Study on the contribution of transport to PM2.5 in typical regions of China using the regional air quality model RAMS-CMAQ

Li, Rong; Mei, Xin; Wei, Lifei; Xiao, Han; Zhang, Meigen; Jing, Yingying

doi:10.1016/j.atmosenv.2019.116856

Cited by 55 publications

(18 citation statements)

References 48 publications

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“…Airborne PM 2.5 (i.e., particulate matter with an aerodynamic diameter of less than 2.5 µm) has attracted increased global attention due to its well-recognized impact on climate, visibility, and human health (Chow et al, 2004;Liu et al, 2016;Foley et al, 2010). In recent years, with the increasingly Published by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Introductionmentioning

confidence: 99%

“…Air pollution issues in Shanghai are complex and our knowledge of its aerosol sources still fall short of being sufficiently quantitative or comprehensive. Past source apportionment studies of PM 2.5 in Shanghai are based on either offline filter-based data that are inherently of a low time resolution (Du et al, 2017;Chang et al, 2018) or emissionsbased numerical models (Li et al, 2015(Li et al, , 2019Shu et al, 2019;Feng et al, 2019). PM 2.5 source apportionment studies using online data as inputs have so far been limited to the major aerosol species (i.e., inorganic ions, carbonaceous components, and elements; Wang et al, 2018), preventing proper separation of aerosol sources dominated by organic compounds.…”

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confidence: 99%

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Source apportionment of PM2.5 in Shanghai based on hourly organic molecular markers and other source tracers

Li¹,

Wang

et al. 2020

Atmos. Chem. Phys.

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Abstract. Identification of various emission sources and quantification of their contributions comprise an essential step in formulating scientifically sound pollution control strategies. Most previous studies have been based on traditional offline filter analysis of aerosol major components (usually inorganic ions, elemental carbon – EC, organic carbon – OC, and elements). In this study, source apportionment of PM2.5 using a positive matrix factorization (PMF) model was conducted for urban Shanghai in the Yangtze River Delta region, China, utilizing a large suite of molecular and elemental tracers, together with water-soluble inorganic ions, OC, and EC from measurements conducted at two sites from 9 November to 3 December 2018. The PMF analysis with inclusion of molecular makers (i.e., MM-PMF) identified 11 pollution sources, including 3 secondary-source factors (i.e., secondary sulfate; secondary nitrate; and secondary organic aerosol, SOA, factors) and 8 primary sources (i.e., vehicle exhaust, industrial emission and tire wear, industrial emission II, residual oil combustion, dust, coal combustion, biomass burning, and cooking). The secondary sources contributed 62.5 % of the campaign-average PM2.5 mass, with the secondary nitrate factor being the leading contributor. Cooking was a minor contributor (2.8 %) to PM2.5 mass while a significant contributor (11.4 %) to the OC mass. Traditional PMF analysis relying on major components alone (PMFt) was unable to resolve three organics-dominated sources (i.e., biomass burning, cooking, and SOA source factors). Utilizing organic tracers, the MM-PMF analysis determined that these three sources combined accounted for 24.4 % of the total PM2.5 mass. In PMFt, this significant portion of PM mass was apportioned to other sources and thereby was notably biasing the source apportionment outcome. Backward trajectory and episodic analysis were performed on the MM-PMF-resolved source factors to examine the variations in source origins and composition. It was shown that under all episodes, secondary nitrate and the SOA factor were two major source contributors to the PM2.5 pollution. Our work has demonstrated that comprehensive hourly data of molecular markers and other source tracers, coupled with MM-PMF, enables examination of detailed pollution source characteristics, especially organics-dominated sources, at a timescale suitable for monitoring episodic evolution and with finer source breakdown.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Source apportionment of PM2.5 in Shanghai based on hourly organic molecular markers and other source tracers

Li¹,

Wang

et al. 2020

Atmos. Chem. Phys.

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“…Shanghai is the financial center of China, with a large population and a total area of 6,340 km 2 . Past source apportionment studies of PM2.5 in Shanghai were either based on offline filters using receptor models (Du et al, 2017;Chang et al, 2018), or emissions with numerical models (Li et al, 2015;Shu et al, 2019;Li et al, 2019;Feng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Source apportionment of PM2.5 in Shanghai based on hourly molecular organic markers and other source tracers

Wang

et al. 2020

Preprint

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Abstract. Identification of various sources and quantification of their contributions are a necessary step to formulating scientifically sound pollution control strategies. Receptor model is widely used in source apportionment of fine particles. However, most of the previous studies are based on traditional filter collection and lab analysis of aerosol chemical species (usually ions, elemental carbon (EC), organic carbon (OC) and elements) as inputs. In this study, we conducted robust online measurements of a range of organic molecular makers and trace elements, in addition to the major aerosol components (ions, OC and EC), in urban Shanghai in the Yangtze River Delta region, China. The large suite of molecular and elemental tracers, together with water-soluble ions, OC and EC, provide data for establishing measurement-based source apportionment methodology for PM2.5. We conducted source apportionment using positive matrix factorization (PMF) and compared PMF solutions with molecular makers added (i.e. MM-PMF) and those without organic markers. MM-PMF identified 11 types of pollution sources, with biomass burning, cooking and secondary organic aerosol (SOA) as the additional sources identified. The three sources accounted for 4.9 %, 2.6 % and 14.7 % of the total PM2.5 mass, respectively. During the whole campaign, the secondary source is an important source of atmospheric pollution, the average contribution of secondary pollution sources is as high as 63.8 % of the total PM2.5 mass. Grouping different sources to secondary and primary, we note that SOC and POC contributed 45.1 % and 54.9 %, respectively. It is worth noting that the contribution of cooking to PM2.5 mass only account for 2.6 %, but it contributed to 10.7 % of OC. Episodic analysis indicated that secondary nitrate was the always the main cause of PM2.5 pollution, while during non-episodic hours, vehicle exhaust made a significant contribution. Through the application of the above-mentioned techniques to the Yangtze River Delta, more insights are gained on the sources, formation mechanism and pollution characteristics of PM2.5 in this region.

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“…The NO2 concentration was highest in winter and then in Autumn, with the lowest NO2 in summer. Since chemical reactions of NOx highly depend on ambient temperature [10], the hot and cold weather in summer and winter were the main drivers of this great seasonal contrast in NO2 levels. Moreover, the more stagnant weather in winter favored accumulation To further examine spatial patterns of NO 2 pollution, seasonal distribution of OMI tropospheric NO 2 VCD is shown in Figure 7.…”

Section: The Characteristics Of So 2 and No 2 Variation During 2005-2017mentioning

confidence: 99%

“…Owing to adverse effects of air pollution on human health and daily life, the Chinese government has implemented continuous emission control measures. The anthropogenic emission sources and their amount in China have undergone dramatic changes during last decades due to both the rapid transition of economic structures and environmental policies [10]. There is also a large spatial difference in anthropogenic emissions sources [11], exerting considerable challenges for making timely and effective improvement measures.…”

Section: Introductionmentioning

confidence: 99%

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

Mei

Chen

et al. 2020

Remote Sensing

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The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

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Study on the contribution of transport to PM2.5 in typical regions of China using the regional air quality model RAMS-CMAQ

Cited by 55 publications

References 48 publications

Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly molecular organic markers and other source tracers

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

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Study on the contribution of transport to PM2.5 in typical regions of China using the regional air quality model RAMS-CMAQ

Cited by 55 publications

References 48 publications

Source apportionment of PM&lt;sub&gt;2.5&lt;/sub&gt; in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM&lt;sub&gt;2.5&lt;/sub&gt; in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM&lt;sub&gt;2.5&lt;/sub&gt; in Shanghai based on hourly molecular organic markers and other source tracers

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

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Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly organic molecular markers and other source tracers

Source apportionment of PM<sub>2.5</sub> in Shanghai based on hourly molecular organic markers and other source tracers