Understanding the sources and spatiotemporal characteristics of VOCs in the Chengdu Plain, China, through measurement and emission inventory
Maimaiti SimayiYuqi ShiZiyan XiJing LiXuena YuHefan LiuQinwen TanDanlin SongLimin ZengSihua LüShaodong Xie
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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 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 quality models have been developed to better understanding the behaviour of the pollutants in the atmosphere, among other applications. One of them is about the influence of emission inventories and large sources on local and regional air quality. Another is the optimal design and assessment of air quality monitoring networks. This work aims at three different goals, (1) Optimal design of a regional air quality network: one-year high resolution (3 km2) meteorological and air quality simulations was applied to establish the past and future most polluted areas over the Galicia (NW of Spain). Simulations was built coupling WRF-ARW meteorological model to CHIMERE v. 2008c air quality model (validated with DELTA Tool), using 2008 year as meteorological basis. For past simulation, a detailed emission inventory was done, based in a combination of the Portuguese (area sources) and Galician (EMIGAL, point and area sources) emission inventories, with EMEP inventory for the year 2008. Future simulation considers a projection of emissions depending on the maxima authorized activities in the region. (2) To evaluate the effects of European air pollutants emissions between 2001 and 2008 over the ozone levels in the NW of the Iberian Peninsula (NWIP). Results show that no effect was obtained in the rural ozone levels of this region, due to the change of European emissions. (3) To evaluate the effect of removing all the emissions of the largest coal-fired power plant (1400 MW) in this region. In this case, a high resolution (3×3 km2) regional emissions inventory over NWIP was also applied, including more than 300 sources as point sources. Suppressing the power plant emissions derived in an increment of ozone levels around it; and, no effect in the ozone levels was observed over the NWIP region and surrounding areas.
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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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Air pollution in Japan has decreased from the elevated levels found during the period of high economic growth. However, in recent years there is increasing concern regarding air pollution caused by fine particulate matter (PM2.5) and its effect on human health. First, we investigated the seasonal and regional characteristics of air pollution caused by PM2.5 using ground measurements in China and Japan. Next, the cause of air pollution was classified as urban air pollution or transboundary air pollution. Air pollution in Fukuoka (in southwestern Japan) is strongly affected by transboundary air pollution. Nearly half of all air pollution in Tokyo (in eastern Japan) may be urban pollution caused by local emissions. PM2.5 high concentration from winter to spring is often caused by transboundary air pollution. On the other hand, PM2.5 high concentration from summer to autumn is greatly influenced by urban pollution. Air pollution caused by PM2.5 high concentration tends to decrease in Japan. This is due to both a reduction in PM2.5 emissions in Japan and a decrease in PM2.5 concentrations in China.
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