Panagiotis Gkirmpas

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable.

Identifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Therefore, it is important to examine how sensor network characteristics affect STE accuracy. This study investigates the impact of different sensor configurations on STE results for a stationary point source in a complex, urban-like environment. The STE methodology employs the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm alongside numerical simulations of a Computational Fluid Dynamics (CFD) model. The STE algorithm is applied across several sensor configurations in three distinct release scenarios and real sensor observations from the Michelstadt wind tunnel experiment, assessing both the number of sensors used and the agreement between measured and modeled concentrations. In general, the results indicate that increasing the number of sensors and the model’s accuracy improves the source parameters estimations. However, there is a specific number of sensors in each release scenario where STE outcomes from randomly selected, high-accuracy, and low-accuracy sensors converge to similar solutions. Overall, the findings provide valuable information for designing sensor configurations in urban areas.

Estimating the parameters of an unidentified toxic pollutant source is crucial for public safety, especially in densely populated urban areas. Implementing source term estimation methods in real-world urban environments is challenging due to complex phenomena and the absence of concentration observational data. This work combines a computational fluid dynamics numerical simulation with the Metropolis–Hastings MCMC algorithm to identify the location and quantify the release rate of an unknown source within the geometry of Augsburg city center. To address the lack of concentration measurements, synthetic observations are generated by a forward dispersion model. The methodology is tested using these datasets, both as directly calculated by the forward model and with added Gaussian noise under different source release and wind flow scenarios. The results indicate that in most cases, both the source location and the release rate are estimated accurately. Although a higher performance is achieved using synthetic datasets without additional noise, high accuracy predictions are also obtained in many applications of noisy measurement datasets. In general, the outcomes demonstrate that the presented methodology can be a useful tool for estimating unknown source parameters in real-world urban applications.