There is a lack of proper research that highlights the impact of institutional quality (IQ) and renewable energy consumption (REC) on the carbon emission (CE). The significance of IQ and REC in the achievement of zero CE is highlighted in this research. The current research reports the effects of these important factors on the consumption-based carbon emissions in the G-7 countries from 1995 to 2018. Based on the outcome of the cointegration test, the long-run connection is recognized between IQ, REC, GDP, exports, imports, and consumption-based CE. The findings also validated that there exist significant decrease and increase in the CE in both the short and long run; for instance, IQ, REC, and exports decrease the CE, while imports and GDP increase the CE. The estimates of causality test showed that policies aimed at improving IQ, REC, GDP, exports, and imports have a significant impact on the CE. Consequently, based on these results, policymakers in the G-7 must prioritize IQ and REC to enhance environmental quality and attain carbon neutrality.

Industrial symbiosis (IS) is an approach that aims to use resources efficiently by cooperating between independent enterprises in raw materials, energy, and similar sectors. As a result of cooperation, businesses gain economic, environmental, and social benefits. Especially in recent years, IS applications have become widespread due to the problems experienced in the supply of resources. The presence of more than one enterprise in cooperation creates a complex network structure in IS applications. In this complex system, many decision problems are encountered in establishing and effectively maintaining the industrial symbiosis network. Operations research techniques are at the forefront of the methods used to solve decision problems. This study examined studies using operations research techniques in industrial symbiosis. Studies were divided into four classes according to the methods they used: exact methods, heuristic methods, multi-criteria decision-making, and simulation. In the literature review, the studies in the Web of Science (WOS) database are systematically presented by scanning with the determined keywords. As a result of the study, it was analyzed which method was preferred and where the methods could be applied in industrial symbiosis.

River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986–2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers’ flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accuracy of predictions is evaluated in the appropriate range, and the present study shows promising results of the current approaches. Consequently, a comparison between the performance of linear stochastic models and complex black-box MLs, reveals that linear stochastic models are more suitable for the current region’s monthly river flow prediction.

The design of an e-waste collection policy is challenging, especially for a country like India, where the economy is a developing state, and there is a large diversity in socio-economic factors. The e-waste collection policy impacts the various stakeholders such as the manufacturer, the raw material producers, the assemblers, the retailers, the generator (households and bulk consumers), the scrap dealers, the smelters, the recyclers, and the regulators. The design of an e-waste collection policy needs to consider the appropriate set of Critical Success Factors (CSFs), which will maximise the e-waste collection providing business sustainability to the stakeholders while satisfying the environmental regulations in the operating locations. Twenty-three CSFs identified and categorised in six implication dimensions for the e-waste collection policy framework based on a literature survey and experts committee view. The fuzzy DEMATEL approach is employed to analyse the CSFs to design an e-waste collection policy in India from a comprehensive perspective. Cause and effect interrelationship is established among the CSFs, and also their impacts are evaluated to segregate the CSFs into cause group (prominent influencing and independent) and effect group (influenced and dependent). The CSFs such as technology involvement, green practices, environmental program, certification and licensing, public ethics and stakeholder's awareness for circular economy are prominent influencing CSFs for e-waste collection policy in India. The current study is expected to provide a platform for policymakers to design the e-waste collection policy.

Wastewater treatment plants (WWTPs) efficiently retain microplastic particles (MPs) generated within urban areas. Among the wastewater treatment steps, disinfection has not been characterized for its potential MPs retention activity, although it has been reported that processes used to abate the bacterial load could also affect MPs concentration. For this reason, we evaluated the MPs concentration across the overall wastewater treatment process and before and after the disinfection step in four small/medium WWTPs located in the north of Italy. Most of the MPs found in the samples were fibers or fragments, smaller than 500 μm, mainly composed of polyethylene, polypropylene, or polyethylene terephthalate. The retention efficiency at the outlets was higher than 94% in all the plants analyzed. More interestingly, the disinfection treatments adopted by the different WWTPs reduced MPs concentration from a minimum of 9.1% (UV treatment) to a maximum of 67.6% (chlorination), promoting a further increase of the overall retention efficiency of the WWTPs from 0.4 to 0.7%. Quantitatively, the disinfection contributes to the MPs reduction in the outlets by retaining 0.5–6.7 million MPs per day, in WWTPs that discharge 2.7–12 million MPs per day. The results of the present work underline the importance of a careful choice of the steps that constitute the wastewater treatment, including disinfection, in order to minimize MPs discharge into the natural ecosystems.

Carbonized products of waste agricultural straws used for soil remediation can reduce impact of heavy metals on soil ecology and crop growth. Here, we demonstrated straw fermentation residues to be suitable for preparation of soil remediation agents by pyrolysis. Lignocellulose degradability of filamentous fungi during fermentation was found to significantly enhance properties of biochar for cadmium (Cd (II))-contaminated paddy soil remediation. Obtained biochars were indicated to have rich oxygen-containing groups, thus showing enhanced removal ability of Cd (II). Adsorption capacity of biochar (BaWS) prepared from wheat straw, which has been fermented by Trichoderma asperellum T-1, reached 105.9 mg g⁻¹, 372.8% higher than that from natural wheat straw (BWS). Fermentation of straws by Trichoderma reesei QM6a can also improve the adsorption performance of biochar, but the effect is much weaker. The content of bioavailable Cd (II) in paddy soil reduced 83.7% within 15 days after addition of 1% BaWS. Significantly, adding 1% BaWS had better effect on increasing soil pH and removing available Cd (II) , than adding 3% BWS. These results suggest that the used dosage of microbial pretreated straw biochar for the remediation of Cd (II)-contaminated paddy soil was only 1/3 of that of conventional biochar. The enhanced property of biochar was attributed to deconstruction of straws by filamentous fungi before being pyrolyzed. Thus, fermented straws were indicated more suitable for the preparation of biochar used as effective soil remediation agents.

Chromium (Cr) contamination in soil poses a serious security risk for the development of medicine and food with ginseng as the raw material. Microbiome are critical players in the functioning and service of soil ecosystems, but their feedback to Cr-contaminated ginseng growth is still poorly understood. To study this hypothesis, we evaluated the effects of microbiome and different Cr exposure on the soil microbial community using Illumina HiSeq high-throughput sequencing. Our results indicated that 2467 OTUs and 1785 OTUs were obtained in 16S and ITS1 based on 97% sequence similarity, respectively. Bacterial and fungal diversity were affected significantly in Cr-contaminated soil. Besides, Cr contamination significantly changed the composition of the soil bacterial and fungal communities, and some biomarkers were identified in the different classification level of the different Cr-contaminated treatments using LEfSe. Finally, a heatmap of Spearman’s rank correlation coefficients and canonical discriminant analysis (CDA) indicated that Chloroflexi, Gemmatimonadetes, Acidobacteria, Verrucomicobia, and Parcubacteria in phylum level and Acidimicrobiia, Gemmatimonadetes, and Deltaproteobacteria in class level were positively correlated with AK, AP, and NO₃⁻-N (p < 0.05 or p < 0.01), but negatively correlated with total Cr and available Cr (p < 0.05 or p < 0.01). Similarly, in the fungal community, Tubaria, Mortierellaceae, and Rhizophagus in the phylum level and Glomeromycetes, Agaricomycetes, and Exobasidiomycetes in the class level were positively correlated with AK, AP, and NO₃⁻-N (p < 0.05 or p < 0.01), but negatively correlated with total Cr and available Cr (p < 0.05 or p < 0.01). Our findings provide new insight into the effects of Cr contamination on the microbial communities in ginseng-growing soil.

Nanoparticles interact with plants to induce a positive, negative, or neutral effect on their growth and development. In this study, we document the positive influence of magnesium oxide (MgO) nanoparticles (NPs) on the morpho-biochemical parameters of Macrotyloma uniflorum (horse gram). Horse gram is a protein and polyphenol-rich legume crop. It is an important part of the human diet and nutrition. When exposed to MgO-NPs, a significant increment in the shoot–root length, fresh biomass, and chlorophyll content of horse gram was evident. Furthermore, there was a 4–20 and 18–127% increase in the accumulation of carbohydrate and protein content on MgO-NP exposure. The antioxidant potential was enhanced by 5–19% on NP treatment as a result of the increase in the accumulation of total polyphenolics. Total phenols and flavonoids were enhanced by 7–20 and 50–84% in the presence of MgO-NPs. The enzyme activity of SOD, CAT, and APX was also enhanced in MgO-NP-exposed horse gram. The observed alterations were also justified by the Pearson correlation. Overall, the MgO-NP-induced morpho-biochemical alterations in horse gram indicated their probable role as a nano-fertilizer. However, it further warrants the need to extensively investigate the responses of various other plant types to MgO-NPs before industry scale application.

Tailoring a material that has a synergistic role as an adsorbent and a photocatalyst for environmental application is an attractive field for research. This article presents a study of facile synthesis of NiO and Ni/NiO with a synergistic role as super adsorbents in the lake of light and photocatalysts under light irradiation. Nano flower-like mesoporous NiO and Ni/NiO were synthesized by the co-precipitation method. XRD, SEM, EDAX, XPS, BET, and DR/UV–Vis spectroscopy techniques were employed for samples’ analysis. The point of zero surface charge of prepared samples was detected by the batch equilibrium method. The adsorption efficiency was investigated in the absence of light using aniline blue as a pollutant model dye. The synergistic effect as an adsorbent and a photocatalyst was investigated under UV and sunlight irradiation. Different parameters affecting the adsorption in the dark have been optimized. The results showed that in the absence of light, the prepared samples are super adsorbents with a maximum adsorption capacity ranging from 210 to 230 mg g⁻¹ and a removal % ranging from 95 to 100% within 2 h. Under UV or sunlight irradiation, the adsorbent/photocatalyst attained a dye removal % of 99.8% within 30 min. The adsorption data matched the pseudo-second-order model, and the equilibrium adsorption data showed compatibility with Langmuir model. The findings of experiments revealed that the adsorption is spontaneous, exothermic, and results in less entropy. Under sunlight irradiation, the dye removal efficiency increased by 19% in the case of Ni/NiO hybrid; it showed a removal efficiency of 99.5% within 30 min under sunlight irradiation versus 80% after 120 min in the dark.

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.