Estimation of anthropogenic heat emissions in urban Taiwan and their spatial patterns (original) (raw)
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Theoretical and Applied Climatology, 2020
This paper is about spatial-temporal variability of atmospheric temperature across Taiwan, an island with diverse local emission sources partly because of its Asian cultural characteristics. To develop a new land use regression (LUR) model for this study, we used the temperature data collected by the Taiwan Central Weather Bureau from 2000 and 2016, while using the data from 2017 as the external data verification to assess model reliability. Because incense and joss money burning is a cultural-specific emission source in Asia, we further included location of temples, cemeteries, and crematoriums as potential predictors. The overall model performance and tenfold cross-validated are R 2 of 0.88 and R 2 of 0.87, respectively, which presents a high level of prediction performance. Moreover, we used our LUR model to estimate urban heat islands intensity (UHII) for six metropolises in Taiwan and found Taichung City has the highest UHII value (4.60°C) among them. These results provide important insights in expanding the remote sensing application on spatial-temporal variation of atmospheric temperature and its further application on UHI effects.
Anthropogenic heat flux estimation over Hong Kong : a multi modelling approach
2015
The main objective of this study is to measure anthropogenic heat flux using satellite remote sensing, and evaluate the results using GIS-based modelling. In cities, anthropogenic heat emissions are sourced mainly for buildings, vehicles and human metabolic activities. The emissions vary significantly over time and space, and are not easily measured, so detailed models of anthropogenic heat emissions are not available for most cities. Previous studies have mainly used three approaches: inventory, energy balance enclosure, and building energy models. The present study is the first to model anthropogenic heat using three different approaches. Thermal infrared Images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Level 1B (ASTER, L1B) satellite are used to estimated anthropogenic heat emissions using the energy balance approach. A new technique is developed to calculate the emissivity with a higher spatial resolution. This study measures urban morphometric parameters using the Geographic Information Systems (GIS) at a much finer scale, compared to previous studies, which only calculate approximate morphometric parameters of the city. Anthropogenic heat emissions are estimated from six different ASTER images of Hong Kong captured between 2007 and 2009. These images represent diurnal (day and night) periods and seasons (summer and winter). In this study, a global anthropogenic heat model called the Large-scale Urban Consumption of energY (LUCY) is adopted for application to Hong Kong, with the spatial resolution increased to 100 m from 2.5-arc minutes (~5-km spatial resolution). The anthropogenic heat emissions in LUCY greatly depend on population density data. Therefore, a new population scheme is introduced, in which daytime population data are included so as to consider the migration of population within grid cells at any given time. Other input parameters like energy consumption, air temperature, traffic II information, and temperature schemes are also modified to match Hong Kong conditions. A GIS-based model using a bottom-up approach is utilized to account for anthropogenic heat emitted from each building block and every road in Hong Kong. Since these emission sources are captured at a finer resolution in the GIS-based model, the model data are used as the reference for evaluating the remote sensing and LUCY models. It may be noted that the model estimations are limited to the times of satellite image acquisition (~ 11am for the day and 11pm for the night) on the given date. In addition, the anthropogenic heat values estimated using the three different models is limited to 100 m spatial resolution. Anthropogenic heat emissions were found to be higher at day compared to nighttime emissions. Likewise, summer emissions were higher than winter ones. In all models, the highest emissions were observed in grid cells with tall commercial, business and residential buildings. Although the intensity levels differed across models, the spatial distributions remained essentially the same. Finally, a significant correlation between anthropogenic heat and UHI was noted when the results of the models were correlated with urban-morphometric parameters and ASTER surface temperature measurements. The anthropogenic heat values estimated using remote sensing agree well with those derived from the GIS-based model. However, there are certain differences in terms of advantages and disadvantages. Overall, the results from all the three models are usable as inputs to climate models. This should be useful to urban planners and other agencies in studies related to UHI.
The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (Rn), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high–density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning. Zhang, Y., Balzter, H. and Wu, X. (2013): Spatial-temporal patterns of urban anthropogenic heat discharge in Fuzhou, China observed from sensible heat flux using Landsat TM/ETM+ data, International Journal of Remote Sensing, 34, 1459–1477, http://hdl.handle.net/2381/27723
Global anthropogenic heat flux database with high spatial resolution
A global database of anthropogenic heat emission (AHE) with high spatial resolution was constructed using a top-down approach. Annual average AHE was estimated from four heating components, based on different sectors of energy consumption. A population-adjustment using nighttime light was created to improve estimating AHE spatial variability in urban areas. A sensitivity function of AHE relative to temperature was derived to provide a way to evaluate AHE monthly variability globally. a b s t r a c t This study developed a top-down method for estimating global anthropogenic heat emission (AHE), with a high spatial resolution of 30 arc-seconds and temporal resolution of 1 h. Annual average AHE was derived from human metabolic heating and primary energy consumption, which was further divided into three components based on consumer sector. The first and second components were heat loss and heat emissions from industrial sectors equally distributed throughout the country and populated areas, respectively. The third component comprised the sum of emissions from commercial, residential, and transportation sectors (CRT). Bulk AHE from the CRT was proportionally distributed using a global population dataset, with a radiance-calibrated nighttime lights adjustment. An empirical function to estimate monthly fluctuations of AHE based on gridded monthly temperatures was derived from various Japanese and American city measurements. Finally, an AHE database with a global coverage was constructed for the year 2013. Comparisons between our proposed AHE and other existing datasets revealed that the problem of overestimation of AHE intensity in previous top-down models was mitigated by the separation of energy consumption sectors; furthermore, the problem of AHE underestimation at central urban areas was solved by the nighttime lights adjustment. A strong agreement in the monthly profiles of AHE between our database and other bottom-up datasets further proved the validity of the current methodology. Investigations of AHE for the 29 largest urban agglomerations globally highlighted that the share of heat emissions from CRT sectors to the total AHE at the city level was 40e95%; whereas that of metabolic heating varied with the city's level of development by a range of 2e60%. A negative correlation between gross domestic product (GDP) and the share of metabolic heating to a city's total AHE was found. Globally, peak AHE values were found to occur between December and February, while the lowest values were found around June to August. The northern mid-latitudes contributed most to the global AHE.
Assessment with satellite data of the urban heat island effects in Asian mega cities
International Journal of Applied Earth Observation and Geoinformation, 2006
This study focuses on using remote sensing for comparative assessment of surface urban heat island (UHI) in 18 mega cities in both temperate and tropical climate regions. Least-clouded day-and night-scenes of TERRA/MODIS acquired between 2001 and 2003 were selected to generate land-surface temperature (LST) maps. Spatial patterns of UHIs for each city were examined over its diurnal cycle and seasonal variations. A Gaussian approximation was applied in order to quantify spatial extents and magnitude of individual UHIs for inter-city comparison. To reveal relationship of UHIs with surface properties, UHI patterns were analyzed in association with urban vegetation covers and surface energy fluxes derived from high-resolution Landsat ETM+ data. This study provides a generalized picture on the UHI phenomena in the Asian region and the findings can be used to guide further study integrating satellite high-resolution thermal data with land-surface modeling and meso-scale climatic modeling in order to understand impacts of urbanization on local climate in Asia. #
Analysis of the Urban Heat Island Effect in Shijiazhuang, China Using Satellite and Airborne Data
Remote Sensing, 2015
The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect
A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes
Atmospheric Environment, 2014
h i g h l i g h t s Urban anthropogenic heat (Q F) was estimated over different spatial-temporal scales. We utilised a novel multi-method approach (inventory and BEM) to estimate Q F. Our approach shows improved Q F sensitivity to weather vs. previous methods. Strong regional variations in Q F exist, especially notable over space and time.
Atmospheric Chemistry and Physics, 2021
Information on the spatiotemporal characteristics of Beijing's urban-rural near-surface air temperature difference, known as the canopy layer urban heat island (UHI), is important for future urban climate management strategies. This paper investigates the variation of near-surface air temperatures within Beijing at a neighbourhood-scale resolution (∼ 100 m) during winter 2016 and summer 2017. We perform simulations using the urban climate component of the ADMS-Urban model with land surface parameters derived from both local climate zone classifications and OpenStreetMap land use information. Through sensitivity simulations, the relative impacts of surface properties and anthropogenic heat emissions on the temporal variation of Beijing's UHI are quantified. Measured UHI intensities between central Beijing (Institute of Atmospheric Physics) and a rural site (Pinggu) during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) campaigns, peak during the evening at ∼ 4.5 • C in both seasons. In winter, the nocturnal UHI is dominated by anthropogenic heat emissions but is underestimated by the model. Higher-resolution anthropogenic heat emissions may capture the effects of local sources (e.g. residential buildings and adjacent major roads). In summer, evening UHI intensities are underestimated, especially during heatwaves. The inability to fully replicate the prolonged release of heat stored in the urban fabric may explain this. Observed negative daytime UHI intensities in summer are more successfully captured when surface moisture levels in central Beijing are increased. However, the spatial correlation between simulated air temperatures and satellite-derived land surface temperatures is stronger with a lower urban moisture scenario. This result suggests that near-surface air temperatures at the urban meteorological site are likely influenced by fine-scale green spaces that are unresolved by the available land cover data and demonstrates the expected differences between surface and air temperatures related to canopy layer advection. This study lays the foundations for future studies of heat-related health risks and UHI mitigation strategies across Beijing and other megacities.
Urban heat island (UHI) is one of the most distinctive characteristics of urban climate. The objective of the presented study was to apply a statistical modelling 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 was to limit input data for the developed modelling method in order to assure transferability of the methodology to different cities. Time series of surface temperature and NDVI were obtained from the MODIS instrument for a 10 year period (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). The air temperature was collected from the in-situ observational network of 21 stations. The study was 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 was described by commonly available land cover metrics. Results showed that the proposed approach provided satisfactory AUHI modelling results for the locations with the least degree of urbanization. The best results were obtained with a simple linear regression model with the iterative procedure to minimize the mean absolute gross error (MAGE). The lowest MAGE for modelled UHI was 1.18°C with 69% of the variance explained. The strongest linear relationship between simultaneous SUHI and AUHI was noted for those station pairs which their surroundings have the highest differences in urbanization, and the highest UHI intensities observed. The strength of the SUHI/AUHI linear relationship decreases gradually with the increasing urbanization of the stations' surroundings.