The concentration of major air pollutants during the movement control order due to the COVID-19 pandemic in the Klang Valley, Malaysia

Latif, Mohd Talib; Dominick, Doreena; Hawari, Nor Syamimi Sufiera Limi; Mohtar, Anis Asma Ahmad; Othman, Murnira

doi:10.1016/j.scs.2020.102660

Cited by 51 publications

(47 citation statements)

References 70 publications

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“…SO 2 , NO 2, CO and O 3 are measured using Thermo Scientific model 43i, model 42i, model 48i and model 49i, respectively [93,94]. RH and TEMP were recorded using a Climatronic AIO 2 Weather Sensor (Climatronic Corporation) [95]. All the ground data were obtained on an hourly basis covering the period from January 2018 to December 2019.…”

Section: Datasetmentioning

confidence: 99%

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Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

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Section: Datasetmentioning

confidence: 99%

“…Flow verification for PM 10 and PM 2.5 measurements using TEOM was conducted once a month. The data removal during the QC check was predominantly due to insufficient measurements and instrument failure, while some perturbed data were also excluded as outliers in a second-level QC check [95].…”

Section: Ground Measurementsmentioning

confidence: 99%

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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“…Therefore, a large decrease in air pollution and positive environmental implications are reported for the first time since World War –II due to the worldwide lockdowns in awake of the COVID-19 pandemic ( Evangeliou et al, 2021 ; Grivas et al, 2020 ; Singh et al, 2021 ). Several studies demonstrated a considerable decrease in atmospheric pollutants across urban areas worldwide due to national lockdowns ( Bauwens et al, 2020 ; Latif et al, 2021 ; Wang & Su, 2020 ). NO 2 concentration has dropped by 20–30%, or even more, in urban centers of China ( Bao & Zhang, 2020 ), India ( Mahato et al, 2020 ), Malaysia ( Abdullah et al, 2020 ), Italy ( Collivignarelli et al, 2020 ), France ( Pazmiño et al, 2021 ), Spain ( Tobías et al, 2020 ), Greece ( Grivas et al, 2020 ), Brazil ( Nakada & Urban, 2020 ), and the USA.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America

Parida

Bar

Kaskaoutis

et al. 2021

Sustainable Cities and Society

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The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (–0.11°C to –2.6°C), USA (–0.70°C) and Canada (–0.27°C) in March–May of the pandemic year 2020 compared to the mean of 2015–2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. –0.46°C to –0.96°C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1–2°C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapour content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation.

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“…The complexity and interconnectedness existing in the process are in line with the law of chaos theory proposed by E. N. Lorenz that suggests that a small change in one part might trigger greatly different scenarios in other parts ( Gleick, 2011 ). Researchers have already proven the impact of COVID-19 in many areas, including pollutant emissions ( Kumar et al., 2020 ), air quality ( Latif, Dominick, Hawari, Mohtar & Othman, 2021 Wang & Li, 2021 ), noise ( Rumpler, Venkataraman & Göransson, 2020 ), human mobility ( Beria & Lunkar, 2021 Shakibaei, de Jong, Alpkökin & Rashidi, 2021 ; Zhang, Qian & Hu, 2020 , the global supply chain( Guan et al., 2020 ) and energy consumption Jiang, Fan & Klemeš, 2021 ; López Prol & O, 2020 . As one of the most important global employers (1 in 10 jobs are directly related to tourism, UNWTO, 2020 , 2020b ) and the major GDP contributor for several countries, tourism is highly vulnerable to risks and disasters internationally ( Farzanegan, Gholipour, Feizi, Nunkoo & Andargoli, 2020 ) and presumably was affected in this pandemic.…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 pandemic: Insights from vacation rentals in twelve mega cities

Liang

Leng

Yuan

et al. 2021

Sustainable Cities and Society

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Coronavirus disease 2019 (COVID-19) is a challenging global problem. COVID-19 has caused shocks to various urban systems, and the tourism industry is no exception. We analyzed the impact on vacation rentals by conducting diachronic data mining on nearly 10 GB of rental data (calendar, listings, and reviews) in twelve highly internationalized megacities distributed across Asia, Europe, America, and Oceania based on the data set from the Inside Airbnb website. All twelve cities were adversely affected. The specific time of the impact is related to the pandemic's outbreak and enforced lockdowns policies. Affected by the epidemic, reservation rates decreased, tourists preferred renting in suburbs instead of city centers, the proportion of foreign tourists in all destinations dropped sharply, tourist sentiment scores fluctuated dramatically especially among foreigners, and people focused less on tourism related activities. This study reveals the changing illustrations of vacation rentals in highly internationalized megacities under the pandemic's influence. It offers a methodological assessment framework to monitor the hospitality sector over time and aims to serve as a reference for preparedness in similar cities worldwide.

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The concentration of major air pollutants during the movement control order due to the COVID-19 pandemic in the Klang Valley, Malaysia

Cited by 51 publications

References 70 publications

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America

Impact of the COVID-19 pandemic: Insights from vacation rentals in twelve mega cities

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