Lead University: Carnegie Mellon University
PI: Neil Donahue, Chemical Engineering, Chemistry, Engineering and Public Policy
Co-PIs: Illah Nourkbash, Robotics
We propose to begin development of an integrated, hierarchical data network for air-quality measurement by deploying a group of low-cost fine-particle sensors produced by Airviz Inc. as part of a year-long measurement campaign in Pittsburgh being undertaken by the EPA-funded Center for Air, Climate and Energy Solutions (CACES). This is the first step of a long-term vision to develop a hierarchical data network anchored by a very limited number of stations containing comprehensive state-of-the-art instrumentation with strategically deployed low-cost sensors that enable extrapolation of the high-quality data in time and space. Our focus is on fine particles, which are the dominant driver of mortality associated with air pollution and consequently the chief target of air pollution standards but also by far the most difficult to measure. Particles are a measurement challenge because they span 4 orders of magnitude in size (and thus 12 orders of magnitude in mass) and also have diverse and highly mixed composition. Our ansatz is that sufficiently accurate (in aggregate) low-cost sensors will have a response that is potentially variable with respect to the size and composition of fine particles, but anchored by limited but high-quality measurements from a suite of state-of-the-art instrumentation, the data from low cost sensors will be able to extend the expensive high-quality data in time and space to generate high-quality spatially and temporally resolved data fields suitable for both health effects research, air-quality policy and planning by public officials, and also presentation for public outreach.