Monitoring, modelling and health impacts of air pollutants arising from the Maptaphut Industrial Estates, Thailand

Uapipatanakul, Somchai (2009) Monitoring, modelling and health impacts of air pollutants arising from the Maptaphut Industrial Estates, Thailand. Doctoral thesis, Northumbria University.

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Abstract

Abstract The Maptaphut Industrial Estate is located on the Gulf of Thailand, Rayong Province. The area, which has been designated as a main centre for the petrochemical industry currently occupies 16 sq km and comprises petrochemical plants, chemical and fertilizer plants, refineries, construction plants, and steel industry; there are also residential and commercial areas (IEAT, 2004). There is a significant population around the site, with 24,000 inhabitants in the immediate vicinity according to Jadsri et a/ (2006). The estate has been held responsible for deaths and hospital admissions due to leaks and accidents dating back as far as 1997. Whilst the environmental and health and safety performance of the estate as a whole has significantly improved over recent years, there are still significant outpatient admission rates to Maptaphut hospital for respiratory illness, as recently reported by Jadsri et al. (2006), raising the question of whether local emissions are significantly contributing to ill health, or whether general background concentrations of pollutants from nearby road sources and from Rayong City are the main contributions. The main aim of this research, therefore, was to accurately model the dispersion of pollutants from the estate, and to attempt to quantify the health impacts of these emissions. The specific objectives of this study were to (a) to characterise meteorological conditions in the Maptaphut area; (b) to develop a multiple linear regression statistical model to characterise and predict atmospheric pollutant concentrations in Maptaphut; (c) to investigate the relationship between air pollution and ill health in Maptaphut using a multiple linear regression statistical model; (d) to evaluate the effectiveness of Gaussian and Computational Fluid Dynamics atmospheric dispersion modelling software packages in predicting ground level pollutant concentrations at points around the industrial estate and (e) to use the results of the dispersion modelling studies to assess the contribution of the industrial estate to the overall atmospheric pollutant load in the Maptaphut area, and from published health impact factors, to assess the overall health impact of the estate. The first objective was to characterise the environmental status, trend, and impacts of air pollution during the period 1998 to 2007. The estate is located in the coastal area; thus, the role of the sea-land breeze has a significant role in the dispersion of air pollutants harmfulness. Data collected for the Maptaphut Industrial Estates area, including regional, temporal and spatial considerations included: meteorological data from 100-metres tall meteorological mast; ambient air quality data from three ambient air quality monitoring stations; industrial emissions data; traffic volume on nearby major roads; and outpatient admissions data at the Maptaphut and Rayong hospitals. Comparisons with the ambient air quality in the Bangkok area were made, and the daily and yearly trends in concentrations of the main air pollutants were analysed. Multiple linear regression models correlating pollutant concentrations with respiratory outpatient admissions rates showed that 03, PMio and NO were statistically significant determinants. The overall correlation had a coefficient of Determination (R2) of 41.4% for one week average data, increasing to 51.2% when air temperature and %RH were included. Accumulation effect of pollutants up to four weeks period exposure does not appear to have an effect. A basic health impact analysis study using the ADMS modelled concentrations and the WHO AirQ tool, along with default risk factors, showed that emissions from the Maptaphut industrial estate account for almost all of the NO2 and SO2 related respiratory illness and between 10 and 27% of the PMio related admissions; this actually represents less than 2% of the total respiratory admissions for this area. Furthermore, statistic models were developed to predict da...

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