Veterinary antibiotics could enter the environment after the application of manure or farm wastewater on soil as fertilizer. In this study, a UPLC-MS/MS analytical method was developed and validated for the simultaneous determination of enrofloxacin (ENR) and ciprofloxacin (CIP) at environmental relevant concentrations in piggery wastewater, piggery wastewater solids, agricultural soil and ground water with good performance characteristics. The method recovery for ENR and CIP was 94.2 and 89.9% in the filtered piggery wastewater, 81.3 and 82% in the wastewater solid material, 78.1 and 76.8% in the soil and 95.6 and 97.3% in the ground water. The Limit of Detection (LOD) and Limit of Quantification (LOQ) for ENR were 21 and 64 ng L⁻¹ and for CIP was 18 and 54 ng L⁻¹, respectively. The method was implemented to monitor ENR and CIP in the wastewater of a piggery facility in Cyprus which applied anaerobic treatment before the final disposal of the reclaimed water. The highest antibiotic concentrations were measured in the wastewater samples collected from the nursery, where ENR is continuously used, with average concentration 31.4 μg L⁻¹ for ENR and 16.0 μg L⁻¹ for CIP. After the anaerobic digester, the two antibiotics were found only on the solid matter of the treated wastewater with an average concentration of 1.7 μg kg⁻¹ for ENR and 1.0 μg kg⁻¹ for CIP. The antibiotics adsorption at pH = 7 on clay soil, quartz sand and on solid matter isolated from the piggery wastewater was found to be higher than 95% for all solid materials. The concentration of the antibiotics in soil samples taken from a field where reclaimed piggery wastewater was applied for 10 years and in samples of groundwater from a nearby well was found for all samples below the LOD.

This paper presents a model to analyze the mechanism of the global contribution of energy usage by product exports. The theoretical analysis is based on the perspective that contribution estimates should be in relatively smaller sectors in which the production characteristics could be considered, such as the productivity distribution for each sector. Then, we constructed a method to measure the global contribution of energy usage. The simple method to estimate the global contribution is the percentage of goods export volume compared to the GDP as a multiple of total energy consumption, but this method underestimates the global contribution because it ignores the structure of energy consumption and product export in China. According to our measurement method and based on the theoretical analysis, we calculated the global contribution of energy consumption only by industrial manufactured product exports in a smaller sector per industry or manufacturing sector. The results indicated that approximately 42% of the total energy usage in the whole economy for China in 2013 was contributed to foreign regions. Along with the primary products and service export in China, the global contribution of energy consumption for China in 2013 by export was larger than 42% of the total energy usage.

Phosphorus (P) adsorpted by sediments, when covered by and mixed with ignited sediments from Meiliang Bay in Tai Lake, was analyzed in the laboratory. Potassium dihydrogen phosphate (KH₂PO₄) was added to the parallel experimental units to simulate periodic external P input. Based on the Langmuir model, the sediments after ignition had a greater Sₘₐₓ (maximum P adsorption), a lower equilibrium phosphorus concentration at zero adsorption (EPC₀), and a lower degree of phosphorus saturation in comparison with sediments without ignition. This was confirmed by the variation in the dissolved inorganic phosphorus in the overlying water. When sediments were mixed with or covered by the ignited sediments, 5.985 and 5.978 mg of input P disappeared from the overlying water, respectively. However, when the sediments were mixed with the ignited sediments, 84.18% of the input P was converted to HCl-P, whereas when they were covered by the ignited sediments, sedimentary P was released, mainly from Fe/Al-P (up to 87.50%). This was attributed to differences in the microenvironments where less-intense anaerobic conditions were formed in the mixed sediments than in the sediments covered by the ignited sediments. This suggests that the injection of ignited sediments into existing sediments enhances their P adsorption and retention. It is favor of the control of the eutrophication with a simple technology.

The aim of the study was to compare the phytoextraction abilities of six tree species (Acer platanoides L., Acer pseudoplatanus L., Betula pendula Roth, Quercus robur L., Tilia cordata Miller, Ulmus laevis Pall.), cultivated on mining sludge contaminated with arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), thallium (Tl), and zinc (Zn). All six tree species were able to survive on such an unpromising substrate. However, A. platanoides and T. cordata seedlings grown on the polluted substrate showed significantly lower biomass than control plants (55.5 and 45.6%, respectively). As, Cd, Cu, Pb, and Tl predominantly accumulated in the roots of all the analyzed tree species with the following highest contents: 1616, 268, 2432, 547, and 856 mg kg⁻¹, respectively. Zn was predominantly localized in shoots with the highest content of 5801 and 5732 mg kg⁻¹ for U. laevis and A. platanoides, respectively. A. platanoides was the most effective in Zn phytoextaction, with a bioconcentration factor (BCF) of 8.99 and a translocation factor (TF) of 1.5. Furthermore, with the exception of A. pseudoplatanus, the analyzed tree species showed a BCF > 1 for Tl, with the highest value for A. platanoides (1.41). However, the TF for this metal was lower than 1 in all the analyzed tree species. A. platanoides showed the highest BCF and a low TF and could, therefore, be a promising species for Tl phytostabilization. In the case of the other analyzed tree species, their potential for effective phytoextraction was markedly lower. Further studies on the use of A. platanoides in phytoremediation would be worth conducting.

In recent years, international research on the toxicity of the heavy metal, antimony, has gradually changed focus from early medical and pharmacological toxicology to environmental toxicology and ecotoxicology. However, little research has been conducted for sources identification and risk management of heavy metals pollution by long-term antimony mining activities. In this study, a large number of investigations were conducted on the temporal and spatial distribution of antimony and related heavy metal contaminants (lead, zinc, and arsenic), as well as on the exposure risks for the population for the Yuxi river basin in the Hunan province, China. The scope of the investigations included mine water, waste rock, tailings, agricultural soil, surface water, river sediments, and groundwater sources of drinking water. Health and ecological risks from exposure to heavy metal pollution were evaluated. The main pollution sources of heavy metals in the Yuxi River basin were analyzed. Remediation programs and risk management strategies for heavy metal pollution were consequently proposed. This article provides a scientific basis for the risk assessment and management of heavy metal pollution caused by antimony basin ore mining.

With an exponential increase in urbanization and industrialization, water pollution is an inevitable consequence of relatively lagging wastewater treatment facilities. The conventional activated sludge process for wastewater treatment primarily emphasizes the removal of harmful substances to maintain increasingly stringent effluent discharged standards, which is considered an energy-intensive technique. Therefore, innovative and sustainable wastewater treatment should pay more attention to energy and resource recovery in dealing with fossil fuel depletion, global-scale energy security, and climate change. A bibliometric analysis was applied to trace wastewater-energy nexus-related research during the period 1991 to 2015, with respect to the Science Citation Index EXPANDED (SCI-EXPANDED) database. Journal of Hazardous Materials, ranking 1st in h-index (79), was the most productive journal (431, 4.5%) during the same time, followed by International Journal of Hydrogen Energy (422, 4.4%) and Water Research (393, 4.1%) journal, the latter owning a topmost journal impact factor. Though, China (2154, 22.5%) was the most productive country, while the USA with highest h-index (88) was the favorest collaborative country. The Chinese Academy of Sciences, China (241, 2.5%) produced the maximum publications. A novel method called “word cluster analysis” showed that the emerging sustainable processes and novel renewable energy application are applied in response to the desire for a net wastewater-energy nexus system. Based on different wastewater types, the emerging energy and sources recovery treatment processes of Anammox, anaerobic digestion, and microbial fuel cells gained extensive innovation. Evaluation indicators including sustainability, life cycle assessment, and environmental impact were appropriately used to dissert feasibility of the novel treatment methods in regard of renewable energy utilization, energy savings, and energy recovery. The transformation of the new concept of “broaden income source, economize on expenditures and exploit inner potential” should be generalized in order to achieve an environmentally sustainable development of wastewater-energy nexus system.

With the concerns of ecological and circular economy along with sustainable development, reverse logistics has attracted the attention of enterprise. How to achieve sustainable development of reverse logistics has important practical significance of enhancing low carbon competitiveness. In this paper, the system boundary of reverse logistics carbon footprint is presented. Following the measurement of reverse logistics carbon footprint and reverse logistics carbon capacity is provided. The influencing factors of reverse logistics carbon footprint are classified into five parts such as intensity of reverse logistics, energy structure, energy efficiency, reverse logistics output, and product remanufacturing rate. The quantitative research methodology using ADF test, Johansen co-integration test, and impulse response is utilized to interpret the relationship between reverse logistics carbon footprint and the influencing factors more accurately. This research finds that energy efficiency, energy structure, and product remanufacturing rate are more capable of inhibiting reverse logistics carbon footprint. The statistical approaches will help practitioners in this field to structure their reverse logistics activities and also help academics in developing better decision models to reduce reverse logistics carbon footprint.

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Solubility is a critical component of physicochemical characterisation of engineered nanomaterials (ENMs) and an important parameter in their risk assessments. Standard testing methodologies are needed to estimate the dissolution behaviour and biodurability (half-life) of ENMs in biological fluids. The effect of pH, particle size and crystal form on dissolution behaviour of zinc metal, ZnO and TiO₂ was investigated using a simple 2 h solubility assay at body temperature (37 °C) and two pH conditions (1.5 and 7) to approximately frame the pH range found in human body fluids. Time series dissolution experiments were then conducted to determine rate constants and half-lives. Dissolution characteristics of investigated ENMs were compared with those of their bulk analogues for both pH conditions. Two crystal forms of TiO₂ were considered: anatase and rutile. For all compounds studied, and at both pH conditions, the short solubility assays and the time series experiments consistently showed that biodurability of the bulk analogues was equal to or greater than biodurability of the corresponding nanomaterials. The results showed that particle size and crystal form of inorganic ENMs were important properties that influenced dissolution behaviour and biodurability. All ENMs and bulk analogues displayed significantly higher solubility at low pH than at neutral pH. In the context of classification and read-across approaches, the pH of the dissolution medium was the key parameter. The main implication is that pH and temperature should be specified in solubility testing when evaluating ENM dissolution in human body fluids, even for preliminary (tier 1) screening.

Sodium chlorate (NaClO₃) is a widely used non-selective herbicide. It is also generated as a byproduct during disinfection of drinking water by chlorine dioxide. In the present work, the effects of NaClO₃ on human erythrocytes were studied under in vitro conditions. Incubation of erythrocytes with different concentrations of NaClO₃ at 37 °C for 90 min resulted in significant hemolysis. Cell lysates were prepared from NaClO₃-treated and untreated (control) erythrocytes and assayed for various biochemical parameters. Methemoglobin levels were significantly increased and methemoglobin reductase activity was reduced upon NaClO₃ treatment. There was a significant increase in protein oxidation and lipid peroxidation with a decrease in reduced glutathione and total sulfhydryl content. This suggests the induction of oxidative stress in erythrocytes upon exposure to NaClO₃. The occurrence of oxidative stress was confirmed by significantly increased generation of reactive oxygen species and lowered antioxidant response of the cells. NaClO₃ treatment also increased nitric oxide levels showing induction of nitrosative stress. The activities of major antioxidant and membrane-bound and metabolic enzymes were significantly altered upon incubation of erythrocytes with NaClO₃. The erythrocytes became more osmotically fragile while electron microscopic images showed gross morphological alterations in NaClO₃-treated cells. These results show that NaClO₃ induces oxidative stress in human erythrocytes, which results in extensive membrane damage and lowers the antioxidant response.