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Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling., (© 2024. The Author(s).)

Our objective is to understand the effectiveness of local and international nutrient pollution mitigation efforts when targeting better water quality in the region's coastal waters. To this end, we developed an integrated modeling framework for the Archipelago Sea located in the Baltic Sea in Northern Europe, conducted what-if analyses for various ambition levels of nutrient abatement, and studied the long-term consequences at the sea basin scale. We demonstrate that in outer parts of the Archipelago Sea, a good eutrophication status can be achieved if the current internationally agreed policy goals for nutrient abatement are successfully met. In inner coastal areas, current goals for phytoplankton biomass could be reached only through extreme mitigation efforts in all polluting sectors and large-scale application of yet poorly tested ecological engineering methods. This result calls for carefully considering the relevance of current threshold values for phytoplankton and its role as a dominant indicator of good ecological status., (© 2023. The Author(s).)

Aim: We use lake phytoplankton community data to quantify the spatio-temporal and scale-dependent impacts of eutrophication, land-use and climate change on species niches and community assembly processes while accounting for species traits and phylogenetic constraints., Location: Finland., Time Period: 1977-2017., Major Taxa: Phytoplankton., Methods: We use hierarchical modelling of species communities (HMSC) to model metacommunity trajectories at 853 lakes over four decades of environmental change, including a hierarchical spatial structure to account for scale-dependent processes. Using a "region of common profile" approach, we evaluate compositional changes of species communities and trait profiles and investigate their temporal development., Results: We demonstrate the emergence of novel and widespread community composition clusters in previously more compositionally homogeneous communities, with cluster-specific community trait profiles, indicating functional differences. A strong phylogenetic signal of species responses to the environment implies similar responses among closely related taxa. Community cluster-specific species prevalence indicates lower taxonomic dispersion within the current dominant clusters compared with the historically dominant cluster and an overall higher prevalence of smaller species sizes within communities. Our findings denote profound spatio-temporal structuring of species co-occurrence patterns and highlight functional differences of lake phytoplankton communities., Main Conclusions: Diverging community trajectories have led to a nationwide reshuffling of lake phytoplankton communities. At regional and national scales, lakes are not single entities but metacommunity hubs in an interconnected waterscape. The assembly mechanisms of phytoplankton communities are strongly structured by spatio-temporal dynamics, which have led to novel community types, but only a minor part of this reshuffling could be linked to temporal environmental change., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd.)

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In situ high-frequency measured turbidity can potentially be used as a surrogate for riverine phosphorus (P) concentrations to better justify the effectiveness of nutrient loss mitigation measures at agricultural sites. We explore the possibilities of using turbidity as a surrogate for total phosphorus (TP) and particulate phosphorus (PP) in four snowmelt-driven rivers draining agricultural clayey catchments. Our results suggest slightly stronger relationship between in situ measured turbidity and PP than between turbidity and TP. Overall, linear TP and PP regressions showed better error statistics in the larger catchments compared with their sub-catchments. Local calibration of the in situ sensors was sensitive to the number of high P concentration discrete water samples. Two optional calibration curves, one with and one without influential data, resulted in a 17% difference in the estimated mean TP concentrations of a snowmelt storm contributing 18% of the annual discharge volume. Accordingly, the error related to monthly mean TP estimates was the largest in spring months at all sites. The addition of total dissolved phosphorus (TDP) improved the model performance, especially for sites where the TDP/TP ratio is large and highly variable over time. We demonstrate how long-term discrete samples beyond sensor deployment can be utilized in the evaluation of the applicability range of the local calibration. We recommend analysing the validity of P concentration estimates, especially during high discharge episodes that contribute substantially to annual riverine nutrient fluxes, since the use of surrogates may introduce large differences into the P concentration estimates based on selected local calibration curves.

The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU's Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying the uncertainties in the status class estimation. We estimated the temporal and spatial variance components, as well as the effect of sampling allocation to the precision and confidence of chlorophyll-a and total phosphorus. Our results suggest that almost 70% of the lake and coastal waterbodies, and 27% of the river waterbodies, were classified without sufficient confidence in these variables. On the other hand, many of the waterbodies produced unnecessary precise metric means. Thus, reallocation of sampling effort is needed. Our results show that, even though the studied variables are among the most monitored status metrics, the unexplained variation is still high. Combining multiple data sets and using fixed covariates would improve the modeling performance. Our study highlights that ongoing monitoring programs should be evaluated more systematically, and the information from the statistical uncertainty analysis should be brought concretely to the decision-making process.

The biological status of European lakes has not improved as expected despite up-to-date legislation and ecological standards. As a result, the realism of objectives and the attainment of related ecological standards are under doubt. This paper gets to the bottom of a river basin management plan of a eutrophic lake in Finland and presents the ecological and economic impacts of environmental and societal drivers and planned management measures. For these purposes, we performed a Monte Carlo simulation of a diffuse nutrient load, lake water quality and cost-benefit models. Simulations were integrated into a Bayesian influence diagram that revealed the basic uncertainties. It turned out that the attainment of good ecological status as qualified in the Water Framework Directive of the European Union is unlikely within given socio-economic constraints. Therefore, management objectives and ecological and economic standards need to be reassessed and reset to provide a realistic goal setting for management. More effort should be put into the evaluation of the total monetary benefits and on the monitoring of lake phosphorus balances to reduce the uncertainties, and the resulting margin of safety and costs and risks of planned management measures.

Implementation of the EU Water Framework Directive (WFD) has set a great challenge on river basin management planning. Assessing the water quality of lakes and coastal waters as well as setting the accepted nutrient loading levels requires appropriate decision supporting tools and models. Uncertainty that is inevitably related to the assessment results and rises from several sources calls for more precise quantification and consideration. In this study, we present a modeling tool, called lake load response (LLR), which can be used for statistical dimensioning of the nutrient loading reduction. LLR calculates the reduction that is needed to achieve good ecological status in a lake in terms of total nutrients and chlorophyll a (chl-a) concentration. We show that by combining an empirical nutrient retention model with a hierarchical chl-a model, the national lake monitoring data can be used more efficiently for predictions to a single lake. To estimate the uncertainties, we separate the residual variability and the parameter uncertainty of the modeling results with the probabilistic Bayesian modeling framework. LLR has been developed to answer the urgent need for fast and simple assessment methods, especially when implementing WFD at such an extensive scale as in Finland. With a case study for an eutrophic Finnish lake, we demonstrate how the model can be utilized to set the target loadings and to see how the uncertainties are quantified and how they are accumulating within the modeling chain.

Sensor networks are increasingly being implemented for environmental monitoring and agriculture to provide spatially accurate and continuous environmental information and (near) real-time applications. These networks provide a large amount of data which poses challenges for ensuring data quality and extracting relevant information. In the present paper we describe a river basin scale wireless sensor network for agriculture and water monitoring. The network, called SoilWeather, is unique and the first of this type in Finland. The performance of the network is assessed from the user and maintainer perspectives, concentrating on data quality, network maintenance and applications. The results showed that the SoilWeather network has been functioning in a relatively reliable way, but also that the maintenance and data quality assurance by automatic algorithms and calibration samples requires a lot of effort, especially in continuous water monitoring over large areas. We see great benefits on sensor networks enabling continuous, real-time monitoring, while data quality control and maintenance efforts highlight the need for tight collaboration between sensor and sensor network owners to decrease costs and increase the quality of the sensor data in large scale applications.