Exposure to PM2.5 in urban area and respiratory health symptoms among urban workers in Klang Valley

Awang, Mohd Fairus; Jalaludin, Juliana; Latif, Mohd Talib; Fandi, Noor Fatihah Mohamad

doi:10.1088/1755-1315/228/1/012015

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Although the better air quality eases those who are suffering from breathing problems, most people will be more susceptible to illness after years of inhaling polluted air. As previous research has shown, air pollution lowers the immunity and ability to fight off infections (Kamaruddin et al, 2015;Suhaimi et al, 2017;Awang et al, 2019;Sopian et al, 2020). Therefore, people with underlying medical conditions would possibly have challenges to fight the coronavirus.…”

Section: Introductionmentioning

confidence: 99%

Demystifying a Possible Relationship between COVID-19, Air Quality and Meteorological Factors: Evidence from Kuala Lumpur, Malaysia

Suhaimi

Jalaludin

Latif

2020

Aerosol Air Qual. Res.

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Air pollution is the culprit to yearly millions of deaths worldwide, deteriorating human health. What is not yet clear is the impact of environmental factors on susceptibility to getting infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The study aimed to determine associations between air quality, meteorological factors, and COVID-19 cases in Kuala Lumpur, Malaysia. Air pollutants and meteorological data in 2018-2020 were obtained from the Department of Environment Malaysia, while daily new COVID-19 cases in 2020 were obtained from the Ministry of Health Malaysia. Data collected were statistically analyzed using the Statistical Package for Social Sciences (SPSS). There were significant differences between PM 10 , PM 2.5 , SO 2 , NO 2 , CO, O 3, and solar radiation in 2019 and 2020 since movement control order (MCO) was implemented on 18 March 2020. Spearman's correlation test showed that COVID-19 cases were positively correlated with PM 10 (r = 0.131, p < 0.001), PM 2.5 (r = 0.151, p < 0.001), SO 2 (r = 0.091, p = 0.003), NO 2 (r = 0.228, p < 0.001), CO (r = 0.269, p = 0.001), and relative humidity (RH) (r = 0.106, p = 0.001), whereas ambient temperature (AT) was negatively correlated with COVID-19 cases (r =-0.118, p < 0.001). Further, multiple linear regression suggested that NO 2 and AT (R 2 = 0.071, p < 0.001, f 2 = 0.08) were the most significant air pollutant and meteorological factors with weak contribution that influenced the incidence of COVID-19 cases in Kuala Lumpur. In general, better air quality, lower RH, higher AT, along with the targeted approach implemented thus far, have proven to curb the spread of this virus infection in Malaysia. This study supports future research in studies documented to understand the potential of transmission, survival, and infection of SARS-CoV-2.

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Section: Introductionmentioning

confidence: 99%

Demystifying a Possible Relationship between COVID-19, Air Quality and Meteorological Factors: Evidence from Kuala Lumpur, Malaysia

Suhaimi

Jalaludin

Latif

2020

Aerosol Air Qual. Res.

Self Cite

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Prediction of PM2.5 concentrations in Malaysia using machine learning techniques: a review

et al. 2021

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Particulate matter (PM), an air pollutant that is detrimental to breathing, is either emitted or formed ambiently. The exposure of respiratory system towards PM2.5, the fine particles of 2.5 micrometres diameter, causes complication for health. Thus, developing pollution control strategies requires the prediction of PM2.5 concentrations. Advancement of technology and computer science knowledge, machine learning (ML) algorithms are used for highly accurate prediction of air pollutant concentrations. Recently, air quality in Smart Cities of Malaysia has been getting worse due to industrialization, emissions from private motor vehicles, and transboundary haze pollution. Therefore, the forecasting of PM2.5 emissions to ensure they are within the statutory limits becomes necessary. Several machine learning methods have been implemented in existing research to predict air pollution concentrations in comparison to PM2.5. However, very few studies have used ML techniques to predict air quality in Malaysia when compared with global studies. Hence, to create awareness on the ML techniques and promote further research in this area, this study reviews and highlights most of the existing ML techniques for the prediction of PM2.5.

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The Impact of Air Pollution on Lung Function: A Case Study on the Rickshaw Pullers in Dhaka City, Bangladesh

Nayeem

Majumder

Hossain

et al. 2020

J. Hum. Environ. Health Promot

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Exposure to PM2.5 in urban area and respiratory health symptoms among urban workers in Klang Valley

Cited by 4 publications

References 13 publications

Demystifying a Possible Relationship between COVID-19, Air Quality and Meteorological Factors: Evidence from Kuala Lumpur, Malaysia

Demystifying a Possible Relationship between COVID-19, Air Quality and Meteorological Factors: Evidence from Kuala Lumpur, Malaysia

Prediction of PM2.5 concentrations in Malaysia using machine learning techniques: a review

The Impact of Air Pollution on Lung Function: A Case Study on the Rickshaw Pullers in Dhaka City, Bangladesh

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