Smart Nutrient Retention Networks: a novel approach for nutrient conservation through water quality management

Nutrients are essential resources for food production but are used inefficiently, and thereby they pollute inland and coastal waters and are lost into the oceans. Nutrient conservation by retention and consecutive reuse would prevent nutrient losses to the atmosphere and downstream ecosystems. We present Smart Nutrient Retention Networks (SNRNs) as a novel management approach to achieve nutrient conservation across networks of connected waterbodies through strategic water quality management. To present the key features of SNRNs, we review existing knowledge of nutrient retention processes in inland waters, water quality management options for nutrient conservation, and nutrient retention models to develop SNRNs. We argue that successful nutrient conservation, even at a local level, through SNRN management strategies requires clearly formulated goals and catchment-wide system understanding. Waterbody characteristics, such as hydraulic residence time and the presence of macrophytes, shape local nutrient retention with potential network-wide cascading effects of improved water quality and are therefore key targets of SNRN management strategies. Nutrient retention models that include the self-reinforcing feedback loop of ecological water quality, nutrient retention, and nutrient loading in networks of inland waters in relation to management options can support the development of SNRNs. We conclude that SNRNs can contribute to sustainable use of nutrients in human food production.

营养盐（Nutrients）是粮食生产的必需资源，但当前其利用效率低下，进而引发内陆与近岸水体污染，并最终流失至海洋。通过留存与循环复用实现营养盐保育，可避免营养盐向大气及下游生态系统流失。本研究提出智能营养盐留存网络（Smart Nutrient Retention Networks, SNRNs）作为一种新型管理手段，通过战略性水质管理，在互联水体网络中实现营养盐保育。为阐明智能营养盐留存网络的核心特征，本研究梳理了内陆水体营养盐留存过程、营养盐保育的水质管理手段，以及用于构建智能营养盐留存网络的营养盐留存模型相关现有研究成果。本研究认为，即便在局地尺度，通过智能营养盐留存网络管理策略实现高效营养盐保育，需制定明确目标，并对流域尺度的生态系统形成整体认知。水体特征（如水力停留时间、大型水生植物的存在）会影响局地营养盐留存，并可能对全网络水质改善产生级联效应，因此是智能营养盐留存网络管理策略的核心调控目标。纳入生态水质、营养盐留存与内陆水体网络营养盐负荷之间自增强反馈环路，并结合管理手段的营养盐留存模型，可为智能营养盐留存网络的构建提供支撑。本研究最终表明，智能营养盐留存网络可助力人类粮食生产中营养盐的可持续利用。