Abstract
ABSTRACT An accurate depiction of temporal and spatial variations in emissions is critical in simulating air quality with atmospheric chemical transport models. Most emission processing systems typically use prescribed profiles to allocate anthropogenic emissions based on the assumptions that the temporal variance is periodical and spatial variance is time-independent. However, these assumptions are not applicable to emission sources heavily influenced by meteorology and holiday activity. In this study, we improved the temporal and spatial allocation of anthropogenic emissions by, first of all, developing a dynamic allocation method for fugitive dust that uses the negative correlation between dust emissions and precipitation, based on hourly rainfall data generated by the Weather Research and Forecasting model. Second, we employed holiday-specific profiles that were established using continuous emission monitoring system and traffic flow monitoring data to allocate power plant and on-road mobile emissions during the Spring Festival period, when human activity differs considerably from that of non-holiday periods. The new dynamic allocation method and holiday-specific profiles were applied to emissions in the Pearl River Delta region as a demonstration. Validated using a chemical transport model, this method obviously improved the model performance for periods with rainfall, with the normalized mean bias (NMB) decreasing by 6.27% for PM10 (particulate matter with a diameter of ≤ 10 µm) and 4.33% for PM2.5 (particulate matter with a diameter of ≤ 2.5 µm). The holiday simulations revealed that the holiday-specific profiles mitigated overestimations of NO2, SO2, and PM10 for the Spring Festival period, with the NMBs decreasing by 37.95%, 18.56%, and 20.83%, respectively. Hence, refining the allocation of emissions improved model simulation and air quality forecasting.
Highlights
Atmospheric chemical transport models (CTMs) that simulate the transport and fate of atmospheric pollutants are critical tools for regulatory decision making, attainment demonstration, and air quality forecasting
We improved the temporal and spatial allocation of anthropogenic emissions by, first of all, developing a dynamic allocation method for fugitive dust that uses the negative correlation between dust emissions and precipitation, based on hourly rainfall data generated by the Weather Research and Forecasting model
Yin et al (2015) found that updating the spatial allocation of volatile organic compound (VOC) emissions from industrial sources using location information derived from Google Earth improved ozone simulations in urban areas; it revealed that the normalized mean bias (NMB) exhibited a regionwide decrease in October by 0.1% to 4.1%
Summary
Atmospheric chemical transport models (CTMs) that simulate the transport and fate of atmospheric pollutants are critical tools for regulatory decision making, attainment demonstration, and air quality forecasting. In several studies, accurate emission allocation has been beneficial for improving air quality simulations and their applications (Gregg et al, 2009; Lindhjem et al, 2012; Yin et al, 2015). Yin et al (2015) found that updating the spatial allocation of volatile organic compound (VOC) emissions from industrial sources using location information derived from Google Earth improved ozone simulations in urban areas; it revealed that the normalized mean bias (NMB) exhibited a regionwide decrease in October by 0.1% to 4.1%
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