The Experts below are selected from a list of 1767 Experts worldwide ranked by ideXlab platform
C S B Grimmond - One of the best experts on this subject based on the ideXlab platform.
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Anthropogenic Heat flux advisable spatial resolutions when input data are scarce
Theoretical and Applied Climatology, 2019Co-Authors: Andrew Gabey, C S B Grimmond, I CapeltimmsAbstract:Anthropogenic Heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and “hot spots” representing 30–40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total QF, but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns.
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evaluation of the surface urban energy and water balance scheme suews at a dense urban site in shanghai sensitivity to Anthropogenic Heat and irrigation
Journal of Hydrometeorology, 2018Co-Authors: Xiangyu Ao, C S B Grimmond, H C Ward, Andrew Gabey, Xiuqun Yang, Ning ZhangAbstract:AbstractThe Surface Urban Energy and Water Balance Scheme (SUEWS) is used to investigate the impact of Anthropogenic Heat flux QF and irrigation on surface energy balance partitioning in a central ...
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Anthropogenic Heat flux estimation from space: results of the first phase of the URBANFLUXES project
Remote Sensing Technologies and Applications in Urban Environments, 2016Co-Authors: Nektarios Chrysoulakis, Mattia Marconcini, Jean-philippe Gastellu-etchegorry, Christian Feigenwinter, Fredrik Lindberg, Fabio Del Frate, Judith Klostermann, C S B Grimmond, Zina Mitraka, Thomas EschAbstract:H2020-Space project URBANFLUXES (URBan ANthrpogenic Heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve Anthropogenic Heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban Heat island and consequently on energy consumption in cities. This will lead to the development of tools and strategies to mitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the Anthropogenic Heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in Heat storage and the turbulent sensible and latent Heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the Anthropogenic Heat flux estimation from the UEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisit times and increase the value of EO data for scientific work and future emerging applications. These observations can reveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budget fluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity for space-borne observations to enable the development of operational services in the field of urban environmental monitoring and energy efficiency in cities.
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investigation of the impact of Anthropogenic Heat flux within an urban land surface model and pilps urban
Theoretical and Applied Climatology, 2016Co-Authors: M J Best, C S B GrimmondAbstract:Results from the first international urban model comparison experiment (PILPS-Urban) suggested that models which neglected the Anthropogenic Heat flux within the surface energy balance performed at least as well as models that include the source term, but this could not be explained. The analyses undertaken show that the results from PILPS-Urban were masked by the signal from including vegetation, which was identified in PILPS-Urban as being important. Including the Anthropogenic Heat flux does give improved performance, but the benefit is small for the site studied given the relatively small magnitude of this flux relative to other terms in the surface energy balance. However, there is no further benefit from including temporal variations in the flux at this site. The importance is expected to increase at sites with a larger Anthropogenic Heat flux and greater temporal variations.
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impact of city changes and weather on Anthropogenic Heat flux in europe 1995 2015
urban climate, 2013Co-Authors: Fredrik Lindberg, C S B Grimmond, N Yogeswaran, Simone Kotthaus, L AllenAbstract:How people live, work, move from place to place, consume and the technologies they use all affect Heat emissions in a city which influences urban weather and climate. Here we document changes to a global Anthropogenic Heat flux (QF) model to enhance its spatial (30′′ × 30′′ to 0.5° × 0.5°) resolution and temporal coverage (historical, current and future). QF is estimated across Europe (1995–2015), considering changes in temperature, population and energy use. While on average QF is small (of the order 1.9–4.6 W m−2 across all the urban areas of Europe), significant spatial variability is documented (maximum 185 W m−2). Changes in energy consumption due to changes in climate are predicted to cause a 13% (11%) increase in QF on summer (winter) weekdays. The largest impact results from changes in temperature conditions which influences building energy use; for winter, with the coldest February on record, the mean flux for urban areas of Europe is 4.56 W m−2 and for summer (warmest July on record) is 2.23 W m−2. Detailed results from London highlight the spatial resolution used to model the QF is critical and must be appropriate for the application at hand, whether scientific understanding or decision making.
Ning Zhang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the surface urban energy and water balance scheme suews at a dense urban site in shanghai sensitivity to Anthropogenic Heat and irrigation
Journal of Hydrometeorology, 2018Co-Authors: Xiangyu Ao, C S B Grimmond, H C Ward, Andrew Gabey, Xiuqun Yang, Ning ZhangAbstract:AbstractThe Surface Urban Energy and Water Balance Scheme (SUEWS) is used to investigate the impact of Anthropogenic Heat flux QF and irrigation on surface energy balance partitioning in a central ...
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numerical simulations on influence of urban land cover expansion and Anthropogenic Heat release on urban meteorological environment in pearl river delta
Theoretical and Applied Climatology, 2016Co-Authors: Ning Zhang, Yan Chen, Xuemei Wang, Xueyuan WangAbstract:Urbanization is an extreme way in which human being changes the land use/land cover of the earth surface, and Anthropogenic Heat release occurs at the same time. In this paper, the Anthropogenic Heat release parameterization scheme in the Weather Research and Forecasting model is modified to consider the spatial heterogeneity of the release; and the impacts of land use change and Anthropogenic Heat release on urban boundary layer structure in the Pearl River Delta, China, are studied with a series of numerical experiments. The results show that the Anthropogenic Heat release contributes nearly 75 % to the urban Heat island intensity in our studied period. The impact of Anthropogenic Heat release on near-surface specific humidity is very weak, but that on relative humidity is apparent due to the near-surface air temperature change. The near-surface wind speed decreases after the local land use is changed to urban type due to the increased land surface roughness, but the Anthropogenic Heat release leads to increases of the low-level wind speed and decreases above in the urban boundary layer because the Anthropogenic Heat release reduces the boundary layer stability and enhances the vertical mixing.
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numerical simulation of the Anthropogenic Heat effect on urban boundary layer structure
Theoretical and Applied Climatology, 2009Co-Authors: Yongsheng Chen, Weimei Jiang, Ning Zhang, X F He, R W ZhouAbstract:In this paper, several methods of incorporating Anthropogenic Heat release into the boundary layer are compared. The best scheme was one that included Anthropogenic Heat release in both the surface energy balance equation and the thermodynamic equations. In addition, it included diurnal variations and a distribution of Heat based on building concentrations. We further investigated the influence of Anthropogenic Heat release on urban boundary layer structure and the urban Heat island, and found that the contribution of Anthropogenic Heat release to the urban Heat island is greatest in the evening and at night, and least at noon. The daily average contribution ratio of Anthropogenic Heat to urban Heat island intensity in the winter is 54.5%, compared with just 43.6% in the summer. Anthropogenic Heat strengthens the vertical movement of urban surface air flow, changing the urban Heat island circulation. It also makes the urban boundary layer more turbulent and unstable, especially in the morning and evening. The degree of influence of Anthropogenic Heat release on local boundary layer structure depends on its importance to the surface energy budget.
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Numerical Study of Anthropogenic Heat Impact on Boundary Layer Characteristics
2008 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008Co-Authors: Yan Chen, Weimei Jiang, Ning ZhangAbstract:Several Anthropogenic Heat release models are tested in a regional atmospheric boundary layer model, the results demostrate that the diurnal variations of antrhropogenic Heat release must been taken account in the model. The contribution of Anthropogenic Heat on local urban micro-climate and boundary layer structure is also analyzed based on the numerical simulations over Hangzhou city, Zhejiang province, China. The results show that: the contribution of Anthropogenic Heat to urban Heat island intensity is greatest in the evening and at night and least in midday time, the daily averaged is 43.6%. The Anthropogenic Heat release leads to a reduce of wind speed at low atmospheric layer and may strengthen urban Heat island circulations. It also strenghten turblence in urban boundary layer, cause a mor unstable atmospheric boundary layer, especially in the morning and evening.
Yasushi Yamaguchi - One of the best experts on this subject based on the ideXlab platform.
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analysis of urban Heat island effect using aster and etm data separation of Anthropogenic Heat discharge and natural Heat radiation from sensible Heat flux
Remote Sensing of Environment, 2005Co-Authors: Soushi Kato, Yasushi YamaguchiAbstract:The urban Heat-island effect occurs as a result of increased sensible Heat flux from the land surface to the atmosphere near cities. Sensible Heat flux consists of two components: exhausted Anthropogenic Heat, and Heat radiation due to solar input. The latter may be enhanced by changes in the usage of artificial land surface. The authors have developed a new method to separate the Anthropogenically discharged Heat and natural Heat radiation from the sensible Heat flux, based on a Heat-balance model using satellite remote sensing and ground meteorological data. This method was applied to ASTER and ETM+ data for the daytime during spring, summer and winter and for the nighttime during autumn in Nagoya, Japan. The increased sensible Heat flux was approximately 100 W/m2 in the central part of the city during the summer. Sensible Heat flux at night during autumn was approximately 0 W/m2, except in urban areas and over bodies of water. During the winter, Anthropogenic Heat accounted for almost all of the sensible Heat flux in urban areas. The contribution of Anthropogenic Heat to sensible Heat flux in spring was lower than the contributions in summer and winter. The Anthropogenic Heat flux was high in industrial areas throughout the year. These results are consistent with the fact that Anthropogenic energy consumption is high in summer and winter and low in spring and autumn.
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Analysis of urban Heat-island effect using ASTER and ETM+ Data: Separation of Anthropogenic Heat discharge and natural Heat radiation from sensible Heat flux
Remote Sensing of Environment, 2005Co-Authors: Soushi Kato, Yasushi YamaguchiAbstract:The urban Heat-island effect occurs as a result of increased sensible Heat flux from the land surface to the atmosphere near cities. Sensible Heat flux consists of two components: exhausted Anthropogenic Heat, and Heat radiation due to solar input. The latter may be enhanced by changes in the usage of artificial land surface. The authors have developed a new method to separate the Anthropogenically discharged Heat and natural Heat radiation from the sensible Heat flux, based on a Heat-balance model using satellite remote sensing and ground meteorological data. This method was applied to ASTER and ETM+ data for the daytime during spring, summer and winter and for the nighttime during autumn in Nagoya, Japan. The increased sensible Heat flux was approximately 100 W/m2 in the central part of the city during the summer. Sensible Heat flux at night during autumn was approximately 0 W/m2, except in urban areas and over bodies of water. During the winter, Anthropogenic Heat accounted for almost all of the sensible Heat flux in urban areas. The contribution of Anthropogenic Heat to sensible Heat flux in spring was lower than the contributions in summer and winter. The Anthropogenic Heat flux was high in industrial areas throughout the year. These results are consistent with the fact that Anthropogenic energy consumption is high in summer and winter and low in spring and autumn. © 2005 Elsevier Inc. All rights reserved.
Deyong Hu - One of the best experts on this subject based on the ideXlab platform.
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mapping china s time series Anthropogenic Heat flux with inventory method and multi source remotely sensed data
Science of The Total Environment, 2020Co-Authors: Shasha Wang, Shanshan Chen, Deyong Hu, Chen Yu, Yufei DiAbstract:Abstract Mapping time-series Anthropogenic Heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of Anthropogenic Heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high Anthropogenic Heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.
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A Partition Modeling for Anthropogenic Heat Flux Mapping in China
Remote Sensing, 2019Co-Authors: Shasha Wang, Shanshan Chen, Deyong Hu, Chen YuAbstract:Anthropogenic Heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating Anthropogenic Heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded Anthropogenic Heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.
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characterizing spatiotemporal dynamics of Anthropogenic Heat fluxes a 20 year case study in beijing tianjin hebei region in china
Environmental Pollution, 2019Co-Authors: Shanshan Chen, Deyong Hu, Man Sing Wong, Chen Yu, Hung Chak HoAbstract:Abstract Rapid urbanization, which is closely related to economic growth, human health, and micro-climate, has resulted in a considerable amount of Anthropogenic Heat emissions. The lack of estimation data on long-term Anthropogenic Heat emissions is a great concern in climate and urban flux research. This study estimated the annual average Anthropogenic Heat fluxes (AHFs) in Beijing–Tianjin–Hebei region in China between 1995 and 2015 on the basis of multisource remote sensing images and ancillary data. Anthropogenic Heat emissions from different sources (e.g., industries, buildings, transportation, and human metabolism) were also estimated to analyze the composition of AHFs. The spatiotemporal dynamics of long-term AHFs with high spatial resolution (500 m) were estimated by using a refined AHF model and then analyzed using trend and standard deviation ellipse analyses. Results showed that values in the region increased significantly from 0.15 W· m−2 in 1995 to 1.46 W· m−2 in 2015. Heat emissions from industries, transportation, buildings, and human metabolism accounted for 64.1%, 17.0%, 15.5%, and 3.4% of the total Anthropogenic Heat emissions, respectively. Industrial energy consumption was the dominant contributor to the Anthropogenic Heat emissions in the region. During this period, industrial Heat emissions presented an unstable variation but showed a growing trend overall. Heat emissions from buildings increased steadily. Spatial distribution was extended with an increasing tendency of the difference between the maximum and the minimum and was generally dominated by the northeast–southwest directional pattern. The spatiotemporal distribution patterns and trends of AHFs could provide vital support on management decision in city planning and environmental monitoring.
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parameterizing Anthropogenic Heat flux with an energy consumption inventory and multi source remote sensing data
Remote Sensing, 2017Co-Authors: Shanshan Chen, Deyong HuAbstract:Anthropogenic Heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined Anthropogenic Heat flux (RAHF) parameterization scheme to estimate the gridded Anthropogenic Heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The Heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing–Tianjin–Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 1018 J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing–Tianjin–Hebei region is about 6.07 W·m−2, and the AHF in urban areas is about in the range of 20 W·m−2 and 130 W·m−2. The maximum AHF value is approximately 130.84 W·m−2, mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing–Tianjin–Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation.
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estimation of urban energy Heat flux and Anthropogenic Heat discharge using aster image and meteorological data case study in beijing metropolitan area
Journal of Applied Remote Sensing, 2012Co-Authors: Deyong Hu, Limin Yang, Ji Zhou, Lei DengAbstract:In order to analyze the mechanism of the urban Heat island, it is paramount and meaningful to estimate the Anthropogenic Heat flux in cities. A case study was carried out to study the energy balance process in Beijing, China, based on a canopy energy balance equation and to estimate the urban energy fluxes and Anthropogenic Heat discharge and their seasonal and spatial variations. Two ASTER images and meteorological observation data from the winter and summer seasons were used for the study. The results showed that: (1) in Beijing, the Anthropogenic Heat discharge flux reached a maximum of 163.76 W m −2 in winter and 288.26 W m −2 in summer. Spatially, the magnitude of the flux was significantly affected by urban land cover types. In winter, the highest value occurred at an urban commercial district with an average value of 47.60 W m −2 . In summer, the highest value occurred at the airport and the industrial areas with the regional average reaching 47.29 W m −2 ; the spatial pattern of the Heat discharge appears to be clustered, with some, localized high-accumulation centers such as in industrial areas and commercial districts. (2) The Anthropogenic discharge was one of the important contributors to the surface-atmosphere energy exchange in cities. The Heat discharge had a positive effect on elevating the surface temperature and formation of the urban Heat island, especially in the summer. The study confirms the importance to account for the impact of the Anthropogenic Heat flux on urban energy budget.
Shanshan Chen - One of the best experts on this subject based on the ideXlab platform.
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mapping china s time series Anthropogenic Heat flux with inventory method and multi source remotely sensed data
Science of The Total Environment, 2020Co-Authors: Shasha Wang, Shanshan Chen, Deyong Hu, Chen Yu, Yufei DiAbstract:Abstract Mapping time-series Anthropogenic Heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of Anthropogenic Heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high Anthropogenic Heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.
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A Partition Modeling for Anthropogenic Heat Flux Mapping in China
Remote Sensing, 2019Co-Authors: Shasha Wang, Shanshan Chen, Deyong Hu, Chen YuAbstract:Anthropogenic Heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating Anthropogenic Heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded Anthropogenic Heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.
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characterizing spatiotemporal dynamics of Anthropogenic Heat fluxes a 20 year case study in beijing tianjin hebei region in china
Environmental Pollution, 2019Co-Authors: Shanshan Chen, Deyong Hu, Man Sing Wong, Chen Yu, Hung Chak HoAbstract:Abstract Rapid urbanization, which is closely related to economic growth, human health, and micro-climate, has resulted in a considerable amount of Anthropogenic Heat emissions. The lack of estimation data on long-term Anthropogenic Heat emissions is a great concern in climate and urban flux research. This study estimated the annual average Anthropogenic Heat fluxes (AHFs) in Beijing–Tianjin–Hebei region in China between 1995 and 2015 on the basis of multisource remote sensing images and ancillary data. Anthropogenic Heat emissions from different sources (e.g., industries, buildings, transportation, and human metabolism) were also estimated to analyze the composition of AHFs. The spatiotemporal dynamics of long-term AHFs with high spatial resolution (500 m) were estimated by using a refined AHF model and then analyzed using trend and standard deviation ellipse analyses. Results showed that values in the region increased significantly from 0.15 W· m−2 in 1995 to 1.46 W· m−2 in 2015. Heat emissions from industries, transportation, buildings, and human metabolism accounted for 64.1%, 17.0%, 15.5%, and 3.4% of the total Anthropogenic Heat emissions, respectively. Industrial energy consumption was the dominant contributor to the Anthropogenic Heat emissions in the region. During this period, industrial Heat emissions presented an unstable variation but showed a growing trend overall. Heat emissions from buildings increased steadily. Spatial distribution was extended with an increasing tendency of the difference between the maximum and the minimum and was generally dominated by the northeast–southwest directional pattern. The spatiotemporal distribution patterns and trends of AHFs could provide vital support on management decision in city planning and environmental monitoring.
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parameterizing Anthropogenic Heat flux with an energy consumption inventory and multi source remote sensing data
Remote Sensing, 2017Co-Authors: Shanshan Chen, Deyong HuAbstract:Anthropogenic Heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined Anthropogenic Heat flux (RAHF) parameterization scheme to estimate the gridded Anthropogenic Heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The Heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing–Tianjin–Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 1018 J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing–Tianjin–Hebei region is about 6.07 W·m−2, and the AHF in urban areas is about in the range of 20 W·m−2 and 130 W·m−2. The maximum AHF value is approximately 130.84 W·m−2, mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing–Tianjin–Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation.
