Urban heat island (UHI) could have significant impacts on building energy consumption by increasing space cooling demand and decreasing space heating demand. However, the impacts of UHI on building ...energy consumption were understudied due to challenges associated with quantifying UHI-induced temperature change and evaluating building energy consumption. In this study, we reviewed existing literature for improving the understanding of UHI impacts on building energy consumption. It was found that UHI could result in a median increase of 19.0% in cooling energy consumption and a median decrease of 18.7% in heating energy consumption. The reported UHI impacts showed strong intercity variations with an increase of cooling energy consumption from 10% to 120% and a decrease of heating energy consumption from 3% to 45%. The UHI impacts also showed clear intra-city variations with stronger impacts in urban center than that in urban periphery. There were significant differences in the method and the data used to evaluate the UHI impacts in previous studies. Four future research focuses were recommended to better understand the UHI impacts on building energy consumption.
•The literature of UHI impacts on building energy consumption was reviewed.•UHI could lead to a median of 19% increase in building cooling energy consumption.•UHI could lead to a median of 18.7% decrease in building heating energy consumption.•UHI impacts showed strong spatial variations within and among cities.•Four future research focuses were recommended for better understanding of UHI impacts.
Urban heat island (UHI), the phenomenon that urban areas experience higher temperatures compared to their surrounding rural areas, has significant socioeconomic and environmental impacts. With ...current and anticipated rapid urbanization, improved understanding of the response of UHI to urbanization is important for developing effective adaptation measures and mitigation strategies. Current studies mainly focus on a single or a few big cities and knowledge on the response of UHI to urbanization for large areas is limited. As a major indicator of urbanization, urban area size lends itself well for representation in prognostic models. However, we have little knowledge on how UHI responds to urban area size increase and its spatial and temporal variation over large areas. In this study, we investigated the relationship between surface UHI (SUHI) and urban area size in the climate and ecological context, and its spatial and temporal variations, based on a panel analysis of about 5000 urban areas of 10km2 or larger, in the conterminous U.S. We found statistically significant positive relationship between SUHI and urban area size, and doubling the urban area size led to a SUHI increase as high as 0.7°C. The response of SUHI to the increase of urban area size shows spatial and temporal variations, with stronger SUHI increase in Northern U.S., and during daytime and summer. Urban area size alone can explain as much as 87% of the variance of SUHI among cities studied, but with large spatial and temporal variations. Urban area size shows higher association with SUHI in regions where the thermal characteristics of land cover surrounding the urban area are more homogeneous, such as in Eastern U.S., and in the summer months. This study provides a practical approach for large-scale assessment and modeling of the impact of urbanization on SUHI, both spatially and temporally.
Display omitted
•We studied relationship between SUHI and urban area size and their spatial and temporal variation in the conterminous U.S.•SUHI increases nonlinearly with the increase of urban area size in a log-linear form.•Doubling urban size increases SUHI as high as 0.7 °C, with larger increase in high latitude areas and in summer and daytime.•Urban area size explains as much as 87% of SUHI variation, with higher value in regions covered by homogenous land cover.
Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using ...pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISAKDE). The linear functions of LST are well fitted by the ISAKDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.
Display omitted
•Quantifying surface urban heat island intensity using the relationship between LST and Impervious Surface Areas.•The impervious surface areas was regionalized within the footprint of remote sensing observation using a Kernel Density Estimation method.•Linear functions of LST were well fitted using the regionalized impervious surface areas.•Slope of the linear function of LST was defined as the surface urban heat island intensity.
► We examined the effects of spatial resolution on relationship between LST and urban greenspace. ► Landscape metrics of greenspace varied by spatial resolution. ► Relationship between LST and ...abundance of greenspace was consistent across spatial resolution. ► Relationship between LST and spatial configuration of greenspace varied by spatial resolution.
Urban heat island (UHI) is a worldwide phenomenon, which causes many ecological and social consequences. Urban greenspace can decrease environmental temperature and thus alleviate UHI effects. Spatial pattern of greenspace, both composition and configuration, significantly affects land surface temperature (LST). Results from previous studies, however, showed inconsistent, or even contradictory relationships between LST and spatial pattern of greenspace, suggesting these relationships may be scale dependent (sensitive to spatial resolution). But few studies have explicitly addressed this issue. This paper examines whether the spatial resolution of the imagery used to map urban greenspace affect the relationship between LST and spatial pattern of greenspace, using Beijing, China as a case study. Spatial pattern of greenspace was measured with seven landscape metrics at three spatial resolutions (2.44m, 10m, and 30m) based on QuickBird, SPOT, and TM imagery. LST was derived from thermal band of Landsat TM imagery. The relationship between LST and spatial pattern of greenspace was examined by Pearson correlation and partial Pearson correlation analysis using census tract as analytical unit. Results showed that landscape metrics of greenspace varied by spatial resolution. Imagery with higher spatial resolution could more accurately quantify the spatial pattern of greenspace. The relationship between LST and abundance of greenspace was consistently negative, but the relationship between LST and spatial configuration of greenspace varied by spatial resolution. This study extended our scientific understanding of the effects of spatial pattern, especial spatial configuration of greenspace on LST. In addition, it can provide insights for urban greenspace planning and management.
We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC ...variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons. We used three LULC datasets: (1) 0.6 m resolution land cover data; (2) 30 m resolution land cover data (NLCD 2001); and (3) 30 m resolution Normalized Difference Vegetation Index data derived from the same ETM+ images (though from different bands) used for LST calculation. We developed ten models to evaluate effects of spatial and thematic resolution of LULC data on the observed relationships between LST and LULC variables for each season. We found that the directions of the effects of LULC variables on predicting LST were consistent across seasons, but the magnitude of effects, varied by season, providing the strongest predictive capacity during summer and the weakest during winter. Percent of imperviousness was the best predictor on LST with relatively consistent explanatory power across seasons, which alone explained approximately 50 % of the total variation in LST in winter, and up to 77.9 % for summer. Vegetation related variables, particularly tree canopy, were good predictor of LST during summer and fall. Vegetation, particularly tree canopy, can significantly reduce LST. The spatial resolution of LULC data appeared not to substantially affect relationships between LST and LULC variables. In contrast, increasing thematic resolution generally enhanced the explanatory power of LULC on LST, but not to a substantial degree.
Urban forests can play an important role in mitigating the impacts of climate change by reducing atmospheric carbon dioxide (CO2). Quantification of carbon (C) storage and sequestration by urban ...forests is critical for the assessment of the actual and potential role of urban forests in reducing atmospheric CO2. This paper provides a case study of the quantification of C storage and sequestration by urban forests in Shenyang, a heavily industrialized city in northeastern China. The C storage and sequestration were estimated by biomass equations, using field survey data and urban forests data derived from high-resolution QuickBird images. The benefits of C storage and sequestration were estimated by monetary values, as well as the role of urban forests on offsetting C emissions from fossil fuel combustion. The results showed that the urban forests in areas within the third-ring road of Shenyang stored 337,000t C (RMB92.02 million, or $ 13.88 million), with a C sequestration rate of 29,000t/yr (RMB7.88 million, or $ 1.19 million). The C stored by urban forests equaled to 3.02% of the annual C emissions from fossil fuel combustion, and C sequestration could offset 0.26% of the annual C emissions in Shenyang. In addition, our results indicated that the C storage and sequestration rate varied among urban forest types with different species composition and age structure. These results can be used to help assess the actual and potential role of urban forests in reducing atmospheric CO2 in Shenyang. In addition, they provide insights for decision-makers and the public to better understand the role of urban forests, and make better management plans for urban forests.
Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving ...factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.
► We studied the driving factors of urban expansion in Beijing from 1972 to 2010. ► Physical, socioeconomic, and neighborhood factors together affected urban expansion. ► The effects of physical and neighborhood factors declined and that of socioeconomic factors increased. ► The relative importance of these driving factors changed with time.
The urban heat island describes the phenomenon that air/surface temperatures are higher in urban areas compared to their surrounding rural areas. Numerous studies have shown that increased percent ...cover of greenspace (PLAND) can significantly decrease land surface temperatures (LST). Fewer studies, however, have investigated the effects of configuration of greenspace on LST. This paper aims to fill this gap using Beijing, China as a case study. PLAND along with six configuration metrics were used to measure the composition and configuration of greenspace. The metrics were calculated based on a greenspace map derived from SPOT imagery, and LST data were retrieved from Landsat TM thermal band. Ordinary least squares regression and spatial autoregression were employed to investigate the relationship between LST and spatial pattern of greenspace using the census tract as the analytical unit. The results showed that PLAND was the most important predictor of LST. A 10 % increase in PLAND resulted in approximately a 0.86 °C decrease in LST. Configuration of greenspace also significantly affected LST. Given a fixed amount of greenspace, LST increased significantly with increased patch density. In addition, the variance of LST was largely explained by both composition and configuration of greenspace. The unique variation explained by the composition was relatively small, and was close to that of the configuration. Results from this study can expand our understanding of the relationship between LST and vegetation, and provide insights for improving urban greenspace planning and management.
High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban ...domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.
•Daily geographically weighted regression models were developed to interpolate Ta.•Gapless MODIS daily LST improves spatiotemporal details of the interpolated Ta.•A 1 km daily Ta data (2003–2016) was created in urban and surrounding areas in U.S.•The method can be extended globally to create unique Ta data in urban studies.
We quantitatively describe the impacts of urbanization on the accumulation of polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) in urban soils as well as their health risks to residents. ...Residential building age, population density, road density, and distance from urban center were used as urbanization level indicators. Significant correlations were found between those urbanization indicators and the amounts of PAHs, Cu, Cd, Pb, Zn and As in residential soils. The exposure time of soils to urban air was the primary factor affecting soil pollution, followed by local road density and population density. Factor analysis suggested that 59.0% of the elevated pollutant concentrations were caused by citywide uniform deposition, and 15.3% were resulted from short-range deposition and/or non-combustion processes. The combined health risks posed by soil PAHs and HMs were aggravated with time and can be expressed as functions of residence age, road density, and other urbanization indicators.
Display omitted
•The soil PAH and HM contents were closely related to urbanization progression.•The PAH and HM contents were primarily affected by soil exposure time.•Local input loads of pollutants correlated with road density and population density.•The combined risks of PAHs and HMs increased with the urban development level.•The carcinogenic risks of PAHs and As were above 10−5 and increased over time.
The health risks of PAHs and HMs in residential soils were connected to building age, population density and road density of the community as well as its distance from urban center.
PDF
