Measurement of cooperative degree among participants in multi-cooperative air pollution control in Beijing and its influencing factors

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Yongping Chen ◽  
Duofen Chang ◽  
Changsheng Meng
Keyword(s):  
Air Pollution ◽  
Influencing Factors ◽  
Pollution Control ◽  
Air Pollution Control

scholarly journals Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing–Tianjin–Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

2020 ◽  
Vol 17 (5) ◽  
pp. 1598
Author(s):  
Jianhui Qin ◽  
Suxian Wang ◽  
Linghui Guo ◽  
Jun Xu
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Influencing Factors ◽  
Pollution Control ◽  
Sunshine Duration ◽  
Spatial Association ◽  
Henan Province ◽  
Air Pollution Control ◽  
Transmission Channel ◽  
Pollution Transmission

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O3 doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O3 pollution control.

A Method for Mining Granger Causality Relationship on Atmospheric Visibility

2021 ◽  
Vol 15 (5) ◽  
pp. 1-16
Author(s):  
Bo Liu ◽  
Xi He ◽  
Mingdong Song ◽  
Jiangqiang Li ◽  
Guangzhi Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Causal Relationship ◽  
Granger Causality ◽  
Influencing Factors ◽  
Pollution Control ◽  
Gradient Boosting ◽  
Air Pollution Control ◽  
Atmospheric Visibility ◽  
Causality Relationship

Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.

The Zhenhua Case: the emergence of civil environmental public interest litigation in China

2021 ◽  
Vol 14 (2) ◽  
pp. 116-128
Author(s):  
Wenjun Yan
Keyword(s):  
Air Pollution ◽  
Environmental Protection ◽  
Public Interest ◽  
Pollution Control ◽  
Environmental Damage ◽  
Air Pollution Control ◽  
Public Interest Litigation ◽  
First Time ◽  
Restoration Cost

Abstract In 2015, the All-China Environment Federation v Dezhou Jinghua Group Zhenhua Corporation Limited case was the first civil environmental public interest litigation (CEPIL) against air pollution in China. Constituting a milestone in the field of air pollution control in China, this case (i) confirms the eligibility of a non-governmental organisation (NGO) to file civil public interest litigations; (ii) discusses remedies for the ecological destruction caused by air pollution; (iii) assesses the ecological and environmental damage using the ‘virtual restoration cost’ method; and (iv) uses public apology as an innovative way for Zhenhua to assume liability. By applying and interpreting several important rules under the Environmental Protection Law of China (EPLC) for the first time, this case sets an example for future CEPILs against air pollution in China.

How to realize the effect of air pollution control? A hybrid decision framework under the fuzzy environment

2021 ◽  
Vol 305 ◽  
pp. 127093
Author(s):  
Yao Tao ◽  
Yunna Wu ◽  
Jianli Zhou ◽  
Man Wu ◽  
Shiman Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Air Pollution Control ◽  
Decision Framework ◽  
Fuzzy Environment
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