浅水水质的恶化是水管理中的一个重要问题。目前,大多数欧洲河流没有达到指令2000/60/EC(水框架指令,WFD),而许多地表水体的健康状况仍然未知。在此背景下,我们提出了一种新的方法来对河流的压力状态进行半定量分析,从与人类活动相关的易于访问的数据开始。拟议的方法旨在解决监测记录普遍缺乏的问题。本研究提出了一种程序(i)评估不同人类活动的相对压力,(ii)使用基于栅格的方法确定沿河不同污染源的分配点,和(iii)确定空间生化水质指数。已开发的指数表示可能同时影响单个河流段的污染源引起的地表水的整体生化状态。这包括所谓的Seveso指令下的机构,受IPPC-IED纪律约束的活动,污水处理厂,和受污染的场所。该方法已经在意大利北部的三条河流上进行了测试,每个人都面临着不同的工业和人为压力:里诺,恩扎,和帕尔马。与监测数据的比较产生了令人信服的结果,证明了拟议指标在再现河流水质空间变异性方面的一致性。虽然需要额外的调查,开发的方法可以作为一个有价值的工具,以支持缺乏或有限的水质监测数据的地区的决策过程和预测研究。
The deterioration of superficial water quality is a significant concern in water management. Currently, most European rivers do not achieve qualitative standards defined by Directive 2000/60/EC (Water Framework Directive, WFD), while the health status of many surface water bodies remains unknown. Within this context, we propose a new methodology to perform a semi-quantitative analysis of the pressure state of a river, starting from easily accessible data related to anthropic activities. The proposed approach aims to address the endemic scarcity of monitoring records. This study proposes a procedure to (i) evaluate the relative pressure of different human activities, (ii) identify allocation points of different pollutant sources along the river using a raster-based approach, and (iii) determine a spatial biochemical water quality index. The developed index expresses the overall biochemical state of surface water induced by pollutant sources that may simultaneously impact a single river segment. This includes establishments under the so-called Seveso Directive, activities subjected to the IPPC-IED discipline, wastewater treatment plants, and contaminated sites. The methodology has been tested over three rivers in Northern Italy, each exposed to different industrial and anthropogenic pressures: Reno, Enza, and Parma. A comparison with monitored data yielded convincing results, proving the consistency of the proposed index in reproducing the spatial variability of the river water quality. While additional investigations are necessary, the developed methodology can serve as a valuable tool to support decision-making processes and predictive studies in areas lacking or having limited water quality monitoring data.