Soil loss and PM10 emissions from agricultural fields in the Junggar Basin over the past six decades

Authors: PI, HUAWEI - Chinese Academy Of Sciences; WEBB, NICHOLAS - New Mexico State University; LEI, JIAQIANG - Chinese Academy Of Sciences; LI, SISI - Translational Medical Center Of Huaihe Clinical College, Henan University

Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/14/2021
Publication Date: 3/10/2022
Citation: Pi, H., Webb, N., Lei, J., Li, S. 2022. Soil loss and PM10 emissions from agricultural fields in the Junggar Basin over the past six decades. Journal of Soil and Water Conservation. 77(2):113-125. https://doi.org/10.2489/jswc.2022.00018.
DOI: https://doi.org/10.2489/jswc.2022.00018

Interpretive Summary: The Junggar Basin in North Xinjiang Province is regarded as one of the important dust sources in China. However, little is known about how much dust is emitted from the region or how dust emissions may have changed in response to land use and climatic changes. This study assesses dust emissions from the agricultural community of the Junggar Basin in North Xinjiang over the past six decades. The Wind Erosion Prediction System (WEPS) was used to simulate annual soil loss and PM10 (particulate matter =10 µm in aerodynamic diameter) emissions at 11 meteorological stations across the Junggar Basin. From 1958 to 2018, annual soil loss and PM10 emissions significantly decreased 0.65 and 0.2 kg m-2 yr-1, respectively. This decrease was likely due to decreasing wind speed, but also associated with increased precipitation and temperature, and decreased solar radiation. Wind erosion occurred most frequently during April and May, accounting for 39% of the annual soil loss and 40% of the annual PM10 emission. Wind erosion risk appeared to decrease during the past six decades in response to observed climate change across the Basin, however the southeast part of the Basin experienced increasing wind erosion risk over the six decades. Projects to control wind erosion risk and combat dust emission should be given priority in the southeast part of the Basin.

Technical Abstract: The Junggar Basin in North Xinjiang Province is regarded as one of the important dust sources in China. There is, however, a lack of information on long-term dust emissions originating from the Basin. In these largely agricultural communities, wind erosion is a major concern that results in a threat to sustainable agriculture and environmental quality. This study assesses dust emissions from the agricultural community of the Junggar Basin in North Xinjiang based on soil and land use types extracted from remote sensing data in response to weather and climate over the past six decades. The Wind Erosion Prediction System (WEPS) was used to simulate annual soil loss and PM10 (particulate matter =10 µm in aerodynamic diameter) emissions at 11 meteorological stations across the Junggar Basin. From 1958 to 2018, annual soil loss and PM10 emissions significantly decreased 0.65 and 0.2 kg m-2 yr-1, respectively. This decrease was likely due to decreasing wind speed, but also associated with increased precipitation and temperature, and decreased solar radiation. Wind erosion occurred most frequently during April and May, accounting for 39% of the annual soil loss and 40% of the annual PM10 emission. In contrast, no erosion occurred in January, February, and December as a result of low temperature (<-8.6 °C), frozen soil conditions and snow cover. Wind erosion risk appeared to decrease during the past six decades in response to observed climate change across the Basin, however the southeast part of the Basin experienced increasing wind erosion risk over the six decades. Projects to control wind erosion risk and combat dust emission should be given priority in the southeast part of the Basin. Frequency of dust events was compared to simulated erosion, a strong linkage was found between simulated soil loss and frequency of dust storms based on regression analysis. This concluded that the modelling was acceptable in the conditions of this study and the results were prone to be reliable.