科研之友
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摘要
Total fine particulate carbon (TC) is an important contributor to fine particulate matter and is measured in routine national monitoring programs. TC contributes to adverse health effects, regional haze, and climate effects. To resolve these adverse effects, there is a need for tools capable of routine and climatological assessments and exploration of the sources contributing to the measured TC. To address this need, a receptor-oriented, Lagrangian particle dispersion model was developed to simulate TC in rural areas, using readily available meteorological and emission inputs. This model was based on the CAPITA (Center for Air Pollution Impact and Trend Analysis) Monte Carlo model (CMC) and simulated the contributions from eight source categories, including biomass burning and secondary organic carbon (SOC) from vegetation. TC removal and formation mechanisms are simulated using a simplified parameterization of atmospheric processes based on pseudo-first-order rate equations. The rate coefficients are empirical functions of meteorological parameters derived from measured, modeled, and literature data. These functions were optimized such that the simulated TC concentrations reproduce the average spatial and seasonal patterns in measured 2008 U.S. TC concentrations, as well as measured SOC fractions at two eastern U.S. sites. The optimized model was used to simulate 2006-2008 rural TC that was evaluated against measured TC. In addition, the model output was compared to TC from a 2006 Eulerian Community Multiscale Air Quality (CMAQ) simulation. It is shown that the CMC model has similar performance metrics as the CMAQ model. Published by Elsevier Ltd.
- 出版日期2012-12
