Effect of windblown dust from local and regional sources on the air quality of the central district in Jinan, China

Chen, Li; Hong, Zhao; Wang, Wenxing; Zhang, Bai; Wang, Zhongliang; Sun, Fengjuan; Hou, Lingling; Liu, Guanghui; Shi, Mengshuang; Miao, Yunge

doi:10.1016/j.atmosres.2016.10.026

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…Wind-blown dust is one of the main components of atmospheric aerosols ( Chen et al, 2017 ), particularly in the so-called “global dust belt,” which extends from the west coast of North Africa, over the Middle East, Central and South Asia, to China ( Prospero et al, 2002 ). The “global dust belt” contributes up to 70% of the current global annual dust emission flux ( Jickells et al, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Aeolian Prokaryotic Communities of the Global Dust Belt Over the Red Sea

et al. 2020

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Aeolian prokaryotic communities (APC) are important components of bioaerosols that are transported freely or attached to dust particles suspended in the atmosphere. Terrestrial and marine ecosystems are known to release and receive significant prokaryote loads into and from the surrounded atmospheric air. However, compared to terrestrial systems, there is a lack of microbial characterization of atmospheric dust over marine systems, such as the Red Sea, which receives significant terrestrial dust loads and is centrally located within the Global Dust Belt. Prokaryotic communities are likely to be particularly important in the Global Dust Belt, the area between the west coast of North Africa and Central Asia that supports the highest dust fluxes on the planet. Here we characterize the diversity and richness of the APC over the Red Sea ecosystem, the only sea fully contained within the Global Dust Belt. MiSeq sequencing was used to target 16S ribosomal DNA of two hundred and forty aeolian dust samples. These samples were collected at ∼7.5 m high above the sea level at coastal and offshore sampling sites over a 2-year period (2015–2017). The sequencing outcomes revealed that the APC in the atmospheric dust is dominated by Proteobacteria (42.69%), Firmicutes (41.11%), Actinobacteria, (7.69%), and Bacteroidetes (3.49%). The dust-associated prokaryotes were transported from different geographical sources and found to be more diverse than prokaryotic communities of the Red Sea surface water. Marine and soil originated prokaryotes were detected in APC. Hence, depending on the season, these groups may have traveled from other distant sources during storm events in the Red Sea region, where the APC structure is influenced by the origin and the concentration of aeolian dust particles. Accordingly, further studies of the impact of atmospheric organic aerosols on the recipient environments are required.

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Section: Introductionmentioning

confidence: 99%

Aeolian Prokaryotic Communities of the Global Dust Belt Over the Red Sea

et al. 2020

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“…Compared with field measurements and wind tunnel experiments, wind erosion modeling, based on field measurements and geospatial data, is a more efficient and effective method to estimate dust emissions from soil wind erosion [23]. The wind erosion prediction system (WEPS), developed by the US Department of Agriculture, is one of the most widely used models for estimating soil erosion and particle production from soil surfaces and has been extensively applied throughout the United States [24][25][26][27][28] and in countries worldwide, such as Canada [29], Argentina [30] and China [31][32][33]. In numerical studies, good agreement (i.e., R 2 ranging from 0.87 to 0.98) was obtained between the measured and the WEPS-based simulated erosion results [34,35].…”

Section: Introductionmentioning

confidence: 99%

Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Gao,

Liu,

Zhang

et al. 2023

Atmosphere

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Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM2.5 emissions from soil erosion, an approach based on an artificial neural network (ANN) model which provides a multilayered, fully connected framework that relates input parameters and outcomes was proposed in this study. Owing to the difficulty in obtaining the actual emissions of soil-derived PM10 and PM2.5 over a broad area, the PM10 and PM2.5 simulated results of the ANN model were compared with the published results simulated by the widely used wind erosion prediction system (WEPS) model. The PM10 and PM2.5 emission results, based on the WEPS, agreed well with the field data, with R2 values of 0.93 and 0.97, respectively, indicating the potential for using the WEPS results as a reference for training the ANN model. The calculated r, RMSE and MAE for the results simulated by the WEPS and ANN were 0.78, 3.37 and 2.31 for PM10 and 0.79, 1.40 and 0.91 for PM2.5, respectively, throughout Southern Xinjiang. The uncertainty of the soil-derived PM10 and PM2.5 emissions at a 95% CI was (−66–106%) and (−75–108%), respectively, in 2016. The results indicated that by using parameters that affect soil erodibility, including the soil pH, soil cation exchange capacity, soil organic content, soil calcium carbonate, wind speed, precipitation and elevation as input factors, the ANN model could simulate soil-derived particle emissions in Southern Xinjiang. The results showed that when the study domain was reduced from the entire Southern Xinjiang region to its five administrative divisions, the performance of the ANN improved, producing average correlation coefficients of 0.88 and 0.87, respectively, for PM10 and PM2.5. The performances of the ANN differed by study period, with the best result obtained during the sand period (March to May) followed by the nonheating (June to October) and heating periods (November to February). Wind speed, precipitation and soil calcium carbonate were the predominant input factors affecting particle emissions from wind erosion sources. The results of this study can be used as a reference for the wind erosion prevention and soil conservation plans in Southern Xinjiang.

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“…Land degradation may result from removing fertile top soil, namely, the clays and organic matter, and leaving the infertile and coarse soil. In addition, fine suspended particulates due to wind erosion is always a concern for air quality for cities located in or around arid and semiarid regions (Chen et al, 2017;Sharratt & Lauer, 2006).…”

Section: Introductionmentioning

confidence: 99%

Wind erosion of soil influenced by clay amendment in the inland Pacific Northwest, USA

Huggins

Sharratt

2020

Land Degrad Dev

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Soil clay content is one of the primary intrinsic soil properties affecting soil erodibility and ecological processes, but few studies have tested the effects of clay amendment on wind erosion of soil. The objective of this study was therefore to evaluate the effect of progressive clay amendment on wind erosion of soil in the inland Pacific Northwest, where there is a high soil erodibility risk due to the arid and semiarid environment and sandy soils. Clay amendment significantly increased crust crushing

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Effect of windblown dust from local and regional sources on the air quality of the central district in Jinan, China

Cited by 12 publications

References 20 publications

Aeolian Prokaryotic Communities of the Global Dust Belt Over the Red Sea

Aeolian Prokaryotic Communities of the Global Dust Belt Over the Red Sea

Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Wind erosion of soil influenced by clay amendment in the inland Pacific Northwest, USA

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