Dept. of Civil Engineering, Catholic University of Murcia, Campus de los Jerónimos, N° 135 Guadalupe, 30107 Murcia, Spain.
Dept. of Civil Engineering, University of Alicante, Carretera San Vicent del Raspeig s/n, 03690 Alicante, Spain.
Sci Total Environ. 2017 Sep 1;593-594:173-181. doi: 10.1016/j.scitotenv.2017.03.161. Epub 2017 Mar 27.
Monitoring of the quality of bathing water in line with the European Commission bathing water directive (Directive 2006/7/EC) is a significant economic expense for those countries with great lengths of coastline. In this study a numerical model based on finite elements is generated whose objective is partially substituting the microbiological analysis of the quality of coastal bathing waters. According to a study of the concentration of Escherichia coli in 299 Spanish Mediterranean beaches, it was established that the most important variables that influence the concentration are: monthly sunshine hours, mean monthly precipitation, number of goat cattle heads, population density, presence of Posidonia oceanica, UV, urbanization level, type of sediment, wastewater treatment ratio, salinity, distance to the nearest discharge, and wave height perpendicular to the coast. Using these variables, a model with an absolute error of 10.6±1.5CFU/100ml is achieved. With this model, if there are no significant changes in the beach environment and the variables remain more or less stable, the concentration of E. coli in bathing water can be determined, performing only specific microbiological analyses to verify the water quality.
根据欧盟浴场水指令（指令 2006/7/EC）对浴场水质量进行监测，对于那些拥有漫长海岸线的国家来说，是一项巨大的经济支出。在这项研究中，生成了一个基于有限元的数值模型，其目标是部分替代沿海浴场水质的微生物分析。根据对 299 个西班牙地中海水域大肠杆菌浓度的研究，确定了影响浓度的最重要变量是：每月日照小时数、平均每月降水量、山羊头数、人口密度、波西多尼亚海草的存在、紫外线、城市化水平、沉积物类型、污水处理率、盐度、离最近排放口的距离和垂直于海岸的波浪高度。使用这些变量，建立了一个绝对误差为 10.6±1.5CFU/100ml 的模型。有了这个模型，如果海滩环境没有重大变化，并且变量保持相对稳定，就可以确定浴场水中的大肠杆菌浓度，只需进行特定的微生物分析即可验证水质。
