With growing global warming issues, the association between technological innovation and environmental pollution has created significant debate in recent years. This paper examines the long-run and causal impact of technological innovation, economic growth, and renewable energy on consumption-based carbon emissions in Japan. The study utilized quarterly data spanning between 1990 and 2015. The study utilized recent econometrics techniques such as Maki co-integration, ARDL bunds test, FMOLS, DOLS, and frequency domain causality techniques. To the author's understanding, no prior studies have been conducted in Japan using consumption-based carbon emissions as a proxy of environmental degradation. Thus, this empirical analysis contributes to the literature. The findings from the ARDL bounds and Maki co-integration tests revealed evidence of co-integration among the series. The results of FMOLS and DOLS reveal that both renewable energy and technological innovation improve the environmental quality, while economic growth harms the quality of the environment. The results of the frequency-domain causality technique reveal that technological innovation, renewable energy, and economic growth can significantly predict consumption-based carbon emissions in Japan. Based on these outcomes, we suggested that Japan's government should be careful when formulating policies that trigger growth, which will have a detrimental impact on the environmental quality. Our empirical outcome also revealed that any policy that encourages renewable energy should be encouraged since it enhances environmental quality.

The present study attempts to investigate some blood parameters of Otolithes ruber during different seasons in terms of both temperature and pollution. For so doing it uses 10 specimens, for each station and season, collected from 5 polluted stations, including Petrochemical, Ghanam, Zangi, Douragh, Patil, and Sajafi as the control group, away from pollution in Musa Creek. The fish are anesthetized with 1ml of clove extract per liter. Their blood samples are taken immediately from the caudal vein, using a heparinized syringe. Afterwards, the serum is separated in a centrifuge with a speed of 6000 rpm for 2 minutes. The desired factors are measured by the Mindray BS200 auto-analyzer and the total protein level, by Bradford's usual laboratory methods. Results show that AST, ALT, ALP, Glucose, and Triglycerides have increased in more polluted stations (P≤0.05). In sheer contrast, total protein and Albumin have decreased as pollution grows (P≥0.05). According to this study, environmental water pollution of the fish has a large impact on the concentration of measured blood parameters, whereas the influence of seasonal changes on most of them is low.

The production rate of industrial and agricultural waste is increasing due to population growth. Soil is the most important receiver of industrial and agricultural waste. Contaminants such as heavy metals in various waste after reception by the soil, immediately become part of the cycle that has different impacts on the environment. Geopolymer, as a chemical stabilizer has the potential to stabilize heavy metals in the soil. In this research, several geopolymers for the stabilization of heavy metals in soil were synthesized. Silicon dioxide (SiO2) and aluminosilicate (Al2SiO4) must be used to produce the geopolymers. Rice husk ash was used as the SiO2 source. Also, Iranian zeolite and sepiolite, and red clay soil were utilized as the source of Al2SiO4. The synthesized geopolymers were investigated for the adsorption of nickel and cadmium. Also, batch and column studies of using geopolymers for the chemical stabilization of heavy metals in soil were conducted. The results revealed a high adsorption capacity of the geopolymers. The zeolite, sepiolite, and red clay geopolymer-soil samples adsorbed 100% of the heavy metals (i.e., Ni and Cd) at a concentration of 100 ppm. The zeolite geopolymer adsorbent adsorbed 57% and 96% of Ni and Cd at a concentration of 1000 ppm, respectively. In general, it was concluded that the use of geopolymer compounds in soils with high heavy metal adsorption capacity could be an efficient approach to prevent groundwater resource pollution.

Transformation of food and beverage industrial sludge into vermicompost into value-added product simultaneously can control gaseous emission. Addition of biochar in the vermicomposting as a bulking agent increases fertilizer value. This research aimed to investigate the effect of biochar amendment on vermicomposting of the food and beverage industry sludge (FBIS) and cow dung (CD) in a different ratio using earthworm Eisenia fetida. We had further investigated the survival rate of E. fetida and the cocoon productions after 35 days of the vermicomposting. Besides, we have also evaluated the seed germination bioassay using Malabar spinach (Basella alba) to determine the toxicity and maturity of produced compost. The survival and cocoon production of E. fetida were higher in vermicompost amended with 10% biochar. Vermicomposting with biochar resulted in a slight pH shift. Reduction in organic carbon (OC) percentage not so significant in biochar added FBIS and CD. An increase in phosphorus and potassium content and a decrease in nitrogen percentage observed; vermicomposting with biochar resulted in higher seed germination, root elongation, and germination index than vermicomposting without biochar.

The agricultural sector uses fertilizers such as urea to add more nutrients to the soil needed for plant growth. Although it is cost-effective in crop production, indiscriminate use of nitrate-based fertilizer may result in behavioural, morphological, and physiological alterations on non-target organisms. This study determined the angiogenesis activity in the chorioallantoic membrane of urea-exposed duck embryos. It also investigated the weight, morphometries, and liver histopathology to gather more information on urea fertilizer's toxicity. It was observed that urea promoted angiogenesis in the CAM of duck embryos, especially at higher concentrations (P<0.05). Embryos treated with urea resulted in an alteration of the head-beak length (P<0.05). However, weight, crown-rump length, forelimb length, and hind limb length were not affected. The developing liver of urea-treated embryos showed distortion of the central vein shape and had larger sinusoidal spaces. The presence of Kupffer cells and lipid droplets were observed in the treated section. Congestion of blood cells, haemorrhage, and necrosis of hepatocytes were also observed in the tissue suggesting the extent of damage caused by the fertilizer. The findings of this study showed multiple developmental effects of urea on duck embryos. Further investigations are needed to shed more light on the toxicity of urea fertilizer on vertebrates.

In this study, water from three urban wetlands of Gurugram – Sultanpur (WS), Damdama (WD), and Basai (WB), was studied for various physicochemical parameters to assess their suitability for the healthy survival of fishes and the results were compared with the limits of these parameters for fish farming. The parameters studied were colour, temperature, pH, alkalinity, hardness, Ca2+- Mg2+ ratio, NO3-, Cl-, SO42-, PO43-, and heavy metals (Fe, Mn, Cr, Cu, Zn, Ni and Pb). The results of the study indicate the majority of studied parameters are beyond the desirable limits in WB; thus, water is most unsuitable for fishes in WB. WB is unsuitable for parameters: colour, alkalinity, hardness, Ca -Mg ratio, NO3-, Cl-, SO42-, PO43-, Cr, Cu, Fe, Mn, Ni and Zn. WS needs consideration for temperature, NO3-, Cu, Ni and Zn, whereas WD needs improvement in temperature, TDS, NO3-, Cr, Cu, Fe, Mn, Ni and Zn concentration for better fish growth. Most of the parameters are high in summer as compared to winter, which is due to the dilution after rainfall. Hence, we recommend timely action for effective measures to improve the water quality of wetlands and their regular monitoring for improved fish habitat.

This paper presents concentrations and sources of Aliphatic and Aromatic Hydrocarbons in the sediments from Babolsar coastal area and the inlet of Babolrood River in the southern side of the Caspian Sea. The concentration of hydrocarbons in 13 sediment samples from the study area were measured by gas chromatography (GC). Total Petroleum Hydrocarbon (TPH) concentrations in sediment samples in the coastal area ranged from 115 to 201 μg/g. In the inlet samples, TPH concentrations were close to each other and ranged from 294 to 367 μg/g. The TPH results showed moderate level of oil pollution in the study area. Total Polycyclic Aromatic Hydrocarbons (ΣPAHs) concentrations in sediment samples inside the inlet ranged from 498 to 702 ng/g, indicating moderate level of pollution. Concentrations of ΣPAHs in sediment samples in the coastal area ranged from 341 to 1703 ng/g, indicating moderate to less than significant level of pollution. Developed indices for pollutant origins showed that hydrocarbons in all sediment samples collected in the study area had petrogenic origin. The results also showed the Babolrood River as the main source of oil pollution in the sediments in the study area.

The main objective of this paper is to propose K-Nearest-Neighbor (KNN) algorithm for predicting NOx emissions from natural gas electrical generation turbines. The process of producing electricity is dynamic and rapidly changing due to many factors such as weather and electrical grid requirements. Gas turbine equipment are also a dynamic part of the electricity generation since the equipment characteristics and thermodynamics behavior change as turbines age and equipment degrade gradually. Regular maintenance of turbines are also another dynamic part of the electrical generation process, affecting performance of equipment as parts and components may be upgraded over time. This analysis discovered using KNN, trained on a relatively small dataset produces the most accurate prediction rates in comparison with larger historical datasets. This observation can be explained as KNN finds the historical K nearest neighbor to the current input parameters and approximates a rated average of similar observations as prediction. This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model predicting NOx emissions. The model can be used to optimize the operational processes for harmful emissions reduction and increasing overall operational efficiency. Latent algorithms such as Principle Component Algorithms (PCA) have been used for monitoring the equipment performance behavior change which deeply influences process paraments and consequently determines NOx emissions. Typical statistical methods of performance evaluations such as multivariate analysis, clustering and residual analysis have been used throughout the paper. This paper incorporates ambient weather conditions, electrical output as well as turbine performance factors to build a machine learning model predicting NOx emissions. The model can be used to optimize the operational processes for harmful emissions reduction and increasing overall operational efficiency. Latent algorithms such as Principle Component Algorithms (PCA) have been used for monitoring the equipment performance behavior change which deeply influences process paraments and consequently determines NOx emissions. Typical statistical methods of performance evaluations such as multivariate analysis, clustering and residual analysis have been used throughout the paper.

Bed Sediment is a dynamic and complex material that plays an important role in the aquatic ecosystems and provides habitat from a highly various community of organisms. To address the major issue, this study, which its substantive subject done for the first time in Iran, aimed to assess the current status of Darreh-rood river's health and quality using SOD rate and its associated factors including Texture, fine-PSD, TOC and TP of bed sediments along with some basic field parameters of river-water. All required samples were collected from 10 sampling points in due course. SOD data with regard to related factors were calculated and analyzed. The rates of SOD ranged from 0.69 to 1.57 g O2/m2/day. Moreover, this index was classified in varied quality domains. Afterwards, a predictive equation was determined among SOD rate and its associated parameters using MATLAB software. Finally, the Results showed that the river quality and health suitability in research area are in categories slightly clean and slightly degraded, in targeted zones during the study period. Also, the increase in TOC and TP concentrations together with a decrease in sediment particle size was led to an increase in SOD-rate accordingly. In conclusion, the consequences of this study under Survivability-based Adaptive Management (SAM) perspective can be used as a rapid diagnostic tool to support water policy decision-makers and other stakeholders to promote the best practices for protecting the health conditions of riverine systems, focusing on selecting the appropriate points for discharging the wastewaters into the receiving water-bodies.

The critical parameters namely initial pH, time and current density largely impact the process efficiency of electrocoagulation (EC). Few works have been done on observing the interaction of these critical parameters and the possible combined effect on the overall pollutant removal efficiency. Therefore, the knowledge of the combined effect of critical parameter interaction would enhance the optimization of EC parameters to attain maximum efficiency with limited resources. Using aluminium electrodes with interelectrode distance of 10 mm on synthetic wastewater, representing biotreated palm oil mill effluent (BPOME), with a set range of initial pH, current density, and time of 3-8, 40-160 mA/cm2 and 15 to 60 minutes, respectively, the effect of the three critical variables was investigated. The optimum Chemical Oxygen Demand (COD) removal of 71.5% was determined at pH 6, current density of 160 mA/cm2 (with current 1.75 A) at EC time of 15 minutes. The experiment was validated with real BPOME, resulting in the removal efficiency of 60.7 % COD, 99.91 % turbidity, 100 % total suspended solids (TSS) and 95.7 % colour. Removal of a large quantity of pollutants in a time span of 15 minutes with optimized parameters in EC is notable for a wastewater treatment alternative that requires no extensive use of chemicals. The interaction of parameters observed in this study indicated a synergistic contribution of initial pH and current density in removing maximum wastewater COD in 15 minutes of EC.