Clean-soil Air Water最新文献

This study investigated the use of fog-spraying deposition of wood cellulose nanofiber (WCNFs) suspensions on tissue paper (TP) to create air filter media. High-purity WCNFs, confirmed using ATR-FTIR spectroscopy, were found to be composed of cellulose I_β structures via x-ray diffraction with a crystallinity of 67%. The measured zeta potential of the aqueous suspension of WCNFs was −13 ± 1 mV, indicating the absence of acid hydration during their isolation. This study used nine grammage levels of sprayed WCNFs (ranging from 0.3 to 10 g/m²) and investigated the influence of tert-butanol (TB) on the performance of the medium. After coating, the specimens were freeze-dried and imaged using FE-SEM to confirm the proper distribution of WCNFs on the TP substrate. The key findings revealed that increasing the grammage level from 0.3 to 2 g/m² led to increased particulate matter (PM) adsorption and a significant pressure drop. However, increasing the grammage level from 2 to 10 g/m² decreased the adsorption efficiency, particularly for PM size of 0.3 µm (PM_0.3). The study concluded that the specimens prepared with a deposition of 2 g/m² grammage level of WCNFs containing TB were the optimal treatment, demonstrating an adsorption efficiency of 94.1% for PM_0.3 and a pressure drop of 123 Pa, compared to the corresponding values for bare TP, which were only 9.1% and 18 Pa, respectively.

The growing environmental impact of tire waste necessitates innovative recycling methods. With global tire production expected to reach 2.67 billion units by 2027, repurposing tire-derived materials in concrete offers a sustainable solution. This study investigates the effects of steel fibers from waste tires on concrete incorporating coconut shells (CSs), palm kernel shells (PKSs), and recycled aggregates. The research examines how different fiber percentages influence fresh properties and strength. A concrete mix ratio of 1:2.19:2.46 (Cement: Fine Aggregate: Coarse Aggregate) was used, with an admixture at 1% by cement weight. Natural aggregates were replaced with recycled concrete aggregates (RCA), CSs, and PKS at 25%, 50%, 75%, and 100%, while steel fibers were added at 0.25%, 0.5%, 0.75%, and 1% by cement weight. Slump, split tensile, and compressive strength tests were conducted. Results showed that steel fibers enhanced cohesion but reduced slump due to increased stiffness. After 28 days of curing, the optimal mix contained 1% steel fibers and 25% aggregate replacements, achieving a balance between workability and strength. In addition, a life cycle and economic assessment indicated that these sustainable mixes can lower CO₂ emissions by 15%–25% and reduce costs by 2%–4%, confirming their potential for affordable, low-carbon construction in Kwara state, Nigeria.

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.