Short-life of aerosol in the ambient atmosphere poses a challenge to scientific community for the monitoring and the modelling of aerosol constituents. Satellite earth observations are a viable option to conduct the spatial aerosol measurements, but lacks of providing high temporal measurements. In this context, global model simulations are likely to provide aerosol data of temporal resolution, which lacks the high spatial resolution. We developed a constrained model which helps overcome the issue of low time resolution by integrating the global model simulations and the satellite observations. A case study was conducted to investigate constrained aerosol constituents at the surface level (e.g. 100 m) over Bhubaneswar city during 2018–2019. The ground-based PM2.5 observations were used to evaluate the model estimates of composite PM2.5 surface concentration using a statistical metric (e.g. normalized mean bias (NMB)). Seasonal average extinction coefficients from CALIPSO satellite retrievals were used as one of the functional constraints of proposed constrained aerosol simulations. Results show a good coherence of PM2.5 between observations and the model estimates (NMB: −2 to + 21%). We observed a linear relation between aerosol constituents and the satellite-based extinction coefficient having rest of the functional constraints and the constants are of less importance. The hygroscopic coefficient show a marginal impact on output values. Atmospheric mixing layers were not included in our model as the satellite estimates take into account height of planetary boundary layer. Moreover, our study focuses on simulation of surface level pollution (e.g. PM2.5) concentrations which does not need entire profile of aerosol.