Are we safe inside? Indoor air quality in relation to outdoor concentration of PM10 and PM2.5 and to characteristics of homes
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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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Indoor Inhalable particulate matter is one of the most important indoor air pollutants among all kinds of indoor air pollutant, Based on the source of the indoor air particulate matter, explains the effects, the distributions and motion patterns as well as, the importance of indoor air quality, the measurements, and the advantages disadvantages of assessment methods. Suggestions for further consideration are given.
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Formaldehyde was the first air pollutant, which already in the 1970s emerged as a specifically non-industrial indoor air quality problem.Yet formaldehyde remained an indoor air quality issue and the formaldehyde level in residential indoor air is among the highest of any indoor air contaminant.Formaldehyde concentrations in 4 different indoor settings (schools, office buildings, new dwellings and occupied dwellings) in Portugal were measured using Photo Ionization Detection (PID) equipment (11,7 eV lamps).All the settings presented results higher than the reference value proposed by Portuguese legislation.Furthermore, occupied dwellings showed 3 units with results above the reference.We could conclude that formaldehyde presence is a reality in monitored indoor settings.Concentration levels are higher than the Portuguese reference value for indoor settings and these can indicate health problems for occupants.
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Abstract Sungshan, Longshan, Nankang and Mucha districts are selected for experimental research on particulate air pollution in Taipei. Air samples are taken from these districts and analyzed for total particulate weight, particle size distribution and chemical composition. The health status of students in some primary schools in these districts is also investigated. It is found that the particulate air pollution in Sungshan, Longshan and Nankang is serious. Lung function of the students in these three districts has been more or less damaged.
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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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The concentrations of air-borne micro-organisms and particulate matter were measured in the air and on solid surfaces in five rooms in a hospital. For comparison the same measurements were also made outdoors. The rooms were chosen because of their different needs in terms of indoor environment quality. The highest bacterial concentrations were found in the air of a patient's room that was `unventilated' other than through its doorway. Some of the rooms were supplied with filtered air. Compared to the rest, these rooms had the lowest microbial concentrations in their air but the highest on surfaces. A correlation was found between the count of the particulate size-range ≥1.0 μm and that of bacteria ( r=0.76) and between the count of the particulate size range ≥5.0 μm and those of bacteria ( r=0.88) and staphylococci ( r=0.80) in indoor air. No correlation was found between the particulate count and the mould count in the indoor air.
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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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