Uncertainty of chemical status in surface waters

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.

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.

Seasonal variation in metal concentration in various tissues of the European chub (Squalius cephalus L.)

Due to the increasing industrialization, metals are discharged into all spheres of the environment, in particular, in river courses, which leads to the need for constant monitoring. Metals do not degrade into harmless end products; they are very persistent and have high potential for accumulation in biota. Metals in the fish body are accumulated in different amounts in the tissue specific matter. In relation to the biotic conditions and fish metabolism, the load of food, or the stage of the reproductive cycle, the seasonal variation of metal levels can be expected. Because of that, the objective of our present study was to analyze 15 metals and metalloids in liver, gills, muscle, and gonads of European chub (Squalius cephalus) throughout the 4 seasons, autumn, winter, spring, and summer. The specimens were collected from two rivers, Pestan and Beljanica at the Kolubara basin, and their concentrations were determined with inductively coupled plasma optical emission spectrometry (ICP-OES). Specimens from both rivers have shown similarities in metal accumulation like the highest accumulation of majority of elements in gills, lowest accumulation of majority of elements in muscle (except for Hg), and higher accumulation of some elements in summer (Cu, Fe, Zn). In addition, Cu and Fe showed affinity for liver, while Ba, Cr, Sr, and Zn were specific for gills. Also, Al, B, Fe, Ni, and Pb did not show significant differences in concentrations among different seasons in all investigated tissues.