Analysis of Spatio-Temporal Variation Characteristics of Main Air Pollutants in Shijiazhuang City

Tui, Yue; Qiu, Jiaxin; Ju, Wang; Fang, Chunsheng

doi:10.3390/su13020941

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…The average mass concentration of O 3 in spring and summer was significantly higher than that in autumn and winter, and showed obvious diurnal variation characteristics. This is consistent with the research results of Tui et al [28]. During the period from 11:00 to 14:00 in summer, the concentration of O 3 rose rapidly, reaching a peak of 111 µg/m 3 at 18:00; then, with the weakening of solar radiation, the temperature decreased, and so did the concentration of O 3 .…”

Section: Seasonal Hourly Mean Changesupporting

confidence: 93%

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

Fang

Shi

et al. 2023

IJERPH

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With cotton production in Xinjiang increasing annually, the impact on the environment of agricultural waste produced to improve production has been reflected. This study selected Bozhou of Xinjiang, the main cotton producing region in northern Xinjiang, as the research object, and collected hourly concentration data of six pollutants from 2017 to 2021, and analyzed the spatial and temporal distribution characteristics of each pollutant. At the same time, Morlet wavelet analysis was used to further analyze the variation period of PM2.5 (PM particles with aerodynamic diameters less than 2.5μm) concentration. The Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) model was used to evaluate the emissions reduction measures for the most polluted month. The results showed that the concentration of particulate matter (PM particles with aerodynamic diameters less than 2.5μm and 10μm) decreased from the southern mountains to the north; moreover, the concentrations of CO (carbon monoxide), NO2 (nitrogen dioxide), and SO2 (sulfur dioxide) in the suburbs were higher than those in the urban center. The concentration of O3 (Ozone) was the highest in summer, while the concentrations of other pollutants were high in autumn and winter. Under the time scale of a = 13, 24, PM2.5 had significant periodic fluctuation. The health risk values of PM2.5 and PM10 in this study were within the scope of the United States Environmental Protection Agency (USEPA) criteria, but it is still necessary to keep a close watch on them. In the context of emissions reduction measures, agricultural sources reduced by 20%, residential sources by 40%, industrial sources by 20%, and transportation sources by 20%; no change in the power source remains. Under these conditions, the daily average value of each pollutant met the first level of the national ambient air quality standard. The research results provide a reference for the local government to formulate heavy pollution emissions reduction policies.

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Section: Seasonal Hourly Mean Changesupporting

confidence: 93%

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

Fang

Shi

et al. 2023

IJERPH

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“…In spring and summer, it is significantly higher than that in autumn and winter. The seasonal pattern of ozone in central Liaoning urban agglomeration is the same as that in other regions, with the overall pattern as summer > spring > autumn > winter [45][46][47]. In summer, the O3 concentration in Shenyang is 131.14 µg/m 3 , and the O3 concentration in Benxi is 95.3 µg/m 3 .…”

Section: Temporal Variationmentioning

confidence: 96%

Temporal and Spatial Analysis of PM2.5 and O3 Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

Zhong

et al. 2022

Sustainability

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The central Liaoning urban agglomeration is an important heavy industry development base in China, and also an important part of the economy in northeast China. The atmospheric environmental problems caused by the development of heavy industry are particularly prominent. Trajectory clustering, potential source contribution (PSCF), and concentration weighted trajectory (CWT) analysis are used to discuss the temporal and spatial pollution characteristics of PM2.5 and ozone concentrations and reveal the regional atmospheric transmission pattern in central Liaoning urban agglomeration from 2015 to 2020. The results show that: (1) PM2.5 in the central Liaoning urban agglomeration showed a decreasing trend from 2015 to 2020. The concentration of PM2.5 is the lowest in 2018. Except for Benxi (34.7 µg/m3), the concentrations of PM2.5 in other cities do not meet the standard in 2020. The ozone concentration in Anshan, Liaoyang, and Shenyang reached the peaks in 2017, which are 68.76 µg/m3, 66.27 µg/m3, and 63.46 µg/m3 respectively. PM2.5 pollution is the highest in winter and the lowest in summer. The daily variation distribution of PM2.5 concentration showed a bimodal pattern. Ozone pollution is the most serious in summer, with the concentration of ozone reaching 131.14 µg/m3 in Shenyang. Fushun is affected by Shenyang intercity pollution, and the ozone concentration is high. (2) In terms of spatial distribution, the high values of PM2.5 are concentrated in monitoring stations in urban areas. On the contrary, the concentration of ozone in suburban stations is higher. The high concentration of ozone in the northeast of Anshan, Liaoyang, Shenyang to Tieling, and Fushun extended in a band distribution. (3) Through cluster analysis, it is found that PM2.5 and ozone in Shenyang are mainly affected by short-distance transport airflow. In winter, the weighted PSCF high-value area of PM2.5 presents as a potential contribution source zone of the northeast trend with wide coverage, in which the contribution value of the weighted CWT in the middle of Heilongjiang is the highest. The main potential source areas of ozone mass concentration in spring and summer are coastal cities and the Bohai Sea and the Yellow Sea. We conclude that the regional transmission of pollutants is an important factor of pollution, so we should pay attention to the supply of industrial sources and marine sources of marine pollution in the surrounding areas of cities, and strengthen the joint prevention and control of air pollution among regions. The research results of this article provide a useful reference for the central Liaoning urban agglomeration to improve air quality.

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“…The evolution trend of air quality differences in UAs was tested by σ convergence, β convergence, and club convergence. In Equation (12), j and i represent the UA and the cities included therein; n j is the number of cities included in UA j; EE ij is the average air quality of UA j; and EE i,t+1 and EE i,t refer to the air quality of UA i at time t + 1 and t, respectively.…”

Section: Convergence Modelmentioning

confidence: 99%

“…Research on air quality has been conducted with regard to two aspects: the spatiotemporal distribution and the influencing factors of air quality. Firstly, relevant studies reveal obvious regional differences in air quality [9][10][11][12]. China's air quality index (AQI) shows a spatial distribution pattern of "high in the north and low in the south" [13].…”

Section: Introductionmentioning

confidence: 99%

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

Xue

Liu

2022

Sustainability

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The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

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Analysis of Spatio-Temporal Variation Characteristics of Main Air Pollutants in Shijiazhuang City

Cited by 11 publications

References 24 publications

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

Temporal and Spatial Analysis of PM2.5 and O3 Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

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