Understanding the ecological status of aquatic ecosystems and the impact of anthropogenic contamination requires correlating exposure to toxicants with impact on biological communities. Several tools exist for assessing the ecotoxicity of substances, but there is still a need for new tools that are ecologically relevant and easy to use. We have developed a protocol based on the substrate-induced respiration of a river biofilm community, using the MicroResp™ technique, in a pollution-induced community tolerance approach. The results show that MicroResp™ can be used in bioassays to assess the toxicity toward biofilm communities of a wide range of metals (Cu, Zn, Cd, Ag, Ni, Fe, Co, Al and As). Moreover, a community-level physiological profile based on the mineralization of different carbon substrates was established. Finally, the utility of MicroResp™ was confirmed in an in-situ study showing gradient of tolerance to copper correlated to a contamination gradient of this metal in a small river. A modified MicroResp™ technique as a tool for measuring induced tolerance to heavy metals of a microbial biofilm community

Chlordecone was applied between 1972 and 1993 in banana fields of the French West Indies. This resulted in long-term pollution of soils and contamination of waters, aquatic biota, and crops. To assess pollution level and duration according to soil type, WISORCH, a leaching model based on first-order desorption kinetics, was developed and run. Its input parameters are soil organic carbon content (SOC) and SOC/water partitioning coefficient (Koc). It accounts for current chlordecone soil contents and drainage water concentrations. The model was valid for andosol, which indicates that neither physicochemical nor microbial degradation occurred. Dilution by previous deep tillages makes soil scrapping unrealistic.Lixiviation appeared the main way to reduce pollution. Besides the SOC and rainfall increases, Koc increased from nitisol to ferralsol and then andosol while lixiviation efficiency decreased. Consequently, pollution is bound to last for several decades for nitisol, centuries for ferralsol, and half a millennium for andosol.

Kinetic EDTA and citrate extractions were used to mimic metal mobilization in a soil contaminated by metallurgical fallout. Modeling of metal removal rates vs. time distinguished two metal pools: readily labile (QM1) and less labile (QM2). In citrate extractions, total extractability (QM1 + QM2) of Zn and Cd was proportionally higher than for Pb and Cu. Proportions of Pb and Cu extracted with EDTA were three times higher than when using citrate. We observed similar QM1/QM2 ratios for Zn and Cu regardless of the extractant, suggesting comparable binding energies to soil constituents. However, for Pb and Cd, more heterogeneous binding energies were hypothesized to explain different kinetic extraction behaviors. Proportions of citrate-labile metals were found consistent with their short-term, in-situ mobility assessed in the studied soil, i.e., metal amount released in the soil solution or extracted by cultivated plants. Kinetic EDTA extractions were hypothesized to be more predictive for long-term metal migration with depth.

The current air pollution by SO2due to anthropogenic pressure in Poland was assessed based on sulfurconcentrations in pine needles (Pinus sylvestrisL.). On 308 monitoring sample plots located in pine standsdistributed across Poland, measurements were conducted in mineral soil layers (0e10 cm, 10e40 cm, 40e100 cm) and in the soil organic layer (þ5e0 cm). Samples of Scots pine foliage (current-year needles)were then collected, and the sulfur concentration in these needles was determined. Based on these data,a map of the spatial variability of sulfur concentrations in pine needles was drawn. The mean sulfurconcentration in the pine needles was 854.8 mg kg�1in dry mass. Higher SO2emissions were noted inregions influenced by industry, such as the Upper Silesia and regions under strong urban pressure. Sulfurconcentrations in Scots pine needles were related to the stands' degrees of defoliation. A comparison ofthe current sulfur concentrations in pine needles from biomonitoring in 2015e2016 with those fromprevious biomonitoring (in 1983e1985, byDmuchowski and Bytnerowicz (1995)showed that air qualityhas improved and SO2emissions have decreased. | Sulfur dioxide, Bioindication, Defoliation, Air quality | 100 | 1-8 | 113559

The multi-layer dynamic model SAFE was applied to the forested catchment Istebna (Southern Poland), to study recovery from acidification. Environmental pollution in the area has been historically high. The model uses data from an intensive monitoring plot established in 1999 in a spruce stand, which was planted in 1880. Observations showed that the soil was depleted of base cations. The measured base saturation in 1999 was between 5 and 8% in the different soil layers. Model predictions assuming full implementation of the UNECE 1999 Gothenburg Protocol and present day base cation deposition show that the base saturation will slowly increase to 20% by 2100. Despite large emission reductions, Istebna still suffers from the very high loads of acidifying input during the past decades. Soil recovery depends on future emissions especially on base cation deposition. The recovery will be even slower if the base cation deposition decreases further. | Soil chemistry; SAFE model; Spruce stands; Southern Poland | 24 | 1-6

Soil magnetic susceptibility (MS) is an important parameter in pollution studies owing to its relationship with atmospheric deposition, and the concomitance of technogenic magnetic particles (TMPs) with potentially toxic elements (PTEs), Fe and Mn. In this study, we performed a detailed soil study under tree canopies for a forest area with high historical TMP-bearing industrial dust deposition. The technogenic sources of magnetic signals in topsoil were analyzed via scanning electron microscope electron dispersive spectroscopy (SEM/EDS), while the minor role of geogenic sources was obtained from soil profile analysis. To our knowledge, this is the first study to show soil TMP distribution in three dimensional (3D) space. In addition, using the data from 275 soil cores and 8250 individual measurements, 3D maps of MS for four tree species were plotted. There is a noticeable difference between coniferous (spruce and pine) and deciduous (beech and oak) species regarding depth of maximum concentration of magnetic particles in the topsoil. For beech and oak, maximum MS values were measured at 3 cm depth; pine and spruce, maximum MS values were measured at 5 cm depth. However, no significant differences were found among tree species in terms of mean MS or PTE contents. This suggests that there is little different among tree species in terms of dust capture over their life span. Significant correlations between MS and other parameters (PTEs and organic matter contents) present new possibilities for spatial 3D analysis of topsoil horizons. | 3D analysis, Magnetic susceptibility, Tree crowns, Forest soil, Contaminants migration | 100 | 1-11 | 273

Supplementary data related to this article can be found at :http://dx.doi.org/10.1016/j.envpol.2015.06.033 | To derive O3 dose-response relationships (DRR) for five European forest trees species and broadleaf deciduous and needleleaf tree plant functional types (PFTs), phytotoxic O3 doses (PODy) were related to biomass reductions.[br/]PODy was calculated using a stomatal flux model with a range of cut-off thresholds (y) indicative of varying detoxification capacities. Linear regression analysis showed that DRR for PFT and individual tree species differed in their robustness. A simplified parameterisation of the flux model was tested and showed that for most non-Mediterranean tree species, this simplified model led to similarly robust DRR as compared to a species- and climate region-specific parameterisation.[br/]Experimentally induced soil water stress was not found to substantially reduce PODy, mainly due to the short duration of soil water stress periods.[br/]This study validates the stomatal O3 flux concept and represents a step forward in predicting O3 damage to forests in a spatially and temporally varying climate.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Among alternative tillage practices, conservation tillage (CT) is a prominent greenhouse gas (GHG) mitigation strategy advocated in wheat cultivation, largely because of its low energy consumption and minimum soil disturbance during cultural operations. This paper examines the agricultural production and GHG emission trade-off of CT vis-à-vis traditional tillage (TT) on wheat farms of Bangladesh. Using a directional distance function approach, the maximum reduction in GHG emissions was searched for within all available tillage technology options, while increasing wheat production as much as possible. The underlying institutional, technical, and other socio-economic factors determining the efficient use of CT were analyzed using a fractional regression model. The average meta-efficiency score for permanent bed planting (PBP) and strip tillage (ST) was 0.89, while that achieved using power tiller operated seeders (PTOS) is 0.87. This indicates that with the given input sets, there is potential to reduce GHG emissions by about 11% for ST and PTOS; that potential is 13% for farmers using PTOS. The largest share of TT farmers cultivate wheat at lower meta-efficiency levels (0.65–0.70) compared to that observed with farmers practicing CT (0.75–0.80). Fractional regression model estimates indicate that an optimal, timely dose of fertilizers with a balanced dose of nutrients is required to reduce GHG emissions. To develop climate smart sustainable intensification strategies in wheat cultivation, it is important to educate farmers on efficient input management and CT together. Agricultural development programs should focus on addressing heterogeneities in nutrient management in addition to tillage options within CT.