Experimental validation of an automated soil leachate monitoring system for agricultural Non-Point source pollution and nutrient run-off to water bodies

Despite advancements, precise on-site measurement of agricultural non-point source pollution and its impact on water quality remains challenging. Existing systems face limitations in addressing spatial variability and temporal fluctuations and accurately capturing nutrient dynamics in water bodies. This study introduces an innovative automated monitoring framework to assess nitrogen (N) and phosphorus (P) contributions from agricultural runoff. This research also presents an innovative mathematical modeling framework and experimental validation for an automated soil leachate collection system (ASLCS) designed to monitor agricultural non-point source pollution, particularly focusing on nitrogen (N) and phosphorus (P) dynamics integrating soil water dynamics, pressure variations, and sensor responses. The model demonstrated exceptional accuracy in simulating soil water content across multiple depths (30, 60, and 90 cm) with remarkably low RMSE values of 0.0038, 0.0044, and 0.0036 m 3 /m 3 respectively. The soil leachate collection rates showed a strong correlation with soil water content (R 2 > 0.98 across all depths), with collection efficiency peaking at soil water contents above 0.28 m 3 /m 3 . Pressure dynamics analysis revealed sophisticated relationships between normalized pressure and volume (R 2 = 0.9911 and 0.9977), while ultrasonic level sensor performance demonstrated high measurement accuracy (R 2 = 0.9902) across operational ranges. The capability of the designed model to monitor nutrient concentrations showed exceptional precision, with validation results indicating high accuracy for NH 4 -N (R 2 = 0.9968, 0–10 mg/L range), NO 3 -N (R 2 = 0.9960, 0–20 mg/L range), and phosphorus (R 2 = 0.9943, 0–5 mg/L range). This integrated approach represented a significant advancement in automated monitoring technology, offering real-time, high-precision tracking of agricultural nutrient leaching while minimizing soil disruption.

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Publication Details

Journal
Ain Shams Engineering Journal
Published
2025-08-26
DOI
https://doi.org/10.1016/j.asej.2025.103713
Citations
1
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.82
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article

Experimental validation of an automated soil leachate monitoring system for agricultural Non-Point source pollution and nutrient run-off to water bodies

Iskander Tlili, Syed Muhammad Zaigham Abbas Naqvi, Jiandong Hu, Muhammad Awais et al.
1 citations
Ain Shams Engineering Journal
Water Quality Monitoring Technologies
0.82
article

Experimental validation of an automated soil leachate monitoring system for agricultural Non-Point source pollution and nutrient run-off to water bodies

Iskander Tlili, Syed Muhammad Zaigham Abbas Naqvi, Jiandong Hu, Muhammad Awais, Hao Zhang, Wei Zhang, Yongqi Chen, Vijaya Raghavan
article en
1 citations

Abstract

Despite advancements, precise on-site measurement of agricultural non-point source pollution and its impact on water quality remains challenging. Existing systems face limitations in addressing spatial variability and temporal fluctuations and accurately capturing nutrient dynamics in water bodies. This study introduces an innovative automated monitoring framework to assess nitrogen (N) and phosphorus (P) contributions from agricultural runoff. This research also presents an innovative mathematical modeling framework and experimental validation for an automated soil leachate collection system (ASLCS) designed to monitor agricultural non-point source pollution, particularly focusing on nitrogen (N) and phosphorus (P) dynamics integrating soil water dynamics, pressure variations, and sensor responses. The model demonstrated exceptional accuracy in simulating soil water content across multiple depths (30, 60, and 90 cm) with remarkably low RMSE values of 0.0038, 0.0044, and 0.0036 m 3 /m 3 respectively. The soil leachate collection rates showed a strong correlation with soil water content (R 2 > 0.98 across all depths), with collection efficiency peaking at soil water contents above 0.28 m 3 /m 3 . Pressure dynamics analysis revealed sophisticated relationships between normalized pressure and volume (R 2 = 0.9911 and 0.9977), while ultrasonic level sensor performance demonstrated high measurement accuracy (R 2 = 0.9902) across operational ranges. The capability of the designed model to monitor nutrient concentrations showed exceptional precision, with validation results indicating high accuracy for NH 4 -N (R 2 = 0.9968, 0–10 mg/L range), NO 3 -N (R 2 = 0.9960, 0–20 mg/L range), and phosphorus (R 2 = 0.9943, 0–5 mg/L range). This integrated approach represented a significant advancement in automated monitoring technology, offering real-time, high-precision tracking of agricultural nutrient leaching while minimizing soil disruption.

Ain Shams Engineering JournalVol. 16(11)
Islamic University of Madinah (SA), Henan Agricultural University (CN), McGill University (CA)
Zero hunger
Openalex Percentile: Top 23%
Water Quality Monitoring Technologies
0.82
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