Urban air pollution during winter is a major challenge in many cities, where emissions from residential heating lead to elevated particulate matter levels. Atmospheric dispersion modelling supports the understanding of spatial and temporal pollution behavior and enables the assessment of source contributions relevant for targeted mitigation. In this study, the ADMS-Urban dispersion model was applied to simulate hourly PM_2.5 and PM₁₀ concentrations across the city of Skopje, North Macedonia. Residential heating was the focus of the analysis, while emissions from road traffic and industrial activities were also included to ensure a realistic representation of the urban emission environment. A representative winter day was analyzed to examine the influence of wind patterns and diurnal boundary-layer height variability on particulate matter dispersion. Modelled concentrations were evaluated against measurements from urban air quality monitoring stations and showed good agreement in reproducing both night-time accumulation and daytime dispersion. The results indicate that household heating using biomass is the dominant contributor to wintertime particulate matter emissions, with PM₁₀ prevailing over PM_2.5. These findings underline the need for targeted emission reduction measures in the residential heating sector and demonstrate the usefulness of short-term dispersion modelling for supporting air quality management strategies in Skopje.

Urban air pollution in Skopje, a city with complex topography, is strongly influenced by traffic emissions, household heating, industrial activities, and meteorological conditions, leading to pronounced spatial and seasonal variability. The objective of this study is to assess the contribution of major urban emission sources to air quality in Skopje, with a focus on traffic pollution, and to quantify their seasonal influence on NO₂, PM₁₀, and PM_2.5 concentrations using a high-resolution urban dispersion modelling approach. The methodology is based on the ADMS-Urban dispersion modelling system, integrating traffic activity data as line sources, together with area sources representing household heating, point sources representing industrial facilities, and seasonally representative meteorological data. Model performance was evaluated through comparison with measurements from official urban monitoring stations. The results show that the model successfully reproduces the observed spatial gradients and seasonal trends of NO₂, PM₁₀, and PM_2.5 concentrations across the urban area. Source contribution analysis indicates that household heating dominates particulate matter pollution throughout the year, while traffic and industrial combustion are the main contributors to NO₂. The isolated traffic contribution exhibits clear seasonal variability, with the highest concentrations occurring during winter due to reduced atmospheric dispersion and increased traffic-related emissions. The model is primarily suitable for assessing spatial patterns and relative source contributions rather than accurate prediction of absolute concentration levels, due to the use of aggregated Tier 1 emission factors. The study confirms that physically based urban dispersion modelling provides a robust framework for identifying pollution hotspots, quantifying traffic contributions, and supporting targeted air quality management strategies in Skopje.

The city of Skopje faces persistent inefficiencies in waste management, characterised by rising waste generation and limited progress in sustainable treatment solutions. This paper addresses the need for a structured approach to identify barriers to implementing Waste-to-Energy (WtE) technology, focusing on incineration-based energy recovery as a potential option for improving waste management while generating energy. Interpretive Structural Modelling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were applied to six categories of implementation challenges: operational, technical, financial, regulatory, social, and environmental. ISM identified hierarchical relationships within each category, while MICMAC classified challenges according to their driving and dependence powers. The results revealed distinct structural patterns across categories, with underlying barriers including inadequate waste separation and collection, lack of integrated waste information, competitive renewable energy prices, regulatory preferences for waste reuse, recycling and reduction over WtE, institutional distrust, and combustion residue management. The findings reveal interconnected barriers with distinct structural roles, while conceptual mitigation measures and relevant SDG linkages are proposed based on the identified barriers and literature. The proposed ISM-MICMAC framework provides a structured approach for analysing WtE implementation barriers and can be adapted to other contexts following appropriate contextual and quantitative assessment.