Clean-soil Air Water最新文献

Due to the potential occurrence of heavy metals in tap water, it is critical to examine their interactions with plastic potable water pipe surfaces, as these pipes may undergo physicochemical degradation induced by residual disinfectants over time. Thus, this study investigated the influence of plastic pipe degradation and water alkalinity on the release of Pb, Cu, and Zn from crosslinked polyethylene type A (PEX-A) pipes after they underwent heavy metal accumulation experiments. For this purpose, an accelerated aging process was conducted by exposing pipes to a solution with a total chlorine concentration of 2500 mg L⁻¹ at 70°C for 6 days. Disinfectant decay and organic leaching characteristics were examined for new and aged pipes in both cold and hot water. Surface chemistry analyses of pipes after accelerated degradation revealed the formation of oxidized carbon surface functional groups and etched-like surface morphology. Aged pipes demonstrated higher heavy metal accumulation in the presence of alkalinity compared to conditions without alkalinity. Cu accumulation was found to be the most prominent, followed by Pb and Zn. The new PEX-A pipes released more metals than aged PEX-A pipes at pH 6.0. This pH was selected to assess the sensitivity of metal release to pH changes under more aggressive water chemistry. For hot water, both new and aged PEX-A pipes exhibited a significant reduction in total chlorine residual concentration after 12 h, with aged pipes showing a faster chlorine decay rate. Hot water also promoted organic leaching from both new and aged pipes at pH 6.0.

The rapid pace of urbanization in developing countries has increasingly disrupted the balance of urban ecosystems, climate well-being, and surrounding habitats, largely due to changes in the urban thermal environment. Motivated by the urgent need to understand and mitigate urban heat impacts, this study aims to analyze long-term diurnal land surface temperature (LST) patterns to support sustainable urban development. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellite data from 2002 to 2022, we conducted a comprehensive multi-temporal analysis of diurnal LST across daily, monthly, seasonal, and annual scales. Advanced soft computing methods, including four Backpropagation Neural Network (BPNN) models with varying transfer functions, were applied to predict and validate LST dynamics in one of the least studied regions of the country. The analysis revealed a clear and consistent warming trend in daytime LST, with summer temperatures increasing by approximately 0.45°C per decade, whereas nighttime LST exhibited more variability, including episodes of cooling in certain years. Inter-annual comparisons showed a net positive trend in daytime temperatures, suggesting potential links to broader climate change patterns. Model performance was strong, with the best BPNN configuration achieving an R² value of 0.88 and RMSE of 1.2 for daytime LST prediction. These findings highlight the critical role of diurnal LST monitoring in urban climate assessment and suggest that machine learning-enhanced remote sensing can effectively support early warning systems and urban planning. We recommend integrating LST trend analysis into regional climate adaptation strategies, especially in climate-sensitive and rapidly urbanizing regions.

Air pollution is a global environmental problem that poses serious threats to human health and quality of life, especially in industrializing and densely populated areas. The use of machine learning techniques in air pollution prediction has become an important tool in air quality management. In this study, PM10 concentrations were estimated using support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN), random forest (RF), gradient boosting (GB), K-nearest neighbor (KNN), linear regression (Lasso), and long short-term memory (LSTM) models, and the models were examined in detail. The results showed that the SVM model performed best with the lowest error values (mean absolute error [MAE]: 4.747 µg/m³ and root mean square error [RMSE]: 6.648 µg/m³), whereas the ANN model achieved the highest accuracy level (R²: 0.791). The KNN model showed the weakest performance with the highest error rates (RMSE: 10.07 µg/m³). Among the LSTM models, the model trained with the Adam optimization algorithm exhibited the best performance compared to other LSTM versions. Among the models used in PM10 estimation, SVM, ANN, and CNN highlight the effectiveness of machine learning models for air pollution estimation and constitute an important reference for studies to be conducted in this field. Focusing on Karaman, a medium-sized industrial city dominated by food-processing industries, it provides a representative framework for similar medium-sized industrial cities facing comparable environmental challenges. The research introduces an integrated and transparent machine-learning approach that combines data preprocessing, outlier correction, normalization, and broad hyperparameter optimization under consistent conditions for eight algorithms. This framework improves the reliability, comparability, and reproducibility of air-quality prediction.

Chittagong, Bangladesh's commercial center, hosts diverse natural resources, including the waterfalls of Mirsarai and Sitakund subdistricts, such as Khaiyachora, Napittarchora, Kupitakhum, Bagbiani, Shuptadhara, and Sohosradhara. These waterfalls provide essential water for drinking, agriculture, and tourism. This study evaluates their ecological health through physicochemical and microbial analysis to ensure sustainable resource management. Water samples were collected across the dry, wet, and winter seasons of 2022, with key parameters measured according to APHA 2018 standards. The mean values obtained were as follows: 26.51°C temperature, 27.73 NTU turbidity, pH 7.27, 0.094 ppm salinity, 321.94 µS/cm electrical conductivity (EC), 149.79 ppm total dissolved solids (TDS), 37.5 ppm TSS, 13.28 ppm silica, 103.5 ppm total hardness, 123.5 ppm HCO₃⁻, 148.29 ppm alkalinity, 26.2 ppm CO₂, 0.0 ppm F⁻, 33.01 ppm Cl⁻, 0.0 ppm Br⁻, 53.82 ppm SO₄²⁻, 0.641 ppm PO₄³⁻, 0.143 ppm NO₃⁻, 0.0 ppm NO₂⁻, 5.45 ppm DO, <0.2 ppm BOD, <0.1 ppm COD, 0.0 most probable number (MPN)/100 mL Escherichia coli, and <1.8 MPN/100 mL total coliform. The results were compared with national and international water quality standards. The water quality index (WQI) for each waterfall was calculated, yielding scores of 31.6 at Khaiyachora, 21.1 at Kupitakhum, 36.8 at Napittarchora, 36.4 at Bagbiani, 22.0 at Sohosradhara, and 22.6 at Shuptadhara. All WQI scores fell below the critical threshold of 100, indicating general suitability for drinking. Spatial analysis detected pollution at Khaiyachora and Napittarchora. Given their ecological and socioeconomic value, policy interventions are needed to ensure sustainable water quality, offering key insights for conservation management.

To provide critical insights into the channel geometry characteristics and hydraulic behavior, a comprehensive hydraulic simulation was conducted using the Hydrologic Engineering Center—River Analysis System (HEC—RAS) software. This analysis focused on the Zayandehrud River in Iran, aiming to enhance flood prevention and mitigate potential damage. As an innovative methodology, the calibrated HEC—RAS results were integrated with the Areal Difference Asymmetry Index (ADAI) and optimal geometric equations to derive the optimized dimensions of the river cross-sections. After achieving optimal geometric parameters, such as width, depth, hydraulic radius, wet perimeter, bank profile, and areas, the optimum cross-section indices (OCIs) were applied. This index quantified the numerical value of each river cross-section relative to its optimal condition, enabling a comprehensive evaluation of cross-sections based on their degree of optimization. On the basis of the watercourse assessment, the river width increased by 346.98%, whereas the depth decreased by 22.94% from upstream to downstream, and the downstream cross-sections were more symmetrical than the upstream cross-sections. The area of the calculated optimal cross-sections gradually increased toward the downstream and was far from the optimal geometry. The increase in values of OCIs, ranging from 5% to 120% from upstream to downstream, indicated a greater need for channel modification in the downstream reaches.

Groynes are widely used river training structures and have proven effective in flow regulation and bank protection. Recently, hybrid groynes have gained popularity in the field of river training engineering. However, systematic approaches for their optimal design and placement remain limited. In the present work, a linked simulation–optimization model is proposed to determine cost-effective hybrid groyne series. At the same time, it also ensures the riverbank stability. The flow simulation model solves the unsteady equations of open channel flow. The optimization model is formulated here in three different formulations to handle different real-field issues. The first formulation is designed to provide complete flexibility in determining all the decision variables, that is, the groyne type, number, length, and position. The second formulation defines some fixed locations of the groynes on the vulnerable river reaches and the optimization model determines the best value of the rest decision variables. The third formulation, however, fixes the groyne types, lengths, and numbers, and the optimization model determines the optimal locations of the same. The proposed optimization models are solved here using the binary coded genetic algorithm (GA). The model is then applied for two hypothetical test cases and reliable results are derived. Out of the three formulations, the first formulation is found to behave more robustly because of no restrictions imposed. Compared to previous optimization studies which focused on individual, uniform, and straight groyne types, the present framework introduces a generalized tool for designing hybrid groyne series.

Domestic hot water production is in high demand. Fossil fuels are the most used means of producing domestic hot water. Unfortunately, these methods have certain drawbacks, such as increased consumption of fossil fuels, risks, and environmental impacts. The use of solar energy non-polluting and abundant resource seems a good alternative. Algeria has a significant solar deposit that remains underexploited to date. This work focuses on the production of domestic hot water using solar energy in Algeria, selecting twelve sites representative of all local climates: coastal, inland, high plateau, and Saharan. For each climate, three thermosiphon solar collectors of different technologies are used: glazed, unglazed, and vacuum. These solar collectors were numerically simulated using RETScreen Expert. The results obtained made it possible to determine the charge recovery rate for all the selected sites. Indeed, for the southern regions, the charge recovery rate varies between 59.7% and 75.9% for the glazed collector, 50% and 80% for the unglazed collector and 40% and 65% for the evacuated collector. For the northern and central regions, the charge recovery rate varies between 46% and 55.8% for the glazed collector, 40% and 65% for the evacuated collector and 20% and 30% for the unglazed collector. The use of all collectors has positive effects, but the evacuated and glazed collectors are more efficient than the unglazed collectors. With such a glazed solar collector, and thanks to the RETScreen Expert simulation, greenhouse gas emissions of 1.7 tonnes can be avoided over the entire lifetime of the installation.