Clean-soil Air Water最新文献

The Fenton and UV–Fenton procedures were utilized in this study to eliminate total organic carbon (TOC) from wastewater generated during actual resin manufacturing. Optimal operating parameter values influencing removal efficiency were identified, including initial H₂O₂ and Fe²⁺ concentrations and total reaction time (t). The residual H₂O₂ concentration was measured using the metavanadate method in all processes. The results indicated that the Fenton process achieved a TOC removal rate of 32.0% at concentrations of 500 mg L⁻¹ for H₂O₂ and 100 mg L⁻¹ for Fe²⁺, with a constant pH of 3.78 and a reaction time of 6 h. In the UV–Fenton process, H₂O₂ concentrations of 500 and 1000 mg L⁻¹ were examined, resulting in 14% and 15% TOC removal efficiencies, respectively. The effect of gradually adding H₂O₂ on the removal efficiency was also investigated in this study. To do this, the Fenton process started with an initial H₂O₂ concentration of 250 mg L⁻¹. Once approximately 80% of this amount was consumed, 250 mg L⁻¹ H₂O₂ was added, and the process continued. A maximum TOC removal of about 71% was achieved by gradually adding H₂O₂ at a 4000 mg L⁻¹ concentration. On the basis of these findings, the gradual addition of H₂O₂, as opposed to an initial dose, proved to be a significant and practical method for removing organic matter from wastewater in the Fenton process.

Floods and droughts are significantly impacted by rainfall, a vital component of the hydrological cycle. This study evaluates long-term rainfall trends using variable-size cluster analysis (VSCA) to examine trends and change points over eight synoptic stations of the Damodar River Basin from 1922 to 2021. The Mann–Kendall (MK) test with Sen's slope estimator reveals monotonic trends and magnitudes, and VSCA analyzes rainfall patterns and change points. Changing climate statistics were summarized using a modified Pettitt–Mann–Whitney test version. Rainfall patterns that changed over time were shown graphically using 3D representations for 100 years of data with a minimum cluster size of 10. VSCA analysis showed a declining trend in rainfall beginning about 1990, with notable variations in 1970–1980 for Bardhaman, Dhanbad, Giridih, and Hazaribag. On the other hand, Koderma and Purulia had rising patterns starting in 1970 and lasting roughly from 1960 to 1980. Most of the time, West-Medinipur showed both declining and no-trend conditions. The MK test and Sen's slope technique revealed a significant negative trend in rainfall, with magnitudes of −1.28, −1.03, −1.67, −0.61, −2.54, and −1.92 mm/year for Bardhaman, Dhanbad, Giridih, Hazaribag, Ramgarh, and West-Medinipur, respectively. Purulia and Koderma displayed rising trends with magnitudes of 0.84. This research enhances our understanding and provides valuable insights for managing water resources.

Groundwater, the largest global source of freshwater, is under increasing stress due to over-extraction, leading to a significant decline in groundwater levels (GWLs) in many regions around the world. This global groundwater crisis, driven by consistent overdraft, seriously threatens water security and requires immediate action for sustainable management strategies. This study aims to predict and forecast monthly GWLs at three critical observation wells, such as Ramachandrapuram, Palakollu, and Jangareddigudem, located in the Lower Godavari River Basin, India, to support sustainable groundwater management. Univariate artificial intelligence (AI) models, namely, random forest (RF), least-squares support vector machine (LS-SVM), and radial basis function SVM (RBF SVM), were utilized for GWL simulation and prediction. The time-series features were extracted from historical groundwater data (January 1998–December 2012) to develop prediction models for training (January 1998–June 2008) and testing (July 2008–December 2012) periods. The models were then applied to project the monthly GWLs from January 2013 to December 2018. RF outperformed LS-SVM and RBF SVM models, achieving R² values of 0.89, 0.86, and 0.82 for Jangareddigudem, Ramachandrapuram, and Palakollu during testing phase. The superior performance of the RF model demonstrates its robustness in modeling GWLs with high predictive accuracy. This data-driven approach, leveraging AI techniques for time-series prediction, presents a novel methodology for GWL estimation in data-sparse regions. The developed models provide valuable insights for sustainable groundwater management and inform policy decisions to mitigate impacts of groundwater overdrafts and ensure long-term water security in vulnerable regions.

The dye manufacturing industry generates substantial volumes of wastewater that contains color, metals, and various toxic chemicals depending on the specific dyes produced. Effective treatment of this complex wastewater is of great importance to ensure compliance with discharge regulations and protect aquatic ecosystems. In this study, the treatability of wastewater samples taken from the dye manufacturing industry at two different times was investigated using adsorption and Fenton oxidation processes. Treatment performance and cost-effectiveness were assessed by using different pH values and activated carbon dosages in the adsorption process, and different Fe²⁺ and H₂O₂ dosages in the Fenton process. The optimum removal of chemical oxygen demand (COD) and color in the adsorption process was achieved at pH 5, and at 20 g L⁻¹ activated carbon, COD and color removal were achieved at above 64.2% and 95%, respectively. In Fenton oxidation studies, a COD removal rate of 56.6% was achieved for wastewater 1 at 3000 mg L⁻¹ Fe²⁺ and 6000 mg L⁻¹ H₂O₂. Similarly, a 60.3% COD removal rate was achieved at 4000 mg L⁻¹ Fe²⁺ and 6000 mg L⁻¹ H₂O₂ in wastewater 2. In the Fenton process, the color removal rate for both wastewaters approached approximately 98%–99%. The cost of wastewater treatment for dye manufacturing wastewater was calculated to be $10.58–15.53 m⁻³ in the adsorption process and $20.57–22.89 m⁻³ in the Fenton oxidation process. Overall, the findings indicate that both adsorption and Fenton processes are effective treatment alternatives for dye manufacturing wastewater, providing significant reductions in COD and color.

As plants gradually age and die, uranium-rich plant residues are at risk of migration and diffusion of accumulated uranium to the surrounding environment under the action of monsoon and rainfall. In this study, we collected roots and stems of Macleaya cordata from restored uranium-rich soils to simulate the decomposition of M. cordata residues under rainfall drenching. We analyzed the characteristics of uranium release, microbial community composition, and functional group changes during the decomposition of residues. The results showed that after 36 days of decomposition, the stems of the plant residues decomposed faster than the roots, whereas the uranium release rate from the stems (65.09%) was greater than that from the roots (59.09%). On the basis of microbial community analysis and infrared spectroscopy, our results show that Galactomyces, Proteobacteria, and Firmicutes (Ascomycota phylum) play critical roles in the degradation of cellulose, hemicellulose, and lignin in M. cordata residues. These results suggest that after the uranium-rich plant residues migrate and disperse with the monsoon, the uranium in the plant is released into the water body under the action of rain, and migrates and disperses with the water body, causing pollution to the surrounding environment.

Hazardous gases such as oxides of nitrogen (NOx) come from fossil fuel combustion and, therefore, require special attention because there is a regular usage of fuel on a day-to-day basis. In the current work, a new methodology is proposed for diesel exhaust treatment involving electrical discharge plasma causing possible catalysis in a combination of two industrial wastes (composite waste) for removal of NOx. A dual-metal film and helical wire reactor were used to generate surface discharge plasma at room temperature and pressure. Five composite wastes (CW), namely, waste tiles + foundry sand, copper slag + red mud, iron tailings + waste tiles, red mud + waste tiles, and foundry sand + red mud, were used to examine their catalytic properties. A 5-kW diesel engine exhaust was sampled for laboratory experiments. The NOx removal efficiency, which was 16% under plasma-alone treatment at a specific energy of 140 J/L, got enhanced to 80%–93% in plasma-catalysis mode when CWs containing metal oxides were introduced into the plasma reactor. Further, it was verified that plasma catalysis with individual wastes yielded less NOx removal efficiency compared to that with CWs (40%–71% against 80%–93%), indicating the synergy of two wastes that are blended in the CWs.

Polyester fabrics are widely used in various fields, from textiles to packaging applications, due to their easy production and low cost. However, the C─C chains and C─O bonds of these polymers make it difficult for them to spontaneously decompose in nature, and it takes decades for polyester fabric to decompose in nature. This, combined with the increasing production rates, causes serious risks to human, living, and environmental health, and concerns about waste pollution are steadily increasing. In this study, the use of greater wax moth (Gallleria mellonella) larvae was proposed as a natural method to accelerate polyester decomposition. For this purpose, polyester fabric was exposed to the larvae. The surface morphology was then examined using scanning electron microscopy (SEM), and chemical changes were analyzed via Fourier-transform infrared spectroscopy (FTIR). SEM images indicated a rapid onset of surface degradation, and FTIR spectra suggested that the larvae caused the fabric to oxidize. These findings suggest that the polyester chains may have been compromised by this oxidation process, whereas the disintegration of the fabric was likely accelerated by the mechanical actions of the larvae. Although further research is required to gain a full understanding of the precise process of larval-induced decomposition, these preliminary results highlight a promising method for breaking down synthetic polymer-based fabrics, potentially contributing to ecological balance and sustainability.

Nano-sized semiconductors, particularly zinc oxide nanoparticles (ZnO-NPs), have gained significant attention due to their unique properties and applications in fields such as solar energy, electronics, biology, and medicine. This study focused on the environmentally friendly synthesis of ZnO-NPs using lavender (Lavandula angustifolia) extract (l-ZnO-NPs) and the effects of two different applications, foliar spraying and integration into hydrogel, on the growth and development of wheat plants. Characterization using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), and x-ray diffraction (XRD) confirmed their excellent purity and crystallinity. According to SEM data, the average particle size distribution of Raw- and l-ZnO-NPs was 92.23 and 82.61 nm, respectively; they were determined as 26.78 and 21.9 nm, according to XRD data. ZnO-NPs suspension increased the seed germination percentage (GP) by 20%, root and shoot lengths by 30%, and chlorophyll content by 15% compared to the control. Raw- and l-ZnO-NPs suspensions exhibited 100% GP at 4000 mg/L concentration. Moreover, the hydrogel treatment enhanced the plant height at 3000 mg/L concentration of Raw- and l-ZnO-NPs content. Foliar and hydrogel treatments promoted the Zn²⁺ uptake, and the maximum values were 4.47 mg/g at 2000 mg/L l-ZnO-NPs concentration and 3.47 mg/g at 1000 mg/L hydrogels contained l-ZnO-NPs concentrations, respectively. This study underscores green-synthesized ZnO-NPs’ potential for sustainable agriculture, providing an eco-friendly method to boost crop productivity. The findings contribute to addressing food security and environmental sustainability amidst the ongoing climate crisis. The water retention properties of hydrogels further address critical challenges like water scarcity in irrigation, exacerbated by climate change.

In the present era of industrial automation, low-cost sensing techniques for accurate liquid level measurement within storage tanks are essential. Storage tanks may contain various sensitive liquids, and changes in their physical properties, as sensed by the sensor, can affect measurement accuracy. An IoT-enabled experimental setup has been established to collect real-time data using low-cost differential pressure, temperature, and turbidity sensors. This work focuses on the detailed characterization of a low-cost differential pressure sensing technique, considering the effects of temperature variation, density, and turbidity. Both in situ and ex situ setups are studied using a differential pressure sensor with an air pocket. The effects of changes in temperature and density are analyzed using a proposed mathematical model and validated through experimental setup. The collected data are preprocessed using filters to remove possible noise and are further used for the estimation of various statistical parameters. For stable water levels, the average root mean square error (RMSE) is less than 0.4 mm (0.16%), and the average standard deviation is less than 0.1 mm. Considering the interrelationship among different parameters, linear and other regression models are developed for comprehensive characterization of the proposed model to ensure accurate measurements. The proposed empirical relationship and regression model show strong correlation between predicted and measured values, with RMSE in the range of 1–2 mm during the filling or draining of the storage tank.