{"id":651,"date":"2025-11-12T13:51:52","date_gmt":"2025-11-12T11:51:52","guid":{"rendered":"https:\/\/webs.uab.cat\/phynest\/?p=651"},"modified":"2025-11-26T12:53:38","modified_gmt":"2025-11-26T10:53:38","slug":"data-driven-approaches-for-tracking-anthropogenic-greenhouse-gas-emissions-changes","status":"publish","type":"post","link":"https:\/\/webs.uab.cat\/phynest\/2025\/11\/12\/data-driven-approaches-for-tracking-anthropogenic-greenhouse-gas-emissions-changes\/","title":{"rendered":"Data-driven approaches for tracking anthropogenic greenhouse gas emissions changes"},"content":{"rendered":"\n
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\"Research<\/figure>\n\n\n\n

Substituir el text dins \u00abResearch project description\u00bb pel seg\u00fcent, modificat per supervisors: \u00bb There is a need to track changes in anthropogenic greenhouse gases (GHGs) emissions in a timely manner to evaluate the effectiveness of emission reduction policies. Typical approaches rely on bottom-up emission inventories which provide no real assessment of concentrations in the atmosphere or a way to track them, or modelling efforts which carry a high degree of uncertainty.<\/p>\n\n\n\n

This proposed research hypothesizes that observation data-driven approaches can enable tracking of emission changes both in urban areas where traffic is a major source and rural areas with agricultural activities contributing significantly to greenhouse gas emissions. Monitoring these sources is crucial for assessing changes in management practices aimed at reducing emissions, a key focus of EU Green Deal targets. Leveraging on the GHG monitoring network recently set up for the Metropolitan Area of Barcelona (https:\/\/urbag.eu\/ghg\/) and eddy covariance measurements in the Delta del Ebre region, the project will pursue three main goals: \u00a0 <\/p>\n\n\n\n

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  1. Build various emission scenarios (i.e., baseline emissions and implementation of low-emission zones) to assess the sensitivity of in-situ GHG monitoring networks to detect changes in emissions. To identify and understand emission patterns, data analysis and machine learning will be used to assess the relationship between changes in atmospheric concentrations, meteorological variability and instrument noise.<\/li>\n\n\n\n
  2. Identify differences in atmospheric observation patterns and emission trends between urban and rural settings to test the sensitivity of monitoring strategies. \u00a0 <\/li>\n\n\n\n
  3. Validate these scenarios through local initiatives (e.g., emissions reduction campaigns within the footprint of instruments).\u00a0 \u00a0 <\/li>\n<\/ol>\n\n\n\n

    The main outcome of this research will be a deeper understanding of how atmospheric observations can indicate the effectiveness of emission reduction plans and provide actionable recommendations.\u00bb<\/p>\n\n\n\n

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      \"Academic<\/figure>\n\n\n\n