Dataset Information

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data.

ABSTRACT: This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.

Supplementary information

The online version contains supplementary material available at 10.1007/s10668-021-01719-z.

SUBMITTER: Mohan Viswanathan P

PROVIDER: S-EPMC8354098 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.

Dataset Information

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data.

Supplementary information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets