Missing values are common in air quality measurements and can lead to biased environmental conclusions if not properly addressed. We developed a new method to reconstruct missing data by using inherent physical relationships between emission sources and measured concentrations. Unlike existing statistical imputation approaches, improves particulate matter speciation accuracy and preserves physically meaningful source information, providing a robust foundation for atmospheric research.