Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model
As a result of considerable changes in rural areas in Northern Thailand, the frequency and intensity of haze outbreaks from particulate pollution, particularly fine particulate matter (PM2.5), has increased in this region. To supplement ground-based monitoring where PM2.5 observation is limited, thi...
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/3190484 |
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| author | Teerachai Amnuaylojaroen |
| author_facet | Teerachai Amnuaylojaroen |
| author_sort | Teerachai Amnuaylojaroen |
| collection | DOAJ |
| description | As a result of considerable changes in rural areas in Northern Thailand, the frequency and intensity of haze outbreaks from particulate pollution, particularly fine particulate matter (PM2.5), has increased in this region. To supplement ground-based monitoring where PM2.5 observation is limited, this study applied a multivariate linear regression model to predict PM2.5 concentrations in 2020 using aerosol optical depth (AOD); meteorological parameters of wind velocity, temperature, and relative humidity; and gaseous pollutants such as SO2, NO2, CO, and O3 from ground-based measurements at three locations: Chiang Mai, Lampang, and Nan provinces in Northern Thailand. Two multivariate linear regression models were conducted in this study. The first model (model 1) is a generic model with meteorological parameters of aerosol optical depth (AOD), temperature, relative humidity, and wind speed. The second model (model 2) includes meteorological parameters and several gaseous pollutants, such as SO2, NO2, CO, and O3. In general, the regression model, which used hourly data from 2020 of the three provinces, adequately characterized the PM2.5 concentrations. The performance of model 2 was good for the prediction of PM2.5 concentrations at Chiang Mai (R2 = 0.52) and Lampang (R2 = 0.60). Model 2 improved the prediction of PM2.5 concentration compared to model 1 for both wet and dry seasons. However, model uncertainties were also present, which lays a foundation for further study. |
| format | Article |
| id | doaj-art-eba51c0bf6d345da8db4fc7ad0eae21f |
| institution | OA Journals |
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| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
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| series | Advances in Meteorology |
| spelling | doaj-art-eba51c0bf6d345da8db4fc7ad0eae21f2025-08-20T02:22:21ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/3190484Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression ModelTeerachai Amnuaylojaroen0Department of Environmental ScienceAs a result of considerable changes in rural areas in Northern Thailand, the frequency and intensity of haze outbreaks from particulate pollution, particularly fine particulate matter (PM2.5), has increased in this region. To supplement ground-based monitoring where PM2.5 observation is limited, this study applied a multivariate linear regression model to predict PM2.5 concentrations in 2020 using aerosol optical depth (AOD); meteorological parameters of wind velocity, temperature, and relative humidity; and gaseous pollutants such as SO2, NO2, CO, and O3 from ground-based measurements at three locations: Chiang Mai, Lampang, and Nan provinces in Northern Thailand. Two multivariate linear regression models were conducted in this study. The first model (model 1) is a generic model with meteorological parameters of aerosol optical depth (AOD), temperature, relative humidity, and wind speed. The second model (model 2) includes meteorological parameters and several gaseous pollutants, such as SO2, NO2, CO, and O3. In general, the regression model, which used hourly data from 2020 of the three provinces, adequately characterized the PM2.5 concentrations. The performance of model 2 was good for the prediction of PM2.5 concentrations at Chiang Mai (R2 = 0.52) and Lampang (R2 = 0.60). Model 2 improved the prediction of PM2.5 concentration compared to model 1 for both wet and dry seasons. However, model uncertainties were also present, which lays a foundation for further study.http://dx.doi.org/10.1155/2022/3190484 |
| spellingShingle | Teerachai Amnuaylojaroen Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model Advances in Meteorology |
| title | Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model |
| title_full | Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model |
| title_fullStr | Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model |
| title_full_unstemmed | Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model |
| title_short | Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model |
| title_sort | prediction of pm2 5 in an urban area of northern thailand using multivariate linear regression model |
| url | http://dx.doi.org/10.1155/2022/3190484 |
| work_keys_str_mv | AT teerachaiamnuaylojaroen predictionofpm25inanurbanareaofnorthernthailandusingmultivariatelinearregressionmodel |