Assessing PM<sub>2.5</sub> in Bahrain From the DPSIR Framework Perspective

Abbas, Hanan; Ghanem, Sara; Abahussain, Asma

doi:10.1109/ieeeconf53624.2021.9667958

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Air pollution levels in the atmosphere are controlled by emission sources, meteorological conditions, and geographical characteristics. Previously, several studies have reported that particulate pollution is a serious environmental issue in Middle Eastern countries, including Saudi Arabia [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], which in addition to arid geographical conditions and frequent sand storms, is encouraged by rapid urbanisation and industrialisation. Saudi Arabia has large scale desert areas, arid or semi-arid climatic conditions, little rain, and strong winds, which play a positive role in enhancing the atmospheric burden of particulates.…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Habeebullah

Munir

Zeb

et al. 2022

Toxics

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In this paper, the emission sources of PM10 are characterised by analysing its trace elements (TE) and ions contents. PM10 samples were collected for a year (2019–2020) at five sites and analysed. PM10 speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM10 concentrations (µg/m3) were 304.68 ± 155.56 at Aziziyah, 219.59 ± 87.29 at Misfalah, 173.90 ± 103.08 at Abdeyah, 168.81 ± 82.50 at Askan, and 157.60 ± 80.10 at Sanaiyah in Makkah, which exceeded WHO (15 µg/m3), USEPA (50 µg/m3), and the Saudi Arabia national (80 µg/m3) annual air quality standards. A GAM model was developed using PM10 as a response and ions and TEs as predictors. Among the predictors Mg, Ca, Cr, Al, and Pb were highly significant (p < 0.01), Se, Cl, and NO2 were significant (p < 0.05), and PO4 and SO4 were significant (p < 0.1). The model showed R-squared (adj) 0.85 and deviance explained 88.1%. PMF identified four main emission sources of PM10 in Makkah: (1) Road traffic emissions (explained 51% variance); (2) Industrial emissions and mineral dust (explained 27.5% variance); (3) Restaurant and dwelling emissions (explained 13.6% variance); and (4) Fossil fuel combustion (explained 7.9% variance).

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Section: Introductionmentioning

confidence: 99%

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Habeebullah

Munir

Zeb

et al. 2022

Toxics

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Atmospheric Emissions from Electricity Generation in Southeast Asia: Development Trend and Policy Responses

Oanh,

Huy

2024

Curr Pollution Rep

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Developing DPSIR Framework for Managing Climate Change in Urban Areas: A Case Study in Jakarta, Indonesia

et al. 2022

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From an environmentally conscious and ecological perspective, the sustainability of cities within the effects of climate change are closely related to the wise use of resources and modifications in the ecological status of the environment. In terms of the ecological environment, the sustainability of smart cities entails meeting present and future societal demands for the environment of the water, land, and air, among others. Environmental and the ecological concerns that arise from rapid climate change and monetary developments are shown in the inconsistency between ecological assets, environmental pollution, and the destruction of nature. In this study, the authors aim to develop a strategy to deal with climate change in urban areas using Remote Sensing and the Driver-Pressure-State-Impact-Response (DPSIR) Framework with a case study in Jakarta Smart City. The DPSIR framework, which will be developed and implemented in the city of Jakarta, is a smarter and more sustainable framework that is evaluated through a systematic evaluation of sustainability with quantitative research using the entropy weight method and Partial Least Square-Structural Equation Modeling (PLS-SEM). These methods evaluate 58 representative elements of environments at the urban level, including the shortcomings of earlier research such as data availability, spatial and temporal constraints, and several related ecological indicators, such as soil pH, wind speed, air quality index as well as land changes in the spatial (spatiotemporal) time series. The results of the study show that in the metropolitan city of Jakarta, the Drivers that are related to climate change are the rate of population growth and the rate of industrial growth which, although increases people’s income and GRDP in Jakarta; it also creates Pressures, namely an increase in the amount of water consumption and in the amount of wastewater. Based on these pressures, the environmental conditions (State) of Jakarta city have undergone several environmental changes, such as loss of water supply, changes in wind speed, changes in rainfall, and increasing concentrations of the Air Pollutant Standard Index. The Impact of these three elements resulted in the increase in household and industrial water consumption, an increase in annual electricity consumption, and deteriorating air quality. Hence, the Response to these four interrelated causal variables is that the Jakarta Provincial Government must increase annual funds for the construction of urban community facilities, increase the production capacity of clean water supply, build environment-friendly wastewater treatment facilities, increase the capacity of waste processing infrastructure and transportation fleets, and educate people to use water wisely to reduce the level of water use.

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Assessing PM_2.5 in Bahrain From the DPSIR Framework Perspective

Cited by 3 publications

References 30 publications

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Atmospheric Emissions from Electricity Generation in Southeast Asia: Development Trend and Policy Responses

Developing DPSIR Framework for Managing Climate Change in Urban Areas: A Case Study in Jakarta, Indonesia

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Assessing PM2.5 in Bahrain From the DPSIR Framework Perspective

Cited by 3 publications

References 30 publications

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

Atmospheric Emissions from Electricity Generation in Southeast Asia: Development Trend and Policy Responses

Developing DPSIR Framework for Managing Climate Change in Urban Areas: A Case Study in Jakarta, Indonesia

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Assessing PM_2.5 in Bahrain From the DPSIR Framework Perspective