Health risk assessment from particulate matters based on fine particle sensor monitoring at Chiang Mai city's underpass, Thailand

Abstract

Chiang Mai has been facing air pollution every year. Particulate matter concentration is more important to monitor which is harmful for human health and environment. Several major air pollutant and metrological factor has been collected from several organizations, including the Department of Highway, Thailand. Transportation is the main source of pollution in the cities. Chiang Mai is the top of tourist destinations in Thailand, high traffic and number of vehicles are increasing every year. Road tunnels or road underpasses are concerned about health exposure to haze users from pollutants air flow. This research builds a model of PM2.5 concentration by multiple linear regression (MLR) model from data station under road tunnel construction in Chiang Mai. Forecasting PM2.5 from developed and fitting model with carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), particulate matter (PM2.5, PM10), wind speed (WS), wind direction (WD), temperature (T), relative humidity (RH), pressure (P), and rainfall (PreH). Analysis of model focused on weekend/weekday and day/night time to compare different of PM concentrations. The correlation analysis reveals positive correlation of PM2.5 and others variance (PM2.5/PM10 r = 0.9995, CH2O/TVOC r = 0.9999, CH2O/CO2 r = 0.9999, TVOC/CO2 r = 0.99998) and negative (Temp/RH r = -0.7967, RH/O3 r = -0.5939)