Analysis and Modeling of Particulate Matter Release of Farmland Soil Under Conservation Tillage Based on Sensor Monitoring for More Sustainable Agricultural Production

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM2.5), inhalable particulate matter (PM10), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s−1 wind speed, and 32.36 mm precipitation—yielding average PM2.5, PM10, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m−3, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m−3 for PM2.5, PM10, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production.

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Publication Details

Journal
Agriculture
Published
2026-05-09
DOI
https://doi.org/10.3390/agriculture16101034
Primary Topic
Aeolian processes and effects
Type
article
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article

Analysis and Modeling of Particulate Matter Release of Farmland Soil Under Conservation Tillage Based on Sensor Monitoring for More Sustainable Agricultural Production

Zhengxin Xu, Longbao Wang, 段改莲, Caiyun Lu et al.
Agriculture
Aeolian processes and effects
article

Analysis and Modeling of Particulate Matter Release of Farmland Soil Under Conservation Tillage Based on Sensor Monitoring for More Sustainable Agricultural Production

Zhengxin Xu, Longbao Wang, 段改莲, Caiyun Lu, Xi Zhang, Lin Jia, Fuyu Ma, Qingjie Wang, Chao Wang
article en

Abstract

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM2.5), inhalable particulate matter (PM10), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s−1 wind speed, and 32.36 mm precipitation—yielding average PM2.5, PM10, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m−3, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m−3 for PM2.5, PM10, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production.

AgricultureVol. 16(10)
Ministry of Agriculture and Rural Affairs (CN), China Agricultural University (CN)
Zero hunger
Openalex Percentile: Top 5%
Aeolian processes and effects
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