Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.

Sources, fate and distribution of inorganic contaminants in the Svalbard area, representative of a typical Arctic critical environment–a review

Global environmental changes not only contribute to the modification of global pollution transport pathways but can also alter contaminant fate within the Arctic. Recent reports underline the importance of secondary sources of pollution, e.g. melting glaciers, thawing permafrost or increased riverine run-off. This article reviews reports on the European Arctic–we concentrate on the Svalbard region–and environmental contamination by inorganic pollutants (heavy metals and artificial radionuclides), including their transport pathways, their fate in the Arctic environment and the concentrations of individual elements in the ecosystem. This review presents in detail the secondary contaminant sources and tries to identify knowledge gaps, as well as indicate needs for further research. Concentrations of heavy metals and radionuclides in Svalbard have been studied, in various environmental elements since the beginning of the twentieth century. In the last 5 years, the highest concentrations of Cd (13 mg kg−1) and As (28 mg kg−1) were recorded for organic-rich soils, while levels of Pb (99 mg kg−1), Hg (1 mg kg−1), Zn (496 mg kg−1) and Cu (688 mg kg−1) were recorded for marine sediments. Increased heavy metal concentrations were also recorded in some flora and fauna species. For radionuclides in the last 5 years, the highest concentrations of 137Cs (4500 Bq kg−1), 238Pu (2 Bq kg−1) and 239 + 240Pu (43 Bq kg−1) were recorded for cryoconites, and the highest concentration of 241Am (570 Bq kg−1) was recorded in surface sediments. However, no contamination of flora and fauna with radionuclides was observed.

Prompt location of indoor instantaneous air contaminant source through multi-zone model-based probability method by utilizing airflow data from coarse-grid CFD model

In order to ensure indoor air quality safety, locating the airborne contaminant sources accurately and quickly is extremely important so that timely measures can be taken to undermine the spread of pollutant and even eliminate the negative effects. Previous studies have shown that the multi-zone model can greatly reduce the inverse calculation time. However, the multi-zone model cannot describe the details of the indoor velocity field, which limits its application in complex multi-zone or large space buildings. On the premise of the accuracy and computational speed, based on the joint probability method, this study adopted the coarse-grid CFD method to speed up the process of acquiring the indoor airflow field, together with the multi-zone model method, to perform the inverse calculation of indoor airborne contaminant source location. In the backward calculation process, we conducted the ‘transpose' of the velocity field to obtain adjoint matrix, instead of computing ‘negative' of the velocity vector to save the calculation time. A two-dimensional ventilation model was utilized to validate the method, which proved the accuracy and time-saving potential of it. This study provides theoretical and practical prospect for the real-time inverse calculation of locating the indoor airborne contaminant sources.

Stochastic Particle Tracking Application in Different Urban Areas in Central Europe: The Milano (IT) and Jaworzno (PL) Case Study to Secure the Drinking Water Resources

Urban areas are typically characterized by the presence of industrial sites, which are often sources of groundwater contamination, posing a serious threat for the groundwater. In such cases, a crucial step is to find a link between the contaminant sources and freshwater supply wells at risk. As a part of the AMIIGA Project, two different stochastic approaches were applied to assess drinking water supply wells vulnerability in Functional Urban Areas in the presence of several chlorinated hydrocarbons sources in an alluvial aquifer in Milano and a pesticide mega site in a complex geological setting in Poland. In the first case study, the innovative Pilot Point Null-Space Monte Carlo forward particle tracking was used, applying a forward solution instead of the classical backtracking, while in the second case was chosen the classical Monte Carlo methodology. Both case studies represent useful application examples, allowing an effective prioritization of expensive remediation actions in order to protect freshwater wells.

Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

A Multidisciplinary Approach to Evaluate the Effectiveness of Natural Attenuation at a Contaminated Site

This study evaluates the natural attenuation of chlorinated hydrocarbons as remediation action in a contaminated site downtown the city of Parma (Italy). To achieve this goal, a combination of new investigation methods (bio-molecular analysis, compound specific isotope analysis, phytoscreening) has been proposed. The approach (named circular multi step) allows to: fully understand the phenomena that occur at the study site, design new investigation activities, and manage best practices. Consequently, each step of the approach improves the conceptual and numerical models with new knowledge. The activities carried out at the study site allowed to detect a contamination of perchloroethylene in a large part of the city of Parma and, of main importance, underneath a kindergarten. The results of the study did not show significant natural attenuation of chlorinated hydrocarbons and that the detected contamination could refer to the same unknown contaminant source. Furthermore, the innovative phytoscreening technique was applied to assess the presence of chlorinated hydrocarbons at the ground level. The plume spread was estimated through numerical modeling starting from potential contaminant sources. This study enhances the knowledge of groundwater flow and contamination in Parma and allows authorities to design new investigation/reclamation activities through management actions.