The Impact of Air Quality and Meteorology on COVID-19 Cases at Kuala Lumpur and Selangor, Malaysia and Prediction Using Machine Learning

被引:4
|
作者
Jalaludin, Juliana [1 ]
Mansor, Wan Nurdiyana Wan [2 ]
Abidin, Nur Afizan [1 ]
Suhaimi, Nur Faseeha [1 ]
Chao, How-Ran [3 ]
机构
[1] Univ Putra Malaysia, Fac Med & Hlth Sci, Dept Environm & Occupat Hlth, Serdang 43400, Malaysia
[2] Univ Malaysia Terengganu, Fac Ocean Engn Technol & Informat, Kuala Terengganu 21030, Malaysia
[3] Natl Pingtung Univ Sci & Technol, Dept Environm Sci & Engn, Neipu 91201, Taiwan
关键词
air pollution; relative humidity; PM2; 5; lockdown; CITIES; OZONE;
D O I
10.3390/atmos14060973
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Emissions from motor vehicles and industrial sources have contributed to air pollution worldwide. The effect of chronic exposure to air pollution is associated with the severity of the COVID-19 infection. This ecological investigation explored the relationship between meteorological parameters, air pollutants, and COVID-19 cases among residents in Selangor and Kuala Lumpur between 18 March and 1 June in the years 2019 and 2020. The air pollutants considered in this study comprised particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O-3), and carbon monoxide (CO), whereas wind direction (WD), ambient temperature (AT), relative humidity (RH), solar radiation (SR), and wind speed (WS) were analyzed for meteorological information. On average, air pollutants demonstrated lower concentrations than in 2019 for both locations except PM2.5 in Kuala Lumpur. The cumulative COVID-19 cases were negatively correlated with SR and WS but positively correlated with O-3, NO2, RH, PM10, and PM2.5. Overall, RH (r = 0.494; p < 0.001) and PM2.5 (r = -0.396, p < 0.001) were identified as the most significant parameters that correlated positively and negatively with the total cases of COVID-19 in Kuala Lumpur and Selangor, respectively. Boosted Trees (BT) prediction showed that the optimal combination for achieving the lowest Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and a higher R-squared (R-2) correlation between actual and predicted COVID-19 cases was achieved with a learning rate of 0.2, a minimum leaf size of 7, and 30 learners. The model yielded an R-2 value of 0.81, a RMSE of 0.44, a MSE of 0.19, and a MAE of 0.35. Using the BT predictive model, the number of COVID-19 cases in Selangor was projected with an R-2 value of 0.77. This study aligns with the existing notion of connecting meteorological factors and chronic exposure to airborne pollutants with the incidence of COVID-19. Integrated governance for holistic approaches would be needed for air quality management post-COVID-19 in Malaysia.
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页数:24
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