Carnegie Mellon University
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Advanced Chemical Transport Modeling of Fine Particulate Matter to Support Exposure Assessment

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posted on 2019-10-18, 19:15 authored by Pablo Garcia RiveraPablo Garcia Rivera
Fine particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) has been associated with public health concerns due to short and long-term exposure. Chemical transport models (CMTs) are frequently used for developing air quality and emissions control policies that protect public health. To evaluate these policies, Chemical transport models (CTMs) must simulate PM2.5 concentrations and their response to changes in emissions accurately. At high resolutions, the geographical distribution of PM2.5 concentrations can have sharp gradients. During the last decades, regulations by the U.S. Environmental Protection Agency have led to significant reductions of the emissions of atmospheric pollutants including PM2.5.
The CTM PMCAMx is used here to assess the impact of increasing model resolution on the model’s ability to predict the variability, sources and population exposure of PM2.5 concentrations at 36 x 36, 12 x 12, 4 x 4 and 1 x 1 km resolutions over the city of Pittsburgh; to evaluate the PMCAMx predictions at various grid resolutions against measurements of PM2.5 concentration and composition; and to estimate the concentration, composition and sources of PM2.5 over 20 years in the U.S.
At the finest resolution, the model successfully resolved intra-urban variations and individual roadways. Pollutants with significant local emissions such as elemental and organic carbon have gradients that can only be resolved at the finest resolution. PMCAMx predicts sulfate, elemental carbon and organic aerosol concentrations well with fractional biases below 10%. Agreement with total PM2.5 measurements is also encouraging with a fractional bias of 3%. Prediction performance improves with increasing resolution reducing the average fractional error from 16% at 36 x 36 km to 12% at 1 x 1 km. EC concentrations reduced by 23% due to a 37% reduction on emissions from 1990 to 2010. SO2 emissions were reduced by 63% causing a large reduction of sulfate concentrations in the northeast of the U.S. A comparison between the predicted and observed reductions of EC, sulfate, and PM2.5 showed excellent correlations.

History

Date

2019-09-27

Degree Type

  • Dissertation

Department

  • Chemical Engineering

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Peter Adams Spyros Pandis

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