Biomass derived from Tamarindus indica partially activated seed coat was investigated for the removal of nitrate ions from aqueous solutions. Batch experiments were performed to evaluate the parameters like pH, contact time, sorbent dose and initial nitrate concentration. pH of the solution played vital role. The maximum sorption observed at pH=7, sorbent dose 300mg, contact time at 120min, initial nitrate concentration 5mg. Physicochemical properties of the biomass were evaluated using scanning electron microscopy (SEM), energy dispersive X-ray analysis and Fourier Transform infra red (FTIR) spectroscopy. The SEM and FTIR data reveals the suitable surface and the presence of chemical functional groups such as hydroxyl, amide, carbonyl strong acid and primary amine on the biosorbent surface contributes to biosorption. The equilibrium isotherms and kinetics were deliberated. Biosorption equilibrium followed Langmuir isotherm. Pseudo second order kinetics provided better correlation of the experimental data in comparison with pseudo-first-order kinetic model. The study indicated that Tamarindus indica partially activated seed coat biomass found to be a novel biosorbent for the removal of nitrates from aqueous solutions.

Hydrogeochemistry and isotope hydrology were carried out to investigate the spatial distribution of fluoride (F−) and the mechanisms responsible for its enrichment in the western region of the Ordos basin, northwestern China. Sixty-two groundwater samples from the unconfined aquifer and fifty-six from confined aquifer were collected during the pre-monsoon (June 2016). Over 77% of groundwater samples from the unconfined aquifer (F− concentration up to 13.30 mg/L) and approximately 66% from confined aquifer (with a maximum F− concentration of 3.90 mg/L) exhibit F− concentrations higher than the Chinese safe drinking limit (1.0 mg/L). High-F− groundwater presents a distinctive hydrochemical characteristic: a high pH value and HCO3− concentration with Ca-poor and Na-rich. Mineral dissolution (e.g., feldspar, calcite, dolomite, fluorite), cation exchange and evaporation in the aquifers predominate the formation of groundwater chemistry, which are also important for F− enrichment in groundwater. Mixing with unconfined groundwater is a significant mechanism resulting in the occurrence of high-F− groundwater in confined aquifer. These findings indicate that physicochemical processes play crucial roles in driving F− enrichment and that may be useful for studying F− occurrence in groundwater in arid and semi-arid areas.

Understanding how mercury (Hg) accumulates in the aquatic food web requires information on the factors driving methylmercury (MeHg) contamination. This paper employs data on MeHg in muscle tissue of three black bass species (Largemouth Bass, Spotted Bass, and Smallmouth Bass) sampled from 21 reservoirs in California. During a two-year period, reservoirs were sampled for total Hg in sediment, total Hg and MeHg in water, chlorophyll a, organic carbon, sulfate, dissolved oxygen, pH, conductivity, and temperature. These data, combined with land-use statistics and reservoir morphometry, were used to investigate relationships to size-normalized black bass MeHg concentrations. Significant correlations to black bass MeHg were observed for total Hg in sediment, total Hg and MeHg in surface water, and forested area. A multivariate statistical model predicted Largemouth Bass MeHg as a function of total Hg in sediment, MeHg in surface water, specific conductivity, total Hg in soils, and forested area. Comparison to historical reservoir sediment data suggested there has been no significant decline in sediment total Hg at five northern California reservoirs during the past 20 years. Overall, total Hg in sediment was indicated as the most influential factor associated with black bass MeHg contamination. The results of this study improve understanding of how MeHg varies in California reservoirs and the factors that correlate with fish MeHg contamination.

Biomass burning (BB) is one of the largest sources of carbonaceous aerosols with adverse impacts on air quality, visibility, health and climate. BB emits a few specific aromatic acids (p-hydroxybenzoic, vanillic, syringic and dehydroabietic acids) which have been widely used as key indicators for source identification of BB-derived carbonaceous aerosols in various environmental matrices. In addition, measurement of p-hydroxybenzoic and vanillic acids in snow and ice cores have revealed the historical records of the fire emissions. Despite their uniqueness and importance as tracers, our current understanding of analytical methods, concentrations, diagnostic ratios and degradation processes are rather limited and scattered in literature. In this review paper, firstly we have summarized the most established methods and protocols for the measurement of these aromatic acids in aerosols and ice cores. Secondly, we have highlighted the geographical variability in the abundances of these acids, their diagnostic ratios and degradation processes in the environments. The review of the existing data indicates that the concentrations of aromatic acids in aerosols vary greatly with locations worldwide, typically more abundant in urban atmosphere where biomass fuels are commonly used for residential heating and/or cooking purposes. In contrast, their concentrations are lowest in the polar regions which are avoid of localized emissions and largely influenced by long-range transport. The diagnostic ratios among aromatic acids can be used as good indicators for the relative amounts and types of biomass (e.g. hardwood, softwood and herbaceous plants) as well as photochemical oxidation processes. Although studies suggest that the degradation processes of the aromatic acids may be controlled by light, pH and hygroscopicity, a more careful investigation, including closed chamber studies, is highly appreciated.

Swine waste is a reservoir of microbial pollutants, including pathogens, antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB); therefore, soil fertilized with swine waste is an essential pathway for the dissemination of microbial pollutants from concentrated swine farms to the public. To rationalize the intervals of swine wastes application and investigate the effects of soil type on the occurrences of microbial pollutants and antibiotic resistance, pot experiments were conducted with three typical soils, humic acrisol, calcaric cambisols and histosols, being collected from south, northwest and northeast China (soil-R, soil-Y and soil-B, respectively). The soils were amended with swine slurry, digestate and chemical fertilizers and then conducted for 172 days. The influence of microbial pollutants and antibiotic resistance in soil posed by digestate application was similar to that of the chemical fertilizers, while swine slurry posed high risks to the soil. Soil-B which had the highest organic matter and neutral pH was least influenced by the swine slurry amendment. tetG, tetM and ermF were persistent ARGs in the slurry treated soil, and their decay rates fitted to first-order kinetics in the order soil-B> soil-Y > soil-R. Putative pathogens showed strong correlations with ARGs, suggesting a risk of dissemination. The initial 43–82 days was the active phase of microbial pollution in slurry treated soil, during which time heavy metals, moisture content, total organic carbon and the microbial community were key factors contributing to changes in antibiotic resistance. Fertilization intervals of livestock wastes should be lengthened over the ARG active phase.

The long-term use of animal manure in agriculture has resulted in estrogen pollution, which poses risks to facility vegetable soils. Owing to the complex soil composition, estrogen may exhibit a variety of behaviors at the water/soil interface. This study demonstrated the role of humic acid (HA) on the 17β-estradiol (E2) adsorption by clay minerals (montmorillonite, kaolinite, and hematite). The interfacial behaviors were investigated using adsorption kinetics and isotherms data. Then, the effects of temperature, pH, and bisphenol A (BPA) on the interactions between humic-mineral complexes and E2 were explored. The adsorption of E2 is an exothermic and spontaneous process, and the addition of HA to minerals significantly promoted their E2 adsorption capacities. Higher pH levels (>10) and the presence of BPA decreased the adsorption capacities of minerals and mineral complexes for E2. Moreover, intercalation, hydrophobic partitioning, π-π interactions and hydrogen bonding could dominate the E2 adsorption onto complexes. These results provided insight into the interfacial behaviors of E2 on the surfaces of humic-mineral complexes and promoted the understanding of the migration and transport of estrogens in soils.

Photocatalytic degradation is an attractive strategy to purify waste water contaminated by macromolecular organics. Compared with the single-component photocatalysts, heterostructures of different semiconductors have been widely used to improve the photocatalytic performance. In this work, we fabricate a hetero-structured photocatalyst consisting of two-dimensional graphitic carbon nitride (g-C3N4) nanosheets and commercial MoO3 microparticles through a simple mixing and annealing process. The photocatalytic performance was evaluated in various dye degradation reactions, especially Rhodamine (RhB) degradation. The MoO3/g-C3N4 composite shown a significant improvement compared with individual MoO3 or g-C3N4 as well as their physical mixture. By applying electron spin resonance (ESR) spin-trap spectra, radical scavenge experiments and electrochemical analysis, we find that a direct Z-scheme charge transfer between MoO3 and g-C3N4 not only causes an accumulation of electrons in g-C3N4 and holes in MoO3, but also boosts the formation of superoxide radical and hydroxyl radical. The superoxide radical and hole dominate the photocatalytic degradation, while the hydroxyl radical plays a negligible role and its production can be suppressed by lowering the pH value.

A biofilm reactor was designed with flat ceramic substrates to remove Co(II), Ni(II) and Zn(II) from industrial wastewater. The ceramics were made of clay and nano-rubber with high mechanical resistance. The surface of the ceramic substrate was modified with neutral fiber and nano-hydroxyapatite. A uniform and stable biofilm mass of 320 g with 2 mm of thickness was produced on the modified ceramic after 3 d. The micro-organisms were identified in the biofilm by polymerase chain reaction (PCR) method. Functional groups of biofilms were identified with a Fourier transform infrared spectrometer (FT-IR). Experiments were designed by central composite design (CCD) using the responsive surface method (RSM). The biosorption process was optimized at pH = 5.8, temperature = 22 °C, feed flux of heavy metal wastewater = 225 ml, substrate flow = 30 ml, and retention time = 7.825 h. The kinetic data was analyzed by pseudo first-order and pseudo second-order kinetic models. Isotherm models and thermodynamic parameters were applied to describe the biosorption equilibrium data of the metal ions on the biofilm-ceramic. The maximum biosorption efficiency and capacity of heavy metal ions were about 72% and 57.21 mg, respectively.

The numerical and mass concentrations of microplastics collected at 36 sites on the surfaces of 29 Japanese rivers were mapped and compared with four basin characteristics (basin area, population density, and urban and agricultural ratios) and six water quality parameters (pH, biochemical oxygen demand (BOD), suspended solids (SS), dissolved oxygen (DO), total nitrogen (T-N), and total phosphorus (T-P)) in each river basin. Microplastics were found in 31 of the 36 sites, indicating that some plastics fragment into small pieces before reaching the ocean. The microplastic concentrations are significantly correlated with urbanisation and population density, indicating that the microplastic concentrations in the river depend on human activities in the river basin. Furthermore, we found a significant relationship between the numerical and mass concentrations and BOD, which is an environmental indicator of river pollution. This result demonstrates that microplastic pollution in river environments has progressed more in polluted rivers with poor water quality than in rivers with good water quality, leading to the conclusion that the sources and inflow processes of microplastics in river environments are similar to those of other pollutants. Our findings can help identify potential sources (i.e., point and non-point sources) of fragmented microplastics to improve waste management in Japan and model the transport fluxes of fragmented microplastics in Japanese rivers using water quality parameters and basin characteristics.

Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer functions are now advancing. This study developed and evaluated an Artificial Neural Network (ANN) model for estimating the transport velocity (V), dispersion coefficient (D) and retardation factor (R) of NaAsO₂, Pb(NO₃)₂, Cd(NO₃)₂, C₉H₉N₃O₂ and CaCl₂ from the basic soil properties. Breakthrough data of the solutes were measured in 14 agricultural soils of Bangladesh by time-domain reflectometry (TDR) in repacked soil columns under unsaturated steady-state water-flow conditions. The transport parameters of the chemicals were determined by analyzing the solute breakthrough data. Bulk density (γ), organic carbon (OC), clay (C) content, pH, median grain diameter (D₅₀) and uniformity coefficient (Cᵤ) of the soils were determined. An ANN model for V, D and R was developed by using data of eight soils, validated/tested with the data of five soils and verified with the data of one soil. Clay content and bulk density of the soils were the most sensitive input variables to the ANN model followed by other soil properties (OC, C, pH, D₅₀ and Cᵤ). The model reliably predicted V, D and R with relative root-mean-square error (RRMSE) of 0.028–0.363, mean error (ME) of – 0.00004 to 0.0005, bias error (BOE%) of 0–0.003 and modeling efficiency (EF) of >0.99. Thus, the ANN model can significantly enhance prediction of pollution transport through soils in terms of cost and effort.