Risk Assessment and Air Quality Study during Different Phases of COVID-19 Lockdown in an Urban Area of Klang Valley, Malaysia

Abstract

Globally, the COVID-19 pandemic has had both positive and negative impacts on humans and the environment. In general, a positive impact can be seen on the environment, especially in regard to air quality. This positive impact on air quality around the world is a result of movement control orders (MCO) or lockdowns, which were carried out to reduce the cases of COVID-19 around the world. Nevertheless, data on the effects on air quality both during and post lockdown at local scales are still sparse. Here, we investigate changes in air quality during normal days, the MCOs (MCO 1, 2 and 3) and post MCOs, namely the Conditional Movement Control Order (CMCO) and the Recovery Movement Control Order (RMCO) in the Klang Valley region. In this study, we used the air sensor network AiRBOXSense that measures carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and particulate matter (PM2.5 and PM10) at Petaling Jaya South (PJS), Kelana Jaya (KJ) and Kota Damansara (KD). The results showed that the daily average concentrations of CO and NO2 mostly decreased in the order of normal days > MCO (MCO 1, 2 and 3) > CMCO > RMCO. PM10, PM2.5, SO2 and O3 showed a decrease from the MCO to RMCO. PJS showed that air pollutant concentrations decreased from normal days to the lockdown phases. This clearly shows the effects of ‘work from home’ orders at all places in the PJS city. The greatest percentage reductions in air pollutants were observed during the change from normal days to MCO 1 (24% to 64%), while during MCO 1 to MCO 2, the concentrations were slightly increased during the changes of the lockdown phase, except for SO2 and NO2 over PJS. In KJ, most of the air pollutants decreased from MCO 1 to MCO 3 except for CO. However, the percentage reduction and increments of the gas pollutants were not consistent during the different phases of lockdown, and this effect was due to the sensor location—only 20 m from the main highway (vehicle emissions). The patterns of air pollutant concentrations over the KD site were similar to the PJS site; however, the percentage reduction and increases of PM2.5, O3, SO2 and CO were not consistent. We believe that local burning was the main contribution to these unstable patterns during the lockdown period. The cause of these different changes in concentrations may be due to the relaxation phases during the lockdown at each station, where most of the common activities, such as commuting and industrial activities changed in frequency from the MCO, CMCO and RMCO. Wind direction also affected the concentrations, for example, during the CMCO and RMCO, most of the pollutants were blowing in from the Southeast region, which mostly consists of a city center and industrial areas. There was a weak correlation between air pollutants and the temperature and relative humidity at all stations. Health risk assessment analysis showed that non-carcinogenic risk health quotient (HQ) values for the pollutants at all stations were less than 1, suggesting unlikely non-carcinogenic effects, except for SO2 (HQ > 1) in KJ. The air quality information showed that reductions in air pollutants can be achieved if traffic and industry emissions are strictly controlled.

Influence of COVID-19 on Environmental Awareness, Sustainable Consumption, and Social Responsibility in Latin American Countries

“…First, regarding the effect on production, the evidence of the initial periods of confinement in different cities and countries showed a relevant decrease in the pollution in urban and rural zones [49,50]. For example, recent research suggests that lockdowns and other restrictions related to COVID-19 have had a positive impact on terms of air quality in countries such as China [51,52], Italy [53,54], Portugal [50], the United States [49], India [55], and Malaysia [56]. In this vein and linked to the idea of sustainable consumption and emissions of waste and pollutants, scholars have suggested that the control of governments, manifested in restrictions on industry emissions and transport, can be the bridge to reaching environmental goals regarding air quality [56].…”

“…For example, recent research suggests that lockdowns and other restrictions related to COVID-19 have had a positive impact on terms of air quality in countries such as China [51,52], Italy [53,54], Portugal [50], the United States [49], India [55], and Malaysia [56]. In this vein and linked to the idea of sustainable consumption and emissions of waste and pollutants, scholars have suggested that the control of governments, manifested in restrictions on industry emissions and transport, can be the bridge to reaching environmental goals regarding air quality [56]. Nonetheless, those findings should be assessed with caution because they are concluded under exceptional circumstances.…”

“…Local governments should consider this social scenario and encourage activities that positively impact our planet from a national perspective. Furthermore, based on the experience and learning of this crisis [56], the authorities should implement policies that facilitate the process of change, which should be related to reducing pollutants and incentives for recycling or efficient water consumption. Similarly, companies must understand this change and offer products and services that respond to clients' preferences.…”

Wavelet Convolutional Neural Network for Forecasting Malaysian PM10 Time Series Data

“…In comparison, those stations with shorter dependencies on their previous values will have more volatile PM 10 values. These different dependencies of hourly PM 10 time series on its previous values for each station could be caused by several factors such as wind speed, humidity, temperature, land terrain and activities in that area [19][20][21].…”

Low‐cost air quality monitoring networks for long‐term field campaigns: A review

“…Several interesting findings were achieved within the reviewed papers, demonstrating the potentiality of LC networks to greatly expand the air quality monitoring capability. LC networks allowed, for example, investigating ship emissions in harbour areas (Jayaratne et al, 2020; Merico et al, 2019), identifying restaurants as an important local contributor to PM 2.5 concentrations (Eilenberg et al, 2020; Kosmopoulos et al, 2022), finding that local sources are responsible for only a fraction (30%) of the total PM 2.5 in the urban core while the rest is transported from other areas (Kosmopoulos et al, 2022), identifying the best hours to practice physical activities in the open air (Nieckarz & Zoladz, 2020), improving air pollution monitoring during special events (Chao et al, 2021; Kuhn et al, 2021), and evaluating the impact on air quality of the Covid‐19 pandemic (Chadwick et al, 2021; Davidović et al, 2021; Mohd Nadzir et al, 2021; Mousavi & Wu, 2021), forest fires (Stavroulas et al, 2020), fireworks (Petäjä et al, 2021), a stone quarry (Molho et al, 2019) and a volcano (Whitty et al, 2020).…”