The influence of land use on the spatial distribution and intensity of heat islands in Slovenia

Heat islands (HI) are a common anthropogenic phenomenon and are defined as artificial surfaces (urban areas) that have a higher average temperature than their surroundings (rural areas). The aim of this work was to determine the influence of land use on the spatial distribution and intensity (HIi ) of HI in Slovenia. The MODIS Land Surface Temperature (LST) and Corine Land Cover (CLC) databases were used to perform the analysis. Within the identified HI, two HI levels were determined based on temperature difference. The results revealed a statistically significant negative correlation between HIi and both forest co%ver and forest fragmentation (forest edge density and ratio of mean forest patch size to HI size). Artificial surface was positively correlated with HIi . The results contribute to the understanding of the spatial distribution of HI and HIi and provide informati%on for spatial planning and policy-making to reduce the negative impact of HI.

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Assessing the Spatial Distribution of Soil PAHs and their Relationship with Anthropogenic Activities at a National Scale

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16244928 ◽

2019 ◽

Vol 16 (24) ◽

pp. 4928 ◽

Cited By ~ 1

Author(s):

Siyan Zeng ◽

Jing Ma ◽

Yanhua Ren ◽

Gang-Jun Liu ◽

Qi Zhang ◽

...

Keyword(s):

Spatial Distribution ◽

Rural Areas ◽

Urban Areas ◽

Negative Impact ◽

Anthropogenic Activities ◽

Linear Regression Analysis ◽

Principal Component ◽

Multiple Linear Regression Analysis ◽

Anthropogenic Factors ◽

Characteristic Ratio

Soil polycyclic aromatic hydrocarbon (PAH) pollution is a major concern due to its negative impact on soil quality around the world. In China, accurate data on soil PAHs and information on the relationship with anthropogenic activities are limited. In this study, about 30,800 samples from 1833 soil sample sites were reviewed from 306 published reports to build a soil PAHs database. Based on the data obtained, the results demonstrated that 24.11% of surface soils in China are heavily contaminated. Meanwhile, the concentration of soil PAHs varied, in the order of independent mining and industrial areas (IMIA) > urban areas > suburban areas > rural areas, and the spatial distribution in China demonstrated a descending trend from north to south. Moreover, the characteristic ratio and PCA-MLR (principal component analysis-multiple linear regression) analysis demonstrated that coal combustion and vehicular exhaust emissions were the main sources of soil PAH pollution in China. On the other hand, provincial total Σ16PAHs in surface soil were significantly correlated with the per square kilometer GDP (gross domestic product) of industrial land, the per capita GDP, as well as the production and consumption of energy. These results indicate that anthropogenic factors have greatly affected the levels of soil PAHs in China. This study improves our understanding on the status and sources of soil PAH contamination in China, thereby facilitating the implementation of strategies of prevention, control, and remediation of soils.

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Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)

Remote Sensing ◽

10.3390/rs13163177 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3177

Author(s):

Talha Hassan ◽

Jiahua Zhang ◽

Foyez Ahmed Prodhan ◽

Til Prasad Pangali Sharma ◽

Barjeece Bashir

Keyword(s):

Urban Heat Island ◽

Rural Areas ◽

Land Surface ◽

Urban Areas ◽

Vegetation Index ◽

Thermal Environment ◽

Urban Heat Islands ◽

Heat Island ◽

Heat Islands ◽

Urban Heat

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

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Evaluation of Community Resilience in Rural China—Taking Licheng Subdistrict, Guangzhou as an Example

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115827 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5827

Author(s):

Jianhong Fan ◽

You Mo ◽

Yunnan Cai ◽

Yabo Zhao ◽

Dongchen Su

Keyword(s):

Spatial Distribution ◽

Rural Communities ◽

Rural Areas ◽

Urban Areas ◽

Rural China ◽

Social Resilience ◽

Learning Abilities ◽

The Sustainable Development ◽

Guangzhou City ◽

Local Sustainability

Resilience of rural communities is becoming increasingly important to contemporary society. In this study we used a quantitative method to measure the resilience regulating ability of rural communities close to urban areas—in Licheng Subdistrict, Guangzhou City, China. The main results are as follows: (1) Rural systems close to urban areas display superior adapting and learning abilities and have a stronger overall resilience strength, the spatial distribution of which is characterized by dispersion in whole and aggregation in part; (2) the resilience of most rural economic subsystems can reach moderate or higher levels with apparent spatial agglomeration, whilst the ecological subsystem resilience and social resilience are generally weaker; the spatial distribution of the former shows a greater regional difference while the latter is in a layered layout; (3) some strategies such as rebuilding a stable ecological pattern, making use of urban resources and cultivating rural subjectivity are proposed on this basis, in order to promote the sustainable development of rural areas and realize rural revitalization. This work also gives suggestion for the creation of appropriate and effective resilience standards specifically targeted for rural community-aiming to achieve the delivery of local sustainability goals.

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Seasonal variation and spatial distribution of carbonaceous aerosols in Taiwan

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-9563-2010 ◽

2010 ◽

Vol 10 (19) ◽

pp. 9563-9578 ◽

Cited By ~ 42

Author(s):

C. C.-K. Chou ◽

C. T. Lee ◽

M. T. Cheng ◽

C. S. Yuan ◽

S. J. Chen ◽

...

Keyword(s):

Spatial Distribution ◽

Rural Areas ◽

Urban Areas ◽

Air Pollutants ◽

Chemical Properties ◽

Anthropogenic Sources ◽

Carbonaceous Aerosols ◽

Urban Rural ◽

Southern Taiwan ◽

Northern Taiwan

Abstract. To investigate the physico-chemical properties of aerosols in Taiwan, an observation network was initiated in 2003. In this work, the measurements of the mass concentration and carbonaceous composition of PM10 and PM2.5 are presented. Analysis on the data collected in the first 5-years, from 2003 to 2007, showed that there was a very strong contrast in the aerosol concentration and composition between the rural and the urban/suburban stations. The five-year means of EC at the respective stations ranged from 0.9±0.04 to 4.2±0.1 μgC m−3. In rural areas, EC accounted for 2–3% of PM10 and 3–5% of PM2.5 mass loadings, comparing to 4–6% of PM10 and 4–8% of PM2.5 in the urban areas. It was found that the spatial distribution of EC was consistent with CO and NOx across the network stations, suggesting that the levels of EC over Taiwan were dominated by local sources. The measured OC was split into POC and SOC counterparts following the EC tracer method. Five-year means of POC ranged from 1.8±0.1 to 9.7±0.2 μgC m−3 among the stations. It was estimated that the POM contributed 5–17% of PM10 and 7–18% of PM2.5 in Taiwan. On the other hand, the five-year means of SOC ranged from 1.5±0.1 to 3.8±.3 μgC m−3. The mass fractions of SOM were estimated to be 9–19% in PM10 and 14–22% in PM2.5. The results showed that the SOC did not exhibit significant urban-rural contrast as did the POC and EC. A significant cross-station correlation between SOC and total oxidant was observed, which means the spatial distribution of SOC in Taiwan was dominated by the oxidant mixing ratio. Besides, correlation was also found between SOC and particulate nitrate, implying that the precursors of SOA were mainly from local anthropogenic sources. In addition to the spatial distribution, the carbonaceous aerosols also exhibited distinct seasonality. In northern Taiwan, the concentrations of all the three carbonaceous components (EC, POC, and SOC) reached their respective minima in the fall season. POC and EC increased drastically in winter and peaked in spring, whereas the SOC was characterized by a bimodal pattern with the maximal concentration in winter and a second mode in summertime. In southern Taiwan, minimal levels of POC and EC occurred consistently in summer and the maxima were observed in winter, whereas the SOC peaked in summer and declined in wintertime. The discrepancies in the seasonality of carbonaceous aerosols between northern and southern Taiwan were most likely caused by the seasonal meteorological settings that dominated the dispersion of air pollutants. Moreover, it was inferred that the Asian pollution outbreaks could have shifted the seasonal maxima of air pollutants from winter to spring in the northern Taiwan, and that the increases in biogenic SOA precursors and the enhancement in SOA yield were responsible for the elevated SOC concentrations in summer.

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Identificação de Ilhas de Calor na Cidade de Campina Grande-PB Utilizando Sensoriamento Remoto (Identification of Heat Islands in Campina Grande City-PB Using Remote Sensing)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v9.2.p614-626 ◽

2016 ◽

Vol 9 (2) ◽

pp. 614 ◽

Cited By ~ 2

Author(s):

Elânia Daniele Silva Araújo

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Rural Areas ◽

Urban Areas ◽

Spatial And Temporal Variability ◽

Heat Islands ◽

Social Nature ◽

Spatial Changes ◽

Remote Sensing Techniques ◽

The City

A intensa urbanização causa diversos problemas de natureza ambiental, climática e social. O crescimento não planejado da população urbana e a remoção da vegetação são fatores que intensificam estes problemas. As temperaturas na cidade são significativamente mais quentes do que as suas zonas rurais circundantes devido às atividades humanas. As intensas mudanças espaciais em áreas urbanas, promovem significativo aumento na temperatura, causando o chamado efeito de Ilha de Calor Urbano (ICU). Campina Grande é uma cidade de tamanho médio que experimentou um crescimento desordenado, desde o tempo do comércio de algodão e, como qualquer cidade de grande ou médio porte, sofre alterações em seu espaço. Dessa forma, este estudo teve por objetivo analisar a variabilidade espaço-temporal da temperatura da superfície (Ts) e detectar ICU, através de técnicas de sensoriamento remoto. Para o efeito, foram utilizadas imagens dos satélites Landsat 5 e 8, dos anos de 1995, 2007 e 2014. Aumentos da Ts foram bem evidentes e foram detectadas duas ICU. Campina Grande mostra um padrão de tendência: o crescimento urbano não planejado é responsável por mudanças no ambiente físico e na forma e estrutura espacial da cidade, o que se reflete sobre o microclima e, em última análise, na qualidade de vida das pessoas. ABSTRACT The intense urbanization causes several problems of environmental, climate and social nature. The unplanned growth of urban population and the vegetation removal are factors that deepen these problems. Temperatures in the city are significantly warmer than its surrounding rural areas due to human activities. Large spatial changes in urban areas promote significant increase in temperature, causing the so-called Urban Heat Island effect (UHI). Campina Grande is a medium-sized town that experienced an uncontrolled growth since the time of the cotton trade and like any large or medium-sized city, undergoes changes in its space. Therefore, this study aimed to analyze surface temperature spatial and temporal variability and to detect potential UHI, through remote sensing techniques. Spectral images from Landsat 5 and 8 satellites were used. Using images from years 1995, 2007 and 2014, considerable increases in temperature were identified and two UHI were recognize. Campina Grande shows a trend pattern: the urban unplanned growth is responsible for changes in the physical environment and in the form and spatial structure of the city, reflecting on people quality of life. Keywords: change detection, surface temperature, heat islands, urbanization.

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Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10120809 ◽

2021 ◽

Vol 10 (12) ◽

pp. 809

Author(s):

Jing Sun ◽

Suwit Ongsomwang

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Decomposition Analysis ◽

Lulc Change ◽

Main Components ◽

The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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Analyses of Nocturnal Temperature Cooling-Rate Response to Historical Local-Scale Urban Land-Use/Land Cover Change

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-10-05014.1 ◽

2011 ◽

Vol 50 (9) ◽

pp. 1872-1883 ◽

Cited By ~ 24

Author(s):

Winston T. L. Chow ◽

Bohumil M. Svoma

Keyword(s):

Land Use ◽

Land Cover ◽

Cooling Rate ◽

Rural Areas ◽

Urban Areas ◽

Local Scale ◽

Urban Land Use ◽

Cooling Rates ◽

Near Surface ◽

Over Time

AbstractUrbanization affects near-surface climates by increasing city temperatures relative to rural temperatures [i.e., the urban heat island (UHI) effect]. This effect is usually measured as the relative temperature difference between urban areas and a rural location. Use of this measure is potentially problematic, however, mainly because of unclear “rural” definitions across different cities. An alternative metric is proposed—surface temperature cooling/warming rates—that directly measures how variations in land-use and land cover (LULC) affect temperatures for a specific urban area. In this study, the impact of local-scale (<1 km2), historical LULC change was examined on near-surface nocturnal meteorological station temperatures sited within metropolitan Phoenix, Arizona, for 1) urban versus rural areas, 2) areas that underwent rural-to-urban transition over a 20-yr period, and 3) different seasons. Temperature data were analyzed during ideal synoptic conditions of clear and calm weather that do not inhibit surface cooling and that also qualified with respect to measured near-surface wind impacts. Results indicated that 1) urban areas generally observed lower cooling-rate magnitudes than did rural areas, 2) urbanization significantly reduced cooling rates over time, and 3) mean cooling-rate magnitudes were typically larger in summer than in winter. Significant variations in mean nocturnal urban wind speeds were also observed over time, suggesting a possible UHI-induced circulation system that may have influenced local-scale station cooling rates.

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Regional Variations of Land-Use Development and Land-Use/Cover Change Dynamics: A Case Study of Turkey

Remote Sensing ◽

10.3390/rs11070885 ◽

2019 ◽

Vol 11 (7) ◽

pp. 885 ◽

Cited By ~ 7

Author(s):

Ustaoglu ◽

Aydınoglu

Keyword(s):

Land Use ◽

Land Use Change ◽

Rural Areas ◽

Urban Areas ◽

Urban Land ◽

Temporal Changes ◽

Rapid Expansion ◽

Land Uses ◽

Hierarchical Levels ◽

Spatio Temporal

. Population growth, economic development and rural-urban migration have caused rapid expansion of urban areas and metropolitan regions in Turkey. The structure of urban administration and planning has faced different socio-economic and political challenges, which have hindered the structured and planned development of cities and regions, resulting in an irregular and uneven development of these regions. We conducted detailed comparative analysis on spatio-temporal changes of the identified seven land-use/cover classes across different regions in Turkey with the use of Corine Land Cover (CLC) data of circa 1990, 2000, 2006 and 2012, integrated with Geographic Information System (GIS) techniques. Here we compared spatio-temporal changes of urban and non-urban land uses, which differ across regions and across different hierarchical levels of urban areas. Our findings have shown that peri-urban areas are growing more than rural areas, and even growing more than urban areas in some regions. A deeper look at regions located in different geographical zones pointed to substantial development disparities across western and eastern regions of Turkey. We also employed multiple regression models to explain any possible drivers of land-use change, regarding both urban and non-urban land uses. The results reveal that the three influencing factors-socio-economic characteristics, regional characteristics and location, and development constraints, facilitate land-use change. However, their impacts differ in different geographical locations, as well as with different hierarchical levels.

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Urban heat island: Aerodynamics or imperviousness?

Science Advances ◽

10.1126/sciadv.aau4299 ◽

2019 ◽

Vol 5 (4) ◽

pp. eaau4299 ◽

Cited By ~ 30

Author(s):

Dan Li ◽

Weilin Liao ◽

Angela J. Rigden ◽

Xiaoping Liu ◽

Dagang Wang ◽

...

Keyword(s):

Green Infrastructure ◽

Rural Areas ◽

Land Surface ◽

Urban Heat Islands ◽

Lower Atmosphere ◽

Geographic Variations ◽

Heat Islands ◽

Urban Heat ◽

The Difference ◽

Urban And Rural Areas

More than half of the world’s population now live in cities, which are known to be heat islands. While daytime urban heat islands (UHIs) are traditionally thought to be the consequence of less evaporative cooling in cities, recent work sparks new debate, showing that geographic variations of daytime UHI intensity were largely explained by variations in the efficiency with which urban and rural areas convect heat from the land surface to the lower atmosphere. Here, we reconcile this debate by demonstrating that the difference between the recent finding and the traditional paradigm can be explained by the difference in the attribution methods. Using a new attribution method, we find that spatial variations of daytime UHI intensity are more controlled by variations in the capacity of urban and rural areas to evaporate water, suggesting that strategies enhancing the evaporation capability such as green infrastructure are effective ways to mitigate urban heat.

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Climate change over UK cities: the urban influence on extreme temperatures in the UK climate projections

Climate Dynamics ◽

10.1007/s00382-021-05883-w ◽

2021 ◽

Author(s):

William J. Keat ◽

Elizabeth J. Kendon ◽

Sylvia I. Bohnenstengel

Keyword(s):

Urban Heat Island ◽

Rural Areas ◽

Land Surface ◽

Urban Areas ◽

Climate Model ◽

Climate Projections ◽

Heat Island ◽

Night Time ◽

Urban Heat ◽

The Uk

AbstractIncreasing summer temperatures in a warming climate will increase the exposure of the UK population to heat-stress and associated heat-related mortality. Urban inhabitants are particularly at risk, as urban areas are often significantly warmer than rural areas as a result of the urban heat island phenomenon. The latest UK Climate Projections include an ensemble of convection-permitting model (CPM) simulations which provide credible climate information at the city-scale, the first of their kind for national climate scenarios. Using a newly developed urban signal extraction technique, we quantify the urban influence on present-day (1981–2000) and future (2061–2080) temperature extremes in the CPM compared to the coarser resolution regional climate model (RCM) simulations over UK cities. We find that the urban influence in these models is markedly different, with the magnitude of night-time urban heat islands overestimated in the RCM, significantly for the warmest nights (up to $$4~^{\circ }$$ 4 ∘ C), while the CPM agrees much better with observations. This improvement is driven by the improved land-surface representation and more sophisticated urban scheme MORUSES employed by the CPM, which distinguishes street canyons and roofs. In future, there is a strong amplification of the urban influence in the RCM, whilst there is little change in the CPM. We find that future changes in soil moisture play an important role in the magnitude of the urban influence, highlighting the importance of the accurate representation of land-surface and hydrological processes for urban heat island studies. The results indicate that the CPM provides more reliable urban temperature projections, due at least in part to the improved urban scheme.

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