An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

Cheng, Nana; Zhang, Cheng; Jing, Deji; Li, Wei; Guo, Tianjiao; Wang, Qiaoli; Li, Sujing

doi:10.1016/j.jes.2020.01.018

Cited by 16 publications

(4 citation statements)

References 35 publications

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“…Source apportionment that quantifies the contribution of sources to air pollutants is the basis for formulating air pollution control strategies and includes two methods of receptor models and air quality models 21 . Receptor models such as chemical mass balance (CMB) 22 , 23 and positive matrix factorization (PMF) 23 – 25 can estimate the relationship between receptors and sources on the basis of measurements. Numerous studies have been conducted to quantify the contribution of emission sources to PM in Chinese cities, especially over the regions of the PRD, YRD and BTH 26 – 28 .…”

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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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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“…Meanwhile, the State Council requires governments at all levels to promote the action of withdrawing from the city and entering the park , resulting in enhanced production concentration within these parks alongside intensified pollutant emissions [ [8] , [9] , [10] , [11] ]. The atmospheric condition in industrial parks is different with that in relatively low polluted areas, due to the characterization of small-scale, dense enterprises and various chemical and complex production processes [ [12] , [13] , [14] ], as well as the intermittent and fugitive emissions of VOCs [ 15 , 16 ] and the inevitable emissions of NO X [ 17 , 18 ], resulting in more complex in O 3 formation rules [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park

Wang,

Sheng,

et al. 2023

Heliyon

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“…The superposition of exogenous sources was most significant for soil pollution. Exogenous sources of HMs included mining, industrial sources (such as the metal smelting plants, thermal power plants and alkylation plants) [ 14 , 15 , 16 , 17 ], agricultural sources (fertilizers, pesticides and livestock manure) [ 18 , 19 ] and traffic sources (wear of metal parts and exhaust emission) [ 20 ]. In addition, the soil of the karst area, compared to that of non-karst area, is characterized by thin regoliths, uneven distribution, and high porosity [ 21 , 22 ], which contributed to the migration of HMs from the surface to the subsoil and even contaminated underground water [ 9 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, numerous methods were utilized in source apportionment of HMs in soils. For instance, correlation analysis and cluster analysis were widely used to qualitatively define these sources [ 30 , 31 ], whereas receptor models, such as principle component analysis (PCA), chemical mass balance (CMB), positive matrix factorization (PMF), UNMIX and absolute principal component analysis followed by multiple linear regression (APCA-MLR), were widely applied to quantitatively analyze the contribution of each source [ 14 , 32 , 33 , 34 ]. Among the mentioned methods, the CMB model, directly analyzing numerical values rather than deviations, had a better-fitting accuracy based on building of the source profile, which improved confidence in the robustness of source apportionment results [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Source Identification and Superposition Effect of Heavy Metals (HMs) in Agricultural Soils at a High Geological Background Area of Karst: A Case Study in a Typical Watershed

Zhang

Liu

et al. 2022

IJERPH

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Exogenous sources and the superposition effect of HMs in agricultural soils made the idenfication of sources complicated in a karst area. Here, a typical watershed, a research unit of the karst area, was chosen as the study area. The smaller-scale study of watersheds allowed us to obtain more precise results and to guide local pollution control. In this study, sources of HMs in agricultural soil were traced by a CMB model. Superposition effects were studied by spatial analysis of HMs and enrichment factor (EF) and chemical fraction analysis. The average concentrations of Cd, Pb, Cr, Cu, Ni and Zn in surface soils were 8.71, 333, 154, 51.7, 61.5 and 676 mg∙kg−1, respectively, which exceeded their corresponding background values. The main sources of Cd, Pb and Zn in agricultural soil were rock weathering, atmospheric deposition and livestock manure, and their contributions were 47.7%, 31.0% and 21.2% for Cd; 7.63%, 78.7% and 13.4% for Pb; and 17.0%, 52.3% and 28.1% for Zn. Cr mainly derived from atmospheric deposition (73.8%) and rock weathering (20.0%). Cu and Ni mainly came from livestock manure (81.3%) and weathering (87.5%), respectively, whereas contributions of pesticides and fertilizers were relatively limited (no more than 1.04%). Cd, Pb, Zn and Cu were easily enriched in surface soils near the surrounding pollution sources, whereas Cr and Ni were easily enriched in the high-terrain area, where there was less of an impact of anthropogenic activities. The superposition of exogenous sources caused accumulation of Cd, Pb and Zn in topsoil, contaminated the subsoil through leaching and improved bioavailability of Cd and Pb, causing high ecological risk for agricultural production. Therefore, Cd and Pb should be paid more attention in future pollution control.

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An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

Cited by 16 publications

References 35 publications

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

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

Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park

Source Identification and Superposition Effect of Heavy Metals (HMs) in Agricultural Soils at a High Geological Background Area of Karst: A Case Study in a Typical Watershed

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