Uncertainty in Environmental Micropollutant Modeling.

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.

Macrosystem community change in lake phytoplankton and its implications for diversity and function.

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.

High-frequency measured turbidity as a surrogate for phosphorus in boreal zone rivers: appropriate options and critical situations.

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.

Correction to: High-frequency measured turbidity as a surrogate for phosphorus in boreal zone rivers: appropriate options and critical situations.

The original version of this article unfortunately contained a mistake.

A practical approach to improve the statistical performance of surface water monitoring networks.

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.

Probabilistic Evaluation of Ecological and Economic Objectives of River Basin Management Reveals a Potential Flaw in the Goal Setting of the EU Water Framework Directive.

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.

Statistical Dimensioning of Nutrient Loading Reduction: LLR Assessment Tool for Lake Managers.

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.

Good eutrophication status is a challenging goal for coastal waters.

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.

Strategies for integrating scientific evidence in water policy and law in the face of uncertainty.

Understanding how human actions and environmental change affect water resources is crucial for addressing complex water management issues. The scientific tools that can produce the necessary information are ecological indicators, referring to measurable properties of the ecosystem state; environmental monitoring, the data collection process that is required to evaluate the progress towards reaching water management goals; mathematical models, linking human disturbances with the ecosystem state to predict environmental impacts; and scenarios, assisting in long-term management and policy implementation. Paradoxically, despite the rapid generation of data, evolving scientific understanding, and recent advancements in systems modeling, there is a striking imbalance between knowledge production and knowledge utilization in decision-making. In this paper, we examine the role and potential capacity of scientific tools in guiding governmental decision-making processes and identify the most critical disparities between water management, policy, law, and science. We demonstrate how the complex, uncertain, and gradually evolving nature of scientific knowledge might not always fit aptly to the legislative and policy processes and structures. We contend that the solution towards increased understanding of socio-ecological systems and reduced uncertainty lies in strengthening the connections between water management theory and practice, among the scientific tools themselves, among different stakeholders, and among the social, economic, and ecological facets of water quality management, law, and policy. We conclude by tying in three knowledge-exchange strategies, namely - adaptive management, Driver-Pressure-Status-Impact-Response (DPSIR) framework, and participatory modeling - that offer complementary perspectives to bridge the gap between science and policy.

Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling.

The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services.

The Anthropocene presents formidable threats to freshwater ecosystems. Lakes are especially vulnerable and important at the same time. They cover only a small area worldwide but harbour high levels of biodiversity and contribute disproportionately to ecosystem services. Lakes differ with respect to their general type (e.g. land-locked, drainage, floodplain and large lakes) and position in the landscape (e.g. highland versus lowland lakes), which contribute to the dynamics of these systems. Lakes should be generally viewed as 'meta-systems', whereby biodiversity is strongly affected by species dispersal, and ecosystem dynamics are contributed by the flow of matter and substances among locations in a broader waterscape context. Lake connectivity in the waterscape and position in the landscape determine the degree to which a lake is prone to invasion by non-native species and accumulation of harmful substances. Highly connected lakes low in the landscape accumulate nutrients and pollutants originating from ecosystems higher in the landscape. The monitoring and restoration of lake biodiversity and ecosystem services should consider the fact that a high degree of dynamism is present at local, regional and global scales. However, local and regional monitoring may be plagued by the unpredictability of ecological phenomena, hindering adaptive management of lakes. Although monitoring data are increasingly becoming available to study responses of lakes to global change, we still lack suitable integration of models for entire waterscapes. Research across disciplinary boundaries is needed to address the challenges that lakes face in the Anthropocene because they may play an increasingly important role in harbouring unique aquatic biota as well as providing ecosystem goods and services in the future.

The value of perfect and imperfect information in lake monitoring and management.

Uncertainty in the information obtained through monitoring complicates decision making about aquatic ecosystems management actions. We suggest the value of information (VOI) to assess the profitability of paying for additional monitoring information, when taking into account the costs and benefits of monitoring and management actions, as well as associated uncertainty. Estimating the monetary value of the ecosystem needed for deriving VOI is challenging. Therefore, instead of considering a single value, we evaluate the sensitivity of VOI to varying monetary value. We also extend the VOI analysis to the more realistic context where additional information does not result in perfect, but rather in imperfect information on the true state of the environment. Therefore, we analytically derive the value of perfect information in the case of two alternative decisions and two states of uncertainty. Second, we describe a Monte Carlo type of approach to evaluate the value of imperfect information about a continuous classification variable. Third, we determine confidence intervals for the VOI with a percentile bootstrap method. Results for our case study on 144 Finnish lakes suggest that generally, the value of monitoring exceeds the cost. It is particularly profitable to monitor lakes that meet the quality standards a priori, to ascertain that expensive and unnecessary management can be avoided. The VOI analysis provides a novel tool for lake and other environmental managers to estimate the value of additional monitoring data for a particular, single case, e.g. a lake, when an additional benefit is attainable through remedial management actions.

Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems.

Climate and land-use change drive a suite of stressors that shape ecosystems and interact to yield complex ecological responses (that is, additive, antagonistic and synergistic effects). We know little about the spatial scales relevant for the outcomes of such interactions and little about effect sizes. These knowledge gaps need to be filled to underpin future land management decisions or climate mitigation interventions for protecting and restoring freshwater ecosystems. This study combines data across scales from 33 mesocosm experiments with those from 14 river basins and 22 cross-basin studies in Europe, producing 174 combinations of paired-stressor effects on a biological response variable. Generalized linear models showed that only one of the two stressors had a significant effect in 39% of the analysed cases, 28% of the paired-stressor combinations resulted in additive effects and 33% resulted in interactive (antagonistic, synergistic, opposing or reversal) effects. For lakes, the frequencies of additive and interactive effects were similar for all spatial scales addressed, while for rivers these frequencies increased with scale. Nutrient enrichment was the overriding stressor for lakes, with effects generally exceeding those of secondary stressors. For rivers, the effects of nutrient enrichment were dependent on the specific stressor combination and biological response variable. These results vindicate the traditional focus of lake restoration and management on nutrient stress, while highlighting that river management requires more bespoke management solutions.

Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland: Evaluation from a Data User's Perspective.

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.

Participatory operations model for cost-efficient monitoring and modeling of river basins--A systematic approach.

The worldwide economic downturn and the climate change in the beginning of 21st century have stressed the need for cost efficient and systematic operations model for the monitoring and management of surface waters. However, these processes are still all too fragmented and incapable to respond these challenges. For example in Finland, the estimation of the costs and benefits of planned management measures is insufficient. On this account, we present a new operations model to streamline these processes and to ensure the lucid decision making and the coherent implementation which facilitate the participation of public and all the involved stakeholders. The model was demonstrated in the real world management of a lake. The benefits, pitfalls and development needs were identified. After the demonstration, the operations model was put into operation and has been actively used in several other management projects throughout Finland.