Journal of Urban Environmental Management

Abstract This study addresses the challenge of estimating Biochemical Oxygen Demand over 5 days (BOD₅) in a remote polar lake, where direct measurement is expensive, time-consuming, and often constrained by limited monitoring data. Using more than four decades of historical water quality records from Lake Oulujärvi, Finland, the research developed predictive models based on observations collected from 34 stations across six sub-basins, with particular attention to stratified periods and depth-resolved measurements. The data were first preprocessed through normalization, detection of missing values, removal of invalid observations, and stratification analysis to improve consistency and model reliability. Three machine learning approaches, including Convolutional Neural Networks (CNN), Random Forest (RF), and XGBoost, were then trained to estimate BOD₅ from routinely monitored water quality variables. The selected predictors included dissolved oxygen (DO), pH, turbidity, electrical conductivity, Secchi depth, water temperature, and sampling time, all of which are readily measurable and physically linked to BOD₅ dynamics. Model performance was optimized using grid search and cross-validation. Among the tested models, the CNN achieved the best results, with validation R² values of 0.88–0.89, followed by RF, and XGBoost. Feature importance analysis highlighted DO, pH, turbidity, electrical conductivity, and sampling time as the most influential variables. The findings show that machine learning can provide accurate, cost-effective, and scalable alternatives for BOD₅ estimation in low-accessibility aquatic systems, supporting improved water quality assessment and management in fragile lake ecosystems under environmental change.

Abstract In valley‑mountainous cities, differences in solar radiation and ventilation can modify the classic “hot core” pattern and create heat foci along the margins. This study examines the spatial pattern of the Surface Urban Heat Island (SUHI) in Khorramabad from 16 Mehr to 16 Aban 1404 (7 October to 6 November 2025) and assesses vegetation's role in moderating land surface temperature (LST). LST was derived from Landsat‑8/9 imagery using a single‑channel algorithm with ERA5 atmospheric parameters. NDVI was calculated from Sentinel‑2 images and resampled to 30‑meter resolution. SUHI was computed as the difference between urban LST and the mean LST of peripheral reference areas, redefined with constraints on elevation, slope, and aspect. Results indicated 27.15% of the urban area in the “neutral” class and 4.82% in the “very hot” class, while 50.61% is cold to cool. Very hot hotspots are concentrated in southern margins (airport, 184th brigade, oil depot, barren lands) and the eastern belt (Mehr Housing to Azad University). Cool islands appear around Kio Lake, along Khorramrud River, and in some central neighborhoods. A strong inverse LST‑NDVI relationship (r = –0.82; R² = 0.71; p < 0.001) revealed that each 0.1‑unit increase in NDVI reduces LST by 2.86°C on average. Based on these findings, low‑cost strategies are proposed: protecting intra‑valley green patches, strengthening the green‑blue network along the river and lake, creating green belts in sensitive margins, restricting construction on steep slopes, and preserving valley‑oriented ventilation corridors to alleviate thermal stress.

Abstract While the role of green infrastructure in mitigating urban heat is acknowledged, the influence of its spatial configuration on thermal performance in arid climates has been less explored. This study quantitatively examines the relationship between the spatial structure of green infrastructure and land surface temperature in the historic desert city of Yazd. Using landscape ecology metrics and Sentinel-2 and Landsat 9 satellite imagery from summer 2024, green spaces were mapped and the Green Space Heat Mitigation Index was calculated. Spatial metrics at the patch, class, and landscape levels were extracted with FRAGSTATS software, and their association with cooling performance was analyzed via multivariate regression.
The results indicate that Yazd's green infrastructure has a highly fragmented and dispersed pattern, consisting of small, isolated patches and lacking large, contiguous green areas. Cooler patches exhibited a higher clumpiness index and lower edge density. Regression analysis revealed that approximately 68% of the variation in the heat mitigation index is explained by three spatial metrics: clumpiness, largest patch index, and edge density. This finding confirms that in arid environments, the spatial organization of green infrastructure has a greater influence on cooling efficiency than its total area does. Designing integrated, compact green spaces with simplified boundaries can optimize thermal performance and reduce the heat island effect. The presented methodology provides a generalizable framework for assessing structure–function relationships in urban landscapes, with practical implications for sustainable design in arid-region cities.

Abstract The transition from a linear economy to a circular economy in municipal plastic waste management is regarded as one of the pivotal challenges in the sustainable development of contemporary cities. This research has been conducted with the objective of comparatively assessing the performance of the European Union (EU) and Iran in plastic waste management through the lens of the circular economy. The comparison is structured around six key parameters: legal and regulatory frameworks, production and consumption patterns, speed and quality of recycling, collection and disposal systems, adoption of emerging technologies, and precision in source separation and waste categorization. Findings reveal that the European Union is advancing systematically toward a circular economy by integrating preventive legislation, binding quantitative targets (e.g., the 55% plastic recycling target by 2030), and cohesive technical infrastructures. As a result, the plastic recycling rate in the EU has reached 26.5%, surpassing its landfilling rate of 23.5%. In contrast, Iran despite enacting foundational legislation (such as the 2004 Waste Management Act) confronts serious structural challenges, including ineffective policy implementation, the absence of integrated recycling infrastructure, heavy reliance on the informal sector, and a lack of transparency in official statistics. These factors have confined Iran’s plastic recycling rate to below 14%, with over 84% of plastic waste landfilled without prior separation. This study demonstrates that an effective transition to a circular economy in Iran necessitates fundamental reforms specifically, rigorous enforcement of regulations, elimination of administered pricing mechanisms, strengthening of supervisory institutions, standardization of practices, data transparency, and the promotion of informed civic participation rather than merely constructing recycling facilities.

Abstract This research investigates the potential of integrating smart materials to optimize energy consumption and enhance thermal performance within Near-Zero Energy Buildings (NZEB), specifically focusing on the “Noor Aram” school located in the challenging cold and mountainous climate of Tabriz, Iran. To achieve high-fidelity results, a detailed four-story model was developed using DesignBuilder software, leveraging the robust EnergyPlus simulation engine for thermal analysis. The study evaluated three distinct performance-enhancement scenarios: the integration of Phase Change Materials (PCM), the application of Low-Emissivity (Low-E) glazing, and the implementation of advanced thermal insulation, comparing each against a standard baseline model.
The simulation findings demonstrate that while all evaluated smart materials contribute to energy savings, their overall efficacy is heavily dictated by the local climate and the specific composition of the building envelope. Advanced thermal insulation emerged as the most critical strategy, yielding a significant 34% reduction in total heating load and facilitating the building’s transition toward NZEB standards. Furthermore, the application of PCMs provided a 23% reduction in annual heating requirements by effectively buffering thermal fluctuations. In contrast, Low-E glazing showed a more marginal impact, primarily due to the limited ratio of transparent openings relative to the total heat loss occurring through opaque surfaces. Consequently, this study concludes that in cold regions, sustainable design must prioritize the optimization of opaque envelopes. It recommends that future building regulations adopt holistic, climate-aware strategies, combining high-performance insulation with strategic thermal mass management.

Abstract Mountain rivers with step pool morphology represent complex hydraulic systems in terms of water quality dynamics; however, the performance of conventional water quality models such as QUAL2Kw, which are designed based on the assumption of uniform flow, has rarely been evaluated in such environments. This study assessed the performance of the QUAL2Kw model in simulating water quality in these rivers. A 66 m reach with step pool structure was selected on the Dohezar River in Mazandaran Province, Iran. Water quality sampling for temperature, dissolved oxygen, nitrate, and inorganic phosphorus was conducted at four monitoring stations (upstream, step crest, step toe, and downstream) over an eight month period under both low flow and high flow conditions. The model was calibrated using the first four months of data and verified with the remaining four months. Results showed under low flow conditions, the QUAL2Kw model performed satisfactorily for all parameters, with NSE exceeding 0.84. However, nitrate and inorganic phosphorus simulations exhibited lower errors compared to temperature and dissolved oxygen, attributed to increased residence time and reduced turbulence, which allowed sufficient time for kinetic reactions. Under high flow conditions, the model overestimated all four quality parameters, particularly at the step pool structure. This error is attributed to reduced residence time and the dominance of physical processes over kinetic reactions. Based on the findings, the model can serve as an efficient tool for water quality simulation in mountain rivers during low flow periods, and its results are valuable for the environmental management of these ecosystems.

Abstract This study was conducted to perform a life cycle environmental assessment of ethylene glycol production in Iran’s petrochemical industry. Using a quantitative and systematic life cycle assessment approach, it aimed to identify and quantify the environmental impacts associated with producing one tonne of ethylene glycol and to determine the key hotspots. The methodology was developed in accordance with the ISO 14040 series. The system boundary was defined as gate-to-gate, and the functional unit was set as “production of one tonne of ethylene glycol.” The results indicated that the environmental impact profile of ethylene glycol production is significantly influenced by utility units and supporting processes. In the global warming impact category, electricity generation—and subsequently steam generation—accounted for the largest share of CO₂-equivalent emissions, highlighting the dominant role of fossil fuel–based energy supply in shaping the product’s carbon footprint. In human health–related categories, including carcinogenic effects and inorganic and organic respiratory impacts, electricity generation was also identified as the primary hotspot, reflecting the substantial contribution of combustion-related emissions and associated pollutants. By contrast, in the non-carcinogenic category, the industrial water production unit was the dominant contributor, underscoring the importance of chemical consumption and management of water-related flows in health-oriented impacts. Overall, the findings confirm that improving the environmental performance of ethylene glycol production requires an integrated approach that simultaneously focuses on optimizing energy use (electricity and steam), reducing combustion emissions, enhancing water and wastewater management, and improving waste management.

Abstract This study inspects the impact of water pollution in the nonlinear uncertainty and high dimensional process to detect early abnormalities or significant changes in the water pollution indicators. A hybrid algorithm based on wavelet techniques and adaptive network fuzzy inference system learning by using high-frequency surrogate relations is introduced. Important qualitative indicators such as phosphate, and nitrate in the water bodies have uncertainties with variations such as dependence, and effectiveness of physical and chemical factors. A case study based on the monitoring program applied to the Potomac River Basin in Virginia, USA, is conducted. In the first step, the high-frequency time series of the main TP index is obtained through the surrogate model and compared with GARCH techniques. By using the wavelet transform, the noise components of the time series are removed and pre-processed. In the next step, it is created by using the ANFIS to identify the main characteristics of water quality. In the last step, the contamination threshold is calculated based on the estimated base pattern for analyzing statistical patterns. The results show that the proposed algorithm has high stability and accuracy because using the surrogate technique has extracted a more accurate model of the behavior of the required water variables. It can be used to manage surface runoff in watersheds to preserve the environment and improve water quality. Based on the results, using ANFIS models in water pollution anomaly results in accurate simulations.
Keywords: water pollution, anomaly, high dimensional process, adaptive network fuzzy inference system, wavelet