PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

Al-Hemoud, Ali; Al-Khayat, Ahmad; Al‐Dashti, Hassan; Li, Jing; Alahmad, Barrak; Koutrakis, Petros

doi:10.1016/j.envc.2021.100215

Cited by 14 publications

(7 citation statements)

References 56 publications

(67 reference statements)

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“…To investigate the results obtained by the previous study (Ghanim 2022 ), excellent research has been conducted in the United Arab Emirates (Mansouri Daneshvar et al 2022 ) that studied how COVID-19 reduces pollution at the local scale of the country. As a result, this study and others have also found that SARS-CoV-2 lockdown reduces air pollution levels in MENA nations including Morocco, Kuwait, Tunisia, Qatar, and Iraq (Khomsi et al 2020 ; Al-Hemoud et al 2021 ; Jribi et al 2020 ; Mahmoud et al 2022 ; Hashim et al 2021a , b ).…”

Section: Thematic Discussionsupporting

confidence: 58%

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Meskher

Belhaouari

Thakur

et al. 2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-23392-z.

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Section: Thematic Discussionsupporting

confidence: 58%

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Meskher

Belhaouari

Thakur

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

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“…In addition, a modeling technique such as a mixed-effect regression model may further explain the variation of PM2.5 as it allows both fixed and random effects in the model. The changes in PM2.5 due to other pollutants and meteorological factors could be determined using a mixed-effect regression model which provides a better understanding of the effect of meteorological parameters and pollutants on PM concentrations (Al-Hemoud et al, 2021).…”

Section: The Relationship Between Air Contaminants and Weather Conditionsmentioning

confidence: 99%

Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Rahman

Hamzah

Latif

et al. 2022

Aerosol Air Qual. Res.

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Particulate matter is the parameter of most concern in air quality monitoring in Malaysia. This study discusses the variations and clustering of PM2.5 recorded from 2018 to 2019 at 65 stations of the Continuous Air Quality Monitoring Network of the Malaysian Department of Environment. PM2.5 concentrations were recorded continuously using a tapered element oscillating microbalance. The cluster analysis was conducted using the Agglomerative Hierarchical Cluster (AHC) method. The results show that the daily average of PM2.5 concentrations ranged between 8 and 31 µg m -3 . The cluster regions were classified into High Pollution Regions (HPR), Medium Pollution Regions (MPR) and Low Pollution Regions (LPR) based on the AHC analysis. The mean concentration of PM2.5 recorded in HPR was significantly higher with 23.04 µg m -3 followed by MPR and LPR. The results also showed that the highest concentration of PM2.5 was recorded during the 2019 haze episode for all three regions, with the air pollutant index indicating very unhealthy and dangerous levels.

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“…The highest PM 2.5 concentration occurred in winter during the day. It has been shown that the PM 2.5 concentration is positively correlated with relative humidity [ 36 ]. In summer, the relative humidity at night is significantly higher during the day, and during the sampling period, the average wind speed at night in Lanzhou was low (1.16 m/s).…”

Section: Resultsmentioning

confidence: 99%

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Zhang

Jia

Wang

et al. 2022

IJERPH

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Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl−, NO3−, SO42−), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.

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PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

Cited by 14 publications

References 56 publications

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Assessment of PM2.5 Patterns in Malaysia Using the Clustering Method

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

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