Use of a MM5–CAMx–PSAT Modeling System to Study SO2 Source Apportionment in the Beijing Metropolitan Region

Huang, Qing; Cheng, Shuiyuan; Perozzi, Rhoda E.; Perozzi, Edmund F.

doi:10.1007/s10666-012-9312-8

Cited by 32 publications

(17 citation statements)

References 25 publications

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“…PSAT (Particulate Matter Source Apportionment Technology) is one major extension of the CAMx model. PSAT was developed from the related ozone source apportionment method and provided PM source apportionment for specific geographic regions and source categories (Huang et al, 2012). Furthermore, PSAT can be used to analyze the source-acceptor relationship of PM 2.5 pollutants and trace SO 2 , SO 2− 4 , NO − 3 , NH + 4 , secondary organic aerosol, Hg, elemental carbon, dust particles, and other primary and secondary particles.…”

Section: Source Apportionmentmentioning

confidence: 99%

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport

et al. 2019

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Abstract. To comprehensively evaluate the effects of the recent “2+26” regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentration 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentration 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly, and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contribution of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 %, respectively, indicating that the recent “Coal to Gas” project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing–Tianjin–Hebei region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 %, respectively. The relative contribution of local emissions to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intentionally reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

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Section: Source Apportionmentmentioning

confidence: 99%

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport

et al. 2019

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“…This was mainly caused by the following reason. (1) In Beijing, the SO 2 were affected by heating sources (Huang et al, 2012). It is well known that the heating season in Beijing is from November to the March in the next year.…”

Section: Seasonal Variationmentioning

confidence: 99%

Temporal and Spatial Characteristics of Ambient Air Quality in Beijing, China

Zhou

Cheng

Chen

et al. 2015

Aerosol Air Qual. Res.

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The ambient air quality in Beijing was comprehensively assessed based on the real-time pollutant concentrations monitored at urban, suburban, roadside, and background sites in 2013. The results showed that the annual average concentration for CO, NO 2 , SO 2 , O 3 , PM 2.5 and PM 10 in 2013 was 2.0 mg m , respectively. The annual average concentration of CO, NO 2 , SO 2 , PM 2.5 and PM 10 was highest in roadside, while that of O 3 was highest in background stations. The mean monthly statistics indicated the maximum concentration of CO, NO 2 , SO 2 , PM 2.5 and PM 10 occurred in January because of larger emissions in heating season, lower wind speed and higher relative humidity (RH), while the minimum was found in July or August due to larger precipitation or photochemical degradation. The peak concentrations of O 3 occurred during May to August due to higher temperature and solar radiation which could promote the photochemistry activity. The monthly variation is also reflected in the corresponding season. Diurnally analysis showed the CO, NO 2 , SO 2 , PM 2.5 and PM 10 in urban and roadside area had two increase phases accompanying with the traffic peaks. Beside the temporal variation, we also found the spatial variation that higher concentrations of O 3 and other pollutants occurred in northern and southern districts/counties, respectively. It could be attributed to the spatial distribution of various pollutant emissions in Beijing and the impact of pollutant transport from neighboring provinces. Moreover, we examined the visibility in Beijing and found its significant correlation with PM 2.5 concentration and RH, respectively. Lastly, the air quality in Beijing was compared with that in other mega cities in the world. The higher pollutant concentrations and PM 2.5 /PM 10 ratio indicated that the mitigation of the air pollution especially the PM 2.5 pollution in Beijing still had a long way to go.

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“…Atmospheric dispersion models have been frequently used to evaluate the sources' contributions to PM concentrations [1,5,7,8,28,35]. Studies have also been carried out to estimate the sources' contributions to ozone [8,[32][33][34], CO [36][37][38][39] and SO 2 [2,40] concentrations. All of these studies were performed with mesoscale atmospheric dispersion models.…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

2018

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Abstract:Developing effective strategies for reducing the atmospheric pollutant concentrations below regulatory threshold levels requires identifying the main origins/sources of air pollution. This can be achieved by implementing so called source apportionment methods in atmospheric dispersion models. This study presents the results of a source apportionment module implemented in the SIRANE urban air-quality model. This module uses the tagged species approach and includes two methods, named SA-NO and SA-NOX, in order to evaluate the sources' contributions to the NO 2 concentrations in air. We also present results of a data assimilation method, named SALS, that uses the source apportionment estimates to improve the accuracy of the SIRANE model results. The source apportionment module and the assimilation method have been tested on a real case study (the urban agglomeration of Lyon, France, for the year 2008) focusing on the NO 2 emissions and concentrations. Results of the source apportionment with the SA-NO and SA-NOX models are similar. Both models show that traffic is the main cause of NO 2 air pollution in the studied area. Results of the SALS data assimilation method highlights its ability in improving the predictions of an urban atmospheric models.

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Use of a MM5–CAMx–PSAT Modeling System to Study SO2 Source Apportionment in the Beijing Metropolitan Region

Cited by 32 publications

References 25 publications

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport

Temporal and Spatial Characteristics of Ambient Air Quality in Beijing, China

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

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Use of a MM5–CAMx–PSAT Modeling System to Study SO2 Source Apportionment in the Beijing Metropolitan Region

Cited by 32 publications

References 25 publications

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations, source apportionment, and the relative contribution of local emission and regional transport

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations, source apportionment, and the relative contribution of local emission and regional transport

Temporal and Spatial Characteristics of Ambient Air Quality in Beijing, China

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

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Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport

Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM<sub>2.5</sub> concentrations, source apportionment, and the relative contribution of local emission and regional transport