Multiple perspectives for modeling regional PM2.5 transport across cities in the Beijing–Tianjin–Hebei region during haze episodes

Zhang, Hanyu; Cheng, Shuiyuan; Yao, Sen; Wang, Xiaoqi; Zhang, Junfeng

doi:10.1016/j.atmosenv.2019.05.031

Cited by 70 publications

(28 citation statements)

References 54 publications

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“…Cluster analysis and scene analysis, based on the backward trajectory model, are effective methods for studying the characteristics of air pollutants’ transport pathways and diffusion [ 15 , 16 , 17 ]. Zhu et al analyzed the pollutant transport path and potential sources during haze days in Hangzhou, Hefei, Nanjing, and Shanghai to characterize the haze pollution in the Yangtze River Delta [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Meng

et al. 2020

IJERPH

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Air pollution has attracted increasing attention in recent years. Cluster analysis, scene analysis, and the potential source contribution function (PSCF), based on the backward trajectory model, were used to identify the transport pathways and potential source regions of PM2.5 and PM10 (particulate matter with an aerodynamic diameter of not more than 2.5 µm and 10 µm) in Changchun in 2018. In addition, the PSCF was slightly improved. The highest average monthly concentrations of PM2.5 and PM10 appeared in March and April, when they reached 53.9μg/m3 and 120.0 μg/m3, respectively. The main potential source regions of PM2.5 and PM10 were generally similar: western Jilin Province, northwestern Inner Mongolia, northeastern Liaoning Province, and the Yellow Sea region. The secondary potential source regions were southern Russia, central Mongolia, western Shandong Province, eastern Hebei Province, and eastern Jiangsu Province. The northwest and southwest directions were found to be the two pathways that mainly affect the air quality of Changchun City. Moreover, the northwestern pathway had a larger potential contribution source area than the southwestern pathway. The airflow in the southwest direction came from Liaoning Province, Shandong Province, and the Yellow Sea region. This mainly occurred in summer; its transmission distance was short; it had a relatively higher weight potential source contribution function (WPSCF) value; it can be regarded as a local source; and its representative pollutants were SO2 (sulfur dioxide), CO (carbon monoxide), and O3 (ozone). The northwestern pathway passed through Russia, Mongolia, and Inner Mongolia. The transmission distance of this pathway was longer; it had a relatively lower WPSCF value; it can be considered as a natural source to a certain extent; it mainly occurred in autumn and, especially, in winter; and the representative pollutants of this pathway were NO (nitric oxide), NOx (nitrogen oxide), PM2.5, and PM10.

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Section: Introductionmentioning

confidence: 99%

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Meng

et al. 2020

IJERPH

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“…The most stringent regulation "Air Pollution Prevention Action Plan" was enacted on September 10, 2013 and implemented on 1 January 2015. Since 2013, the real-time monitoring of six air pollutants, i.e., particles with an aerodynamic diameter equal to or less than 2.5 µm (PM 2.5 ), particles with an aerodynamic diameter equal to or less than 10 µm (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ),…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China

Zhou

et al. 2020

Atmosphere

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Air pollution shows a generally decreasing trend from the north to the south in China since 2013 (GB3095-2012, the current standard for monitoring). However, an opposite observation was recorded in 2017, especially in winter. In this study, we collected monitoring data of six air pollutants in 2016 and 2017, from a northern city (Beijing) and a southern city (Nanjing) for comparison. As air pollution was highly dependent upon meteorological conditions, we further analyzed their relationships to explain this abnormal phenomenon. Seasonal averaged PM2.5, PM10, SO2, CO, and NO2 were negatively correlated with wind scale (WS) while 8-h O3 exhibited an opposite relationship. Relative humidity (RH) has opposite effects on the concentrations of different pollutants in Beijing and Nanjing. The 8-h O3 showed the closest positive correlation with temperature (T), which is due to its formation mechanism. In Beijing, decreased RH, together with more wind from northwest in winter, resulted in an improved air quality in 2017. In Nanjing, WS, RH, T, and wind direction fluctuated within a narrow range in each season, leading to relatively stable pollutant concentrations. These results suggest that meteorological conditions are important factors to evaluate the air quality and implement control measures.

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“…Partial cities close to Beijing are shown in Figure 1, including Zhangjiakou, Chengde, Qinhuangdao, Langfang, and Tianjin. [35][36][37] This article uses these regions for experiments. In particular, the distance is likely to cause the oxide propagation time lag.…”

Section: Study Area and Data Preprocessingmentioning

confidence: 99%

Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

Zhang

Chen

et al. 2020

Concurrency and Computation

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The formation of haze depends on the complex evolution behavior of SO 2 , NO 2 , and CO. We explore the influence of surrounding urban oxides on haze formation in Beijing. From the perspective of time evolution, multifractal detrended cross-correlation analysis (MF-DCCA) method quantitatively shows that all oxides reveal persistent cross-correlations (h xy : 0.640 sim; 0.995) in the short term and long term. The correlation characteristics of the same oxide in different regions are compared. In the short term, Zhangjiakou (SO 2 /NO 2) and Tianjin (CO) have the strongest multifractal features, while Zhangjiakou (SO 2 /NO 2 /CO) is more prominent in the long term. Their sources are mainly Fat-tailed. Ignoring the characteristics of timing, the fitting degree (R 2) of LightGBM model to PM2.5 concentration regression is 0.862 after the addition of neighboring oxides, an increase of 2.0%. CatBoost's R 2 improves by 6.4%. The feature importance score indicates that Langfang's SO 2 and Chengde's CO contribute the most to the formation of PM2.5. This study has certain reference value for the formulation of oxide emission strategy in the surrounding area of Beijing.

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Multiple perspectives for modeling regional PM2.5 transport across cities in the Beijing–Tianjin–Hebei region during haze episodes

Cited by 70 publications

References 54 publications

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China

Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

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Multiple perspectives for modeling regional PM2.5 transport across cities in the Beijing–Tianjin–Hebei region during haze episodes

Cited by 70 publications

References 54 publications

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Pollution Characteristics, Transport Pathways, and Potential Source Regions of PM2.5 and PM10 in Changchun City in 2018

Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China

Study on the influence of surrounding urban SO2, NO2, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms

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Study on the influence of surrounding urban SO₂, NO₂, and CO on haze formation in Beijing based on MF‐DCCA and boosting algorithms