Most of the population globally lives in areas exceeding prior and current WHO guidelines for fine particulate matter (PM_2.5), highlighting the persisting need for implementing emission reduction strategies. Given the complex transport and transformation processes that airborne species undergo in the atmosphere, chemical transport models can aid in designing and prioritizing air pollution mitigation actions. However, detailed chemical transport models often require substantial computational power and expertise. For that reason, reduced complexity models have emerged as an alternative, incorporating some of the information from chemical transport models while drastically reducing the technical complexity and computational demand. In this work, we build a local implementation of the Intervention Model For Air Pollution, InMAP, at high spatial resolution for a large urban area, in Bogot&aacute;, Colombia. As input for the reduced complexity model, we carried out a detailed 12-month simulation with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 4.1. To achieve sufficiently high spatial-resolution for urban air quality, the model was configured with three nested domains of 27x27 km, 9x9 km, and 3x3 km respectively. When compared with surface station data, the modeled annual mean PM_2.5 showed a +3.3 % overestimation at the city-scale. Furthermore, the WRF-Chem simulation accurately captured the structure of the observed PM_2.5 time series at daily, weekly and seasonal time-scales. The InMAP base fields showed a slight under-prediction relative to WRF-Chem, but overall, the correlation between the WRF-Chem and InMAP modeled PM_2.5 fields was high, with <em>R</em>² = 0.92. InMAP sensitivity was tested for three emission reduction scenarios of varying complexity, by comparing the marginal concentrations against simulations with the full chemical transport model. The scenarios ranged in complexity, from primary-PM reductions only, to scenarios exploring moderate and severe city-wide emissions reductions from diesel powered mobile sources. Although InMAP marginal PM_2.5 fields were linearly correlated with the corresponding WRF-Chem sensitivities, a strong overestimation in predicted PM_2.5 variations were shown for all emission reduction scenarios considered. For the simpler scenarios where only primary PM was reduced InMAP sensitivity was a factor of 2 that of WRF-Chem, while for the more complex emission reduction scenarios involving also reduction in gas-phase emissions, InMAP overestimated PM_2.5 concentrations by a factor of 5. The driver in InMAPs overestimated PM_2.5 sensitivity in the scenarios involving gas-phase precursors was a large overestimation of secondary organic aerosols and particulate nitrate. The results of this work suggest that InMAP can be used to scan for potential emission reduction scenarios at the urban-scale, specially when those scenarios involve mostly primary PM emission reductions. However, our analysis indicates that studies aiming to carry out assessments using the absolute reductions in concentration from InMAP should first calibrate its sensitivities against a full chemical transport model run.