The Metha-BioSol project aims to assess the impact of anaerobic digestion residues (digestates) on the biological and ecological quality of soils using an operational dashboard of indicators. This dashboard includes measurements of soil physical and chemical status, bio-indicators of soil biological communities and indicators of soil biological functions and soil health status. The project consisted of two parts: 1) To evaluate, under controlled conditions, the impact of different types of digestates taking into account the soil type, and on experimental sites, the impact of repeated digestate applications; and 2) To establish a national network of agricultural farms and conduct an assessment of the impact of digestate application combined with various agronomic practices in different pedoclimatic contexts. The results obtained from both controlled conditions and experimental sites showed that the response of bio-indicators to digestate application was dependent on the type of digestate, the pedoclimatic context, and the recurrence of these applications. The establishment of the farm network also provided the first elements for creating a reference framework for interpreting the impact of digestate use in varied territorial and sociotechnical contexts.
Rivers act as the primary harmonizer of the critical zone, reflecting the confluence of processes that regulate nutrient cycling, water quality, and energy flow within ecosystems. Understanding water quality dynamics and their relationship with hydrological and biogeochemical processes warrants the study of river chemistry at a detailed temporal resolution, but this is often hindered by logistical challenges in the acquisition of such data. Recent efforts in automated data acquisition, such as the RiverLab “lab-on-a-field” setup (Floury et al. 2017) have equipped us with a toolbox to peek into riverine systems at unprecedentedly fine temporal scales. This assemblage consists of a primary circuit that continuously samples water from the middle of a stream, and measures various physico-chemical parameters using probes. A portion of this water is also routed through a filtration system towards two ion chromatographs housed in the setup, allowing the measurement of dissolved ions. This system thus enables sub-hourly measurement of major ions and near-continuous monitoring of pH, Temperature, Conductivity, etc. However, despite these advances in data acquisition, technical challenges remain. The datasets often contain gaps due to practical limitations like sensor drift and malfunction, power outages, etc. These induce gaps in the multivariate time series datasets, and can impede the reliable application of statistical techniques, introduce bias in model outputs and obscure periodicities. Collectively, these pose a risk of misconstruing the true water quality patterns. To address these challenges and for an efficient understanding of high-frequency stream chemistry, here we present a novel two-pronged approach: Reconstruction and forecasting of stream chemistry using machine learning (ML) techniques; and Understanding trends and seasonalities in stream chemistry using Singular Spectrum Analysis (SSA). Reconstruction and forecasting of stream chemistry using machine learning (ML) techniques; and Understanding trends and seasonalities in stream chemistry using Singular Spectrum Analysis (SSA). We used one year of high-frequency stream chemistry measurements from the RiverLab at Orgeval Critical Zone Observatory (Loumagne and Tallec 2013), a constituent site of the OZCAR network (Gaillardet et al. 2018) of French Critical Zone Observatories. By incorporating the temporal structure and physico-chemical parameters (Discharge, Temperature, Conductivity, pH) as inputs, multiple ML-based models were developed and applied to predict the dissolved major ions in the stream. Preliminary results hold considerable promise, with the mean absolute percentage error (MAPE) on Cl - prediction remaining below 5% for forecasts extending up to one year. This underscores the potential of ML-assisted approaches in complementing cost-effective and accessible water quality monitoring, owing to the lower operating costs and near-continuous data output provided by the probes. Following this, we discuss the applications of Singular Spectrum Analysis (SSA) on the reconstructed RiverLab data. SSA is a non-parametric technique that combines aspects of classical time series analysis and multivariate statistics (e.g. Principal Component Analysis) to decompose the time series into trends, seasonalities and residual components without relying on the assumption of stationarity (Vautard and Ghil 1989). This study marks one of the first applications of SSA in hydrogeochemistry, and is proposed as a novel tool for exploring concentration-discharge (C-Q) relationships, water quality forecasting, anomaly detection and interpretation of periodic signals in streams.
Wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health at the population level by analyzing chemical and biological markers in wastewater (Singer et al., 2023). Wastewater-based surveillance (WBS), a subset of WBE, involves the consistent and targeted measurement of health indicators in wastewater samples (UNEP, 2023). This study explores the feasibility of using BIOFIRE® diagnostic panels—originally designed for clinical pathogen detection—for environmental surveillance through wastewater testing ( FilmArray® Panels , n.d.). Two research configurations of BIOFIRE® panels were developed for this purpose. Configuration 1 includes 33 assays targeting respiratory pathogens, antimicrobial resistance genes, and bacterial markers. Configuration 2 comprises 22 assays focused on enteric viruses, bacteria, and parasitic pathogens. These panels integrate sample preparation, nucleic acid extraction, nested multiplex PCR, and melt curve analysis, with real-time Cp values displayed via the FIREWORKS™ software platform to estimate pathogen signal strength. To evaluate performance, wastewater samples were collected and tested throughout 2023 at two study sites: SUEZ ( Our Group - SUEZ Group , n.d.), a wastewater management company, research center in France and the Enteric Virus Laboratory at the University of Barcelona ( UB - University of Barcelona - UB , n.d.). Both concentrated and non-concentrated influent samples were analyzed. The BIOFIRE® panels showed strong correlation with standard PCR methods and demonstrated robust detection across a wide range of pathogens including SARS-CoV-2, Influenza A/B, Respiratory Syncytial Virus, seasonal coronaviruses, Norovirus, Rotavirus, and various bacterial and AMR targets. Notably, Adenovirus , Norovirus, and several bacterial species were consistently detected across all samples and sites. These findings support the feasibility of using BIOFIRE® research panels for wastewater-based surveillance. The panels offer a promising approach for expanding environmental monitoring beyond SARS-CoV-2 to a broader spectrum of pathogens, contributing to the surveillance of environmental pathogens.
