Significance of non-DLVO interactions on the co-transport of levofloxacin and titanium dioxide nanoparticles in porous media.

With the application of engineered nanomaterials and antibiotics in the fields of medicine, aerospace, new energy and agriculture, the associated contamination is detected widely in soil-groundwater systems. It is of great scientific and practical significance to deeply explore the environmental interface process between nanoparticles and antibiotics for the scientific assessment of environmental fate and ecological environmental risks, as well as the development of new composite pollution control technologies. In this study, the co-transport behaviors of positively charged titanium dioxide nanoparticles (TiO2-NPs) and negatively charged levofloxacin (LEV) in quartz sand (QS) are investigated in this study. The results show that TiO2-NPs hardly flow out when transported alone in the column because of its positive charge, which creates a strong attraction with the negatively charged quartz sand on the surface. When TiO2-NPs co-migrate with LEV in porous media, the presence of LEV promotes the transport of TiO2-NPs, while the presence of TiO2-NPs inhibits LEV transport. Non-XDLVO interactions based on molecular dynamics (MD) simulations can help explain the observed promotion and inhibition phenomena as well as the correlation between TiO2-NPs and LEV. The results indicate that TiO2-LEV complexes or aggregates can be formed during the co-transportation process of TiO2-NPs and LEV in porous media. As flow velocity increases from 0.204 cm min-1 to 1.630 cm min-1, both the transport capacities of TiO2-NPs and LEV are enhanced significantly. Under the condition of high citric acid (CA) concentration (15 mmol L-1), the transport capacity of TiO2-NPs is slightly inhibited, while the transport capacity of LEV is enhanced. This study provides new insights into the transport of nanometallic oxides and antibiotics in porous media, which suggests that non-XDLVO interactions should be considered together when assessing the environmental risks and fate of nanometallic oxides and antibiotics in soil-groundwater systems.

Temporal and spatial variations in the sub-daily precipitation structure over the Qinghai-Tibet Plateau (QTP).

Precipitation is a vital component of the global atmospheric and hydrological cycles and influencing the distribution of water resources. Even subtle changes in precipitation can significantly impact ecosystems, energy cycles, agricultural production, and food security. Therefore, understanding the changes in the precipitation structure under climate change is essential. The Qinghai-Tibet Plateau (QTP) is a region sensitive to global climate change and profoundly impacts the atmospheric water cycle in Asia and even globally, rendering it a hot topic in climate change research in recent years. Few studies have examined on the sub-daily scale precipitation structure over the QTP. In this paper, the characteristics of sub-daily precipitation on the QTP were systematically investigated from multiple perspectives, including the concentration index, skewness (the third standardized moment of a distribution), and kurtosis (the fourth standardized moment of a distribution). The results indicated that the frequency of moderate-intensity nighttime precipitation on the QTP generally increased, and the analysis of both the concentration index and kurtosis (skewness) suggested that extreme precipitation was more frequent in the southwestern foothills of the QTP. Furthermore, potential high-risk areas for natural disasters were identified on the QTP, and found that the southeastern part of the plateau constituted a potential hotspot area for flood disasters. Given the complexity of climate change, a comprehensive analysis of the spatiotemporal characteristics of diurnal and nighttime precipitation changes on the QTP could help reveal the regularity of precipitation changes. This has significant implications for forecasting, warning, disaster preparedness, and mitigation efforts on the QTP.

Quantification of two-site kinetic transport parameters of polystyrene nanoplastics in porous media.

In this study, a combination of column experiments, interface chemistry theory and transport model with two-site kinetics was used to systematically investigate the effect of pH on the transport of polystyrene nanoparticles (PSNPs) in porous media. The porous media containing quartz sand (QS) and three kinds of clay minerals (CMs)-kaolinite (KL), illite (IL) and montmorillonite (MT), was used in column experiments to simulate the porous media in the soil-groundwater systems. Experimental results showed that the inhibitory effect of CMs on the transport of PSNPs is weakened as pH increases. The two-dimensional (2D) surface of the DLVO interaction energy (2D-pH-DLVO) was built to calculate the interactions between PSNPs and CMs under different conditions of pH. Results suggested the inflection point of PSNP-QS, PSNP-KL, PSNP-IL and PSNP-MT are 2.42, 3.30, 2.84 and 3.69, respectively. Most importantly, there was a significant correlation between the two-site kinetic parameters related to PSNPs transport and the DLVO energy barrier (DB). The contributions of the interactions of PSNPs-PSNPs and PSNPs-minerals were determined for PSNPs transport in porous media. The critical values of pH related to the migration ability of PSNPs in porous media could be determined by a combination of column experiments, 2D-pH-DLVO and PSNPs transport model. The critical values of pH were 2.95-3.01, 3.22-3.51, 2.98-3.02, 3.31-3.33 for the migration ability of PSNPs in QS, QS + KL, QS + IL and QS + MT porous media, respectively. The stronger migration ability of PSNPs under high pH conditions is attributed to the enhanced deprotonation of the media surface and increased negative surface charge, which increases the electrostatic repulsion between PSNPs and porous media (QS, CMs). Moreover, the agglomeration of PSNPs usually is weaker and the average particle size of agglomerates is smaller under the condition of high pH, thus leading to the stronger migration ability of PSNPs under high pH conditions.

Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a.

Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.

Investigating transport kinetics of polystyrene nanoplastics in saturated porous media.

Understanding the fate and transport of polystyrene nanoparticles (PSNPs) in porous media under various conditions is necessary for evaluating and predicting environmental risks caused by microplastics. The transport kinetics of PSNPs are investigated by column experiment and numerical model. The surface of DLVO interaction energy is calculated to analyze and predict the adsorption and aggregation of PSNPs in porous media, which the critical ionic strength of PSNPs can be accurately investigated. The results of the DLVO energy surface suggest that when the concentration of Na+ increases from 1 mM to 50 mM, the DLVO energy barrier of PSNPs-silica sand (SS) decreases from 78.37 kT to 5.46 kT. As a result, PSNPs are easily adsorbed on the surface of SS and the mobility of PSNPs is reduced under the condition of a high concentration of Na+ (PSNPs recovery rate decreases from 62.16% to 3.65%). When the concentration of Ca2+ increases from 0.1 mM to 5 mM, the DLVO energy barrier of PSNPs-SS decreases from 12.10 kT to 1.90 kT, and PSNPs recovery rate decreases from 82.46% to 4.27%. Experimental and model results showed that PSNPs mobility is enhanced by increasing initial concentration, flow velocity and grain size of SS, while the mobility of PSNPs with larger particle diameter is lower. Regression analysis suggests that kinetic parameters related to PSNPs mobility are correlated with DLVO energy barriers. The environmental behavior and mechanism of PSNPs transport in porous media are further investigated in this study, which provides a scientific basis for the systematic and comprehensive evaluation of the environmental risk and ecological safety of nano-plastic particles in the groundwater system.

Effects of clay minerals on the transport of polystyrene nanoplastic in groundwater.

Microplastics are widely detected in the soil-groundwater environment, which has attracted more and more attention. Clay mineral is an important component of the porous media contained in aquifers. The transport experiments of polystyrene nanoparticles (PSNPs) in quartz sand (QS) mixed with three kinds of clay minerals are conducted to investigate the effects of kaolinite (KL), montmorillonite (MT) and illite (IL) on the mobility of PSNPs in groundwater. Two-dimensional (2D) distributions of DLVO interaction energy are calculated to quantify the interactions between PSNPs and three kinds of clay minerals. The critical ionic strengths (CIS) of PSNPs-KL, PSNPs-MT and PSNPs-IL are 17.0 mM, 19.3 mM and 21.0 mM, respectively. Experimental results suggest KL has the strongest inhibition effect on the mobility of PSNPs, followed by MT and IL. Simultaneously, the change of ionic strength can alter the surface charge of PSNPs and clay minerals, thus affecting the interaction energy. Experimental and model results indicate both the deposition rate coefficient (k) and maximum deposition (Smax) linearly decrease with the logarithm of the DLVO energy barrier, while the mass recovery rate of PSNPs (Rm) exponentially increases with the logarithm of the DLVO energy barrier. Therefore, the mobility and associated kinetic parameters of PSNPs in complex porous media containing clay minerals can be predicted by 2D distributions of DLVO interaction energy. These findings could help to gain insight into understanding the environmental behavior and transport mechanism of microplastics in the multicomponent porous media, and provide a scientific basis for the accurate simulation and prediction of microplastic contamination in the groundwater system.

Effects of polyamide microplastic on the transport of graphene oxide in porous media.

With the rapid development of the nano-material and chemical industry, more and more microplastic (MP) and nano-material were discharged into the environment. In this study, a two-dimensional (2D) surface of Extended Darjaguin-Landau-Verwe-Overbeek (XDLVO) is proposed to quantitatively investigate the effect of polyamide (PA) on the transport of graphene oxide (GO) in porous media. The influences of mass fraction of PA, flow rate, GO concentration, ionic type and strength on the migration of GO in saturated porous media are investigated by column experiments and numerical models. The two-dimensional (2D) surfaces of XDLVO interaction energy between GO and GO, GO and QS, GO and PA, are firstly calculated to analyze the transport of GO in saturated porous media. Experimental results suggest the mobility of GO is enhanced when flow velocity and initial concentration of GO are increased. However, the mobility of GO is inhibited when the mass fraction of PA and ionic strength are increased. More important, the inhibitory effect of divalent cations on GO migration is stronger than that of monovalent cations. Simultaneously, XDLVO results suggest that ionic types and strengths are important factors affecting the mobility of GO in porous media, and the critical ionic strength is observed from the continuous variation of the secondary minimum trap of XDLVO interaction energy. Model results show that there is a linear relationship between the logarithm of the secondary minimum trap of XDLVO interaction energy and the parameters related to GO mobility, which suggests XDLVO energy surface has an important application significance in the accurate quantification of GO mobility in porous media. These findings contribute to GO transport affected by microplastic in porous media, thus laying a significant foundation for the environmental risk and contamination remediation.

Interannual and seasonal variations of permafrost thaw depth on the Qinghai-Tibetan Plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest.

An accurate estimation of thaw depth is critical to understanding permafrost changes due to climate warming on the Qinghai-Tibetan Plateau (QTP). However, previous studies mainly focused on the interannual changes of active layer thickness (ALT) across the QTP, and little is known about the changes in the seasonal thaw depth. Machine learning (ML) is a critical tool to accurately estimate the ALT of permafrost, but a direct comparison of ML with deep learning (DL) in ALT projection regarding the model performance is still lacking. Here, ML, namely random forest (RF), and DL algorithms like convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks were compared to estimate the interannual changes of ALT and seasonal thaw depth on the QTP. Meteorological series, in-situ collected ALT observations, and geospatial information were used as predictors. The results show that both ML and DL methods are capable of estimating ALT and seasonal thaw depth in permafrost areas. The CNN and LSTM models developed using longer lagging times exhibit better performance in thaw depth prediction while the RF models are either mediocre or sometimes even worse as the lagging time increases. The results show that the ALT from 2003 to 2011 on the QTP exhibits an increasing trend, especially in the northern region. In addition, 68.8%, 88.7%, 52.5%, and 47.5% of the permafrost regions on the QTP have deepened seasonal thaw depth in spring, summer, autumn, and winter, respectively. The correlation between air temperature and permafrost thaw depth ranges from 0.65 to 1 with the time lag ranging from 1 to 32 days. This study shows that ML and DL can be effectively used in retrieving ALT and seasonal thaw depth of permafrost, and could present an efficient way to figure out the interannual and seasonal variations of permafrost conditions under climate warming.

Simulation of regional groundwater levels in arid regions using interpretable machine learning models.

Regional groundwater level forecasting is critical to water resource management, especially for arid regions which require effective management of groundwater resources to meet human and ecosystem needs. In this study Machine Learning and Deep Learning approaches - Support Vector Machine, Generalized Regression Neural Network, Decision Tree, Random Forest (RF), Convolutional Neural Network, Long Short Term Memory and Gated Recurrent Network- have been used to simulate the groundwater levels in the lower Tarim River basin (LTRB) which is an extreme dryland. The results showed that models developed here with easily available input data such as relative humidity, flow volume and distance to the riverbank can fully utilize spatiotemporally inconsistent groundwater monitoring data to predict the spatiotemporal variation of the groundwater system in arid regions where exist intermittent flow. The shapely additive explanations method was used to interpret the RF model and discover the effect of meteorological, hydrological and environmental variables on the regional groundwater. These explanations showed that the flow volume, the distance to the river channel and reservoir have a critical impact on groundwater changes. Within 300 m distance to the riverbank, groundwater would mainly be influenced by the flow volume and the distance to the reservoir. While far from the riverbank, these effects decreased gradually further away from the river course. The RF prediction results showed that in the next three years (2021-2023), the groundwater level on average may decline to -6.4 m, and the suitable areas for natural vegetation growth would be limited to 39% if no water conveyance in the LTRB. To guarantee the stability of ecosystems in the LTRB, it is necessary to convey the water annually. These results can support spatiotemporal predictions of groundwater levels predominantly recharged by intermittent flow, and form a scientific basis for sustainable water resources management in arid regions.

Spatial distribution and source apportionment of polycyclic aromatic hydrocarbons in typical oasis soil of north-western China and the bacterial community response.

Oases environments in oases to be sensitive to anthropogenic activity because of ecological fragility. Polycyclic aromatic hydrocarbon (PAH) pollution resulting from anthropogenic activity leads to ecological degradation in oases. To examine the impact of anthropogenic activity on the oasis ecological environment, the present study focused on the spatial distribution and source apportionment of soil PAHs and bacterial community responses in typical oases in Xinjiang, China. The results showed that the soil PAH level were higher in the city centres of Urumqi (9-6340 µg kg-1), Aksu (8-957 µg kg-1) and Korla (8-1103 µg kg-1) and lower in the centres of Hotan city (11-268 µg kg-1) and Qira county (7-163 µg kg-1). Source apportionment suggested that gasoline emissions, diesel emissions, vehicle emissions, coal combustion, coke processing and biomass burning were the sources of soil PAHs. The integrated lifetime cancer risks of soil PAH exceeding the guideline safety values (10-6) recommended by United States Environmental Protection Agency. The ingestion and dermal exposure pathways caused the greatest health risk (contribution ≤82%). Additionally, in the soil with low PAH concentrations, the richness and evenness of the soil bacterial community were great, and the molecular ecological network (MEN) structure was complex. Among populations, Proteobacteria and Actinobacteria (relative abundance ≥17%) are the main dominant species in the bacterial communities and the keystone species in the MEN.

Effects of nanometer alumina and humic acid on the retention and transport of hexavalent chromium in porous media.

Column experiments were conducted to investigate the effects of ion type, ion strength, humic acid (HA), and nanometer alumina (NA) particles on the transport of hexavalent chromium (HC) in saturated porous media. A one-dimensional model is developed to simulate the migration of HC affected by NA particles. The results show that nano-alumina particles would enhance the mobility of HC in saturated porous media. However, the influence of NA on the migration of HC in porous media is complex. When the concentration of NA reaches 30 mg/L, HC has minimum retention parameter and best mobility. The transport of HC also is affected by ion strength and ion type. Higher ionic strength would decrease the retention of HC and enhance its mobility. Compared with sodium ion, calcium ion has larger effects on the transport of HC. Moreover, HA can improve the mobility of HC in saturated porous media, but the corresponding promoting effect decreases with the increase of HA concentration. As nanometer contaminants and HC come into the subsurface environment, findings from this study elucidate the key factors and processes controlling the transport of HC in porous media, which can promote the prediction and assessment of HC in the groundwater system.

Bacterial community variations with salinity in the saltwater-intruded estuarine aquifer.

Bacterial community has been significantly enrolled in the biogeochemical cycling of the coastal subsurface ecosystem. The bacterial community variations with salinity have been extensively investigated in the surface environment, such as lake, soil, and estuary, but not in the subsurface environment. Here we explore the responses of bacterial populations to the salinity and other environmental factors (EFs) by considering both the abundant and rare sub-community in a coastal Holocene groundwater system. Our study results indicate that the bacterial diversity was independent of the salinity in both the abundance and rare sub-community. Besides diversity, no flourishing of abundant bacteria relative abundance is observed with increasing or decreasing salinity. Yet the rare taxa exhibit a bio-growth with salinity, which has a significant correlation (p < 0.001) with sulfate concentration. The responses of the abundant sub-community taxa to nutrients, temperature, pH, and dissolved oxygen are insensitive. However, the correlation between δ18O, δD and the entire community diversity is significant, which demonstrates the bacterial community is affected by the groundwater origin. Besides, not all the species in one class or order are necessarily shaped by the same factor. To quantify the impact of EFs on the community properties, analyses in different taxonomic levels is suggested. These findings imply that the spatial organization of microbial communities is complicated and influenced by multiple factors on a regional scale. The investigated results are useful for understanding biogeochemical processes in the coastal groundwater.

Using an ensemble Kalman filter method to calibrate parameters of a prediction model for chemical transport from soil to surface runoff.

Water pollution from surface runoff is an important non-point pollution source, which has been a great threat to our environment. The model proposed by Gao et al. (2004) is of great significance to solve the non-point source pollution problem, which is a numerical advection-diffusion equation (ADE) model for chemical transport from soil to surface runoff. The ensemble Kalman filter (EnKF), the data assimilation (DA) method, is easy to be implemented and widely used in hydrology field. In this study, we use the EnKF method to update model state variables such as chemical concentrations in surface runoff and calibrate model parameters such as water transfer rate in Gao et al. (2004) under different study cases, while other model parameters are assumed to be known. The observations are generated from the simulation results based on synthetic real parameters. The objective of this study was to extend the application of the EnKF to the ADE-based prediction model of chemical transport from soil to surface runoff. The results of the predicted chemical concentration in the surface runoff with EnKF are greatly improved than those without EnKF in comparison with the observations, and the updated parameters are close to the real parameters. We explored feasibility of the EnKF method from six factors, including the initial parameter estimate, the ensemble size, the influence of multi-parameters, the assimilation time interval, the infiltration boundary conditions, and the relationship between the standard deviations of the observation error and initial parameter. Different study strategies are proposed for different factors. For assimilation time interval, the key observation can reduce the assimilation frequency. With the situation of much larger observation error covariance than the prediction covariance, we analyzed influences of the standard deviation of the observation error and initial parameter on the feasibility of the EnKF method. According to the study results, it is concluded that the EnKF is efficient to update the parameter for the ADE-based prediction model of chemical transport from soil to surface runoff.

Environmental behavior and potential driving force of bisphenol A in the Elbe River: A long-term trend study.

As an endocrine disruptor, a deep understanding of the environmental behavior and potential driving force of bisphenol A (BPA) is helpful for developing a mitigation strategy and reducing the exposure risk to the public. Based on long-term monitoring data from 2004 to 2016, this study systematically evaluated the long-term trend, periodic characteristics, and potential risks of BPA in the Elbe River in the state of Saxony, Germany. Multiple advanced statistical approaches were employed for data mining. Pettitt's test was used to determine the main change points of BPA that occurred from 2008 to 2011. The Mann-Kendall test showed a decreasing trend in BPA concentrations (slope: -0.087 to -0.112, P < 0.05) over the past 13 years, particularly in the wet seasons (slope: -0.730 to -0.038, P < 0.05). Wavelet analysis revealed similar periodicities of BPA among stations (which experienced 4-5 oscillations in the first major period). The ARIMA model forecasted the mean BPA concentration as ranging from 9 to 41 ng L-1 in the subsequent 3 months, which was similar to that in the last 3 months (20-42 ng L-1). Besides, the highest hazard quotients (>0.3) were documented for Chironomus riparius, Oryzias latipes, Potamopyrgus antipodarum, and Hydra vulgar, which indicates that BPA may threaten their growth and development. The hazard index values for non-cancer risk of BPA no greater than 6.47 × 10-9 (HQ far below 1), which suggests that BPA did not pose a significant threat to human health. Because BPA pollution is closely related to industrial activities, a long-term decline in BPA concentrations could be attributed to the reduced number of factories, limited discharge, and improved decontamination efficiency. However, the minimal change in the BPA concentration in the near future could reflect periodic fluctuations.

Isotopic and hydrochemical evidence for the salinity origin in the coastal aquifers of the Pearl River Delta, Guangzhou, China.

To better understand the origin of the saline groundwater in the Pearl River Delta (PRD), China, water samples were collected from local aquifers, rainfall regions and rivers for isotopic and hydrochemical analysis. The hydraulic connections between the aquifers in the study area were tested by analyzing a series of water samples from different months in one hydrological year (January 2017-January 2018). The total dissolved solids (TDS) results show that the highly saline groundwater only occurs in the granites, which indicates that the TDS distribution depends on the permeability of the aquifer material. Variations in the TDS and stable hydrogen and oxygen isotope ratios (δ2H and δ18O, respectively) of the water samples from different months reflect a dynamic balance among evaporation and precipitation in a hydrological year. Additionally, the very old radiocarbon (14C) ages and undetectable amounts of tritium (3H) in most of the groundwater samples suggest that the residence time of the groundwater in the aquifer is high. In general, the saline groundwater (TDS >5 g/L) in the area mainly originated primarily from seawater intrusion in the past. Meanwhile, the water contents of saline groundwater were affected by evaporation and long-term geochemical processes, such as water-rock, sulfate reduction, methanogenesis and ion exchange. The fresh groundwater in the area is from modern meteoric precipitation recharge.

Comparison of the groundwater microbial community in a salt-freshwater mixing zone during the dry and wet seasons.

To gain a better understanding of the microbial community in salt-freshwater mixing zones, in this study, the influence of seasonal variation on the groundwater microbial community was evaluated by high throughput 16S rDNA gene sequencing. The results showed that notable changes in microbial community occurred in a salt-freshwater mixing zone and the groundwater samples in the dry season were more saline than those in the wet season. The increase in precipitation during the wet season relieved local seawater intrusion. Microbial diversity varied greatly with seasons, while no obvious change pattern was found. Proteobacteria was identified as the dominant phylum in all samples. The genus Hydrogenophaga dominated in the dry season, while the genus Acidovorax dominated in the wet season. Dissolved oxygen affected the diversity of the microbial communities during the dry and wet season, while groundwater level had a strong influence on the structure of microbial communities. Phylogenetic molecular network analysis of the microbial communities indicated that increased seawater intrusion led to a more compact microbial network and strengthening the groundwater microbial interactions.

Characterizing microbial diversity and community composition of groundwater in a salt-freshwater transition zone.

A salt-freshwater transition zone due to seawater intrusion to groundwater promotes changes in microbial diversity and community composition in a coastal aquifer. The main purpose of this study is to explore the effect of seawater intrusion on the groundwater quality in a salt-freshwater transition zone and identify the microbial fingerprints of seawater intrusion. The changes in microbial community diversity response to the seawater intrusion were characterized by comparing the community structures of the microbes in fresh groundwater, seawater, and salty groundwater from various monitoring wells at different depths using the high throughput 16S rDNA gene sequencing. Results show that seawater had the lowest taxon richness and evenness, and the irrigation water had the highest richness and evenness. Statistical analysis showed that DO%, ORP, and Cl- affected microbial distribution in the groundwater; while DO% was a main environmental factor influencing microbial community diversity. The analysis of microbial community structures indicates that the order Oceanospirillales and the family Alteromonadaceae could be used as indicators of seawater intrusion.

The influence of heavy metals in road dust on the surface runoff quality: Kinetic, isotherm, and sequential extraction investigations.

This study examines the adsorption and desorption characteristics of heavy metals in road dust (RD) for the aspect of integrated stormwater management. The chemical fractionations of Cu, Zn, Ni, and Cd were determined by a three-step sequential extraction protocol. Pseudo-first-order and Pseudo-second-order kinetic models, along with Langmuir, Freundlich, and Temkin isotherms were adopted to simulate the batch experimental data. The proportional shift of metals' chemical fractionations in original RD, adsorption equilibrium, and desorption equilibrium were determined. Results show that RD has a remarkable affinity to adsorb heavy metal within a short time. The adsorption processes were well described by the Pseudo-second-order kinetic model (R2 = 0.98-0.99) and Freundlich isotherm (R2 = 0.89-0.98) for most of the given metals indicating that the chemical adsorption was probably the rate-controlling step and the binding energy for each site was not identical. The maximum adsorption capacities for Cu, Cd, Zn, and Ni were 6300 mg kg-1, 5800 mg kg-1, 4000 mg kg-1, and 3200 mg kg-1, respectively. A linear fit to the equilibrium pH and the total amounts of the adsorbed metals indicates a strong pH-dependent adsorption. According to the proportional shift of metals' chemical fractionations during the adsorption and desorption processes, the exchangeable fractions of heavy metals in RD were irreversible. It suggests that a portion of the surface sites of RD would be not exchangeable once it was occupied.

Concentration decline in response to source shift of trace metals in Elbe River, Germany: A long-term trend analysis during 1998-2016.

Monitoring spatial and temporal chemical status of water bodies is crucial to assist environmental policy, identify the chemical fingerprints, and further reduce the source orientated pollutants. Elbe River is one of the major rivers affected by anthropogenic activities in vicinity countries. This study assessed the spatiotemporal changes in response to source shift of Cd, Cu, Ni, Pb, and Zn in the suspended particulate matter (SPM) at upstream, midstream, and downstream of the Elbe River reach in Saxony state, Germany. The average contents of trace metals in SPM was found in the order of Zn (676 mg/kg) ¼ Pb (79 mg/kg) > Cu (74 mg/kg) > Ni (48 mg/kg) ¼ Cd (3.2 mg/kg). According to the Mann-Kendall trend test, Cd, Cu, Pb, and Zn showed significant declines over 1998-2016. The results of source apportionment indicate industrial, urban, natural, and historical mining sources influencing the metal contents in the Elbe River of Saxony. The contributions of industrial and urban pollution decreased by 58.2% from 1998 to 2007 to 2008-2016. The contribution of the natural source was steady over the last two decades.

Diversity and predictive metabolic pathways of the prokaryotic microbial community along a groundwater salinity gradient of the Pearl River Delta, China.

Almost half of the groundwater in the Pearl River Delta (PRD) contains salt water originally derived from paleo-seawater due to the Holocene transgression, which then generates intense physicochemical gradients in the mixing zone between freshwater and saltwater. Although some studies have been conducted on the hydrological and geochemical characteristics of groundwater in the PRD to monitor the intrusion of seawater, little attention has been paid to the microbial community of this particular region. In this study, we implemented a high-throughput sequencing analysis to characterize the microbial communities along a salinity gradient in the PRD aquifer, China. Our results indicated that the microbial community composition varied significantly depending on the salinity of the aquifer. The presence of abundant anaerobic microorganisms of the genera Desulfovibrio and Methanococcus in certain saltwater samples may be responsible for the gas generation of H2S and CH4 in the stratum. In saline water samples (TDS > 10 g/L), the linear discriminant analysis effect size (LEfSe) analysis found two biomarkers that usually live in marine environments, and the aquifers of the PRD still contained large quantity of saltwater, indicating that the impact of the paleo-seawater has lasted to this day. The predictive metagenomic analysis revealed that the metabolic pathways present in the groundwater samples studied, included the degradation of pesticides and refractory organics (dichlorodiphenyltrichloroethane (DDT), atrazine and polycyclic aromatic hydrocarbons), matter cycling (methane, nitrogen and sulfur), and inorganic ion and mineral metabolites. This study can help enhance our understanding of the composition of the microbial assemblages and its implications as an environmental indicator in an aquifer affected by saltwater intrusion.