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Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI)
in the city of Wrocław, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of
different seasons, were selected for analysis. GWR results were compared with global regression mod...
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... magnitude of the UHI was calculated as the air temperature difference dT=T U −T R measured at the same time on stations U and R (Fig. 1, Table 1). Also, the occurrence of UHI in the city center was calculated as the frequency of dT stated above. Detailed average, extreme, and frequency of UHI values in Wrocław in the period April 1997-March 2000 were introduced by Szymanowski (2004Szymanowski ( , 2005 and Szymanowski and Kryza (2009). UHI phenomena in the city center rises the annual mean temperature by 1.0 K. Thermal excess is weaker in large housing estates (0.7 K) and in residual areas (0.3 K). Similarly to other cities of this size, the average magnitude of UHI in the night is two to three times higher than the average value for daytime. The maximum difference between the city center and suburban areas may exceed 9 K ( Szymanowski and Kryza 2009). Positive values of UHI in the central parts of the city are observed during >96% of night hours and >80% of daytime, but strong UHI effect (>5.0 K) are measured in 3.8% of night hours and only randomly during daytime. The annual cycle of the UHI magnitude is dependent on meteorological conditions and the release of artificial heat. The most favorable conditions for UHI occur in warm season, but due to increasing convective cloudiness in the mid-summer, the highest values are observed in May and August. Secondary maximum of UHI intensity is observed in January (heating season), and the minima are observed in October and February. More detailed analysis of UHI in Wroclaw is provided by Szymanowski (2004Szymanowski ( , 2005 and Szymanowski and Kryza ...
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... one of the objectives of the paper is the determina- tion of the best interpolator of UHI, exactly the same air temperature measurements, gathered with automatic mobile meteorological stations, as in the former study were used (Szymanowski and Kryza 2009), and the reader is referred there for details on measurement and processing methodology. Seven UHI cases were observed in years 2001-2002 during nighttime with relatively weak winds (<4 ms −1 ) and cloudless or moderately cloudy skies (Table 2). All UHI cases analyzed can be classified as radiative in origin. The frequency of the night hours with similar UHI is 31.3%, based on measurements gathered in period April 1997-March 2000 in Wrocław. The former studies on the UHI in Wroclaw revealed that the increase of wind speed to over 4 ms −1 at night, irrespective of cloudiness, causes a considerable reduction of the UHI magnitude (Szymanowski 2005). The meas- urements were performed during the UHI stabilization phase (approximately equal cooling rates at urban and rural stations) to avoid fast changes in UHI magnitude (Haeger-Eugensson and Holmer 1999;Runnalls and Oke 2000). Finally, measurements from 206 points were selected along routes systematically to represent different land-use categories with some densification over the most interesting and geometrically diverse areas in the city center ( Fig. 1). ...
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... is a mid-sized city (293 km 2 ; ∼640,000 inhab- itants) located in SW Poland (51°N, 17°E). The average elevation of the city is ∼120 m a.s.l., and the terrain is relatively flat; therefore, the local climate is practically not affected by changes in elevation. The city is located along the Odra River. Approximately 31.4% of Wrocław is a built-up area, consisting of city and mixed series of the "local climate zone" classification system by Stewart and Oke (2009). The remaining areas of the city are mostly agricultural areas (cropped and bared fields; 28.9%), urban greenspace with semi-natural forests and grasslands (36.6%), and water-3.1% (Fig. ...
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... GWR is a method for modeling spatial heterogeneity to achieve higher accuracy in analyzing location-affected correlations, which indicates that in each geographical coordinate, the correlation between the dependent and independent variables is different according to the variety of coefficients of the model on the point [33,36]. The coefficients of the model can be estimated at any point in the place. ...
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... It was found when taking the effects of neighbouring elements (e.g. grid cell or landscape patch) into account by using spatial regression models (Chun et al., 2018;Yin et al., 2018;Dai et al., 2018;Galletti et al., 2019;Guo et al., 2020) or Geographically Weighted Regression (GWR) models (Buyantuyev et al., 2010;Li et al., 2010;Su et al., 2012;Szymanowski et al., 2012;Deilami et al., 2018;Liu et al., 2019;Zhang et al., 2019), higher explanatory or predictive power can be obtained to model/assess the relationship between SUHI variations and the contributing factors. This implies that SUHI at the local scale is influenced by the proximity to and the spatial distribution of nearby warming/cooling factors, and this influence decays as the distance increases. ...
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... During a series of studies conducted in Wroclaw in the early 2000s by Szymanowski et al. [7,16,43,47], the existence of an AUHI was confirmed. It occurred both in the early night hours and during the day in the summer and winter months. ...
... Despite the fact that AUHI has been fairly well documented in all major Polish metropolises: in Warsaw [49], Kraków [50], Poznań [19], Łódź [32,51] and Wrocław [7,16,43,47], the presence of the underground heat island has, in this part, of Europe has not been analyzed yet. Several works of researchers from the University of Wroclaw just touched on the topic of GWT beneath the city and municipal well-field [52,53] without deeper analysis of its character. ...
In the face of climate change and constantly progressing urbanization processes, so-called heat islands are observed with growing frequency. These phenomena are mainly characteristic of large cities, where increased air and land surface temperatures form an atmospheric (AUHI) or surface (SUHI) urban heat island (UHI). Moreover, UHIs have also been recognized in the underground environments of many cities worldwide, including groundwater (GUHI). However, this phenomenon is not yet as thoroughly studied as AUHI and SUHI. To recognize and characterize the thermal conditions beneath the city of Wrocław (SW, Poland), we analyze the groundwater temperature (GWT) of the first aquifer, measured in 64 wells in 2004–2005. The study aimed to identify groundwater urban heat islands (GUHI) in Wrocław. Therefore, we used a novel approach to gather data and analyze them in predefined seasonal periods. Meteorological data and satellite imagery from the same period allowed us to link GWT anomalies to the typical conditions that favor UHI formation. GWT anomaly related to the GUHI was identified in the central, urbanized part of Wrocław. Moreover, we found that the GUHI phenomenon occurs only seasonally during the winter, which is related to the city’s climate zone and anthropogenic heat sources. Comparing our results with previous works from other cities showed untypical behavior of the observed anomalies. In contrast to AUHI and SUHI temperatures, the GWT anomalies detected in Wrocław are characterized by seasonal transitions from a heat island in winter to a cold lake in summer. Such a transitional character of GUHI is described for the first time.
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... Alternatively to the aforementioned interpolation geocoding method, regression can be used to solve interpolation problems (Szymanowski & Kryza, 2012). This allows for more complex models to potentially learn policy and construction year patterns. ...
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... To improve the spatial resolution with which the outdoor air temperature is observed from an AWSN, various interpolation methods have been used in the literature [19,20,21,22,23,24,25,26,27]. Estimates provided by these interpolation methods are relatively accurate at positions near weather stations. ...
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Increased urbanisation is boosting the intensity and frequency of the Urban Heat Island (UHI) effect in highly developed cities. The advances in satellite measurement are facilitating the analysis of this phenomenon using Land Surface Temperature (LST) as an indicator of the Surface UHI (SUHI). Due to the spatial implications of using remote sensing data, this research developed ArcUHI, a Geographic Information System (GIS) add-in for modelling SUHI. The tool was designed in ArcGIS, which was bound with R to run machine learning algorithms in the background. ArcUHI was tested using the metropolitan area of Madrid (Spain) in 2006, 2012 and 2018 as a case study. The add-in was found to predict observed LST with high accuracy, especially when using Random Forest Regression (RFR). Building height and albedo were identified as the main drivers of SUHI, whose magnitude and extension increased with time. In view of these results, strategic roof and wall greening was suggested as a measure to mitigate the street canyon effect entailed by buildings and offset the heat retention capacity of built-up surfaces.
... Wroclaw is one of the few Polish cities whose urban climatic environment has been widely studied and described in recent years. Studies by [24,25] revealed that the processes shaping the city's climate are consistent with those described in other major urban areas around the world. This prompted us to study the impact of specific urban climate factors on the distribution and richness of the epiphytic bryophyte species. ...
... However, this increase, strongly dependent on urban features and weather conditions, generally occurs systematically from the periphery towards the city center [23,28]. The maps of seven cases of UHI in Wrocław used in this study were prepared by [25]. The UHI episodes were characterized by different magnitudes of the phenomenon; therefore, before averaging, each map was normalized to the range (0, 1). ...
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There is still a lack of knowledge on the effect of urban environmental factors on bryophyte species distribution and richness. The goal of this study was to fill that gap. We assumed the hypothesis that the urban heat island is the most important factor affecting epiphytic bryophyte species in urban space. The survey was based on a network of 500 one hectare study plots, scattered throughout the city of Wrocław (SW Poland). A set of 27 environmental factors was assessed in the field, as well as by the collection, processing, and interpretation of satellite imagery, LiDAR scans, and climatological data. Canonical correspondence analysis was used to evaluate the significance of the effect of the studied variables on the distribution of bryophyte epiphytes. The effect of the normalized difference vegetation index on epiphytic bryophyte distribution and richness was the strongest. The effects of the urban heat island as well as the tree species diversity appeared weaker, though significant. Among the tree stands features, the supply of European ash Fraxinus excelsior and tree height appeared to be the strongest. Maintaining afforested areas rich in old tree individuals with cooler and more humid microclimates seems to be crucial to the keeping of epiphytic bryophyte species diversity in the urban landscape.
... The inverse distance weighting has been adopted in different environmental studies [51][52][53]. In the GWR model, the relationships between the dependent and independent variables vary spatially [54]. The prediction performance of the GWR model can be improved by adjusting the regression coefficients according to the location. ...
Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity-Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000-2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000-2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium-and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth
... Stache et al. demonstrated that significant differences in thermal behavior between different types of urban vegetation surfaces occur [17]. Statistical models have limited applicability because they require additional analysis (calibration) for every city [18,19]. Of particular note is the paper by Theeuwes et al. [20], in which several factors of UHI formation are taken into account. ...
... where t is the time interval for averaging; it should be t > l/V. Thus, the presented equations allow us to estimate the heat fluxes characterizing the heat inflow to the UHI region, while Equation (19) allows estimation of the UHII in the form of the temperature increment. We call this model an energy model because it does not allow us to analyze the heterogeneity of the air temperature distribution in the city depending on the building density, location of large enterprises, and other factors. ...
... It should be noted that Equation (19) proposed for calculating the UHII includes not only meteorological parameters measured with our equipment, but also the parameter hUHI, which was not analyzed in this paper. However, the comparison of the UHII calculated by Equation (19) with direct measurements by the mobile station has shown that the parameter hUHI for Tomsk can be taken constant and equal to 100 m. We can assume that for any chosen city this parameter will be constant as well. ...
Despite the fact that the presence of a heat island over a city was established quite a long time ago, now there is no versatile algorithm for the determination of the urban heat island intensity. The proposed models either take into account only one or several factors for the formation of an urban heat island or do not consider physical reasons for the difference in thermodynamic conditions between a city and countryside. In this regard, it is impossible to make a forecast and determine the optimal methods for reducing the urban heat island intensity for an arbitrarily chosen city in a wide range of its characteristics and climatic conditions. This paper studies the causes for the formation of an urban heat island in order to develop the quantitative model of this process through the determination of the difference in radiation fluxes of various nature between a city and countryside (background area). A new equation allowing the intensity of an urban heat island in different seasons and different times of day, as well as under various atmospheric conditions, to be calculated from meteorological parameters measured at a stationary observation station is proposed. The model has been tested through the comparison of the results of numerical simulation with direct measurements of the heat island in Tomsk with a mobile station. It is shown that the main contributors to the formation of the heat island in Tomsk are anthropogenic heat emissions (80–90% in winter, 40–50% in summer) and absorption of shortwave radiation by the urban underlying surface (5–15% in winter, 40–50% summer). The absorption of longwave radiation by the urban underlying surface, absorption by atmospheric water vapor and other constituents, and heat consumption for evaporation are insignificant. An increase in the turbulent heat flux is responsible for the outflow of 40–50% of absorbed energy in summer and 20–30% in winter.
