Journal of Urban Environmental Management

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 In this study, three intelligent modeling approaches, including Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network–Multilayer Perceptron, and Artificial Neural Network–Radial Basis Function, were employed to simulate 14 water quality parameters, in the Halil River over a long-term monthly dataset spanning 52 years. Streamflow discharge was used as an input variable in some models. The performance of the developed models was evaluated using statistical indicators including the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Willmott’s index of agreement (d). The results indicated that although all three artificial intelligence models demonstrated high capability in simulating the investigated water quality parameters, the ANFIS model outperformed the other models in most cases. Specifically, for SAR, values of R² = 0.99 and RMSE = 1.04 were obtained; for %Na, R² = 0.99 and RMSE = 3.02; and for SO₄, R² = 0.93 and RMSE = 0.61. Among the input scenarios, model (b), which incorporates five easily measurable parameters, is recommended as a practical and efficient model, as it reduced RMSE by approximately 65% compared to model (a) (based solely on discharge). The findings of this study demonstrate that intelligent machine learning techniques can be effectively used to estimate water quality parameters in cases where direct measurements are unavailable, based on other measured variables, thereby reducing laboratory analysis costs.

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 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.