In this study, a new straw-iron composite material (ST@Fe) was synthesized through impregnation and freeze-drying process for persulfate (PS) activation to degrade reactive black 5 (RB5). Scanning electron microscope, Brunauer–Emmett–Teller, Fourier transform infrared spectrometry, and X-ray photoelectron spectroscopy confirmed that straw owns huge pore structure and varieties of organic functional groups, including hydroxyl carboxyl groups, which could effectively adsorb and complex iron ions. The interaction between the active iron particles in ST@Fe and straw generated Fe²⁺ for PS activation, effectively degrading over 94.80% of RB5 at an initial concentration of 20 ppm in 100 min with a specific degradation capacity of 18.97 min⁻¹ per unit of iron ions. ST@Fe/PS system demonstrated high tolerance in a wide initial pH range, which could gradually attack the RB5 molecular structure and significantly reduce the mineralization of water. Quenching experiments and electron paramagnetic resonance demonstrated the efficient generation of ROS including sulfate radicals, hydroxyl radicals, and singlet oxygen, and confirmed the dominance of sulfate radicals in the degradation process. The continuous degradation capacity and reusability of ST@Fe were also evaluated, which proved that the contaminant could be effectively degraded even after multiple cycles in the simulated textile wastewater, indicating its potential for use in practical remediation. This work provided a new method for the preparation of modified functional materials for the degradation of organic pollutants in textile wastewater and posed a novel strategy for the utilization of waste biomass.

The last decade increasing attention to litter accumulation in the marine environment revealed the issue of the terrestrial fluxes of plastic pollution into the oceans. However, limited studies investigate the macro-litter abundance and composition particularly in freshwater ecosystems and the adjusted coastal environment. In this study, we conducted an empirical analysis of anthropogenic litter (AL) in two freshwater streams and their estuaries of Lesvos island (Greece), strongly affected by the waste mismanagement in the Moria refugee camp. A total of 1,622 litter items were collected from five sites during the field campaign between the 3rd and 6th of June 2019. Among these, artificial polymer items (mainly drink bottles, single-use plastic bags, cutlery, and trays) irrespective of the site location constituted the major part ranging from 46 to 91% of total AL by mass. Drink bottles and plastic cutlery are found in higher percentage at Moria camp river and its estuary, while packaging and bag litter are more abundant at upstream sites. The operation of the camp has increased the AL abundance 13 times more than the reference site, causing severe threat to human health and to freshwater and marine environment. It is therefore important to improve the waste management system at (inter)national (change in production and packaging policy) and local scale (introducing sustainable solutions to refugee camp design) by promoting recycling and circular economy projects to diminish AL pollution.

Groundwater contamination source recognition involves the recovery of contamination source time series release histories from observation data. In the present study, a linear source contamination recognition task was addressed. When using a simulation–optimization inverse framework to solve the recognition task, high calculated expense and high dimensional search space always hinder the task efficiency. Moreover, traditional surrogate methods face obstacle of handling with time-sequence data. Therefore, a novel stacked chaos gate recurrent unit (SCGRU) neural network was proposed as a surrogate model to precisely emulate the sequence to sequence mapping relationship of a high computational running simulation model. To address the challenge of high dimensional search, a mixed-integer programming strategy was employed to reduce the dimension of unknown variables. Furthermore, a hybrid sparrow search algorithm (HSSA) was implemented to alleviate being trapped into local optimum. In particular, the proposed SCGRU-HSSA framework was utilized to determine the length and release intensities during the stress period of a linear source. Based on the results obtained, the following conclusions were derived: (1) SCGRU can replace the origin simulation model with high accuracy and fast running speed; (2) when using chaos sine mapping and a Cauchy mutation strategy, the SSA escaped from the local optimum, improving the search efficiency of the recognition task; and (3) SCGRU-HSSA methodology is stable and reliable in recognizing features of linear source contamination.

To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear nature due to the random nature of climate conditions. So, one way to solve is solar radiation prediction and solar energy prediction using more accurate techniques. Also, energy business and power system control units require more accuracy along with very short to large duration prediction in advance. So, to complete the requirement many prediction techniques are used and among them, Artificial Neural Network (ANN) and Fuzzy are more accurate and reliable techniques. In this paper basically, a literature study for solar radiation and energy prediction using ANN and Fuzzy logic techniques has been carried out. Many studies are reviewed and then selected some most accurate, reliable, and relevant studies for further study. ANN models with different algorithms such as feed-forward back-propagation-based ANN, Multi-layer feed-forward-based ANN model, Linear regression with ANN model, GNN-based model are reviewed in the study. ANN models with different input parameters combinations and the different number of neurons were also reviewed. Fuzzy logic-based and Adaptive Neuro-Fuzzy interface (ANFIS)-based different models have been reviewed and observed that the ANFIS technique performs better. From the study, it has been noted that ANN and Fuzzy logic employed models are most effective for estimation than any other empirical models. It is found that solar radiation and energy prediction models are dependent on input parameters more. At last, highlighted some possible research opportunities and areas for better efficiency of the results.

Taking the green credit policy in 2012 as a quasi-natural experiment, this paper has investigated the impact of green credit policy on Chinese firms’ green strategy choices by using the panel data of A-share listed firms from 2008 to 2019. The results reveal that green credit improves firms’ green innovation overall. In terms of time, the green-washing behavior of listed firms will increase significantly in the early stage of the implementation of green credit policy, but as time goes by, such green behavior of firms will be detected, which in turn will motivate firms to improve green innovation. Furthermore, the green credit policy has a more significant effect on green innovation of firms in localities under high environmental regulation, economically developed regions, and without other alternative financing channels. Firms located in regions with economically underdeveloped and low environmental regulation are more inclined to adopt the behavior of green-washing environmental information. Besides, after the implementation of the green credit policy, green innovation can improve corporate financial, environmental, and social performance, while green-washing behavior will damage corporate financial, environmental, and social performance. Our findings contribute to the literature on green credit policy, corporate green innovation, and environmental information disclosure, and also provide policy implications for improving the quality of green credit policy in the future.

A total of fifty groundwater samples were collected in the western part of Nizamabad district, Telangana State, India. The results obtained were compared with the IS 10500 standard, which shows more than 80% of the samples are unfit for drinking purposes. The results were also analyzed to know the irrigation suitability, where about 80% of the samples are fit for agricultural use. For the major ions in groundwater quality for irrigation and drinking, contradictory locations exist which are majorly caused by geogenic (silicate weathering minerals) and anthropogenic (fertilizers, manure, and industrial effluents) sources. The hydrochemical facies of CaCl and mixed CaMgCl and NaCl water types were dominant in pre- and post-monsoon seasons. The spatial distribution diagrams of the major ions were high concentration in northern areas followed by western and south-western portions. As per the groundwater quality of pollution index (GQPI), most of the study region (62%) comes under the low contamination zone and the rest (38%) under the moderate to unacceptable zone. Factor analysis reveals that the study region is predominant of weathering, ion exchange, and anthropogenic inputs of major contamination of groundwater quality.

This study aims to evaluate the environmental and employment impacts of China’s value-added trade using the gross exports decomposition method and balance of avoided factor content. The major findings are that China’s value-added trade overall leads to increases in global emissions and employment, which are mainly affected by the characteristics of China’s exports. However, China’s imports indirectly promote global emissions reductions and have a significant driving effect on foreign employment. Trade through onefold value chains plays a dominant role in determining both impacts. Simple global value chain (GVC)-related trade contributes most to reducing global emissions but decreases global employment as well. Complex GVC-related trade overall increases global employment and emissions. At the bilateral level, the impacts are varied, and there are some instances of complex GVC-related bilateral trade that contribute to global emission mitigation. A win–win situation for the environment and employment can be achieved in China’s bilateral trade with some countries through specific value chain routes. These findings can provide useful information for balancing trade growth, carbon emission reduction, and job creation and may indicate the future direction of China’s upgrade in the GVCs and help allocate emission reduction responsibilities.

Low wheat production is linked to soil degradation, low organic matter, temperature variation, and nutrient depletion in soils of semiarid regions. Nitrogen is mostly applied as urea to meet crop requirements; however, excessive N application may pollute the environment and contaminate groundwater. The current studies explored possible ways for decreasing N losses (NH₃ volatilization and NO₃ leaching) and improving N availability for wheat production in alkaline soil. The ZnO was coated on urea (1% Zn coating) to get zinc-coated urea (ZnU), and both urea and ZnU were incubated in laboratory at recommended rate (RR), i.e., 150 kg N ha⁻¹ and 80% (N of RR), after further coating with inhibitors [N-(n-butyl) thiophosphoric triamide (NBPT) at 1% of urea and 4-amino-1,2,4-triazole (ATC) at 2% of urea], thus creating six treatments. The results showed higher NH₃–N loss at day 4 and thereafter a decreasing trend reaching to minimum at day 14. The cumulative NH₃–N volatilization from urea alone was found higher (28.99%), except ATC treatments producing statistically similar losses due to restriction in nitrification process. In greenhouse, the treatments were tested in wheat cultivars (Faisalabad 2008 and Lasani) for crop growth, nutrient (N, P, K, and Zn) uptake, and yield parameters, where 80% of RR treatment, i.e., NBPT + ZnU₈₀, was found at par with full RR as commercial products, especially comparable to ZnU (at RR) that produced the highest chlorophyll (53.65unit value), net leaf photosynthetic rate (19.64 μmol CO₂ m⁻² s⁻¹), plant biomass (208.13 g/pot), grain yield (63.65 g/pot), and nutrient (NPK and Zn) accumulation in grain of Fsd-2008 cultivar. In field trial, NBPT + ZnU₈₀ also outperformed and produced the highest physiological efficiency (PE), agronomic efficiency (AE), and nitrogen recovery efficiency (REN); the treatment also found statistically similar with ZnU (at RR) that produced the maximum plant height (95.4 cm), plant biomass (11.58 t/ha), grain yield (4.69 t/ha), and 1000-grain weight (42.55 g). The relative NO₃ leaching was found lower in 80% N treatments, yet leaching was not significant from either treatment at the three stages of crop growth. Overall, current studies revealed the effectiveness of NBPT-amended urea (followed by ZnU) with 20% saving of N inputs through higher N availability for plant uptake that could benefit growers as well as conserve environment.

Crop straw mulching is an important organic supplement in sustainable agriculture; however, the effect of increased organic matter on the diversity of micropredators such as myxobacteria and the correlation between myxobacteria and microorganisms have been little explored. In the current investigation, high-throughput sequencing was performed to analyze the myxobacterial community composition in a wheat-corn rotation experimental field with 6-year straw mulching and fertilization treatments. The results reveal no significant influence of straw mulch application on myxobacterial α-diversity (P < 0.05). NMDS (nonmetric multidimensional scaling) and perMANOVA results indicate the significant influence of straw mulching application on myxobacterial community composition (P < 0.05), and several groups, including Haliangiaceae, Polyangiaceae, and Archangiaceae, also varied in soil aggregates. RDA (redundancy analysis) results show that TOC (total organic carbon) was the most important factor affecting the myxobacterial community structure. In addition, RDA and random forest analysis results show the contribution of myxobacterial community structure to soil bacterial community α- and β-diversity, especially in the 0.25–1 mm and < 0.25 mm soil aggregate fractions. In conclusion, we suggest that the variation in myxobacterial community structure may be a driver of bacterial α- and β-diversity in soil microhabitats and might be a cause of soil microbial community changes. Our results are fruitful for finding more efficient ways to use straw from waste for the betterment of sustainable agriculture by analyzing changes in myxobacterial community structure.

Little evidence has demonstrated the association between health conditions and cooking water. The purpose of this study was to explore the relationship, using a representative sample of 10,531 subjects selected from the China Family Panel Study (CFPS). The usage rate of surface-exposed water showed a slight upward trend from 2010 to 2018. The adjusted odds ratio (OR) of chronic diseases with surface-exposed and well water was 1.140 (95% CI: 0.989–1.315) and 0.902 (95% CI: 0.839–0.969), respectively, with reference to tap/purified water. Surface-exposed water increased the likelihood of a worsening health change by 25.5% (OR: 1.255; 95% CI: 1.123–1.411), while well water was associated with poor self-rated health (OR: 1.169; 95% CI: 1.094–1.249). As such, surface-exposed water was associated with chronic diseases and worsening changes in health, and well water was negatively associated with chronic diseases. Although efforts to improve quality of drinking water have been made in China for decades, our conclusions reveal that water quality still remains a critical public livelihood issue among middle-aged and elderly populations. More in-depth research is required on whether the disinfection ingredients of tap water may increase the risk for chronic disease.