scholarly journals Research on the changes and influencing factors of Air pollution Policy in China

2021 ◽  
Vol 632 ◽  
pp. 052041
Author(s):  
Chunfang Liu
Keyword(s):  
Air Pollution ◽  
Influencing Factors ◽  
Air Pollution Policy

Modeling the joint impacts of ozone and aerosols on crop yields in China: An air pollution policy scenario analysis

2021 ◽  
Vol 247 ◽  
pp. 118216
Author(s):  
Tianyi Zhang ◽  
Xu Yue ◽  
Nadine Unger ◽  
Zhaozhong Feng ◽  
Bangyou Zheng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Scenario Analysis ◽  
Crop Yields ◽  
Policy Scenario ◽  
Air Pollution Policy

scholarly journals Extracting relationship between air pollution and precipitation using spatio-temporal analysis in Tehran metropolis

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Shokouh Dareshiri ◽  
Mohammadreza Sahelgozin ◽  
Maryam Lotfian ◽  
Jens Ingensand
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Regression Model ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Human Life ◽  
Pearson Correlation ◽  
Influencing Factor ◽  
Polynomial Equation ◽  
Temporal Analysis

<p><strong>Abstract.</strong> Precipitation is one of the main stages of the water cycle, and it is required for the organisms to survive on the planet. In contrast, air pollution is a phenomenon that has greatly affected the human life nowadays. Population growth, development of factories and increasing number of fossil fuel vehicles are the most influencing factors on air pollution. In addition to understand nature of precipitation and air pollution, finding relationship between these two phenomena is necessary to make appropriate policies for reducing air pollution. Furthermore, studying trends of precipitation and air pollution in the past, is helpful to forecast the times and places with less precipitation and more air pollution for a better urban management. In this study, we tried to extract any probable relationship between these two parameters by investigating their monthly measured amounts in 22 municipal districts of Tehran in three epochs of time (2009, 2013 and 2017). Carbon Monoxide (CO) was considered as the indicator of air pollution. Results of the study show that the parameters have a significant relationship with each other. By using Pearson Correlation Coefficient and One-Way Variance (ANOVA) test, relationship between the data for each month and for each district of Tehran were studied separately. As the time has passed and the air pollution has increased, the correlation between the parameters in districts has decreased. In addition, during the cold months of the year, the correlations decrease since the fact that precipitation is not the only influencing factor on the air pollution due to the rise of air “Inversion”. Finally, the polynomial regression model of carbon monoxide based on precipitation was extracted for each of the three years. The model suggests a degree three polynomial equation. The obtained coefficients from the regression model show that the relationship between parameters was stronger in the years with more rainfalls. This can be due to the more significant impact of other influencing factors on air pollution, such as population density, wind direction, vehicles and factories in the areas or conditions with a less rainfall.</p>

scholarly journals Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1742 ◽  
Author(s):  
Ruoyu Yang ◽  
Weidong Chen
Keyword(s):  
Air Pollution ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Population Pressure ◽  
Spatial Data Analysis ◽  
So2 Emissions ◽  
Political Movement ◽  
The Impact

In order to study the present situation regarding SO2 emissions in China, problems are identified and countermeasures and suggestions are put forward. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.

The characteristics of air pollution induced by the quasi-stationary front: Formation processes and influencing factors

2020 ◽  
Vol 707 ◽  
pp. 136194 ◽  
Author(s):  
Xin Wu ◽  
Mengren Li ◽  
Jinsheng Chen ◽  
Hong Wang ◽  
Lingling Xu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Influencing Factors ◽  
Formation Processes ◽  
Stationary Front

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.

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