Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Sun, Xue; Luo, Xiao-San; Xu, Jiangbing; Zhao, Zhen; Chen, Yan; Wu, Li-Chun; Chen, Qi; Zhang, Dan

doi:10.1038/s41598-019-40426-8

Cited by 36 publications

(26 citation statements)

References 47 publications

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“…e NO 2 is mainly located in southern Jiangsu. e reason for this phenomenon may be due to the influence of monsoon climate and the development of local heavy industry, the particulate matter concentration in the inland area of northern Jiangsu is relatively high, while the NO 2 content is significant due to the population and car ownership in southern Jiangsu [39]. O 3 is higher in spring and summer, and the other five main pollutants are the highest in winter.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Spatiotemporal Distribution of Air Pollution Characteristics in Jiangsu Province, China

Song

Yang

Liu

et al. 2019

Advances in Meteorology

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Following the deepening of climate change and the increasing industrialization in recent years, the problem of air pollution in cities has become increasingly prominent. Based on the data of air pollutants and meteorological elements in Jiangsu Province, China (2013–2017), we analyze the spatiotemporal characteristics of air pollution. The results show that the air-quality index (AQI) in Jiangsu Province decreased from 2013 to 2017 and the highest AQI is in winter and the lowest in the summer, while its values in coastal cities of Jiangsu are less than those of inland cities. For the temporal distribution of primary pollutants, PM2.5, PM10, SO2, NO2, and CO present the same trend under seasonal and monthly time scales, i.e., winter is higher and summer is lower; however, the other secondary pollutant, O3, presents opposite characteristics under the same time scale: it has higher concentration levels in summer and lower in winter. For the spatial distribution, PM2.5 and PM10 are in good concord: the higher values are found in the west of Jiangsu Province and lower in the east. For the spatial distribution of NO2, this presents higher concentrations in south and lower concentrations in north according to the position of Yangzi River, while the distribution of O3 concentration is opposite to that of NO2. The meteorological elements selected are related to air pollution, the AQI is significantly negatively correlated with monthly temperature (including average, minimum, and maximum temperatures), monthly average water vapor pressure, monthly precipitation, and monthly sunshine duration; the correlation coefficients are –0.852, –0.846, –0.850, –0.797, –0.727, and –0.599, respectively. As far as the relationships between air pollutions are concerned, there is a significant positive correlation between AQI, PM2.5, PM10, SO2, and NO2, while O3 is remarkably negatively correlated with other pollutants and AQI. The most prominent correlations are distinguished into two groups: one is AQI, PM2.5, and PM10, with correlation coefficients of 0.876 and 0.966, and the other is SO2, NO2, with correlation coefficient of about 0.9.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Spatiotemporal Distribution of Air Pollution Characteristics in Jiangsu Province, China

Song

Yang

Liu

et al. 2019

Advances in Meteorology

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“…The MAE and RMSE of the Kriging circular mode method are the smallest, which indicates that the method can reflect the spatial and temporal distribution pattern of Xi'an and five provinces air quality scientifically. Therefore, the circular function in Kriging method is used as the generation method of air quality surface data 24,25 .…”

Section: Spatial Interpolation and General G Spatial Interpolation Imentioning

confidence: 99%

Research on PM2.5 concentration based on dissipative structure theory: a case study of Xi’an, China

Sun

Hong

Liu

et al. 2020

Sci Rep

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PM2.5 pollution has become a serious urban environmental problem, especially in developing countries with increasing urbanization. Understanding the proportion of PM2.5 generation sources has laid a foundation for better PM2.5 concentration reduction This paper used Point of Interesting (POI)data, building profile data of Xi’an, PM2.5 concentration and wind monitoring data of five provinces near Xi’an as the basic data. And this paper studied the spatial distribution of various buildings in Xi’an, the temporal and spatial distribution of PM2.5 in Xi’an and the five provinces, and found that the spatial distribution of PM2.5 concentration in Xi’an and the building distribution in Xi’an does not match. Based on this, a quantitative model of PM2.5 concentration in Xi’an, energy consumption, wind, and other factors is established through the qualitative and quantitative analysis of PM2.5 concentration in Xi’an. Entropy theory and dissipative structure theory are applied to analyze this phenomenon. The results show PM2.5 in Xi’an mainly comes from the spread of PM2.5 in the five provinces. The PM2.5 generated by energy consumption in Xi’an is not enough to cause serious PM2.5 pollution. And further suggestions on how to reduce PM2.5 concentration in Xi’an are put forward.

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“…With the rapid industrialization and urbanization process over the past three decades, atmospheric pollution caused by large energy consumption has become one of the most ubiquitous and concerning issues in China [11]. The air quality in more than three-quarters of cities in China exceeded the national air quality standards in 2016 [12]. In order to solve the air pollution problem, the Chinese government proposed a range of moves to control the air pollution, such as phase-out of backward production facilities, increasing urban green space, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…For the past several years, researchers have conducted numerous studies on the spatio-temporal variations of PM 2.5 concentration and the influence of human activities and natural conditions on it [12,[18][19][20]. For instance, Chu and Bilal [16] used integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models for mapping PM 2.5 based on satellite-derived Aerosol Optical Depth (AOD) data in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Wang

Samat

Abuduwaili

et al. 2020

IJERPH

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With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.

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Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013–2017 by geostatistics

Cited by 36 publications

References 47 publications

Spatiotemporal Distribution of Air Pollution Characteristics in Jiangsu Province, China

Spatiotemporal Distribution of Air Pollution Characteristics in Jiangsu Province, China

Research on PM2.5 concentration based on dissipative structure theory: a case study of Xi’an, China

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

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