Agricultural non-point source (NPS) pollution has long been recognized as a pervasive threat to water quality worldwide, driven primarily by diffuse contaminants such as nitrogen and phosphorus carried by surface runoff from cultivated lands. In China alone, data from 2017 reveal staggering discharges of 1.4149 million tons of total nitrogen and 212 thousand tons of total phosphorus from agricultural activities. Among these pollutants, emissions originating from cropping systems constitute a significant fraction—accounting for 51% of nitrogen and 36% of phosphorus releases. Despite extensive efforts to monitor and manage these sources, conventional farmland runoff monitoring techniques exhibit pronounced limitations that constrain their effectiveness and practical applicability on broader scales.
Traditional approaches, including runoff pool measurements and manual water sampling, suffer from spatial constraints and operational vulnerabilities. Runoff pools typically cover limited areas and are frequently disrupted during intense rainfall events, which undermines continuous data collection. Manual sampling methods, while targeted, impose significant labor demands and frequently fail to capture temporally comprehensive datasets, compromising the representativeness of the collected information. Moreover, extrapolating data derived from small experimental plots to field-scale conditions introduces substantial uncertainties, diminishing confidence in pollution load assessments. Against this backdrop, an urgent need has emerged for technological solutions capable of automated, large-scale, and continuous monitoring of agricultural NPS pollution that can reliably reflect real-world conditions.
Responding to these challenges, a research team led by Wenchao Li of Hebei Agricultural University in collaboration with Lingling Hua from Beijing University of Agriculture has pioneered a novel online monitoring system. Designed specifically for continuous cropping farmland, the system harnesses a serial pipeline infrastructure integrating diversion trenches, online flow measurement instruments, and dynamic acquisition devices. This configuration facilitates real-time, automated sampling of surface runoff, thereby overcoming the deficiencies of traditional monitoring schemes. By implementing strategically placed diversion trenches and pipelines to channel runoff centrally, the system achieves extensive spatial coverage, dramatically reducing the physical footprint and construction costs compared to conventional runoff pools.
One of the key innovations underpinning this system is its ability to extend monitoring across several hundred hectares of farmland through the deployment of a networked pipeline system. This design supersedes the limited tens of square meters coverage typical of traditional runoff pools, enabling a far more comprehensive assessment of pollutant dynamics at field scale. Online flowmeters coupled with advanced water quality sensors measure critical parameters such as flow rates, total nitrogen, total phosphorus, and chemical oxygen demand (COD) continuously. Additionally, an automated sampling mechanism, triggered by a rainfall sensor, sequentially collects representative water samples corresponding to individual precipitation events. This automated response ensures complete temporal coverage of runoff episodes and mitigates the traditional issues of manual sampling latency and poor temporal resolution.
Another transformative aspect of the system lies in its remote data transmission and control capabilities. Utilizing wireless communication technologies, monitoring data are transmitted in real-time to central management platforms, allowing stakeholders to visualize trends instantaneously. Embedded alert functionalities notify operators of abnormal water quality conditions, enabling swift emergency interventions. This integration substantially elevates the responsiveness and efficiency of NPS pollution management, bridging the gap between data acquisition and actionable insights.
Field validations of this innovative monitoring system were conducted in the Baiyangdian Basin located within the Xiong’an New Area, Hebei Province. The system demonstrated remarkable stability and precision in capturing complex runoff dynamics over an extended monitoring period from July to August 2023. Notably, it accurately detected the runoff lag phenomenon following the August 11 rain event; runoff formation commenced approximately 24 hours post-precipitation and subsequently intensified, closely aligning with corresponding meteorological measurements. Under scenarios involving extreme heavy rainfall, the system’s capacity for elevated monitoring frequencies effectively tracked rapid hydrological fluctuations, showcasing its robustness in capturing complex environmental processes.
The technological advancements embodied in this online monitoring system have been formally recognized by the Agricultural Ecology and Resource Protection Station of China’s Ministry of Agriculture and Rural Affairs. It has been designated as a key technology for the comprehensive management of agricultural NPS pollution, reflecting its potential to fundamentally improve pollution source assessments. Compared to conventional experimental plot methods, the data generated by this system offer enhanced relevance to actual agricultural production settings, thereby furnishing more accurate parameter inputs for pollution load modeling and management decision-making.
As this technology gains wider adoption, it is poised to play a pivotal role in forthcoming national pollution source censuses and environmental monitoring campaigns. By providing detailed, real-time insights into the spatial and temporal dynamics of nutrient runoff, it enables policymakers to develop targeted, effective intervention strategies that reconcile agricultural productivity with ecological sustainability. Ultimately, the system’s deployment represents a significant step forward in safeguarding freshwater resources, supporting the restoration and preservation of aquatic ecosystems.
This research not only advances the scientific understanding of NPS pollution mechanisms but also delivers practical, scalable solutions for environmental monitoring and governance. The modular nature of the serial pipeline design allows for flexible adaptation to diverse agricultural landscapes and cropping systems. Future enhancements may incorporate machine learning algorithms for predictive analytics and integration with broader watershed management frameworks. The convergence of real-time sensing technologies, data analytics, and environmental engineering embodied in this work exemplifies the transformative potential of innovative monitoring systems in addressing chronic pollution challenges.
In conclusion, the development of an online monitoring system based on diversion trenches and serial pipelines marks a paradigm shift in agricultural NPS pollution management. By effectively addressing the spatial and temporal limitations of traditional methods, it enables comprehensive, continuous, and automated surveillance of pollutant flows at scales relevant to modern agricultural production. Its successful field application underscores the feasibility and benefits of such integrated technological solutions, offering a blueprint for sustainable agricultural water management practices worldwide.
Subject of Research: Not applicable
Article Title: An innovative approach to monitoring non-point source pollution at a field scale: online monitoring system for continuous cropping with a serial pipeline
News Publication Date: 15-Sep-2025
Web References: http://dx.doi.org/10.15302/J-FASE-2024596
References: Li, W., Hua, L., et al. (2025). An innovative approach to monitoring non-point source pollution at a field scale: online monitoring system for continuous cropping with a serial pipeline. Frontiers of Agricultural Science and Engineering. DOI: 10.15302/J-FASE-2024596
Image Credits: Peipei FENG, Gaofei YIN, Qingyi ZHU, Tongyang LI, Bin XI, Xiaoyuan XU, Huiqing JIAO, Hongda WEN, Lingling HUA, Wenchao LI
Keywords: Agriculture
