Monitoring water quality in urban stream is of utmost importance for water resources managers, who are pressured to optimize monitoring schemes in order to reduce costs. The present study aims to use the results of a 2-year-long water quality monitoring program of an urban stream in Portugal to identify improvement opportunities. The urban stream under study was subjected to wastewater treatment plants effluent discharges, leachates from a major sealed landfill, low-class housing effluents, and nonpoint sources of pollution. Contributing watersheds are mostly artificial surfaces and agricultural land, which irrigate directly from the river. River water quality was evaluated on 11 sampling locations for 24 months from October 2013 to September 2015. The present paper describes statistical analysis of the results obtained for 12 physicochemical parameters in order to optimize the monitoring scheme. Cluster analysis detected a seasonal variation in the water quality and a spatial pattern based on the major point sources of pollution. A factor analysis showed that the parameters that mostly contribute to water quality assessment in this urban river are alkalinity, ammonia, electrical conductivity, pH, temperature, and dissolved oxygen. Results suggest that the monitoring efforts-and associated costs-may be reduced by decreasing monitoring frequency, sampling points, and monitored parameters. The statistical analysis described in this study may be replicated in other water quality monitoring programs, providing useful and important information for the systematic and iterative assessment of the adequacy of water quality sampling programs towards a sustainable management of water quality surveillance.

中文翻译：

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关于监测城市河流水质的考虑：葡萄牙的一个案例研究。

对于水资源管理人员而言，监测城市溪流中的水质至关重要，他们面临着优化监测方案以降低成本的压力。本研究旨在利用葡萄牙城市河流一项为期两年的水质监测计划的结果来确定改善的机会。所研究的城市溪流受到废水处理厂的废水排放，主要密封垃圾填埋场的渗滤液，低级房屋废水和非点源污染的影响。造成影响的集水区主要是人工地表和农田，它们直接从河中灌溉。2013年10月至2015年9月，在11个采样点进行了24个月的河流水质评估。本文描述了对12个理化参数获得的结果的统计分析，以优化监测方案。聚类分析发现了水质的季节性变化和基于主要污染源的空间格局。因子分析表明，对该城市河流的水质评估最主要的参数是碱度，氨，电导率，pH，温度和溶解氧。结果表明，可以通过降低监视频率，采样点和监视参数来减少监视工作量和相关成本。本研究中描述的统计分析可以在其他水质监测计划中重复使用，