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Air quality
Published in Stephen Battersby, Clay's Handbook of Environmental Health, 2023
Measurement is frequently concerned with providing data for the purposes of monitoring pollutant concentrations against the predetermined standards and objectives. The results can then inform policy decisions at local, regional and national government levels. Monitoring provides raw measurements of air pollutant concentrations, which can then be analysed and interpreted. Measuring different pollutants in the air allows an assessment to be made of how pollutants interact with each other and how they relate to traffic levels or industrial activity. There are two types of method for the measurement of air pollution generally in use; one can be termed automatic and the other non-automatic. Once the purpose for monitoring is confirmed, the specific site can be selected. This may involve some practical challenges. To overcome these, pre-planning is required and issues to consider include: the presence of utilities such as electricity at a potential site; whether a hard standing is required then checking for subterranean services such as drains is necessary.
Air and air quality
Published in Stephen Battersby, Clay's Handbook of Environmental Health, 2016
Monitoring provides raw measurements of air pollutant concentrations, which can then be analysed and interpreted. Analysis of the continuous measurements allows us to assess how bad air pollution is from day to day, which areas are worse affected than others and whether levels are rising or falling over time. By measuring different pollutants we can see how pollutants interact with each other and how they relate to traffic levels or industrial activity. By analysing the relationship between meteorology and air quality, we can also predict which weather conditions will give rise to episodes with high levels of pollution.
Photochemical oxidants and ambulance dispatches for asthmatic symptoms in Tokyo
Published in International Journal of Environmental Health Research, 2022
Mihye Lee, Sachiko Ohde, Shinichi Ishimatsu
Tokyo Metropolis consists of 67 municipalities (23 Special Wards, 26 cities, 5 towns, and 8 villages). Among them, the 23 Special Wards (‘Ku’ in Japanese) are the most populated areas located in the east part of Tokyo. Parts of those populated municipalities had multiple monitoring stations within their area, whereas other rural areas (town- or village-level) did not have any. In the former case, we used the average air pollutant concentrations within the area. For the latter case, we assigned the measurements of monitoring stations that are closest to the centroids of the municipality. The distribution of monitoring sites in Tokyo Metropolis is shown in Figure 1. The gray area represents the Special 23-Ku area and the other areas are relatively less urbanized compared to it.
Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces
Published in International Journal of Environmental Health Research, 2022
Longyan Cai, Mazhan Zhuang, Yin Ren
The monthly concentrations of NO2, PM2.5 and O3 at different monitoring sites are shown in Figure 2, revealing significant spatiotemporal variability in the concentrations of these pollutants. For example, the lowest value of PM2.5 was observed at NWR station, with a value of 6.04 μg/m3, and the highest (59.42 μg/m3) was found at TGXD station. Furthermore, the interquartile ranges suggest that a large variability in air pollutant concentrations existed between the months. Some details about the monitoring sites are presented in Table 1. Clearly, complex spatial distributions for each pollutant existed, thus, air pollutant concentrations in urban areas and suburbs were analyzed to further understand the spatial distribution patterns of air pollution.
Estimating PM2.5 concentrations with statistical distribution techniques for health risk assessment in Bangkok
Published in Human and Ecological Risk Assessment: An International Journal, 2020
Tin Thongthammachart, Wanida Jinsart
Multiple linear regression (MLR) was applied in the urban areas of several studies for forecasting concentrations of particulate matter (both PM10 and PM2.5) by using meteorological parameters and co-pollutants as the predictors or the input data (Abdullah et al.2017; Nazif et al.2018; Ul-Saufie et al.2011). The MLR technique has basically two main advantages: simple computation and easy implementation. MLR technique has been developed in order to forecast particulate matter in urban areas in different cities such as Finland, Greece, Malaysia, China and Iran (Eldrandaly and Abu-Zaid 2011; Li and Wang 2017; Ul-Saufie et al.2012; Vlachogianni et al.2011). These studies used the measured ambient air pollutants and meteorological data to compute and develop their MLR equations. Their equations have shown to predict air pollutant concentrations with remarkable success. In addition, the development of an MLR equation (which includes meteorological factors) can be very useful in predicting pollutant concentrations. Thus, the MLR technique was selected to develop equations for forecasting the PM2.5 concentrations in this study. Then, the measured PM2.5 concentrations in the monitored areas and predicted PM2.5 concentrations in the non-monitored areas were quantified an inhalation health risk assessment (HRA). The HRA method was adopted from the regulatory protocol of the USEPA and quantified as the hazard quotient (HQ) for non-carcinogenic substance exposure.