Clean-soil Air Water最新文献

Rapid socioeconomic developments take the price of prominent environmental pollution problems, which necessitate a delicate equilibrium between urban development and environmental protection through governments’ scientific planning. This study delves into the sustainable trajectory for urban agglomerations, focusing on the Beijing–Tianjin–Hebei (BTH) urban agglomeration in China. Employing the STIRPAT model and Conley–Ligon model, it assesses the impact of socioeconomic factors on environmental pollution and their spillover effects. The study incorporates three distinct developmental scenarios designed to simulate regional economic growth and environmental protection. Our findings reveal a substantial influence of local socioeconomic development and spatial spillover effects from neighboring counties within the BTH region on local PM_2.5 concentration. Economic development level and environmental governance expenditure emerge as pivotal factors affecting the increase and decrease of PM_2.5 concentration, with spillover effects observed in neighboring counties. The effectiveness of balancing air pollution and the economy is better under the Zoning Cooperated scenario than under the local Government Planning scenario and trend extrapolation scenario. This means that more than 96% of the 100 million people in the counties will live in areas with clean air. In light of these findings, the research proposes pertinent policy recommendations, offering a novel and efficacious solution for urban agglomerations in rapidly developing countries or regions worldwide grappling with the intricate trade-off between air pollution and economic development.

Trench weirs constructed across streams and rivers have been utilized for generations of hydropower due to their simplicity, cost-effectiveness, and minimal disruption to natural water courses. This type of weir consists of rack bars to control the deposition of sediments and floating debris. This article presents the investigations of the effects of different parameters, particularly the spacing of rack bars, specific energy of flow in the approach channel, velocity head, and porosity of rack bars on the discharging ability of trench weirs. Due to their greater flexural rigidity, flat bars are chosen in the current investigation from a structural perspective. The experiment was carried out in an open channel taking different combinations of bed slope, rack slope, and spacing of flat rack bars while testing under free flow conditions and having a pre-defined discharge in the approach channel. In addition, the discharge characteristics and coefficient of discharge (C_d) of the trench weir have been examined with varying clear spacing of rack bars, specific energy of approach flow, velocity head, and porosity of rack bars. The findings indicate that, in free flow conditions, C_d decreased as the porosity, clear spacing of rack bars, and specific energy of the approach flow increased. Further, during the analysis of experimental data, it was observed that C_d does not show any systematic pattern with velocity head. Thus, this study provides valuable insights on trench weir performance and the variables affecting discharge characteristics of trench weirs.

Modern agriculture faces significant environmental challenges due to toxic contaminants like lead (Pb), which infiltrate the food chain and pose severe risks to all living organisms, including humans. Bioremediation, utilizing microorganisms to mitigate contamination, offers a sustainable and cost-effective solution. In this study, a fungal strain, PH7, was isolated from the rhizosphere of Parthenium hysterophorus and identified as Talaromyces purpureogenus through genetic analysis of ITS 1 and ITS 4 rRNA regions. Preliminary screenings revealed its ability to solubilize phosphate and produce key plant growth regulators, including indole acetic acid (IAA) and salicylic acid (SA), alongside beneficial metabolites like phenolics, sugars, proteins, lipids, and flavonoids. The strain demonstrated substantial Pb tolerance, up to 800 µg/g, while enhancing the antioxidant defense system in liquid culture. Under 500 µg/g Pb stress, maize (Zea mays L.) exhibited a significant reduction in root length (29.2%), shoot length (30.4%), fresh weight (24.5%), and dry weight (53.5%). However, treatment with T. purpureogenus markedly improved these parameters, along with chlorophyll and carotenoid levels. The treated plants also showed enhanced antioxidant activity, including elevated levels of enzymatic and nonenzymatic antioxidants. These findings highlight the dual role of T. purpureogenus as a bioremediant and biofertilizer, capable of restoring Pb-contaminated soils and enhancing agricultural productivity and plant health.

Surface soil moisture (SSM) refers to the capacity of the top layer of soil to hold moisture. It is an essential part of the budget for surface water. Soil moisture monitoring is crucial to reduce the effects of precipitation deficits and determine the best ways to manage natural ecosystems in the face of climate change. The current study collected daily SSW data from MERRA-2 for the Tel River Basin in Odisha, India, from 2001 to 2020 with a spatial resolution of 0.5° × 0.625°. To forecast SSW time series (SSWTS) one step ahead, this study examines the reliability of three deep learning (DL) models: gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural network (simpleRNN). This study aims to address the following research questions: (1) How accurately can DL models predict SSWTS? (2) Which DL model—GRU, LSTM, or simpleRNN—is the most reliable for SSW forecasting? (3) How can the uncertainty in the predicted SSW be quantified and analyzed? Further, in an uncertainty investigation on SSW projected values, a Wilson score technique was employed to evaluate the uncertainty of the DL methods. GRU has outdone the other two models in forecasting monthly SSW with a 12-lookback timestep with a lower error for all the stations. The model appeared more accurate as it declined in gradient on larger sequencing samples. GRU's ability to remember significant prior knowledge, whereas discarding irrelevant data may assist in finding a novel, dependable solution for SSWTS forecasting.

Acid mine drainage (AMD) has a low pH and contains harmful metals, making it a severe problem in the mining industry. Neutralization treatment using slaked lime is widely applied to remove potentially toxic metals and adjust the pH. However, this generates neutralized sludge containing large amounts of harmful metals. Therefore, in this study, we propose a bacterial bioprocess for removing metals before neutralization. Acid- and metal-tolerant bacteria were isolated from neutral soils and utilized as As removers from AMD. The Paenathrobacter sp. strain H1 removed As (43.6%) and Fe (10.6%) from AMD in a single-batch test (pH 1.95; initial concentrations were 6.13 and 283 mg L⁻¹, respectively). Repeated batch tests using fresh cells enhanced the As removal ratio, achieving successful removal of As (95.3%) and Fe (75.5%). Although further research is required, this study has substantial implications for the development of a sustainable AMD treatment to suppress harmful waste generation.

Timely and reliable estimates of reference evapotranspiration (ET₀) are imperative for robust water resources planning and management. Applying machine learning (ML) algorithms for estimating ET₀ has been evolving, and their applicability in different sectors is still a compelling field of research. In this study, four Gaussian process regression (GPR) algorithms—polynomial kernel (PK), polynomial universal function kernel (PUK), normalized poly kernel (NPK), and radial basis function (RBF)—were compared against widely used random forest (RF) and a simpler locally weighted linear regression (LWLR) algorithm at a humid subtropical region in India. The sensitivity analysis of the input variables was followed by application of the best combination of variables in algorithm testing and training for generating ET₀. The results were then compared against the Penman–Monteith method at both daily and monthly time steps. The results indicated that ET₀ is least sensitive to wind speed at 2 m height. Additionally, at a daily time step, RF, followed by PUK, generated the best results during both training and testing phases. In contrast, at a monthly time step, using multiple model evaluation matrices, PUK followed by RF performed best. These results demonstrate the application of the ML algorithms is subjected to user-required time steps. Although this study focused on Northwest India, the findings are relevant to all humid subtropical regions across the world.

Drought is an abnormal condition characterized by dry weather which can continue for days, months, and years. Drought often has major effects on the ecosystems and agriculture of vulnerable regions leading to catastrophe on the local economies. Deep learning was employed in this study to forecast hydrological drought in the Wardha River basin in Maharashtra, Vidarbha region, India. Monthly streamflow data from 1971 to 2020 for the Wardha River serve as the basis for analysis. The study calculates the standardized streamflow index (SSI) at several timescales (3, 6, 9, 12, and 24 months). Deep learning models, specifically the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model, are employed for drought prediction within the study region. The models are trained with data spanning from 1971 to 2005 and tested against data from 2006 to 2020. Predictions are made for lead time scales of 6 and 12 months by considering lagged SSI values. Drought event lead time scale forecasts will serve as an early warning strategy. The 6- and 12-month lead times of the SSI forecast could be used as a warning for anticipated drought conditions. The study assesses model efficiency by comparing the root mean square error (RMSE) and mean absolute error (MAE) between the LSTM and MLP models. The results indicate that the LSTM model performs better for higher time scales in predicting hydrological drought, whereas the MLP model demonstrates superior predictive capabilities for lower time scales of drought index.