The project is part of the most recent requests from Space Agencies which consider the estimation of PM concentrations a priority objective for future Earth Observation (EO) missions. In accordance with the United Nations 2030 Agenda, whose sustainable development objectives include Target 11.6 on reducing the environmental impact of cities, and with the guidelines of the World Health Organization, PM concentrations in cities must be weighted considering data on population distribution. The project investigates the possibility of integrating medium resolution AOD data (MISR, MODIS and VIIRS) with MAIA's AOD products. This combination will act as input for downscaling procedures, using AI techniques, for the extraction of AOD time series at high spatial resolution. This improved AOD data together with satellite images from different sources, both high (such as PRISMA, Sentinel-2, Sentinel-3) and medium resolution (MISR, MODIS, VIIRS and simulated data from MAIA), will be used to train the AI ​​model to perform the extraction of fine particulate matter (PM) concentration at an intra-urban scale. Satellite data/products such as Land Cover Land Use (LCLU) and Land Surface Temperature (LST), meteorological data and other predictors, will be used to estimate what factors influence the spatial variability of PM concentrations at intra-scale. urban. With the information obtained on particulate matter, the seasonal and monthly exposure of the population to PM can be assessed through the extraction of the SDG 11.6.2 indicator (Annual mean levels of fine particulate matter in cities), which takes into account the spatialized demographic data calculated using the dasymetric method.