International audience | Treatment Wetlands (TW) are employed in the treatment of wastewater, participating in a decentralised and sustainable urban water management. Their designs and usages vary greatly across the world, which limit the aggregation of comparable data for deeper understanding of the mechanisms ruling over water treatment. We developed a hybrid modelling methodology to combine mechanistic and empirical pre-sizing models, applied to a context of low amount of data and knowledge on Horizontal and Vertical Flow TW. We first collected data from the scientific literature and introduced a data quality validation step providing reliability weights associated with each observation. We secondly trained and compared machine-learning models on the assessed dataset. We finally tested two hybridizations with the tank-in-series model, and predicting an optimized surface with a uncertainty interval. This methodology is reproducible and we believe hybrid models can provide more accurate and reliable predictions, therefore facilitating implementations of TW in urban plannings.

Worldwide, coastal waters contain pollutants such as nutrients, plastics, and chemicals. Rivers export those pollutants, but their sources are not well studied. Our study aims to quantify river exports of nutrients, chemicals, and plastics to coastal waters by source and sub-basin worldwide. We developed a new MARINA-Multi model for 10,226 sub-basins. The global modelled river export to seas is approximately 40,000 kton of nitrogen, 1,800 kton of phosphorous, 45 kton of microplastics, 490 kton of macroplastics, 400 ton of triclosan and 220 ton of diclofenac. Around three-quarters of these pollutants are transported to the Atlantic and Pacific oceans. Diffuse sources contribute by 95–100 % to nitrogen (agriculture) and macroplastics (mismanaged waste) in seas. Point sources (sewage) contribute by 40–95 % to phosphorus and microplastics in seas. Almost 45 % of global sub-basin areas are multi-pollutant hotspots hosting 89 % of the global population. Our findings could support strategies for reducing multiple pollutants in seas.

Many tropical coastal ecosystems face human pressures related to tourism, land or sea use. We developed a practical procedure to involve stakeholders in an early stage of an ecological research project to map the Social-Ecological System (SES) in our case study Lac Bay, Bonaire island, as well as to identify and prioritize ecological research questions and nature management options in relation to a recent new threat: massive sargassum landings. In our procedure we used the Group Model Building methodology for identifying drivers, key variables and feedback loops in this SES. The underlying mechanisms of driving feedbacks were revealed and shared during these sessions. We identified and prioritized urgent ecological research questions for the conservation of seagrass beds and mangrove forests, and practical measures for nature management in Lac Bay. Both were used in follow-up scientific research and nature management plans, illustrating the applicability of our procedure for early science-stakeholder interaction.

Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and characterizing macroplastics in aquatic environments. Despite numerous studies focusing on bands of interest in the shortwave infrared spectrum, the high cost of sensors in this range makes it difficult to mass-produce them for long-term and large-scale applications. Therefore, we present the assessment and transfer of various machine learning models across four datasets to identify the key bands for detecting and classifying the most prevalent plastics in the marine environment within the visible and near-infrared (VNIR) range. Our study uses four different databases ranging from virgin plastics under laboratory conditions to weather plastics under field conditions. We used Sequential Feature Selection (SFS) and Random Forest (RF) models for the optimal band selection. The significance of homogeneous backgrounds for accurate detection is highlighted by a 97 % accuracy, and successful band transfers between datasets (87 %–91 %) suggest the feasibility of a sensor applicable across various scenarios. However, the model transfer requires further training for each specific dataset to achieve optimal accuracy. The results underscore the potential for broader application with continued refinement and expanded training datasets. Our findings provide valuable information for developing compelling and affordable detection sensors to address plastic pollution in coastal areas. This work paves the way towards enhancing the accuracy of marine litter detection and reduction globally, contributing to a sustainable future for our oceans.

Cryptic species are rarely considered in ecotoxicology, resulting in misleading outcomes when using a single morphospecies that encompasses multiple cryptic species. This oversight contributes to the lack of reproducibility in ecotoxicological experiments and promotes unreliable extrapolations. The important question of ecological differentiation and the sensitivity of cryptic species is rarely tackled, leaving a substantial knowledge gap regarding the vulnerability of individual cryptic species within species complexes. In times of agricultural intensification and the frequent use of pesticides, there is an urgent need for a better understanding of the vulnerability of species complexes and possible differences in adaptive processes. We used the cryptic species complex of the aquatic amphipod Gammarus roeselii, which comprises at least 13 genetic mtDNA lineages and spans from small-scale endemic lineages in Greece to a large-scale widely distributed lineage in central Europe. We exposed eleven populations belonging to four lineages to the neonicotinoid thiacloprid in an acute toxicity assay. We recorded various environmental variables in each habitat to assess the potential pre-exposure of the populations to contaminants. Our results showed that the populations differed up to 4-fold in their tolerances. The lineage identity had a rather minor influence, suggesting that the cryptic species complex G. roeselii does not differ significantly in tolerance to the neonicotinoid thiacloprid. However, the observed population differentiation implies that recent pre-exposure to thiacloprid (or similar substances) or general habitat contamination has triggered adaptive processes. Though, the extent to which these mechanisms are equally triggered in all lineages needs to be addressed in the future. Our study provides two key findings: Firstly, it shows that observed phylogenetic differences within the G. roeselii species complex did not reveal differences in thiacloprid tolerance. Second, it confirms that differentiation occurs at the population level, highlighting that susceptibility to toxicants is population-dependent. The population-specific differences were within the range of accepted intraspecific variability from a regulatory standpoint. From an evolutionary-ecological perspective, it remains intriguing to observe how persistent stresses will continue to influence tolerance and whether different populations are on distinct pathways of adaptation. Given that the potential selection process has only lasted a relatively short number of generations, it is crucial to monitor these populations in the future, as even brief exposure periods significantly impact evolutionary responses.

A study on the biodiversity and isolation of freshwater algae from some reservoirs of Mae Rim Campus, Chiang Mai Rajabhat University, Chiang Mai Province, collected algal samples and assessed the water quality at four reservoirs, including Wiang Bua Reservoir, Ma Lang Por Reservoir, Education Auditorium reservoir, and Kru Noi Garden Reservoir. One hundred and six species of algae belonging to 8 phyla were found. The most prominent species were Cylindrospermopsis philippinensis, Trachelomonas volvocina, Peridiniopsis sp., and Coelastrum astroideum, respectively. The overall water quality was categorized as clean according to some physical and chemical parameters by the National Environmental Board of Thailand. However, high BOD values were detected at some sampling points. The algae isolation included 8 isolates, which could be utilized for various purposes in the future, such as biomass, protein, polysaccharide energy, bioactive compounds, antioxidant substances, wastewater treatment, environmental indicators, algal toxins, and phylogenetic studies. All strains were stored at the Centre of Excellence of Biodiversity Research and Implementation for Community, Chiang Mai Rajabhat University, for conservation and future development purposes.

Heavy metal contamination in soil poses a significant environmental challenge globally, affecting agricultural productivity and human health. Phytoremediation, using plants to extract and detoxify heavy metals, presents a promising solution. This study investigates the novel potential of Echium plantagineum, a metal-tolerant species, in phytostabilization and phytoremediation and explores the role of metallothioneins in heavy metal reduction. A comprehensive literature review identified known metallothioneins involved in heavy metal reduction across various plant species. Moreover, genome annotation and gene prediction of Echium plantagineum were performed, predicting a total of 39,520 proteins. This comprehensive protein list facilitates the identification of metallothioneins or other metal-related proteins with potential functional roles in heavy metal tolerance, suggesting new targets to improve the effectiveness of phytoremediation. The sequences of these proteins were utilized to construct a protein BLAST database, against which known metallothioneins protein sequences from other plant species were subjected to BLAST searches, resulting in 41 top hits. Subsequent 3D modeling, structural analysis, protein-metal virtual screening, and functional annotation of the proteins revealed novel high affinities of Ctr copper transporter, zinc/iron permease, and nicotianamine synthase proteins with nickel, zinc, and zinc ion, suggesting their unexplored roles in the uptake of aforementioned ligands. Notably, this study identifies novel metallothioneins proteins in Echium plantagineum, highlighting their role in metal tolerance and phytoremediation.

Soil erosion in the agricultural landscape of Assam has been impacting the livelihoods of millions. In administrative regions like districts, which are vulnerable to natural disasters like floods and bank erosion, GIS-based soil erosion estimating studies can help planners and policymakers identify areas of soil erosion to implement scientific conservation measures. The main purpose of this study is to estimate soil loss and to determine soil loss zones in the Morigaon district of Assam. The Revised Universal Soil Loss Equation (RUSLE) combined with GIS has been incorporated into the present study. The five parameters of RUSLE, namely, rainfall-runoff erosivity, soil erodibility, topographic factor, cover management, and conservation practices, are individually estimated from relevant and authentic data sources, and all these parameters are quantified in GIS. The research findings show that 46.89% of areas in the district are in moderate soil loss zone, eroding 0.78 ton/ha/year, 34.27% of areas are in low soil loss zone, 15.36% of areas are in high soil loss zone, eroding about 12.22 ton/ha/year and 3.47% of areas are in a very high soil loss zone, eroding 192.8 ton/ha/year. The high soil loss zones mainly cover the riverine areas and bare lands in the district. As per our estimation, there is an average of 205.85 tonnes of soil loss in the district per hectare per year.

Monitoring water quality changes in any body of water is crucial as it directly relates to climate change. Evaluating the quality and quantity of fresh water for various uses is essential to maintaining safe water sources now and in the future. This study examined the water quality of the Main Outfall Drain River (MOD) in the eastern part of Al-Qadisiya Governorate at three sites over four seasons in 2023, using the Iraqi Water Quality Index (IQ-WQI). In most cases, the concentrations of dissolved oxygen (DO), biochemical oxygen demand (BOD5), and total dissolved solids (TDS) exceeded allowable limits for freshwater and aquatic life protection. The major contributing parameters to the river’s low water quality were TDS, BOD5, turbidity, and DO. The use of the MOD for discharging agricultural effluents led to increased levels of TDS, BOD5, and turbidity. Temporal variation indicated that the summer season had the highest values compared to other seasons due to increased evaporation and low water discharge. Spatial variation showed the IQ-WQI of the sites in descending order from very poor water to unsuitable, with Site 3 having double the TDS concentrations compared to other sites. This increase may be attributed to the impact of the Al-Dalmag Marsh discharge canal, which comes into contact with the MOD at this site. Sensitivity analysis using backward linear regression was applied to predict the IQ-WQI and determine the most influential parameters on the IQ-WQI score. The test was conducted for two sets of water parameters (from the IQ-WQI calculation) and included 7 parameters for each freshwater and aquatic life use, obtaining different models.