Assessment of PM₁₀ Emission Sources for Priority Regulation in Urban Air Quality Management Using a New Coupled MM5-CAMx-PSAT Modeling Approach

Abstract

In this study, a new method was proposed to systematically identify the local PM 10 emission sources for priority regulation in urban air quality management through a coupled MM5-CAMx-PSAT modeling system. Parameters of emission source contribution ratio (ESCR) and normalized local ESCR were introduced to reflect the source contribution in terms of its total emission amount and per unit emission amount, and were used for identifying the emission sources for priority regulation. The proposed method was then applied to a case study in Beijing, China. Three scenarios were examined, including (a) analysis of only seven PM 10 emission source categories for the entire Beijing; (b) analysis of just 13 emission districts in Beijing; and (c) comprehensive analysis of seven emission source categories in each emission district. The following emissions were identified for priority regulation: (a) stationary emissions from the urban center of Beijing and Fengtai districts; (b) industrial fugitive emissions from Chaoyang, Fengtai, and Shijingshan districts; (c) road dust emissions from the urban center of Beijing, Chaoyang, Fengtai, Shijingshan, and Haidian districts; (d) construction site dust emissions from the urban center of Beijing, Chaoyang, Fengtai, and Haidian districts; (e) bare land emissions from Chaoyang district; and (f) vehicle exhaust emissions from the urban center of Beijing, Chaoyang, Fengtai, and Haidian districts. Results indicated that the proposed method could be successfully implemented at different levels for different air quality management purposes.

Application of MM5-CAMx-PSAT Modeling Approach for Investigating Emission Source Contribution to AtmosphericSO2Pollution in Tangshan, Northern China

“…In terms of examining the response of atmospheric pollutant concentration to perturbations in pollutant emission rate, a number of approaches have been proposed in the past years by using various models [4,11,16,17]. Particularly, a technique named particulate matter source apportionment technology (PSAT) [18] has been implemented in CAMx to provide source apportionment for primary and secondary particulate matter (PM) species according to emission source categories and their geographic locations [19]. This technique is useful for identifying emission sources that significantly contribute to gaseous or PM pollution.…”

Sulfate formation during heavy winter haze events and the potential contribution from heterogeneous SO<sub>2</sub> + NO<sub>2</sub> reactions in the Yangtze River Delta region, China

“…To investigate whether the SO 2 + NO 2 reactions in aerosol water could help better predict the enhanced sulfate formation during haze periods in the Yangtze River Delta (YRD) region, we implemented a parameterized SO 2 +NO 2 reactive uptake mechanism in the Comprehensive Air Quality Model with Extensions (CAMx), which is a widely used air quality model in China (e.g., Wang et al, 2009;Huang et al, 2012;Li et al, 2013Li et al, , 2015Jia et al, 2017). Our parameterization specifically incorporated RH and aerosol pH dependencies derived from measurement data during the 2015 Beijing haze event (Wang et al, 2016).…”

Improvement of PM10 prediction in East Asia using inverse modeling

“…Chatani et al (2011) developed a framework for a high-resolution, three-dimensional regional air quality simulation; their suspended PM prediction in Japan also showed under-predictions by À37% to À61% NMB, differing by seasons. To our knowledge, most modeling studies in the East Asia region showed under-predictions compared to the observations (Koo et al, 2008(Koo et al, , 2012Shimadera et al, 2009;Huang et al, 2012;In and Kim, 2010;Li et al, 2013;Chatani et al, 2011. One of the main reasons for underestimations in the aerosol simulation of PM10 in particular is the uncertainty of emissions.…”