Extreme temperatures in urban environments exacerbate thermal discomfort and intensify the Urban Heat Island (UHI) effect, particularly during peak warm periods. Pavements, which constitute a significant portion of urban surfaces, contribute significantly to heat retention, whereas soil and vegetated areas aid in cooling through lower heat storage and higher moisture retention. Accurate forecasting of soil and pavement surface temperatures is critical for developing effective UHI mitigation strategies. This paper explores the application of Resistance-Capacitance (RC) models, a type of grey-box model, for soil surface temperature prediction. Unlike purely physics-based and data-driven models, RC models integrate physical principles with data-driven insights, balancing accuracy and interpretability. The proposed methodology is validated using real-world data from a dike in the Netherlands, where an optimal RC model is identified through an iterative process based on the Akaike Information Criterion (AIC). Results demonstrate that a two-node RC model provides a reliable balance between complexity and predictive accuracy, achieving an R² of 0.862 and a mean absolute error (MAE) of 0.675°C. These findings highlight the feasibility of applying RC models for soil temperature prediction while maintaining physical interpretability. Future research could extend this methodology to various soil types and urban surfaces, including pavements, to further enhance predictive capabilities and inform climate-responsive urban design.

This paper presents the preliminary results of field trials conducted to investigate the air permeability of waste at the Braambergen landfill located near the city of Almere, the Netherlands. Pressure variations were monitored in surrounding wells during air extraction tests using differential pressure transducers. The magnitude of the pressure response to gas abstraction indicates suitability of the method to investigate waste permeability and the swiftness of the pressure response indicated good connectivity within the investigated well field. The obtained air permeability values showed a trend where permeability decreased as the distance between two wells increased, suggesting higher permeability in closer proximity to a well. Although the values are comparable to those reported in other landfills, the differences can be explained by the influence of site-specific factors on permeability.

In this study we tested to what extent grain count data from a laser particle counter, when enriched with granulometric data, can lead to accurate measurements of aeolian sediment fluxes in the field. Field experiments were conducted at Koksijde beach (Belgium) with a vertical array of five Wenglor fork sensors and co-located vertically stacked mesh sand traps. Sand collected in the traps was used to both obtain the reference values for sediment flux as well as to obtain granulometric data at the five Wenglor sensor elevations. Grain counts were transformed to sediment fluxes by combining the granulometric data with the grain size dependent, effective detection width concept. It was found that the limitation of the Wenglor sensor to have a minimum detectable grain size, well within the diameter range of sand grains in transport, could easily be corrected for through a linear relation of Wenglor detectable sediment flux with total sediment flux. However, we found that the Wenglor derived fluxes deviated from the sand trap derived fluxes in an inconsistent manner, both in the vertical and over time, which made us conclude that there is no uniform calibration possible to match the Wenglor data with the trap data. This suggests that further studies using optical aeolian transport sensors should focus on analysing the raw photoelectric signal rather than on internally processed count data.