Cities need to design, implement and assess emission abatement policies to meet increasingly stringent air quality goals and decarbonization targets. Within this context, we present a novel approach to consistently integrate source apportionment, assess emission abatement measures and their impact on population exposure to key pollutants (NO2, O3 and PM2.5). The system pivots on two key concepts: i) massive anonymized mobile network data that are used to depict population dynamics and to generate origin–destination matrices needed to compute road traffic emissions and ii) the Decoupled Direct Method in Three Dimensions implemented in the CMAQ chemical-transport model (CMAQ-DDM-3D). Ambient concentration sensitivities to changes on precursor emissions provided by DDM are consistently used for source apportionment analysis and for the development of a reduced form model (RFM) able to estimate concentration changes over the city with 500 × 500 m resolution with very low computational burden, allowing for multiple simulations. Road traffic was identified as a key local source, contributing 8.5 ug/m3 and 2.1 ug/m3 to NO2 and PM2.5 urban background annual mean levels, respectively. The RFM was able to replicate the behavior of the full chemical-transport model for a moderate emission reduction scenario with correlation coefficient of virtually 1 for all pollutants. The RFM also fulfilled the modelling quality indicator (MQI) introduced by the new Directive (EU) 2024/2881 for all NO2, O3 and PM2.5 relevant metrics for the baseline year (2022). Although the performance remains acceptable for the annual mean (MQI < 1 for most locations), the results were found to deteriorate for 2023 and 2024 due to changing meteorology. SIMAD exposure results identify residency as a key factor, resulting in an 8% higher exposure to NO2 for the lower socioeconomic bracket and suggesting that future strategies should be optimized for health benefits and environmental justice in Madrid.
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