Department of Environmental Sciences and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, South Korea.
Empresa Pública Municipal de Agua Potable y Alcantarillado de Ibarra EMAPA-I, Antonio José de Sucre 7-77 and Pedro Moncayo Street, Ibarra, Imbabura, Ecuador.
Environ Monit Assess. 2018 Mar 30;190(4):259. doi: 10.1007/s10661-018-6639-x.
Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.
湖泊 Yahuarcocha 的污染和富营养化过程正在加剧，因此对水质进行持续监测对于更好地了解该生态系统中发生的情况至关重要。在这项研究中，使用时空分析结合地理信息系统（GIS）确定了关键传感器位置，以评估天气特征、人为活动和其他非点污染源的影响。建立了一个水质监测网络，在 13 个月的时间内，在七个采样点的每一个点上获取 14 个物理化学和微生物参数的数据。使用模式识别技术（如聚类分析（CA）和判别分析（DA））进行时空统计分析，对湖泊中最重要的水质参数进行分类和识别。根据最重要参数浓度变化插值的模糊叠加，将原始监测网络减少到四个最佳传感器位置。
