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This study describes an application of hybrid modelling for an atmospheric variable in the Krško basin. The hybrid model is a combination of a physics-based and data-driven model and has some properties of both modelling approaches. In the authors’ case, it is used for the modelling of an atmospheric variable, namely relative humidity in a particular location for the purpose of using the predictions of the model as an input to the air-pollution-dispersion model for radiation exposure. The presented hybrid model is a combination of a physics-based atmospherical model and a Gaussian-process (GP) regression model. The GP model is a probabilistic kernel method that also enables evaluation of prediction confidence. The problem of poor scalability of GP modelling was solved using sparse GP modelling; in particular, the fully independent training conditional method was used. Two different approaches to dataset selection for empirical model training were used and multiple-step-ahead predictions for different horizons were assessed. It is shown in this study that the accuracy of the predicted relative humidity in the Krško basin improved when using hybrid models over using the physics-based model alone and that predictions for a considerable length of horizon can be used.

An experiment was carried out using a scale model of a tall building, with the goal of investigating the role of individual buildings in the dispersion of air pollution. Pollutant dispersion around an isolated building with a height-to-length aspect ratio of 1.4 is investigated using simultaneous particle image velocimetry and planar laser induced fluorescence. Dye is released from a ground-level point source five building heights upstream of the tall building. It was found that in this case the scalar plume was dispersed laterally strongly by the building, but only slightly vertically. It is hypothesized that this is due to 94% of the plume impinging below the stagnation point on the front of the building and being drawn into the horseshoe vortex. We expect this fraction would be lower in a case in which the building is in an array of smaller buildings, and that this would lead to more vertical dispersion.

Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller d p particulates (i.e., PM 1 , PM 2.5 , and PM 4 ), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM 1 and PM 2.5 ) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM 7 , PM 10 , and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM 2.5 , 53.47% lower in PM 10 , and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments. Graphical Abstract

Air quality in dense urban environments is a growing concern, especially in rapidly developing cities. In the face of growing traffic associated with urbanisation, there is evidence for high levels of pollutant concentration at street level which is influenced by building forms. In this paper, we examine the potential effects of high-rise, cluster developments permitted by the local planning authorities in the newly established Port City development in Colombo, Sri Lanka. We designed possible building forms based on specific guidelines for the development in terms of plot coverage, floor area ratio, and maximum height. The three-dimensional building clusters were simulated using the RANS RNG k-epsilon turbulence model, to determine pollutant dispersion of a complex street formation in a high-dense high-rise building cluster, within the development and the surrounding context (existing Colombo). Results show that while increased porosity within the built fabric facilitates better pollution dispersion, a low correlation was seen between wind velocity and pollution concentration, especially in deep narrow high-rise canyons. Dispersion patterns at street level and at the urban canopy differed with each built form and are dependent on each canyon geometry. Thus, the study highlights the need for building regulations to take a holistic approach to capture the various elements of a complex urban cluster rather than the current two-dimensional parameters proposed for Port City, Colombo.

Accurate measurement and inference of phase difference between two time series are critical across several fields, including signal processing, economic dynamics, and air pollution research. Wavelet methods offer advantages over traditional approaches by allowing time–frequency localization and adaptability to non-stationary signals, which makes them widely used for phase difference estimation. However, existing methods do not provide a statistical test to determine whether a measured phase difference reflects a true underlying relationship between the signals or is merely an artifact of measurement errors or randomness. In this paper, we propose a bootstrap method to fill this gap. Our method is particularly suited to the analysis of non-standard data distributions and complex temporal dependencies. Extensive simulations demonstrate its desirable power and control of type-I error. Furthermore, we apply the method to study air pollution dispersion in China and elucidate the factors influencing phase differences.

Urban population growth has led to increased air pollution, influenced by disrupted wind patterns and the heterogeneous distribution of pollutants. Although the relationship between urban form and air quality is well recognized, it is often examined in isolation and through simplified urban geometries. This study addresses these limitations by numerically analyzing pollutant dispersion in densely populated hillside areas using idealized but topographically representative building geometries. A three-dimensional microclimatic simulation is conducted with ENVI-met software, incorporating parametric slope angles and building height variations. The results demonstrate that both slope steepness and building height significantly affect local pollutant concentrations: steeper slopes and taller buildings are associated with higher peak pollution values in the environment. Additionally, the simulation results show that vegetation is critical in mitigating pollution, acting as a natural barrier that enhances dispersion. These findings highlight the need for slope-sensitive urban planning and strategically integrating vegetation in hillside developments to improve air quality in complex urban terrains.

Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.

Air pollution poses a major threat to health, environment, and global climate. Characterizing the emission parameters responsible for air contamination can help formulate appropriate response plans. We propose an advanced methodology that uses Markov Chain Monte Carlo (MCMC) sampling within a Bayesian inference framework to invert for emission parameters of air contamination in an urban environment. We also use a high-resolution Lagrangian dispersion model to provide microscale wind computations as well as pollution concentration values in the presence of urban features with high complexity. Buildings and land use features were all integrated in a realistic urban setup that represents the region of King Abdullah University of Science and Technology, KSA. Boundary meteorological conditions acquired from a Weather Research and Forecasting (WRF) model simulation were employed to obtain the mesoscale wind field. We design numerical experiments to infer two common types of reference observations, a pollutant concentration distribution and point-wise discrete concentration values. The local L 2 norm and global Wasserstein distance are investigated to quantify the discrepancies between the observations and the model predictions. The results of the conducted numerical experiments demonstrate the advantages of using the global optimal transport metric. They also emphasize the sensitivity of the inverted solution to the available observations. The proposed framework is proven to efficiently provide robust estimates of the emission parameters.

One of the consequences of higher mobility is the increase in air pollution. The increase in traffic volume at intersections impacts delays and congestion, thus producing higher air pollution concentrations. This research aimed to identify intersection performance and analyze air quality at existing conditions, then propose traffic management scenarios that increase intersection performance and reduce air pollution at a time. This study was taken on Soekarno-Hatta – M.T. Haryono – D.I. Panjaitan intersection, in Malang. The intersection performance was at the worst level of service (LOS F) for each approach from traffic analysis using the Indonesian Highway Capacity Manual. At this condition, the prediction of air pollutant parameters was out of standard level. The CO concentrate was lower than the standard level, while NO was higher than the standard level. Traffic management was proposed by controlling the movements at the intersection approaches and the number of phases at the intersection. The third alternative can improve intersection performance from F to C and decrease air pollution. The concentration reductions are about ±37.4% for NOx and ±61.3% for CO from the road median to the end of the road.

The motivation for this work stems from the increased number of high-rise buildings/skyscrapers all over the world, and in London, UK, and hence the necessity to see their effect on the local environment. We concentrate on the mean velocities, Reynolds stresses, turbulent kinetic energies (TKEs) and tracer concentrations. We look at their variations with height at two main locations within the building area, and downstream the buildings. The pollution source is placed at the top of the central building, representing an emission from a Combined Heat and Power (CHP) plant. We see how a tall building may have a positive effect at the lower levels, but a negative one at the higher levels in terms of pollution levels. Mean velocities at the higher levels (over 60 m in real life) are reduced at both locations (within the building area and downstream it), whilst Reynolds stresses and TKEs increase. However, despite the observed enhanced turbulence at the higher levels, mean concentrations increase, indicating that the mean flow has a greater influence on the dispersion. At the lower levels (Z < 60 m), the presence of a tall building enhanced dispersion (hence lower concentrations) for many of the configurations.