Loading...
Please wait, while we are loading the content...
Similar Documents
Evaluation of Machine Learning Models for Estimating $PM_{2.5}$ Concentrations across Malaysia
| Content Provider | MDPI |
|---|---|
| Author | Zaman, Nurul Kanniah, Kasturi Kaskaoutis, Dimitris Latif, Mohd |
| Copyright Year | 2021 |
| Abstract | Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks. |
| Starting Page | 7326 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11167326 |
| Journal | Applied Sciences |
| Issue Number | 16 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-09 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Remote Sensing Pm2.5 Himawari-8 Random Forest Support Vector Regression Air Pollution Malaysia |
| Content Type | Text |
| Resource Type | Article |
