Keywords
PM2.5; Dispersion Model; Environmental Modelling; Transportation; Air Pollution; Sensor Network; Health Impact Assessment; Ubiquitous Sensing; Citizen Science
Location
Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale
Start Date
18-6-2014 2:00 PM
End Date
18-6-2014 3:20 PM
Abstract
The availability of new small and low-cost particulate matter monitors has enabled the collection of large quantities of data to calibrate traffic-related air pollution models in urban areas. We present laboratory findings on the performance of our Portable University of Washington Particle (PUWP) monitor compared to a reference instrument, and its application to air quality modeling for epidemiologic studies. The instrument has been calibrated in laboratory chamber studies as well as in field collocation studies against reference instruments. We are currently examining the use of the PUWP to calibrate air pollution. In our laboratory studies, four PUWP monitors were placed in a chamber with varying concentrations of generated monodispersed particles measured by TSI aerodynamic particle sizer (APS). The experiment was repeated for particle sizes 0.75-6 µm. A linear relationship between raw sensor measurements of PUWP monitors and the APS was found for concentrations below 50 µg/m3 (with less than 10% measurement error). To examine the use of the PUWPs for calibrating air quality models, using simulations, we demonstrate deployment of the PUWPs, and inverse modeling using an atmospheric dispersion equation to identify emissions hotspots along a major roadway. Simple maps of measured concentrations may be biased by monitor placement, and can result in incorrect inference on emissions hotspots. However, we found that with sufficiently accurate measurements and enough monitors, it was possible to use inverse modeling to derive robust inference on emissions hotspots. Our findings may help guide future deployments of distributed low-cost sensor networks that have specific goals for informing the management of urban environmental processes, such as transportation systems, to reduce adverse human health impacts.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Use of low-cost particle monitors to calibrate traffic-related air pollutant models in urban areas
Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale
The availability of new small and low-cost particulate matter monitors has enabled the collection of large quantities of data to calibrate traffic-related air pollution models in urban areas. We present laboratory findings on the performance of our Portable University of Washington Particle (PUWP) monitor compared to a reference instrument, and its application to air quality modeling for epidemiologic studies. The instrument has been calibrated in laboratory chamber studies as well as in field collocation studies against reference instruments. We are currently examining the use of the PUWP to calibrate air pollution. In our laboratory studies, four PUWP monitors were placed in a chamber with varying concentrations of generated monodispersed particles measured by TSI aerodynamic particle sizer (APS). The experiment was repeated for particle sizes 0.75-6 µm. A linear relationship between raw sensor measurements of PUWP monitors and the APS was found for concentrations below 50 µg/m3 (with less than 10% measurement error). To examine the use of the PUWPs for calibrating air quality models, using simulations, we demonstrate deployment of the PUWPs, and inverse modeling using an atmospheric dispersion equation to identify emissions hotspots along a major roadway. Simple maps of measured concentrations may be biased by monitor placement, and can result in incorrect inference on emissions hotspots. However, we found that with sufficiently accurate measurements and enough monitors, it was possible to use inverse modeling to derive robust inference on emissions hotspots. Our findings may help guide future deployments of distributed low-cost sensor networks that have specific goals for informing the management of urban environmental processes, such as transportation systems, to reduce adverse human health impacts.
