Identifying Dominant Emission Sectors for Air Quality in Argentina using Partial Least Squares Path Modeling (PLS-PM) on WRF-Chem Simulations.

A Partial Least Squares Path Modeling (PLS-PM) methodology was employed to identify the dominant emission sectors responsible for the concentrations of major air pollutants. This approach leverages factor analysis and principal component analysis to differentiate pollutant concentration levels into clusters and establish causal relationships with specific emission sectors. As categorical variables (measurable data) for this model, seven WRF-Chem simulations were conducted over a single domain encompassing Argentina for April 2019. These simulations utilized the GEAA-AEI emission inventory, following a sectorized approach. Five simulations incorporated emissions from individual sectors (Energy, Transport, Livestock, Residential, Industrial), one simulation included emissions from all sectors, and a control simulation was conducted with anthropogenic emissions deactivated. The PLS-PM analysis facilitated the creation of a color map figure that distinguishes areas impacted by each sector. This distinction is particularly evident for transport and livestock emissions at low-pollution levels, while hotspots related to energy sector emissions are discernible in high-pollution areas. This modeling approach holds promise for extracting additional information about areas with high pollution levels from future WRF-Chem simulations or observational data, thereby enabling the identification of contributing sources.