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Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1

Oumami, Safae; Arteta, Joaquim; Guidard, Vincent; Tulet, Pierre; Hamer, Paul David

Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols.

The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types.

The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions.

Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ±5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America.

The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors.


Combined Contaminant Levels from Local Harvested Food Items in the Norwegian–Finnish–Russian Border Region

Nalbandyan-Schwarz, Anna; Pedersen, Kristine Bondo; Evenset, Anita; Heimstad, Eldbjørg Sofie; Sandanger, Torkjel Manning; Myllynen, Päivi; Rautio, Arja

This paper presents the results of a multidisciplinary study with the aim of assessing the potential combined risk from consuming locally harvested food products in the Euro-Arctic region of Norway, Finland, and Russia. The three important contaminant groups—radioactive substances, heavy metals, and persistent organic pollutants (POPs)—were measured in food samples such as berries, mushrooms, fish, birds, reindeer, and moose; they were sampled in 2013–2015. To assess the combined pollution levels and investigate the trends, similarities, and variations between different contaminant groups, subsequent multivariate statistical analysis was performed. The results showed that, in general, the levels of radioactive substances, toxic elements, and POPs were below the permitted EU maximum content in food products. However, statistical analysis revealed some correlations, similarities, and peculiarities between the accumulation of different contaminants in various species, which allowed for a better understanding of the mechanisms of accumulation and interaction between different contaminant groups. It also gave a better insight into the possible added risks and helped pinpoint species that could serve as reference markers for the accumulation of different contaminants in food. Mushrooms, fish, and reindeer were found to be important markers in the combined risk assessments for the contents of metals and radioactive substances. Further research, as well as the development of methodologies for combined assessments, are recommended.



Emission ensemble approach to improve the development of multi-scale emission inventories

Thunis, Philippe; Kuenen, Jeroen; Pisoni, Enrico; Bessagnet, Bertrand; Banja, Manjola; Gawuc, Lech; Szymankiewicz, Karol; Guizardi, Diego; Crippa, Monica; Lopez-Aparicio, Susana; Guevara, Marc; de Meij, Alexander; Schindlbacher, Sabine; Clappier, Alain

Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory.

The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors.

At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors).

The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.


Towards seamless environmental prediction–development of Pan-Eurasian EXperiment (PEEX) modelling platform

Mahura, Alexander; Baklanov, Alexander; Makkonen, Risto; Boy, Michael; Petäjä, Tuukka; Lappalainen, Hanna K.; Nuterman, Roman; Kerminen, Veli-Matti; Arnold, Stephen R.; Jochum, Markus; Shvidenko, Anatoly; Esau, Igor; Sofiev, Mikhail; Stohl, Andreas; Aalto, Tuula; Bai, Jianhui; Chen, Chuchu; Cheng, Yafang; Drofa, Oxana; Huang, Mei; Järvi, Leena; Kokkola, Harri; Kouznetsov, Rostislav; Li, Tingting; Malguzzi, Piero; Monks, Sarah; Poulsen, Mads Bruun; Noe, Steffen M.; Palamarchuk, Yuliia; Foreback, Benjamin; Clusius, Petri; Rasmussen, Till Andreas Soya; She, Jun; Sørensen, Jens Havskov; Spracklen, Dominick; Su, Hang; Tonttila, Juha; Wang, Siwen; Wang, Jiandong; Wolf, Tobias; Yu, Yongqiang; Zhang, Qing; Zhang, Wei; Zhang, Wen; Zheng, Xunhua; Li, Siqi; Li, Yong; Zhou, Putian; Kulmala, Markku

The Pan-Eurasian Experiment Modelling Platform (PEEX-MP) is one of the key blocks of the PEEX Research Programme. The PEEX MP has more than 30 models and is directed towards seamless environmental prediction. The main focus area is the Arctic-boreal regions and China. The models used in PEEX-MP cover several main components of the Earth’s system, such as the atmosphere, hydrosphere, pedosphere and biosphere, and resolve the physical-chemical-biological processes at different spatial and temporal scales and resolutions. This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system, online integrated, forward/inverse, and socioeconomical modelling, and other approaches with a particular focus on applications in the PEEX geographical domain. The employed high-performance computing facilities, capabilities, and PEEX dataflow for modelling results are described. Several virtual research platforms (PEEX-View, Virtual Research Environment, Web-based Atlas) for handling PEEX modelling and observational results are introduced. The overall approach allows us to understand better physical-chemical-biological processes, Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks, impact, consequences, etc. for population, environment and climate in the PEEX domain. This work was also one of the last projects of Prof. Sergej Zilitinkevich, who passed away on 15 February 2021. Since the finalization took time, the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

Taylor & Francis


Understanding individual heat exposure through interdisciplinary research on thermoception

Serrano, Paloma Yáñez; Bieńkowska, Zofia; Boni, Zofia; Chwałczyk, Franciszek; Hassani, Amirhossein

Extreme heat events are more frequent and more intense globally due to climate change. The urban environment is an additional factor enhancing the effects of heat. Adults above 65 years old are especially at risk due to their poorer health, physiology and socio-economic situation. Yet, there is limited knowledge about their experiences of summer heat, their actual heat exposure and how they negotiate their thermal comfort through different adaptation practices. In conventional research on heat exposure and thermal comfort, very little attention is given to individual behaviour and subjective experiences. To understand how older adults feel the heat in the city we study their thermoception, which we conceptualise as an embodied knowledge about bodily sensations, thermal environments and adjustments to heat. This article stems from interdisciplinary research conducted in Warsaw and Madrid in the summers of 2021–2022. We combine and juxtapose data from ethnographic research and from physical measurements of temperature gathered in people’s homes, to show on a microscale how we can study and understand the diversity in individual heat exposure more holistically. We demonstrate that to understand the consequences of heat for vulnerable populations it is crucial to study thermoception, the subjective experiences of heat, in addition to analysing their thermal environments. With the use of a unique methodology, this article shows how similar weather conditions are experienced differently by people from the same cities, depending on the materiality of their dwellings, availability of cooling devices, as well as everyday habits and their individual bodies. We discuss the social, material and temporal adjustments participants made to deal with heat, to showcase their agency in affecting their individual heat exposure. The article emphasises the role of social sciences and qualitative methods in research on individual heat exposure and argues for the co-production of knowledge on the topic.

Palgrave Macmillan


Zürich II Statement on Per- and Polyfluoroalkyl Substances (PFASs): Scientific and Regulatory Needs

DeWitt, Jamie C.; Glüge, Juliane; Cousins, Ian T.; Goldenman, Gretta; Herzke, Dorte; Lohmann, Rainer; Miller, Mark; Ng, Carla A.; Patton, Sharyle; Trier, Xenia; Vierke, Lena; Wang, Zhanyun; Adu-Kumi, Sam; Balan, Simona; Buser, Andreas M.; Fletcher, Tony; Haug, Line Småstuen; Huang, Jun; Kaserzon, Sarit; Leonel, Juliana; Sheriff, Ishmail; Shi, Ya-Li; Valsecchi, Sara; Scheringer, Martin

Per- and polyfluoroalkyl substances (PFASs) are a class of synthetic organic chemicals of global concern. A group of 36 scientists and regulators from 18 countries held a hybrid workshop in 2022 in Zürich, Switzerland. The workshop, a sequel to a previous Zürich workshop held in 2017, deliberated on progress in the last five years and discussed further needs for cooperative scientific research and regulatory action on PFASs. This review reflects discussion and insights gained during and after this workshop and summarizes key signs of progress in science and policy, ongoing critical issues to be addressed, and possible ways forward. Some key take home messages include: 1) understanding of human health effects continues to develop dramatically, 2) regulatory guidelines continue to drop, 3) better understanding of emissions and contamination levels is needed in more parts of the world, 4) analytical methods, while improving, still only cover around 50 PFASs, and 5) discussions of how to group PFASs for regulation (including subgroupings) have gathered momentum with several jurisdictions proposing restricting a large proportion of PFAS uses. It was concluded that more multi-group exchanges are needed in the future and that there should be a greater diversity of participants at future workshops.

American Chemical Society (ACS)


Mapping Plastic and Plastic Additive Cycles in Coastal Countries: A Norwegian Case Study

Marhoon, Ahmed Mohamed Jaffar Marhoon A; Las Heras Hernandez, Miguel; Billy, Romain Guillaume; Beat Müller, Daniel; Verones, Francesca

The growing environmental consequences caused by plastic pollution highlight the need for a better understanding of plastic polymer cycles and their associated additives. We present a novel, comprehensive top-down method using inflow-driven dynamic probabilistic material flow analysis (DPMFA) to map the plastic cycle in coastal countries. For the first time, we covered the progressive leaching of microplastics to the environment during the use phase of products and modeled the presence of 232 plastic additives. We applied this methodology to Norway and proposed initial release pathways to different environmental compartments. 758 kt of plastics distributed among 13 different polymers was introduced to the Norwegian economy in 2020, 4.4 Mt was present in in-use stocks, and 632 kt was wasted, of which 15.2 kt (2.4%) was released to the environment with a similar share of macro- and microplastics and 4.8 kt ended up in the ocean. Our study shows tire wear rubber as a highly pollutive microplastic source, while most macroplastics originated from consumer packaging with LDPE, PP, and PET as dominant polymers. Additionally, 75 kt of plastic additives was potentially released to the environment alongside these polymers. We emphasize that upstream measures, such as consumption reduction and changes in product design, would result in the most positive impact for limiting plastic pollution.


A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements

Logothetis, Stavros-Andreas; Salamalikis, Vasileios; Kazantzidis, Andreas

Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.