Surveying topographical changes and climate variations to detect the urban heat island in the city of Málaga (Spain)

Abstract

The main aim of this research was to detect a possible urban heat island (UHI) in the tourist city of Málaga (Spain). To achieve this goal, different methods to validate annual and monthly temperature data have been used, and the internal variability of the UHI with respect to the topographical changes was also surveyed. Our results showed two types of atmospheric thermal gradients: one characterized by a positive gradient according to an elevation above sea level, and another with a negative gradient related to rural areas. The maximum impact of the UHI was found to be 9.1 ºC for an instantaneous event, 4.4 ºC for daily minimum temperatures in August, with a maximum weighted average difference of 2.3 ºC between data from urban and rural stations. We conclude that the detection of UHI is useful as a tool to help urban planners, in order to reduce the intensity of possible climate changes in cities. It is also concluded that UHI is not only a meteorological phenomenon in Málaga city but a climatic phenomenon, present during most days and across the transect of increasing intensity from the sea to the city.

A Regional Geography Approach to Understanding the Environmental Changes as a Consequence of the COVID-19 Lockdown in Highly Populated Spanish Cities

“…This agrees with all the reports related to the IPCC panels (e.g., [30,31]) from recent investigations related to climate change in Mediterranean cities and all over the world [32]. Our results agree with previous studies related to heat islands and aridity published in these cities (e.g., [33][34][35][36][37][38][39]), although we have to also consider that less commuting during the shutdown may have resulted in less heat waste, resulting in a slightly reduced urban heat island effect and thereby reducing local temperatures. Therefore, it is difficult to confirm that the COVID-19 pandemic and lockdowns had an impact on air temperatures in the highly populated cities in Spain in 2020 due to the reduction of human activities and, subsequently, transport.…”

Exploring long‐term datasets of land use, economy, and demography variations in karst wetland areas to detect possible microclimate changes

“…We can also hypothesise that there has been the appearance of an urban heat island formation (Duy et al, 2017), which influences the surrounding eco‐environment, which also was observed in Mediterranean cities (Krüger et al, 2018; Senciales‐González et al, 2020). According to the correlation analysis, the urban land area, air temperature, and Huangcaozhou Karst Wetland changed from 1984 to 2019.…”

“…Whereas urban lands showed significant positive correlations with GDP (0.517) and population (0.392). (IPCC, 2014;Liu et al, 2020;Qu, 2019;Root et al, 2003) We can also hypothesise that there has been the appearance of an urban heat island formation (Duy et al, 2017), which influences the surrounding eco-environment, which also was observed in Mediterranean cities (Krüger et al, 2018;Senciales-González et al, 2020).…”

Are Cities Truly Dispersed? A Long-Term Analysis of Vertical Profile of Settlements in Athens’ Metropolitan Region

“…Urban growth has implications for the likelihood of climate change and for the social costs that climate change will impose on the world's quality of life because it affects greenhouse gas production and local temperatures [82,83]. Some authors found that densification and compactness of the core cites can generate heat islands, which can be a factor to motivate decentralization because of the vertical growth of the cities [84][85][86]; however, it is difficult to find a clear justification between vertical growth using climate quality as key factor. Considering only the first two components (Figure 8) and plotting cases (years) and variables (topographic and territorial predictors), the PCA biplot classifies years in three main groups according to the economic cycle: (i) years from 1983 to 1998 (with the exception for 1993) associated with the lower left quadrant of the biplot (negative scores on both components); (ii) years from 1999 to 2010 associated with the higher left quadrant of the biplot (negative scores on Component 1 and positive scores on Component 2); (iii) years from 2011 to 2019 associated with the right quadrants (positive scores on Component 1) with a particularly heterogeneous pattern.…”