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Asian Journal of Atmospheric Environment - Vol. 16 , No. 2

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 16, No. 1
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Mar 2022
Received 11 Aug 2021 Revised 27 Dec 2021 Accepted 13 Jan 2022

Association of Air Pollutant Index (API) on SARS-CoV-2 of Coronavirus Disease 2019 (COVID-19) in Malaysia
Samsuri Abdullah1), 2), * ; Muhammad Azhari Imran1) ; Amalina Abu Mansor3) ; Ku Mohd Kalkausar Ku Yusof3) ; Nazri Che Dom4) ; Siti Khamisah Saijan5) ; Siti Rohana Mohd Yatim4) ; Ali Najah Ahmed6) ; Marzuki Ismail2), 3)
1)Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Malaysia
2)Institute of Tropical Biodiversity and Sustainable Development, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Malaysia
3)Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Malaysia
4)Faculty of Health Sciences, Universiti Teknologi MARA, UiTM Cawangan Selangor, 42300 Puncak Alam, Selangor, Malaysia
5)Malaysia Genome Institute, National Institutes of Biotechnology Malaysia Jalan Bangi Lama, 43000 Kajang, Selangor
6)Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang, Selangor Darul Ehsan 43000, Malaysia

Correspondence to : * Tel: +60-9-668-3491 E-mail:

Copyright © 2022 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Funding Information ▼


Malaysia reported its first COVID-19 case on January 25, 2020, and the cases have continued to grow, necessitating the implementation of additional measures. Hence, determining the factors responsible for the significant increase in COVID-19 cases is the top priority issue for the government to take necessary action and ultimately restrain this virus before the vaccine availability. Researchers had predicted that air pollution had an indirect relationship with COVID-19 in terms of virus infections. As a result, this study focuses on the link between the Air Pollutant Index (API) and COVID-19 infections. The initial data set consists of daily confirmed COVID-19 cases in Malaysia and API readings obtained from the Ministry of Health (MOH) and the Department of the Environment (DOE). The results show that Klang (S22) recorded the highest mean of API which at 62.70 while the lowest is at Limbang (S37) (25.37). Next, due to the implementation of Movement Control Order (MCO) in Malaysia and reducing social movement, 27 stations recorded a good level of API compare to the stations that recorded moderate and unhealthy levels. There is positive relationship between API and COVID-19 at each of the region which are North 0.4% (R2=0.004), Central 2.1% (R2=0.021), South 0.04% (R2=0.0004), East 1.6% (R2=0.016), Sarawak 0.2% (R2=0.002), meanwhile Sabah recorded negative correlation at 4.3% (R2=0.043). To conclude, the API value did not have a strong relationship with the rising number of COVID-19 daily cases.

Keywords: COVID-19, Air pollutant index, Malaysia, Prevalence, Virus infection


The progression of the disease is correlated with aspects such as older age, smoking habit, and the respiratory and cardiovascular diseases (Zhou et al., 2020). High air pollutants concentration might induce and worsen the COVD-19 cases at certain area (Gupta et al., 2020). The current global spread of SARS-CoV-2 coronavirus (COVID-19) began as a contagious event in the Chinese city of Wuhan in late December 2019 and erupted and was proclaimed a pandemic throughout Europe, the United States of America, and other parts of the world, including Malaysia, by March 2020. The disease was isolated from patients in Wuhan in early 2020 by the scientific community. The genetic sequencing of the new coronavirus has required real-time diagnostic tests to evolve rapidly (Wang et al., 2020).

The first findings found that fever, cough, and myalgia are the most prevalent symptoms at the onset of the illness sputum development, headache and diarrhea were the less frequent symptoms (Cheng et al., 2020; Sharma et al., 2020; Wang et al., 2020). The progression of the condition is associated with conditions such as old age, smoking history, elevated blood pressure, and heart disease (Gautam, 2020; Zhou et al., 2020). The WHO announces the COVID-19 epidemic as a Public Health Emergency of International Concern due to its high degree of infectivity and its violent history, it has been granted pandemic status. Pneumonia infections transmitted by a novel coronavirus (COVID-19) is an infectious disease, with some similarity to previous infections documented over time such as Severe Acute Respiratory Syndrome (SARS-CoV) that occur on 2002-2003 and Middle East Respiratory Syndrome (MERS-CoV) that spread on 2012-2015 with some variations in its phenotypic and genotypic composition that may affect their pathogenic mechanisms.

Malaysia recorded the first COVID-19 cases on 25th January 2020 (Ministry of Health Malaysia, 2020). From thereon, the number of cases particularly in March 2020 has continued to increase. This rise of COVID-19 disease in Malaysia has called for a few steps to be taken, involve the development of a screening system to rapidly detect cases; cause of the symptoms urgent isolation and comprehensive surveillance of cases, and close contact quarantine for those tested positive for COVID-19. Intending to isolate the origins of the COVID-19 outbreak, the Malaysian Government declared the enforcement of the Movement Control Order (MCO). Human-to-human transmission between close contacts has occurred since mid-December 2019 and has steadily expanded over the upcoming month. On the other hand, optimizing air quality by minimizing both serious and long average concentrations will help protect cities from COVID-19 and reduce pressure on health facilities (Stieb et al., 2020).

Exposure to air pollution had a very well association with heightened threats and severe effects of the spread of disease, including COVID-19, 2009 H1N1, and 1918 Spanish influenza pandemics (Clay et al., 2019; Morales et al., 2017). Exposure to criteria pollutants is known to cause respiratory and various other diseases that make people more vulnerable to infectious diseases close to COVID-19 (Mahnoor et al., 2020). Latest reports have closely linked COVID-19 mortality to long-term exposure to air pollution in the United States (Shoari et al., 2020). Among various controlling factors, environmental factors, especially air pollution play an essential role in the emergence of an influenza virus with pandemic potential. The severity of respiratory problems and lung infection will increase when there is a significant combination of air pollutant concentrations and virus infection at the same time. COVID-19 is a respiratory disease and particulate pollution is strongly linked with respiratory diseases. Air pollution is readily associated with respiratory infections such as chronic obstructive pulmonary disease (COPD). COVID-19 is mainly transmitted by droplets and contact. Aerosol transmission is possible when people have prolonged exposure to high concentrations of aerosols. Research has demonstrated that respiratory viruses are transmittable among individuals via contacting directly or indirectly, or coarse or small droplets and SARS-CoV-2 is transmittable through direct or indirectly salivary route (To et al., 2020). Wong et al. (2010) denoted that the likelihood of transmitting influenza by aerosols could be reduced by improving ventilation design and prevention of generating aerosols. It is believed that transmitting by aerosols is plausible due to the high risk of cross-infection among physicians, nurses, and personnel (Hoseinzadeh et al., 2017). Another study proved that the SARS-CoV-2 is probably transmitted via aerosols produce during therapeutic actions (Huang et al., 2020). COVID-19 is spread by the airborne route. There are currently few studies that define the pathophysiological characteristics of COVID-19, and there is great uncertainty regarding its mechanism of spread. Current knowledge is largely derived from similar coronaviruses, which are transmitted from human-to-human through respiratory fomites. An epidemiological investigation of 198 early cases in Wuhan revealed that only 22% of patients had direct exposure to the marketplace, 32% were in contact with the suspected cases and 51% had no contact with either of the source (Ali et al., 2020). However, the virus was capable of efficient human-to-human transmission, and similar to MERS, reports of nosocomial propagation were also documented (Ali et al., 2020). This situation necessitated the need for the implementation of measures to abstain from transmissions. Thus, this study is aimed to investigate the association of Air Pollutant Index (API) towards COVID-19 infections in Malaysia.


In Malaysia, the areas that reported COVID-19 were not in line with Air Quality Monitoring Station (AQMS). Thus, the nearest AQMS were taken to investigate the relationship between API with the reported COVID-19 confirmed cases (Table 1). Data of API have been collected from the Air Pollutant Index Malaysia (available at (DOE, 2021) and Malaysian Department of Environment from 18 March 2020 to 28 February 2021. The status of air quality in Malaysia is displayed on an hourly basis by the Malaysian Department of Environment (DOE) via the Air Pollutant Index (API). There are six criteria pollutants measured, including fine particulate matter (PM2.5), coarse particulate matter (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ground-level ozone (O2). Before the execution of API, the sub-index for each criteria pollutants are calculated, and the maximum sub-index is considered as the API, showing the status of air quality at that particular area. The monitoring was under the concession of DOE and Transwater Sdn. Bhd. which covers urban, suburban, industrial, and background stations (Ash’aari et al., 2020). Most of the time, the sub-index of API was dominated by PM2.5. Specifically, the PM2.5 played an important role for Angiotensin-Converting Enzyme 2 (ACE2), but in order to represent the overall air quality status, the API is used as a representative for all pollutants. The exposure of high particulate matter will induce ACE2 expressions. ACE2 receptor usually exists at respiratory systems. The full-length protein structure of ACE2 consists of an N-terminal and a C-terminal domain with a single transmembrane helix and an intracellular segment. ACE2 is expressed in different tissues, such as renal, cardiovascular, and gastrointestinal. ACE2 is also present in lung alveolar epithelial cells, enterocytes of the small intestine, arterial and venous endothelial cells, and arterial smooth muscle cells. ACE2 was previously identified as an entry receptor for SARS-CoV and HCoV-NL63. Higher expressions of ACE2 may prolong the virus life cycle, enhance virus replication and mediate penetration of the virus into the host cell. It has been reported that SARS-CoV-2 spike glycoprotein may use ACE2 as a receptor to gain entry into human cells, in a way similar to that of SARS-CoV. COVID-19 daily cases data have been acquired from the website of COVID-19 Malaysia (available at (MOH, 2021) from 18 March 2020 to 28 February 2021. Data in this study had been analyzed to determine the trend of API during the COVID-19 pandemic in Malaysia. These data were then translated into bar graph for clear observation regarding the trend. After getting the trend, the indication of air quality status at study areas is investigated. Descriptive statistics indices were evaluated (Abdullah et al., 2020), and for the inferential statistics, it aims to correlate the relationship between API and COVID-19 cases using Microsoft Excel Spreadsheet® 2019.

Table 1. 
Selected AQMS in line with COVID-19 in Malaysia.
Region State Label Stations
North Perlis S1 Kangar
Kedah S2 Langkawi
Kedah S3 Alor Setar
Kedah S4 Kulim Hi-Tech
Pulau Pinang S5 Seberang Perai
Perak S6 Taiping
Perak S7 Tasek Ipoh
Perak S8 Seri Manjung
East Pahang S9 Rompin
Pahang S10 Temerloh
Pahang S11 Jerantut
Pahang S12 Balok Baru Kuantan
Terengganu S13 Kemaman
Terengganu S14 Kuala Terengganu
Terengganu S15 Besut
Kelantan S16 Tanah Merah
Kelantan S17 Kota Bharu
Central Kuala Lumpur S18 Cheras
Putrajaya S19 Putrajaya
Selangor S20 Kuala Selangor
Selangor S21 Petaling Jaya
Selangor S22 Klang
South Negeri Sembilan S23 Seremban
Negeri Sembilan S24 Port Dickson
Melaka S25 Alor Gajah
Melaka S26 Bandaraya Melaka
Johor S27 Segamat
Johor S28 Batu Pahat
Johor S29 Kluang
Johor S30 Larkin
Johor S31 Kota Tinggi
Johor S32 Tangkak
Sabah S33 Tawau
S34 Sandakan
S35 Kota Kinabalu
S36 Keningau
Sarawak S37 Limbang
S38 Miri
S39 Bintulu
S40 Mukah
S41 Kapit
S42 Sibu
S43 Sarikei
S44 Sri Aman
S45 Samarahan
S46 Kuching


Table 2 summarizes the main statistical index which are minimum, maximum, median, average, standard deviation, variance, skewness, and kurtosis values of API based on AQMS in Malaysia. There are 46 locations of API at all-region in Malaysia that nearest to COVID-19 recorded areas (Fig. 1). Data have skewness ranged from -0.63 to 1.31 and kurtosis ranged -0.85 to 1.90. While for median are ranged from 23 to 61. Lastly, the range for standard deviation and variance are 7.25-18.74 and 52.55-351.10, respectively. The ranges of minimum to maximum API value for the North region (S1-S8), are from 2-97 while for the East (S9-S17), are from 8-139. Next, for Central (S18-S22) and South (S23-S32) are 17-116 and 8-124, respectively. Lastly, for Sabah (S33-S36) and Sarawak (S37-46) the range is between 9-71 and 8-90, respectively. In between this study period, Klang (S22) was found to be the most station-dominant air pollutant compared to others. The average API recorded was 62.70 and categorized as moderate according to the New Malaysian Ambient Air Quality Standard (NMAAQS). Since Port Klang is one of the industrial towns with a high volume of heavy vehicles (the monitoring site is also located just beside the main road of Port Klang), PM10 mass concentrations appear to accumulate in this area (Mohamad et al., 2015). A study by Rahman et al. (2015) stated that air pollution sources in Klang are PM10, and might has influence by relative humidity, and atmospheric temperature. This indicates that, in addition to CO and NO2, PM10 was a significant air pollutant in the Klang Valley. The lowest API has been recorded at Limbang (S37) (25.37). Limbang is considered as the background station (rural). The distinct differences in NO2, CO, and SO2 variations revealed different origins for them. According to Zhou et al. (2020), the Rural Residential Coal Combustion (RRCC) for heating had a significant effect on air quality, adding 36.1, 9.1, and 16.1 percent of SO2, NOx, and PM2.5 to the atmosphere, respectively, in the winter. According to the findings from Qiao et al. (2020), each 1 g/m3 increase in PM1, PM2.5, PM10, and NO2 was linked to 14.9%, 14.6%, 7.3%, and 16.5% increased risk of osteoporosis, respectively. Air contamination in China’s rural population is thought to be responsible for 20.29 to 24.36% of osteoporosis incidents. In Malaysia, cooking activity has led to a substantial increase in exposure from increasing concentrations in PM2.5 during a COVID-19 lockdown (maximum average concentration at 52.2 μg/m3) (Ezani et al., 2020).

Table 2. 
Descriptive statistic of API based on Air Quality Monitoring Stations.
Region State Stations AQMS Min Max Mean Med Stdev Var Skew Kurt
North Perlis S1 Kangar 13 97 44.92 47 13.28 176.24 -0.05 -0.37
Kedah S2 Langkawi 2 71 39.49 38 12.99 168.73 0.08 -1.11
Kedah S3 Alor Setar 15 96 44.81 47 15.01 225.16 0.12 -0.56
Kedah S4 Kulim Hi-Tech 15 79 46.89 51 12.77 163.07 -0.31 -0.75
Pulau Pinang S5 Seberang Perai 20 88 51.35 53 11.50 132.26 -0.30 -0.17
Perak S6 Taiping 11 88 48.33 52 13.88 192.56 -0.34 -0.34
Perak S7 Tase Ipoh 21 87 53.06 54 10.57 111.73 -0.42 0.21
Perak S8 Seri Manjung 16 97 51.99 53 12.02 144.54 -0.08 0.45
East Pahang S9 Rompin 16 135 48.17 45 18.74 351.10 0.70 0.25
Pahang S10 Temerloh 10 91 48.29 52 13.21 174.47 -0.63 0.18
Pahang S11 Jerantut 8 79 44.33 47 13.87 192.37 -0.20 -0.85
Pahang S12 Balok Baru Kuantan 14 139 42.15 42 11.77 138.51 0.10 0.76
Terengganu S13 Kemaman 12 66 42.40 44 12.03 144.62 -0.51 -0.74
Terengganu S14 Kuala Terengganu 12 92 50.43 54 13.96 194.83 -0.54 0.56
Terengganu S15 Besut 11 78 41.96 43 13.46 181.26 -0.16 -0.78
Kelantan S16 Tanah Merah 10 98 50.48 53 15.28 233.39 -0.29 0.09
Kelantan S17 Kota Bharu 11 97 49.28 53 13.43 180.39 -0.41 0.38
Central Kuala Lumpur S18 Cheras 21 100 53.00 54 9.04 81.69 -0.74 1.14
Putrajaya S19 Putrajaya 22 116 55.15 55 9.65 93.09 -0.31 1.90
Selangor S20 Kuala Selangor 17 85 52.91 54 11.75 138.13 -0.46 0.17
Selangor S21 Petaling Jaya 27 112 61.70 60 10.87 118.14 0.49 1.04
Selangor S22 Klang 35 94 62.70 61 8.11 65.74 0.61 0.64
South Negeri Sembilan S23 Seremban 18 87 47.82 51 10.12 102.39 -0.48 -0.09
Negeri Sembilan S24 Port Dickson 21 124 48.39 51 10.44 108.94 -0.08 0.76
Melaka S25 Alor Gajah 18 80 46.04 49 12.51 156.58 -0.28 -0.83
Melaka S26 Bandaraya Melaka 16 101 48.36 51 10.74 115.30 -0.41 -0.31
Johor S27 Segamat 13 87 47.22 51 11.45 131.20 -0.71 0.13
Johor S28 Batu Pahat 12 89 42.07 44 13.39 179.17 -0.08 -0.53
Johor S29 Kluang 8 66 36.79 37 12.34 152.18 -0.08 -0.84
Johor S30 Larkin 21 101 51.46 53 9.55 91.22 -0.58 1.03
Johor S31 Kota Tinggi 10 74 35.36 33 11.45 131.06 0.44 -0.62
Johor S32 Tangkak 21 86 48.09 50 9.82 96.42 0.16 0.93
Sabah S33 Tawau 10 63 27.77 27 7.25 52.55 0.80 1.81
S34 Sandakan 14 60 35.94 35 8.79 77.22 0.11 -0.56
S35 Kota Kinabalu 9 60 28.06 25 11.95 142.88 0.79 -0.39
S36 Keningau 14 71 37.81 36 11.53 133.02 0.16 -1.13
Sarawak S37 Limbang 9 60 25.37 23 9.88 97.54 1.22 1.31
S38 Miri 17 90 44.59 47 11.57 133.88 -0.18 -0.57
S39 Bintulu 13 74 40.67 41 12.62 159.29 -0.11 -1.05
S40 Mukah 10 66 26.11 25 9.37 87.71 1.10 1.43
S41 Kapit 9 67 26.68 25 7.81 60.99 1.28 2.61
S42 Sibu 9 87 31.74 29 10.95 119.85 0.96 1.15
S43 Sarikei 12 68 26.73 24 9.53 90.84 1.31 1.64
S44 Sri Aman 8 84 28.32 25 11.33 128.30 1.05 1.16
S45 Samarahan 11 66 28.62 27 10.26 105.30 1.02 0.65
S46 Kuching 13 71 34.37 33 10.97 120.36 0.58 -0.34

Fig. 1. 
The mean of API at Air Quality Monitoring Stations.

Table 3 shows the percentage level of API in study areas. According to the Malaysian Department of Environment (2021), the API levels between 0 to 50 signified a good level. Good API shows that the study areas contain low pollution and do not impose negative health effects. No restrictions on public outdoor events, and encourages people to live a healthier lifestyle. The API level ranged from 51 to 100, indicating a moderate status. The areas were reported to have moderate emissions and no restrictions on outdoor activities. Finally, during the study, several locations reported an unhealthy level of API (101-200). Elderly, pregnant women, kids, and people with heart and lung problems suggested staying indoor and the government advises high-risk individuals to restrict their recreational behaviors. The good API level in the north area ranges from 30.12% to 70.81%. The highest percentage at Good was 70.81% in Langkawi (S2) (sub-urban), while the lowest percentage was in Tasek Ipoh (S7) (30.12%) (urban). In the East region. Balok Baru Kuantan (S12) (industrial) has the highest percentage (68.41%), while Kuala Terengganu (S11) (urban) has the lowest percentage (34.75%). Kuala Selangor (S20) (rural) had the highest percentage (30.78%) in the central area, while Klang (S22) (suburban) had the lowest percentage (2.41%). In the southern area, Kota Tinggi (S31) (sub-urban) recorded the highest percentage (83.86%) compared to Larkin (S30) (urban) that has the lowest percentage (32.12%). In Sabah, the percentage varies from 77.10 to 98.82%, with Tawau (S33) (sub-urban) being the most dominant (98.82%) at the good API level. Finally, in Sarawak, the good level of API ranges from 57.10% to 98.48%, with Kapit (S44) (rural) recording the highest percentages. In the North, the moderate API level ranged from 29.19% to 69.88%. Tasek Ipoh (S7) (urban) has the highest percentage, while Langkawi (S2) (sub-urban) has the lowest. In the East, Balok Baru Kuantan (S12) (Industrial) had the lowest percentage. The levels for central range from 75.09% to 97.59%. Cheras (S18) (urban), Putrajaya (S19) (sub-urban), Kuala Selangor (S20) (rural), Petaling Jaya (S21) (sub-urban), and Klang (S22) (sub-urban) were the study areas in this field. These areas in the region had an API rating of more than 50% at a moderate API level. At South, Larkin (S30) (urban) recorded the highest percentages in this region followed by Bandaraya Melaka (S26) (urban) that recorded 54.58%. Sabah and Sarawak were ranges from 1.18%-22.9% and 1.52%-42.9%, respectively. Keningau (S36) (background) and Miri (S38) (sub-urban) are recorded the highest percentages (22.9% & 42.9%) respectively. This study also revealed 0.6% and 0.08% from the east region for unhealthy API level located at Rompin (S9) (rural) and Balok Baru Kuantan (S12) (industrial) respectively. Next, at central region, Putrajaya (S19) (sub-urban) is recorded 0.11% while Petaling Jaya (S21) (sub-urban) recorded 0.17%. Lastly, at South, Port Dickson (S24) (sub-urban) produced 0.05% while Bandaraya Melaka (S26) (urban) and Larkin (S30) (urban) recorded only at 0.01% each.

Table 3. 
Percentage indication level of API at study areas.
Region State Station AQMS Good
(0-50) (%)
(51-100) (%)
(101-200) (%)
North Perlis S1 Kangar 56.69 43.31 0 100
Kedah S2 Langkawi 70.81 29.19 0 100
Kedah S3 Alor Setar 55.60 44.4 0 100
Kedah S4 Kulim Hi-Tech 49.18 50.82 0 100
Pulau Pinang S5 Seberang Perai 37.08 62.92 0 100
Perak S6 Taiping 44.94 55.06 0 100
Perak S7 Tasek Ipoh 30.12 69.88 0 100
Perak S8 Seri Manjung 35.71 64.29 0 100
East Pahang S9 Rompin 58.80 40.59 0.60 100
Pahang S10 Temerloh 41.81 58.19 0 100
Pahang S11 Jerantut 56.40 43.60 0 100
Pahang S12 Balok Baru Kuantan 68.41 31.51 0.08 100
Terengganu S13 Kemaman 64.22 35.78 0 100
Terengganu S14 Kuala Terengganu 34.75 65.25 0 100
Terengganu S15 Besut 62.90 37.10 0 100
Kelantan S16 Tanah Merah 38.62 61.38 0 100
Kelantan S17 Kota Bharu 40.57 59.43 0 100
Central Kuala Lumpur S18 Cheras 24.91 75.09 0 100
Putrajaya S19 Putrajaya 20.41 79.48 0.11 100
Selangor S20 Kuala Selangor 30.78 69.22 0 100
Selangor S21 Petaling Jaya 7.85 91.98 0.17 100
Selangor S22 Klang 2.41 97.59 0 100
South Negeri Sembilan S23 Seremban 47.77 52.23 0 100
Negeri Sembilan S24 Port Dickson 46.43 53.52 0.05 100
Melaka S25 Alor Gajah 51.91 48.09 0 100
Melaka S26 Bandaraya Melaka 45.41 54.58 0.01 100
Johor S27 Segamat 47.15 52.85 0 100
Johor S28 Batu Pahat 65.30 34.70 0 100
Johor S29 Kluang 81.93 18.07 0 100
Johor S30 Larkin 32.12 67.87 0.01 100
Johor S31 Kota Tinggi 83.86 16.14 0 100
Johor S32 Tangkak 52.00 48.00 0 100
Sabah S33 Tawau 98.82 1.18 0 100
S34 Sandakan 92.83 7.17 0 100
S35 Kota Kinabalu 91.12 8.88 0 100
S36 Keningau 77.10 22.90 0 100
Sarawak S37 Limbang 95.49 4.51 0 100
S38 Miri 57.10 42.9 0 100
S39 Bintulu 67.77 32.23 0 100
S40 Mukah 97.02 2.98 0 100
S41 Kapit 98.48 1.52 0 100
S42 Sibu 90.35 9.65 0 100
S43 Sarikei 95.98 4.02 0 100
S44 Sri Aman 93.94 6.06 0 100
S45 Samarahan 94.12 5.88 0 100
S46 Kuching 88.76 11.24 0 100

Fig. 2 shows that recorded API at a good level is higher compared to a moderate level. This shows that better air quality is formed during the COVID-19 pandemic. Research from Abdullah et al. (2020) and Othman and Latif (2021) reported that the introduction of MCO greatly reduced human activities, resulting in lower air emissions and improved human health in Malaysia. Other than that, during the COVID-19 lockdown, Mahato et al. (2020) found a 36.84% decline in CO in the megacity of Delhi, which they attribute to closed highways, industrial factories, and power plants. A study by Rahman et al. (2021) stated that PM2.5, NO2, SO2, O2, and CO concentrations in Dhaka City decreased by 26%, 20.4%, 17.5%, 9.7%, and 8.8%, respectively during the partial and absolute lockdowns, respectively, relative to the time before the lockdown. The introduction of a lockout strategy to contain COVID-19 transmission was critical in lowering pollution levels. During the first partial lockdown, from March 1 to April 21, Baghdad’s air quality index (AQI) improved by 13% relative to prelockdown levels. NO2, PM2.5, and PM10 concentrations in Baghdad decreased by 6%, 8%, and 15%, respectively but O2 levels increased by 13% during the first partial and absolute lockdowns from March 1 to April 21. NO2 and PM2.5 levels fell by 20% and 2.5%, respectively, during the second partial lockout, which lasted from June 14 to July 24 while O2 and PM10 levels increased by 525% and 56%, respectively (Hashim et al., 2021). According to Wang et al. (2021), The air quality index (AQI) was decreased by 15.2%, and NO2, PM10, PM2.5, and CO concentrations were reduced by 37.8%, 33.6%, 21.5%, and 20.4%, respectively. We discovered that traffic controls, especially the restriction of intra-city travel intensity (TI), had a major heterogeneous impact on NO2, with a reduction of approximately 13.6%, and that any oneunit rise in control measures intensity decreased air pollution concentrations by approximately 2-4%.

Fig. 2. 
Percentage of API Level at Air Quality Monitoring Stations.

According to Table 4 and Fig. 3, as the API variable increased by one unit, the number of COVID-19 cases increased by 0.114 in the north. In this region, the R2 was only 0.4%. The number of COVID-19 cases increased by 1.831 in central areas as the API variable increased by one unit, resulting in a 2% of R2. Following that, as the API variable increased by one unit, the COVID-19 cases increased by 0.102, resulting in a 0.04% R2 in the south area. When the API variable was increased by one unit, the COVID-19 cases increased by 0.062 in the east. This area had an R2 of 1.5%. Meanwhile, in Sabah, the number of COVID-19 cases decreased by 0.821 as the API value increased by one unit, resulting in a negative relationship with an R2 of 4%. Finally, as the API value rose by one unit and R2 was 0.2%, Sarawak recorded a 0.065 rise in COVID-19 cases. Based on the result, it is observed that there is a slight correlation between COVID-19 cases and API due to the exposure from the emission of industrialization activity, vehicle smoke, and road dust. We also can see that the ambient air did not contribute much to the rising of COVID-19 in Malaysia. Li et al. (2021) have also confirmed that the Air Quality Index (AQI) in China promotes the transmission of COVID-19 cases (R2=0.13 and 0.223) in Wuhan and XiaoGan. Furthermore, in Milan, Italy, Zoran et al. (2020) have also justified that the COVID-19 new daily cases have positive significant relationship with the air quality index. In Singapore, it is reported that the pollutant standard index is also positively correlated with COVID-19 cases (r=0.35) (Lorenzo et al., 2021). Moreover, several air pollutants concentration are positively correlated with COVID-19 cases in Mexico, which the evaluated correlation values are 0.77-0.80 (Tello-Leal and Macia-Hernandez, 2020). But, conversely, Sangkham et al. (2021) said that the daily confirmed COVID-19 cases are negatively associated with the air quality index in Bangkok (r=0.458), in line for Sabah region in this study. As advised by the MOH (2021), the community needs to practicing the 3W which are washing the hands using soap and sanitizer, wear the face mask when going outdoor, and warning from the government need to be alerted. Furthermore, the society also needs to avoid the 3C which mean close conversation, crowded place, and confined spaces to avoid the COVID-19 transmission from occurring. These actions had effective towards the society in curbing the COVID-19 spread. The spread of the COVID-19 outbreak is strongly associated with population movements in our culture, which may intensify the spread of novel coronaviruses and pose a serious threat to human life and public health (Wang et al., 2021). The environmental factor like temperature might plays an important role in the survival and transmission of viruses. Unusual temperature variation is an important risk factor for respiratory diseases. Temperature fluctuations can increase the mortality rate and enhanced influenza-related mechanisms. Influenza viruses live lengthier on surfaces or in droplets in cold and dry air, which increases the possibility of succeeding contagion (Hoseinzadeh et al., 2020). Previously, the temperature is a significant factor influencing the infectious diseases such as SARS and influenza. Temperature and its changes affected the SARS outbreak (Lin et al., 2006). Tan et al. (2005) found that the temperature was lower in the 2003 SARS outbreak and there is an increased risk of daily incidence. A study by Casanova et al. (2010) shows that the SARS-CoV strain of the infection was seen to endure longer on surfaces at a lower temperature. The SARS-CoV day by day rate of cases during the endemic was 18-overlap in lower temperatures contrasted with higher temperatures (Lin et al., 2006). Furthermore, Chan et al. (2011) reported that the reasonability of different sorts of SARS Covids was diminished with high temperatures. Their study likewise recommends that tropical nations have a generally safe of SARS Covid disease as contrasted and moderately chilly nations. Higher temperatures have been shown to be protected against the transmission of the SARS in 2002-2003 (Lin et al., 2006), perhaps because of the diminished endurance of the SARS-CoV on surfaces at higher temperatures (Chan et al., 2011). For MERS-CoV, Gardner et al. (2019) found that it has a negative relationship among temperature and case occurrences for a situation case-crossover investigation. Unfortunately, Altamimi et al. (2019) found that the high temperature was one of the contributors to increased MERS-CoV cases. The speculation is drawn from the inverse relationship between warm temperature and viral infections, including influenza and other coronaviruses like MERS-CoV (Fagbo et al., 2017; Lowen et al., 2007).

Table 4. 
Summary of linear regression between API and COVID-19.
Region Equation (y=mx+c) R2 p-value
North C19=0.114 (API)+2.054 0.004154 p<0.05
Central C19=1.831 (API)-45 0.021279 p<0.05
South C19=0.102 (API)+13 0.000412 p>0.05
East C19=0.062 (API)+0.227 0.015648 p<0.05
Sabah C19=-0.821 (API)+71.56 0.043024 p>0.05
Sarawak C19=0.065 (API)+2.22 0.002565 p<0.05

Fig. 3. 
The relationship of API and COVID-19.

Prata et al. (2020) examine the role of temperature on the combined number of COVID-19 cases in Brazil. The Generalized Additive Model (GAM) developed to reveal that there is an inverse relationship between temperature and daily cumulative COVID-19 cases. In-depth, they found that on every 1°C increase of temperature, will decrease 4.9% of COVID-19 cases in Brazil. Li et al. (2020) found that the daily temperature (R2=0.126, p<005) and daily lowest temperature (R2=0.143, p<0.05) were predominantly correlated with COVID-19 incidence, in inverse correlation by using simple linear regression analysis. Moreover, Mandal and Panwar (2020) found that the monthly average environment temperature has a strong negative correlation of total cases (r=-0.45), active cases (r=-0.42), and cases per million (r=-0.50) via Spearman correlation analysis. They added that a chilly climate might be an extra danger factor for COVID-19 cases. In Bangladesh, Haque and Rahman (2020) found that high temperature significantly reduces the transmission of COVID-19. They found the peak spread COVID-19 occurred at an average temperature of 26°C. In a study by Shahzad et al. (2020), the research found that there exists negative correlation between temperature and COVID-19 for several provinces; Guangdong (r=-0.3038346, p<0.05), Henan (r=-0.5006191, p<0.05), Jiangxi (r=-0.5178269, p<0.05), Shandong (r=-0.5246733, p<0.05), and Jiangsu (r=-0.5211593, p<0.05), while some provinces show positive correlation; Hubei (r=0.5356257, p<0.05), Zhenjiang (r=0.4941642, p<0.05), Hunan (r=0.4372964, p<0.05), Anhui (r=0.5098245, p<0.05), and Heilongjiang (r=0.6612333, p<0.05). Xie and Zhu (2020) also used the GAM to determine the relationship between mean temperature and COVID-19 confirmed cases in China like Prata et al. (2020). Conversely, they found that mean temperature has a positive linear relationship with the number of COVID-19 cases over 122 cities when the temperature is below 3°C. For every 1°C increase is associated with a 4.861% increase in the daily number of COVID-19 confirmed cases. Menebo (2020) found that the maximum temperature (r=0.374, p<0.05) and normal temperature (r=0.293, p<0.05) were positively and significantly correlated with COVID-19. Azuma et al. (2020) revealed that COVID-19 is significantly associated with the increase in daily temperature or sunshine hours. This suggests that an increase in person-to-person contact due to increased outing activities on a warm and/or sunny day might promote the transmission of COVID-19. Interestingly, Runkle et al. (2020) stated that the temperature did not exhibit a strong association with COVID-19 in US cities. This is corroborated by To et al. (2021), who discovered no correlation between ambient temperature and COVID-19 incidence. The concept that lower or higher temperatures will limit COVID-19 transmission is not fully supported by various data. These contradictory findings regarding the effect of temperature on COVID-19 transmission emphasise the importance of conducting more investigation in a range of geographic areas and over long time periods. MOH (2020) also recorded that until November 2020, there are 119 clusters reported to be workplace-related. Of that number, a total of 36 clusters have been declared ended while another 83 clusters are still active to this day. A total of 77,201 individuals were screened in which a total of 12,079 cases were found to be COVID-19 positive. This involved 4,398 cases of citizens and 7,681 cases were non-citizens. This shows that the infection of COVID-19 is involved in the workplace that specifically in indoor spaces.


This research should ideally serve as a starting point for a better understanding of the factors affecting COVID-19 transmission and spread. Additionally, the findings imply that air quality should be prioritised, since it would help prevent the spread of infectious diseases such as COVID-19. Integrated solutions to avert pandemics comparable to COVID-19 should be developed not just in terms of medicine and wellness, but also in terms of sustainability and environmental science. Characterization of PM2.5 is ideally a significant step in detecting the presence of SARS-CoV-2 genes in the air as the PM2.5 has always dominated the API. Additional factors to consider include temperature, traffic volume, industrial activity, and biomass burning. Serious action must be done to halt the spread of COVID-19, with a particular emphasis on the installation of severe lockdowns that can significantly reduce the number of new confirmed cases of COVID-19 on a daily basis.


Universiti Teknologi MARA (UiTM) supported this research through the Young Talent Research Grant (600-RMC/YTR/5/3 (007/2021)). Additionally, we would like to express our gratitude to the Air Quality Division of the Malaysian Department of Environment for acquiring air quality data. We are extremely appreciative of the frontliners’ efforts during this trying time. We wish all who have been directly impacted by COVID-19 better days ahead.


The authors declare no conflicts of interest.

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