Composition analysis of PM2.5 at multiple sites in Zhengzhou, China: implications for characterization and source apportionment at different pollution levels

Liu, Xiaohan; Jiang, Nan; Zhang, Ruiqin; Yu, Xue; Li, Shengli; Miao, Qing-Qing

doi:10.1007/s11356-020-10943-5

Cited by 11 publications

(4 citation statements)

References 83 publications

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“…Therefore, the SO 2 has the largest impact and plays a dominant role in the large fluctuation part of the haze coupling system except PM 10 and PM 2.5 . The finding is consistent with the source apportionment experiment of PM 2.5 in Zhengzhou [ 47 , 48 , 49 ]. In 2010, the dominant components of PM 2.5 in Zhengzhou are secondary ions (sulfate and nitrate) and carbon fractions, which soluble ions and total carbon contribute 41% and 13% of PM 2.5 mass, respectively [ 47 ].…”

Section: Coupling Detrended Fluctuation Analysis (Cdfa) Of Air Pollut...supporting

confidence: 90%

“…Jiang et al (2017) [ 48 ] conduct the sample source apportionment experiment of PM 2.5 during the periods from 2012 to 2015 in Zhengzhou, and Chemical Mass Balance results show that the contributions of major sources (i.e., nitrate, sulfate, biomass, carbon and refractory material, coal combustion, soil dust, vehicle, and industry) of PM 2.5 are 13%, 16%, 12%, 2%, 14%, 8%, 7%, and 8% in heavily polluted days, with sulfate and nitrate as major ions. In 2016, the sum of the concentrations of

, NO 3− , and NH 4+ increase with the aggravation of pollution level, and the secondary aerosols is the main source of PM 2.5 , which accounts for 38.4% on heavy pollution days in Zhengzhou [ 49 ].…”

Section: Coupling Detrended Fluctuation Analysis (Cdfa) Of Air Pollut...mentioning

confidence: 99%

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Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Linan

Wang²,

Wang

2022

IJERPH

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This paper investigates the multifractal characteristics of six air pollutants using the coupling detrended fluctuation analysis method. The results show that coupling correlations exist among the air pollutants and have multifractal characteristics. The sources of multifractality are identified using the chi square test. The coupling strengths between different pollutants are quantified. In addition, the coupling contribution of a series in the haze system is calculated, and SO2, as the main pollutant, plays a key role in the pollution system. Moreover, the Kriging interpolation method is used to analyze the spatial characteristic on coupling contribution of SO2. The spatial analysis of coupling strength for air pollutants will provide an effective approach for pollution control.

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Section: Coupling Detrended Fluctuation Analysis (Cdfa) Of Air Pollut...supporting

confidence: 90%

Section: Coupling Detrended Fluctuation Analysis (Cdfa) Of Air Pollut...mentioning

confidence: 99%

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Linan

Wang²,

Wang

2022

IJERPH

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“…This mode has a relatively complete process of transport, diffusion and settlement and is an efficient and quick method to analyse the source of pollutants and determine the transmission path. At present, the HYSPLIT trajectory model has been widely used at home and abroad to study the diffusion and transport of pollutants [12,20,21]. The author calculated the backward air mass trajectory for 36 h from January 1, 2014, to December 31, 2018, using the HYSPLIT V4.9 model.…”

Section: Backward Air Mass Trajectorymentioning

confidence: 99%

Distribution Characteristics of Six Criteria Air Pollutants Under Different Air Quality Levels in Cangzhou City, China

Wang¹,

Zhao²,

Xu³

2022

JHER

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The problem of urban air pollution has caused widespread concern and solving the problem of air pollution has become a primary research focus. Cangzhou is one of the "2+26" cities in the air pollution transmission channel in the Beijing-Tianjin-Hebei (BTH) region, and its regional advantage is obvious. To study the distribution characteristics of major air pollutants, the air quality index (AQI) and mass concentrations of six criteria air pollutants, including PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 , from 2014 to 2018 were used. Furthermore, by employing the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the air pollutant concentration level, temporal variations and air mass trajectory characteristics under different air quality levels in Cangzhou city were analysed. The results showed that the mass concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 and CO and PM 2.5 /PM 10 increased successively with increasing pollution level, while the mass concentration of O 3 was at a level of slight pollution, which first increased and then decreased. In the case of serious pollution, PM 2.5 and PM 10 were 3.3 and 2.4 times the Chinese Ambient Air Quality Standard (CAAQS) Grade II standard, respectively, and PM 2.5 /PM 10 was 0.71 times the standard, indicating that as pollution increased, the air pollution gradually became composed of mainly fine particles. The air quality was dominantly good and light, accounting for 73.4% to 84.7% of the total air quality from 2014 to 2018, respectively. The ambient air quality improved annually; the proportion of excellent and good days increased from 42.9% to 63.8%, and the proportion of severe and serious pollution days decreased from 12.2% to 3.7%. The diurnal variations in air pollutants were different under different air quality levels. The air mass trajectory analysis showed that as the pollution level increased, the proportion of eastern and easterly air masses decreased, and the proportion of western and westerly air masses increased gradually. Compared with the CAAQS Grade II standard, the excessive levels of particulate matter increased, and PM 2.5 was the most serious.

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“…Urban PM is mainly generated from sources such as vehicle emissions, road/soil dust, biomass burning, agricultural emissions, and regional transport aerosols; however, studies have not yet quantified the contribution of each source or explained the formation mechanism of PM 5 , 47 . In recent years, several studies have quantified the source apportionment of PM 2.5 in Zhengzhou by using receptor models 46 , 48 – 50 . Nevertheless, the contributions of local emissions and regional transport to PM in Zhengzhou and Henan Province remain unclear, which makes it difficult to understand the source and formation mechanism of PM in this region.…”

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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Composition analysis of PM2.5 at multiple sites in Zhengzhou, China: implications for characterization and source apportionment at different pollution levels

Cited by 11 publications

References 83 publications

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China

Distribution Characteristics of Six Criteria Air Pollutants Under Different Air Quality Levels in Cangzhou City, China

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

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