Exploring the Impact of COVID-19 on the Air Quality in China
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Abstract At the end of 2019, the COVID-19 emerged in Wuhan, China. It has since put global public health institutions on high alert. People in China reduced their traveling, and production has stopped nationwide during the height of the epidemic. This study explores the effects of these COVID-19-derived changes on air quality in China. Air quality data of 367 cities around China were analyzed. The daily air quality index and air pollutant concentrations (CO, O 3 , NO 2 , SO 2 , PM10, and PM2.5) were collected and compared the epidemic period (23.1.2020-23.3.2020) with the preceding two months (22.11.2019-22.1.2020) and the parallel period the year before (23.1.2019-23.3.2019).To compare, we calculated the daily average number of cities with pollution, and the trend in air quality index change. The air quality in the 50 cities with the highest number of confirmed COVID-19 cases and Wuhan was also analyzed. During the period between 23.1.2020 and 23.3.2020, the number of cities with excellent air quality was significantly higher than that in the other two periods. The concentrations of PM2.5, PM10, NO 2 , SO 2 , CO, and O 3 decreased significantly during this period. The most significant decreases were in PM10 and NO 2 . The number of cities with good air quality in the later period was significantly higher than a year before. The air quality has improved significantly during the COVID-19 outbreak. The reason for this change might be changes in human activities such as reduced transportation and production stoppage.Keywords:
Air Pollution Index
2019-20 coronavirus outbreak
Air pollution has become a severe threat and challenge in China. Focusing on air quality in a heavily polluted city (Weifang Cty), this study aims to investigate spatial and temporal distribution characteristics of air pollution and identify the influence of weather factors on primary pollutants in Weifang over a long period from 2014-2018. The results indicate the annual Air quality Index (AQI) in Weifang has decreased since 2014 but is still far from the standard for excellent air quality. The primary pollutants are O3 (Ozone), PM10 (Particles with aerodynamic diameter ≤10 µm), and PM2.5 (Particles with aerodynamic diameter ≤10 µm); the annual concentrations of PM10 and PM2.5 show a significant reduction but that of O3 is basically unchanged. Seasonally, PM10 and PM2.5 show a U-shaped pattern, while O3 exhibits inverted U-shaped variations, and different pollutants also present different characteristics daily. Spatially, O3 exhibits a high level in the central region and a low level in the rural areas, while PM10 and PM2.5 are high in the northwest and low in the southeast. Additionally, the concentration of pollutants is greatly affected by meteorological factors, with PM2.5 being negatively correlated with temperature and wind speed, while O3 is positively correlated with the temperature. This research investigated the spatiotemporal characteristics of the air pollution and provided important policy advice based on the findings, which can be used to mitigate air pollution.
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Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM 2.5 and PM 10 . The influence of relative humidity was most significant for SO 2 and it also had a great influence on O 3 , while wind speed had a great influence on NO 2 . The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives.
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Analyze the air pollution index (API) variation characteristics in Chongqing, and the relationship between API and meteorological elements. Found that air quality of Chongqing is basically at level II. Primary pollutant is inhalable particle (PM10). Summer is the season with best air quality, while winter is the most polluted season. Visibility, precipitation, sunshine and other meteorological elements have a great effect on the variation of air quality. The correlation between air pollution index and visibility is the most significant, which is-0.37. However, inter-monthly variation of monthly total rainfall, monthly total sunshine duration and API shows obvious reverse phase variation.
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In order to reveal the law of the influence of meteorological changes on air pollutants, the author collected the air pollution situation and meteorological data of Wuhan in 2017.Through the analysis of meteorological data and air pollutant data, the time-varying curves of Wuhan air quality index (AQI) and major air pollutants PM2.5, PM10, SO2, NO2, CO and O3 were obtained. Through correlation analysis, the relationship curves of AQI, air pollutants and temperature, rainfall and wind changes are given. The results showed that AQI, PM2.5 and PM10 showed significant u-shaped changes at the monthly scale, with a low level from June to October and a high level at the beginning and end of each year. SO2, NO2 and CO showed "V" changes on the monthly scale, with the lowest points appearing in June, July and July respectively. O3 concentration change in "Λ" type change appears on scales, the highest point in July. Through air pollution index and meteorological data correlation analysis found that: with the increase of temperature, AQI, PM2.5, SO2, NO2, CO concentration decreases, and elevated O3 concentration, PM10 concentration changes with the temperature has little to do; Changes in wind direction, wind is one of the important factors affecting atmospheric pollutants concentration changes, no wind for a long time, can lead to air pollutants gathering, but the wind changes can scrape pollutants, may also put the pollutant of the nonlocal, caused by imported pollution; Spring and winter rainfall can remove air pollutants and improve air quality.
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The characteristic of air quality and its influencing factors are analyzed based on the daily report data of urban air quality and meteorological data in Harbin from 2001 to 2012. The seasonal change of the air pollution index( API) in Harbin showed relatively high in winter and spring and relatively low in summer and fall. The values of API were obviously higher in heating season than that in non-heating season. In Harbin,the key pollutant was inhaleble particles. In 75. 4% of the days,the air pollution levels showed good and moderate. During the 12 years,a significant decreasing trend of API indicates the improvement of air quality in Harbin. The variations of meteorological parameters can influence the level of air pollutants,which show a positive correlations between API and wind speed,and a negative correlations between API and rainfall,air temperature, and visibility. The pattern and origination of the middle-polluted air mass appeared in Harbin was analyzed by backward trajectory analysis.
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Air quality monitoring study at Govt. Board (MPCB) was undertaken. The secondary data were collected from the MPCB web site from the period 1 Feb. to 17 Nov.32014.Twenty3 four hours air monitoring sampling was carried out by using continuous monitoring station (analysis Unit) and Reparable Suspended particulate matter sampler. Air Quality Index (AQI) and Time Series Analysis for Seasonal variation was employed for analysis of ambient air quality monitoring data. The criteria for pollutants included in the study were sulphur dioxide (SO (RSPM) below 10 micron partials. The results indicated that the RSPM extensively contribute toward air pollution at this location throughout study period.
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The 2004-2009 urban air quality monitoring data in Luzhou city was analyzed to find out the changing trends.It is shown that the air quality of Luzhou in the past 6 years was good,the average air pollution index was 80 and the number of days more than second air quality standard occupied 72.33%.The air quality became better gradually,especially the average concentration of SO2 and PM10 dropped obviously as well as that of NO2 increased a little.The distributions of these air pollutants varied with time and space.The average concentration of SO2 and PM10 was lower in summer than in winter;the seasonal character of NO2 was not obvious.The average concentrations of the three air pollutants at Lantianxianqiao and Xiaoshishangmatou stations were higher than those at the Zhongshan environmental monitoring station.
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The climate change may be mitigated, and intra air quality assessment and local human well-being can benefit from a decrease in emission of pollutant content in the air. Monitoring the quality of the air around us is one way to do this. However, a location with various emission sources and short-term fluctuations in emissions in both time and space, and changes in winds, temperature, and precipitation creates a complex and variable pollution concentration field in the atmosphere. Therefore, based on the time and location where the sample is obtained, the measurement conducted are reflected in the monitoring results. This study aims to investigate one of India's most polluted cities' air quality measurements by greenhouse gas emissions. Using the Mann-Kendall and Sen's slope estimators, the research piece gives a statistical trend analysis of several air contaminants based on previous pollution data from Mumbai, India's air quality index station. In addition, future levels of air pollution may be correctly forecasted using an autoregressive integrated moving average model. This is followed by comparing different air quality standards and forecasts for future air pollution levels.
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