Urban Temperature Research Articles

Bioassessment of Macroinvertebrate Communities Influenced by Gradients of Human Activities.

This study explores the impact of anthropogenic land use changes on the macroinvertebrate community structure in the streams of the Cangshan Mountains. Through field collections of macroinvertebrates, measurement of water environments, and delineation of riparian zone land use in eight streams, we analyzed the relationship between land use types, stream water environments, and macroinvertebrate diversities. The results demonstrate urban land use type and water temperature are the key environmental factors driving the differences in macroinvertebrate communities up-, mid-, and downstream. The disturbed streams had lower aquatic biodiversity than those in their natural state, showing a decrease in disturbance-sensitive aquatic insect taxa and a more similar community structure. In the natural woodland area, species distributions may be constrained by watershed segmentation and present more complex community characteristics.

Adapting Remote Sensing Technique to Study the Changes in the Spatial Cover and the Urban Temperatures – A Case Study of Indore City, Madhya Pradesh

Adapting Remote Sensing Technique to Study the Changes in the Spatial Cover and the Urban Temperatures – A Case Study of Indore City, Madhya Pradesh

Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.

The functional microclimate of an urban arthropod pest: Urban heat island temperatures in webs of the western black widow spider

Urbanization alters natural landscapes and creates unique challenges for urban wildlife. Similarly, the Urban Heat Island (UHI) effect can produce significantly elevated temperatures in urban areas, and we have a relatively poor understanding of how this will impact urban biodiversity. In particular, most studies quantify the UHI using broad-scale climate data rather than assessing microclimate temperatures actually experienced by organisms. In addition, studies often fail to address spatial and temporal complexities of the UHI. Here we examine the thermal microclimate and UHI experienced in the web of Western black widow spiders (Latrodectus hesperus), a medically-important, superabundant urban pest species found in cities across the Western region of North America. We do this using replicate urban and desert populations across an entire year to account for seasonal variation in the UHI, both within and between habitats. Our findings reveal a strong nighttime, but no daytime, UHI effect, with urban spider webs being 2–5 °C warmer than desert webs at night. This UHI effect is most prominent during the spring and least prominent in winter, suggesting that the UHI need not be most pronounced when temperatures are most elevated. Urban web temperatures varied among urban sites in the daytime, whereas desert web temperatures varied among desert sites in the nighttime. Finally, web temperature was significantly positively correlated with a spider's boldness, but showed no relationship with voracity towards prey, web size, or body condition. Understanding the complexities of each organism's thermal challenges, the “functional microclimate”, is crucial for predicting the impacts of urbanization and climate change on urban biodiversity and ecosystem functioning.

Planning for Heat Resilience and the Future of Residential Electricity Usage

Community resilience refers to the ability for a geographic area to respond and adapt to acute shocks and long-term stresses. Geography research is well positioned to examine how communities can adapt to the compounding effects of climate change. Our work aims to analyze the factors that influence residential electricity in the context of increasing urban temperatures in Maricopa County, Arizona, coupled with the COVID-19 pandemic. Using census tracts as our basis for analysis, we quantified factors that influence electricity use related to neighborhood characteristics, home structures, social situations, pricing policies, and work-from-home estimates. Our findings suggest that the structure of the home (e.g., number of rooms and house size) influences the variation in residential electricity use, whereas trees and small-area temperatures were not factors. We then compared above-median-income census tracts to those that were below the median income. We found fewer factors influenced electricity use in the lower income census tracts and that they were also related mostly to the home structure. Our analysis did not reveal that working from home was significant, but we did find that the average household size was significant and that the amount of influence increased from 2019 to 2020, suggesting that the stay-at-home policies from the pandemic did affect electricity use. As we consider and implement work-from-home strategies, families need to recognize that the home structure plays a crucial role in determining electricity usage.

Variation the in relationship between urban tree canopy and air temperature reduction under a range of daily weather conditions

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00–14:00, −1.62 °C), afternoon (14:00–17:00, −1.19 °C), and morning (8:00–11:00, −1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings −0.92 °C and afternoons −0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00–20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00–23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons −1.78 °C [-1.78, −1.78], cloudy midday −1.17 °C [-1.19, −1.15], clear midday −1.12 °C [-1.12, −1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement.

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

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

Plant Diversity Distribution along an Urbanization Gradient and Relationships with Environmental Factors in Urban Agglomerations of Henan Province, China

Urbanization induces rapid plant environmental modifications, leading to alterations in plant diversity distribution patterns and plant homogenization. However, how plant diversity is distributed along urbanization gradients in regional urban agglomerations and its relationship with environmental factors are not well defined. In three nearby Henan Province Chinese cities—Zhengzhou, Kaifeng, and Zhongmu—along an urbanization gradient, the distribution pattern of plant diversity was quantified. Both native and non-native plants found in urban green spaces were taken into consideration. A total of 176 plant quadrats were selected and separated into three urbanization gradient types using space-constrained hierarchical clustering: urban core, urban suburb, and urban outskirt. Polynomial fitting was used to characterize the spatial distribution patterns of plants along the urbanization gradient, and Pearson correlation and the Mantel test were employed to examine the effects of environmental factors, including longitude, latitude, altitude, distance from the urban center, temperature, and illumination, on plant diversity. A total of 313 vascular plant species, comprising 137 woody species and 176 herbaceous species, were examined. Along the three urbanization gradients, remarkable variations in plant diversity for woody and herbaceous species were observed. The spatial patterns of plant diversity were consistent across cities, whereas woody plants and herbaceous plants displayed the opposite behavior. Distance to the city center and temperature were the most substantial environmental effect factors for the diversity of woody plants, whereas light factors had a major impact on herbaceous plants. These findings show different life-type plants are affected differently by urbanization, and they offer managers and planners a recommendation for increasing urban plant diversity by executing various interventions throughout the urban gradient.

Empowering urban climate resilience and adaptation: Crowdsourcing weather citizen stations-enhanced temperature prediction

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

Quantifying the Impact of Urban Growth on Urban Surface Heat Islands in the Bangkok Metropolitan Region, Thailand

The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration of populations, a global trend, notably exemplified in megacities such as Bangkok, Thailand. The global trend of urbanization has witnessed unprecedented growth in recent decades, with cities transforming into megametropolises that profoundly impact changes in urban temperature, specifically the urban heat island (UHI) phenomenon induced by the rapid growth of urban areas. Elevated urban concentrations lead to increased city density, contributing to higher temperatures within the urban environment compared to the surrounding areas. The evolving land-use surface has assumed heightened significance due to urban development, necessitating accelerated efforts to mitigate urban heat islands. This study aims to quantify the influence of urban growth on urban surface temperature in Bangkok and its surrounding areas. The inverse relationship between urban temperature and land surface temperature (LST), coupled with urban area density, was examined using Landsat 5 and 8 satellite imagery. The analysis revealed a positive correlation between higher temperatures and levels of urban growth. Areas characterized by high-rise structures and economic activities experienced the most pronounced impact of the heat island phenomenon. The city exhibited a notable correlation between high density and high temperatures (high–high), signifying that increased density contributes to elevated temperatures due to heat dissipation (significant correlation of R2 = 0.8582). Conversely, low-temperature, low-density cities (low–low) with a dispersed layout demonstrated effective cooling of the surrounding area, resulting in a significant correlation with lower local temperatures (R2 = 0.7404). These findings provide valuable insights to assist governments and related agencies in expediting planning and policy development aimed at reducing heat in urban areas and steering sustainable urban development.

Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020

To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017–2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (−3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (−0.06 %) and PRE (−1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

High‐resolution dataset of nocturnal air temperatures in Bern, Switzerland (2007–2022)

AbstractTo prepare for a hotter future, information on intra‐urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high‐resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine‐scaled low‐cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium‐sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter‐annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low‐cost measurement devices during daytime, the dataset is currently limited to night‐time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.

Exploring the Nonlinear Interplay between Urban Morphology and Nighttime Thermal Environment

The nighttime thermal environment affects people's nighttime leisure activities and energy consumption. While increasing studies have examined the interplay between urban morphologies and daytime temperature, a gap exists in understanding the nonlinear relationship in the nighttime thermal environment. To address this, the study employs a data-driven ensemble model to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) thermal environment data for four seasons, aligning it with the scale of urban morphology. Furthermore, an additive interpretation algorithm investigates the impact of nonlinear factors shaping the urban thermal environment during nighttime. The findings reveal that in contrast to daytime temperatures, built environment variables exert a more pronounced effect on nighttime surface temperatures than natural variables. Specifically, plot ratio and building height are the greatest contributors to the warming observed across all seasons. On the other hand, increasing the sky view factor of streets proves to be an effective strategy for mitigating nighttime temperatures. Overall, our study sheds new light on the complex interplay between urbanization and the nighttime thermal environment, assisting planners in understanding how the built environment affects the urban temperature and sustainable development path.

Impacts of local climate zone mapping quality on urban near-surface air temperature simulation in WRF-UCM

Accurate simulation of urban near-surface air temperature (SAT) is essential for understanding climate dynamics. The reliability of the simulated SAT depends largely on the representation of urban morphology. Local Climate Zones (LCZ), as a sort of urban morphology representation, have been widely applied for SAT simulation based on the Weather Research and Forecasting Model coupled with urban canopy model (WRF-UCM). However, how the LCZ mapping quality affects the accuracy of SAT simulation remains underexplored. Here we investigate the relationship between the LCZ mapping quality and the accuracy of SAT simulation in WRF-UCM. The city of Guangzhou, China, is selected as the case study area. Six LCZ classifications are used to simulate SAT at the 1 km resolution, and the results are validated using the meteorological observations. The simulations suggest that the average values of root mean square error decrease if the more accurate LCZ classifications are used. The simulated SAT is also sensitive to the local mapping quality of LCZ classifications within 3 to 7 km windows. Our findings can provide insights for modelers to develop more accurate simulations of SAT, which are fundamental to the understanding of climate change impacts and promote relevant policy making toward sustainable cities.

Based on Correlation Analysis and K-Means: An Anomaly Detection Algorithm for Seasonal Time- Series Data

Anomaly detection is widely used in fields like data processing, intrusion detection, and financial fraud prevention, helping to avoid potential accidents and economic losses. In time series anomaly detection, which deals with numerical sequences over time (e.g., urban temperatures, sales data, stock market trends), the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is an excellent choice. This paper presents an improved anomaly detection algorithm tailored for seasonal time series data. By combining autocorrelation coefficients with the K-means algorithm, precise clustering results down to the date level are provided, subsequently employing the DBSCAN algorithm for detection, the enhanced algorithm is capable of capturing a greater number of local anomalies. Experiment conducted on daily temperature data from Beijing and Sanya in 2023, the enhanced algorithm exhibited a respective increase of 11.6% and 78% in anomaly detection compared to the original algorithm, thus affirming the feasibility of the approach.

Dynamics of land surface temperature: Insights into vegetation, elevation, and air pollution in Bengaluru

Bengaluru, among the five fastest-growing cities of India, is facing the challenge of urban heat islands (UHI) and temperature variations across its landscape. Monitoring the temperature fluctuations is crucial for understanding the city's microclimate and the impact of urbanization on its environment. Land Surface Temperature (LST) is a significant metric for this purpose. The present study examined the relationship of LST with vegetation, elevation, and air pollutants during the summer and winter seasons for 4-years (2019–2022). Air pollutants and weather parameters data from air quality monitoring stations was used. The study found negative relationship of LST with NDVI and Digital Elevation Model (DEM) during both seasons. The relationship between LST and individual air pollutants was variable during the summer and winter seasons and R2 ranged between 0.52 and 0.79. The relationship became stronger (R2 between 0.69 and 0.82 during summer and R2 between 0.74 and 0.80 during winter season) when all the parameters were considered simultaneously. The result from the Taguchi method showed that different air pollutants had variable impact over change in LST and the order of their effect was found to be PM2.5 > SO2 > O3 > CO > NO2. The results indicate that O3 (summer) and PM2.5 (winter) were the major contributors to rising LST. The present study has applications in future scientific research as well as it can be utilized to integrate mitigation strategies for increasing temperatures.

Assessment of soil microclimate in an urban park of Budapest, Hungary

Investigation of urban parks is a particular research section in the frame of urban geology, with increasing social importance. Both natural and anthropogenic factors affect these green sites, giving special scientific importance to their investigation. Thus, the behavior of such a ’composite’ system is expected to be quite complex. Based on the general circumstances, a research project was introduced in 2016 in the former Mining and Geological Survey of Hungary (now: Supervisory Authority for Regulatory Affairs). The ongoing project was focused on the behavior of urban parks. Within the frame of this research, between 2016 and 2019, field studies were started in 4 parks of Budapest. This paper is targeting one of these parks, namely Honvéd square. Our objective is to understand the behavior of urban parks under special conditions (e.g., during heat island effects) by getting information from well-defined positions. This can be essential to perform more sustainable water management in an urban park. Within the frame of the field works soil temperature and soil moisture measurements were being recorded manually every week in every park, at four different locations within a park. Observation points were selected to describe the different microclimates of the parks. A statistical analysis of the data reveals that the urban heat island (UHI) effect is reflected in soil temperatures at a citywide scale and that by moderating urban soil surface temperature extremes, trees and shrubs may help to reduce the adverse impacts of urbanization on microclimate, soil processes, and human health. It can be stated, that beside the manual measurements, the automated soil temperature detection was significantly influenced by soil depth at the Honvéd square park. At 100 cm below the surface, the soil temperature is relatively constant. It was approved that not only do deeper soil layers undergo less drastic seasonal temperature fluctuations but also the changes taking place lag further behind those of shallower soil layers.

Exploring urban land surface temperature with geospatial and regression modelling techniques in Uttarakhand using SVM, OLS and GWR models

Given the climate change challenges, Uttarakhand has become crucial for examining land dynamics and regional climate interactions. This study employed a Support Vector Machine (SVM) for land use and land cover mapping for 2024, achieving 94% accuracy and a Kappa coefficient of 0.90, indicating robust mapping. Key land indices such as NDVI, NDWI, NDBI, NDSI, and NDBaI were calculated, along with Land Surface Temperature (LST) from Landsat 8 imagery. These indices were selected for their relevance in representing vegetation health (NDVI), measuring water content (NDWI), assessing urban areas (NDBI), identifying snow cover (NDSI), and highlighting the barren land (NDBaI), which all influence LST. Hotspot analysis with Getis-Ord Gi∗ revealed spatial distribution patterns of LST. Regression analysis showed significant relationships: a strong positive correlation between LST and NDBI (R2 = 0.78) and a substantial negative correlation between LST and NDSI (R2 = −0.80). The strong positive correlation highlights how urbanization contributes to rising surface temperatures, while the substantial negative correlation underscores the cooling effect of snow cover, which is particularly relevant as reduced snow cover could lead to higher LST in the context of climate change. These correlations offer deeper insights into how land cover changes can exacerbate or mitigate climate impacts in Uttarakhand. Two regression models were used for statistical modeling and spatial analysis: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). In OLS, the results reveal non-stationarity (p = 0.000) with an R2 value of 0.79 while GWR significantly enhanced performance, achieving an R2 value of 0.94. The improved performance of GWR (R2 = 0.94) compared to OLS (R2 = 0.79) can be attributed to GWR’s ability to account for spatial non-stationarity. This method allows for variations in relationships between LST and explanatory variables across different locations, effectively capturing local patterns that OLS may overlook. Spatial autocorrelation analysis, utilizing Moran’s I, exhibited a decrease from 0.606 (OLS) to 0.02 (GWR), This reduction indicates that GWR effectively captures spatial non-stationarity, minimizing residual autocorrelation by modeling local relationships between LST and its predictors that often remain in global models like OLS, thereby demonstrating its advantages in heterogeneous regions. The findings underscore the importance of employing GWR to better elucidate the connection between LST and its predictors, specifically in regions characterized by spatial non-stationarity, thereby offering insights crucial for informed decision-making amidst changing climatic conditions.

How Does PM2.5 Impact the Urban Vertical Temperature Structure? A Case Study in Nanjing

How Does PM2.5 Impact the Urban Vertical Temperature Structure? A Case Study in Nanjing

Urban land surface temperature forecasting: a data-driven approach using regression and neural network models

The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants.