| Title |
Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre |
| ID_Doc |
67522 |
| Authors |
Azhari, A; Halim, NDA; Mohtar, AAA; Aiyub, K; Latif, MT; Ketzel, M |
| Title |
Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre |
| Year |
2021 |
| Published |
Sustainability, 13, 10 |
| DOI |
10.3390/su13105402 |
| Abstract |
Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4-65.9 mu g/m(3) and 30.4-43.7 mu g/m(3) respectively, compared to during the 30% traffic reduction run ranging at 40.5-59.5 mu g/m(3) and 29.9-40.3 mu g/m(3) respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 mu g/m(3) and 28.2 mu g/m(3) respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution. |
| Author Keywords |
particulate matter; vehicular emission management; air pollution dispersion model |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000662524000001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/13/10/5402/pdf?version=1620812418
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