Polycyclic aromatic hydrocarbons like benzo(a)pyrene (BaP) in atmospheric particulate matter pose a threat to human health because of their high carcinogenicity. In the atmosphere, BaP is mainly degraded through a multiphase reaction with ozone, but the fate and atmospheric transport of BaP are poorly characterized. Earlier modeling studies used reaction rate coefficients determined in laboratory experiments at room temperature, which may overestimate/underestimate degradation rates when applied under atmospheric conditions. Moreover, the effects of diffusion on the particle bulk are not well constrained, leading to large discrepancies between model results and observations. We show how regional and global distributions and transport of BaP can be explained by a new kinetic scheme that provides a realistic description of the temperature and humidity dependence of phase state, diffusivity, and reactivity of BaP-containing particles. Low temperature and humidity can substantially increase the lifetime of BaP and enhance its atmospheric dispersion through both the planetary boundary layer and the free troposphere. The new scheme greatly improves the performance of multiscale models, leading to better agreement with observed BaP concentrations in both source regions and remote regions (Arctic), which cannot be achieved by less-elaborate degradation schemes (deviations by multiple orders of magnitude). Our results highlight the importance of considering temperature and humidity effects on both the phase state of aerosol particles and the chemical reactivity of particulate air pollutants.

A description of the new air quality downscaling model – the urban EMEP (uEMEP) and its combination with the EMEP MSC-W model (European Monitoring and Evaluation Programme Meteorological Synthesising Centre West) – is presented. uEMEP is based on well-known Gaussian modelling principles. The uniqueness of the system is in its combination with the EMEP MSC-W model and the “local fraction” calculation contained within it. This allows the uEMEP model to be imbedded in the EMEP MSC-W model and downscaling can be carried out anywhere within the EMEP model domain, without any double counting of emissions, if appropriate proxy data are available that describe the spatial distribution of the emissions. This makes the model suitable for high-resolution calculations, down to 50 m, over entire countries. An example application, the Norwegian air quality forecasting and assessment system, is described where the entire country is modelled at a resolution of between 250 and 50 m. The model is validated against all available monitoring data, including traffic sites, in Norway. The results of the validation show good results for NO₂, which has the best known emissions, and moderately good for PM₁₀ and PM_2.5. In Norway, the largest contributor to PM, even in cities, is long-range transport followed by road dust and domestic heating emissions. These contributors to PM are more difficult to quantify than NO_x exhaust emission from traffic, which is the major contributor to NO₂ concentrations. In addition to the validation results, a number of verification and sensitivity results are summarised. One verification showed that single annual mean calculations with a rotationally symmetric dispersion kernel give very similar results to the average of an entire year of hourly calculations, reducing the runtime for annual means by 4 orders of magnitude. The uEMEP model, in combination with EMEP MSC-W model, provides a new tool for assessing local-scale concentrations and exposure over large regions in a consistent and homogenous way and is suitable for large-scale policy applications.

The air quality downscaling model uEMEP and its combination with the EMEP MSC-W chemical transport model are used here to achieve high-resolution air quality modelling at street level in Europe. By using publicly available proxy data, this uEMEP–EMEP modelling system is applied to calculate annual mean NO₂, PM_2.5, PM₁₀, and O₃ concentrations for all of Europe down to 100 m resolution and is validated against all available AIRBASE monitoring stations in Europe at 25 m resolution. Downscaling is carried out on annual mean concentrations, requiring special attention to non-linear processes, such as NO₂ chemistry for which frequency distributions are applied to better represent the non-linear NO₂ chemistry. The downscaling shows significant improvement in NO₂ concentrations for which the spatial correlation has been doubled for most countries and bias reduced from −46 % to −18 % for all stations in Europe. The downscaling of PM_2.5 and PM₁₀ does not show improvement in spatial correlation but does reduce the overall bias in the European calculations from −21 % to −11 % and from −39 % to −30 % for PM_2.5 and PM₁₀, respectively. There is improved spatial correlation in most countries after downscaling of O₃ and a reduced positive bias of O₃ concentrations from +16 % to +11 %. Sensitivity tests in Norway show that improvements in the emission and emission proxy data used for the downscaling can significantly improve both the NO₂ and PM results. The downscaling development opens the way for improved exposure estimates, improved assessment of emissions, and detailed calculations of source contributions to exceedances in a consistent way for all of Europe at high resolution.

Ground-level ozone (O₃) has emerged as a significant air pollutant in China, attracting increasing attention from both the scientific community and policymakers. Chemical transport models (CTMs) serve as crucial tools in addressing O₃ pollution, with frequent applications in predicting O₃ concentrations, identifying source contributions, and formulating effective control strategies. The accuracy and reliability of the simulated O₃ concentrations are typically assessed through model performance evaluation (MPE). However, the wide array of CTMs available, variations in input data, model setups, and other factors result in a broad range of differences between simulated and observed O₃ concentrations, highlighting the necessity of standardized benchmarks in O₃ evaluation. Building upon our previous work, this study conducted a thorough literature review of CTM applications simulating O₃ in China from 2006 to 2021. A total of 216 relevant articles out of a total of 667 reviewed were identified to extract quantitative MPE results and key model configurations. From our analysis, two sets of benchmark values for six commonly used MPE metrics are proposed for CTM applications in China, categorized into “goal” benchmarks representing optimal model performance and “criteria” benchmarks representing achievable model performance across a majority of studies. It is recommended that the normalized mean bias (NMB) for hourly O₃ and daily 8 h maximum O₃ concentrations should ideally fall within ±15 % and ±10 %, respectively, to meet the goal benchmark. If the criteria benchmarks are to be met, the NMB should be within ±30 % and ±20 %, respectively. Moreover, uncertainties in O₃ predictions due to uncertainties in various model inputs were quantified using the decoupled direct method (DDM) in a commonly used CTM. For the simulation period of June 2021, the total uncertainty of simulated O₃ ranged from 4 to 25 µg m⁻³, with anthropogenic volatile organic compound (AVOC) emissions contributing most to the uncertainty regarding O₃ in coastal regions and with O₃ boundary conditions playing a dominant role in the northwestern region. The proposed benchmarks for assessing simulated O₃ concentrations, in conjunction with our previous studies on PM_2.5 and other criteria air pollutants, represent a comprehensive and systematic effort to establish a model performance framework for CTM applications in China. These benchmarks aim to support the growing modeling community in China by offering a robust set of evaluation metrics and establishing a consistent evaluation methodology relative to the body of prior research, thereby helping to establish the credibility and reliability of CTM applications. These statistical benchmarks need to be periodically updated as models advance and as better inputs become available in the future.

Polycyclic aromatic hydrocarbons (PAHs) significantly impact human health due to their persistence, toxicity, and potential carcinogenicity. Their global distribution and regional changes caused by emission changes, especially over areas in developing countries, remain to be understood along with their health impacts. This study implemented a PAH module in the global–regional nested Atmospheric Aerosol and Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) to investigate the global distribution of PAHs and the change in their health risks from 2013 to 2018 in China. An evaluation against observations showed that the model could capture well the spatial distribution and seasonal variation in Benzo[a]pyrene (BaP), the typical indicator species of PAHs. On a global scale, the annual mean concentrations are the highest in China followed by Europe and India, with high values exceeding the target values of 1 ng m⁻³ over some areas. Compared with 2013, the concentration of BaP in China decreased in 2018 due to emission reductions, whereas it increased in India and southern Africa. However, the decline is much smaller than for PM_2.5 during the same period. The concentration of BaP decreased by 8.5 % in Beijing–Tianjin–Hebei (BTH) and 9.4 % in the Yangtze River Delta (YRD). It even increased over areas in the Sichuan Basin due to changes in meteorological conditions. The total incremental lifetime cancer risk (ILCR) posed by BaP only showed a slight decrease in 2018, and the population in east China still faced significant potential health risks. The results indicate that strict additional control measures should be taken to reduce the pollution and health risks of PAHs effectively. The study also highlights the importance of considering changes in meteorological conditions when evaluating emission changes from concentration monitoring.