Associations between co-exposure to multiple metals and renal function: a cross-sectional study in Guangxi,China

The association between co-exposure to multiple metals and renal function is poorly understood. We aimed to evaluate the individual and joint effects of metal exposure on renal function in this study. We performed a cross-sectional study including 5828 participants in Guangxi, China, in 2019. Urine concentrations of 17 metals were detected by inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression model and restricted cubic spline (RCS) were applied to investigate the association of individual metal exposure with renal dysfunction. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to assess the co-exposure effects of the metals. Participants with the highest quartile of urinary Cu were at 1.84-fold (95% confidence interval (CI): 1.20–2.87) increased risk of renal dysfunction compared with the lowest quartile. The highest quartiles of urinary Sr, Cs, V, Ba, and Se were associated with 0.27-fold (95% CI: 0.17–0.43), 0.33 (95% CI: 0.19–0.53), 0.41 (95% CI: 0.25–0.65), 0.58 (95% CI: 0.36–0.90), and 0.33 (95% CI: 0.19–0.56) decreased risk of renal dysfunction compared with their lowest quartile, respectively. Furthermore, urinary Ba and Cu were non-linearly correlated with renal dysfunction. The WQS analysis showed that mixed metal exposure was inversely associated with renal dysfunction (OR = 0.47, 95% CI: 0.35–0.62), and Sr accounted for the largest weight (52.2%), followed by Cs (32.3%) in the association. Moreover, we observed a potential interaction between Cu, Cs, and Ba for renal dysfunction in BKMR model. Exposure to Se, Sr, Cs, V, and Ba is associated with decreased risk of renal dysfunction, whereas an increased risk is associated with Cu exposure. Co-exposure to these metals is negatively associated with renal dysfunction, and Sr and Cs are the main contributors to the associations.

     

Efficient production of hydrogen peroxide in microbial reverse-electrodialysis cells coupled with thermolytic solutions

● Appreciable H₂O₂ production rate was achieved in MRCs utilizing NH₄HCO₃ solutions. ● Carbon black outperformed activated carbon as the catalyst for H₂O₂ production. ● The optimum carbon black loading for H₂O₂ production on air-cathode was 10 mg/cm². ● The optimum number of cell pairs was determined to be three. ● A maximum power density of 980 mW/m² was produced by MRCs with 5 cell pairs. H₂O₂ was produced at an appreciable rate in microbial reverse-electrodialysis cells (MRCs) coupled with thermolytic solutions, which can simultaneously capture waste heat as electrical energy. To determine the optimal cathode and membrane stack configurations for H₂O₂ production, different catalysts, catalyst loadings and numbers of membrane cell pairs were tested. Carbon black (CB) outperformed activated carbon (AC) for H₂O₂ production, although AC showed higher catalytic activity for oxygen reduction. The optimum CB loading was 10 mg/cm² in terms of both the H₂O₂ production rate and power production. The optimum number of cell pairs was determined to be three based on a tradeoff between H₂O₂ production and capital costs. A H₂O₂ production rate as high as 0.99 ± 0.10 mmol/(L·h) was achieved with 10 mg/cm² CB loading and 3 cell pairs, where the H₂O₂ recovery efficiency was 52 ± 2% and the maximum power density was 780 ± 37 mW/m². Increasing the number of cell pairs to five resulted in an increase in maximum power density (980 ± 21 mW/m²) but showed limited effects on H₂O₂ production. These results indicated that MRCs can be an efficient method for sustainable H₂O₂ production.     

Effect of humic substances on the anaerobic digestion of secondary sludge in wastewater treatment plants: a review

Environmental Chemistry Letters - Anaerobic digestion is a promising technology for energy recovery from secondary sludge, yet the presence of humic substances in wastewater limits anaerobic...     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

Enhanced performance of alkali-modified Bi2WO6/Bi0.15Ti0.85O2 toward photocatalytic oxidation of HCHO under visible light

Environmental Science and Pollution Research - Photocatalytic oxidation of formaldehyde (HCHO) is considered as one of the promising ways to resolve indoor air HCHO pollution. TiO2 has been well...     

Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016

An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatio-temporal trends and the factors which influence PM_2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000–2016 were chosen as the research object, the PM_2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM_2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM_2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM_2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM_2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high–high type and the low–low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM_2.5 concentrations in the UAs.

     

Catalytic decomposition of dioxins and other unintentional POPs in flue gas from a municipal waste incinerator (MWI) in China: a pilot testing

Unintentionally produced persistent organic pollutants (UPOPs) include polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), dioxin-like polychlorinated biphenyls (dl-PCBs), pentachlorobenzene (PeCBz), and hexachlorobenzene (HxCBz). With the booming of municipal waste incinerators (MWIs) in China, the emission of UPOPs has generated great concern. As an alternative technology of dioxin control, catalytic decomposition has not been used in China, mainly due to the absence of national demonstration projects. Also, the simultaneous removal of various UPOPs has not been well investigated.In this study, a pilot-scale selective catalytic oxidative (SCO) system using a self-developed honeycomb catalyst was built and tested in a typical municipal waste incinerator (MWI) of China. The original concentration of PCDD/Fs in flue gas after the treatment of activated carbon injection (ACI) still exceeded the national emission standard (0.1 ng I-TEQ/Nm³), while the concentrations of PeCBz and HxCBz were one order of magnitude higher than that of PCDD/Fs. For the testing temperature varying from 300 to 200 °C, the removal efficiency of PCDD/Fs range from 39 to 95 %, followed by dl-PCBs with the range of 56–89 %. PeCBz and HxCBz were also removed, though their removal efficiencies were lower than those of PCDD/Fs and dl-PCBs. Both temperature and degree of chlorination influence the removal efficiencies.     

Nano-MnO<Subscript>2</Subscript>-mediated transformation of triclosan with humic molecules present: kinetics,products, and pathways

It has been shown that manganese dioxide (MnO₂) can mediate transformation of phenolic contaminants to form phenoxyl radical intermediates, and subsequently, these intermediates intercouple to form oligomers via covalent binding. However, the reaction kinetics and transformation mechanisms of phenolic contaminants with humic molecules present in nano-MnO₂-mediated systems were still unclear. In this study, it was proven that nano-MnO₂ were effective in transforming triclosan under acidic conditions (pH 3.5–5.0) during manganese reduction, and the apparent pseudo first-order kinetics rate constants (k?=?0.0599–1.5314 h^?1) increased as the pH decreased. In particular, the transformation of triclosan by nano-MnO₂ was enhanced in the presence of low-concentration humic acid (1–10 mg L^?1). The variation in the absorption of humic molecules at 275 nm supported possible covalent binding between humic molecules and triclosan in the nano-MnO₂-mediated systems. A total of four main intermediate products were identified by high-resolution mass spectrometry (HRMS), regardless of humic molecules present in the systems or not. These products correspond to a suite of radical intercoupling reactions (dimers and trimers), ether cleavage (2,4-dichlorophenol), and oxidation to quinone-like products, triggered by electron transfer from triclosan molecules to nano-MnO₂. A possible reaction pathway in humic acid solutions, including homo-coupling, decomposition, oxidation, and cross-coupling, was proposed. Our findings provide valuable information regarding the environmental fate and transformation mechanism of triclosan by nano-MnO₂ in complex water matrices.     

Three-dimensional simulation of reservoir temperature and pollutant transport by the lattice Boltzmann method

Predicting the three-dimensional (3D) transport processes of reservoir temperature and pollutants is essential for water environmental protection and restoration, and introducing the lattice Boltzmann (LB) method into this prediction is necessary because of its simple algorithm, straightforward implementation of boundary conditions, and high computation efficiency. In this paper, a triple-distribution function (TDF) LB model for flow-temperature-concentration coupling simulations is introduced. Some essential techniques for implementing this method in 3D reservoirs are also described, including the treatment of water surface fluctuation, the consideration of surface heat exchange, and the hardware acceleration using the graphics processing unit (GPU). Two cases verified the proposed model, and then, the temporal-spatial variations of flow, temperature, and pollutants in the upper reservoir of a pumped-storage power station during both pumping and generating modes were analyzed to demonstrate its applicability. In the reservoir, the water forms several circulations, the cold water from the inlet flows as an undercurrent firstly, and then spread laterally, and the spreading of pollutants directly relates to the flow velocity. The results of flow, temperature, and concentration fields in different working conditions are consistent with model tests and physical laws, which shows good prospects of the proposed LB model.

     

Response relationships between abrupt seasonal temperature changes/warming (cooling) hiatuses in China and their influencing factors

Environmental Science and Pollution Research - The mechanisms of abrupt seasonal temperature changes and warming (cooling) hiatuses remain unclear. Clarifying how they respond to various...