Assessment of Energy-Related Sources, Factors, and Transitions Using Novel High-Resolution Ambient Air Monitoring Networks and Personal Monitors
Pat Breysse (CDC), D’Ann Williams (Johns Hopkins), Howard Katz (Johns Hopkins), Ben Zaitchik (Johns Hopkins); Jordan Peccia (Yale); Branko Kerkez (University of Michigan)
Centers for Disease Control (Atlanta, GA), Johns Hopkins (Baltimore, MD), Yale (New Haven, CT), University of Michigan (Ann Arbor, MI)
We aim to determine the fraction of observed heterogeneity in regional air pollution and personal exposure that is due to energy-related factors (e.g., transportation, power generation). The objectives are to: 1) develop novel online multipollutant monitors to simultaneously measure air pollutants and greenhouse gases; 2) deploy a high-resolution, multipollutant stationary monitoring network to quantify variability in pollutant concentrations at high spatiotemporal resolution; and use source apportionment methods to assess contributions from energy-related sources; and 3) deploy novel multipollutant sensors as personal monitors to evaluate temporally-resolved personal exposures with detailed time-activity information; and use source apportionment methods to assess contributions from energy-related sources.
We will use novel multipollutant wireless monitors in a high-resolution fixed network (100+ sites) and as personal samplers in a case study area (Baltimore). We propose the first large distributed network of robust low-cost monitors to characterize modifiable energyrelated factors affecting air pollution, and to observe changes in air quality and personal exposure from energy-related sources. Sampling will include diverse communities by housing stock, socioeconomic status, and sustainable aspects to understand spatiotemporal variability in pollutant concentrations, source contributions, and exposures across in an urban setting.
We will produce a novel stationary and personal assessment framework with cutting-edge technologies (chemical sensors, distributed sensing, wireless networking) to simultaneously estimate concentrations for 9+ pollutants at high temporal resolution. There is growing interest in using new sensor technologies to create high-granularity monitoring networks to supplement existing larger fixed site monitoring programs. Increased granularity will allow air quality managers more flexibility in monitoring locations. Small-scale source apportionment and other statistical methods will be used to elucidate influences of energy-related sources and examine emissions before and after energy transition events (e.g., power plant decommissioning). We will quantify energy-related sources that impact personal exposures considering regional, local, and residential sources; transportation modes (car, rail, bus, bicycle); and aspects of the built environment.