Examining land surface temperature and relations with the major air pollutants: A remote sensing research in case of Tehran

Fuladlu, Kamyar; Altan, Haşim

doi:10.1016/j.uclim.2021.100958

Cited by 35 publications

(10 citation statements)

References 38 publications

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“…Other factors such as air pollutants, building characteristics, population density, etc. may also have an impact on the heat island effect, Fuladlu and Altan (2021) studied land surface temperature and its relationship with major air pollutants and proved that CO and O 2 positively correlated with LST, and PM 2.5 , SO 2 , NO 2 were negatively correlated with altitude. These factors may not have been considered in this study, and more diverse influencing factor data should be studied in the future.…”

Section: Discussionmentioning

confidence: 99%

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Shang,

Xu,

Liu

et al. 2023

SAGE Open

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In recent years, with economic development, urbanization has been accelerating. In the past 100 years, the global average temperature has increased by 0.5°C, and according to the predictions of most global climate models, the temperature will continue to increase by 1.5°C to 3.0°C in the next 100 years. Under the influence of global warming, the urban heat island effect problem is becoming more and more serious, bringing much harm. Studying the thermal effect and its influencing factors is of great significance for sustainable urban development. In this study, the seasonal and interannual surface temperature changes of the study area are inverted based on the SW-TES algorithm, and the LST inversion of the algorithm is simply verified. At the same time, a cell dichotomous model was established to explore the influencing factors of the urban heat island effect and the influence of different land use types and normalized vegetation indices in the Hangzhou metropolitan area was analyzed. The results showed that the overall heating trend of the study area showed a heating trend, the expansion rate of regional construction land was relatively fast, and the scale of urban land was increasing. Based on the results of surface temperature changes of areas with different land types, we obtained the temperature trends of each land type [from 2005 to 2018] and found that there was a negative correlation between surface temperature and normalized vegetation index. This study provides a theoretical basis to evaluate the urban heat island effect and analyze factors that impact it and would promote sustainable urban development.

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Section: Discussionmentioning

confidence: 99%

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Shang,

Xu,

Liu

et al. 2023

SAGE Open

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“…The results from studies conducted in the Nile Delta region show a positive relationship between LST and air pollution particles such as NO2, SO2, and PM10. Moreover, LST demonstrates a positive correlation with multiple associated pollutants, including CO2 and O3, indicating that LST is a crucial component of studies for understanding the changing patterns of NO2 concentration [12], [27].…”

Section: A Relationship Of No2 With Vegetation and Lstmentioning

confidence: 99%

“…Vegetation plays a crucial role in influencing air quality through processes such as photosynthesis and the absorption of air pollutants [1], [11]. Additionally, changes in land cover and land use directly impact land surface temperature, altering environmental conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO₂ Concentrations: A Google Earth Engine-Driven Investigation

Zheng,

Haseeb,

Tahir

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO2) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO2, LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019-2022. The study evaluates NO2 concentrations vegetation health using indices Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), and LST variations. The analysis reveals a notable increase in NO2 during both summer and winter, with approximately 0.021 (×10 3 mol/m 2 ) and 0.03 (×10 3 mol/m 2 ) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×10 3 mol/m 2 ) and 0.007 (×10 3 mol/m 2 ) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 ℃ in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 ℃ in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO2 and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO2 and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO2 levels and temperatures is critical for policymakers and urban planners. These insights contribute to the Sustainable Development Goal (SDG-11), fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.

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“…Studies on the effect of COVID-19 lockdowns are limited, meaning reduced human activities, such as energy and lighting consumption and its trade-off with LST, air temperature, SUHI, and air quality parameters underexplored. Studies in the literature could be classified into three main topics considering this research's main focus: (1) SUHI, human activity, and pandemic lockdowns [ [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] ]. For instance, in the study conducted in Melbourne by Wai et al [ 34 ], authors mentioned that the UHI intensity was 1 °C lower than the past five year's averages during the July to August month (Winter lockdown).…”

Section: Introductionmentioning

confidence: 99%

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

Jamei

Seyedmahmoudian

et al. 2022

Energy Strategy Reviews

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Examining land surface temperature and relations with the major air pollutants: A remote sensing research in case of Tehran

Cited by 35 publications

References 38 publications

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO₂ Concentrations: A Google Earth Engine-Driven Investigation

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

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Examining land surface temperature and relations with the major air pollutants: A remote sensing research in case of Tehran

Cited by 35 publications

References 38 publications

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Study of Urban Heat Island Effect in Hangzhou Metropolitan Area Based on SW-TES Algorithm and Image Dichotomous Model

Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

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Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO₂ Concentrations: A Google Earth Engine-Driven Investigation