محیط زیست و مهندسی آب

Water quality is a critical determinant of ecological and public health, making its regular assessment essential for sustainable development. This study aims to estimate the Water Quality Index (WQI) using multiple water parameters—pH, temperature, dissolved oxygen (DO), conductivity, faecal coliform, and nitrate-nitrite nitrogen. The dataset, sourced from Kaggle, comprises water samples collected across 18 Indian states. A weighted arithmetic WQI approach is employed to compute the index values. To forecast WQI, four regression models, linear regression, decision tree, random forest, and gradient boosting, are applied. Model performance is evaluated using the coefficient of determination (R²). Among all models, gradient boosting achieved the highest prediction accuracy, with an R² value of 0.94, significantly outperforming the others. The results highlight the effectiveness of machine learning in modelling complex environmental parameters and forecasting water quality. This study demonstrates that data-driven approaches can support timely decision-making for water resource management and public health interventions.

Water scarcity is a reality in arid and semi-arid regions, leading to competition for limited water resources among agricultural, domestic, and industrial needs. In this study, sesame seed residue (Sr) adsorbents and biochar produced at 400 (B1), 500 (B2), and 600 °C (B3) were used to remove salt ions from agricultural saline wastewater. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) patterns, and Brunauer-Emmett-Teller (BET) theory were used to determine the different properties of the adsorbents. Four adsorbent treatments were batch tested at salinity levels of 5, 10, 20, and 35 dS/m in three replicates. Ion concentrations of Na+, Ca2+, Mg2+, Cl-, SO42-, and HCO3-, and primary and secondary EC before and after adsorption were measured. Various parameters, such as contact time, initial concentration of salt ions in the water, and isotherm and adsorption kinetics, were investigated. The results showed that in the four salinity levels of 5, 10, 20, and 35 dS/m, the maximum value of solute adsorption was 24.4, 57.0, 139.9, and 308.6 mg/g for adsorbent B3, respectively. The largest and smallest decreases in saline water EC occurred for B3 and Sr at salinity levels of 35 and 5 dS/m, respectively, which reduced the water salinity by approximately 31 and 3%, respectively. While low-cost adsorbents derived from agricultural waste are capable of adsorbing salt or removing contaminants from aquatic environments, they also have limitations that have led to ongoing research.

This study delivers a comprehensive environmental sustainability assessment of ultrafiltration membranes in desalination pretreatment and advances a decision-support framework to mitigate manufacturing-related ecological burdens. The impact assessment was performed using life cycle assessment (LCA) in SimaPro V9.4 with a cradle-to-cradle approach. The Recipe method, incorporating midpoint and endpoint indices, along with the IPCC method, was used to evaluate the consequences of membrane fabrication via the phase inversion method. Material flows and energy consumption were modeled based on the operating conditions of the membrane manufacturing process. Sensitivity analysis was conducted to identify opportunities for technical improvements at an industrial scale. Results indicated that the most significant environmental impacts are related to terrestrial ecotoxicity and resource depletion. Electricity production notably contributed to most impact categories, especially global warming potential. The sensitivity analysis revealed that reducing electricity consumption at an industrial scale or switching to renewable energy sources could substantially decrease environmental impacts. Consequently, conducting a product sustainability assessment using LCA is vital for identifying environmental hotspots within a product’s or process’s supply chain, followed by process optimization through pollution prevention strategies and environmental performance improvements.

Understanding climate change trends, especially precipitation, has played a significant role in the hydrological cycle and the severity of recent droughts in the Mesopotamian Plain and other western and southwestern regions. In the present study, precipitation changes trend in western and southwestern Iran and the occurrence of droughts in the Mesopotamian Plain from 1989 to 2022 were investigated using the independent t-test in SPSS software, and analyzing statistical variables including mean, median, mode, standard deviation, coefficient of variation, range of variation, skewness, maximum, and minimum. The spatial distribution of annual rainfall has shown a noticeable downward trend, and rainfall in the country has become more concentrated and uniform in terms of time in recent years. The precipitation concentration index also showed that the highest index is related to the southwestern regions, and the lowest concentration index is assigned to the northern regions. Accordingly, the increase in the intensity of seasonality and concentration of precipitation in the southern parts indicates high variability and precipitation irregularity, as a result, an increase in the incidence of drought. The results obtained indicate a decreasing trend in annual and seasonal precipitation and, subsequently, an increase in the occurrence of droughts in the Mesopotamian Plain.

Emissions of greenhouse gases, including carbon dioxide, harm the environment. Therefore, many international researchers are concerned about the use of fossil fuels. So, to prevent further irreversible climate change, it is necessary to reduce the temperature and CO2 emissions by 2050. For this purpose, agricultural wastes are very effective and economical sources as one of the methods of CO2 capture. The main objective of this research was to investigate the role of agricultural wastes in carbon dioxide capture and the factors affecting their performance with the aim of protecting the environment. The present research is of the type of applied research and analytical-descriptive in terms of research method. The findings showed that agricultural wastes (biomass) like bamboo wastes, olive stones, peanut shells, walnut shells, sugarcane bagasse, cottonwood, rice straw, and coffee grounds are effective in CO2 capturing. they are also inexpensive and non-toxic, and show an adsorption capacity of up to 21 mmol/g and a surface area of up to 3900 m2/g. while petroleum or coal-based adsorbents have an adsorption capacity of about 160-900 mg/g and a surface area between 850-3800 m2/g.

This research aims to enhance the accuracy of water quality predictions using machine learning models. The focus was on evaluating the performance of the Random Forest (RF) model and its hybrid version with a Genetic Algorithm (GA-RF) in predicting biochemical oxygen demand (BOD) and dissolved oxygen (DO) in the Nahand River Basin, Iran. The hybrid model was developed using eleven years of daily water quality data from 2013 to 2023, incorporating 11 input variables—including total nitrogen, total phosphate, nitrite, nitrate, phosphate, nephelometric turbidity unit, water temperature, air temperature, electrical conductivity, pH, and flow. Additionally, six scenarios were created using different combinations of these inputs. The models' performance was statistically evaluated through the coefficient of determination (R²), root mean square error (RMSE), Nash-Sutcliffe efficiency (NS), and Willmott’s index of agreement (WI). Results demonstrated that GA-RF consistently outperformed the standalone RF. In BOD prediction, the GA-RF-6 and RF-5 models achieved R² values of 0.563 and 0.548, respectively. For DO prediction, GA-RF-5 and RF-6 yielded R² values of 0.81 and 0.792, respectively. The findings indicate that integrating the Genetic Algorithm with Random Forest can enhance predictive accuracy in water quality assessments, supporting more informed and sustainable water resource management decisions.

The governance of water quality and aquatic ecosystems remains a critical challenge in environmental law and policy. This study explores how predictive modeling can complement legal frameworks to improve water quality interventions. By collecting comprehensive data—including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate, and turbidity—from various U.S. watersheds, baseline conditions and significant seasonal and spatial variability were identified. Using multivariate regression and Lasso modeling, BOD levels were predicted, with model validity confirmed through cross-validation. Optimization methods were then applied to develop strategies based on enforceable legal thresholds. All key indicators showed significant improvements, highlighting the effectiveness of data-informed thresholds in guiding compliance. Seasonal changes notably influenced water quality; for instance, pH levels consistently dropped during rainy seasons, with the Rio Grande showing a decline from 6.8 to 6.5 due to acidic runoff. DO levels also decreased during wet periods, likely because of lower oxygen solubility and increased organic matter decomposition. These findings support dynamic, multi-level regulation that integrates scientific data with legal standards. This integrated approach is crucial for addressing persistent and complex environmental issues, ensuring that policy and regulation are both scientifically grounded and practically enforceable.

Wide application of nanoparticles causes considerable environmental, health, and safety problems. CuO NPs have been studied for their potential agricultural applications. This systematic review followed three main steps: literature search, study selection, and data extraction. The searches were conducted in PubMed and Scopus using core keywords and synonyms. Also, VOSviewer software was used to visualize and assess bibliographic data. Our study showed that the accumulation of CuO nanoparticles in the tissues of medicinal plants can have both positive and negative effects, depending on the plant species, concentration, and exposure duration. NPs once inside plants, they allow for multiple applications, they can cross the cell membrane and enter with various organelles and biomolecules such as DNA, RNA, and proteins, and can also transport DNA and chemicals into the plant cell. Furthermore, these NPs can be taken up by plants and accumulate in various plant tissues, raising concerns about their potential impact on human health if ingested via the food chain. If copper intake exceeds the human tolerance level, it causes toxic effects such as hemolysis, jaundice, liver cirrhosis, and even death. Further research is needed to determine the safe and effective application method and optimal concentration of CuO NPs in agriculture.

This study examined and compared the adsorption behavior and fluidity of two common adsorbents: activated carbon made from jujube seeds and titanium oxyhydroxide (TiO(OH)2) used in carbon dioxide capture. The activated carbon was produced through chemical activation with potassium hydroxide as the activating agent at a 2:1 weight ratio to the biomass. To assess the characteristics of the adsorbents, techniques such as SEM, BET, FTIR, and TGA were employed. The carbon dioxide adsorption capacity was tested at 25 and 50 °C. Regeneration was performed at 120 °C with a gas flow rate of 50 cm³/min. Adsorption was conducted for 1 hour, and desorption for 30 minutes, using 10% and 90% carbon dioxide concentrations in equilibrium with nitrogen. Results indicated that activated carbon had a significantly higher carbon dioxide adsorption capacity than TiO(OH)2 under the same conditions. To evaluate the fluidity of the different adsorbents, a gas-solid fluidized bed apparatus was used. Adding 5% by weight of hydrophobic silica nanoparticles to the adsorbents improved fluidity and increased bed expansion, due to reduced cohesive interactions between particles. The comparison of results highlights the considerable effectiveness of activated carbon as an efficient adsorbent for carbon dioxide capture.

Zeolite is well known for its excellent adsorption properties, making it a popular choice for water and wastewater treatment. However, clinoptilolite, the most common type of natural zeolite, has a relatively low capacity for ammonium adsorption. This study aimed to improve its performance by treating natural clinoptilolite with 1 M NaOH and KOH solutions. Batch experiments were conducted to examine how solution pH, contact time, initial ammonium concentration, adsorbent amount, and type of chemical modifier affect adsorption efficiency. The highest adsorption capacity was achieved with NaOH-treated clinoptilolite under optimal conditions: pH 8, 120 minutes contact time, 100 mg/L initial ammonium, and 4 g/L adsorbent, reaching a capacity of 17.03 mg/g. Desorption tests using 0.1 M NaCl over five cycles confirmed the reusability of the modified material with minimal performance loss. Isotherm modeling closely matched both Langmuir and Freundlich models, suggesting the coexistence of monolayer and multilayer adsorption processes. Overall, the modified clinoptilolite shows strong potential as an efficient and economical adsorbent for ammonium removal in wastewater treatment.

To estimate potential evapotranspiration, many relationships based on the climatic conditions of different regions have been proposed by researchers, which need to be evaluated and calibrated before applying them to specific locations. Each of these relationships has different functions depending on the climatic conditions. This study aimed to optimize temperature and radiation models for estimating ET0 at the Jolfa synoptic station. The Penman-Monteith model served as a benchmark for evaluating these models. Calibration was performed using linear and power equations. The t-test results indicated that there was no significant difference between the ET0 values obtained from the Hargreaves, Linacre, and Jensen-Haise methods compared to the standard FAO56-Penman-Monteith method. Among the methods used, the Thornthwaite method had the highest coefficient of determination, with values above 0.891 across all months. The Hargreaves and Linacre methods showed the strongest correlation with the Penman-Monteith method after Thornthwaite, with determination coefficients of 0.883 and 0.874, respectively. Consequently, using the Thornthwaite, Hargreaves, and Linacre methods, after applying calibration coefficients, is recommended for estimating ET0 in the study area.

This research explored the root causes of hidden pollution and key factors affecting spatial changes, as well as identifying the best inputs for water quality modeling. The study used principal component analysis (PCA), artificial neural network models (MLP), gene expression programming (GEP), and support vector machine (SVM) to achieve its objectives. The dataset included 11 different parameters collected monthly over 10 water years (2012-2021) from the Zohreh River, Iran. Initially, PCA was applied to reduce parameters and calculate the Water Quality Index (WQI). Two input models (parameters before and after PCA) were then created using artificial intelligence to determine the most accurate model for predicting the WQI. The Kaiser-Meyer-Olkin measure (KMO) was 0.6524, indicating the dataset was suitable for factor analysis. Bartlett's sphericity test was also significant at the 0.05 alpha level. PCA identified five significant principal components, explaining 70.66% of the total variance. The combined SVM and PCA model showed the best prediction ability, with an R² of 0.889, RMSE of 0.052, and MAE of 0.038.

This study explores the social impacts of the Alborz reservoir dam on local communities, particularly focusing on residents living upstream and downstream of the dam. The research emphasizes the role of social capital, communication networks, and local participation in shaping the outcomes of dam construction. Using a qualitative approach and purposive sampling, data were collected through in-depth interviews with 346 individuals, including local officials, knowledgeable informants, and ordinary residents. The data were analyzed through a three-stage coding process, open, axial, and selective coding, and social capital was quantitatively assessed using numerical averaging methods. Findings reveal that residents downstream of the dam experienced predominantly positive impacts, such as increased social trust, enhanced local interactions, and greater social participation. In contrast, upstream residents, particularly those in 18 affected villages, faced significant negative consequences, including a decline in social capital, loss of social networks, and decreased participation in community activities. Additionally, the study highlights the very limited involvement of stakeholders in decision-making processes related to the dam, with their views largely disregarded. The research recommends strategies aimed at mitigating adverse social effects and fostering greater community engagement to promote sustainable and equitable water resource management.

The improper management of municipal solid waste in Iran has had significant negative impacts on both the environment and public health. Implementing an Environmental Impact Assessment (EIA) process can help identify the effects of municipal waste management (MWM) practices on various environmental components. Therefore, this research aims to conduct an EIA of the MWM practice in Qaen city, South Khorasan province, Iran. To achieve this, after visiting the waste disposal site and gathering the necessary information, the EIA of the MWM practice was performed using the rapid impact assessment matrix (RIAM) method. According to the results, the MWM practice, which involves the collection, accumulation, incineration of solid waste, and burial of the resulting ash, had significant adverse effects on the environment. The most negative impacts were observed in the physical-chemical environment, with a score of -241, followed by the biological-ecological environment at -197, the sociological-cultural environment at -184, and the economic-operational environment at -73. Furthermore, the MWM practice had moderate or significant negative effects on approximately 58% of environmental components. Among these, around 54% were classified as permanent effects, while 42.3% were considered irreversible effects.

This systematic review examines the relationship between economic complexity (ECI), institutional quality (IQ), and environmental performance (EP). This review bridges the critical gap in understanding how IQ mediates ECI's environmental impacts. For this purpose, we analyze 76 studies of environmental economics from 2017 to 2024, including ECI, which is increasingly used in the literature. The research is organized to respond to three main questions of research evolution, the literature focus, and implications. The results show that ECI significantly influences ecological footprints and carbon emissions, with effects varying by country development and institutional strength. ECI can reduce the EF in countries with robust institutions and higher levels of development. Our review reveals the Environmental Kuznets Curve (EKC) as the dominant framework in the literature. Key policy implications include strengthening institutions, promoting renewable energy, and tailoring strategies to country-specific contexts. Regarding the Sustainable Development Goals, this study highlights the essential role of the interplay of economic complexity, institutions, and environmental outcomes.

The escalating global energy demand has led to increased fossil fuel combustion, elevating atmospheric CO₂ concentrations and exacerbating climate change. This study investigates the enhancement of CO₂ adsorption capacity in biomass-derived materials through amine functionalization, comparing nanocrystalline cellulose (NCC) and activated carbon (AC). Hairy NCC was synthesized via TEMPO-mediated oxidation of cotton linter, while AC was produced through pyrolysis of elderberry kernels. The amine-modified adsorbents were characterized using FTIR spectroscopy and SEM imaging, with CO₂ capture performance evaluated through thermogravimetric analysis at 25°C and 50°C under varying CO₂ concentrations (10-90 vol.%). Results revealed that at 25°C and 90 vol.% CO₂, unmodified NCC and AC exhibited adsorption capacities of 1.74 and 2.78 mmol/g, respectively. After monoethanolamine (MEA) modification, NCC demonstrated improved performance (2.25 mmol/g), while AC capacity decreased to 1.72 mmol/g due to amine-induced pore blockage. The superior performance of amine-modified NCC highlights its potential as an efficient, sustainable alternative to conventional AC for CO₂ capture applications. This study provides critical insights into biomass-derived adsorbents for mitigating anthropogenic CO₂ emissions, with implications for developing cost-effective carbon capture technologies.

Cationic dyes like methylene blue (MB) and basic red 46 (BR-46) pose significant environmental risks due to their toxicity and persistence in aquatic systems. This study aims to enhance adsorption efficiency by modifying a melamine-based metal-organic framework (MOF). By altering the solvent during synthesis, a porous structure, [Cu(ɳ1-OAc)(µ-OC2H5)(MA)]2 (CMP-Et), was successfully derived from [Cu(ɳ1-OAc)(µ-OCH3)(MA)]2 (CMP-Me) using low-cost ethanol as a green solvent. The materials were characterized using XRD, FT-IR, and SEM. The removal efficiency of MB and BR-46 at an initial concentration of 20 mg/l was found to be 80 and 37%, respectively. The adsorption capacity of CMP-Et for the removal of MB and BR-46 was calculated to be 161 and 74 mg/g, respectively. The adsorption data for these dyes were consistent with the Langmuir and the Freundlich/Temkin isotherms, respectively, and followed pseudo-second-order kinetics. This work demonstrates the potential of cost-effective MOFs synthesized from inexpensive solvents like ethanol for efficient textile wastewater treatment.

یکی از راهکارهای مطلوب جهت فائق آمدن بر چالش‌های زیست‌محیطی، تربیت و آموزش نیروی انسانی دانش‌آموختگان دانشگاهی با نگرش زیست‌محیطی است. لذا هدف پژوهش حاضر مدل‌سازی رابطه بین نگرانی‌ها و نگرش زیست‌محیطی دانشجویان علوم کشاورزی و منابع طبیعی خوزستان بود. جامعه آماری، شامل دانشجویان تحصیلی سال 1402- 1401، در دانشگاه کشاورزی و منابع طبیعی خوزستان بودند. حجم نمونه با استفاده از فرمول کوکران 161 نفر تعیین شد و نمونه‌گیری نیز به روش طبقه‌ای-تصادفی با انتساب متناسب انجام شد. بر اساس نتایج به‌دست‌آمده این پژوهش از مدل‌سازی معادلات ساختاری متغیر نگرانی‌های زیست‌محیطی با نگرش دانشجویان نسبت به محیط‌زیست معنی‌دار شده است. ازاین‌رو، برای تغییر این رویه می‌توان بکارگیری رهبران عقیده مانند اساتید مشهور دانشگاهی و سایر افراد مشهور و با برگزار ی سخنرانی در سمینارهای دانشگاهی درباره استفاده از احترام به حفاظت از محیط زیست دیدگاه‌های دانشجویان نسبت به محیط زیست تغییر دهد این امر به نوبه خود نگرش آنها به حفاظت از محیط زیست افزایش می‌یابد.

هدف از این تحقیق، پایش و پیش‌آگاهی از تغییرات ET0 دشت مغان تحت تأثیر تغییر اقلیم است. محاسبه ET0 به روش PMF56 و با استفاده از نرم‌افزار CROPWAT انجام شد. از داده‌های 30 سال (2022-1993) ایستگاه همدید پارس‌آباد مغان و خروجی گزارش CMIP6 شامل مدل HadGEM3-GC31-LL و سناریوهای SSP1-2.6، SSP2-4.5 و SSP5-8.5 استفاده شد. نتایج نشان داد که بارش دشت مغان از mm 261 در دوره پایه (2023-1993)، ممکن است در انتهای قرن حاضر به mm 361 نیز افزایش یابد. دمای کمینه و بیشینه تحت بدبینانه‌ترین سناریو (SSP5-8.5) به‌ترتیب از 9.95 و °C 21.12 در دوره پایه، به 04/16 و °C 68/27 در انتهای قرن خواهد رسید. تحت تأثیر تغییر اقلیم، ET0 نیز در آینده افزایش خواهد یافت. شدت تأثیر تغییر اقلیم بر ET0 دشت مغان بیشتر در ماه‌های گرم سال است و بیش‌ترین افزایش تحت سناریو SSP5-8.5 خواهد بود. میانگین ET0 دشت مغان از mm/year 1114 در دوره پایه، با 20 درصد افزایش به mm/year 1334 تا پایان قرن می‌رسد. با توجه به سهم بیشتر مصرف آب ایران در بخش کشاورزی، تغییرات ET0 به‌ویژه در مناطقی مانند دشت مغان که عمده کشت محصولات کشاورزی در آنجا است، باید در برنامه‌‌های مهندسی و مدیریت منابع آب مورد توجه قرار گیرد.

هدف از پژوهش حاضر اصلاح خصوصیات مکانیکی خاک و پی به وسیله سنتز ژئوپلیمر بود. خاک مصرفی در پژوهش حاضر، از نوع ماسه بادی است که از km 30 شمال شرقی شهر تهران که در معرض فرسایش قرار داشته است تهیه شد. جهت بررسی خصوصیات مکانیکی خاک از آزمایشات تعیین حدود اتربرگ، ضریب نفوذپذیری خاک، ارزش ماسه‌، pH خاک، رطوبت بهینه خاک، مقاومت فشاری محصور نشده و آزمایش دانه بندی استفاده شد. نتایج نشان داد ضریب یکنواختی و ضریب انحنا به ترتیب برابر 5/2 و 23/1 هستند، بنابراین خاک بد دانه‌بندی شده است. بر اساس درصد لای و رس خاک، از نوع ماسه است. نتایج آزمایش دانه‌بندی برابر 99/4%است لذا خاک از نوع درشت دانه و بد دانه بندی شده است. چگالی خاک g/cm³ 65/2 به دست آمد، که نشان داد خاک ماسه و از نوع کوارتزی است و به دلیل عدم داشتن خاصیت چسبندگی، دارای مقاومت فشاری صفر است. ضریب نفوذپذیری cm/s 3-10×1، ارزش ماسه‌ای 79%،pH خاک 7/7 به دست آمد. نتایج نشان داد نمونه ژئوپلیمر خاک حاصل از دیاتومیت، بیش‌ترین مقاومت را دارا است. عامل اصلی در فرآیند ژئوپلیمریزاسیون SiO2 بود. با توجه به داشتن مقاومت تقریباً برابر با سیمان، جایگزین مناسیبی برای سیمان است.