Particulate matter (PM) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand.

BACKGROUND

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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.