Development and implementation of eco-genomic tools for aquatic ecosystem biomonitoring: the SYNAQUA French-Swiss program

The effectiveness of environmental protection measures is based on the early identification and diagnosis of anthropogenic pressures. Similarly, restoration actions require precise monitoring of changes in the ecological quality of ecosystems, in order to highlight their effectiveness. Monitoring the ecological quality relies on bioindicators, which are organisms revealing the pressures exerted on the environment through the composition of their communities. Their implementation, based on the morphological identification of species, is expensive because it requires time and experts in taxonomy. Recent genomic tools should provide access to reliable and high-throughput environmental monitoring by directly inferring the composition of bioindicators’ communities from their DNA (metabarcoding). The French-Swiss program SYNAQUA (INTERREG France-Switzerland 2017–2019) proposes to use and validate the tools of environmental genomic for biomonitoring and aims ultimately at their implementation in the regulatory bio-surveillance. SYNAQUA will test the metabarcoding approach focusing on two bioindicators, diatoms, and aquatic oligochaetes, which are used in freshwater biomonitoring in France and Switzerland. To go towards the renewal of current biomonitoring practices, SYNAQUA will (1) bring together different actors: scientists, environmental managers, consulting firms, and biotechnological companies, (2) apply this approach on a large scale to demonstrate its relevance, (3) propose robust and reliable tools, and (4) raise public awareness and train the various actors likely to use these new tools. Biomonitoring approaches based on such environmental genomic tools should address the European need for reliable, higher-throughput monitoring to improve the protection of aquatic environments under multiple pressures, guide their restoration, and follow their evolution.     

Potential sources and meteorological factors affecting PM<Subscript>2.5</Subscript>-bound polycyclic aromatic hydrocarbon levels in six main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks

A yearlong sampling campaign (2012–2013) was conducted in six major cities of the Veneto region to investigate the spatial-temporal trends and the factors affecting the polycyclic aromatic hydrocarbon (PAHs) variations and identify the local sources. Sixty samples per city were collected for analyses in every alternate month (April, June, August, October, December, and February): 10 samples per sampling site in 10 consecutive days of the months selected. Samples were ultrasonically extracted with acetonitrile and processed through high-performance liquid chromatography. Total Σ-PAH concentrations ranged from 0.19 to 70.4 ng m^?3 with a mean concentration of 11.5 ng m^?3. The mean benzo[a]pyrene (BaP) concentration reached 2.0 ng m^?3, which is two-times higher than the limit set by the European Union. BaP contributed for 17.4% to the total concentration of PAHs, which showed the same pattern across the region with maxima during cold months and minima in the warm period. In this study, PAHs showed an inverse relationship with temperature, solar radiation, wind speed, and ozone. According to this study, biomass burning for household heating and cooking, followed by gaseous PAHs absorption on particles due to low atmospheric temperature, were the main reasons for increasing PAHs concentration in winter. Health risk, evaluated as lifetime lung cancer risk (LCR), showed a potential carcinogenic risk from the airborne BaP_TEQ six-fold higher in the cold season than in the warm one. Diagnostic ratios and conditional probability functions were used to locate the sources, and results confirmed that local emission, overall domestic heating, and road transport exhausts were responsible for higher concentration rates of PAHs as well as of PM_2.5.     

Pre-incubation in soil improves the nitrogen fertiliser value of hair waste

Global generation of human hair waste and its disposal at landfills could contribute to the leaching of nitrates into ground water. High concentrations of nitrogen (N) and other elements suggest that the waste could be a source of plant nutrients and differences in ethnic hair types could affect nutrient release and fertiliser value. The objective of this study was to determine the effects of hair type, as an N source, and pre-incubation time on dry-matter yield, nutrient uptake by spinach (Spinacia oleracea L.) and residual soil nutrients. Salons in Pietermaritzburg provided bulk African and Caucasian hair waste, without distinguishing age, sex, health status or livelihood of the individuals. The hair waste was analysed for elemental composition. A pot experiment was set up under glasshouse conditions. The hair waste was incorporated (400 kg N ha^?1) into a loamy oxisol and pre-incubated for 0, 28, 56 and 84 days before planting spinach. Potassium (K) and phosphorus (P) were corrected to the same level for all treatments. Spinach seedlings were then cultivated for 6 weeks. Shoot dry-matter and the uptake of all nutrients, except P, were increased by the pre-incubation of hair. African hair pre-incubated for 28 days resulted in greater dry-matter, N, K, Mn and S uptake than Caucasian hair. Increasing pre-incubation resulted in a decline in the residual soil pH and exchangeable K. The findings suggested that pre-incubation improves the N fertiliser value of hair and that African hair has greater value than Caucasian hair when pre-incubated for a short period.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

Groundwater Modeling in Agricultural Watershed under Different Recharge and Discharge Scenarios for Quaternary Aquifer Eastern Nile Delta,Egypt

Different scenarios of recharge and discharge were assessed for sustainable management of groundwater in Quaternary aquifer east of Nile Delta. MODFLOW was utilized to investigate the effect of land use change and damming construction in the upstream of the Nile River on the current and short-term groundwater management strategies. The interpretive transient simulation was performed between 2004 and 2016 after steady-state calibration in 2004, and transient state from 2004 to 2013 with different irrigation recharges associated with land use change in this period. Sensitivity analysis was performed for hydraulic conductivities, recharge, and conductance parameters. The predictive transient simulation was run till 2023 under three scenarios of increasing pumping rates by 15, 30, and 50% for agriculture expansion and specified head reduction of Port Said Canal by 0.2, 0.4, and 0.6 m associated with the reduction of Nile water levels after Grand Ethiopian Residence Dam, GERD operation in 2017. Results from the in- and out-flow budgets showed that groundwater aquifer is stable at the current rate of pumping till 2023. Groundwater heads decreased by 0.2 and 0.42 m in the southern section, and a slight increase in the northern part was noticed for the first and second scenarios, respectively. When additional pumping stress is applied (50% increase), groundwater head dropped by 0.66 m, and the storage is no longer able to maintain the aquifer capacity after 2020 (worst-case scenario).     

A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

Influence of Clear Fellings on the Bryophyte Component of the Broad-leaved Forests of the Bashkir Cis-Ural Region

The change in the composition of the bryophyte component of succession communities that occur in the process of natural regeneration at the site of clear felling of the indigenous elm—maple–linden forests of the Bashkir Cis-Ural Region has been studied. The vulnerability of species to the consequences of felling is affected by their substrate confinement, ecological amplitude in relation to the factors of temperature, humidity, variability of soil moisture, and type of life strategy. In the secondary aspen forests, the absence or low constancy of nemoral epiphytic and ground mosses was noted.     

Effects of Topographical Heterogeneity and Dispersal Limitation on Species Turnover in a Temperate Mountane Ecosystem: a Case Study in the Henan Province,China

Species turnover patterns can be inconsistent due to differences in the dispersal ability of different growth forms. Here, species of trees, shrubs, herbs, and bryophytes in the Xiaoqinling National Nature Reserve in China were analyzed to determine patterns of species turnover along an elevation and spatial gradient. Variance partitioning was used to assess the relative contribution of topographic heterogeneity and dispersal limitation to species turnover. Our results suggest that the effect of dispersal limitation is more important than topographic heterogeneity on species turnover in temperate mountane ecosystems in the study area. Dispersal limitation has a greater effect on trees species turnover than on shrubs, herbs or bryophytes species turnover.