Biofiltration and disinfection codetermine the bacterial antibiotic resistome in drinking water: A review and meta-analysis

• Published data was used to analyze the fate of ARGs in water treatment. • Biomass removal leads to the reduction in absolute abundance of ARGs. • Mechanism that filter biofilm maintain ARB/ARGs was summarized. • Potential BAR risks caused by biofiltration and chlorination were proposed. The bacterial antibiotic resistome (BAR) is one of the most serious contemporary medical challenges. The BAR problem in drinking water is receiving growing attention. In this study, we focused on the distribution, changes, and health risks of the BAR throughout the drinking water treatment system. We extracted the antibiotic resistance gene (ARG) data from recent publications and analyzed ARG profiles based on diversity, absolute abundance, and relative abundance. The absolute abundance of ARG was found to decrease with water treatment processes and was positively correlated with the abundance of 16S rRNA (r² = 0.963, p<0.001), indicating that the reduction of ARG concentration was accompanied by decreasing biomass. Among treatment processes, biofiltration and chlorination were discovered to play important roles in shaping the bacterial antibiotic resistome. Chlorination exhibited positive effects in controlling the diversity of ARG, while biofiltration, especially granular activated carbon filtration, increased the diversity of ARG. Both biofiltration and chlorination altered the structure of the resistome by affecting relative ARG abundance. In addition, we analyzed the mechanism behind the impact of biofiltration and chlorination on the bacterial antibiotic resistome. By intercepting influent ARG-carrying bacteria, biofilters can enrich various ARGs and maintain ARGs in biofilm. Chlorination further selects bacteria co-resistant to chlorine and antibiotics. Finally, we proposed the BAR health risks caused by biofiltration and chlorination in water treatment. To reduce potential BAR risk in drinking water, membrane filtration technology and water boiling are recommended at the point of use.     

Photosynthesis and related metabolic mechanism of promoted rice (Oryza sativa L.) growth by TiO2 nanoparticles

• The rice growth was promoted by nano-TiO₂ of 0.1–100 mg/L. • Nano-TiO₂ enhanced the energy storage in photosynthesis. • Nano-TiO₂ reduced energy consumption in carbohydrate metabolism and TCA cycle. Titanium dioxide nanoparticle (nano-TiO₂), as an excellent UV absorbent and photo-catalyst, has been widely applied in modern industry, thus inevitably discharged into environment. We proposed that nano-TiO₂ in soil can promote crop yield through photosynthetic and metabolic disturbance, therefore, we investigated the effects of nano-TiO₂ exposure on related physiologic-biochemical properties of rice (Oryza sativa L.). Results showed that rice biomass was increased >30% at every applied dosage (0.1–100 mg/L) of nano-TiO₂. The actual photosynthetic rate (Y(II)) significantly increased by 10.0% and 17.2% in the treatments of 10 and 100 mg/L respectively, indicating an increased energy production from photosynthesis. Besides, non-photochemical quenching (Y(NPQ)) significantly decreased by 19.8%–26.0% of the control in all treatments respectively, representing a decline in heat dissipation. Detailed metabolism fingerprinting further revealed that a fortified transformation of monosaccharides (D-fructose, D-galactose, and D-talose) to disaccharides (D-cellobiose, and D-lactose) was accompanied with a weakened citric acid cycle, confirming the decrease of energy consumption in metabolism. All these results elucidated that nano-TiO₂ promoted rice growth through the upregulation of energy storage in photosynthesis and the downregulation of energy consumption in metabolism. This study provides a mechanistic understanding of the stress-response hormesis of rice after exposure to nano-TiO₂, and provides worthy information on the potential application and risk of nanomaterials in agricultural production.     

High heterogeneity of bacterioplankton community shaped by spatially structured environmental factors in West Lake,a typical urban lake in eastern China

Environmental Science and Pollution Research - Elucidating the bacterioplankton spatial distribution patterns and its determinants is a central topic in ecological research. However, research on...     

Compartmentation and effects of lead (Pb) in the collembolan,Folsomia candida

Environmental Science and Pollution Research - The impact of soil lead (Pb) pollution on survival, growth, and reproduction of the collembolan, Folsomia candida, and Pb compartmentation in its gut...     

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.     

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).     

Natural Resource Use with Vague-Transferability in Property Right

The purpose of this paper is to study the impacts of the vagueness about the transfer of the harvest right on the use of a natural resource. We develop one-period non-cooperative game frameworks to examine whether the choice of the resource owner to be hazy about the transmission of the harvest contract is optimal. In the perfect information situation, we show that it is optimal for the resource owner to forbid the transfer of the harvest right instead of being hazy about it when the transfer fees are lesser than the collected net punishment fines of the monitoring costs even though the resource would be inefficiently used. In particular, we find in this case that the government’s announcement to forbid the transfer of the right should be public. We further show that there are subgame Nash equilibria in the perfect information context and a unique Nash equilibrium in the imperfect information setting. Specifically, we show that, when the transfer fees are greater or equal to the (net) punishment fines, there is a Nash equilibrium both in perfect and imperfect information situations, precisely when the owner chooses to allow the transfer of harvest title and the resource harvester transfers it.     

Design,Modelling and Assessment of Emission Scenarios Resulting from a Network of Wood Biomass Boilers

The use of wood biomass as a fuel for domestic and industrial heating systems allows for a reduction of CO₂ emissions at a global scale, but it may also result in worse local air quality conditions, due to their emissions of particulate matter. The aim of this study is to assess the actual trend of atmospheric pollution in a study area, assuming that all heating systems are replaced by small size biomass boilers linked to the buildings through district heating network. Ground level concentrations of particulate matter, emitted by different heating systems, are therefore evaluated through numerical simulations performed by means of an atmospheric dispersion model (Sirane). As a first step, we have compared the environmental impact of a woodchip boilers network with that given by the use of traditional heating systems, i.e. wood stoves and natural gas boilers. As a second step, we have analysed the impact of such a network taking into account different emission scenarios, related to different boilers operating conditions. Results show that the environmental performances of a woodchip boilers network can be optimized by combining it with other renewable sources of energy devoted to the supply of hot water. The adopted analysis methodology can be applied to other real or hypothetic punctual sources on the territory.     

The Impact of Unilateral Commitment on Transboundary Pollution

To reach a common target of environmental quality, countries can choose to commit to a stream of pollution abatement right from the beginning of the game or decide upon abatement at each moment of time. Though most of the previous literature studies homogeneous strategies where no country or all countries commit to a (same) predefined policy, reality goes along a different way: some countries make more efforts than others to reduce pollutant emission. The main novelty of this paper resides in the introduction of this kind of heterogeneous strategic behavior currently observed among large pollution nations. We find that the pollution level can be lower under heterogeneous than under homogeneous strategies. A stringent environmental quality target will induce the committed player to produce an abatement effort that more than compensates the free-riding attitude of the non-committed player.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.