Clean-soil Air Water最新文献

Excessive industrial release of trace elements may pose a great risk to the environment. Conventional remediation treatments have considerable limitations, making less expensive new technologies an important research area. Batch experiments were conducted at room temperature to investigate the use of two lignite-derived humic products (nano-humus and humic powder) and a cattle manure biochar as adsorbents in the removal of cadmium and zinc from laboratory synthesized water. Nano-humus was most effective in adsorbing Cd(II), wherein 89% adsorption was rapidly achieved in 15 min. The adsorbed amount of Cd(II) and Zn(II) both increased with initial metal ion concentrations from 25 to 125 mg L⁻¹. Despite being produced from the same sources, nano-humus and humic powder showed different properties and adsorption behaviors. The adsorption mechanism of nano-humus followed the Freundlich isotherm model and pseudo-second-order kinetic model, indicating multilayer chemisorption. Humic powder followed the pseudo-second-order kinetic model, although it had a low isotherm model fit, implying chemisorption-dominated adsorption. Cattle manure biochar followed the Freundlich isotherm and pseudo-first-order kinetics model, suggesting diffusion-dominated multilayer adsorption. Of the three adsorbents tested, nano-humus had greatest potential as an effective and inexpensive material for metal remediation.

Accurate rainfall-runoff analysis is essential for water resource management, with artificial intelligence (AI) increasingly used in this and other hydrological areas. The need for precise modelling has driven substantial advancements in recent decades. This study employed six AI models. These were the support vector regression model (SVR), the multilinear regression model (MLR), the extreme gradient boosting model (XGBoost), the long-short-term memory (LSTM) model, the convolutional neural network (CNN) model, and the convolutional recurrent neural network (CNN-RNN) hybrid model. It covered 1998–2006, with 1998–2004 for calibration/training and 2005–2006 for validation/testing. Five metrics were used to measure model performance: coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root-mean square error (RMSE), and RMSE-observations standard deviation ratio (RSR). The hybrid CNN-RNN model performed best in both training and testing periods (training: R² is 0.92, NSE is 0.91, MAE is 10.37 m³s⁻¹, RMSE is 13.13 m³s⁻¹, and RSR is 0.30; testing: R² is 0.95, NSE is 0.94, MAE is 12.18 m³s⁻¹, RMSE is 15.86 m³s⁻¹, and RSR is 0.25). These results suggest the hybrid CNN-RNN model is highly effective for rainfall-runoff analysis in the Potteruvagu watershed.

There are very few reports about water purification and its influence on indigenous microorganisms by effective microorganisms (EM) in the fields. This study investigated the effect of EM on water purification and microbial community via in vitro and field experiments. In in vitro water purification experiments, we examined values for chemical oxygen demand (COD), total phosphorus, and total nitrogen (TN). In systems in which the active EM solutions were at a concentration of less than 1:2000 of the total water sample, the values after 28 days were equal to or lower than those of the control. In systems using 1:200 and 1:100 active EM solutions, COD and TN values were significantly lower in artificial wastewater compared to control. Conversely, they were significantly higher than the control in the pond water. When the active EM solutions were added to the pond water, total microbial plate counts were higher than the control after 28 days for all concentrations of the EM active solutions. Excessive use of EM caused deterioration of water quality and increase of microbial counts. The results of field experiments for 4 years suggested that EM treatment did not purify the pond water, establish introduced EM, or affect indigenous microorganisms.

The DRAINMOD-NII model was used to evaluate the water table behavior and nitrate movement using field data from the Thehri experimental field in Muktsar district, southwest Punjab, India. To optimize the drainage design parameters for efficient nitrogen management, a scenario analysis of water table depth, drain outflows, and nitrogen load was carried out for different drain depths and spacings. The rice–wheat cropping system was followed for 2 years, from 2018 to 2020. The conventional subsurface drainage system was installed in the study area. The recorded data consisted of daily drain outflows, water table depths, daily nitrate concentrations in the drain outflows, and relative crop yields. The DRAINMOD-NII model was calibrated and validated for 2018–2019 and 2019–2020, respectively. The model's reliability was assessed by comparing the measured and predicted values of daily drain outflows, water table depth, daily nitrate loads, and relative crop yields during each season. A better agreement was found between the observed and simulated values. Root mean square error, Nash–Sutcliffe model efficiency, R², and percentage bias values during the calibration period ranged from 1.76 to 7.5; 0.48 to 0.88; 0.70 to 1.00; and −6.04 to 5.02, respectively, for the recorded parameters. A similar statistical evaluation was also performed during the validation period. The scenario analysis of drain depth and spacing concluded that a depth of 1.3 m and spacing of 42 m could be optimal for better crop yield and lesser nitrogen losses. Hence DRAINMOD NII model is a tool for drainage systems design.

The hydrodynamic lake model is an important tool for lake management and decision-making. When model results are analyzed by traditional analysis methods, the multisource heterogeneous data are not expressed systematically and intuitively, which leads to the inability to extract useful information efficiently. In order to solve the above problems, a three-dimensional dynamic visualization analysis method of the hydrodynamic lake model (3DV-HLM) is proposed by coupling the hydrodynamic lake model with the three-dimensional dynamic visualization technology. Chaohu Lake was taken as an example to verify the feasibility of the method. Thirteen working conditions were set up and the simulated water flows changing with space and time were analyzed and compared by the 3DV-HLM method. The results show that the 3DV-HLM method proposed in this study is more systematic and effective in the expression of multisource heterogeneous information than the traditional analysis methods. It is easier to discover rules and obtain useful information from huge data set by the 3DV-HLM method. Besides, the intuitive display of the model results by the 3DV-HLM method is close to the real environment, which can enhance the understanding of the hydrodynamic characteristics of the lake by the water environment managers.

The present study was conducted to address four key questions: (i) What are the levels of submicron particulate matter at the study area?, (ii) which are the major contributing sources of these particles?, and (iii) is there any seasonal changes in the levels of pollutants at the study site? Thus, the study was conducted at an urban residential site of Jaipur City, India, to determine the elemental and ionic composition of toxic elements associated with PM₁ using inductively coupled plasma optical emission spectroscopy and ion chromatography to reveal specific sources. Monitoring was done for a period of 8 months between October 2020 and May 2021 considering three seasons: winter (December–February), pre-monsoon (March–May), and post-monsoon (October–November). PM₁ samples were found to be highly enriched with Ag, Cd, B, Ni, and Zn. PM₁ mass concentrations were observed to be greater in winter (104.13 ± 30.16 µg m⁻³) and lower in the pre-monsoon season (83.62 ± 19.40 µg m⁻³). Ion concentrations (Cl⁻, NO₃²⁻, and SO₄²⁻) followed a similar pattern to PM₁ concentrations. Source apportionment by positive matrix factorization at the study site revealed six major sources of pollutants (soil dust, agro-based industry, automobile industry, salt aerosols, industrial activities, and biomass burning).

To fulfill a huge demand that is arising globally due to the skyrocketing population, the textile industry is shifting toward cheaper, sturdier, enduring fabrics. Apparently, innovations are turning out to be banes instead of boons, as they are generating a lot of waste, leading to the destruction of the environment. Microfibers are one such example of an emerging environmental contaminant with several irreversible, health, and ecosystem repercussions. This study deals with the effects of temperature on the generation of microfibrils from washing machines. Three different temperatures ranging from lower to higher were considered. The net weight of microfibers released from higher temperatures was found to be 1132.5 ± 41.3 mg/20 L using gravimetric analysis. The fibers released from the higher temperature, that is, 60°C, were 2.7 and 1.6 times higher than those released from colder temperatures, 30 and 40°C, respectively. The length and diameter of these microfibers were in the microplastic size range. The polyester fiber was found to be released in higher amounts after identification with Fourier transform infrared and Raman spectroscopy. The results of this study can help consumers implement sustainable behavior and regulations to lessen the release of microfibers from washing household textiles.

Numerical model using 2D Richard’s equation in cylindrical polar coordinate system was developed to assess the soil wetting patterns in a drip irrigation system. A fully implicit finite-difference scheme was used to solve equations with suitable initial and boundary conditions. The developed model was validated with an experimental result available in the literature, which shows a high level of concordance with low values of RMSE and R2 greater than 0.93 for all discharge rates. Furthermore, the versatility and applicability of the model were demonstrated for complex subsurface conditions considering homogeneous and heterogeneous soil profiles. Water ponding on the surface was observed in clay loam soil, contrasting with deep percolation in sandy loam soil. This underscores the significance of adjusting both discharge rate and irrigation frequency in accordance with prevailing soil conditions.

The analysis of UV filters (UVFs) in water has become increasingly important due to their adverse effects on aquatic organisms and humans. This study describes a method for the determination of benzophenone derivatives UVF in wastewater samples. The selected UVFs are 2-hydroxy-4-methoxybenzophenone (BP-3), 2,4-dihydroxybenzophenone (BP-1), 4-hydroxybenzophenone (4HB), 2,2′-dihydroxy-4-methoxybenzophenone (DHMB) and lastly, 4,4′-dihydroxybenzophenone (4DHB). The method includes solid-phase extraction (SPE) of analytes from wastewater followed by on-line derivatization with bis(trimethylsilyl)trifluoroacetamide (BSTFA) and analysis with GC-MS/MS. Method validation studies resulted in good recoveries (86–112%), relative standard deviation RSD = 0.8 and 7.3%, the limits of detection LODs = 1.00–10.8 ng/L, and the limits of quantification LOQs = 3.00–32.3 ng/L. The method was successfully applied to domestic wastewater samples collected from influent and effluent of touristic hotels’ biological wastewater treatment plants. BP-3 (24–1765 ng/L), BP-1 (8–703 ng/L), 4HB (26–96 ng/L), and 4DHB (20–22 ng/L) were the common benzophenone derivatives in the influent wastewater while effluent contained mainly BP-1 (8–32 ng/L), 4HB (12–57 ng/L) and 4DHB (20–102 ng/L). These results indicate that BP-3 and BP-1 are biodegraded in the treatment processes. However, 4HB and 4DHB are resistant to degradation and they are the main benzophenone metabolites discharged to receiving media.