SIMBAD is a simplified model that estimates concentration changes due to emission scenarios. It performs well in simpler cases but lacks accuracy in complex scenarios involving multiple pollutants and sectors. Despite this, SIMBAD is an efficient and accurate tool for assessing air quality impacts and supporting decision making.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1368,1318,1321,1300,1320],"targets":[1313,1304,1314,1317],"pubblicazioni_tipologie":[773],"class_list":["post-202226","pubblicazioni","type-pubblicazioni","status-publish","hentry","tag-air-quality","tag-development-scenarios-en","tag-emissions","tag-industrial-sector","tag-sustainable-development-en","targets-decision-makers","targets-industrial-world","targets-press-media-en","targets-research","pubblicazioni_tipologie-isi-article-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":188402,"post_author":"464","post_date":"2024-06-13 15:10:06","post_date_gmt":"2024-06-13 13:10:06","post_content":"","post_title":"Energy scenarios and governance support","post_excerpt":"Provide the authorities governing the energy system with technical support based on quantitative models and analysis in order to respond appropriately and optimally to the country\u2019s obligations of energy and climate planning and the definition of energy strategies for decarbonization.\nProvide useful assessments for the investments needed for the plan for National Ecological Transition.\n","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"energy-scenarios-and-governance-support","to_ping":"","pinged":"","post_modified":"2024-07-02 10:32:27","post_modified_gmt":"2024-07-02 08:32:27","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/energy-scenarios-and-governance-support\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"
The reconstruction of airborne pollutant concentration fields based on emission reduction scenarios is a complex task. Simulations with chemistry and transport models (CTM) are computationally expensive and not suited for iterative optimisation that could require the evaluation of a great number of scenarios.<\/p>\n
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To address this, data-driven surrogate models have been used to approximate the relationship between emission and ambient air concentrations. In this study, a different approach is presented. SIMBAD is a simplified model based on CAMx (Comprehensive Air Quality Model with Extensions) and the Direct Decoupled Method (DDM) algorithm that estimates concentration changes due to variations in emission fields. SIMBAD was validated by comparing PM10 and NO2 concentrations with CAMx simulations implementing the same emission variations.<\/p>\n
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The testing scenarios involved different emission reductions and precursor species to assess SIMBAD’s performance in both simple and complex cases. The model’s performance in reproducing the non-linear nature of atmospheric processes was satisfactory, showing an average Root Mean Square Error (RMSE) always lower than 0.2 \u03bcg\/m3 and a normalised bias below 2%.<\/p>\n
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Slightly lower accuracy was found in more complex scenarios involving multiple pollutants and sectors modified simultaneously. Overall, SIMBAD proved to be an efficient and accurate tool for evaluating the impacts of energy policies on air quality, providing valuable insights for policymakers and researchers alike.<\/p>\n","scarica_file":false,"link_estreno":[{"link_text":"Download Publication","link":"https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/2701\/1\/012074"}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2023-08-31","autori":"M. P. Costa, E. De Angelis, G. Pirovano (RSE S.p.A.)","destinazione":"Journal of Physics, Vol. 2701","rif_rse":"23012392"},"satellite_post_url":""},"yoast_head":"\n