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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (4) : 44    https://doi.org/10.1007/s11783-021-1478-3
RESEARCH ARTICLE
Impact of anthropogenic heat emissions on meteorological parameters and air quality in Beijing using a high-resolution model simulation
Hengrui Tao1,2,3, Jia Xing2,3(), Gaofeng Pan1, Jonathan Pleim4, Limei Ran5, Shuxiao Wang2,3, Xing Chang2,3, Guojing Li6, Fei Chen7, Junhua Li2,3()
1. Aviation University of Air Force, Changchun 130022, China
2. School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
3. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
4. Research Triangle Park, Environmental Protection Agency, NC 27711, USA
5. East Remote Sensing Laboratory, United States Department of Agriculture, NC 20250, USA
6. Unit No. 96941 of Chinese People’s Liberation Army, Beijing 100041, China
7. Unit No. 31010 of Chinese People’s Liberation Army, Beijing 100081, China
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Abstract

• The Large scale Urban Consumption of energY model was updated and coupled with WRF.

• Anthropogenic heat emissions altered the precipitation and its spatial distribution.

• A reasonable AHE scheme could improve the performance of simulated PM2.5.

• AHE aggravated the O3 pollution in urban areas.

Anthropogenic heat emissions (AHE) play an important role in modulating the atmospheric thermodynamic and kinetic properties within the urban planetary boundary layer, particularly in densely populated megacities like Beijing. In this study, we estimate the AHE by using a Large-scale Urban Consumption of energY (LUCY) model and further couple LUCY with a high-resolution regional chemical transport model to evaluate the impact of AHE on atmospheric environment in Beijing. In areas with high AHE, the 2-m temperature (T2) increased to varying degrees and showed distinct diurnal and seasonal variations with maxima in night and winter. The increase in 10-m wind speed (WS10) and planetary boundary layer height (PBLH) exhibited slight diurnal variations but showed significant seasonal variations. Further, the systematic continuous precipitation increased by 2.1 mm due to the increase in PBLH and water vapor in upper air. In contrast, the precipitation in local thermal convective showers increased little because of the limited water vapor. Meanwhile, the PM2.5 reduced in areas with high AHE because of the increase in WS10 and PBLH and continued to reduce as the pollution levels increased. In contrast, in areas where prevailing wind direction was opposite to that of thermal circulation caused by AHE, the WS10 reduced, leading to increased PM2.5. The changes of PM2.5 illustrated that a reasonable AHE scheme might be an effective means to improve the performance of PM2.5 simulation. Besides, high AHE aggravated the O3 pollution in urban areas due to the reduction in NOx.
Keywords Anthropogenic heat emissions      LUCY      High-resolution      Meteorological parameters      Air quality     
Corresponding Author(s): Jia Xing,Junhua Li   
Issue Date: 03 August 2021
 Cite this article:   
Hengrui Tao,Jia Xing,Gaofeng Pan, et al. Impact of anthropogenic heat emissions on meteorological parameters and air quality in Beijing using a high-resolution model simulation[J]. Front. Environ. Sci. Eng., 2022, 16(4): 44.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1478-3
https://academic.hep.com.cn/fese/EN/Y2022/V16/I4/44
Name Grid resolution Emission AHE
noAHE_1km 1 km × 1 km 1 km No
AHE_1km 1 km × 1 km 1 km LUCY
noAHE_3km 3 km × 3 km 3 km No
AHE_3km 3 km × 3 km 3 km LUCY
Tab.1  Experimental design for WRF-CMAQ simulations
Fig.1  Spatial distributions of monthly average AHE, ?T2, ?WS10, ?PBLH, and ?Q2 (The delta (?) represents the difference between AHE_1km and noAHE_1km. The research areas are in Beijing and its surroundings, the same as below).
Fig.2  Diurnal time-series of AHE, ?T2, ?WS10, ?PBLH in Beijing-urban.
Fig.3  Spatial distribution of AHE, ?QV, ?PBLH and ?RAIN at a monthly scale and different types of precipitation.
Level Beijing-urban Beijing-suburban
AH WS10 PBLH PM2.5 AH WS10 PBLH PM2.5
VI a 13.0 0.03 65.8 −20.2 2 0.016 12.6 −3
b 0 1.45 196 184.4 0 1.25 152.3 125.4
c 2.2% 33.6% −11.1% 1.3% 8.3% −2.4%
V a 12.8 0.02 40.1 −14.9 1.92 0.016 15.8 −4.3
b 0 1.4 220.5 153.4 0 1.37 171.9 101.1
c 1.7% 18.2% −9.7% 1.1% 9.2% −4.3%
IV a 12.9 0.11 27.96 −5.2 1.93 0.02 10.2 −1
b 0 1.98 228.5 42.2 0 2 240.5 20.5
c 5.7% 12.2% −12.3% 1.1% 4.2% −4.9%
III a 12.7 0.16 43.7 −3.4 1.96 0.02 12.1 −0.35
b 0 2.1 285.6 27.3 0 3.7 442 11.4
c 7.5% 15.3% −12.5% 0.6% 2.74% −3%
II a 12.9 0.23 113 −1.2 1.91 0.02 6.6 0.19
b 0 3.7 985 11.1 0 6 1083 8.6
c 6.2% 11.5% −10.5% 0.3% 0.6% 2.2%
I a 12.9 0.16 118 −0.16 1.93 0.02 1.7 0.14
b 0 4.2 1260 12.8 0 6.65 1421 6.1
c 3.9% 9.4% −1.3% 0.3% 0.1% 2.3%
Tab.2  The differences of simulated AH, T2, WS10, PBLH, PM2.5 between AHE_1km and noAHE_1km (denotes as variable “a”), simulated value in noAHE_1km (denotes as variable “b”) and the ratio of the above two (denotes as variable “c”) under different PM2.5 pollution levels in December
Fig.4  Spatial distribution of AHE, ?T2, ?WS10, ?PBLH, PM2.5, and ?PM2.5 during a heavy pollution period at 02:00, 08:00, 14:00, 20:00.
Fig.5  The diurnal variation time-series of ?WS10 and ?PM2.5 in the Beijing-exurb during heavy pollution period.
Fig.6  Spatial distribution of O3, ?O3, ?NO2, ?NO, and ?NOx at a monthly scale and across different pollution levels.
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