissions by calculating limits of detection and quantification for the BEACO2N network. We then use the BEACO2N network to quantify air pollutant and CO2 emissions from a continuous point source emitter, namely the Richmond Chevron Refinery in Richmond, CA. Using the Gaussian plume model and several hundred plume observations made over 2 years, we calculate the CO2 emission rate for the refinery and find it in good agreement with external inventories and measurements. We additionally calculate a CO emission rate, not previously constrained by external observations, and find that inventories likely severely underestimate CO emissions from the refinery. We conclude by describing prospects for detection and characterization of other point emission sources, including preliminary work on maritime shipping emissions.Overall, the work here shows that low-cost, dense sensor networks provide unique insight into emission sources within urban areas. Given the increasing deployment of similar networks across the globe, the methods explored here show great promise for unlocking insights into pollutant sources, emission magnitudes, and trends in greenhouse gases and air quality in cities worldwide.