Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (3): 324-336    DOI: 10.1007/s40333-025-0006-0     CSTR: 32276.14.JAL.02500060
Research article     
PM10 dust emission in the Erenhot-Huailai zone of northern China based on model simulation
WANG Yong1,2,3, YAN Ping2,3,*(), WU Wei4, WANG Yijiao2,3, HU Chanjuan1, LI Shuangquan1
1Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China
2Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
4College of Urban and Environmental Sciences, Hubei Normal University, Huangshi 435002, China
Download: HTML     PDF(567KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The Erenhot-Huailai zone, as an important dust emission source area in northern China, affects the air quality of Beijing City, Tianjin City, and Hebei Province and human activities in this zone have a profound impact on surface dust emission. In order to explore the main source areas of surface dust emission and quantify the impacts of human activities on surface dust emission, we investigated the surface dust emission of different land types on the Erenhot-Huailai zone by model simulation, field observation, and comparative analysis. The results showed that the average annual inhalable atmospheric particles (PM10) dust emission fluxes in arid grassland, Hunshandake Sandy Land, semi-arid grassland, semi-arid agro-pastoral area, dry sub-humid agro-pastoral area, and semi-humid agro-pastoral area were 4.41, 0.71, 3.64, 1.94, 0.24, and 0.14 t/hm2, respectively, and dust emission in these lands occurred mainly from April to May. Due to the influence of human activities on surface dust emission, dust emission fluxes from different land types were 1.66-4.41 times greater than those of their background areas, and dust emission fluxes from the main dust source areas were 1.66-3.89 times greater than those of their background areas. According to calculation, the amount of PM10 dust emission influenced by human disturbance accounted for up to 58.00% of the total dust emission in the study area. In addition, the comparative analysis of model simulation and field observation results showed that the simulated and observed dust emission fluxes were relatively close to each other, with differences ranging from 0.01 to 0.21 t/hm2 in different months, which indicated that the community land model version 4.5 (CLM4.5) had a high accuracy. In conclusion, model simulation results have important reference significance for identifying dust source areas and quantifying the contribution of human activities to surface dust emission.

Key words： northern China      classification of land type      model simulation      dust emission      human disturbance     
Received: 26 June 2024      Published: 31 March 2025
Corresponding Authors: *YAN Ping (E-mail: yping@bnu.edu.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
WANG Yong
YAN Ping
WU Wei
WANG Yijiao
HU Chanjuan
LI Shuangquan
Cite this article:

WANG Yong, YAN Ping, WU Wei, WANG Yijiao, HU Chanjuan, LI Shuangquan. PM10 dust emission in the Erenhot-Huailai zone of northern China based on model simulation. Journal of Arid Land, 2025, 17(3): 324-336.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0006-0     OR     http://jal.xjegi.com/Y2025/V17/I3/324

Model i mi (%) Dv,i (m) σg,i
1 3.6 0.832×10-6 2.1
2 95.7 4.820×10-6 1.9
3 0.7 19.380×10-6 1.6
Table 1 Mass fraction (mi), particle size of mass median (Dv,i), and geometric standard deviation (σg,i) of dust source model i
Model j Dj,min (m) Dj,max (m) Model j Dj,min (m) Dj,max (m)
1 0.1×10-6 1.0×10-6 3 2.5×10-6 5.0×10-6
2 1.0×10-6 2.5×10-6 4 5.0×10-6 10.0×10-6
Table 2 The minimum and maximum particle sizes of particle size group j
Fig. 1 Regional climate (a) and land type (b) classification and distribution of background area (c) in the Erenhot-Huailai zone, northern China
Fig. 2 Field dust observation. (a), instruments; (b), DustTrak 8540; (c), DS-2 two-dimensional ultrasonic wind speed sensor.
Fig. 3 Annual PM10 dust emission flux based on model simulation during 2017-2020. (a), 2017; (b), 2018; (c), 2019; (d), 2020.
Code Land type Average PM10 dust emission flux (t/hm2)
I1 Arid grassland 4.41
I2 Hunshandake Sandy Land 0.71
I3 Semi-arid grassland 3.64
I4 Semi-arid agro-pastoral area 1.94
I5 Dry sub-humid agro-pastoral area 0.24
I6 Semi-humid agro-pastoral area 0.14
Table 3 Average PM10 dust emission fluxes of different land types based on model simulation during 2017-2020
Fig. 4 Average PM10 dust emission flux in different seasons based on model simulation during 2017-2020. (a), spring; (b), summer; (c), autumn; (d), winter.
Fig. 5 Average wind speed in different seasons during 2017-2020. (a), spring; (b), summer; (c), autumn; (d), winter.
Fig. 6 Average monthly PM10 dust emission flux based on model simulation during 2017-2020. (a), January; (b), February; (c), March; (d), April; (e), May; (f), June; (g), July; (h), August; (i), September; (j), October; (k), November; (l), December.
Fig. 7 Comparison of PM10 dust emission fluxes between land type and their background area based on model simulation during 2017-2020. (a), 2017; (b), 2018; (c), 2019; (d), 2020. I1, arid grassland; I2, Hunshandake Sandy Land; I3, semi-arid grassland; I4, semi-arid agro-pastoral area; I5, dry sub-humid agro-pastoral area; I6, semi-humid agro-pastoral area.
Fig. 8 Comparison of dust emission flux between field observation and model simulation
[1]   Alfaro S C, Gomes L. 2001. Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas. Journal of Geophysical Research, Atmospheres, 106(D16): 18075-18084.
[2]   Anisimov A, Tao W C, Stenchikov G L, et al. 2017. Quantifying local-scale dust emission from the Arabian Red Sea coastal plain. Atmospheric Chemistry and Physics, 17(2): 993-1015.
[3]   Aviv R, Meni B, Itzhak K. 2020. Dust emission thresholds in loess soil under different saltation fluxes. Applied Science, 10(17): 5949, doi: 10.3390/app10175949.
[4]   Bolinder M A, Crotty F, Elsen A, et al. 2020. The effect of crop residues, cover crops, manures and nitrogen fertilization on soil organic carbon changes in agroecosystems: A synthesis of reviews. Mitigation and Adaptation Strategies for Global Change, 25: 929-952.
[5]   Carslaw K S, Boucher O, Spracklen D V, et al. 2010. A review of natural aerosol interactions and feedbacks within the Earth system. Atmospheric Chemistry and Physics, 10(4): 1701-1737.
[6]   Chappell A, Webb N P, Butler H J, et al. 2013. Soil organic carbon dust emission: An omitted global source of atmospheric CO2. Global Change Biology, 19(10): 3238-3244.
doi: 10.1111/gcb.12305 pmid: 23897802
[7]   Cui M C, Lu H Y, Sweeney M, et al. 2015. PM10 emission flux in the Tengger Desert and Mu Us Sand field, northern China, measured by PI-SWERL. Chinese Science Bulletin, 60(17): 1621-1630.
[8]   Dong G R, Li C Z, Jin J, et al. 1987. Some results of soil erosion based on wind tunnel simulation experiment. Chinese Science Bulletin, 24(4): 297-301. (in Chinese)
[9]   Donovan M, Monaghan R. 2021. Impacts of grazing on ground cover, soil physical properties and soil loss via surface erosion: A novel geospatial modelling approach. Journal of Environmental Management, 287: 112206, doi: 10.1016/j.jenvman.2021.112206.
[10]   Du H Q, Wang T, Xue X. 2017. Potential wind erosion rate response to climate and land-use changes in the watershed of the Ningxia-Inner Mongolia reach of the Yellow River, China, 1986-2013. Earth Surface Processes and Landforms, 42(13): 1923-1937.
[11]   Du H Q, Fan Y W, Luo L H, et al. 2023. Identification of natural and anthropogenic sources and the effects of climatic fluctuations and land use changes on dust emissions variations in the Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 340(15): 109628, doi: 10.1016/j.agrformet.2023.109628.
[12]   Duce R A. 1995. Sources, distributions, and fluxes of mineral aerosols and their relationship to climate. Aerosol Forcing of Climate, 17: 43-72.
[13]   Gillette D A. 1988. Threshold friction velocities for dust production for agricultural soils. Journal of Geophysical Research: Atmospheres, 93(D10): 12645-12662.
[14]   Han Z E, Cui W, Li J R, et al. 2024. Effect of soil moisture content on wind erosion rate of frozen aeolian sand. Journal of Desert Research, 44(1): 228-234. (in Chinese)
doi: 10.7522/j.issn.1000-694X.2023.00120
[15]   Ishizuka M, Mikami M, Leys J, et al. 2008. Effects of soil moisture and dried raindroplet crust on saltation and dust emission. Journal of Geophysical Research: Atmospheres, 113: D24212, doi: 10.1029/2008JD009955.
[16]   Kok J F, Mahowald N M, Fratini G, et al. 2014. An improved dust emission model-Part 1: Model description and comparison against measurements. Atmospheric Chemistry & Physics, 14(23): 13043-13061.
[17]   Kok J F, Ridley D A, Zhou Q, et al. 2017. Smaller desert dust cooling effect estimated from analysis of dust size and abundance. Nature Geoscience, 10(4): 274-278.
[18]   Kok J F, Ward D S, Mahowald N M, et al. 2018. Global and regional importance of the direct dust-climate feedback. Nature Communications, 9(1): 241, doi: 10.1038/s41467-017-02620-y.
[19]   Laurent B, Marticorena B, Bergametti G, et al. 2006. Modeling mineral dust emissions from Chinese and Mongolian deserts. Global and Planetary Change, 52(1-4): 121-141.
[20]   Liang P, Chen B, Yang X P, et al. 2022. Revealing the dust transport processes of the 2021 mega dust storm event in northern China. Science Bulletin, 67(1): 21-24.
doi: 10.1016/j.scib.2021.08.014 pmid: 36545954
[21]   Lu H, Shao Y P. 2001. Toward quantitative prediction of dust storms: An integrated wind erosion modelling system and its applications. Environmental Modelling & Software, 16(3): 233-249.
[22]   Lu Z Q, Dou Y Y, Wang J Z, et al. 2024. Impacts of different types of ecosystem conversion on soil wind erosion in key ecological functional zone in Inner Mongolia Autonomous Region. Bulletin of Soil and Water Conservation, 44(4): 247-256. (in Chinese)
[23]   Lyu X, Li X B, Wang H, et al. 2021. Soil wind erosion evaluation and sustainable management of typical steppe in Inner Mongolia, China. Journal of Environmental Management, 277: 111488, doi: 10.1016/j.jenvman.2020.111488.
[24]   Mahowald N, Ward D S, Kloster S, et al. 2011. Aerosol impacts on climate and biogeochemistry. Annual Review of Environment and Resources, 36: 45-74.
[25]   Meng Z J, Dang X H, Gao Y, et al. 2018. Interactive effects of wind speed, vegetation coverage and soil moisture in controlling wind erosion in a temperate desert steppe, Inner Mongolia of China. Journal of Arid Land, 10(4): 534-547.
doi: 10.1007/s40333-018-0059-1
[26]   Niu L N, Shao Q Q, Ning J, et al., 2023. The assessment of ecological restoration effects on Beijing-Tianjin Sandstorm Source Control Project area during 2000-2019. Ecological Engineering, 186: 106831, doi: 10.1016/j.ecoleng.2022.106831.
[27]   Okin G S. 2005. Dependence of wind erosion and dust emission on surface heterogeneity: Stochastic modeling. Journal of Geophysical Research: Atmospheres, 110(D11): 1371-1380.
[28]   Shao Y P. 2001. A model for mineral dust emission. Journal of Geophysical Research: Atmospheres, 106(D17): 20239-20254.
[29]   Shao Y P, Wyrwoll K H, Chappell A, et al. 2011. Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4): 181-204.
[30]   Shi M J, Yang Z L, Stenchikov G L, et al. 2016. Quantifying the impacts of landscape heterogeneity and model resolution on dust emissions in the Arabian Peninsula. Environmental Modelling and Software, 78: 106-119.
[31]   Sweeney M R, Lu H Y, Cui M C, et al. 2016. Sand dunes as potential sources of dust in northern China. Science China-Earth Sciences, 59(4): 760-769.
[32]   Wang G X, Tuo W Q, Du M Y. 2004. Flux and composition of wind-eroded dust from different landscapes of an arid inland river basin in north-western China. Journal of Arid Environments, 58(3): 373-385.
[33]   Wang R D, Li Q, Zhang C L, et al. 2021a. Comparison of dust emission ability of sand desert, gravel desert (Gobi), and farmland in northern China. Catena, 201: 105215, doi: 10.1016/j.catena.2021.105215.
[34]   Wang R D, Li Q, Wang R J, et al. 2021b. Influence of wind velocity and soil size distribution on emitted dust size distribution: A wind tunnel study. Journal of Geophysical Research: Atmospheres, 126(7): e2020JD033768, doi: 10.1029/2020JD033768.
[35]   Wang S S, Yu Y, Zhang X X, et al. 2021c. Weakened dust activity over China and Mongolia from 2001 to 2020 associated with climate change and land-use management. Environmental Research Letters, 16(12): 124056, doi: 10.1088/1748-9326/ac3b79.
[36]   Wang X S, Zhao W W, Liu S L, et al. 2022. Ecosystems impact on aeolian dust emissions in Inner Mongolia from 2001 to 2018. Geoderma, 422: 115938, doi: 10.1016/j.geoderma.2022.115938.
[37]   Wang Y, Yan P, Wang Y J, et al. 2025. Dust emission from different land use types based on the PI-SWERL test. Catena, 248: 108577, doi: 10.1016/J.CATENA.2024.108577.
[38]   Wang Z C, Yang J, Chen S H, et al. 2016. Impact of warmer climate on agricultural and animal husbandry climate resources in eastern Inner Mongolia. Journal of Arid Land Resources and Environment, 30(4): 132-137. (in Chinese)
[39]   Webb N P, Pierre C. 2018. Quantifying anthropogenic dust emissions. Earth's Future, 6(2): 286-295.
[40]   Wu B, Li X S, Liu W, et al. 2006. Desertification control regionalization and rehabilitation countermeasures of source area of the sand and dust endangering Beijing-Tianjin. Scientia Silvae Sinicae, 42(10): 65-70. (in Chinese)
[41]   Wu C L, Lin Z H, He J X, et al. 2016. A process-oriented evaluation of dust emission parameterizations in CESM: Simulation of a typical severe dust storm in East Asia. Journal of Advances in Modeling Earth Systems, 8(3): 1432-1452.
[42]   Xuan J, Liu G L, Du K. 2000. Dust emission inventory in Northern China. Atmospheric Environment, 34(26): 4565-4570.
[43]   Yan H, Wang S Q, Wang C Y, et al. 2005. Losses of soil organic carbon under wind erosion in China. Global Change Biology, 11(5): 828-840.
[44]   Yang Y, Russell L M, Lou S J, et al. 2017. Dust-wind interactions can intensify aerosol pollution over eastern China. Nature Communications, 8(1): 15333, doi: 10.1038/ncomms15333.
[45]   Zender C S, Newman D, Torres O. 2003. Spatial heterogeneity in aeolian erodibility: Uniform, topographic, geomorphic, and hydrologic hypotheses. Journal of Geophysical Research: Atmospheres, 108(D17): 4543, doi: 10.1029/2002JD003039.
[46]   Zhang G F, Azorin-Molina C, Shi P J, et al. 2019. Impact of near-surface wind speed variability on wind erosion in the eastern agro-pastoral transitional zone of Northern China, 1982-2016. Agricultural and Forest Meteorology, 271: 102-115.
[47]   Zhang H Y, Fan J W, Cao W, et al. 2018. Response of wind erosion dynamics to climate change and human activity in Inner Mongolia, China during 1990 to 2015. Science of the Total Environment, 639: 1038-1050.
[1] Xijiri, ZHOU Ruiping, BAO Baorong, Burenjirigala . Spatiotemporal patterns and drivers of cultivated land conversion in Inner Mongolia Autonomous Region, northern China[J]. Journal of Arid Land, 2024, 16(9): 1197-1213.
[2] JIN Junfang, YIN Shuyan, YIN Hanmin. Impact of land use/land cover types on surface humidity in northern China in the early 21st century[J]. Journal of Arid Land, 2022, 14(7): 705-718.
[3] ZHANG Chunlai, WANG Xuesong, CEN Songbo, ZHENG Zhongquan Charlie, WANG Zhenting. Separating emitted dust from the total suspension in airflow based on the characteristics of PM10 vertical concentration profiles on a Gobi surface in northwestern China[J]. Journal of Arid Land, 2022, 14(6): 589-603.
[4] WANG Shiqing, TAO Zefu, SUN Piling, CHEN Sijia, SUN Huiying, LI Nan. Spatiotemporal variation of forest land and its driving factors in the agropastoral ecotone of northern China[J]. Journal of Arid Land, 2022, 14(1): 1-13.
[5] CAO Huicong, YAN Dandan, JU Yuelin. Drought and flood characteristics in the farming- pastoral ecotone of northern China based on the Standardized Precipitation Index[J]. Journal of Arid Land, 2021, 13(12): 1244-1259.
[6] Mahdi BOROUGHANI, Sima POURHASHEMI, Hamid GHOLAMI, Dimitris G KASKAOUTIS. Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia[J]. Journal of Arid Land, 2021, 13(11): 1103-1121.
[7] Long MA, Hongyu LI, Tingxi LIU, Longteng LIANG. Abrupt temperature change and a warming hiatus from 1951 to 2014 in Inner Mongolia, China[J]. Journal of Arid Land, 2019, 11(2): 192-207.
[8] Linli CUI, Jun SHI, Yue MA, Xiaochen LIU. Variations of the thermal growing season during the period 1961-2015 in northern China[J]. Journal of Arid Land, 2018, 10(2): 264-276.
[9] LI Jinchang, LIU Haixia, SU Zhizhu, FAN Xiaohui. Changes in wind activity from 1957 to 2011 and their possible influence on aeolian desertification in northern China[J]. Journal of Arid Land, 2015, 7(6): 755-764.