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The paper evaluates the long-term (2007–2016) impact of biogenic volatile organic compound (BVOC) emissions on urban ozone patterns over central Europe, specifically focusing on the contribution of urban vegetation using a regional climate model coupled offline to a chemistry transport model. BVOCs are emitted by terrestrial ecosystems, and their impact is considered especially important over NOx-rich environments such as urban areas. The study evaluates the impact of BVOC emissions on ozone (O3), formaldehyde (HCHO), and hydroxyl radical (OH) near-surface concentrations, showing an increase in summer ozone by 6 %–10 % over large areas in central Europe due to their emissions. It also demonstrates a substantial increase in formaldehyde concentrations. Additionally, the impact of BVOC emissions on hydroxyl radical concentrations shows a decrease over most of the modeled region by 20 %–60 %, with some increases over urban areas. Impacts on peroxy radicals (HO2 and higher RO2) are shown too. Importantly, the study explores the partial role of urban vegetation in modulating ozone and evaluates its contribution to the overall ozone formation due to all BVOC emissions. The findings reveal that urban BVOC emissions contribute to around 10 % of the total impact on ozone and formaldehyde concentrations in urban areas, indicating their significant but localized influence. The study also conducts sensitivity analyses to assess the uncertainty arising from the calculation of the urban fraction of BVOC emissions. The results show that the impact of urban BVOC emissions responds to their magnitude nearly linearly, with variations up to 4-fold, emphasizing the importance of accurately quantifying the urban BVOC fluxes. Overall, the study sheds light on the intricate relationship between urban vegetation, BVOC emissions, and their impact on atmospheric chemistry, providing valuable insights into the regional chemistry of BVOC emissions over central Europe and the causes of urban ozone pollution. [ABSTRACT FROM AUTHOR]

This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) is among the air pollutants representing the most critical threat to human health in Europe. For designing strategies to mitigate this kind of air pollution, it is essential to identify and quantify the sources of its components. Here, we utilized the regional chemistry transport model CAMx (Comprehensive Air Quality Model with Extensions) to investigate the relationships between emissions from different categories and the concentrations of PM 2.5 and its secondary components over Central Europe during the period 2018–2019, both in terms of the contributions of emission categories calculated by the particle source apportionment technology (PSAT) and the impacts of the complete removal of emissions from individual categories (i.e., the zero-out method). During the winter seasons, emissions from other stationary combustion (including residential combustion) were the main contributor to the domain-wide average PM 2.5 concentration (3.2 µgm-3), and their removal also had the most considerable impact on it (3.4 µgm-3). During the summer seasons, the domain-wide average PM 2.5 concentration was contributed the most by biogenic emissions (0.57 µgm-3), while removing emissions from agriculture–livestock had the most substantial impact on it (0.46 µgm-3). The most notable differences between the contributions and impacts for PM 2.5 were associated with emissions from agriculture–livestock, mainly due to the differences in nitrate concentrations, which reached up to 4.5 and 1.25 µgm-3 in the winter and summer seasons, respectively. We also performed a sensitivity test of the mentioned impacts on PM 2.5 on two different modules for secondary organic aerosol formation (SOAP and VBS), which showed the most considerable differences for emissions from other stationary combustion (in winter) and road transport (in summer). [ABSTRACT FROM AUTHOR]

Air pollution nowadays represents the most significant environmental health risk in Europe, with fine particulate matter (PM2.5) being among the pollutants with the most critical threat to the human health, especially in urban areas. Identifying and quantifying the sources of PM2.5 components are essential prerequisites for designing effective strategies to mitigate this kind of air pollution. In this study, we utilized the numerical weather prediction model WRF (Weather Research and Forecast Model) coupled with the chemistry transport model CAMx (Comprehensive Air quality Model with Extensions) to investigate the relationships between emissions (with a primary focus on emissions covering a wide range of anthropogenic activities) and the concentrations of total PM2.5 and its secondary components (ammonium, nitrate, sulfate, and secondary organic aerosol (SOA)) in the region of Central Europe (with a more detailed focus on six large cities in this region, namely Berlin, Munich, Vienna, Prague, Budapest, and Warsaw) during the period 2018-2019 using the PSAT (Particulate Source Apportionment Technology) tool implemented in CAMx and the zero-out method (an extreme case of the brute-force method), which makes this study, taking into account the differentiation of individual GNFR sectors of anthropogenic activity, the only one of its kind for this region. The use of the PSAT tool showed, among other things, that during the winter seasons, emissions from other stationary combustion (including residential combustion), boundary conditions, road transport, and agriculture-livestock contribute most extensively to the average PM2.5 concentrations (their domain-wide average contributions are 3.2, 2.1, 1.4, and 0.9 µgm-3, respectively), while during the summer seasons, the average PM2.5 concentrations are mainly contributed by biogenic emissions, followed by emissions from road transport, industrial sources, and boundary conditions (their domain-wide average contributions are 0.57, 0.31, 0.28, and 0.27 µgm-3, respectively). In contrast, the most considerable average seasonal impacts on the concentration of PM2.5 when modeling with the SOAP mechanism activated (i.e., with the same SOA formation mechanism that is implemented when using the PSAT tool; we named this sensitivity experiment as the SOAP experiment) are caused by the overall reduction of emissions from other stationary combustion, agriculture-livestock, road transport, and agriculture-other during the winter seasons (their domain-wide averages are 3.4, 2.9, 1.4, and 1.1 µgm-3, respectively), while during the summer seasons, they are induced by emissions from agriculture-livestock, road transport, industrial sources, and other stationary combustion (0.46, 0.45, 0.34, and 0.29 µgm-3, respectively). Further, we revealed that the differences between the contributions of emissions from anthropogenic sectors to PM2.5 concentration and the impacts of these emissions on PM2.5 concentration in the SOAP experiment are predominantly caused by the secondary aerosol components (due to the acting of oxidation-limiting and/or indirect effects). Moreover, the most substantial of these differences, in terms of daily averages in the cities (reaching up to = 15 µgm-3 in some of them during winter time) and seasonal averages for the winter and summer seasons (reaching up to 4.5 and 1.25 µgm-3, respectively), are associated with emissions from agriculture-livestock, mainly due to differences in nitrate concentrations. Finally, we performed one more sensitivity experiment (named the VBS experiment) based on the zero-out method, in which gas-aerosol partitioning and chemical aging of organic aerosol were activated using the 1.5-D VBS scheme, and we also added the estimates of intermediate-volatility and semivolatile organic compounds. We found that their application, in comparison with the results of the SOAP experiment, mainly increases the average seasonal impacts on the concentration of 35 PM2.5 caused by the overall reduction of emissions from other stationary combustion and road transport during the winter seasons (the increases reach up to 12 and 4 µgm-3, respectively) and mainly by increasing the average seasonal impact on the concentration of PM2.5 produced by the overall reduction of emissions from road transport during the summer seasons (the increase reach up to 2.25 µgm-3). [ABSTRACT FROM AUTHOR]

The PALM 6.0 model system has been rapidly developed in the recent years with respect to its capability to simulate physical processes within urban environments. In this regard, it includes e.g. energy-balance solvers for building and land surfaces, a radiative transfer model to account for multiple reflections and shading, as well as a plant-canopy model to consider the effects of plants on the (thermo)dynamics of the flow. This study provides a thorough evaluation of modelled meteorological, air chemistry and wall-surface quantities against dedicated in-situ measurements taken in an urban environment in Prague, Dejvice, Czech Republic. Measurements included e.g. monitoring of air quality and meteorology in street canyons, surface temperature scanning with infrared camera and monitoring of wall heat fluxes. Large-eddy simulations (LES) for multiple days within two summer and three winter episodes that are characterized by different atmospheric conditions were performed with the PALM model driven by boundary conditions obtained from a mesoscale model. For the simulated episodes, the resulting temperature, wind speed and concentrations of chemical compounds within street canyons agreed well with the observations, except the LES did not adequately capture nighttime cooling near the surface at certain meteorological conditions. In some situations, less turbulent mixing was modelled resulting in higher near-surface concentrations. At most of the surface evaluation points the simulated wall-surface temperature agreed fairly well with the observed one regarding its absolute value as well as daily amplitude. However, especially for the winter episodes and for modern buildings with multi-layer walls, the heat transfer through the wall is partly not well captured leading to discrepancies between the modelled and observed wall-surface temperature. Furthermore, we show that model results depend on the accuracy of the input data, particularly the temperatures of surfaces affected by nearby trees strongly depend on the spatial distribution of the leaf area density, land-surface temperatures at grass surfaces strongly depend on the initial soil moisture, or wall-surface temperatures depend on the correct prescription of wall material parameters, though these parameters are often not available with sufficient accuracy. Moreover, we also point out current model limitations, here we particularly focus on implications with respect to the discrete representation of topography on a Cartesian grid, complex heterogeneous facades, as well as glass facades that are not well represented in terms of radiative processes. With these findings presented, we aim to validate the representation of physical processes in PALM as well as to point out specific shortcomings. This will help to build a baseline for future developments of the model and for improvements of simulations of physical processes in an urban environment. [ABSTRACT FROM AUTHOR]