scholarly journals Anthropogenic Heat Flux Estimation Based on Luojia 1-01 New Nighttime Light Data: A Case Study of Jiangsu Province, China

2020 ◽  
Vol 12 (22) ◽  
pp. 3707
Author(s):  
Zhongli Lin ◽  
Hanqiu Xu
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Urban Climate ◽  
Power Estimation ◽  
Anthropogenic Heat ◽  
Economic Activities ◽  
Estimation Model ◽  
Nighttime Light Data ◽  
Anthropogenic Heat Flux ◽  
Nighttime Light

With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTLnor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R2 of 0.8444 along with the smallest RMSE of 4.8277 W·m−2 and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m−2. Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m−2, followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m−2, while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m−2. This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively.

scholarly journals Revising the definition of anthropogenic heat flux from buildings: role of human activities and building storage heat flux

2021 ◽  
Author(s):  
Yiqing Liu ◽  
Zhiwen Luo ◽  
Sue Grimmond
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Energy Balance ◽  
Human Activities ◽  
Building Energy ◽  
Heat Emission ◽  
Heat Fluxes ◽  
Anthropogenic Heat ◽  
Anthropogenic Heat Flux ◽  
The Difference

Abstract. Buildings are a major source of anthropogenic heat emissions, impacting energy use and human health in cities. The difference between building energy consumption and building anthropogenic heat emission magnitudes and time lag and are poorly quantified. Energy consumption (QEC) is a widely used proxy for the anthropogenic heat flux from buildings (QF,B). Here we revisit the latter’s definition. If QF,B is the heat emission to the outdoor environment from human activities within buildings, we can derive it from the changes in energy balance fluxes between occupied and unoccupied buildings. Our derivation shows the difference between QEC and QF,B is attributable to a change in the storage heat flux induced by human activities (∆So-uo) (i.e., QF,B = QEC − ∆So-uo). Using building energy simulations (EnergyPlus) we calculate the energy balance fluxes for a simplified isolated building (obtaining QF,B, QEC, ∆So-uo) with different occupancy states. The non-negligible differences in diurnal patterns between QF,B and QEC caused by thermal storage (e.g. hourly QF,B to QEC ratios vary between −2.72 and 5.13 within a year in Beijing, China). Negative QF,B can occur as human activities can reduce heat emission from building but are associated with a large storage heat flux. Building operations (e.g., open windows, use of HVAC system) modify the QF,B by affecting not only QEC but also the ∆So-uo diurnal profile. Air temperature and solar radiation are critical meteorological factors explaining day-to-day variability of QF,B. Our new approach could be used to provide data for future parameterisations of both anthropogenic heat flux and storage heat fluxes from buildings. It is evident that storage heat fluxes in cities may also be impacted by occupant behaviour.

scholarly journals A Partition Modeling for Anthropogenic Heat Flux Mapping in China

2019 ◽  
Vol 11 (9) ◽  
pp. 1132 ◽  
Author(s):  
Shasha Wang ◽  
Deyong Hu ◽  
Shanshan Chen ◽  
Chen Yu
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Spatial Distribution ◽  
High Resolution ◽  
Urban Areas ◽  
Thermal Environment ◽  
Modeling Method ◽  
Anthropogenic Heat ◽  
Regional Characteristics ◽  
Anthropogenic Heat Flux

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.

ANthropogenic heat FLUX estimation from Space

2017 ◽  
Author(s):  
Nektarios Chrysoulakis ◽  
Mattia Marconcini ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
C.S.B Grimmong ◽  
Christian Feigenwinter ◽  
...  
Keyword(s):  
Heat Flux ◽  
Anthropogenic Heat ◽  
Heat Flux Estimation ◽  
Anthropogenic Heat Flux ◽  
Flux Estimation

scholarly journals Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Climate ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 75 ◽  
Author(s):  
Ilias Agathangelidis ◽  
Constantinos Cartalis ◽  
Mat Santamouris
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Urban Heat Island ◽  
Urban Form ◽  
Heat Exposure ◽  
Geographical Information ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Anthropogenic Heat Flux ◽  
Urban Heat

Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top–down and bottom–up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.

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