scholarly journals Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

2018 ◽  
Vol 10 (9) ◽  
pp. 2960 ◽  
Author(s):  
Yixiao Li ◽  
Zhaoxin Dai ◽  
Xianlin Liu
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Influencing Factors ◽  
Human Activities ◽  
Contemporary Society ◽  
Spatial Characteristics ◽  
Temporal Characteristics ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
The Government

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime.

scholarly journals The Impact of “Coal to Gas” Policy on Air Quality: Evidence from Beijing, China

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3876 ◽  
Author(s):  
Zhe Liu ◽  
Xueli Chen ◽  
Jinyang Cai ◽  
Tomas Baležentis ◽  
Yue Li
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Energy Use ◽  
Northern China ◽  
Heating System ◽  
Use Efficiency ◽  
The Government ◽  
The Impact ◽  
Beijing China

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The “coal to gas” policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the “coal to gas” policy on air quality. Strong evidence shows that the “coal to gas” policy has significantly improved the air quality in Beijing. On average, the “coal to gas” policy reduced sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the “coal to gas” policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the “coal to gas” policy, so as to alleviate the air pollution in Beijing, China.

scholarly journals 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

2020 ◽  
Vol 17 (17) ◽  
pp. 6274 ◽  
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.

scholarly journals Analysis on Spatio-Temporal Characteristics and Influencing Factors of Industrial Green Innovation Efficiency—From the Perspective of Innovation Value Chain

2021 ◽  
Vol 14 (1) ◽  
pp. 342
Author(s):  
Pengzhen Liu ◽  
Liyuan Zhang ◽  
Heather Tarbert ◽  
Ziyu Yan
Keyword(s):  
Influencing Factors ◽  
Value Chain ◽  
High Efficiency ◽  
Industrial Structure ◽  
Coastal Region ◽  
Green Innovation ◽  
Clear Trend ◽  
Temporal Characteristics ◽  
Spatio Temporal ◽  
The Government

Green innovation has become an important combination of high-quality economic growth and ecological sustainability. In this paper, the super-efficiency network SBM model was used to measure the two-stage green innovation efficiency of the industrial technology research and development (R&D) stage and achievement transformation stage in China (30 provinces and cities) from 2009 to 2019. The results show the following points. Firstly, in terms of temporal series, the efficiency of technology R&D and achievement transformation has experienced three stages of “upward-declining-revitalized period”. Secondly, in terms of spatial trend, the industrial green innovation efficiency gradually increases from northwest to southeast. The high-efficiency areas are still concentrated in the eastern coastal region, with a clear trend towards balanced development in the central and western regions. Finally, openness, industrial structure, government technical expenditures, enterprise scale, and environmental regulation all have different degrees of impact on the efficiency of green innovation in the two stages. Based on the above, this paper is helpful for the government to formulate laws and regulations and coordinate the level of regional economic development and clarify the spatio-temporal characteristics and influencing factors of the efficiency of green innovation.

scholarly journals Haze Influencing Factors: A Data Envelopment Analysis Approach

2019 ◽  
Vol 16 (6) ◽  
pp. 914 ◽  
Author(s):  
Yi Zhou ◽  
Lianshui Li ◽  
Ruiling Sun ◽  
Zaiwu Gong ◽  
Mingguo Bai ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Influencing Factors ◽  
Human Activities ◽  
Social Progress ◽  
Observation Data ◽  
Data Envelopment ◽  
Coal Consumption ◽  
Stochastic Dea ◽  
The Government ◽  
Meteorological Elements

This paper investigates the meteorological factors and human activities that influence PM2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relationship between the meteorological elements and human activities and PM2.5 pollution. The results are summarized by the following: (1) Among all five primary indexes, social progress, energy use and transportation are the most significant for PM2.5 pollution. (2) Among our selected 14 secondary indexes, coal consumption, population density and civil car ownership account for a major portion of PM2.5 pollution. (3) Human activities are the main factor producing PM2.5 pollution. While some meteorological elements generate PM2.5 pollution, some act as influencing factors on the migration of PM2.5 pollution. These findings can provide a reference for the government to formulate appropriate policies to reduce PM2.5 emissions and for the communities to develop effective strategies to eliminate PM2.5 pollution.

scholarly journals Impact of air pollution on human activities: Evidence from nine million mobile phone users

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251288
Author(s):  
Wei Chen ◽  
YingHua He ◽  
Shiyuan Pan
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Mobile Phone ◽  
Human Activities ◽  
Home Ranges ◽  
Shopping Malls ◽  
Government Regulations ◽  
Median Distance ◽  
Hourly Data ◽  
Number Of Individuals

To measure the effects of air pollution on human activities, this study applies statistical/econometric modeling to hourly data of 9 million mobile phone users from six cities in China’s Zhejiang Province from December 18 to 21, 2013. Under a change in air quality from “Good” (Air Quality Index, or AQI, between 51 and 100) to “Heavily Polluted” (AQI between 201 to 300), the following effects are demonstrated. (i) Consistent with the literature, for every one million people, 1, 482 fewer individuals are observed at parks, 95% confidence interval or CI (−2, 229, −735), which represents a 15% decrease. (ii) The number of individuals at shopping malls has no statistically significant change. (iii) Home is the most important location under worsening air quality, and for every one million people, 63, 088 more individuals are observed at home, 95% CI (47, 815, 78, 361), which represents a 19% increase. (iv) Individuals are on average 633 meters closer to their home, 95% CI (529, 737); as a benchmark, the median distance from home ranges from 300 to 1900 meters across the cities in our sample. These effects are not due to weather or government regulations. We also provided provisional evidence that individuals engage in inter-temporal activity substitutions within a day, which leads to mitigated (but not nullified) effects of air pollution on daily activities.

AMBIENT PARTICULATE MATTER (PM10 AND PM2.5) AIR POLLUTION IN DESNIANSKY DISTRICT IN KYIV

2017 ◽  
Vol 2017 (67) ◽  
pp. 31-37
Author(s):  
O. Turos ◽  
◽  
T. Maremukha ◽  
I. Kobzarenko ◽  
A. Petrosian ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ambient Particulate Matter ◽  
Pm10 And Pm2.5

PROBLEMS CAUSED BY SMALL PIG FARM ODOR AROUND THE WESTERN SONGKHLA LAKE BASIN

2017 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
Papontee Teeraphan
Keyword(s):  
Air Pollution ◽  
Lake Basin ◽  
Urgent Problem ◽  
The Public ◽  
Pig Farms ◽  
Pig Farm ◽  
The Government ◽  
The Moment ◽  
Source Of Information ◽  
Proper Solutions

Pollution is currently a significant issue arising awareness throughout the world. In Thailand, pollution can often be seen in any part of the country. Air pollution is pointed as an urgent problem. This pollution has not damaged only to human health and lives, it has destroyed environment, and possibly leading to violence. In Phattalung, air pollution is affecting to the residents’ lives. Especially, when the residents who are mostly agriculturists have not managed the waste resulted from the farm. In Phattalung, at the moment, there are many pig farms, big and small. Some of them are only for consuming for a family, some, however, are being consumed for the business which pigs will be later purchased by big business companies. Therefore, concerning pollution, the researcher and the fund giver were keen to focus on the points of the air pollution of the small pig farms. This is because it has been said that those farms have not been aware on the pollution issue caused by the farms. Farm odor is very interesting which can probably lead to following problems. The researcher also hopes that this research can be used as a source of information by the government offices in order to be made even as a policy or a proper legal measurement. As the results, the study shows that, first, more than half of the samples had smelled the farm odor located nearby their communities, though it had not caused many offenses. Second, the majority had decided not to act or response in order to solve the odor problem, but some of them had informed the officers. The proper solutions in reducing offenses caused by pig farm odor were negotiation and mediation. Last, the majority does not perceive about the process under the Public Health Act B.E. 2535.

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

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