Smart cities are generally equipped with Air Quality Monitoring Stations (AQMS) as public infrastructure to have an overall perception of the air quality. However, the spatial density of the samples from the available public AQMS infrastructure is low, with a high cost of deployment and maintenance. Due to the spatial variation of the air quality and sparse deployment of AQMSs within a city, it is impossible to reliably obtain the air quality of a location far from a deployed AQMS. This paper provides a framework called AQuaMoHo that augments this existing system with a low-cost alternative that can even help the residents of a city to accurately monitor the air quality at any location in the town. AQuaMoHo relies on a low-cost thermo-hygrometer (THM) along with a GPS to populate various meteorological and demographic features, which are then used to predict the air quality reliably from any location. From a thorough study over two different cities, we observe that the proposed framework can significantly help annotate the air quality data at a personal scale.