Exposure to a single metal has been reported to damage renal function in humans. However, information regarding the association between multiple-metal exposure and markers for early renal impairment in different sexes among the young adult Taiwanese population is scarce. We assessed the association between exposure to arsenic (As), cadmium (Cd), and lead (Pb), and early renal impairment markers using urinary microalbumin (MA), β2-microglobulin (β2MG), and N-acetyl-beta-D-glucosaminidase (NAG) by analyzing 157 young adults aged 20‒29 years, in Taiwan. Inductively coupled plasma mass spectrometry was used to determine urinary As, Cd, and Pb levels. Regression models were applied to different sex groups. The results showed that after adjusting for potential confounding factors and each metal, urinary Cd levels were significantly positively associated with urinary MA (β = 0.523, 95% CI: 0.147–0.899) and β2MG (β = 1.502, 95% CI: 0.635–2.370) in males. However, the urinary Cd level was significantly positively associated with only urinary NAG (β = 0.161, 95% CI: 0.027–0.296) in females. This study thus indicates that the effect of exposure to metals (especially Cd) on early renal impairment among young adults in Taiwan is sex-specific. Our study results could contribute toward developing early intervention programs for decreasing the incidence of renal dysfunction. Further studies are warranted to confirm our findings and clarify the potential mechanisms involved.

Using MXene as substrate, CoOOH@MXene with different mass content of CoOOH were prepared and used to active peroxymonosulfate (PMS) for the sulfamethoxazole (SMX) degradation. The sample characterizations demonstrated the successful preparation of CoOOH@MXene. CoOOH@MXene possessed much higher BET surface area (183.82 m²/g) than CoOOH (85.36 m²/g) and MXene (6.89 m²/g) due to the good dispersibility of CoOOH particles on MXene. Due to its large surface area, 1.3CoOOH@MXene displayed the best catalytic performance for the degradation of SMX. With 0.2 g/L of 1.3CoOOH@MXene and 0.5 mM of PMS, 20 μM of SMX was completely eliminated in 10 min. The degradation followed pseudo-first-order kinetic model well, with rate constants of 0.33 min⁻¹ for 1.3CoOOH@MXene and 0.054 min⁻¹ for CoOOH. Influencing factors of initial pH, catalyst dosage, PMS concentration, SMX concentration, and co-existing anions on SMX degradation were assessed systematically. Recycling tests verified the excellent reusability and stability of the catalyst. Quenching experiments and electron paramagnetic resonance analysis substantiated that ¹O₂ played a leading role. Moreover, the intermediates were identified, and degradation pathways and activation mechanism of CoOOH@MXene for PMS were proposed. This work may highlight the application of MXene with transition metals in PMS activation.

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal–Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson’s correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia–nitrogen (NH₃-N), nitrate-nitrogen (NO₃-N), chlorophyll-a (Chl-a), fluoride (F⁻), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD₅, COD, NH₃-N, NO₃-N, F⁻, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

The refined assessment of the spatiotemporal characteristics of droughts is of great significance for drought evaluation. Based on monthly precipitation and temperature grid data (1961–2019) in the middle reaches of the Yellow River basin (MYRB), the standardized precipitation evapotranspiration index (SPEI) was calculated at monthly, seasonal, and annual scales. The run theory was used to extract the drought features at the monthly scale, and the spatiotemporal characteristics of different drought levels were analyzed using Mann–Kendall mutation tests and spatial interpolation. The Moran’ I was used to analyze the spatial heterogeneity of droughts. The results showed that the drought trend in the MYRB increased from 1961 to 2019, with the SPEI exhibiting an overall decreasing rate of − 0.1145/decade. Decreasing rates were observed in spring (− 0.1356/decade), summer (− 0.0362/decade), and autumn (− 0.0745/decade), whereas an increasing rate was observed in winter (0.0781/decade). Only extreme droughts were long term, with an intensity as low as − 22.29. The highest frequencies were observed for mild–moderate droughts, which mainly showed high-value clusters in the western and central regions. The frequencies of severe–extreme droughts mainly presented low-value clusters in the northern and southwestern areas. The frequencies of mild and severe droughts exhibited significant spatial cluster characteristics, while the drought intensity showed non-significant spatial clusters and a random distribution. The high and low values of drought intensity were mainly clustered in the middle–upper reaches. The research results provide reference for disaster prevention and mitigation, agricultural planning, and water resource allocation in the MYRB.

Solar energy is a promising form of energy that has the potential to meet all of the world’s energy needs. Only half of the sun’s energy reaches the earth’s surface, even though it is more enough for meeting the world’s energy need. Though there is a great deal of solar energy utilization technologies available, solar parabolic dish collector system got researchers focus because of its higher thermal energy conversion efficiency and its unique advantages. Several researchers have been enlightening new and emerging technologies in several countries. Hence, the authors would like to emphasize the progress in this while exercising an extensive review of different solar concentrating techniques using solar parabolic dish collector in order to produce heat and electrical power using direct and indirect energy conversion devices with wide range of applications. Their design advancement and progress applications in recent years particularly in related field are discussed too.

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6–3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063 g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018 g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6–3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312 g/l, 65.15 and − 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

The present work reports the use of natural alkaline extract from coconut husk ash as a precipitating agent for metal oxide nanoparticles synthesis. The abundance of K₂O and K₂CO₃ in it makes the extract highly basic and could be the alternative source of basic media in the laboratory. In this study, highly photoactive zinc oxide nanoparticles have been synthesized using water extract of waste coconut husk ash in a green approach which is considered as replacement of homogeneous base like NaOH and KOH. The formation of zinc oxide nanoparticles at different pH of the solution of coconut husk ash was confirmed through powder XRD, BET, SEM–EDX, UV–Vis, FTIR, and photoluminescence spectroscopy. The photocatalytic performance of the samples was evaluated through the degradation of methylene blue (MB) and methyl orange (MO) under solar irradiation which undergo degradation around 97% and 68% within 120 min, respectively. The high photocatalytic activity and rate constant could be attributed to the large surface area due to small particle size that could provide quicker photon absorption and reduction of charge carrier recombination. This current work introduces a new method to reduce energy consumption for the synthesis of highly photoactive low-cost zinc oxide nanoparticles.

The cadmium (Cd) and micronutrient contents in grains were used as screening indicators through a pot experiment, and the hierarchical cluster analysis was used to select wheat cultivars with low Cd and high micronutrient contents. The potential human health risks caused by wheat intake and the relationship between the Cd concentration in wheat grains and 12 agronomic traits were also investigated using the risk assessment model and logistic equation fitting, respectively. Yannong-23, Zhongmmai-175, and Luyuan-502, the main wheat cultivars promoted in the Huang-Huai-Hai region of China, were screened for low Cd accumulation and high micronutrient. Health risk assessment results demonstrated that children showed a high noncarcinogenic risk and that adults posed a high carcinogenic risk. The results of the agronomic trait analysis showed that low-Cd accumulation wheat cultivars had high spikelet number and fresh and dry weights of root, stem, and leaf (p < 0.05). Logistic curve fitting results showed that among all agronomic traits, the root dry weight was the most suitable factor with remarkable goodness of fit and showed a significant negative correlation. The Cd concentration in wheat grains could be predicted by the logistic curve equation obtained by fitting this trait. Results provided theoretical support for the safe use of slightly to moderately contaminated farmland, formulation of health risk management policies for different populations, and breeding of high-quality wheat.

Herbicide application and residue accumulation in farm soils have deleterious effects on non-target fauna such as earthworms. Although previous studies have documented both positive and deleterious effects of herbicides on soil biota, reports are rare on possible toxicity reduction by raising soil total antioxidant capacity (TAC). Here we review the impact of pretilachlor, a herbicide on the morpho-histology and physiology of the earthworm Eudrilus eugeniae in soil amended with farmyard manure (FYM), poultry manure (PM) and vermimanure (VM), sources of antioxidants over a period of 168 h. The results indicated a significant spike in the TAC of amended soils relative to control. Dermal undulation, setal aberrations, muscular anomaly, protein and lipid peroxidation variations in the activities of lactate dehydrogenase (LDH) and catalase (CAT) were significantly less in animals from amended soils. The maximum percent increase in protein (314%) and reductions in LPX (87%), LDH (87.9%) and CAT (87.3%) were observed in the earthworm from VM-amended soil. The increase in TAC was also maximum (109.9%) in soil amended with VM. A significant negative correlation between soil TAC with the biochemical parameters was observed and confirmed through receiver operator characteristics (ROC) and principal component analysis (PCA). The novelty of the present study includes exploring the missing link between the antioxidant level of organically amended soil and the herbicide-induced oxidative stress in the earthworm E. eugeniae. We concluded that soils with high levels of antioxidants could reduce oxidative damage in E eugeniae due to herbicide toxicity.

Environmental degradation has been a major concern for nations globally in recent years as carbon emissions have increased. Environmental degradation, if not controlled, is one of the dangers faced by humankind, and achieving sustainable development goals is not possible without improving environmental quality. Thus, reliable carbon emission measurement plays an important role in developing an effective climate strategy to address the current environmental issues. Following the trade-adjusted carbon emission measure, an effective climate strategy can be formulated especially following the 17 United Nation Sustainable Development Goals that are intended to lead to improvements toward a sustainable future. To fill the gap in the literature, we empirically explore the interrelationship between foreign capital flows and environmental quality measured by trade-adjusted consumption-based carbon dioxide (CCO₂) emissions for a panel of 125 countries in 1990–2018 by revisiting the pollution haven hypothesis (PHH). The results obtained using system GMM analysis show that FDI has a significantly positive link with CCO₂ in Asia and Africa, but the links between these two variables are insignificant in the Latin American, Caribbean, and European regions. In the cases of the full-sample and developing countries, a significantly positive relationship is found between FDI and CCO₂. In the case of income-based samples, results reveal that FDI is the cause of environmental degradation in low-income, lower-middle-income, and upper-middle-income countries. These findings suggest that developing countries should adopt environmentally friendly policies to attract foreign investors by setting strict regulations on environmental pollution control to achieve sustainable development goals (SDGs).