This paper highlights the advantages of an affordable multi-wavelength ground-based camera, called WaltRCam, for monitoring Aerosol Optical Depth (AOD) in a clear case over a peri-urban area. To simulate the performance of this low-cost camera, for which data are not yet available, we use data from an expensive hyperspectral camera (HSI) to mimic its characteristics. Our methodology is based on the construction of look-up tables using the DART (Discrete Anisotropic Radiative Transfer) 3D radiative transfer model. DART simulates the different spectra observed by the WaltRCam camera, which then provides the AODs for all image pixels in near-real-time. Moreover, DART is coupled to a 3D scale-model of the city of Toulouse (dating from 2014) to model complex urban geometries and to associate specific optical properties to the various objects that make up the environment. Moreover, we use a neural-network-based method to recognize the various objects in the image in order to take into account only pixels common between the observations. In this way, we take account of changes to the peri-urban area, such as vegetation growth, construction, demolition of buildings, etc. The results of this study show that the WaltRCam camera, by capturing eight wavelengths, can deliver convincing results compared with ground and satellite reference data, with a correlation coefficient of 0.9 and an average RMSE of less than 0.02.