Data-driven approaches for tracking anthropogenic greenhouse gas emissions changes

Substituir el text dins “Research project description” pel següent, modificat per supervisors: ” 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.

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:  

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.
2. Identify differences in atmospheric observation patterns and emission trends between urban and rural settings to test the sensitivity of monitoring strategies.  
3. Validate these scenarios through local initiatives (e.g., emissions reduction campaigns within the footprint of instruments).   

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.”

• Master’s degree in Environmental Science, Physics, Atmospheric Science, Earth System Science, or related field.
• Experience with time-series analysis, modeling, and machine learning techniques is desirable.
• Knowledge in programming languages such as Python, R, or Matlab.
• Familiarity with greenhouse gas measurement techniques is desirable.
• Experience with large datasets, meteorological models, and spatial data is desirable.
• Good communication skills and interest in science communication.

URBAG (Integrated System Analysis of Urban Vegetation and Agriculture), led by Gara Villalba Mendez, studies how combinations of urban, peri-urban, and green spaces can optimize performance in terms of local and global environmental impacts. The group is responsible for implementing the greenhouse gas monitoring network in the Metropolitan Area of Barcelona, led by Vanessa Monteiro, which is designed to capture the city’s heterogeneity by including its diverse land uses.

The Biogeotrace Lab, led by Ariane Arias Ortiz, studies ecosystem–atmosphere interactions (carbon and energy fluxes) using micrometeorology, isotopic tracers, and long-term time series. As part of MERS at ICTA-UAB, the lab provides expertise in biogeochemical and gas flux dynamics across aquatic–terrestrial interfaces, supporting quantitative and statistical approaches to model and predict ecosystem responses to biophysical drivers.