Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data

Chen, Qian; Yang, Xuchao; Ouyang, Zutao; Zhao, Naizhuo; Jiang, Qinghong; Ye, Tingting; Jun, Qi; Yue, Wenze

doi:10.1016/j.envpol.2020.115183

Cited by 34 publications

(24 citation statements)

References 70 publications

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“…For instance, the use of air conditioning and motorized vehicles directly releases waste heat into the air and contributes to warming. Generally, higher urbanization ratios indicate higher anthropogenic heat releases (Q. Chen et al., 2020; Iamarino et al., 2012; Sailor, 2011). With increased urbanization ratios and populations, the contributions of anthropogenic heat to warming are also increasing (Oke, 2002).…”

Section: Discussionmentioning

confidence: 99%

Impacts of Continuously Increasing Urbanization Ratios on Warming Rates and Temperature Extremes Observed Over the Beijing Area

Jiang

Wei

2021

JGR Atmospheres

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Evaluations of the impacts of increasing urbanization ratios on air temperatures are less quantitative and accurate due to the lack of high‐resolution land cover data sets. Here, based on the latest released high‐resolution (30‐m) land cover data from 1985 to 2017, annual urbanization ratios around each station in Beijing were calculated. Warming rates and temperature extremes were compared between nonrural stations (17 stations with continuously increasing urbanization ratios) and purely rural stations (3 stations with continuous urbanization ratios of zero). Although urbanized areas only occupied 22.2% of the total area in Beijing, urbanization ratios at nonrural stations already reached an average of 62.4% (10 km2)−1 in 2017, and their average rate reached 15.1% (10 km2)−1 decade−1. From 1985 to 2017, each 10% increase in the urbanization ratio with 10 km2 had led to additional annual warming of 0.14 ± 0.11, 0.17 ± 0.08, and 0.30 ± 0.17°C at nonrural stations for daily maximum (Tmax), mean (Tmean), and minimum (Tmin) air temperatures, respectively. Meanwhile, each 10% increase in the urbanization ratio with 10 km2 had led to additional changes of 11.6 ± 5.1, 12.7 ± 5.8, and −11.1 ± 5.2 days at nonrural stations for hot days, hot nights, and chilly nights, respectively. Additionally, the inner‐annual urbanization contributions to the warming increased significantly at rates of 10.7% decade−1 and 14.1% decade−1 for Tmean and Tmin, and reached 49.0% and 52.6% in 2017, respectively. The inner‐annual urbanization contributions to the increasing hot nights and the decreasing chilly nights changed at rates of 16.7% decade−1 and 15.6% decade−1, and reached 57.8% and 53.1% in 2017, respectively.

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Section: Discussionmentioning

confidence: 99%

Impacts of Continuously Increasing Urbanization Ratios on Warming Rates and Temperature Extremes Observed Over the Beijing Area

Jiang

Wei

2021

JGR Atmospheres

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“…Thus, this study used Cubist regression models to generate gridded BECCE intensity estimates that were subsequently used for dasymetric disaggregation of prefectural-level BECCE data into gridded cells with 1 km 2 spatial resolution, following the same principles as used in the downscaling approach of PLS regression fitting. This process has been applied successfully to Cubist-based dasymetric anthropogenic heat emission mapping by Chen et al [28]. The Cubist models were implemented using the Cubist Package in the R environment.…”

Section: Building Cubist Regression Modelsmentioning

confidence: 99%

“…However, due to diverse demographic structures, socioeconomic patterns, and climatic conditions, the advanced artificial intelligence methods rather than linear regression models are more suitable for scientific estimation of BECCE, because they can explore complex nonlinear relations between BECCE and these variables, especially for large study areas. Recently, some machine learning algorithms such as Random Forest and Cubist models have been successfully applied to estimate population [26], electronic power consumption [27], and anthropogenic heat emission [28], which are relevant to BECCE estimation. Nevertheless, there are hardly any attempts at using those models with more BECCE-related ancillary data integrated to refine BECCE mapping.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

et al. 2021

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The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.

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“…They found that the contribution of anthropogenic heat source emissions to the urban heat island reached 29.6% [ 106 ]. Qian et al (2020) generated a gridded anthropogenic heat flux benchmark dataset with a spatial resolution of 1 km based on machine learning; they found that the anthropogenic heat emissions in the city center were 60–190 W/m 2 , and the largest value of anthropogenic heat emissions in the industrial zone was 415 W/m 2 [ 107 ]. These research results show that anthropogenic heat has become a major component of urban thermal environment research.…”

Section: Anthropogenic Heat Emissions and The Ustementioning

confidence: 99%

Simulation of the Urban Space Thermal Environment Based on Computational Fluid Dynamics: A Comprehensive Review

Huo

Chen

Geng

et al. 2021

Sensors

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Rapid urbanization has made urban space thermal environment (USTE) problems increasingly prominent. USTE research is important for improving urban ecological environment and building energy consumption. Most studies on USTE research progress have focused on meteorological observations and remote sensing methods, and few studies on USTE are based on computational fluid dynamics (CFD). During the past two decades, with the increasing applications of CFD in USTE research, comprehensively summarizing the phased results have become necessary tasks. This paper analyzes the current research status of CFD-based USTE simulation from six perspectives. First, we summarize the current research status of USTE simulation with CFD models that integrate ground observations and remote sensing technology. Second, we define and classify the spatial scope of CFD-based USTE simulations at different scales. Third, we systematically analyze the quantitative relationships among urban land type, the underlying surface structure, water bodies, green space and the corresponding changes in CFD-based USTE simulations. Fourth, we quantitatively analyze the impact of anthropogenic heat in CFD-based USTE simulations. Fifth, we summarize the corresponding USTE mitigation measures and methods based on the CFD simulation results. Finally, the outlooks and the existing problems in current research on CFD simulations of the USTE are analyzed.

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Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data

Cited by 34 publications

References 70 publications

Impacts of Continuously Increasing Urbanization Ratios on Warming Rates and Temperature Extremes Observed Over the Beijing Area

Impacts of Continuously Increasing Urbanization Ratios on Warming Rates and Temperature Extremes Observed Over the Beijing Area

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

Simulation of the Urban Space Thermal Environment Based on Computational Fluid Dynamics: A Comprehensive Review

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