Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones

Yuan, Yuan; Li, Chengwei; Geng, Xiaolei; Yu, Zhaowu; Fan, Zhengqiu; Wang, Xiangrong

doi:10.1016/j.envint.2022.107574

Cited by 40 publications

(13 citation statements)

References 91 publications

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“…The UHI effect is not solely dependent on economic development [19,37] . Instead, other factors, such as population density, can play a signi cant role in nighttime [11,47] . This may explain why Nepal, a densely populated country [48] , showed an extremely high SUHII increasing rate during SN in our study.…”

Section: Discussionmentioning

confidence: 99%

“…Given the projected increase in urban population to 68% by 2050 [ 10] , it is both urgent and crucial to investigate the trends and patterns of UHI changes to ensure human and environmental wellbeing in the urban context. UHI can be in uenced by various socio-economic and biophysical factors [11] , which can vary across countries and climate zones, and modulate the UHI's seasonal and diurnal variability [12] . The intensity and trends of UHI may vary across different countries or cities depending on factors such as urban development and climate differences [13] .…”

Section: Introductionmentioning

confidence: 99%

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The Growing Threat of Global Urban Heat Island Effects in Lower-Middle- and Low-Income Countries

Yuan

Xu²,

et al. 2023

Preprint

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Urbanization has undergone significant growth in recent decades, but we still lack information regarding the disparity caused by the urban heat island (UHI) effect associated with urban development. Upon analyzing 4,832,660 urban grids worldwide from 2003 to 2018, we found that there is an uneven distribution in the growth of surface urban heat island intensity (SUHII) across countries in four distinct economic levels. The SUHII gradually decreased after 2006 and 2004 in high and upper-middle income groups, respectively. However, increasing trend was observed in the low and lower-middle income groups. Specifically, during the daytime, 27% of low-income regions exhibit a largest-scale net increasing trend, while the lower-middle-income group has the largest area (19.10%) with a net increasing trend during the nighttime. The growth rate of SUHII in high- and upper-middle-income country urban areas is close to the average urbanization level of these countries, but the growth rate of SUHII in lower-middle- and low-income country cities is much faster. Our findings highlight that urban areas in lower-middle- and low-income countries face more urgent UHI issues and require targeted interventions for mitigation. High- and upper-middle-income countries need shoulder greater responsibility in mitigating the UHI effects by offering technical expertise and guidance to economically disadvantaged nations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Growing Threat of Global Urban Heat Island Effects in Lower-Middle- and Low-Income Countries

Yuan

Xu²,

et al. 2023

Preprint

Self Cite

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“…A significant clinical challenge lies in accurately determining the risk of CRC‐related LMs and conducting timely imaging screening 21 . Numerous studies employing ML and AI technology 22 have contributed to the improved prognosis of CRC patients with remarkable results. However, the majority of these studies rely on costly high‐throughput sequencing genetic data or high‐quality imaging or pathology data 23 .…”

Section: Discussionmentioning

confidence: 99%

“…It is based on Shapley values, a game‐theoretic concept developed by economist Lloyd Shapley to determine the importance of individuals by calculating their contributions to cooperation. This method has received much attention in AI interpretability research and has contributed significantly to advancing the clinical applications of models 21,22 . The Shapley value interpretation is an additive feature attribution method that interprets a model's predicted value as a linear function of a binary variable.

g ()(, z^{'}) goodbreak= ϕ_{0} goodbreak+ \sum_{j = 1}^{M} ϕ_{j} z_{j}^{'}

z^{'} \in {\{0 ， 1\}}^{M}

ϕ_{j} \in R

where g is the explanatory model (3a), z is the coalition vector, M is the maximum coalition size (3b), and ϕ j ∈ R is the feature attribution of feature j .…”

Section: Methodsmentioning

confidence: 99%

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An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Li,

Wang,

Cai

et al. 2023

Cancer Medicine

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BackgroundThe significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.AimOur study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free‐text medical record data and structured laboratory data to predict LM in postoperative CRC patients.MethodsWe used a robust dataset of 1463 patients and leveraged state‐of‐the‐art natural language processing (NLP) and machine learning techniques to construct a two‐layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two‐tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free‐text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score‐based nomogram using the top 13 valid predictors identified in our study.ResultsThe proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs.ConclusionThis fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision‐making.

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Quantitative comparison of seasonal and diurnal characteristics for urban heat using meteorological monitoring data

Gao,

Zhao,

Han

2024

Int. J. Environ. Sci. Technol.

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Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones

Cited by 40 publications

References 91 publications

The Growing Threat of Global Urban Heat Island Effects in Lower-Middle- and Low-Income Countries

The Growing Threat of Global Urban Heat Island Effects in Lower-Middle- and Low-Income Countries

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

Quantitative comparison of seasonal and diurnal characteristics for urban heat using meteorological monitoring data

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