Optimal design of river monitoring network in Taizihe River by matter element analysis

The study on optimal design of river monitoring network using modified approaching degree model: a case study of the Liaohe River, Northeast China

This paper proposes a quantitative method to optimize the existing river monitoring network based on a modified approaching degree model, T test, and Euclidean distance. In this study, the Liaohe River located in Liaoning province, China, was taken as a research object. Samples were collected from 8 sampling sites throughout the monitoring network, and water quality parameters were analyzed every 2 months from January 2009 to December 2010. The results show that the average concentrations of the ammonia nitrogen (NH₄⁺-N) and chemical oxygen demand (COD) were beyond grade III of the Environmental Quality Standards for Surface Water of China (GB3838-2002), and they were the main water quality parameters. After optimization, the number of monitoring sections along the Liaohe River was reduced to five from the original eight, thus saving 37.5% of the monitoring cost; meanwhile, there is no significant difference between the un-optimized and optimized monitoring networks, and the optimized monitoring network remains to be able to perform as good as the original one. In addition, the total data attainment rate was improved greatly, and the duplicate setting degree of monitoring points decreased significantly compared with other optimal methods. The optimized monitoring network proves to be more efficient, reasonable, and economically feasible, so this quantitative method can help optimize the changing orderly river monitoring networks.

Methodological design for the macro-location of a micropollutants monitoring network in tropical rivers: a case study in Cauca River

Establishing scientifically the macro-location of a micropollutants monitoring network in tropical Andean rivers is a complex process, because information gathering is restricted by high-cost of analysis and limited availability of analytical techniques, which lead to inadequate sampling strategies that hinder the representativeness of samples. Thus, this work proposes a methodology for determining the number of representative sampling sections in a micropollutant monitoring network to characterise the ecological risk in tropical Andean torrential rivers. The proposed methodology consists of four stages: identification of the potential sampling units by Spline interpolation; calculation of the number of representative sections for a stratified sampling with an acceptable level of confidence and error; spatial allocation of the potential sampling units into sections by hierarchical cluster analysis; and representation of the spatial distribution of the sampling sections through a geographic information system (GIS). The proposed methodology is dynamic, and therefore, it can be revisited as more data are obtained in the subsequent years; it has the possibility of being applied to other inter-Andean valley rivers that interact with the tropical Andean sloppy mountains and serves as a tool for decision making by environmental authorities regarding the optimisation of the existing monitoring networks in terms of micropollutants to promote sustainable management of water resources. The proposed methodology is applied in the Upper Cauca River Basin (UCRB), which is located in southwest Colombia, South America.

Optimizing a coastal monitoring network using a water-quality response grid (WRG)-based sampling design for improved reliability and efficiency

Marine monitoring in Bohai Sea is delivered within three networks by lacking appropriate sampling and assessment methodologies. Water-quality response grid (WRG)-based sampling design using optimization and multi-factors assessment can reliably detect a variety of environmental impacts. Which includes 5 steps: selects environmental reference factors, divides the sampling grid, sets the initial stations, optimizes the sampling stations, and assesses the proposed network's reproducibility and efficiency. We applied this method to the Bohai Sea, the networks proposed here have 225 stations for optimized special surveys (OSS) and 181 stations for optimized operational monitoring (OOM), accounting for 46.5% and 37.4% of the original station totals, respectively. Besides, the reproducibility and efficiency index (REI) of OSS and OOM stations approximately 15.4% and 13.3% higher than three current monitoring networks on average among multi-factors in 4 seasons. Thus, the method can improve the reproducibility, efficiency and land-sea spatial matching of monitoring network.

Optimal river monitoring network using optimal partition analysis: a case study of Hun River, Northeast China

River monitoring networks play an important role in water environmental management and assessment, and it is critical to develop an appropriate method to optimize the monitoring network. In this study, an effective method was proposed based on the attainment rate of National Grade III water quality, optimal partition analysis and Euclidean distance, and Hun River was taken as a method validation case. There were 7 sampling sites in the monitoring network of the Hun River, and 17 monitoring items were analyzed once a month during January 2009 to December 2010. The results showed that the main monitoring items in the surface water of Hun River were ammonia nitrogen (NH₄⁺-N), chemical oxygen demand, and biochemical oxygen demand. After optimization, the required number of monitoring sites was reduced from seven to three, and 57% of the cost was saved. In addition, there were no significant differences between non-optimized and optimized monitoring networks, and the optimized monitoring networks could correctly represent the original monitoring network. The duplicate setting degree of monitoring sites decreased after optimization, and the rationality of the monitoring network was improved. Therefore, the optimal method was identified as feasible, efficient, and economic.

Insights from a Systematic Search for Information on Designs, Costs, and Effectiveness of Poliovirus Environmental Surveillance Systems

Poliovirus surveillance plays a critical role in achieving and certifying eradication and will play a key role in the polio endgame. Environmental surveillance can provide an opportunity to detect circulating polioviruses prior to the observation of any acute flaccid paralysis cases. We completed a systematic review of peer-reviewed publications on environmental surveillance for polio including the search terms "environmental surveillance" or "sewage," and "polio," "poliovirus," or "poliomyelitis," and compared characteristics of the resulting studies. The review included 146 studies representing 101 environmental surveillance activities from 48 countries published between 1975 and 2016. Studies reported taking samples from sewage treatment facilities, surface waters, and various other environmental sources, although they generally did not present sufficient details to thoroughly evaluate the sewage systems and catchment areas. When reported, catchment areas varied from 50 to over 7.3 million people (median of 500,000 for the 25% of activities that reported catchment areas, notably with 60% of the studies not reporting this information and 16% reporting insufficient information to estimate the catchment area population size). While numerous studies reported the ability of environmental surveillance to detect polioviruses in the absence of clinical cases, the review revealed very limited information about the costs and limited information to support quantitative population effectiveness of conducting environmental surveillance. This review motivates future studies to better characterize poliovirus environmental surveillance systems and the potential value of information that they may provide in the polio endgame.

Dynamic water quality evaluation based on fuzzy matter-element model and functional data analysis, a case study in Poyang Lake

Comprehensively evaluating water quality with a single method alone is challenging because water quality evaluation involves complex, uncertain, and fuzzy processes. Moreover, water quality evaluation is limited by finite water quality monitoring that can only represent water quality conditions at certain time points. Thus, the present study proposed a dynamic fuzzy matter-element model (D-FME) to comprehensively and continuously evaluate water quality status. D-FME was first constructed by introducing functional data analysis (FDA) theory into a fuzzy matter-element model and then validated using monthly water quality data for the Poyang Lake outlet (Hukou) from 2011 to 2012. Results showed that the finite water quality indicators were represented as dynamic functional curves despite missing values and irregular sampling time. The water quality rank feature curve was integrated by the D-FME model and revealed comprehensive and continuous variations in water quality. The water quality in Hukou showed remarkable seasonal variations, with the best water quality in summer and worst water quality in winter. These trends were significantly correlated with water level fluctuations (R = -0.71, p < 0.01). Moreover, the extension weight curves of key indicators indicated that total nitrogen and total phosphorus were the most important pollutants that influence the water quality of the Poyang Lake outlet. The proposed D-FME model can obtain scientific and intuitive results. Moreover, the D-FME model is not restricted to water quality evaluation and can be readily applied to other areas with similar problems.