Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Environment, Health & Social Impact Unit, Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
BMC Public Health. 2021 Nov 24;21(1):2149. doi: 10.1186/s12889-021-12217-2.
The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country's most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM prediction for different seasons using meteorological factors.
The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM concentration were then developed, namely annual, summer, rainy, and winter seasons.
The maximum daily PM concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM was significantly different for both stations. CO was moderately related to PM in the summer season. The PM summer model was the best MLR model to predict PM during haze episodes. In both stations, it revealed an R of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM in the winter season.
In conclusion, the MLR models are effective at estimating PM concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R of 0.61 and 0.52 for stations 65 and 73, respectively.
泰国北部地区每年都会遭遇霾事件和跨境空气污染，空气中积聚的颗粒物，尤其是 PM，对人体健康造成不利影响。清莱府是该国最受欢迎的旅游目的地之一，也是重要的经济中心。本研究旨在开发和比较使用气象因素预测不同季节 PM 的最佳拟合模型。
空气污染和天气数据由污染控制部（PCD）从 2011 年到 2018 年按小时在两个站点采集。然后，针对 PM 浓度，分别为全年、夏季、雨季和冬季，开发了四个不同的逐步多元线性回归（MLR）模型。
两个站点的夏季最高日 PM 浓度，雨季最低日浓度。两个站点的 PM 季节性变化差异显著。夏季 CO 与 PM 中度相关。PM 夏季模型是预测霾事件期间 PM 的最佳 MLR 模型。在两个站点中，它分别显示出 R 值为 0.73 和 0.61。相对湿度和大气压力呈负相关，而温度在夏季和雨季与 PM 浓度呈正相关。而在冬季，气压与 PM 呈正相关。
总之，MLR 模型可有效用于估算每个季节的局部 PM 浓度。两个站点的年度 MLR 模型均表明，R 值分别为 0.61 和 0.52，预测效果良好。
