Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Hereher, Mohamed E.; Eissa, Rasha; Alqasemi, Abduldaem S.; Kenawy, Ahmed M. El

doi:10.1007/s11356-021-17383-9

Cited by 28 publications

(11 citation statements)

References 61 publications

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“…We first generate random points in ArcGIS Pro 2.9.1 over the Brussels capital region and Liege city with 100 m spacing between the points to avoid spatial autocorrelation. We consider 100 m as minimum distance as spatial variability of urban temperatures is around 100 m (Bechtel et al 2015;Li et al 2021;Hereher et al 2022). We then use the "extract values to point" tool, to extract values of all the parameters mentioned above to the randomly generated points.…”

Section: Data Processingmentioning

confidence: 99%

Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

Joshi

Aliaga

Teller

2023

The Urban Book Series

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An abundance of impervious surfaces like building roofs in densely populated cities make green roofs a suitable solution for urban heat island (UHI) mitigation. Therefore, we employ random forest (RF) regression to predict the impact of green roofs on the surface UHI (SUHI) in Liege, Belgium. While there have been several studies identifying the impact of green roofs on UHI, fewer studies utilize a remote-sensing-based approach to measure impact on Land Surface Temperatures (LST) that are used to estimate SUHI. Moreover, the RF algorithm, can provide useful insights. In this study, we use LST obtained from Landsat-8 imagery and relate it to 2D and 3D morphological parameters that influence LST and UHI effects. Additionally, we utilise parameters that influence wind (e.g., frontal area index). We simulate the green roofs by assigning suitable values of normalised differencevegetation index and built-up index to the buildings with flat roofs. Results suggest that green roofs decrease the average LST.

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Section: Data Processingmentioning

confidence: 99%

Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

Joshi

Aliaga

Teller

2023

The Urban Book Series

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“…Therefore, it is obvious that the high mean concentrations of this pollutant are apparent in all the metropolitan agglomerations of the GCC countries, such as Kuwait City, Dammam, Manama, Doha, and Dubai (Figure 5a) during the period from 2005-2020. The maximum monthly concentrations The distribution of NO 2 is correlated mainly to the road traffic and to the indoor use of stoves, heaters, and fossil-fuel-burning appliances [39]. Therefore, it is obvious that the high mean concentrations of this pollutant are apparent in all the metropolitan agglomerations of the GCC countries, such as Kuwait City, Dammam, Manama, Doha, and Dubai (Figure 5a) during the period from 2005-2020.…”

Section: Greenhouse Gases and Air Pollutionmentioning

confidence: 99%

“…At the monthly level (Figure 4c) and in contrast to the CO2 emissions, winter and fall witnessed maximum SO2 emissions, while the minimum was recorded during June and July. The distribution of NO2 is correlated mainly to the road traffic and to the indoor use of stoves, heaters, and fossil-fuel-burning appliances [39]. Therefore, it is obvious that the high mean concentrations of this pollutant are apparent in all the metropolitan agglomerations of the GCC countries, such as Kuwait City, Dammam, Manama, Doha, and Dubai (Figure 5a) during the period from 2005-2020.…”

Section: Greenhouse Gases and Air Pollutionmentioning

confidence: 99%

Climate Change during the Third Millennium—The Gulf Cooperation Council Countries

Hereher

2022

Sustainability

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The Gulf Cooperation Council (GCC) is a union occurring in the arid subtropical belt of the world. Contemporary climate change is a serious environmental issue at the regional and global levels. The main purpose of this study was to investigate the changes and trends in the regional climate in the GCC countries in terms of the land surface temperatures and surface anomalies, precipitation, and sea surface temperatures over the first two decades of this millennium. Research data exclusively relied on satellite remote sensing. Spatial, statistical, and cartographic analyses were performed to extract, manipulate, and display thematic maps reflecting the changes and trends of the regional climate. The results showed that notable climate changes were obvious and frequent throughout the GCC countries, with symptoms influencing the atmosphere, lithosphere, and the hydrosphere of the region. However, a prominent gradient in the severity of climate change occurred from north to south. Remarkably, serious impacts were observed in Kuwait and eastern Saudi Arabia, while the least effects were recorded in Oman. The study denotes the competence of remote sensing for monitoring regional climate change.

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“…The Sentinel-5P is a sun-synchronous, near-polar orbiting satellite that flies at an altitude of 817 km above the earth's surface at an ascending point with an equator crossing time of 13:30 (local time), providing daily global coverage. Three alternative data streams could be used to monitor air pollutants using TROPOMI: near-real-time (NRTI), offline (OFFL), and reprocessing (RPRO) [20] [21]. The images were obtained using Google Earth Engine, a cloud-based platform for planetary-scale geospatial analysis that applies Google's massive computational capabilities to a variety of highimpact societal issues such as deforestation, drought, disaster, disease, food security, water management, climate monitoring, and environmental protection.…”

Section: Datasetmentioning

confidence: 99%

Urban monitoring application in new administrative capital in Egypt using machine learning techniques and Sentinel 2 data

Fathy¹,

ElGharbawi²,

Amin

et al. 2022

Port-Said Engineering Research Journal

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Recently, Egypt has been undergoing rapid development in the urban and transportation sectors with a focus on the New Administrative Capital. This requires temporal monitoring and assessment for such projects with low cost, high accuracy, and simple application. Therefore, sentinel-2 data was utilized to assess the effectiveness of several methods for urban classification and monitoring. The study discusses three main approaches: histogram threshold, spectral indices, and machine learning techniques. In machine learning, we focus on K-nearest neighbor (KNN), Linear discrimination analysis (LDA), and Random Forest (RF) due to their robustness and simplicity. It was found that the RF technique presents the highest accuracy for our study region with 98.8%. Moreover, this study monitored the effect of such development on air pollution in the New Administrative Capital using sentinel-5P data which showed an increase in the CO and NO2 levels during the last five years by nearly 11% and 75% respectively, with a noticeable drop during the year 2020 due to the Covid19 lockdown. The classification model was trained and verified using ground truth data obtained on the 10 th of Ramadan City. Then, it was applied to monitoring urban development and the associated environmental impact in the New Administrative Capital in Egypt.

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Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Cited by 28 publications

References 61 publications

Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

Predicting Urban Heat Island Mitigation with Random Forest Regression in Belgian Cities

Climate Change during the Third Millennium—The Gulf Cooperation Council Countries

Urban monitoring application in new administrative capital in Egypt using machine learning techniques and Sentinel 2 data

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