Dust emission inventory in Northern China

Wind erosion is largely determined by wind erosion climatic erosivity. In this study, we examined changes in wind erosion climatic erosivity during 4 seasons across northern China from 1981-2016 using 2 models: the wind erosion climatic erosivity of the Wind Erosion Equation (WEQ) model and the weather factor from the Revised Wind Erosion Equation (RWEQ) model. Results showed that wind erosion climatic erosivity derived from the 2 models was highest in spring and lowest in winter with high values over the Kumtag Desert, the Qaidam Basin, the boundary between Mongolia and China, and the Hulunbuir Sandy Land. In spring and summer, wind erosion climatic erosivity showed decreasing trends in whole of northern China from 1981-2016, whereas there was an increasing trend in wind erosion climatic erosivity over the Gobi Desert from 1992-2011. For the weather factor of the RWEQ model, the difference between northern Northwest China and the Gobi Desert and eastern-northern China was much larger than that of the wind erosion climatic erosivity of the WEQ model. In addition, in contrast to a decreasing trend in the weather factor of the RWEQ model over southern Northwest China during spring and summer from 1981-2016, the wind erosion climatic erosivity of the WEQ model showed a decreasing trend for 1981-1992 and an increasing trend for 1992-2011 over southern Northwest China. According to a comparison between dust emission and wind erosion climatic erosivity, the 2 models have the ability to project changes in future wind erosion in northern China.

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Performance of KMA-ADAM3 in Identifying Asian Dust Days over Northern China

Atmosphere ◽

10.3390/atmos11060593 ◽

2020 ◽

Vol 11 (6) ◽

pp. 593

Author(s):

Sang-Boom Ryoo ◽

Jinwon Kim ◽

Jeong Hoon Cho

Keyword(s):

Dust Emission ◽

Northern China ◽

Asian Dust ◽

Probability Of Detection ◽

Soil Types ◽

Dust Source ◽

Aerosol Model ◽

Dust Generation ◽

Source Regions ◽

Dust Events

Recently, the Korea Meteorological Administration developed Asian Dust Aerosol Model version 3 (ADAM3) by incorporating additional parameters into ADAM2, including anthropogenic particulate matter (PM) emissions, modification of dust generation by considering real-time surface vegetation, and assimilations of surface PM observations and satellite-measured aerosol optical depth. This study evaluates the performance of ADAM3 in identifying Asian dust days over the dust source regions in Northern China and their variations according to regions and soil types by comparing its performance with ADAM2 (from January to June of 2017). In all regions the performance of ADAM3 was markedly improved, especially for Northwestern China, where the threat score (TS) and the probability of detection (POD) improved from 5.4% and 5.5% to 30.4% and 34.4%, respectively. ADAM3 outperforms ADAM2 for all soil types, especially for the sand-type soil for which TS and POD are improved from 39.2.0% and 50.7% to 48.9% and 68.2%, respectively. Despite these improvements in regions and surface soil types, Asian dust emission formulas in ADAM3 need improvement for the loess-type soils to modulate the overestimation of Asian dust events related to anthropogenic emissions in the Huabei Plain and Manchuria.

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Dust emission factors for environment of Northern China

Atmospheric Environment ◽

10.1016/s1352-2310(98)00339-2 ◽

1999 ◽

Vol 33 (11) ◽

pp. 1767-1776 ◽

Cited By ~ 36

Author(s):

Jie Xuan

Keyword(s):

Dust Emission ◽

Northern China ◽

Emission Factors

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Simulation of a Severe Sand and Dust Storm Event in March 2021 in Northern China: Dust Emission Schemes Comparison and the Role of Gusty Wind

Atmosphere ◽

10.3390/atmos13010108 ◽

2022 ◽

Vol 13 (1) ◽

pp. 108

Author(s):

Jikang Wang ◽

Bihui Zhang ◽

Hengde Zhang ◽

Cong Hua ◽

Linchang An ◽

...

Keyword(s):

Dust Storm ◽

Dust Emission ◽

Northern China ◽

Pm10 Concentration ◽

Storm Event ◽

Air Quality Model ◽

Wind Model ◽

Dust Source ◽

Source Regions ◽

Sand And Dust Storm

Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM10 concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM10 concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM10 concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM10 concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM10 concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM10 concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM10 concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM10 concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

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Mineral Dust Cycle

Oxford Research Encyclopedia of Environmental Science ◽

10.1093/acrefore/9780199389414.013.46 ◽

2016 ◽

Author(s):

Irina Sokolik

Keyword(s):

Atmospheric Aerosols ◽

Environmental Changes ◽

Mineral Dust ◽

Global Climate ◽

Dust Emission ◽

Northern China ◽

Dust Storms ◽

Atmospheric Composition ◽

System Response ◽

Beneficial Effects

There is scientific consensus that human activities have been altering the atmospheric composition and are a key driver of global climate and environmental changes since pre-industrial times (IPCC, 2013). It is a pressing priority to understand the Earth system response to atmospheric aerosol input from diverse sources, which so far remain one of the largest uncertainties in climate studies (Boucher et al., 2014; Forster et al., 2007). As the second most abundant component (in terms of mass) of atmospheric aerosols, mineral dust exerts tremendous impacts on Earth’s climate and environment through various interaction and feedback processes. Dust can also have beneficial effects where it deposits: Central and South American rain forests get most of their mineral nutrients from the Sahara; iron-poor ocean regions get iron; and dust in Hawaii increases plantain growth. In northern China as well as the midwestern United States, ancient dust storm deposits known as loess are highly fertile soils, but they are also a significant source of contemporary dust storms when soil-securing vegetation is disturbed. Accurate assessments of dust emission are of great importance to improvements in quantifying the diverse dust impacts.

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