Air quality monitoring sensor networks focusing on air pollution measure pollutants that are regulated by the authorities, such as CO, NO2, NO, SO2, O3, and particulate matter (PM10, PM2.5). However, there are other pollutants, such as black carbon (BC), which are not regulated, have a major impact on health, and are rarely measured. One solution is to use proxies, which consist of creating a mathematical model that infers the measurement of the pollutant from indirect measurements of other pollutants. In this paper, we propose a robust machine learning proxy (RMLP) framework for estimating BC based on nonlinear machine learning methods, calibrating the low-cost sensors (LCSs), and adding robustness against noise and data missing in the LCS. We show the impact of LCS data aggregation, denoising and missing imputation on BC estimation, and how the concentrations estimated by the BC proxy approximate the values obtained by a reference instrument with an accurate BC sensor.