Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions.

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO₂ and PM_2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO₂, PM₂.₅) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring. To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method's capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation-particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO₂ and BC than PM_2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM₂. ₅ (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.

This study focuses on mapping the concentrations of pollutants of interest to health (NO₂, black carbon (BC), PM_2.5, and particle number concentration (PNC)) down to the street scale to represent the population exposure to outdoor concentrations at residences. Simulations are performed over the area of Greater Paris with the WRF-CHIMERE/MUNICH/SSH-aerosol chain, using either the top-down inventory EMEP or the bottom-up inventory Airparif, with correction of the traffic flow. The concentrations of the pollutants are higher in streets than in the regional-scale urban background, due to the strong influence of road traffic emissions locally. Model-to-observation comparisons were performed at urban background and traffic stations and evaluated using two performance criteria from the literature. For BC, harmonized equivalent BC (eBC) concentrations were estimated from concomitant measurements of eBC and elemental carbon. Using the bottom-up inventory with corrected road traffic flow, the strictest criteria are met for NO₂, eBC, PM_2.5, and PNC. Using the EMEP top-down inventory, the strictest criteria are also met for NO₂, eBC, and PM_2.5, but errors tend to be larger than with the bottom-up inventory for NO₂, eBC, and PNC. Using the top-down inventory, the concentrations tend to be lower along the streets than those simulated using the bottom-up inventory, especially for NO₂ concentrations, resulting in fewer urban heterogeneities. The impact of the size distribution of non-exhaust emissions was analysed at both regional and local scales, and it is higher in heavy-traffic streets. To assess exposure, a French database detailing the number of inhabitants in each building was used. The population-weighted concentration (PWC) was calculated by weighting populations by the outdoor concentrations to which they are exposed at the precise location of their home. An exposure scaling factor (ESF) was determined for each pollutant to estimate the ratio needed to correct urban background concentrations in order to assess exposure. The average ESF in Paris and the Paris ring road is higher than 1 for NO₂, eBC, PM_2.5, and PNC because the concentrations simulated at the local scale in streets are higher than those modelled at the regional scale. It indicates that the Parisian population exposure is underestimated using regional-scale concentrations. Although this underestimation is low for PM_2.5, with an ESF of 1.04, it is very high for NO₂ (1.26), eBC (between 1.22 and 1.24), and PNC (1.12). This shows that urban heterogeneities are important to be considered in order to represent the population exposure to NO₂, eBC, and PNC but less so for PM_2.5.

This study aimed to evaluate the wind environment in step-up and step-down urban canyons through a computational numerical experiment using the computational fluid dynamics (CFD) model. Spatial structural conditions were considered according to the location of high-rise buildings, and the changing wind patterns inside canyons were compared and analyzed by varying the building heights. Under the step-up to step-down condition, wind velocity inside the canyon weakened, a vertical vortex formed, and vertical air flow separated; additionally, in shallow and deep canyons, wind velocity and detailed flow differed slightly according to each additional condition. For the step-down to step-up condition, the building located in the center appeared to be isolated, and a general wind environment phenomenon consistent with the step-up and step-down structures was observed. However, depending on the isolated area, an additional roof-top canyon was formed, and the wind field in the canyon was found to affect the wind velocity and detailed flow in other canyons. The wind velocity components of the inflow and outflow winds into the canyon differed based on the step-up to step-down or step-down to step-up conditions, and according to the conditions in the first and second canyons. Furthermore, the vertical wind velocity components were greatly affected by the step-up and step-down structures. Accordingly, the height and structural location of the building could affect various phenomena, such as the separation of vortices and air currents inside the canyon, and a variable wind environment was formed according to a series of conditions for the building.

We investigated the effects of wall- and tree-type fences on the airflow and fine particular matter (PM_2.5) concentration around a school using a computational fluid dynamics (CFD) model. First, we validated the simulated wind speeds and PM_2.5 concentrations against measured values, and the results satisfied the recommended criteria of the statistical validation indices used. Then, we evaluated the fence effects for 16 inflow directions by conducting numerical simulations with different fence types and heights. With east–southeasterly inflow, relatively high PM_2.5 from the road was transported to the school. However, the wall-type fence prevented the PM_2.5 from the road from entering the school, and the PM_2.5 concentration decreased significantly downwind of the fence. With east–northeasterly inflow, the horizontal wind speed decreased due to the drag caused by the tree-type fence, resulting in a shift in the flow convergence region. The PM_2.5 concentration decreased in the region of strengthened upward flow. This occurred because the number of pollutants transported from the background decreased. A comparison of the two fence types revealed that the effect of the tree-type fence on inbound pollutants was more significant, due to increased upward flows, than the effect of the wall-type fence.