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Urban air quality is an important part of human well-being, and its detailed and precise modeling is important for efficient urban planning. In this study the potential sources of errors in large eddy simulation (LES) runs of the PALM model in stable conditions for a high-traffic residential area in Prague, Czech Republic, with a focus on street canyon ventilation, are investigated. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes in meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale turbulent kinetic energy (TKE), adjusting the profiles of parameters of the synthetic turbulence generator in PALM, and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modeling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease in concentration overestimation in the range 30 %–66 % while exhibiting a negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors which occurred during such extremely stable conditions. [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]

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We present the process of and difficulties in acquiring the proper boundary conditions (BCs) for the state-of-the-art large-eddy simulation (LES)-based PALM model system. We use the mesoscale Weather Research and Forecasting (WRF) model as a source of inputs for the PALM preprocessor and investigate the influence of the mesoscale model on the performance of the PALM model. A total of 16 different WRF configurations were used as a proxy for a multi-model ensemble. We developed a technique for selecting suitable sets of BCs, performed PALM model simulations driven by these BCs, and investigated the consequences of selecting a sub-optimal WRF configuration. The procedure was tested for four episodes in different seasons of the year 2019, during which WRF and PALM outputs were evaluated against the atmospheric radiosounding observations. We show that the PALM model outputs are heavily dependent on the imposed BCs and have different responses at different times of the day and in different seasons. We demonstrate that the main driver of errors is the mesoscale model and that the PALM model is capable of attenuating but not fully correcting them. The PALM model attenuates the impact of errors in BCs in wind speed, while for the air temperature, PALM shows variable behavior with respect to driving conditions. This study stresses the importance of high-quality driving BCs and the complexity of the process of their construction and selection. [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]

We present the process and difficulties of acquiring the proper initial and boundary conditions (IBC) for the state-of-the-art LES based model PALM (Parallelized Large-Eddy Simulation Model). We use the mesoscale model WRF (Weather Research and Forecasting model) as a source of inputs for the PALM preprocessor, and investigate the influence of the mesoscale model on the performance of the PALM model. Sixteen different WRF configurations were used as a proxy for a multi-model ensemble. We developed a technique for selecting the suitable sets of IBC, performed PALM model simulations driven by them, and investigated the consequences of selecting a sub-optimal WRF configuration. The procedure was tested for four episodes during different seasons of the year 2019, evaluating WRF and PALM outputs against the atmospheric radio sounding observations. We show that the PALM model outputs are heavily dependent on the imposed IBC, and have different responses for different times of the day, and different seasons. We demonstrate that the main driver of errors is the mesoscale model, and that the PALM model is capable of attenuating, but not fully correcting them. The PALM model attenuates the impact of errors in IBC in wind speed, while for the air temperature, PALM shows variable behavior with respect to driving conditions. This study stresses the importance of high-quality driving IBC, and the complexity of the process of their construction and selection. [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 μg m-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 μg m-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 μg m-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 μg m-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 μg m-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 μg m-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 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 μg m-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 μg m-3). [ABSTRACT FROM AUTHOR]

The development of integrated urban services requires the implementation of informative tools that provide a balance between quality, time and costs for air quality assessment. Within this framework, three modeling techniques with different levels of complexity were compared during a winter inversion episode against PM 10 concentrations measured in a built-up area in Prague (Czech Republic) characterized by heavy traffic. Although the Gaussian model ATEM satisfied the common statistical-performance criteria, the predictions poorly represented the spatial variability of concentrations in the study domain. The Lagrangian model GRAL provided a better simulation of the effects of terrain and vortice formation inside street canyons, but tended to overpredict the influence of these phenomena. Finally, the most sophisticated of the three models, the Large-Eddy Simulation model PALM, demonstrated the best performance based on an exhaustive analysis of the model outputs in the temporal and spatial dimensions. After model comparison, a sensitivity test of the selected models to the driving meteorology and emissions inputs was carried out. While advanced models can simulate complex urban environments, their suitability for use in urban planning is subject to further considerations, such as computational cost, user expertise, and the usefulness of the output. Thanks to increasing computation power and intensive work on the entire modeling chain, sophisticated models could become routine tools for use in regulatory applications, contributing to future integrated urban service provision. • Selection of dispersion models should include a spatio-temporal assessment. • Meteorological input and resuspension emissions are important sources of uncertainty. • LES models provide a thorough set of data valuable for integrated urban services. [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]

Sensitivity of the PALM model 6.0 is tested in a real urban environment in the vicinity of a typical crossroad in a densely built-up residential area in Prague, Czech Republic. Two types of scenarios are employed. First are the synthetic scenarios altering mainly surface and material parameters such as albedo, emissivity or wall conductivity, testing sensitivity of the model simulations to potentially erroneous setting of model inputs. Second, real-life type scenarios are analyzed, in which commonly considered urban heat island mitigation measures are applied, such as greening of the streets or changing surface materials. For the first-type scenarios, surface parameters used in radiation balance equations are found to be the most sensitive overall followed by volumetric heat capacity and thermal conductivity of walls. Other parameters show limited average effect, however, some can still be significant in some parts of the day, such as surface roughness in the morning hours. Second type, the mitigation scenarios, show urban vegetation to be the most effective measure, especially when considering both physical and biophysical temperature indicators. Influence of both type scenarios was also tested for air quality, specifically PM10 dispersion which generally shows behaviour opposite to thermal indicators, ie., improved thermal comfort brings deterioration of PM10 concentrations. [ABSTRACT FROM AUTHOR]

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially. [ABSTRACT FROM AUTHOR]