Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NO_x from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality.

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.

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM₁₀ dispersion in a 1.6 × 2 km² urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution.

Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations.