A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China

Yang, Junhua; Kang, Shichang; Ji, Zhenming; Chen, Xintong; Yang, Shulan; Lee, Shao Yi; Foy, B. de; Chen, Deliang

doi:10.1016/j.atmosres.2019.104787

Cited by 15 publications

(6 citation statements)

References 65 publications

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“…Due to the distinctive valley basin topography of Lanzhou, coupled with the role of the Mongolian High in causing downdrafts, the occurrence of mountain airflows at night strengthens the inversion, which is intensified during the night in winter. These conditions are not conducive to the dispersion of pollutants ( Figure 2 ) [ 59 , 60 ]. Therefore, in winter, the contribution of vehicle emissions, coal-fired, and industrial emissions sources was higher at night than during the day [ 17 ].…”

Section: Causes Of the Differences Between Daytime And Nighttime Pm ...mentioning

confidence: 99%

“…To address the problem of increased dustiness during windy weather and minimize its impact on air quality, the Chengguan District in Lanzhou has changed the sprinkler operating time from 0630 to 1330. In Lanzhou, the temperature in summer is so high that there is an increased usage of sprinklers, which promote the settling of airborne dust and reduces the atmospheric dust content [ 60 , 61 ]. Consequently, in summer, the proportion of soil dust is lower during the day than at night.…”

Section: Causes Of the Differences Between Daytime And Nighttime Pm ...mentioning

confidence: 99%

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Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Zhang

Jia

Wang

et al. 2022

IJERPH

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Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl−, NO3−, SO42−), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.

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Section: Causes Of the Differences Between Daytime And Nighttime Pm ...mentioning

confidence: 99%

Section: Causes Of the Differences Between Daytime And Nighttime Pm ...mentioning

confidence: 99%

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Zhang

Jia

Wang

et al. 2022

IJERPH

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“…They have been recognized as a useful tool for air pollution controls due to their ability and large spatial coverage to quantify the transport impacts of regional air pollutants 31 – 35 . Many studies have been conducted to quantify the contributions of regional sources to PM over severely polluted regions of China, such as BTH 36 , PRD 37 – 39 , YRD 40 , North China Plain (NCP) 41 , and western China 42 , 43 , by using air quality models, such as the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) 44 and the Community Multiscale Air Quality model (CMAQ) 45 . They mostly used air quality models to simulate the concentration of particulate matter in the study area at a time when the region is prone to heavy air pollution (generally, the simulation time is half a month to one month).…”

Section: Introductionmentioning

confidence: 99%

Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China

Wang,

Min

et al. 2023

Sci Rep

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In this study, we simulated the spatial and temporal processes of a particulate matter (PM) pollution episode from December 10–29, 2019, in Zhengzhou, the provincial capital of Henan, China, which has a large population and severe PM pollution. As winter is the high incidence period of particulate pollution, winter statistical data were selected from the pollutant observation stations in the study area. During this period, the highest concentrations of PM2.5 (atmospheric PM with a diameter of less than 2.5 µm) and PM10 (atmospheric PM with a diameter of less than 10 µm) peaked at 283 μg m-3 and 316 μg m-3, respectively. The contribution rates of local and surrounding regional emissions within Henan (emissions from the regions to the south, northwest, and northeast of Zhengzhou) to PM concentrations in Zhengzhou were quantitatively analyzed based on the regional Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). Model evaluation showed that the WRF/Chem can accurately simulate the spatial and temporal variations in the PM concentrations in Zhengzhou. We found that the anthropogenic emissions south of Zhengzhou were the main causes of high PM concentrations during the studied episode, with contribution rates of 14.39% and 16.34% to PM2.5 and PM10, respectively. The contributions of anthropogenic emissions from Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.94% and 7.29%, respectively. The contributions of anthropogenic emissions from the area northeast of Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.42% and 7.18%, respectively. These two areas had similar contributions to PM pollution in Zhengzhou. The area northeast of Zhengzhou had the lowest contributions to the PM2.5 and PM10 concentrations in Zhengzhou (5.96% and 5.40%, respectively).

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“…The emission inventory method, diffusion models, and receptor models are popular methodologies for quantitatively analyzing the contribution of various sources to particle pollution in multiple cities (Zhang K et al 2019;Liu et al 2020;Yang JH et al 2020;Zhang WJ et al 2020). However, due to the limitations of each method, there were great differences in the results of source analysis on particulate pollution.…”

Section: Introductionmentioning

confidence: 99%

“…It is concluded that these pollutants come mainly from 9 sources: industrial boilers and kilns, industrial processes, coal‐fired power plants, vehicle emissions, dust, volatile sources, residents' living activities, agricultural sources, and natural sources. The emission inventory method, diffusion models, and receptor models are popular methodologies for quantitatively analyzing the contribution of various sources to particle pollution in multiple cities (Zhang K et al 2019; Liu et al 2020; Yang JH et al 2020; Zhang WJ et al 2020). However, due to the limitations of each method, there were great differences in the results of source analysis on particulate pollution.…”

Section: Introductionmentioning

confidence: 99%

Observations on the particle pollution of the cities in China in the Coronavirus 2019 closure: Characteristics and lessons for environmental management

Yao

Niu

Zhang

et al. 2021

Integr Envir Assess & Manag

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Particulate matter in the air seriously affects human health and has been a hot topic being discussed. Because of the COVID-19 lockdown of the cities in China, sources of This article is protected by copyright. All rights reserved. Accepted Article the particulate matter including gasoline burning vehicles, dust producing building sites, coal-fired factories, almost all stopped working since the end of January 2020. It was not until early April that outdoor activities recovered. Ten cities were selected as the observation sites in the period from 12/19/2019 to 04/30/2020, covering the periods of the pre-closure, the closure and the gradual resumption. 11720 groups of data were obtained and four indicators were applied to assess the characteristics of the particle pollution in the period. The quality of the atmospheric environment was visually influenced by human activities in the four months. The PM10 concentrations decreased slightly in February and March, and then began to increase gradually after April with the gradual recovery of production. The concentrations of PM2.5 reduced greatly in most regions especially in northern cities, during the closure and maintained a relatively stable level in the following three months. The trends of PM10 and PM2.5 indicated that the reduced human activities during the COVID-19 lockdown made the concentrations of the particulate matter in the air decreased, and the difference between the PM10 and PM2.5 trends might be due to the different sources of the two particles and their different aerodynamics. However, during the closure, the pollution of the particulate matter in the cities still remained at a high level, which indicated that some ignored factors except the outdoor production activities, automobile exhaust and sites' dust might have been contributing much to the PM10 and PM2.5 concentrations, and the traceability of the particulate matter should be paid further attention in environmental management.

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A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China

Cited by 15 publications

References 65 publications

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China

Observations on the particle pollution of the cities in China in the Coronavirus 2019 closure: Characteristics and lessons for environmental management

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