We perform a source attribution for tropospheric and ground-level ozone using a novel technique that accounts separately for the contributions of the two chemically distinct emitted precursors (reactive carbon and oxides of nitrogen) to the chemical production of ozone in the troposphere. By tagging anthropogenic emissions of these precursors according to the geographical region from which they are emitted, we determine source–receptor relationships for ground-level ozone. Our methodology reproduces earlier results obtained via other techniques for ozone source attribution, and it also delivers additional information about the modelled processes responsible for the intercontinental transport of ozone, which is especially strong during the spring months. The current generation of chemical transport models used to support international negotiations aimed at reducing the intercontinental transport of ozone shows especially strong inter-model differences in simulated springtime ozone. Current models also simulate a large range of different responses of surface ozone to methane, which is one of the major precursors of ground-level ozone. Using our novel source attribution technique, we show that emissions of NO_x (oxides of nitrogen) from international shipping over the high seas play a disproportionately strong role in our model system regarding the hemispheric-scale response of surface ozone to changes in methane, as well as to the springtime maximum in intercontinental transport of ozone and its precursors. We recommend a renewed focus on the improvement of the representation of the chemistry of ship NO_x emissions in current-generation models. We demonstrate the utility of ozone source attribution as a powerful model diagnostic tool and recommend that similar source attribution techniques become a standard part of future model intercomparison studies.

A system for source attribution of tropospheric ozone produced from both NO_x and volatile organic compound (VOC) precursors is described, along with its implementation in the Community Earth System Model (CESM) version 1.2.2 using CAM4. The user can specify an arbitrary number of tag identities for each NO_x or VOC species in the model, and the tagging system rewrites the model chemical mechanism and source code to incorporate tagged tracers and reactions representing these tagged species, as well as ozone produced in the stratosphere. If the user supplies emission files for the corresponding tagged tracers, the model will produce tagged ozone tracers which represent the contribution of each of the tag identities to the modelled total tropospheric ozone. Our tagged tracers preserve O_x. The size of the tagged chemical mechanism scales linearly with the number of specified tag identities. Separate simulations are required for NO_x and VOC tagging, which avoids the sharing of tag identities between NO_x and VOC species. Results are presented and evaluated for both NO_x and VOC source attribution. We show that northern hemispheric surface ozone is dominated year-round by anthropogenic emissions of NO_x, but that the mix of corresponding VOC precursors changes over the course of the year; anthropogenic VOC emissions contribute significantly to surface ozone in winter–spring, while biogenic VOCs are more important in summer. The system described here can provide important diagnostic information about modelled ozone production, and could be used to construct source–receptor relationships for tropospheric ozone.

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Tropospheric ozone (O₃) is an important air pollutant that affects human health, ecosystems, and climate. The contributions of O₃ precursor emissions from different geographical source regions to the O₃ concentration can help to quantify the effects of local versus remotely transported precursors on the O₃ concentration in a certain area. This study presents a “tagging” approach within the WRF-Chem model that attributes O₃ concentration in several European receptor regions to nitrogen oxide (NO_x) emissions from within and outside of Europe during April–September 2010. We also examine the contribution of these different precursor sources to various O₃ metrics and their exceedance events. Firstly, we show that the spatial distributions of simulated monthly mean MDA8 from tagged O₃ source regions and types for late spring, summer, and early autumn 2010 varies with season. For summer conditions, O₃ production is dominated by national and intra-European sources, while in the late spring and early autumn intercontinental transported O₃ is an important contributor to the total O₃ concentration. We have also identified shipping activities in the Mediterranean Sea as an important source of O₃ for the Mediterranean countries, as well as the main contributor to high modelled MDA8 O₃ concentration in the Mediterranean Basin itself. Secondly, to have a better understanding of the origin of MDA8 O₃ exceedances, we compare modelled and observed values of MDA8 O₃ concentration in the Po Valley and Germany–Benelux receptor regions, revealing that the contribution from local sources is about 41 % and 38 % of modelled MDA8 O₃ during the exceedance days, respectively. By examining the relative contributions of remote NO_x sources to modelled and observed O₃ exceedance events, we determine that model underrepresentation of long-range O₃ transport could be contributing to a general underestimation of modelled O₃ exceedance events in the Germany–Benelux receptor region. Thirdly, we quantify the impact of local vs. non-local NO_x precursors on O₃ production for each European receptor region using different O₃ metrics. The comparison between mean, MDA8 and 95th percentile O₃ metrics accentuates the importance of large contributions from locally emitted NO_x precursors to the high end of the O₃ distribution. When we compare the vegetation and health metrics, we notice that the SOMO35 and AOT40 indexes exhibit rather similar behaviour, while the W126 index accentuates the importance of local emissions. Overall, this study highlights the importance of a tagging approach to quantify the contribution of local and remote sources to the MDA8 O₃ concentration during several periods as well to different O₃ metrics. Moreover, this method could be applied to assess different mitigation options.

With NO₂ limit values being frequently exceeded in European cities, complying with the European air quality regulations still poses a problem for many cities. Traffic is typically a major source of NO_x emissions in urban areas. High-resolution chemistry transport modelling can help to assess the impact of high urban NO_x emissions on air quality inside and outside of urban areas. However, many modelling studies report an underestimation of modelled NO_x and NO₂ compared with observations. Part of this model bias has been attributed to an underestimation of NO_x emissions, particularly in urban areas. This is consistent with recent measurement studies quantifying underestimations of urban NO_x emissions by current emission inventories, identifying the largest discrepancies when the contribution of traffic NO_x emissions is high. This study applies a high-resolution chemistry transport model in combination with ambient measurements in order to assess the potential underestimation of traffic NO_x emissions in a frequently used emission inventory. The emission inventory is based on officially reported values and the Berlin–Brandenburg area in Germany is used as a case study. The WRF-Chem model is used at a 3 km × 3 km horizontal resolution, simulating the whole year of 2014. The emission data are downscaled from an original resolution of ca. 7 km × 7 km to a resolution of 1 km × 1 km. An in-depth model evaluation including spectral decomposition of observed and modelled time series and error apportionment suggests that an underestimation in traffic emissions is likely one of the main causes of the bias in modelled NO₂ concentrations in the urban background, where NO₂ concentrations are underestimated by ca. 8 µg m⁻³ (−30 %) on average over the whole year. Furthermore, a diurnal cycle of the bias in modelled NO₂ suggests that a more realistic treatment of the diurnal cycle of traffic emissions might be needed. Model problems in simulating the correct mixing in the urban planetary boundary layer probably play an important role in contributing to the model bias, particularly in summer. Also taking into account this and other possible sources of model bias, a correction factor for traffic NO_x emissions of ca. 3 is estimated for weekday daytime traffic emissions in the core urban area, which corresponds to an overall underestimation of traffic NO_x emissions in the core urban area of ca. 50 %. Sensitivity simulations for the months of January and July using the calculated correction factor show that the weekday model bias can be improved from −8.8 µg m⁻³ (−26 %) to −5.4 µg m⁻³ (−16 %) in January on average in the urban background, and −10.3 µg m⁻³ (−46 %) to −7.6 µg m⁻³ (−34 %) in July. In addition, the negative bias of weekday NO₂ concentrations downwind of the city in the rural and suburban background can be reduced from −3.4 µg m⁻³ (−12 %) to −1.2 µg m⁻³ (−4 %) in January and from −3.0 µg m⁻³ (−22 %) to −1.9 µg m⁻³ (−14 %) in July. The results and their consistency with findings from other studies suggest that more research is needed in order to more accurately understand the spatial and temporal variability in real-world NO_x emissions from traffic, and apply this understanding to the inventories used in high-resolution chemical transport models.

Increased tropospheric ozone (O₃) and high temperatures affect human health during heat waves. Here, we perform a source attribution that considers separately the formation of German surface ozone from emitted NO_x and volatile organic compound (VOC) precursors during two peak ozone events that took place in 2015 and 2018 which were associated with elevated temperatures. Results showed that peak ozone concentrations can be primarily attributed to nearby emissions of anthropogenic NO_x (from Germany and immediately neighboring countries) and biogenic VOC. Outside of these high ozone episodes, baseline ozone concentrations are attributed primarily to long-range transport, with ozone due to remote anthropogenic NO_x emissions and methane oxidation adding to the tropospheric ozone background. We show that a significant contribution to modeled O₃ coming from German NO_x or VOC emissions occurs mostly in southern Germany, emphasizing that the production of ozone depends on the local interplay between NO_x and VOC precursors. Shipping activities in the Baltic and North seas have a large impact on ozone predicted in coastal areas, yet a small amount of ozone from these sources can also be seen far inland, showing the importance of transported ozone on pollution levels. We have also shown that changes in circulation patterns during the peak O₃ episodes observed in Germany during the 2015 and 2018 heat waves can affect the contribution of different NO_x emission sources to total O₃; thus, the possible influence of multiple upwind source regions should be accounted for when mitigation strategies are designed. Our study also highlights the good correlation between ozone coming from German biogenic VOC emissions and total ozone, although the diurnal variation in the ozone coming from biogenic sources is not dominated by the diurnal variation in biogenic emissions, and the peaks of ozone from biogenic sources are disconnected from local emission peaks. This suggests that the formation of O₃ from local German biogenic VOC emissions is not the sole factor that influences the ozone formation, and other meteorological and chemical processes affect the diel variation of ozone with a biogenic origin. Overall, this study helps to demonstrate the importance of a source attribution method to understand the sources of O₃ in Germany and can be a useful tool that will help to design effective mitigation strategies.