Predicting Concentration of Air Pollution Using AERMOD Model and Estimating Its Health Impact on the Population Near Biomass Power Plants in Khon-Buri District, Nakhon Ratchasima Province

Abstract

Although biomass power plants play a role in enabling Thailand to produce electricity in response to economic development, the potential issue of air pollution should not be overlooked. This study aimed to predict the air pollution concentration from biomass power plants using the AERMOD air quality modeling and assess its health impacts on the population residing within a 10-kilometer radius of the plant in the Khon-Buri District of Nakhon Ratchasima Province. During the period from October 2019 to September 2020, air samples from four potentially affected communities within the radius were analyzed. These communities were Subkanlueng Subdistrict health promoting hospital, Chorakhehin Subdistrict health promoting hospital, Khon-Buri Tai Subdistrict municipality, and Ban-Clongyang (Moonbon Ubpatum) School. The analysis results showed that the concentration of total suspended particles (TSP), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) was lower than the national ambient air pollution standard in all four locations. In terms of health impacts, the most prevalent symptoms observed was fatigue (13.5%), followed by blurred vision (12.6%). The analysis of the relationship between the pollutants and particulate matter with a diameter less than 2.5 micrometers (PM_2.5) and health symptoms using binary logistic regression revealed that only PM_2.5 exposure was significantly associated with multiple symptoms including nasal congestion, runny nose, burning nose, sore throat, dry cough, cough with mucus, trouble breathing, headache, fatigue, burning and itchy eyes. For instance, an increase of 1 microgram per cubic meter of PM_2.5 was associated with a 1.01 times higher likelihood of experiencing nasal congestion. In light of these results, the researchers recommend (1) the establishment of an air pollution health risk assessment center that utilizes AERMOD model to monitor and alert the public to the impact of air pollution, and (2) the development of database system linking health and environmental factors for the use in proactive monitoring and warning of health disaster caused by environmental pollution.