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

Abstract

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.

Unveiling the health consequences of air pollution in the world’s most polluted nations

“…To our knowledge, this study represents a novel empirical investigation in the existing literature, providing unique insights from both the scope and methodological application perspectives in exploring the relationships between air pollution and the predictors of health outcomes. The contributions of this study can be summarized as follows: First, our results build upon prior findings, e.g., 7 , 22 , 23 , and 16 by emphasizing that pollutant-endogenous predictors are significant in explaining health outcomes, but the importance of pollutant external drivers cannot be overlooked, specifically in comprehensive and macro-level policy discussions. Second, while prior literature contributes valuably to enhancing the contemporary body of knowledge on the impact of air pollution on health outcomes, our study stands out with its nuanced policy-oriented perspective.…”

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

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Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

“…This longer-term perspective offers insights into the evolution of energy service use and energy markets, providing valuable empirical evidence for developing climate policies over extended timescales [3]. Raising regional and country-level ambitions will be crucial to meet interlinked energy and climate objectives, especially in the urban environment [4,5]. New, clean and renewable energy resources are vital to meet this demand and to tackle environmental issues [6].…”

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“…Such data could be collected by installing wind masts to measure wind speed at different heights. However, installing and operating wind masts are very expensive and time-consuming practices [5]. An alternative approach involves utilizing wind measurements obtained from meteorological stations within a dense network, which are typically taken at a height of 10 m above ground level (a.g.l.).…”