A Decision Support Tool For Water Pollution and Eutrophication Prevention In Groundwater-dependent Shallow Lakes From Periurban Areas Based On The DPSIR Framework

Romanelli, Asunción; Lima, María Lourdes; Ondarza, Paola M.; Esquius, Karina Soledad; Massone, Héctor

doi:10.1007/s00267-021-01498-3

Cited by 11 publications

(3 citation statements)

References 42 publications

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“…The drivers-pressure-state impact response (DPSIR) is a conceptual framework, widely used to categorize indicators in a cause-effect chain for policymaking and decision support (Duan et al 2021, Romanelli et al 2021, Kosamu et al 2022. It is a result of strategy recommendation for integrated environmental assessments (EEA 1999) where drivers are linked to pressure then to state, impact and response indicators respectively (figure 2).…”

Section: Need To Develop a Causal Networkmentioning

confidence: 99%

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Suresh

Tang

Vliet

et al. 2023

Environ. Res. Lett.

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Eutrophication is a major global concern in lakes, caused by excessive nutrient loadings (nitrogen and phosphorus) from human activities and likely exacerbated by climate change. Present use of indicators to monitor and assess lake eutrophication is restricted to water quality constituents (e.g., total phosphorus, total nitrogen) and does not necessarily represent global environmental changes and the anthropogenic influences within the lake’s drainage basin. Nutrients interact in multiple ways with climate, basin conditions (e.g., socio-economic development, point-source, diffuse source pollutants), and lake systems. It is therefore essential to account for complex feedback mechanisms and non-linear interactions that exist between nutrients and lake ecosystems in eutrophication assessments. However, the lack of a set of water quality indicators that represent a holistic understanding of lake eutrophication challenges such assessments, in addition to the limited water quality monitoring data available. In this review, we synthesize the main indicators of eutrophication for global freshwater lake basins that not only include the water quality constituents but also the sources, biogeochemical pathways and responses of nutrient emissions. We develop a new causal network (i.e., multiple links of indicators) using the DPSIR (drivers-pressure-state-impact-response) framework that highlights complex interrelationships among the indicators and provides a holistic perspective of eutrophication dynamics in freshwater lake basins. We further review the 30 key indicators of drivers and pressures using seven cross-cutting themes: (i) hydro-climatology; (ii) socio-economy; (iii) land use; (iv) lake characteristics; (v) crop farming and livestock; (vi) hydrology and water management; (vii) fishing and aquaculture. This study indicates a need for more comprehensive indicators that represent the complex mechanisms of eutrophication in lake systems, to guide the global expansion of water quality monitoring networks, and support integrated assessments to manage eutrophication. Finally, the indicators proposed in this study can be used by managers and decision-makers to monitor water quality and set realistic targets for sustainable water quality management to achieve clean water for all, in line with Sustainable Development Goal 6.

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Section: Need To Develop a Causal Networkmentioning

confidence: 99%

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Suresh

Tang

Vliet

et al. 2023

Environ. Res. Lett.

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“…Two lake basins, La Brava and Los Padres, are highly susceptible to pollution and algal blooms, which can lead to algal bloom outbreaks. By combining a conceptual modelling tool (the DPSIR framework) with the development of a quantitative model (the Multi-Criteria Decision Making Model), the researchers propose a methodology for assessing water pollution problems that provides a practical and flexible assessment tool for the integrated assessment of eutrophication and agrochemicals in shallow lakes that depend on groundwater, as well as guidelines for decision-making environmental managers in developing countries in the context of medium-sized cities [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

Bai,

Niu,

Zhao

et al. 2024

Water

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The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms.

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“…Borja et al (2006) Niemeijer and de Groot (2008), Troian et al (2021)) and support decision making (e.g. Mysiak et al (2005), Pyrgaki et al (2021), Romanelli et al (2021)). In this thesis the framework is used as a structural frame to contextualize the environmental pharmaceutical pollution and its anthropogenic drivers as well as societal responses in an integrated assessment (see Figure 1-1).…”

Section: Pharmaceutical Pollution In Integrated Assessments Of Freshw...mentioning

confidence: 99%

An integrated assessment of pharmaceuticals in water systems

Wöhler¹

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A Decision Support Tool For Water Pollution and Eutrophication Prevention In Groundwater-dependent Shallow Lakes From Periurban Areas Based On The DPSIR Framework

Cited by 11 publications

References 42 publications

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Recent advancement in water quality indicators for eutrophication in global freshwater lakes

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

An integrated assessment of pharmaceuticals in water systems

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