The long-term assessment of air quality on an island in Malaysia

Halim, Nor Diana Abdul; Latif, Mohd Talib; Ahamad, Fatimah; Dominick, Doreena; Chung, Jing Xiang; Juneng, Liew; Khan, Firoz

doi:10.1016/j.heliyon.2018.e01054

Cited by 29 publications

(13 citation statements)

References 68 publications

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“…Lockdown of the production sectors, restriction on human mobility, and controlled public transportation system have resulted in the lowering of emission of pollutants. Studies by various scholars have shown how the lockdown has improved the ambient air quality, like in India (Gautam 2020 ; Mahato et al 2020 ; Sikarwar and Rani 2020 ; Srivastava et al 2020 ), the USA (Berman and Ebisu 2020 ), Mexico (Méndez-Arriaga 2020 ), Kazakhstan (Kerimray et al 2020 ), Iran (Abdul Halim et al 2018 ), China (Fan et al 2020 ; Zambrano-monserrate et al 2020 ) especially in Wuhan (Cole et al 2020 ; Lu et al 2020 ; Sicard et al 2020 ; Song et al 2016 ; Wang and Su 2020 ), Barcelona in Spain (Tobías et al 2020 ), Sao-Paulo in Brazil (Nakada and Urban 2020 ), Milan in Italy (Collivignarelli et al 2020 ), and Salé City in Morocco (Otmani et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India

Sarkar

Das²,

Mukhopadhyay

2020

Environ Dev Sustain

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The worldwide spread of COVID-19 caused a nationwide lockdown in India from 24 March 2020 and was further extended up to 3 May 2020 to break off the transmission of novel Coronavirus. The study is designed to assess the changes in air quality from the pre-lockdown period to the during lockdown period in Kolkata and Howrah municipal corporation, West Bengal, India. GIS-based techniques include the spatial and temporal distribution of pollutants using interpolation method, and on the other hand, statistical methods like analysis of variance (ANOVA) was applied to determine the mean differences two phases and correlation matrix helps to understand the changing association of the pollutants in pre- and during lockdown phases. Significant correlations have been found among the pollutants, ANOVA (Two-Way) has shown the significant mean difference of NAQI between the two phases, F (1,611) = 465.723, p < 0.0001; pairwise comparison for Ballygunge has shown the highest mean difference 108.194 at p < 0.0001 significant level between lockdown and pre-lockdown phase. Significant positive correlation has been found between PM 2.5 , PM 10 (0.99*); PM 2.5, NO 2 (0.81*); PM 10 , NO 2 (0.81*); CO, NO 2 (0.77*) and some negative correlations have also been found between O 3 , NO (− 0.15); O 3 and NH 3 (− 0.36) in the pre-lockdown phase. The reduction amount of mean concentration from the pre-lockdown phase to during lockdown of the main pollutants like PM 2.5 , PM 10 and NO 2 are ~ 58.71%, ~ 57.92% and ~ 55.23%. Near Rabindra Bharati University constant emission of PM 2.5 , 10 and NO 2 have been recorded due to the nearby Cossipore thermal power station.

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Section: Introductionmentioning

confidence: 99%

Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India

Sarkar

Das²,

Mukhopadhyay

2020

Environ Dev Sustain

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“…Loss of significance due to urban levels (low versus high) was likely the result of mixed land use and configurations within varying urban form [74,75]. The findings do suggest a general increase in ambient air pollution during tourist season as observed in other parts of the world [11,76,77]. Some social events, typically well-attended by SKN residents and visitors, did coincide with sampling schedules, most of them annual recreational affairs that are associated with increased traffic volumes as well as outdoor cook stove use [78] (e.g.…”

Section: Discussionmentioning

confidence: 67%

Ambient air pollution per specific land use types and activities in an urbanizing Eastern Caribbean Country, St. Kitts and Nevis

Whittaker

Deziel

Zhang

et al. 2020

Environ. Res. Commun.

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Urban development can have negative impacts on the environment through various mechanisms. While many air quality studies have been carried out in more developed nations, Eastern Caribbean (EC) countries remain understudied. This study aims to estimate the concentrations of air pollutants in the EC nation of St. Kitts and Nevis. Transport, recreation and construction sites were selected randomly using local land use records. Pollutant levels were measured repeatedly for numerous 1-hour intervals in each location between October 2015 and November 2018. Weather trends and land use characteristics were collected concurrent to sampling. Across 27 sites, mean NO 2 , O 3 , SO 2 , PM 10 and PM 2.5 levels were 26.61 ppb (range: 0-306 ppb), 11.94 ppb (0-230 ppb), 27.9 ppb (0-700 ppb), 52.9 μg m −3 (0-10,400 μg m −3 ) and 29.8 μg m −3 (0-1556 μg m −3 ), respectively. Pollutants were elevated in high urban areas and generally significantly positively correlated with each other, with the exception of PM 10 . NO 2 levels in construction areas were generally comparable to those in transportation areas and higher than in recreation areas. O 3 levels were lower in construction than recreation and transport areas. SO 2 concentrations were lower in construction and recreation compared to transport sites. Construction and recreation PM 10 levels exceeded transport sites, while PM 2.5 was highest in construction areas. Additional bivariate and multivariate analysis were conducted to assess whether various meteorological, temporal and land use factors including rain, tour season and urban features explained variability in air pollutant concentrations. Tourist season and specific months, more than any other factors, contributed most to variability in pollutant concentrations. These new measurements of air pollution concentrations in an understudied nation may have important implications for health outcomes among exposed EC residents, and provide critical data for future exposure and epidemiologic research and environmental policy.

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“…It was also used to detect the major contributors to air pollution in Jiangsu Province. A high SO 2 /NO 2 ratio (>0.60) indicates significant contributions from point sources, while a low ratio (0.04~0.12) signifies more contributions from mobile sources [83,84]. High values of the SO 2 /NO 2 ratio (>0.60) suggest that the source of NO 2 and SO 2 pollutants were primarily from industry during the period of 2005−2020 (Figure 4).…”

Section: Ratio Of So 2 /No 2 Indicator For Pollution Levelmentioning

confidence: 99%

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

et al. 2021

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Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

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The long-term assessment of air quality on an island in Malaysia

Cited by 29 publications

References 68 publications

Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India

Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India

Ambient air pollution per specific land use types and activities in an urbanizing Eastern Caribbean Country, St. Kitts and Nevis

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

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