Atmospheric Urban Heat Island Research Articles

Effect of COVID-19 Lockdown on Urban Heat Island Dynamics in Prague, Czechia

Urban heat islands (UHI) are a well-known phenomenon adversely affecting human health and urban environments. The worldwide COVID-19 lockdown in 2020 provided a unique opportunity to investigate the effects of decreased emission of air pollution and anthropogenic heat flux (AHF) on UHI. Although studies have suggested that reduced AHF during lockdown decreased atmospheric UHI (AUHI) and surface UHI (SUHI), these results contain inherent uncertainties due to unaccounted weather variability and urban-rural dynamics. Our study comprehensively analyzes the impact of the COVID-19 lockdown on AUHI and SUHI in Prague, Czechia. By selecting days with similar weather conditions, we examined changes in mean SUHI using MODIS satellite images and in AUHI based on air temperature from Prague weather stations for the Lockdown period during March–April 2020 versus a Reference period from March–April 2017–2019. Our results show that, in comparison to the Reference period, the Lockdown period was associated with a 15% (0.1 °C) reduction of SUHI in urbanized areas of Prague and a 0.7 °C decline in AUHI in the city center. Additionally, the observed decreases in satellite-based aerosol optical depth and nitrogen dioxide by 12% and 29%, respectively, support our hypothesis that the weakened UHI effects were linked to reduction in anthropogenic activities during the lockdown. Revealing the largest decrease of mean SUHI magnitude around the periphery of Prague, which has predominantly rural land cover, our study emphasizes the need to consider the effects of urban-rural dynamics when attributing changes in SUHI to AHF. Our findings provide additional insights into the role of reduced anthropogenic activities in UHI dynamics during the COVID-19 lockdown and offer policymakers a comprehensive understanding of how the complex interaction between urban and rural microclimate dynamics influences the SUHI phenomenon.

Temporal and Spatial Urban Heat Islands in a Coastal Brazilian Area of Tropical Climate

This study explores the urban heat island (UHI) phenomenon in the Metropolitan Region of Vitória (MRV), Brazil. Two methods were employed: the traditional approach estimating the Atmospheric Urban Heat Island (UHI) using air temperature data, and the quantiles method assessing the Surface Urban Heat Island (SUHI) through medium spatial resolution images. The findings demonstrate the existence of an average heat island effect, as estimated by both UHI (+0.9 °C) and SUHI (+2.03 °C). The intensity of the atmospheric UHI is linked with higher air temperature values around midday. Additionally, the study reveals that UHI values in the MRV are more pronounced during spring and summer SUHI estimates peak during the summer months. The utilization of orbital remote sensing enables the identification of hotspots throughout the metropolitan area, with the highest temperatures observed in industrial areas, suburban neighborhoods, and densely populated regions. The advantage of the UHI method lies in its long time series, providing high temporal resolution and statistical significance over an extended period. Conversely, the SUHI method offers the advantage of considering data across the entire domain with high spatial resolution. Thus, these two methods complement each other in identifying the heat island phenomenon.

Assessment of the Spatiotemporal Variability of Urban Heat Island Intensity in Colombo Metropolitan Area


 
 
 During the last few decades, the Colombo Metropolitan Area (CMA) has experienced rapid population growth and unplanned urbanization, resulting in the replacement of natural landcover with more impervious surfaces. The primary approach of current study was to determine the gradients of air temperature and assess the spatiotemporal variation of atmospheric urban heat island (AUHI) intensity in the CMA considering a range of rural sites extending in different radial directions from the urban center. A total of sixteen rural observation sites that extend from an urban center in four directions were selected within the CMA, with four sites 3.5 km apart from each other in any direction. The air temperatures at each site were measured using HOBO UX-100-003 temperature data loggers for an 8-day period from 21st to 28th, of February 2022. The normalized difference vegetation index (NDVI) and normalized difference building index (NDBI) were used to determine the influence of land use/land cover on air temperatures. The results revealed that air temperature had a strong positive correlation with NDBI. Thus, the current study suggests that the impervious surface/s is a significant predictor of air temperature over the study area. The higher air temperatures were observed at the urban center in relation to the selected suburban and rural observation sites. Hence, the current study confirms that AUHI exists within the selected study area over the CMA. It was found that AUHI tends to be present throughout the day in the CMA. However, it becomes strong between 12.00 A.M. and 6.00 A.M and mostly weakens in the late afternoon hours. Furthermore, this study found that the timing and magnitudes of AUHI vary from direction to direction across the study area depending on the site-specific characteristics of the rural observation sites. The current study emphasizes that site-specific land use/land cover characteristics can have a significant impact on the interpretation of UHI study results even within a single metropolitan area. Therefore, taking only one rural site might not be effective in determining the UHI intensity and a range of sites need to be considered (to select from) for in-depth analysis of UHI in any given area. The findings of the current study will provide firsthand knowledge for urban planners to identify future trends in UHI in the CMA and will promote further UHI research in this area.
 Keywords: Rural observation site, Atmospheric urban heat island, Normalized Difference Vegetation Index
 
 

Annual Comparison of the Atmospheric Urban Heat Island in Rome (Italy): An Assessment in Space and Time

This study examined the atmospheric urban heat island (UHI) phenomenon within the city of Rome (Italy) and its effects on building energy demand. Weather data from 2020 and 2022 collected from six meteorological stations were considered. A Geographic Information System (GIS) was used to analyze the landscape, correlating the percentage of impermeable surfaces with UHI intensity values in each area. Dynamic simulations were conducted using different climatic data to estimate the heating and cooling energy demands for two representative residential buildings. The findings revealed significant differences in the climatic conditions between urban and rural areas, primarily due to temperature increases. The UHI intensities reached maximum values of 4.67 °C and 3.54 °C in 2020 and 2022. In urban areas, the UHI has positive effects on the heating energy demand but results in a significant increase in energy demand for cooling. Considering a building type constructed between 1900 and 1950, a variation of up to 33.03% in the heating energy demand in urban areas compared to rural areas was calculated, along with a variation of up to 81% for cooling. In contrast, considering a more recent building type constructed between 1991 and 2005, the corresponding values reached up to 36.47% and 75.7%.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R > 0.86, p < 0.05) and AUHII (R > 0.68, p < 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

Diurnal characterization of the atmospheric urban heat island over urban hot agglomerations

Abstract The urban heat island ($\mathrm{UHI}$) is a physical climatic phenomenon; its effects in built-up areas can be observed in terms of air temperatures above the atmosphere, as well as in terms of thermal comfort under the urban canopy. This study aims to carry out a diurnal characterization of the atmospheric UHI over the agglomeration of $\mathrm{Guelma}$, Algeria during summertime. The characterization is organized into three main steps. First, quantifying the $\mathrm{UHI}$ intensity. Second, highlighting the factors contributing to affect the thermal environment in downtown $\mathrm{Guelma}$ and finally, defining an appropriate scale to measure it over $\mathrm{Guelma}$’s agglomeration of Guelma. The $\mathrm{UHI}$ quantification was done by calculating the positive differences of urban temperatures in daytime based on in situ and static approach. The results indicate that the maximum daily difference reached $4.8$ to $5.1{}^{\circ}\mathrm{C}$ with a positive difference of $6.1{}^{\circ}\mathrm{C}$ at $10:00$ a.m. and $6.4{}^{\circ}\mathrm{C}$ at noon, which means that $\mathrm{Guelma}$’s agglomeration is affected by the atmospheric UHI phenomenon. Depending on the difference of urban temperatures, we have defined appropriate scale to quantify the intensity of UHI phenomenon over Guelma’s agglomeration as follows: ${UHI}_{intensity}=0.3688\times \boldsymbol{\Delta }{T}_{\boldsymbol{urb}}+3.3157.$

A multi-perspective study of atmospheric urban heat island effect in China based on national meteorological observations: Facts and uncertainties

The atmospheric urban heat island (AUHI) effect, traditionally measured by in-situ sensors mounted on fixed meteorological stations, has been extensively studied by different and imperfect methods. However, facts and uncertainties of the AUHI estimates revealed by the different methods are not well understood at a large scale. Here we examined the spatial-temporal variations of the AUHI effects from multiple perspectives in China's 86 large cities as revealed by national-level meteorological observations at 2-m height from 1981 to 2017. We find relatively consistent patterns of larger urban heating effects in daily minimum temperature, winter, and Northeast China than their counterparts in terms of multiyear mean intensity (AUHII), long-term trend (△AUHII), and contribution to local warming (according to the CTRUMR “urban minus rural” and CTROMR “observation minus reanalysis” methods). Concurrently, a cooling impact or a reduction in the heating effect has been observed in some cities randomly, especially in daily maximum temperature. On average across cities, the AUHII, △AUHI, CTRUMR, and CTROMR for the daily mean temperature amount to 0.33 °C, 0.013 °C 10a−1, 53 %, and 23 % at an annual mean time scale, respectively. Nevertheless, the poor representativeness of weather stations, discrepancies among the quantification methods, nonlinearity of the long-term tendencies, and coupling effects with rural crop land use activities lead to large uncertainties of the AUHI estimates. Our results emphasize the limitations of national-level meteorological stations in characterizing AUHI in China and suggest that the urban heat island remains a “well described but rather poorly understood” phenomenon warranting further investigation by a combined uses of multiple techniques like high-density sensor networks, remote sensing techniques, and high-resolution numerical models.

Uncertainty of city-based urban heat island intensity across 1112 global cities: Background reference and cloud coverage

As an urban heat island (UHI) is closely associated with a wide variety of environmental issues, it is necessary to monitor its spatiotemporal variations in an accurate and timely manner. However, the accuracy of surface urban heat island intensity (SUHII) estimation is greatly challenged by three issues. Namely, less attention has been given to city-based global studies, △SUHII (SUHII difference) is induced when various methods are used to the determine background reference, and uncertainty arises in the methods for coping with missing land surface temperature (LST). Thus, this paper quantitatively evaluated these different methods and their impacts on spatiotemporal SUHII variations across 1112 global cities. There are two major findings: (1) the modified equal area-rural (MEA-R) method can overcome the limitations of the other methods. The fixed buffer (FB) method is limited by the difficulty of buffer selection and the inapplicability of a single fixed buffer for various cities, particularly in large-scale studies. The simplified urban-extent (SUE) method is challenged by cluster discrepancies and data obsolescence issues, and the modified equal area-suburban (MEA-S) method underestimates SUHII resulting from strong human activities in suburban areas. The daytime △SUHII induced by different methods for the reference definition reaches 0.62 K, accounting for 42% of the average SUHII. △SUHII is 0.34 K (23%) at night. △SUHII with evident spatiotemporal variations indicates that the different methods used to define the background reference introduce a considerable uncertainty to SUHII research. (2) After comparing methods for coping with missing LST, the first spatial and after temporal aggregation with threshold removal (FSAT-T) method is demonstrated to provided improved robustness to data missing over other methods. Daytime △SUHII is over 0.1 K in 62% of cities, and nighttime △SUHII is over 0.1 K in 45% of cities. Strong latitudinal and seasonal variations of △SUHII induced by the different methods used to cope with missing data introduce uncertainty and incomparability to SUHII research. Moreover, various image quality control (QC) methods and the effects from these methods on SUHII are discussed. The accuracy of QC methods changes with the error distribution of the image quality. Further investigations are required to determine the optimal QC method. The questions of whether different background definition affects the atmospheric UHI were also discussed. In this paper, a promising strategy for estimating city-based SUHII is proposed and a global-scale database based on the Google Earth Engine (GEE) is established to support further research on the variations and controls of SUHI.

Spatial downscaling method for air temperature through the correlation between land use/land cover and microclimate: A case study of the Greater Tokyo Area, Japan

Since the development of remote sensing, studies on urban heat islands (UHIs) have mainly focused on surface UHI (SUHI), which is more than the atmospheric UHI (AUHI). Remote sensing advancements provide strong technical support for SUHI studies. However, the acquisition of air temperature (AT), especially high-resolution large-scale AT data, remains inconvenient. In this study, we selected the Greater Tokyo Area as a case study. We used the extra-trees (ET) model to downscale the AT from 1 km to 250 m based on the regression relationship among AT, digital elevation model (DEM), and land use/land cover (LULC). The downscaled AT results were obtained after residual fitting. Finally, we compared the downscaled AT with meteorological station data to verify accuracy. The results indicated the following: (1) the ET model and the combination of independent variables, including DEM and LULC, could be applied to AT downscaling research; (2) the downscaling accuracy of highly heterogeneous regions was significantly higher than that of highly homogeneous regions; and (3) high-resolution AT data could accurately describe the AUHI of the study area. Finally, an accurate, efficient AT downscaling method based on high-resolution LULC was proposed.

Spatiotemporal Variations in Atmospheric Urban Heat Island Effects and Their Driving Factors in 84 Major Chinese Cities

Accelerating urbanization seriously intensifies urban heat island effects in China, which in turn affects regional environment and human health. However, the spatiotemporal patterns of atmospheric urban heat island effects remain poorly understood in China as previous research is mostly based on satellite-sensed radiation temperatures. Using long-term daily meteorological observations from 1960 to 2017, this study explored the geographical distribution of atmospheric urban heat islands over diurnal, intra-annual, and inter-annual timescales in 84 major cities in China. The results show that on average, the intensity of the urban heat island reaches(0.9±1.1)℃, although large geographical variations were detected. The intensity was, overall, larger in northern China than in southern China; North China showed the largest annual intensity of(1.4±1.4)℃, and seasonal intensity variations were high in northeast and northwest China. Here, significantly higher intensity effects were measured at night[(1.2±1.1)℃] than during the day[(0.5±1.2)℃], and in summer than winter during the day, and in winter than summer during the night. Inter-annually, the annual mean urban heat island effect has increased by an average of 0.040℃ per decade, although this trend weakens after 2009. Furthermore, we found that the spatial patterns of atmospheric heat island intensity was greatly affected by climatic background conditions and the location of meteorological stations, while the long-term trends were strongly influenced by the impervious surface area. This study improves understanding of the atmospheric urban heat island effect in China and provides important insights for formulating urban land-use strategies to alleviate high temperatures and heatwaves.

Time Evolution of the Surface Urban Heat Island

AbstractAtmospheric and surface urban heat islands (UHI) originate from common energetic processes, but the status of scientific knowledge on their time evolution is highly disparate. The diurnal cycles of atmospheric UHI are well known based on years of continuous measurements in cities; the cycles of surface UHI, however, cannot be measured continuously or in situ. In this article, we aim to reconcile these differences. We begin with a synthesis of previous work on the diurnal evolution of surface UHI, which leads to a novel but historically minded approach to the research problem. The approach involves a combination of microscale and mesoscale urban climate models, each of which is forced with universally described urban and rural surface parameters and atmospheric profiles. With these models, we produce theoretical time‐temperature curves for the surface UHI that are comparable to the classic curves of atmospheric UHI. This work prompts a critical look at the use of satellite thermal imagery to assess heat islands and heat risks in cities. To that end, we recommend new and more functional definitions of surface temperature. Conceptually, these represent “incomplete” temperatures defined by specific facets of the urban environment.

Urban heat islands in the Arctic cities: an updated compilation of in situ and remote-sensing estimations

Abstract. Persistent warm urban temperature anomalies – urban heat islands (UHIs) – significantly enhance already amplified climate warming in the Arctic. Vulnerability of urban infrastructure in the Arctic cities urges a region-wide study of the UHI intensity and its attribution to UHI drivers. This study presents an overview of the surface and atmospheric UHIs in all circum-Arctic settlements (118 in total) with the population larger than 3000 inhabitants. The surface UHI (SUHI) is obtained from the land surface temperature (LST) data products of the Moderate Resolution Imaging Spectroradiometer (MODIS) archive over 2000–2016. The atmospheric UHI is obtained from screen-level temperature provided by the Urban Heat Island Arctic Research Campaign (UHIARC) observational network over 2015–2018. Several other UHI studies are included for comparisons. The analysis reveals strong and persistent UHI during both summer and winter seasons. The annual mean surface UHI magnitudes vary from −0.6 ∘C (Hammerfest) to 4.3 ∘C (Murmansk). Thus, the observed UHI is likely an important climatic factor that must be included in future adjustment of urban construction, safety, and environmental quality codes.

The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index

The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.

Satellite identification of atmospheric-surface-subsurface urban heat islands under clear sky

The urban heat island (UHI) is a major topic in the study of urban climates. However, comprehensive research on the atmospheric UHI (UHIAtm), surface UHI (UHISurf), and subsurface UHI (UHISub) simultaneously has yet not been reported. Using the MODIS land surface temperature and atmospheric profile data during the 2010–2016 period, we investigated the diurnal, seasonal, and vertical variations of UHIAtm, UHISurf, and UHISub in Beijing. The major findings include but are not limited to the following: As the altitude increases from 1000 hPa (near the surface) to 700 hPa (~3 km), the daytime UHIAtm intensity in summer decreases piecewise linearly with a rapid decline above 850 hPa (~1.5 km), while in spring and autumn it decreases after a slight increase in the lower atmosphere. The nighttime UHIAtm intensity decreases approximately linearly in all seasons with a rapid (gradual) decline in winter (summer). As the depth increases from the surface to ~0.2 m, the daytime intensity from UHISurf to UHISub decreases in summer but increases in the other seasons, while the nighttime trends are opposite to the daytime ones. The diurnal (seasonal) variation of UHISub intensity converges to the daily (annual) mean as the depth reaches ~0.5 m (10 m). These new findings have theoretical and practical implications for in-depth understanding of the urban thermal environment from the boundary layer to the subsurface.

Urban Heat Island in Mediterranean Coastal Cities: The Case of Bari (Italy)

In being aware that some factors (i.e. increasing pollution levels, Urban Heat Island (UHI), extreme climate events) threaten the quality of life in cities, this paper intends to study the Atmospheric UHI phenomenon in Bari, a Mediterranean coastal city in Southern Italy. An experimental investigation at the micro-scale was conducted to study and quantify the UHI effect by considering several spots in the city to understand how the urban and physical characteristics of these areas modify air temperatures and lead to different UHI configurations. Air temperature data provided by fixed weather stations were first compared to assess the UHI distribution and its daily, monthly, seasonal and annual intensity in five years (from 2014 to 2018) to draw local climate information, and then compared with the relevant national standard. The study has shown that urban characteristics are crucial to the way the UHI phenomenon manifests itself. UHI reaches its maximum intensity in summer and during night-time. The areas with higher density (station 2—Local Climate Zone (LCZ) 2) record high values of UHI intensity both during daytime (4.0 °C) and night-time (4.2 °C). Areas with lower density (station 3—LCZ 5) show high values of UHI during daytime (up to 4.8 °C) and lower values of UHI intensity during night-time (up to 2.8 °C). It has also been confirmed that sea breezes—particularly noticeable in the coastal area—can mitigate temperatures and change the configuration of the UHI. Finally, by analysing the frequency distribution of current and future weather scenarios, up to additional 4 °C of increase of urban air temperature is expected, further increasing the current treats to urban liveability.

Monitoring and spatio-temporal analysis of UHI effect for Mansa district of Punjab, India

Urban heat island (UHI) is one of the most important climatic implications of urbanization and thus a matter of key concern for environmentalists of the world in the twenty-first century. The relationship between climate and urbanization has been better understood with the introduction of thermal remote sensing. So, this study is an attempt to understand the influence of urbanization on local temperature for a small developing city. The study focuses on the investigation of intensity of atmospheric and surface urban heat island for a small urbanizing district of Punjab, India. Landsat 8 OLI/TIRS satellite data and field observations were used to examine the spatial pattern of surface and atmospheric UHI effect respectively, for the month of April, 2018. The satellite data has been used to cover the larger geographical area while field observations were taken for simultaneous and daily temperature measurements for different land use types. The significant influence of land use/land cover (LULC) patterns on UHI effect was analyzed using normalized built-up and vegetation indices (NDBI, NDVI) that were derived from remote sensing satellite data. The statistical analysis carried out for land surface temperature (LST) and LULC indicators displayed negative correlation for LST and NDVI while NDBI and LST exhibited positive correlation depicting attenuation in UHI effect by abundant vegetation. The comparison of remote sensing and in-situ observations were also carried out in the study. The research concluded in finding both nocturnal and daytime UHI effect based on diurnal air temperature observations. The study recommends the urgent need to explore and impose effective UHI mitigation measures for the sustainable urban growth.

Review of methods for retrieving urban heat islands

Urban heat island is one of the alarming phenomena that exacerbate urban microclimate. Measuring this magnitude in time and space is crucial to obtain a general overview of the thermal environment at a multiscale level ranging from the district scale to the city scale including its outskirts areas. These measurements allow the detection of black spots that can be used as a reference to fight against thermal anomalies resulting from anthropogenic causes and then to develop metropolitan areas more resilient to microclimate modifications, thus ensuring a real sustainable development of the environment and the territory. The main purpose of this paper is to provide the state of the art of different techniques and methods that have been developed to retrieve the spatial distribution of different types of urban heat island implemented in the urban climatology field. These different techniques are regrouped into three main categories namely In-Situ measurements, Remote Sensing observation methods and the modeling approach and allow to provide two types of urban heat island: the atmospheric urban heat island that includes the canopy and boundary layers; and the surface urban heat island. This paper provides also a detailed benchmarking of the different approaches of urban heat island modeling and gives a general overview of the strengths and weaknesses of each technique.

Statistical Modeling of Urban Heat Island Intensity in Warsaw, Poland Using Simultaneous Air and Surface Temperature Observations

Urban heat island (UHI) is one of the most distinctive characteristics of urban climate. The objective of this study is to apply a statistical modeling of the nocturnal atmospheric UHI based on the relationship between observed air temperature from ground stations and remotely sensed temperature of the urban surface. The goal of the approach is to limit input data for the developed modeling method in order to assure transferability of the methodology in different cities. Time series of surface temperature and normalized difference vegetation index are obtained from the MODIS instrument for a 10-year period (2008–2017). The air temperature is collected from the in-situ observational network of 21 stations. The studies are conducted for different locations with gradual changes in urbanization in order to assess the impact of urbanization on the relationship between simultaneous air and surface UHI. The urbanization is described by commonly available land cover metrics. Results showed that the proposed approach provides satisfactory AUHI modeling results for the locations with the least degree of urbanization. The best results are obtained with a simple linear regression model with the iterative procedure to minimize the mean absolute gross error (MAGE). The lowest MAGE for modeled UHI is 1.18 °C with 69% of the variance explained. The strongest linear relationship between simultaneous SUHI and AUHI is noted for those station pairs whose surroundings have the highest differences in urbanization, and the highest UHI intensities are observed. The strength of the SUHI/AUHI linear relationship decreases gradually with the increasing urbanization of the stations' surroundings.

The urban heat island and the features of the flora in the Lublin City area, SE Poland

Various forms of human activity in large cities contribute to the creation of a specific climate and new environmental conditions for plants. One of the most important results is the so-called atmospheric urban heat island (UHI). The aim of this study was to compare the thermal conditions in the Lublin City center with those of suburban areas, and so confirm the existence of the UHI and then analyze the influence of thermal conditions on features of the flora. The analysis of the air temperatures was based on data from 2000 to 2014 provided by meteorological stations in the Lublin City center and its nearest surroundings. Floristic data were collected during field studies between 2012–2014 and included species richness and frequency, life forms, and synecological groups of the flora and its elements. The thermal requirements of species were defined on the basis of ecological indicator values (EIVs). Our research confirmed the presence of the UHI in the Lublin City center. Over the study period, the mean temperature in the city center was by 0.87°C higher than that in the suburban areas. The largest differences in mean annual air temperature between the city center and the suburbs were recorded in 2007 and 2011–2013. In total, 552 species were recorded, including six life-form and synecological groups, 246 thermophilous and 436 heliophilous species. The species richness, proportion of therophytes, alien, thermo- and heliophilous species decreased with distance from the city center. The thermal conditions expressed by the EIV L and T ranged from L = 4.5, T = 3.8 in the city center, to L = 3.1, T = 3.4 in the suburbs. An ordination analysis facilitated determination of the relationships between characteristics of the flora, the EIV values, and land use classes. An increasing level of anthropopressure was found to be positively correlated with thermal conditions and the flora traits.

Interaction between urban heat island and urban pollution island during summer in Berlin

Urban Heat Island (UHI) and Urban Pollution Island (UPI) are two major problems of the urban environment and have become more serious with rapid urbanization. Since UHI and UPI can interact with each other, these two issues should be studied concurrently for a better urban environment. This study investigated the interaction between the UHI and UPI in Berlin, through a combined analysis of in-situ and remote sensing observations of aerosols and meteorological variables in June, July, and August from 2010 to 2017. The atmospheric UHI (AUHI), surface UHI (SUHI), atmospheric UPI (AUPI), and near-surface UPI (NSUPI) were analyzed. The SUHI and AUPI are represented by the remote sensing land surface temperature (LST) and aerosol optical depth (AOD), and the AUHI and NSUPI are represented by the in-situ air temperature and Particulate Matter (PM10) concentrations. The study area shows spatial consistency between SUHI and AUPI, with higher LST and AOD in the urban areas. UHI strengthens the turbulent dispersion of particles in the urban areas, decreasing the NSUPI. The NSUPI intensity shows a negative relationship with the AUHI intensity, especially at night with a correlation coefficient of −0.31. The increased aerosols in urban atmosphere reduce the incoming solar radiation and increase the atmospheric longwave radiation in the urban areas. The response of the surface to the change of absorbed radiation is strong at night and weak during the day. This study estimates that the SUHI intensity is enhanced by around 12% at clear night by the increased absorbed radiation in the urban areas using an attribution method. The goal of this paper is to strengthen the understanding of the interactive influence between UHI and UPI and provide a basis for designing mitigation strategies of UHI and UPI.