Investigation into the Mechanism of the Impact of Sunlight Exposure Area of Urban Artificial Structures and Human Activities on Land Surface Temperature Based on Point of Interest Data DOI Creative Commons
Yu-Chen Wang, Yu Zhang, Nan Ding

et al.

Land, Journal Year: 2024, Volume and Issue: 13(11), P. 1879 - 1879

Published: Nov. 10, 2024

With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores impact of sunlight exposure areas artificial structures activities on land surface temperature (LST) in Hefei Xuzhou, using Landsat 9 data, Google imagery, nighttime light Point Interest (POI) data. Building shadow distributions road were derived, geospatial analysis methods applied assess their LST. The results indicate that roofs roads are primary factors affecting LST, with a more pronounced while anthropogenic plays prominent role Hefei. influence building facades is relatively weak, population density shows limited geographical detector model reveals interactions between roof key drivers LST increases. Based these findings, planning should focus optimizing layouts heights, enhancing greening roads, reducing structures. Additionally, strategically utilizing shadows minimizing emissions can help lower local temperatures improve thermal environment.

Language: Английский

Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India DOI
Gourav Suthar, Saurabh Singh,

Nivedita Kaul

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101204 - 101204

Published: April 25, 2024

Language: Английский

Citations

12
High spatial and temporal resolution multi-source anthropogenic heat estimation for China DOI
Jiangkang Qian, Linlin Zhang, Uwe Schlink

et al.

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 203, P. 107451 - 107451

Published: Jan. 21, 2024

Language: Английский

Citations

8
Empowering urban climate resilience and adaptation: Crowdsourcing weather citizen stations-enhanced temperature prediction DOI Creative Commons
Daniel Castro Medina, MCarmen Guerrero Delgado, José Sánchez Ramos

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 101, P. 105208 - 105208

Published: Jan. 14, 2024

The growing impact of climate change, including extreme weather events, represents a significant challenge for humanity. With most the world's population living in urban areas, heat island effect and anthropogenic contribute to elevated city temperatures. This increase warming threatens human health demands deeper understanding thermal distribution environments. Collecting accessible widespread temperature data areas is essential address this challenge. study aims develop methodology anticipating environments, leveraging Citizen Weather Stations (CWS) as valuable crowdsourcing sources. ultimate goal create predictive model that estimates temperatures based on government meteorological station forecasts, improving planning, regulating temperature-based routes, preventing issues vulnerable populations, enhancing livability. divided into three fundamental stages: acquisition through CWS with citizen collaboration, development evaluation optimal forecast models stations (SWS) data, its exploitation terms utility applicability. encompasses collection filtering ensure usefulness implement reliable models. resulting tool facilitates informed decision-making precise seasonal event planning effectively addressing challenges extrapolation contributing more effective adaptation mitigation strategies change heatwaves. results obtained probe feasibility using predict which has been demonstrated accurately. achievement, proven be source context. Also, process described applied case effective, discarding approximately 34.87% data. achieved by detecting eliminating anomalies, considering availability, adhering specific quality criteria. Finally, developed prediction ability optimally estimate temperatures, utilizing provided (SWS). performance indicators support claim. For linear regression model, Mean Squared Error (MSE) 2.177 an R-squared (R2) 0.960 are obtained, while neural network, MSE 1.284 R2 0.976 achieved.

Language: Английский

Citations

5
Estimation of gridded anthropogenic heat flux at the optimal scale by integrating SDGSAT-1 nighttime lights and geospatial data DOI Creative Commons

Biyun Guo,

Deyong Hu, Shasha Wang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 125, P. 103596 - 103596

Published: Dec. 1, 2023

Diverse human activities in megaregions have generated excellent anthropogenic heat (AH), which disrupts the urban energy balance and affects climate. However, few studies investigated spatial scale effect (SSE) of estimating multi-source AH flux (QF). Hence, this study proposes a method, i.e., nighttime light index (NTLI)-based top-down inventory model, to investigate optimal (So) for gridded QF at county level mapping products central region Guangdong-Hong Kong-Macao Greater Bay Area (GBA) megaregion 2018. The method combines multi-scale (10–500 m) enhanced NTLI with model. We by integrating Sustainable Development Science Satellite 1 (SDGSAT-1) lights (NTL), points interest, road network data. Then we analyzed SSE estimation determine So evaluated accuracy characteristics So. results showed that (1) six components varied between 10 m 450 m, related difference source pattern; (2) considering accuracy, numerical characteristics, detail critical features, was 300 m; (3) proposed reduced error more effectively than NTL-based root mean square (RMSE) decreased 2.45–21.20 %, goodness fit (R2) increased 2.17–13.66 % among components; (4) our product outperformed previous heterogeneity accuracy. This first explored captured information So, could provide valuable knowledge micro-climate.

Language: Английский

Citations

7
Public responses to heatwaves in Chinese cities: A social media-based geospatial modelling approach DOI Creative Commons
Mingxuan Dou, Yandong Wang, Mengling Qiao

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104205 - 104205

Published: Oct. 6, 2024

Language: Английский

Citations

2
Exploring the relationship of land surface parameters and air pollutants with land surface temperature in different cities using satellite data DOI
Ruchi Bala, Vijay Pratap Yadav,

D. Nagesh Kumar

et al.

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(7), P. 2958 - 2975

Published: June 18, 2024

Language: Английский

Citations

1
A Framework Analyzing Climate Change, Air Quality and Greenery to Unveil Environmental Stress Risk Hotspots DOI Creative Commons
Priyanka Rao, Patrizia Tassinari, Daniele Torreggiani

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2420 - 2420

Published: July 1, 2024

Rapid urbanization has resulted in increased environmental challenges, compounding worries about deteriorating air quality and rising temperatures. As cities become hubs of human activity, understanding the complex interplay numerous elements is critical for developing effective mitigation solutions. Recognizing this urgency, a framework to highlight hotspots with issues emerges as comprehensive approach that incorporates key criteria such surface urban heat island intensity (SUHII), index (HI) (AQI) assess address web stressors grip landscapes. Employing multicriteria decision analysis approach, proposed framework, named risk hotspot mapping (ERHMF), innovatively applies analytic hierarchy process at sub-criteria level, considering long-term trends recent fluctuations HI AQI. Climate change impact been symbolized through temperatures, reflected by over two decades. The robustness correctness weights have assessed computing consistency ratio, which came out 0.046, 0.065 0.044 SUHII, AQI HI, respectively. Furthermore, delves into nexus between vegetation cover, elucidating role green spaces mitigating risks. Augmented spatial demographic data, ERHMF adeptly discerns high-risk areas where stress converges development, vulnerable population concentrations status, thereby facilitating targeted management interventions. framework’s effectiveness demonstrated regional case study Italy, underscoring its ability pinpoint inform specific policy quantitative undertaken sub-administrative level revealed approximately 6,000,000 m2 land Bologna are classified being under high extremely stress, 4,000,000 lying only within group (90–100). Similarly, 1,000,000 Piacenza Modena levels (80–90). In conclusion, presents holistic methodology delineating hotspots, providing essential insights policymakers, planners stakeholders, potential enhance overall resilience foster sustainable development efforts.

Language: Английский

Citations

0
Investigating Variations in Anthropogenic Heat Flux along Urban–Rural Gradients in 208 Cities in China during 2000–2016 DOI Creative Commons

Ling Cui,

Qiang Chen

Buildings, Journal Year: 2024, Volume and Issue: 14(9), P. 2766 - 2766

Published: Sept. 3, 2024

Anthropogenic heat emissions, which are quantified as anthropogenic flux (AHF), have attracted significant attention due to their pronounced impacts on urban thermal environments and local climates. However, there remains a notable gap in research regarding the distinctions distribution of emissions (AHEs) along urban–rural gradients. To address this gap, present study introduces new concept—the island (ArUHI)—where AHF within areas is higher than that background areas. quantitatively describe magnitude spatial extent ArUHI effect, two metrics—namely, intensity (ArUHII) footprint (ArUHIFP)—are introduced. We conducted comprehensive across 208 cities China investigate spatiotemporal patterns variations gradients during period 2000–2016. In addition, we explored how complex interactions between land cover building form components affect changes Additionally, analyzed economic zones city sizes alter footprint. The results showed 97% (201/208) Chinese exhibited effect from 2000 2016. modeled value substantial increase nearly fivefold, increasing 5.55 ± 0.19 W/m2 26.84 0.99 over time. Regarding footprint, analysis revealed that, for majority (86% or 179 out 208), ranged 1.5 5.5 times City yielded influences values. Building forms were significantly positively correlated with AHF, R2 values 0.94. This contributes understanding effects driving factors China, providing valuable insights climate studies enhancing our surface mechanisms.

Language: Английский

Citations

0
Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China DOI Creative Commons
Jie Deng,

Geying Lai,

Ao Fan

et al.

Computational Urban Science, Journal Year: 2024, Volume and Issue: 4(1)

Published: Sept. 11, 2024

Abstract The middle and lower reaches of the Yangtze River are frequently affected by Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, weak winds, all which affect urban heat island (UHI) effect. Currently, there few studies on influence WPSH UHI In this study, we analysed temporal spatial distributions effect establishing two scenarios: with without WPSH. We calculated intensity proportion index (UHPI) analyse geographical detector method was then used factors influencing UHI. results indicate strong during day provincial capitals some developed cities. area larger under than years UHPI at both night, although more pronounced night. affecting daytime mainly POP NTL, O3 plays a large role control. main night AOD, NTL were control, interactions multi-factors daytime, DEM nighttime. It found that enhanced control WPSH, diurnal differed ultimately provides realistic suggestions for mitigating areas

Language: Английский

Citations

0
Analysis of Radiative Heat Flux Using ASTER Thermal Images: Climatological and Volcanological factors on Java Island, Indonesia DOI Creative Commons

Dini Andriani,

Supriyadi Supriyadi,

Muhammad Aufaristama

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 36, P. 101376 - 101376

Published: Oct. 9, 2024

Language: Английский

Citations

0