Analysis of Air Pollution Influencing Factors of PM2.5 Secondary Particles by Random Forest

<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>

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

International Journal of Environment and Pollution ◽

10.1504/ijep.2021.10037408 ◽

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

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Influencing Factors of Students’ Online Learning Satisfaction During the COVID-19 Outbreak: An Empirical Study Based on Random Forest Algorithm

Learning Technologies and Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-66906-5_10 ◽

2021 ◽

pp. 103-114

Author(s):

Yuzhuo Zhang ◽

Po Zhang ◽

Hongji Yang ◽

Kangrui Zhao ◽

Chunmei Han

Keyword(s):

Online Learning ◽

Random Forest ◽

Empirical Study ◽

Influencing Factors ◽

Random Forest Algorithm ◽

Learning Satisfaction

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

Sustainability ◽

10.3390/su11061742 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1742 ◽

Cited By ~ 6

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.

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

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.136194 ◽

2020 ◽

Vol 707 ◽

pp. 136194 ◽

Cited By ~ 1

Author(s):

Xin Wu ◽

Mengren Li ◽

Jinsheng Chen ◽

Hong Wang ◽

Lingling Xu ◽

...

Keyword(s):

Air Pollution ◽

Influencing Factors ◽

Formation Processes ◽

Stationary Front

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051598 ◽

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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How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176274 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6274 ◽

Cited By ~ 2

Author(s):

Zhiyu Fan ◽

Qingming Zhan ◽

Chen Yang ◽

Huimin Liu ◽

Meng Zhan

Keyword(s):

Air Pollution ◽

Regression Analysis ◽

Particulate Matter ◽

Socioeconomic Factors ◽

Influencing Factors ◽

Geographically Weighted Regression ◽

Meteorological Factors ◽

Central China ◽

Weighted Regression ◽

Pm2.5 And Pm10

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

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A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

Environmental Health ◽

10.1186/s12940-021-00825-9 ◽

2022 ◽

Vol 21 (1) ◽

Author(s):

Luca Boniardi ◽

Federica Nobile ◽

Massimo Stafoggia ◽

Paola Michelozzi ◽

Carla Ancona

Keyword(s):

Machine Learning ◽

Land Use ◽

Air Pollution ◽

Random Forest ◽

Citizen Science ◽

Learning Approach ◽

Machine Learning Approach ◽

Forest Models ◽

Random Forest Models ◽

The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

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Exploring Influencing Factors of Chronic Obstructive Pulmonary Disease Based on Elastic Net and Bayesian Network

10.21203/rs.3.rs-1163618/v1 ◽

2022 ◽

Author(s):

Dichen Quan ◽

Jiahui Ren ◽

Hao Ren ◽

Liqin Linghu ◽

Xuchun Wang ◽

...

Keyword(s):

Air Pollution ◽

Family History ◽

Influencing Factors ◽

Smoking Status ◽

Elastic Net ◽

Household Air Pollution ◽

Shanxi Province ◽

Chronic Obstructive ◽

Targeted Prevention ◽

Network Relationship

Abstract This study aimed to construct Bayesian networks(BNs) to analyze the network relationship between those influencing factors and COPD, and to explore their intensity of effect on COPD through network reasoning. Elastic Net and Max-Min Hill-Climbing(MMHC) hybrid algorithm were adopted to screen the variables on the monitoring data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. After variables selection by Elastic Net, 10 variables closely related to COPD were selected finally. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients’ cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network relationship between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.

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