Relationships between Meteorological and Particulate Matter Concentrations (PM2.5 and PM10) during the Haze Period in Urban and Rural Areas, Northern Thailand

Meteorological parameters play a crucial role in the ambient air quality of urban and rural environments. This study aims to investigate the relationship between meteorological parameters (including temperature, relative humidity, and wind speed) and the concentrations of PM2.5 and PM10 in the urban area and the rural area, northern Thailand during the haze period (January to April) from 2016 to 2020. Statistical analyses of the Spearman-Rank correlation coefficient and the multivariate gaussian regression were used to investigate the relationships. The secondary data of ambient PM2.5 and PM10 concentration and meteorological parameters were acquired from the Thai Pollution Control Department. The measurements are obtained using the Beta Ray attenuation method. The results showed that approximately 24% to 65% of daily average PM2.5 concentrations in the urban area over the study period exceeded Thailand’s National Ambient Air Quality Standards. The average PM2.5/PM10 ratios in the urban and the rural areas over the haze period were 0.69 and 0.66, respectively. Our analysis established a significant correlation between atmospheric temperature (r = 0.624) and relative humidity (r = −0.722) with the concentrations of PM2.5 and PM10. In both areas, PM2.5 and PM10 concentrations were also positively correlated with temperature. In contrast, relative humidity was significantly related with the decrease of PM2.5 and PM10 concentrations. However, wind speed does not affect PM2.5 and PM10 concentrations. Additionally, the daily backward trajectories using the hybrid-single particle Lagrangian integrated trajectory model also demonstrated air mass movement in March mostly came from the southwesterly direction, which moved through the highlands, the large biomass burned areas, upwind neighboring provinces, and transboundary transports before reaching the air monitoring stations. Our findings improve the understanding of particulate matter pollution and meteorological patterns during annual haze periods in the urban and rural areas. We expect the output of this study can help improve existing haze mitigation measures for improving the prediction accuracy of air pollution under variable meteorological parameters.