Comparative analysis of particulate matter (PM2.5, PM10) and trace gases (SO2, NO2, O3) in between satellite derived data and ground based instruments
Zainab MushtaqPargin BangotraSamreen SajadAlok Sagar GautamManish SharmaK. P. SinghYogesh KumarPoonam JainSuman SumanSneha Gautam
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Abstract The present 12 weeks (4 October 2021 to 26 December 2021) study emphasizes on examining the various air quality parameters i.e. PM 2.5 , PM 10 , SO 2 , NO 2 and O 3 over four different sampling stations i.e. Dwarka (28°32' ,28°38' N ,77°0' ,78°8' E) Knowledge Park III (29.496152°N, 77.536011°E), Sector125 (28.5438° N, 77.3310° E) and Vivek Vihar (28.6712° N, 77.3177° E) using ground-based instruments and satellite remote sensing observation (MERRA-2, OMI and Aura Satellite). The ground based observation shows the mean concentrations of PM 2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m -3 , 274 µg m -3 , 294 µg m -3 , and 365 µg m -3 respectively. The ground based instrumental concentrations of PM 2.5 and O 3 were higher than the satellite observations, while as for SO 2 and NO 2 , the mean concentration of satellite based monitoring was higher as compared to others pollutants. A very strong correlations were observed among PM 2.5 , PM 10 , SO 2 , NO 2 and O 3 . A negative and positive weak correlation were observed among pollutant and various meteorological parameters. It has been observed that the wind direction is one of the most prominent parameter to alter the variation of these pollutants. Overall, the present study provides an insight into the noticeable behavior in air pollutants loading trends and, in general, is in less agreement with that relating the findings with those recently recounted by satellite observations.Keywords:
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Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently. (Less)
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This study explored the relationship between the actual level of air pollution and residents’ concern about air pollution. The actual air pollution level was measured by the air quality index (AQI) reported by environmental monitoring stations, while residents’ concern about air pollution was reflected by the Baidu index using the Internet search engine keywords “Shanghai air quality”. On the basis of the daily data of 2068 days for the city of Shanghai in China over the period between 2 December 2013 and 31 July 2019, a vector autoregression (VAR) model was built for empirical analysis. Estimation results provided three interesting findings. (1) Local residents perceived the deprivation of air quality and expressed their concern on air pollution quickly, within the day on which the air quality index rose. (2) A decline in air quality in another major city, such as Beijing, also raised the concern of Shanghai residents about local air quality. (3) A rise in Shanghai residents’ concern had a beneficial impact on air quality improvement. This study implied that people really cared much about local air quality, and it was beneficial to inform more residents about the situation of local air quality and the risks associated with air pollution.
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This study gathered and processed the available air quality daily reports in 86 cities throughout China in 2001-2011. Urban air quality was assessed in terms of the evolution of the key pollutants, the pollution level, and the PM10 (particulate matter with an aerodynamic diameter < 10 microm) concentrations. The authors conclude that PM10 is the most important pollutant in Chinese cities, especially after the national sulfur dioxide (SO2) controls during the 11th Five Year Plan (FYP; 2006-2010). A notable advance was the reduction of extremely heavily polluted days with air pollution index (API) above 150 from 7% in 2001 to 1% in 2011 in the all-city average. In addition, the average API-derived PM10 concentrations continually decreased during the past 11 yr. Additionally, the pollution pattern of "more severe from south to north "in China became less obvious due to the decline of PM10 concentrations in the northern cities and the more obvious regional characteristics of air pollution. Nevertheless, more pollutants should be included in the API system to fully reflect the air quality status and guide future air pollution controls in Chinese cities.Air quality daily report, the only publicly accessible observation database in the past decade, provides valuable insight into the air quality in Chinese cities. Using this data set, this paper assesses the status and change of urban air quality in China in 2001-2011, during which great effort was made to mitigate urban air pollution. It is valuable for the further refinement of national air quality control strategies, and the needs of updating the present daily report system are implicated.
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This chapter presents the most common approaches of the regulatory framework for air quality and air dispersion modeling. The first major federal initiative in the United States to regulate air quality was the Clean Air Act of 1963. The Clean Air Act required the Environmental Protection Agency (EPA) to develop air quality standards. Air emissions are regulated by the states. They are responsible to ensure that the air quality standards are met. Each state (U.S.) and province (Canada) has its own air quality standards, based on the local conditions, industry, and the like. A screening technique in air dispersion modeling is the use of a simple model such as SCREEN3 to calculate the worst-case scenario resulting from a proposed air pollution source. When screening techniques predict the ambient air quality objectives to be exceeded, a refined modeling technique must be used to evaluate the proposed pollution source.
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The methodology of the recently developed Daily Air Quality Index (DAQx) and Long-term Air Quality Index (LAQx) is explained. Both indices consider air pollutants frequently monitored at long-term stations within official air pollution control networks. Therefore, they enable an assessment of the integral air pollution, which reflects the ambient air consisting of a mixture of air pollutants more realistic. Both air quality indices are impact related with respect to people. On the basis of results of extensive investigations in environmental medicine and toxicology, they quantify the impacts of a mixture of air pollutants, which is typical of the ambient air, on well-being and health of people in the form of six index classes and ranges of index values, respectively. To analyse the sensitivity of DAQx and LAQx, air pollutant data for the period 1995-2003 were used. They originate from selected stations within the official air pollution monitoring network in the South-West of Germany, which are characterised by different emission conditions.
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The air quality in Taiwan, at present, is determined by a pollution standard index (PSI) that is applied to areas of possible serious air pollution and Air Quality Total Quantity Control Districts (AQTQCD). Many studies, both in Taiwan and in other countries have examined the characteristics and levels of air pollution with PSI. This study uses air quality data collected from eight automatic air quality monitoring stations in an AQTQCD in central Taiwan and discusses the correlation between air quality variables with statistical analysis in an attempt to accurately reflect the difference of air quality observed by each monitoring station as well as to establish an air quality classification system suitable for the whole Taiwan. After using factor analysis (FA), seven air pollutants are grouped into three factors: organic, photochemical, and fuel. These three factors are the dominant ones in regards to the air quality of central Taiwan. Cluster analysis is used to classify air quality in central Taiwan into five clusters to present different characteristics and pollution degrees of air quality. This research results should serve as a reference for those involved in the review of air quality management effectiveness and/or the enactment of management control strategies.
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Air pollution is the result of economic growth and urbanization. Air pollution has been progressively recognized as a serious problem for cities, through widespread effects on health and well-being. There is less concern from stakeholders about greenness and air pollution mitigating factors in an urban area. This research targeted to indicate the spatial dissemination of greenery, air quality levels (PM2.5, PM10, CO2, and AQI), and exposure to air quality-related health risks for the people in the urban area.The data were collected by measuring air quality at transportation stations and manufacturing industries with Air visual pro, then observing and mapping greenness in the city within the administrative boundary by GIS (street greenery, forest, availability of greenness in the manufacturing industry), and lastly questionnaire and interview were employed for air quality-related health issues. Then, the air quality data were analyzed by using USAQI standards and health messages. Both quantitative and qualitative research approach had employed to explore air pollution levels, availability of greenness, and air quality-related health issues. Moreover, Health questionnaires and greenness were correlated with air quality levels by a simple linear regression model.The result indicated that there was unhealthy air quality in the transportation and manufacturing industries. The measured air quality showed in a range of 50.13-96.84 μg/m3 of PM2.5, 645-1764 ppm of CO2, and 137-179 Air quality index (AQI). The highest mean of PM2.5 and air quality concentrations at Addis Ababa transportation stations and manufacturing sites ranged between 63.46 and 104.45 μg/m3 and 179-326, respectively. It was observed with less street greenery and greenness available in residential, commercial areas, and manufacturing industries. The pollution level was beyond the limit of WHO standards. The result has shown a health risk to the public in the city, particularly for drivers, street vendors, and manufacturing industry employees. Among 480 respondents, 57.92% experienced health risks due to air pollution by medical evidence.High health risks due to industries and old motor vehicles in the city need to be reduced by introducing policies and strategies for low-carbon, minimizing traveling distance, encouraging high occupancy vehicles, and promoting a green legacy in the street network and green building.
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Both the air quality index (AQI) and indicatory air pollutants of Anqing, Hefei, and Suzhou near central China from 2017 to 2019, and the impact of COVID-19 epidemic prevention and control actions on air quality were investigated. The combined data for the three cities from 2017 to 2019 indicated that the lowest AQI (averaged 78.1) occurred in the summer season, for which the AQI proportions for classes I, II, III, IV, V, and VI were 25.6%, 49.9%, 21.9%, 2.7%, 0%, and 0%, respectively. The highest (AQI average of 112.6) was in winter, for which the proportions were 7.4%, 39.5%, 33.3%, 12.5%, 7.2%, and 0.1%, respectively. PM2.5, PM10, and NO2 in order were the most important indicatory air pollutants for AQI classes IV, V, and VI, which all prevailed in winter and spring, while O3 was the indicatory air pollutant that occurred most in summer. The COVID-19 event, which triggered global attention, broke out at the end of 2019. This study also investigated and compared the air quality levels in the three cities from January to March 2017–2019 with those in 2020. The results showed that during February 2020, in the three cities, the average ambient air concentrations of PM2.5, PM10, SO2, CO, and NO2 were 41.9 µg m–3, 50.1 µg m–3, 2.18 ppb, 0.48 ppm, and 8.97 ppb, and were 46.5%, 48.9%, 52.5%, 36.2%, and 52.8%, respectively, lower than those in the same month in 2017–2019, respectively. However, the O3 average concentration (80.6 ppb) did not show significant fluctuations and even slightly increased by 3.6%. This is because a lower concentration of NO2 resulted in constraints on the reaction of NO + O3, so the O3 level could not be effectively further reduced. In addition, this study also analyzed and compared the five highest daily AQIs from February 2017–2019 with those of 2020 for the three cities. The mean AQI for the 5 days with the highest daily AQI (averaged 122.6) in February 2020 was 45.1% lower than that for February 2017–2019 (averaging 223.2), and the indicatory air pollutant was always PM2.5, which decreased by 46.7% (from 173.6 to 92.6 µg m–3). It is clear that during the COVID-19 epidemic prevention and control action periods, the air quality near central China improved significantly.
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