Since water shortage has been a serious challenge in Iran, long-term investigations of alternative water resources are vital. In this study, we performed long-term (1979–2018) model simulation at seven locations (costal, desert, mountain, and urban conditions) in Iran to investigate temporal and spatial variation of dew formation. The model was developed to simulate the dew formation (water and ice) based on the heat and mass balance equation with ECMWF-ERA-Interim (European Centre for Medium-Range Weather Forecasts–Re-Analysis) meteorological data as input. According to the model simulation, the maximum mean yearly cumulative dew yield (~65 L/m²) was observed in the mountain region in the north part of Iran with a yearly mean cumulative dew yield was ~36 L/m². The dew yield showed a clear seasonal variation at all selected locations with maximum yields in winter (mean monthly cumulative 3–8 L/m² depending on the location). Here we showed that dew formation is frequent in northern Iran. In other areas, where there was suffering from water-stress (southern and central parts of Iran), dew can be a utilized as an alternative source of water. The dew yield during 2001–2014 was lower than the overall mean during the past 40 years a result of climate change in Iran.

Model evaluation against experimental data is an important step towards accurate model predictions and simulations. Here, we evaluated an energy-balance model to predict dew formation occurrence and estimate its amount for East-African arid-climate conditions against 13 months of experimental dew harvesting data in Maktau, Kenya. The model was capable of predicting the dew formation occurrence effectively. However, it overestimated the harvestable dew amount by about a ratio of 1.7. As such, a factor of 0.6 was applied for a long-term period (1979–2018) to investigate the spatial and temporal variation of the dew formation in Kenya. The annual average of dew occurrence in Kenya was ~130 days with dew yield > 0.1 L/m²/day. The dew formation showed a seasonal cycle with the maximum yield in winter and minimum in summer. Three major dew formation zones were identified after cluster analysis: arid and semi-arid regions; mountain regions; and coastal regions. The average daily and yearly maximum dew yield were 0.05 and 18; 0.9 and 25; and 0.15 and 40 L/m²/day; respectively. A precise prediction of dew occurrence and dew yield is very challenging due to inherent limitations in numerical models and meteorological input parameters.

Dew condensation on passive collectors can supplement water availability across different climates, but its prediction remains uncertain because condensation depends on both atmospheric conditions and collector properties. This study evaluated a physically based model of dew condensation potential (horizontal condenser surface) using field observations from four sites: Helsinki (Finland), Maktau (Kenya), Ajaccio (Corsica, France), and Bordeaux (France). Simulations were primarily driven by ERA5 reanalysis data, while on-site weather station data were used for sensitivity analysis where available. Near-future dew behavior at the European sites was further examined using EURO-CORDEX climate data. The model reproduced the general timing and cumulative progression of condensation, but predicted yields were generally greater than observed yields, especially at Helsinki and Maktau. The best agreement was obtained by Bordeaux, where the experimental setup closely matched the model assumptions. On-site weather stations data improved agreement, highlighting the importance of site-specific nocturnal conditions. CORDEX simulations showed clear site differences, with Bordeaux having the highest and most stable projected condensation potential, Ajaccio frequent but low-intensity condensation, and Helsinki lower annual totals but comparable yield per dew night. The results show that the model was useful for comparing relative condensation potential across climates and identifying dew seasons, but accurately estimating the harvestable dew requires better representation of collector properties such as geometry and surface.

Urban air quality strategies increasingly rely on transitioning to battery electric vehicles (BEVs), yet their impact on non-exhaust aerosol emissions remains uncertain. This study uses high-resolution Large Eddy Simulation (LES) to investigate aerosol concentrations in a planned Helsinki neighborhood where a highway corridor is being converted into a residential boulevard. We consider three scenarios with varying BEVs shares: a baseline year 2022 (10 % of BEVs), and projected years 2035 (60 %) and 2040 (100 %). The findings reveal a "dual impact" of vehicle electrification. Increased BEV shares significantly reduce particle number concentrations (PN_2.5), with a projected 60 % decrease by 2040 compared to the baseline. Conversely, fine particle mass (PM_2.5) is projected to increase by approximately 15 % by 2040. This divergence occurs because while BEVs eliminate tailpipe exhaust, their greater weight increases unregulated non-exhaust emissions (NEE) from tire and brake wear. Results show high spatial variation, with pollutants concentrated along boulevard roads and limited penetration into residential blocks. This study underscores the challenges of BEV adoption in realistic urban environments and provides vital insights for sustainable urban planning and pollution mitigation strategies.

Urban air quality strategies increasingly rely on transitioning to battery electric vehicles (BEVs), yet their impact on non-exhaust aerosol emissions remains uncertain. This study uses high-resolution Large Eddy Simulation (LES) to investigate aerosol concentrations in a planned Helsinki neighborhood where a highway corridor is being converted into a residential boulevard. We consider three scenarios with varying BEVs shares: a baseline year 2022 (10 % of BEVs), and projected years 2035 (60 %) and 2040 (100 %). The findings reveal a "dual impact" of vehicle electrification. Increased BEV shares significantly reduce particle number concentrations (PN_2.5), with a projected 60 % decrease by 2040 compared to the baseline. Conversely, fine particle mass (PM_2.5) is projected to increase by approximately 15 % by 2040. This divergence occurs because while BEVs eliminate tailpipe exhaust, their greater weight increases unregulated non-exhaust emissions (NEE) from tire and brake wear. Results show high spatial variation, with pollutants concentrated along boulevard roads and limited penetration into residential blocks. This study underscores the challenges of BEV adoption in realistic urban environments and provides vital insights for sustainable urban planning and pollution mitigation strategies.

Urban air quality strategies increasingly rely on transitioning to battery electric vehicles (BEVs), yet their impact on non-exhaust aerosol emissions remains uncertain. This study uses high-resolution Large Eddy Simulation (LES) to investigate aerosol concentrations in a planned Helsinki neighborhood where a highway corridor is being converted into a residential boulevard. We consider three scenarios with varying BEVs shares: a baseline year 2022 (10 % of BEVs), and projected years 2035 (60 %) and 2040 (100 %). The findings reveal a "dual impact" of vehicle electrification. Increased BEV shares significantly reduce particle number concentrations (PN_2.5), with a projected 60 % decrease by 2040 compared to the baseline. Conversely, fine particle mass (PM_2.5) is projected to increase by approximately 15 % by 2040. This divergence occurs because while BEVs eliminate tailpipe exhaust, their greater weight increases unregulated non-exhaust emissions (NEE) from tire and brake wear. Results show high spatial variation, with pollutants concentrated along boulevard roads and limited penetration into residential blocks. This study underscores the challenges of BEV adoption in realistic urban environments and provides vital insights for sustainable urban planning and pollution mitigation strategies.