Quantification of sources of atmospheric pollutants is crucial for regulatory purposes as well as for atmospheric science in general. Due to many physical limitations in observation and modeling, the existing methodologies have many simplifying assumptions, e.g. linear observation model or uncorrelated emission values, which cause inevitable bias in pollutant estimates.
Linear and bilinear models arise in many research areas including statistics, signal processing, machine learning, approximation theory, or image analysis.