Fires are a large source of non-methane organic gas (NMOG) emissions to the global atmosphere. These emissions can contribute to the formation of secondary pollutants such as ozone and particulate matter. However, the abundance and impacts of these emissions are uncertain and historically not well constrained. In this presentation, I will describe recent efforts to expand the representation of NMOGs from fires in a global model (GEOS-Chem) as well as the evaluation of the resulting simulation against airborne observations from the FIREX-AQ and ARCTAS campaigns. We use this expanded model to make the first estimate of the fire contribution to OH reactivity (OHR). We find that fires make an important contribution to global mean surface OHR (15%), and can be a dominant source of reactivity (up to 75%) over fire source regions. This work highlights the importance of representing the emissions and chemical oxidation of the suite of NMOGs emitted from fires in models.

How to cite: Heald, C. and Carter, T.: Modeling global fire emissions of organics and their impact on reactivity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1713, https://doi.org/10.5194/egusphere-egu23-1713, 2023.

Aircraft emissions consist of carbon dioxide (CO₂), nitrogen oxides (NO_x), sulfur dioxide (SO₂) and particulate matter (black carbon, sulfate) and water vapour. The non-CO₂ effects have been recently evaluated as twice the CO₂ effects regarding their radiative forcing of climate in 2018 [1]. Among the non-CO₂ effects, nitrogen oxides emissions impact several greenhouse gases concentrations. Increased tropospheric ozone production results in a positive radiative forcing (climate impact), but the subsequent increased OH concentrations enhance methane chemical destruction, thus decreasing stratospheric water vapour and the methane-linked background ozone levels in the troposphere. The net radiative forcing caused by the aircraft NO_x emissions is evaluated as a net positive forcing but still shows important uncertainties.

In order to investigate representation of key mechanisms involved for climate forcing, in the framework of the ACACIA (Advancing the Science for Aviation and Climate) EU project, six global chemistry-climate models have been used to reevaluate the climate effects of NO_x and aerosol aircraft emissions on atmospheric composition following a common protocol. As a first step, the standard runs have been assessed regarding ozone, carbon monoxide (CO), water vapour and reactive nitrogen (NO_y) against the IAGOS airborne measurements during 1994-2018, separately in the upper troposphere and in the lower stratosphere.

As a second step, the models have been used to assess the impact of NO_x and aerosol emissions on atmospheric composition. The subsonic aircraft perturbations are calculated based on the CEDS aircraft emission inventories [2] for the present-day conditions and based on different socioeconomic scenarios [3] for future (2050) conditions. Several sensitivity simulations will be presented in order to investigate the sensitivity of the results to background atmospheric conditions (present, future) and to lightning emissions. The changes in atmospheric composition will be presented and compared for the different models and scenarios.

Acknowledgement:

This study was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875036 within the Aeronautics project ACACIA.

References:

[1] D.S. Lee, D.W. Fahey, A. Skowron, M.R. Allen, U. Burkhardt, Q. Chen, S.J. Doherty, S. Freeman, P.M. Forster, J. Fuglestvedt, A. Gettelman, R.R. De León, L.L. Lim, M. T. Lund, R.J. Millar, B. Owen, J.E. Penner, G. Pitari, M.J. Prather, R. Sausen, and L. J. Wilcox, Atmospheric Environment 244, 117834 (2021)

[2] R. M. Hoesly, S. J. Smith, L. Feng, Z. Klimont, G. Janssens-Maenhout, T. Pitkanen, J. J. Seibert, L. Vu, R. J. Andres, R. M. Bolt, T. C. Bond, L. Dawidowski, N. Kholod, J. Kurokawa, M. Li, L. Liu, Z. Lu, M. C. P. Moura, P. R. O’Rourke, and Q. Zhang, Geosci. Model Develop. 11, 369-408 (2018)

[3] M. J. Gidden, K. Riahi, S. J. Smith, S. Fujimori, G. Luderer, E. Kriegler, D. P. van Vuuren, M. van den Berg, L. Feng, D. Klein, K. Calvin, J. C. Doelman, S. Frank, O.Fricko, M. Harmsen, T. Hasegawa, P. Havlik, J. Hilaire, R. Hoesly, J. Horing, A. Popp, E. Stehfest, and K. Takahashi, Geosci. Model Develop. 12, 1443-1475 (2019)

How to cite: Cohen, Y., Hauglustaine, D., Bellouin, N., Eastham, S., Lund, M. T., Matthes, S., Skowron, A., and Thor, R.: Impact of aircraft NOx and aerosol emissions on atmospheric composition : a model intercomparison, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8605, https://doi.org/10.5194/egusphere-egu23-8605, 2023.

Aviation’s growth has historically outpaced technological and operational improvements to mitigate emissions. Despite the short-term slowdown due to COVID-19, the sector’s growth is forecast to resume in the coming years. Being a unique sector in terms of the altitude that the majority of the emissions are deposited in, aviation contributes to air pollution near and far from airports, in the form of PM_2.5, ozone, and NO₂, through a series of chemical and physical pathways. Aviation’s growth is heterogeneous globally, with emissions in some regions (e.g., Asia) growing faster than elsewhere (e.g., Europe and North America).

Using recent aviation emissions inventories and future forecasts, and the GEOS-Chem global atmospheric chemistry-transport model, we quantify aviation’s global air quality and associated human health impacts in recent years and under different future atmospheric pathways. Both emissions during landing and take-off operations and emissions during cruise are assessed in different regions globally. We isolate the air quality changes attributable purely to the growth of aviation emissions, and those associated with the evolving (background) atmospheric composition which affects the nonlinear pathways between aviation emission and air pollution formation. Given the long timelines associated with the aviation sector, our results highlight the need for the integrated assessment of present-day and future aviation impacts together with those of other evolving sources.

How to cite: Dedoussi, I. and Quadros, F.: Modeling aviation’s air quality impacts over time in the context of the changing atmospheric composition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14938, https://doi.org/10.5194/egusphere-egu23-14938, 2023.

Freshly emitted ship exhaust gas contributes to the local reduction of boundary layer ozone levels through the reaction of the emitted nitric oxide with ozone and the formation of NO₂. However, after a certain time of plume aging and depending on meteorological conditions as well as the availability of other oxidizing species, new ozone will be formed from NO₂. This implies that ship emissions will act as an ozone source in some distance from the ship. Considering the new and stricter recommendations for ozone limit values from the World Health Organization, it becomes important to analyze and quantify conditions for ozone formation through ship emissions in coastal areas. This study investigates impacts of various parameters like the season, diurnal cycle, cloud coverage and emitted substances on the rate of ozone destruction and formation in ship exhaust gas plumes with the local scale chemistry transport model EPISODE-CityChem. First results show the highest potential of ozone formation under clear-sky daytime conditions during the summer season. A total solar radiation >300 Wm^-2 is necessary for ozone formation in Central European latitudes. A maximum ozone formation rate was found for a plume aging time of ~4 hours. A lower NO/NO₂ emission ratio as well as lower CO emissions show tendencies of stronger O₃ formation in the aged plume. The highest ozone formation goes together with a maximum loss rate of carbon monoxide (CO) and the hydroperoxyl radical (HO₂) as well as the maximum formation of the hydroxyl radical (OH). This indicates that the effects of ship emissions on coastal air quality is highly variable and depends, beneath meteorological influences, on the distance of the ship to the coast and the mixture of pollutants in the plume. 

How to cite: Badeke, R., Schneider, K., and Matthias, V.: Analyzing ozone formation conditions for aged ship plumes in a local scale chemistry transport modeling study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5758, https://doi.org/10.5194/egusphere-egu23-5758, 2023.

Air pollution is a significant concern for society due to its negative impact on human health. The ability to forecast pollutant concentrations and predict extreme pollution events is essential for mitigating their harmful effects. These events are commonly presented as compound and can have serious consequences (Zscheischler et al., 2020) for public health, making it important to identify and understand the variables that contribute to them.

In this study, we used state-of-the-art techniques based on artificial intelligence and machine learning methodologies to analyze data from a selection of European Mediterranean cities in order to identify the meteorlogical and preceding pollution variables that best explain extreme pollution events involving PM10, O₃and NO₂. Furtheremore, the role of the combined effect of these variables and recent climatic conditions is also examined on excess mortality rates using the likelihood multiplication factor parameter (Ridder et al., 2020) as well as the importance given by Random Forest Regression  Models.

Our results show that these events show a clear compound nature, understanding the non-linearity changes in the intensity of compound events. The main driver factors depend on the pollutant specie as well as the season of the year.

References
Ridder N. N., Pitman A. J., Westra S., Ukkola A., Do H. X., Bador M., Hirsch A. L., Evans J. P., Di Luca A., Zscheischler J. (2020). Global hotspots for the occurrence of compound events. Nature communications 11(1), 1–10.
Zscheischler J., Martius O., Westra S., Bevacqua E., Raymond C., Horton R. M., van den Hurk B., AghaKouchak A., Jézéquel A., Mahecha M. D., et al. (2020). A typology of compound weather and climate events. Nature reviews earth & environment 1(7), 333–347.

How to cite: Sánchez-Jiménez, F., Raluy-López, E., Segado-Moreno, L., García-Fernández, E., Montávez, J. P., and Jiménez-Guerrero, P.: Extreme Compound Events of Air Pollution and Effects on European Mediterranean Cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14559, https://doi.org/10.5194/egusphere-egu23-14559, 2023.

With the drastic actions initiated by the Chinese authorities to improve air quality, the level of several secondary pollutants including near-surface ozone still has been increasing significantly between years 2013 and 2020, most notably in the Northern China Plain. Alleviating ozone pollution requires a quantitative understanding of the different processes that contribute to the photochemical formation and destruction of secondary species. It also requires that the budget of fast-reacting radicals that are directly involved in photochemical oxidation processes be investigated in detail. Here, we used a modified regional chemical transport model (the WRF-Chem) to analyze the influence of different photochemical processes on the entire geographical area covered by China. This analysis provides a quantitative estimate of the different factors that affect the oxidation capacity of the atmosphere in different regions of the country. Besides that, our model simulations will assess the relative importance of different photochemical processes that contribute to the budget of near-surface ozone in different chemical environments and provide some theoretical considerations on which our analysis is based. This study should support to reduction of ozone and other secondary pollutants in China.

How to cite: Dai, J., Brasseur, G., Vrekoussis, M., Zhang, Y., and Zhang, H.: The Atmosphere’s Oxidizing Capacity in China: Role and Influence of different photochemical processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9274, https://doi.org/10.5194/egusphere-egu23-9274, 2023.

Air traffic facilitates our society’s requirements for mobility. However, air traffic also contributes to climate change. Especially in view of the 2°C-climate target, it is important to make aviation eco-efficient. Here, the project Eco2Fly aimed at revising the estimate of the climate impact of aviation, by means of numerical simulations as well as in-situ and remote sensing measurements. Eco2Fly was a DLR funded project, which focuses on the climate impact of aviation, how atmospheric processes can be better understood and how we can reduce the climate impact of aviation. In this poster we focus on the aviation climate impact assessment.

Lee et al. (2021) published already a comprehensive climate impact assessment two years ago, which gives an excellent overview of the different climate species and their contribution to nowadays climate impact. Here, we like to add some new methods and processes to a climate impact assessment. One point is the difference between perturbation und tagging approach. While the perturbation approach provides the impact of changed emissions in the chemistry-climate system, the tagging approach gives the contribution of one sector to the total climate impact. Additionally, new insights from numerical simulations for the direct and indirect aerosol impact were obtained.

It is important for such a climate impact assessment, to use model results which are based on a realistic spatial distribution of emissions as different emission inventories can cause significantly different climate impact estimates, despite unchanged total emissions. In cooperation with the DLR project TraK (Transport and Climate) emission calculation and climate modelling approaches are applied to assess the climate impacts of the 2019 aviation emissions.

How to cite: Dahlmann, K., Grewe, V., Matthes, S., Hendricks, J., Righi, M., Weder, C., Mertens, M., and Brinkop, S.: Eco2Fly - An aviation climate impact assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15883, https://doi.org/10.5194/egusphere-egu23-15883, 2023.

Dry deposition is a major sink of ozone in the planetary boundary layer (PBL). In this study, we investigate how different PBL parameterizations influence the simulations of surface ozone and its dry deposition fluxes over eastern China using the Weather Research and Forecasting Model coupled to Chemistry (WRF-Chem), and quantify the contributions of dry deposition to ozone change rates in the PBL with an integrated process rates (IPR) analysis method. As the exacerbated ozone pollution in urban areas of China has aroused extensive concern, we limit our discussion to the model results for three main city agglomerations: Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD) and Pearl River Delta (PRD). Firstly, three PBL schemes applying distinct turbulence closures are employed to examine the model sensitivities, including nonlocal closed Yonsei University (YSU), local closed Mellor–Yamada–Janjić (MYJ) and hybrid local-nonlocal scheme Asymmetric Convective Model v2 (ACM2), each coupling with one or two specific surface layer schemes. The results show that using different PBL schemes leads to the uncertainty of 6.5~18.5% for surface ozone concentration simulations, and of 3.6~15.3% for accumulated ozone dry deposition fluxes, while scarcely impacts the simulated surface ozone diurnal cycles. Among all schemes, MYJ generally calculates the lowest surface ozone concentration and dry deposition flux, especially during nighttime. According to the multiple linear regression analysis, the differences in dry deposition fluxes are dominated by the differences in surface ozone levels, and the contributions of differences in dry deposition velocities are more substantial at nighttime than at daytime. Secondly, by switching ozone dry deposition on-off based on simulation with YSU scheme, we find the absence of ozone dry deposition enhances surface ozone levels by 24~30% during daytime and by 61~82% during nighttime over the three regions. The IPR analyses indicate that when adding dry deposition process, the positive contributions of vertical mixing are elevated by 1~4 μg m^-3 hr^-1, partially compensating the accessorial negative contributions from dry deposition itself (by up to -8 μg m^-3 hr^-1) to the change rates of ozone within the PBL. Furthermore, IPR analysis is also conducted for the model results in July 20 to 23 over BTH, during which period YSU_MM5 simulates contrary ozone trend against MYJ_Eta scheme. In this episode, the choice of PBL schemes influences significantly on dry deposition, gas chemistry and advective transport processes, and the absolute contribution by simulation deviations of dry deposition process account 13% for the difference in gross ozone trend. Our study quantifies the influence of different PBL parameterization schemes on ozone simulations, emphasizing the importance of dry deposition process on governing the ozone change near surface and in the PBL.

How to cite: Li, D., Zhang, L., Liu, Z., Zhou, M., and Zhao, Y.: The influences of dry deposition process on surface ozone simulations under different planetary boundary layer parameterization schemes over eastern China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3001, https://doi.org/10.5194/egusphere-egu23-3001, 2023.

Multiple simulations were conducted using Environment and Climate Change Canada’s Global Environmental Multiscale-Modelling Air-quality and CHemistry (GEM-MACH) in order to evaluate the model’s predictive capabilities for concentrations, height of plume emissions, and deposition for regions within and downwind of the Canadian Oil Sands.  The innermost domain of the nested setup was 1350x1345 km in extent, centered on the Canadian provinces of Alberta and Saskatchewan, with a grid-cell size of 2.5 km.  Successive model science updates were carried out in a series of 15-month simulations covering the period August 1, 2017 through October 31, 2018.  The simulations were compared to local (Wood Buffalo Environmental Association, Lakeland Industry and Community Association, Peace River Area Monitoring Program), provincial (Alberta Precipitation Quality Monitoring Program (APQMP)), and national (National Air Pollution Surveillance (NAPS) and Canadian Air and Precipitation Monitoring Network (CAPMoN)) monitoring network data for the entire period, as well as to aircraft and ground-based measurement intensive data from August 2017, April 2018 and June to July 2018. 

The series of simulations included successive updates to the model’s gas-phase chemistry, secondary organic aerosol formation, photolysis rate calculations, particle speciation, plume rise, inorganic heterogeneous chemistry, cloud processing of gases and aerosols, gas reactions on particle surfaces, the addition of a tracer for the emissions, transport and deposition of total organic carbon gas, the addition of H₂S as a transported species, and numerous updates to the model’s input emissions making use of inventory and observation-based emissions.  While the model evaluation is still underway, the evaluation thus far has identified key chemical and physical processes relevant to the Oil Sands area, which will be highlighted in this presentation, including:

(1) Concentrations of particulate base cations are dominated by fugitive dust, , and exhibit strong seasonality (higher in summer than winter).  This seasonality can be reasonably well simulated by the model if coarse mode emissions of fugitive dust are shut off at temperatures slightly below the freezing point of water;

(2) Low biased model surface ozone predictions from January through April are potentially due to insufficient simulated Troposphere / Stratosphere exchange, in turn identifying the process as a driver for springtime ozone in the area;

(3) The concentrations of NO₂, particulate matter and nitric acid are all linked via a combination of surface reactions transforming NO₂ to HONO and HNO₃, and inorganic heterogeneous chemistry, with the former reaction probabilities being highly uncertain;

(4) Aircraft-based estimates of total organic carbon gas emissions and deposition used as a tracer within the model suggest high molecular mass hydrocarbons are emitted as gases from OS facilities and are being deposited in the surrounding area.  Conventional gas-phase deposition algorithms may not explain observed deposition rates; absorptive partitioning to landscape surfaces is presented as a possible alternative pathway for deposition.

How to cite: Makar, P., Lee, C., Akingunola, A., Gong, W., Stroud, C., Zhang, J., Majdzadeh, M., Ghahreman, R., Miller, S., Fathi, S., Lupu, A., Katal, A., Liggio, J., Hayden, K., Staebler, R., Strawbridge, K., Edgerton, E., Landis, M., White, E., and Moussa, S. and the remainder of the Oil Sands Modelling and Measurement Team: Key Processes in Canadian Oil Sands Gas and Aerosol Chemistry Identified through Model Evaluation Against Monitoring Network and Aircraft-Based Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9306, https://doi.org/10.5194/egusphere-egu23-9306, 2023.

Although anthropogenic emissions have decreased during the last two decades, air pollution is still problematic in many regions in Europe. In 2021, the World Health Organisation has released updated air quality guideline levels that base on newest medical research and have drastically decreased in comparison to the previous levels, except for SO₂. These levels reflect the minimum concentration for which evidence of adverse health effects have been found. This study analyses the air quality in Europe using simulations by EURAD-IM for the year 2016 with optimized emissions to evaluate guideline exceedances for PM_2.5, NO₂, and O₃. All air pollutant concentrations in Europe exceed the WHO2021 annual guideline levels by a large amount. High PM_2.5 and O₃ concentrations are homogeneously distributed across Europe with 99 % and 100 % of the EU38 population (European Union and eleven other European states) exposed to concentrations above the guideline levels. High NO₂ concentrations are linked to densely populated areas. However, 61% of the EU38 population is exposed to concentrations above the guideline level. For PM_2.5, the effect of different aerosol classes on the bulk mass and different PM reduction scenarios are analysed. NO₃^- shows the largest contribution to total PM_2.5 while primary anthropogenic aerosols are mainly responsible for extremely high PM_2.5 concentrations of up to 30 μg m⁻³. In the extreme case of completely removing different aerosol classes from the bulk mass, jointly removing NO₃^- and primary anthropogenic aerosols as well as removing all secondary inorganic aerosols decrease PM_2.5 annual concentrations to below 5 μg m⁻³, which is the WHO2021 annual guideline level, in most parts of Europe. However, a more realistic scenario, in which the decrease of aerosol concentrations linearly follows the targeted anthropogenic emission reductions by the European Union, still shows an exceedance of the annual WHO2021 guideline levels by a factor of 2 - 3 throughout Europe. Thus, besides implementing strong emission reduction plans, other ways of removing air pollutants from the atmosphere need to be designed and implemented.

How to cite: Franke, P., Lange, A. C., and Kiendler-Scharr, A.: Assessing WHO2021 air quality guideline levels in Europe: Population exposure and PM2.5 reduction potential, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15917, https://doi.org/10.5194/egusphere-egu23-15917, 2023.

Long-term personal air pollution exposures estimates from nationwide cohorts are useful in studies of the relationship between air pollution exposure and chronic diseases such as diabetes or cardiovascular disease. Ignoring space-time activity patterns and neglecting mobility in exposure assessment may lead to incorrect exposure distributions and bias in downstream exposure health relations. In our study we estimate personal air pollution exposures nationwide and across socio-economic and age profiles to identify the relevance of location or profiles on exposure analysis.

We developed a set of characteristic diurnal activity profiles that we use to calculate exposures for each home address in Switzerland. The profiles are specified by different characteristics such as age group, social economic status, or commute type (e.g. by car, bicycle, on foot). Potential working locations are retrieved from origin-destination matrices for a particular profile, derived from the annual Structural Survey data from the Swiss Population Census (https://www.bfs.admin.ch/bfs/en/home/statistics/population/surveys/se.html), at the level of municipalities. Commute trips between residential and work location are then calculated using the shortest route on OpenStreetMap data. For each profile and each residential address, we run an agent-based model in Monte Carlo mode, generating a database of personal long-term exposures to NO₂ and PM_2.5 for further epidemiological analysis.

Our activity-based mobility simulation provides a representative description of space-time activities of individuals. We present the model results at all unique 1.8 million residential address locations in Switzerland. We compare the exposure assigned from residential address alone to the exposures derived from 20 different activity profiles and present the differences between profiles. We also demonstrate the spatial variability of exposures per profile and the associated uncertainty.

The generated exposure database can be used for epidemiological analysis of large-scale cohorts, and enables follow-up studies to evaluate whether including commuting and other activities and therefore more detailed estimates of individual exposure results in more accurate risk estimates in health studies.

How to cite: Schmitz, O., de Hoogh, K., Probst-Hensch, N., Jeong, A., Flückiger, B., Vienneau, D., Hoek, G., Kyriakou, K., Vermeulen, R. C. H., and Karssenberg, D.: Applying space-time activity data and socio-economic profiles to assess the variation of personal air pollution exposures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12311, https://doi.org/10.5194/egusphere-egu23-12311, 2023.

Exposure to air pollution, especially aerosols, can lead to adverse health effects. Particle toxicity has been generally linked to its mass concentration. However, toxicity is determined by the chemical composition of the aerosol, which varies greatly depending on pollution sources. Reactive oxygen species (ROS) can be formed in the lung when particles are inhaled and can trigger inflammatory processes. Transition metals strongly contribute to ROS, among which copper, iron and manganese. However, these metals are generally not included in current emission inventories and chemistry-transport models. Thus, to ultimately model aerosol toxicity, it would be necessary to add these three metals into a chemistry-transport model, and use it to provide inputs to a lung chemistry model (Lelieveld et al, 2021).

Country-wise copper emissions are available from the EMEP database (https://www.ceip.at/) for the majority of European Union countries. Methods for evaluating emissions differ from country to country, and it is necessary to homogenize them to standardize emissions, at least for the main emitting countries around France which is the main target of the study. For iron and manganese, European scale inventories do not exist yet. Data from the French Citepa and ADEME, from EMEP reports, SPECIEUROPE and a bibliography of 16 references were used to build these two bottom-up inventories. Many sources have been studied: abrasion of tires, brakes and road for the road transport sector, wear of catenaries, brakes and rails for the rail sector, combustion of coal, biomass and of petroleum, the burning of motor oils, and the incineration of waste.

Also, long term measurements from several dozens of rural, urban and traffic sites were collected to build a large database. These data come from EBAS website (https://ebas-data.nilu.no/), UKAIR website (https://uk-air.defra.gov.uk/data/), CARA program (Favez et al., 2021), the Spanish IDAEA and the French Atmo Auvergne-Rhône-Alpes and Marseille-Longchamp. Fe/Cu and Mn/Cu ratios were calculated for both the bottom-up emission inventories and measurements data. As these ratios turned out to be significantly lower in emission inventories, observed ratios were used to adjust Fe and Mn emissions from Cu ones.

Using these two inventories, emission ratios with coarse particles for each country and sector were created and applied to the spatialized particles emission data. We then implemented the obtained spatialized Cu, Fe and Mg emission inventories into the CHIMERE chemistry-transport model, to simulate ambient copper, iron and manganese mass concentrations in Europe for the year 2014.

How to cite: Dubois, N., Foret, G., Beekmann, M., Siour, G., Vida, M., Andre, J.-M., Moukhtar, S., Uzu, G., Jaffrezo, J.-L., Conil, S., Le Berre, L., Marchand, N., Chazeau, B., Gille, G., Alastuey, A., Querol, X., Reche, C., Socquet, S., Bret, C., and Duval, M.: European modeling of atmospheric copper, iron and manganese as key players for aerosol toxicity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12878, https://doi.org/10.5194/egusphere-egu23-12878, 2023.

Fine particulate matter (PM_2.5) pollution and its long-term exposure is one of the most critical environmental and public health risks in India, and has caused an enormous disease burden. However, the particle load is being shared from various sectoral contributions and a significant proportion found at any specific state would have originated from distant sources that are often outside the immediate jurisdiction and control of local authorities. This poses an injustice to some of the states which are emitting less emission but suffering from excess health burden. This study quantifies the burden apportionment under three model scenarios (baseline, advanced technology, and sustainable development) for the years 2015 and 2030, which forecast the future PM_2.5 exposure based on current emission, and partial or full implementations of cost-effective interventions, as part of the Global Burden of Diseases, injuries, and Risk factor study. In 2030, the total excess mortality burden is projected to be 0.79-0.8 million (0.57-1.1) and DALYs burden of 24.1-24.3 million (16.6-30.4) from regional and sectoral emissions, respectively. Low SDI states would have a higher share in India. The state itself, outside, and neighbouring state emissions would be the leading contributors under the regional emission scenario; however, emissions from the secondary, power plant, high stack, transport and waste sectors would lead to higher burden apportionments in 2030. IHD, COPD, and type-2 diabetes would be the leading causes of health burden, especially in adults. The results add evidence for prioritizing sectors and efficacy for revising relevant environment standards and health policies. Immediate adaptation to the best available cost-effective modern technologies (SDS scenario) in households and commercial emission sectors is extremely necessary to reduce the substantial avoidable deaths and disease burden from this major environmental risk factor, improving the medical infrastructure and awareness in low and middle SDI states that are commensurate with the magnitude of air pollution.

How to cite: Sarkar, D. and Dey, S.: Burden Apportionment of India: Prioritizing the Regional and Sectoral Emissions to Maximize the Health Benefit at Near Future, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10754, https://doi.org/10.5194/egusphere-egu23-10754, 2023.

With semiconductor technology gradually approaching its physical and thermal limits, Graphics processing units (GPUs) are becoming an attractive solution in many scientific applications due to their high performance. This paper presents an application of GPU accelerators in air quality model. We endeavor to demonstrate an approach that runs a PPM solver of horizontal advection (HADVPPM) for air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM from its original Fortran form to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU-GPU communication frequency, increasing the size of data computation on GPU, and optimizing the GPU memory access order to improve the overall computing performance of CAMx. Finally, a heterogeneous, hybrid programming paradigm (MPI+CUDA) is presented and utilized with the GPU-HADVPPM on GPU clusters. When the consistency of its results is verified, offline experiment results show that running GPU-HADVPPM on one K40 and V100 GPU can achieve up to 845.4x and 1113.6x acceleration. By implementing a series of optimization schemes, the CAMx model coupled with GPU-HADVPPM resulted in a 12.7x and 94.8x improvement in computational efficiency using a GPU accelerator card on a K40 and V100 cluster, respectively. The multi-GPU acceleration algorithm enables 3.9x speedup with 8 CPU cores and 8 GPU accelerators on V100 cluster.

Figure 1. The calling and computation process of the HADVPPM function on the CPU-GPU.

Figure 2. (a) The offline performance of the HADVPPM scheme on CPU and GPU. The unit of the wall times for the offline performance experiments is millisecond(ms); (b) The total elapsed time of CAMx-CUDA V1.3 on multiple GPUs. The unit of elapsed time for experiments is seconds (s). The orange bar indicates the elapsed time of CAMx on the CPU, the blue bar shows the elapsed time on the CPU-GPU heterogeneous platform, and the red line indicates its speedup ratio on the heterogeneous platform.

How to cite: Cao, K. and Wu, Q.: GPU-HADVPPM: high-efficient parallel GPU design of the Piecewise Parabolic Method (PPM) for horizontal advection in air quality model (CAMx), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4859, https://doi.org/10.5194/egusphere-egu23-4859, 2023.

Inorganic heterogeneous chemistry (the reactions taking place between inorganic components of the gas-particle system) is one of the most complex and computationally demanding parts of atmospheric chemistry models. Accurate and highly computationally efficient algorithms for carrying out these calculations are essential for these models. Here we present a revised and updated approach for carrying out these calculations, called HETV2.

HETV2 updates the original HETV metastable state subroutines (Makar et al., 2003) expanding the aerosol system to include base cations (Mg²⁺, K⁺, Ca²⁺, Na⁺), and partitioning between chlorine, ammonium, and nitrate ions and HCl, NH₃ and HNO₃ gases. HETV2 is based on the algorithms of ISORROPIA II (Fountoukis and Nenes, 2007), with several key improvements for accuracy and computational efficiency of the calculations. First, the accuracy and stability of polynomial roots have been improved by using a Taylor series expansion of the quadratic formula, for times when the coefficients differ by orders of magnitude. Second, the new algorithms in HETV2 enforce mass conservation for cases where all species are present and the ratio of total base cations to sulfate is between 1.0 and 2.0. Third, the code has been optimized using a “vectorization by gridpoint” approach, allowing a single call to each subroutine for n sets of input conditions, reducing the subroutine call factor overhead. Fourth, the code has been optimized to remove unnecessary calculations, and the programming language has been updated from Fortran 77 to Fortran 90. Fifth, all subroutines that require bisection to obtain an equilibrium solution (i.e., the ‘major systems’) have had their root-finding method updated to the ‘Interpolate, Truncate and Project (ITP)’ method (Oliveria et al., 2021); the ITP method can obtain superlinear convergence, and therefore may significantly reduce the number of iterations, and hence the computational time, required to obtain the same result as ISORROPIA II. The new algorithms significantly improve both the computational speed and accuracy for inorganic heterogeneous chemistry calculations relative to ISORROPIA II. In this talk, we will describe the inorganic heterogeneous chemistry systems that are solved, the improvements to the algorithms, and compare the computational speed of ISORROPIA II to the new HETV2 code (depending on the chemical subspace examined, the new code is up to 2x faster than ISORROPIA II).

References                                                                                                

Fountoukis, C., & Nenes, A., 2007. ISORROPIA II: A computationally efficient thermodynamic equilibrium model for Aerosols. Atmospheric Chemistry and Physics, 7(17), 4639–4659.

Makar, P. A., Bouchet, V. S., & Nenes, A., 2003. Inorganic Chemistry calculations using HETV—a vectorized solver for the SO₄²⁻–NO₃⁻–NH₄⁺ system based on the ISORROPIA algorithms. Atmospheric Environment, 37(16), 2279–2294.

Oliveira, I. F., & Takahashi, R. H., 2021. An enhancement of the bisection method average performance preserving Minmax optimality. ACM Transactions on Mathematical Software, 47(1), 1–24.

How to cite: Miller, S., Makar, P., and Lee, C.: HETV2: An update the vectorized inorganic chemistry solver HETV to include Na+-Cl--Ca2+-K+-Mg2+ in the metastable state option based on ISORROPIA II algorithms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7812, https://doi.org/10.5194/egusphere-egu23-7812, 2023.

Below-cloud scavenging is the process of aerosol removal from the atmosphere between cloud-base and the ground by precipitation (e.g. rain or snow), and affects aerosol number/mass concentrations, lifetime and distributions. An accurate representation of precipitation phases is important in treating below-cloud scavenging as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. To study cloud processes and precipitation chemistry, we examined representation of below-cloud aerosol scavenging of in the current GEM-MACH model, including a revised approach in precipitation phase partitioning and implementing a new aerosol below-cloud scavenging scheme (from Wang et al., 2014) and comparing with the GEM-MACH’s existing scavenging scheme, based on Slinn (1984). 

Overall, the multi-phase partitioning and Wang et al. (2014) scavenging scheme improve GEM-MACH performance as compared with observations. Including multi-phase approach leads to a decrease on SO₄^2- scavenging and impacts the below-cloud scavenging of SO₂ into the aqueous phase. The impact of the new scheme on wet deposition of NO₃^- and NH₄⁺ varies, with both increases and decreases in wet scavenging, and is more important at specific cloud locations. The two aerosol scavenging rates differ during liquid precipitation in the 0.1-1 µm size range mostly at high precipitation intensity. The two aerosol scavenging schemes diverge for aerosols smaller than 1 µm for solid precipitation at lower intensity (R=0.01 mm/h), while at higher precipitation intensities (R=10 mm/h), the two schemes show larger differences for aerosols larger than 1 µm. The changes on the speciated particles (sulphate, nitrate and ammonium) are consistent with the changes in the wet scavenging, leading to higher modelled concentrations of particulate sulphate in the atmosphere.

How to cite: Ghahreman, R., Gong, W., Makar, P., Lupu, A., Cole, A., Banwait, K., Lee, C., and Akingunola, A.: Representation of Precipitation Phases and a New Parameterization for Below-Cloud Scavenging in Regional Air Quality modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8498, https://doi.org/10.5194/egusphere-egu23-8498, 2023.

Plume rise is commonly parameterized based on ambient atmospheric conditions and emission source metrics (e.g. stack effluent temperature and exit momentum), with empirical formulae (e.g., Briggs, 1984) employed in large-scale air-quality models (e.g. Environment and Climate Change Canada’s GEM-MACH model). Past evaluations against observed plume heights emitted from industrial sources (e.g., Canadian Oil Sands) have attributed the discrepancies between observed and predicted plume heights to various causes, such as spatial variability of meteorological fields between observation and stack locations and/or inaccuracies in model meteorological predictions. It has been shown that stack-location-specific meteorology and layered (vertical) calculation of plume buoyancy can improve predicted plume heights (Akingunola et al. 2018).  However, more recent observations have shown that predicted plume heights remain biased low relative to aircraft observations of well-characterized SO₂ plumes, particularly under colder winter conditions, and demonstrate the need for further improvements to plume rise predictions. 
We introduce a new algorithm for plume rise calculation, which incorporates thermodynamic effects of the emitted water vapour from industrial stack combustion sources on the resulting calculation of plume height. The high temperature effluent from these stacks usually contain significant amounts of combustion-generated water. As the plume rises and cools, this water vapour condenses, increasing plume temperature and buoyancy through the release of latent heat, which can result in additional plume rise. We have developed a revised plume rise algorithm for implementation within the regional models, through combining the Briggs’ empirical parameterization with concepts of cloud parcel thermodynamic effects for the release or uptake of latent heat associated with the phase change of water. Our results show significant improvement in model plume rise prediction, through evaluation against SO₂ plumes observed during a 2018 aircraft campaign over the Canadian Oil Sands. We also discuss results from long-term (15-month duration) model simulations with the new versus the original algorithm, along with evaluations against aircraft-based and surface monitoring network observed concentrations. The potential impact of the condensed in-plume liquid water on aqueous phase chemistry will also be discussed. This work is the first plume rise algorithm to incorporate the effects of latent heat release of both combustion-emitted and in-plume ambient-entrained water, for implementation in air quality models. 

References

• Akingunola, A., Makar, P. A., Zhang, J., Darlington, A., Li, S.-M., Gordon, M., Moran, M. D., and Zheng, Q.: A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands, Atmos. Chem. Phys., 18, 8667–8688, https://doi.org/10.5194/acp-18-8667-2018, 2018.
• Briggs, G. A.: Plume rise and buoyancy effects, atmospheric sciences and power production, in: DOE/TIC-27601 (DE84005177), edited by: Randerson, D., TN, Technical Information Center, US Dept. of Energy, Oak Ridge, USA, 327–366, 1984.

How to cite: Fathi, S., Makar, P., Gong, W., Gordon, M., Zhang, J., and Hayden, K.: A New Plume Rise Algorithm – Incorporating the Thermodynamic Effects of Water for Plume Rise Prediction in Air Quality Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7984, https://doi.org/10.5194/egusphere-egu23-7984, 2023.

The Ensemble Kalman Filter (EnKF) has been employed for updating the initial condition, and promising results have been reported. Unlike the variational assimilation method, the advantages of EnKF are flow-dependent background error covariance which is important in a fast-developing air quality system. However, assimilation of air quality observations often suffers from insufficient model background error due to a small ensemble spread when applying EnKF methods. This study suggests methods for effectively increasing model background error covariance (BEC) by perturbing prognostic variables and employing multiple physics parameterizations in the atmospheric chemical transport model.

This study developed an aerosol data assimilation system with the WRF-Chem model and EnKF approach. In spite of considering flow-dependent BEC, the baseline run analysis exhibits poor performance, primarily due to the small ensemble spread. This study conducted new two effective methods for increasing ensemble spread: one considering the uncertainty of model physics and the other considering the uncertainty in the prognostic variables. Both methods improved the quality of surface PM analysis substantially, compared with the baseline run. And the DA_all experiment which incorporates both uncertainty in model physics and prognostic variables, demonstrates the best performance. Physical perturbation and multiplicative perturbation have a non-linear relationship. The forecast skill is also improved. With the substantial increase of BEC, the revised EnKF system has significantly improved the PM_2.5 forecast skills.

How to cite: Lee, S. and Lee, M.-I.: Effective Methods for Increasing Model Background Error in the Ensemble Kalman Filtering in Aerosol Data Assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11371, https://doi.org/10.5194/egusphere-egu23-11371, 2023.

Despite continuous improvement during recent decades, state of the art global chemistry-climate models (CCMs) are still showing biases compared to observational data, illustrating remaining difficulties and challenges in the simulation of atmospheric processes governing ozone production and decay. Therefore, CCM output is frequently bias-corrected in studies seeking to explore changing air quality burdens and associated impacts on human health (e.g., Rieder et al., 2018). Here we assess the strengths and limitations of different bias correction techniques for CCM simulations with focus on maximum daily 8-hour average surface ozone. Ozone fields are chosen as ozone is known as regional pollutant and thus shows smaller spatial heterogeneity in its burden than e.g. particulate matter. Within our comparison a set of different innovative, as well as, common bias correction techniques are applied to output of selected global coupled CCMs contributing hindcast simulations to the Coupled Model Intercomparison Project Phase 6 (CMIP6). For bias correction and evaluation, we utilize gridded observational data for the European and US domains according to Schnell et al. [2014]. The statistical bias-correction techniques applied and compared are quantile mapping, delta-function, relative and mean bias correction. As surface ozone pollution is commonly associated with a strong seasonal cycle, the adjustment techniques are applied to model data on monthly basis, and skill scores for individual bias correction techniques are compared across individual CMIP6 models for both seasonal and annual timescales over the period 1995-2014. Our results highlight large differences among individual bias correction techniques and advocate for the use of more complex correction strategies involving corrections across the spatio-temporal distribution of the ozone field.

References:

Rieder, H.E., Fiore A.M., Clifton, O.E., Correa, G., Horowitz, L.W., Naik, V.: Combining model projections with site-level observations to estimate changes in distributions and seasonality of ozone in surface air over the U.S.A., Atmos. Env., 193, 302-315, https://doi.org/10.1016/j.atmosenv.2018.07.042, 2018.

Schnell, J. L., Holmes, C. D., Jangam, A., and Prather, M. J.: Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model, Atmos. Chem. Phys., 14, 7721–7739, https://doi.org/10.5194/acp-14-7721-2014, 2014.

How to cite: Stähle, C., Rieder, H. E., Fiore, A. M., and Checa-Garcia, R.: An assessment of the performance of bias correction techniques for surface ozone burdens simulated by global chemistry-climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9830, https://doi.org/10.5194/egusphere-egu23-9830, 2023.

A critical measure for our understanding of the complex non-linear processes which determine atmospheric composition is through the use of Chemical Transport Models and Earth System Models. In order to evaluate the veracity of these models, observations are required, however the availability and quality of these observations serves as a major impediment to this process. The most temporally consistent measurements have been made at the surface by established measurement networks, typically for the purpose of monitoring local exceedances of air quality limits. There are multiple networks which report this data, in disparate formats, requiring harmonisation to allow for synthesis.

On the occasion that evaluation efforts use data from multiple networks, there is typically little to no detail given about the methodology used for the data synthesis across the different networks, or regarding the quality assurance (QA) or station classifications employed to subset the data. Therefore, evaluation efforts across different research groups are often incomparable.

As a response to this common challenge, we established GHOST (Globally Harmonised Observational Surface Treatment). GHOST can be succinctly stated as an effort to standardise the data / metadata from the major public reporting networks which provide in situ atmospheric measurements at the surface. In total the dataset comprises of ~20 billion processed measurements, for ~200 components, across 32 networks, from 1970 to 2022. This represents the biggest collection of harmonised atmospheric composition surface measurements ever composed. 

Substantial efforts have been made towards standardising almost every facet of provided data / metadata from across the networks. On top of this, additional metadata was added by processing various commonly utilised globally gridded datasets (e.g. land use), as well as adding temporal classifications per measurement (e.g. weekday / weekend). As the dataset spans many decades, metadata is handled dynamically and allowed to vary through the record, important for instances when there are changes in measurement instrumentation or the measurement position.

Major efforts were made for the standardisation of the numerous metadata fields detailing measurement procedures, with all measurements linked to a dictionary of standard measurement methods and standard instruments. Great effort was also spent in the standardisation of station classifications, providing large flexibility for the subsetting of stations. Rather than dropping any measurements which are labelled as potentially erroneous by the measurement provider, standardised data flags are associated with each individual measurement. On top of this, GHOST own QA flags are also associated per measurement.

All data is now freely available to the community.

How to cite: Bowdalo, D., Basart, S., Guevara, M., Jorba, O., and Pérez García-Pando, C.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14689, https://doi.org/10.5194/egusphere-egu23-14689, 2023.

The implementation of the Air Pollution Prevention and Action Plan (2013–2017) in China has led to a significant decrease in anthropogenic emissions. However, at the same time, ozone (O­₃), a secondary pollutant formed from nitrogen oxides (NO_x) and volatile organic compounds (VOCs) in the presence of sunlight, has increased in the large urban agglomerations of China. To better understand the reasons behind this increase, high-quality anthropogenic emission inventories are needed. In this study, we compared ozone precursor emissions in China from various anthropogenic emission inventories (EIs), including the national EI, Multi-resolution Emission Inventory for China (MEIC), and three global EIs from the Copernicus Atmosphere Monitoring Service (CAMs), Community Emission Data System (CEDS), and Hemispheric Transport of Air Pollution project (HTAP). Differences in emission magnitudes, trends, and spatial distributions were investigated. Global-scale and regional EIs were homogenized by sector and specie to obtain ‘harmonized’ EIs. These harmonized inventories were then used to drive WRF-Chem simulations for the winter and summer of 2017, and the results for each EI were evaluated against observations from the air quality monitoring network developed by the Ministry of Environmental Protection of China. The outcome of this study denotes that using harmonized regional and global EIs can significantly improve the performance of the numerical models when simulating the atmospheric composition of large agglomerations in China. 

How to cite: Zhang, Y., Brasseur, G., Granier, C., Daskalakis, N., Poulidis, A. P., Qu, K., Dai, J., and Vrekoussis, M.: Towards an integrated anthropogenic emission inventory for China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10220, https://doi.org/10.5194/egusphere-egu23-10220, 2023.

NO₂ is an important air pollutant and has been recognized for its hazardous impact on human health. Although routine in-situ measurements of NO₂ are available in many regions of the earth, models for regional chemistry and transport (RCT) are often used to predict trace gas concentrations where no direct measurements are available. An important aspect of realistic NO₂ modelling is to use accurate NO_x emissions with high temporal resolution. The standard practice is to use a monthly or yearly resolved emission inventory in combination with sector-specific hourly emission weights (“temporal profiles”) in order to simulate diurnal, weekly, and seasonal emission patterns. Temporal profiles are typically derived from empirical data, e.g. car counts on highways, and have been known to improve RCT simulations significantly. Nonetheless, in comparison against in-situ measurements, simulated NO₂ concentrations are usually too low at daytime and too high at nighttime, with relative deviations of up to 50 %. This hints towards faulty temporal emission profiles.

We present a novel method to determine improved temporal emission profiles for NO_x emissions in a WRF-Chem simulation for May 2019 in central Europe. The temporal profiles are determined in an iterative procedure that consists of running the simulation, comparing the simulated NO_x concentrations to in-situ reference measurements, and adjusting the hourly temporal profiles to compensate deviations between simulation and reference values. In a subsequent intercomparison of model results with observational datasets (surface concentrations from in-situ measurements, tropospheric vertical column densities from the TROPOMI satellite instrument, and concentration profiles from MAX-DOAS retrievals), we validate our simulation results. In particular, the typical NO₂ underestimation at noontime is resolved and the monthly average of simulated vertical column densities deviates less than 7% from the TROPOMI reference data.

How to cite: Kuhn, L., Beirle, S., Kumar, V., Osipov, S., Pozzer, A., Bösch, T., Kumar, R., and Wagner, T.: Towards more realistic modelling of tropospheric NO2 with WRF-Chem using fine-tuned temporal emission profiles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3818, https://doi.org/10.5194/egusphere-egu23-3818, 2023.

Traffic is a significant contributor to anthropogenic air pollution. Although inland shipping constitutes only a small part of the total traffic and transports cargo very energy-efficient, inland vessels emit not negligible amounts of pollutants like nitrogen oxides and particulate matter. In order to quantify the contribution of inland transportation to the air pollution as well as to develop necessary strategies to mitigate current emissions, exact knowledge about vessel-born emission rates along waterways are required.

A mechanistic model has been developed to simulate fuel demand and emission rates of inland vessels with high temporal and spatial resolution. It links ship positions reported in the signals of the Automatic Identification System (AIS) with representative emission factors of inland vessel engines. The resistance, propulsion and brake power are calculated for each point of a vessel’s trajectory. Emission rates for quasi steady-state estimations of the brake power will be obtained via power-related emission factors. The integral of a large number of these point values ​​provides a quantification of inland shipping emissions along a waterway, e.g., in section-related emission rates.

This new model was applied on relevant German inland waterways. We present current exhaust emission rates and an example for a simulation to evaluate the mitigation potential of a modernized fleet.

How to cite: Weber, D., Stachel, H., Schröder, M., and Noss, C.: Estimation of exhaust emissions of inland shipping - a case study on German waterways, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14890, https://doi.org/10.5194/egusphere-egu23-14890, 2023.

The global-to-meso-scale dispersion model System for Integrated modeling for atmospheric composition (SILAM) has been implemented over the Indian region for operational air quality forecast. The regional SILAM model generates 96 hours forecasts over a domain at 3kmx3km horizontal resolution. The meteorological forcing is provided from the operational 3 km WRF model. The initial condition is derived from the forecast of the previous cycle of the regional SILAM model and the boundary condition is supplied from the global version of the model. Predicted mixing ratios of trace gases in the atmosphere are compared with the available ground-based observations over the North-Western Indian domain for two consecutive years (2021 and 2022). The paper presents a comprehensive evaluation of SILAM model performance against the observed surface data for trace gases (Surface O₃, NO_2, and CO). The spatial and temporal variability of trace gases over the domain is well simulated by the model.  The model was also able to catch well the seasonality of trace gases (Surface Ozone, NO₂, and CO) over the selected Indian domain. The forecast is found to be very skillful for trace gases and has been helping the air pollution control authorities in India to make informed decisions.

How to cite: Sinha, P., Jena, C., Kumar, A., Soni, V. K., and Jindal, T.: Simulation of trace gases over the Indian domain: evaluation of the SILAM model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-672, https://doi.org/10.5194/egusphere-egu23-672, 2023.

Seasonal Variation of Simulated Carbonaceous Fine Particulate Matter over
the Indian Region by using WRF-Chem with two emission Inventories:

EDGAR-HTAP and SAFAR-2018

Praveen Kumar1

, Gufran Beig1,4, Vikas Singh2

, B.S. Murthy1

, B.R. Bamniya3

1
Indian Institute of Tropical Meteorology, Pune- 411008
2 National Atmospheric Research Laboratory, Gadanki, AP-517112
3Mohan LalSukhadiya University, Udaipur, 313001

4National Institutes of Advanced Studies (NIAS), Indian Institute of Science (IISc) Campus,

BANGALORE-560012

Abstract:

This study better evaluates Black Carbon (BC) using Weather Research and Forecasting model
coupled with chemistry (WRF-Chem) v3.9.1.1, with newly developed emission inventory
SAFAR-2018 and global one EDGAR-HTAP for the year 2018. The simulation provides a view
of the seasonal and regional pattern BC concentrations, confirmed by comparing surface
meteorological parameters and BC concentrations to the MERRA reanalysis for the Indian
region. It found that the model simulated surface C-PM concentration with SAFAR-2018
emission inventory is slightly overestimated, but simulation with EDGAR emission inventory is
under-estimated than MERRA. It also found that the model-simulated meteorological parameters
( e.g., wind speed at 2 m, the surface temperature at 2 m, and Planetary Boundary Layer height
showed better agreement with observation. Compared to the simulation with EDGAR emission
inventory, simulated geographic patterns of seasonal mean BC with SAFAR-2018 emission
inventory exhibit good agreement with MERRA. In the IGP region, the concentration of BC
showed the highest peak during the winter, followed by the post-monsoon season compared to
other subcontinents of India. The correlation coefficients of annual hourly time series of surface
BC_sf (SAFAR-2018 emission) concentrations with MERRA over India were higher (0.92) with
RMSE 0.45. These correlations were higher than those (EDGAR emission) observed for the
surface BC_ed (R = 0.91 & RMSE = 0.48) concentrations. It appears that model simulation with
SAFAR-2018 emission inventory well-captured pattern and magnitude over the Indian region,
increasing magnitude of BC concentrations may help to quantify better the effect on climate and
atmospheric condition over the Indian region.

How to cite: Kumar, P.: Seasonal Variation of Simulated Carbonaceous Fine Particulate Matter overthe Indian Region by using WRF-Chem with two emission Inventories:EDGAR-HTAP and SAFAR-2018, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15761, https://doi.org/10.5194/egusphere-egu23-15761, 2023.

The Korean Air Chemistry Modeling System (K_ACheMS) has been developed to enhance the predictability of PM_2.5 in South Korea. In the current version (v2.0) of K_ACheMS, two meteorological models are used to produce meteorological fields. The first model is version 4.1.5 of the Weather Research and Forecasting (WRF) model. The WRF v4.1.5 model is initialized using four cycles (00Z, 06Z, 12Z, and 18Z) of real-time Global Forecasting System (GFS) data from the National Oceanic and Atmospheric Administration (NOAA). The other model is the Regional Data Assimilation and Prediction System (RDAPS), which is the regional operational model of the Korean Meteorological Administration (KMA) based on the Unified Model (UM) developed by the Met Office of the United Kingdom.

For air quality model simulations, the Community Multi-scale Air Quality version GIST (CMAQ-GIST) model has been developed based on the CMAQ v5.2.1 model. The CMAQ-GIST model mainly uses a modified version of the Statewide Air Pollution Research Center 07 (SAPRC07TC) chemical mechanism with several important updates, including the following: (i) daytime HONO photo-chemistries; (ii) heterogeneous HO₂ reactions; (iii) gas- and aqueous-phase halogen chemistries; and (iv) new yield data for SOA formation acquired from multiple smog chamber experiments conducted under typical conditions of northeast Asia. In order to update chemical initial conditions for the CMAQ-GIST model simulations, a three-dimensional variational (3D-VAR) method is applied to the operational mode of K_ACheMS.

Here, we introduce the development of the K_ACheMS v2.0 and present the current performances of the operational mode of K_ACheMS v2.0 by comparing the ground-based observations in South Korea.

How to cite: Yu, J., Song, C. H., and Kim, M. C.: Development of the Korean Air Chemistry Modeling System version 2.0 (K_ACheMS v2.0) and its performance of operational mode, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10089, https://doi.org/10.5194/egusphere-egu23-10089, 2023.

As the Beijing-Tianjin-Hebei (BTH) region of China is one of the key areas with PM_2.5 air pollution and various control policies, to project the future air quality in this region under different climate scenarios and emission scenarios is of great significance. Under the comprehensive considering meteorology and anthropogenic emission, the target accessibility of 35 µg/m³ annual mean PM_2.5 concentration (the Interim Target-1 by World Health Organization) in 2030, similar to the command of the carbon emissions peak and carbon neutrality, will be analyzed based on the sensitivity experiments. This study explored and quantified the influence of climate change and anthropogenic emission on the future air quality in BTH region under the future climate scenario RCP8.5, RCP4.5, and RCP2.6 with the baseline and reduced emission inventory (Base and EIT1 scenarios). The future air quality research modeling system including global climate model BNU-ESM, regional meteorological model WRF, emission process model SMOKE and air quality model CMAQ is utilized. The BNU-ESM provided the global meteorological field, and the more specific meteorological data simulated by WRF using dynamical downscaling method were adopted to drive the SMOKE model to calculate emission inventory and CMAQ model to generate air pollutant results. The results show that the future PM_2.5 concentrations over BTH still present the seasonal variation, higher in winter and lower in summer. Moreover, the annual PM_2.5 concentrations under various climate scenarios and under Base emission scenario are almost consistent, about 40µg/m³. However, the annual PM_2.5 concentrations over BTH under the identical RCP4.5 climate scenario and under diverse emission scenario shows huge differences. The annual PM_2.5 concentrations under EIT1 emission scenario is 37.5% less than the value under Base emission scenario and could achieve the annual target of 35µg/m³. Besides, the shape of high PM_2.5 concentrations follows the area with high emission inventory. In a word, the future PM_2.5 concentrations over BTH region is highly related to anthropogenic emission by human activities, while the climate change in 2030 has little impact on the future air quality over BTH region. That indicates emission reduction is significantly required to achieve the new Chinese PM_2.5 target in 2030.

How to cite: Li, D. and Wu, Q.: The Influence of Climate Change and Human Activities on the Future Air Quality in the Beijing-Tianjin-Hebei Region of China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5090, https://doi.org/10.5194/egusphere-egu23-5090, 2023.

In an ever-growing demand for solar energy production and technologies, atmospheric aerosols pose a great challenge for solar power suppliers due to their high spatio-temporal variability, their direct influence on the scattering and absorption of the incoming solar radiation, and their role as cloud condensation nuclei, indirectly affecting cloud formation and precipitation. Hence, atmospheric aerosols are a preponderant element of an accurate weather and solar forecasting system. Located in Southwestern Europe, the Iberian Peninsula (IP) represents one of the regions with the highest solar power potential in Europe, but it is frequently affected by forest fires and high-concentration dust episodes with their origin in the major African deserts during the warmer months. In this study, a model setup using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is evaluated during an extreme dust episode that affected the IP in August 2010, aiming to perform aerosol-radiation-cloud interactions studies over the region. Model results, such as particle concentration and aerosol optical properties, are compared against different in-situ observations and remote sensing data from regional air quality stations and from the Aerosol Robotic Network (AERONET) to assess the model performance under these kinds of events.

How to cite: Silva, R., Carvalho, A. C., Carvalho, D., and Rocha, A.: Validation of a WRF-Chem setup aiming aerosol-radiation-cloud interactions studies over the Iberian Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12970, https://doi.org/10.5194/egusphere-egu23-12970, 2023.

Air quality remains a key topic for human wellbeing worldwide. Ozone (O₃) is still one of the most toxic and ecologically detrimental air pollutants in Europe and supplies a crucial impact factor for the air quality planning as millions of people are exposed to O₃ levels above the WHO guidelines. The chemical reaction processes leading to the formation of ozone are well documented in literature for long: O₃ is not emitted but rather formed through complex chemical reactions from precursor emissions such as nitrogen oxides or biogenic volatile organic compounds. Processes influencing ozone variability are highly sensitive to several meteorological parameters such as temperature, moisture or solar radiation. These processes can impact the emission rate of ozone precursors, the chemical production and destruction as well as the rate of ozone loss through dry deposition. The ambient air pollution for ozone is often assessed and forecasted using chemical transport models (CTMs). These CTMs aim to reproduce observed ozone variability as good as possible by comprehensively accounting the abovementioned processes.

To evaluate CTMs and identify directions for improvement multi-model intercomparison studies have proven very useful on the past. Often the ensemble mean or median shows a better model skill than the ensemble members. The quality of model (ensemble) results is normally assessed by calculating a number of statistical indicators in a paired comparison to measured timeseries. In addition, to assess the model quality and uncertainty one can use a dynamic evaluation. The dynamic evaluation relates the model error to input data such as the meteorology. The degree to which changes in ozone levels caused by varying meteorological conditions are then evaluated. This allows to assess whether numerical models can capture the chemical response to temperature, humidity or another meteorological parameter.

In this study we also make use of such an ensemble assessment to evaluate the multi-model performance and the skill for each ensemble member. We conducted air pollution simulations for four models (LOTOS-EUROS, REM-CALGRID, COSMO-MUSCAT and WRF-CHEM) across Germany for January 1^st to December 31^st, 2019. The models show a very consistent picture in the ranking of the model skill. Main differences between the four ensemble members we found for ozone episodes, the timing of daytime maxima or even the representation of the nighttime concentration. We further enhanced the understanding of the modelled ozone response to temperature and humidity and provided an in-depth understanding for differences occurring in the ozone production rates for all participating models separated by season and region. First results indicate main differences in the ozone productivity especially for warm and humid conditions during the ozone season. The COSMO-MUSCAT and REM-CALGRID models show largest variability for ozone production rates with respect to temperature and humidity. The overall best performance can be seen for LOTOS-EUROS and WRF-CHEM.

How to cite: Thürkow, M., Butler, T., Pfäfflin, F., Heinold, B., and Schaap, M.: Dynamic ozone evaluation using a model intercomparison study for Germany., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10884, https://doi.org/10.5194/egusphere-egu23-10884, 2023.

Aviation is concerned by its climate effects which results from CO₂ and non-CO₂ effects, comprising NO_x-induced changes of atmospheric ozone and methane. Here climate-chemistry models are required to advance our understanding on induced changes of reactive species and the associated radiative forcing associated to aviation emissions. Evaluation of such comprehensive models is key in order to be able to investigate associated uncertainties can use observational datasets from research infrastructures like IAGOS and DLR aircraft measurement campaign data.

We use the MECO(n) system which is a “MESSy-fied ECHAM and COSMO nested n-times”, relying on the Modular Earth Submodel System (MESSy) framework. For this purpose, both models have been equipped with the MESSy infrastructure, implying that the same process formulations (MESSy submodels) are available for both models. Modelled atmospheric distributions from the multi-scale model system MECO(n) are systematically compared to observational data from aircraft measurements in the upper troposphere and lower stratosphere. Nudging of meteorology to reanalysis data, and special diagnostics available within the modular MESSy infrastructure are implemented in the numerical simulations. Online sampling along aircraft trajectories allows to extract model data with a high temporal resolution (MESSy submodel S4D), in order to evaluate model representation and key processes. Beyond systematic evaluation with IAGOS scheduled aircraft measurements, particular focus on those episodes where dedicated measurements from aircraft campaigns are available. We present an analysis of reactive species, NO_y and ozone, which also identifies those weather pattern and synoptic situations where aviation contributes strong signals. We evaluate model representation of the NO_x-induces effect on radiatively active species ozone and hydroxyl radical in both model instances, ECHAM5 and COSMO. This is key for advancing the scientific understanding of NO_x-induced effects from aviation non-CO₂ effects required in order to quantify potential compensation and trade-offs and eventually in order to identify robust mitigation options for sustainable aviation.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875036 (ACACIA, Advancing the Science for Aviation and Climate) and has been supported by the DLR-Projekt Eco2Fly. This work uses measurement data from the European Research Infrastructure CARIBIC/IAGOS. High-Performance Super Computing simulations have been performed by the Deutsches Klima-Rechenzentrum (DKRZ, Hamburg) and the Leibniz-Rechenzentrum (LRZ, München).

How to cite: Matthes, S., Nickl, A.-L., Peter, P., Mertens, M., Jöckel, P., Ziereis, H., Harlaß, T., and Zahn, A.: Aviation-induced changes of atmospheric composition in the UTLS in the multi-scale Earth system model MECO(1), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15843, https://doi.org/10.5194/egusphere-egu23-15843, 2023.

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change, and mitigate their impacts.  However, errors in air quality forecasts limit their value in long-term decision-making processes. Thus, increasing the accuracy of forecasts is of significant importance.

In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate air quality forecasts for the CONUS domain for June 1^st through September 29^th. Our study spans the seven years from 2015 to 2021, and covers the months when there is a high risk of wildfires. CMAQ is an open-source Cartesian modeling system that simulates the concentrations of atmospheric pollutants at regional scales using emission data and meteorological inputs. We generate these meteorological inputs using the Unified Forecast System (UFS) numerical weather prediction model. We create daily 48-hr forecasts of fine particulate matter (PM2.5), ozone, and related species. We have also included a Carbon Monoxide-FIRE (CO-FIRE) tracer in CMAQ, which tracks CO emitted by wildfires.

Our study consists of three parts. First, we analyze the performance of the CMAQ air quality and UFS meteorological forecasts over seven years of simulation for every EPA defined region using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs the best in the east of the CONUS with the lowest RMSE (2 µg/m3) while in the west, where there is a high risk of wildfires, the model has the highest RMSE of up to 8 µg/m3. Temporally, the model under/over-estimates the PM2.5 concentrations during the day/night time, respectively. Next, we quantify the uncertainties in the model’s prediction, and we explore the reasons behind the model biases. Finally, we employ the state-of-the-art Analog Ensemble (AnEn) method to improve the accuracy of the forecasts and quantify the forecast improvements by AnEn. To achieve this, AnEn relies on the current deterministic forecasts, here generated from the CMAQ model, and the past archive of analogous predictions with relative prior observations. By considering the history of predictions along with the current forecast, AnEn has previously shown a significant increase in the accuracy of probabilistic forecasts by requiring significantly less computation resources compared to model-based ensembles. Despite the challenges of using AnEn for wildfires, we hypothesize that it will significantly improve the CMAQ model forecast in the proposed scenarios.

How to cite: Golbazi, M., Kumar, R., and Alessandrini, S.: Enhancing air quality forecasts across the contiguous United States (CONUS) during wildfires using an Analog-based post-processing methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10607, https://doi.org/10.5194/egusphere-egu23-10607, 2023.

We will show some preliminary results of our NOAA Joint Technology Transfer Initiative (JTTI) 2-year project with the goal of applying a machine learning (ML) post-processing to improve the Community Multi-scale Air Quality (CMAQ) model operational air quality forecasts issued over the US by National Air Quality Forecasting Capability (NAQFC) at NOAA/NCEP. Specifically, we have tested an extension of the analog ensemble (AnEn) model currently implemented at the NAQFC from point-based to 2D gridded predictions.

The AnEn utilizes a training dataset comprising predictions from CMAQ and corresponding observations of the quantity to be predicted (i.e., O₃ or PM_2.5) to generate future ensemble predictions based on past observations. The ensemble is constructed for a given deterministic CMAQ forecast by collecting past observations corresponding to the best matching past CMAQ forecasts (called analogs) to the current CMAQ prediction.

We have conducted a preliminary application of the AnEn to reduce the errors of CMAQ PM_2.5 and ozone surface gridded concentrations using a combination of past gridded chemical reanalysis from the Copernicus Atmosphere Monitoring Service (CAMS) Near-Real-Time model with measurements from AirNow stations. The analog method requires a continuous training dataset of hourly values of observed chemical concentrations obtained by merging the CAMS surface PM_2.5 and ozone fields with the respective observations from the AirNow network using the Satellite-Enhanced Data Interpolation technique (SEDI) (Dinku et al. 2015). SEDI removes the bias from CAMS analysis and short-term forecast fields while preserving the AirNow-measured values at the station locations.

We will first show validation of the SEDI bias-corrected CAMS concentrations against AirNow PM_2.5 and ozone-measured concentrations from stations not used in the SEDI correction process. Then, we will verify the performance of the whole forecasting system in some regions of the contiguous United States in the 0-72 hours lead time range.

How to cite: Alessandrini, S.: Post-Processing of CMAQ forecast for Improving Air Quality Predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5238, https://doi.org/10.5194/egusphere-egu23-5238, 2023.

Forecast biases in the meteorological fields have long been recognized as the main limitation on accuracy and predictability of air quality forecast. However, the quantitative studies on the impacts of meteorological forecast biases on air quality forecast are insufficient, and its mechanisms in different seasons are still unclear. In this study, series of forecasts from 2-Day (24 ~ 48-hour) to 7-Day (144 ~168-hour) for January, April, July, and October of 2018 are conducted over the Beijing-Tianjing-Hebei (BTH) region and the impacts of meteorological forecast bias on surface PM_2.5 concentration forecast at each leading-time are analyzed. The results show that the forecast biases of surface PM_2.5 concentration caused by meteorological forecast biases keep increasing with the increasing leading-time in all seasons. The impacts of meteorological forecast biases are strongest in spring, with the forecast bias of PM_2.5 concentrations up to 187% in the 7-Day forecast, followed by autumn (123%), summer (112%) and winter (80%). By the contribution analysis of relevant processes and their changes with the increase of leading-time, it is found that the dry deposition, transport and PBL mixing are the main processes contributing to the growth of forecast bias of surface PM_2.5 concentrations. In addition, the correspondence between the contributions of relevant processes and the meteorological factor are examined. It is found that the changes of contributions of processes are closely related to the difference in the forecasted meteorological factors such as surface winds, wind fields at 850hPa, PBL height, shortwave radiation and precipitations at each leading-time. This study highlights the importance of meteorological forecast bias in surface PM_2.5 concentration forecasting, and provides useful information for the improvement of air quality forecast accuracy and supports the policies of air pollution controlling.

How to cite: Du, Q., Zhao, C., Feng, J., Yang, Z., Xu, J., Gu, J., Zhang, M., Xu, M., and Lin, S.: Impacts of meteorological biases on forecasting surface PM2.5 concentration over the Beijing-Tianjing-Hebei region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11352, https://doi.org/10.5194/egusphere-egu23-11352, 2023.

As the majority of the population is exposed to air pollutant levels above the WHO guidelines, poor air quality remains one of the key challenges to increase human wellbeing in Europe. To further improve the ambient air quality it is important to know the extent to which the different anthropogenic activities contribute to the population exposures of particulate matter, nitrogen oxides and ozone. Source attribution is a process of tracing pollution levels back to its origin. Within the LOTOS-EUROS chemistry transport model a labelling technique has been developed and applied extensively for particulate matter and nitrogen oxides. So far, ozone source apportionment was not available.

The existing labelling approach was extended for ozone inspired by the implementation of the TOAST module in WRF-CHEM (Lupascu and Butler, 2019). We implemented an additional labelling family for the Ox family. For all reactions in which NO is oxidized to NO₂ by a peroxide (RO₂ or HO₂₎ the NO₂ produced in the Ox family receives the origin of the NO from the NOy family. By capitalizing on the existing labelling modules of LOTOS-EUROS we benefit from the more efficient implementation than simply increasing the number of tracers in the chemical mechanism.     

The new implementation was applied for the year 2019 by adopting zooming approach with a European domain and a higher resolution nest across northwestern Europe. Evaluation against measurements shows that the model is well capable to reproduce the observed variability across the country. During winter time in northwestern Europe the regional background ozone levels are largely determined by influx from the hemispheric background. Regional production from road transport and other combustion sources are important from July to September, whereas increased levels during spring show considerable contributions from ozone formed in southern Europe. To investigate in how far two modelling systems agree or deviate from each other in terms of source apportionment, simulations with LOTOS-EUROS and WRF-CHEM with a consistent model setup and labelling strategy were performed. The results of this analysis will also be shown at the meeting.

Lupaşcu, A. and Butler, T.: Source attribution of European surface O3 using a tagged O3 mechanism, Atmos. Chem. Phys., 19, 14535–14558, https://doi.org/10.5194/acp-19-14535-2019, 2019

How to cite: Schaap, M., Kranenburg, R., Thuerkow, M., Lupascu, A., and Butler, T.: Ozone source apportionment with a tagging approach in the LOTOS-EUROS model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12861, https://doi.org/10.5194/egusphere-egu23-12861, 2023.

A large fraction of the European population is exposed to atmospheric pollutant concentration levels above health-related thresholds, leading to excess mortality and morbidity. Densely populated urban areas are generally more concerned than rural regions due to higher emission of atmospheric pollutants especially from the road network. Atmospheric modelling is a necessary tool to assess urban scale air-quality for both research and operational purposes. It provides a spatially and temporally resolved information for several gaseous and particulate species. It is also used for forecast and scenario evaluation for policy making. 

Modelling atmospheric composition at street level is challenging because pollutant concentration within street-canyons depends largely on local emissions but also on the transport of polluted air masses from remote areas. Therefore, regional scale modelling and local applications must be combined to provide accurate simulations of the atmospheric composition at urban scale. In our study we compare two such strategies. In both cases the regional scale chemistry-transport model CHIMERE, fed by WRF meteorological fields, provides urban background concentrations. To simulate the local component of pollutant concentrations over roads we use i) the statistical sub grid-scale approach embedded in the chemistry-transport model and ii) the street-network model MUNICH. Simulation results over the city of Paris from both modelling approaches are compared to in-situ measurements of the local air-quality network for all available traffic monitors.

The major challenge of this inter comparison exercise is to find a consistent configuration setup for both models allowing a one-to-one comparison of the simulations. To do so we had to implement the same chemical and dynamical mechanisms for gases and suspended particles in both models. We also tested several vertical discretizations to obtain a consistent first-layer depth. Different turbulence parametrizations, including or not the urban canopy model (UCM) within WRF, were compared to obtain stable results for concentrations. We show these latter are particularly sensitive to the parametrization of the anthropogenic heat flux. To obtain realistic heat fluxes and satisfactory results for both modelling strategies we have to include in the simulations all three of the following aspects i) a highly resolved land-cover database (CORINE) ; ii) a three urban class distinction in the UCM and iii) the sub-grid scale urban fraction.

Results of two-month wintertime simulations for NOx, NO2, PM2.5 are discussed. The street-network approach provides better results for both gases and particles especially at high-traffic highways. The conclusion is less straightforward at low-traffic roads. Our results highlight the need to develop a consistent coupling of the street-network model MUNICH with the regional scale chemistry transport model CHIMERE to accurately simulate gases and particulate matter concentrations over the street network.

How to cite: Squarcioni, A., Valari, M., Roustan, Y., Dugay, F., Kim, Y., Lugon, L., Sartelet, K., and Vigneron, J.: Comparison of street-level atmospheric pollutant concentrations simulated with a subgrid-scale against a street-network model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5882, https://doi.org/10.5194/egusphere-egu23-5882, 2023.

Vegetation is an important component for modulating urban air quality. It can affect scalar transport, which is one of the most concerning processes in urban environment, by organizing the airflow around. In previous urban environment studies, the morphological characteristics of vegetation were usually parameterized as porosity or leaf area index (LAI), performing a spatially uniform aerodynamic behaviour. Whereas, a vegetation element has complex multi-scale structures. Predicting the scalar transport around the vegetation accurately is challenging due to the simplified parameterization. The transport processes around multi-scale vegetation should be further studied.

In this study, a fractal tree model is built to preserve the length scales of sub-branches. The large-eddy simulation (LES) is employed to investigate the scalar transport around the multi-scale tree. The spatial distribution of aerodynamic behaviour and scalar transport after and over the tree is determined. The scalar transport characteristics are compared with previous studies, demonstrating the importance of preserving sub-scales in urban air quality research.

How to cite: Liu, S. and Liu, C.-H.: Airflow and scalar transport around a single fractal tree based on large-eddy simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10633, https://doi.org/10.5194/egusphere-egu23-10633, 2023.

BACKGROUND:

Distinguished from the real-time forecast task, the hourly mapping in this work stands for spatiotemporal interpolations of long-term (e.g., annual) average concentrations of air quality in hourly intervals. These long-term-averaged hourly maps facilitate environmental epidemiology studies by enabling dynamic environmental exposures assessment based on human time activities. Over the past decade, several mobile campaigns have been conducted where high-frequency sensors were mounted on vehicles roaming around the entire studied region. Since not all locations can be measured repeatedly for every hour, there are rarely previous studies using opportunistic mobile measurements to reconstruct long-term NO₂ hourly maps. In this work, we evaluated the merit of land-use regression (LUR) models that use mobile measurements paired with land-use and traffic predictors to interpolate hourly air pollution concentrations at fine spatial resolutions.

METHOD:

We monitored 1-second NO₂ concentrations in Amsterdam with two Google StreetView cars, from 8:00 to 20:00 on weekdays from May 2019 to March 2020 (5.7 Million measurements). These measured GPS points were aggregated into 50m road segments and divided into one-hour intervals. Using this hourly mobile data as the response, we explored two spatiotemporal LUR models, namely ST-Kriging (the spatiotemporal version of kriging methods) and GTWR (Geographical and Temporal Weighted Regression), and two spatial LUR models implemented separately in each hour, namely RF_LUR (a LUR model based on the random forest) and LSR (Stepwise Linear Regression). Model performance was assessed by averaging measurements over the same period collected from independent routine monitoring stations in Amsterdam (RIVM, n=9).

RESULT:

The hourly averaged mobile measurements of NO₂ across the city varied from 40 (rush hours) to 28 ug/m³ (non-rush hours). Routine measurements (RIVM) showed significantly different patterns in road types (major vs residential roads) and seasons (winter vs summer). Therefore, the spatiotemporal models were trained separately for these four scenarios and then merged their predictions into the final maps. GTWR captured more accurate spatiotemporal correlations than Kriging methods under the limitation of opportunistic mobile data and temporally static covariates (ST-Kriging: R² = 0.35, MAE, RMSE = 9.46, 12.14 ug/m³, GTWR: R² = 0.50, MAE, RMSE = 6.07, 7.64 ug/m³). Better overall accuracy and more smoothing distributions in both space and time were captured by the spatiotemporal models as compared to spatial models separated in each hour (LSR: R² = 0.47, MAE, RMSE = 6.48, 8.04 ug/m³; RF_LUR: R² = 0.33, MAE, RMSE = 10.33, 14.74 ug/m³). The spatiotemporal distribution of NO₂ predictions was found to strongly follow the intra-urban commuting pattern.

 CONCLUSION:

The spatiotemporal LUR model is able to capture spatiotemporal correlations hidden in opportunistic mobile measurements. The reconstructed spatiotemporal maps can be broadly applied to estimate human exposure to NO₂ considering time-activity patterns.

How to cite: Yuan, Z., Kerckhoffs, J., Shen, Y., Hoek, G., and Vermeulen, R.: Hourly LUR modeling of hyperlocal NO2 using mobile monitoring data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13814, https://doi.org/10.5194/egusphere-egu23-13814, 2023.

To perform realistic high-resolution air quality modeling in a polluted urban area, the WRF (Weather Research and Forecasting) model was used with an embedded large-eddy simulation (LES) module and with online chemistry. As an illustration, the numerical experiment was conducted in the polluted megacity of Hong Kong, which is characterized by multi-type pollution sources as well as complex topography. The multi-resolution simulations from mesoscale to LES scales were evaluated by comparing the calculated fields with ozone sounding profiles and with the observations at surface monitoring stations. The comparison shows that both mesoscale and LES simulations reproduced well the mean concentrations of the chemical species and their diurnal variations at the background sites; however, the mesoscale simulations largely underestimated the NO_Xconcentrations and overestimated O₃ near roadside stations due to the coarse representation of the traffic emissions. The LES simulations improved the agreements with the measurements near the road traffic, and the LES with highest spatial resolution (33 m) provided the best results. The LES simulations showed more detailed structures of the spatial distributions of chemical species than the mesoscale simulations, indicating the capability of LES of resolving high-resolution photochemical transformations in urban areas like Hong Kong. The LES simulations showed similar trends with the mesoscale model in the evolution of the profiles of the chemical species with the development of the boundary layer over a diurnal cycle. The vertical fluxes of the chemical species are stronger in the 33 m LES than the 100 m LES, because the high-resolution LES can better resolve the turbulent eddies.

How to cite: Wang, Y., Ma, Y.-F., Brasseur, G., and Wang, T.: Large-eddy simulation of air quality in Hong Kong, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12152, https://doi.org/10.5194/egusphere-egu23-12152, 2023.