Evaluation of optimization methods for solving the receptor model for chemical mass balance

Abstract

The chemical mass balance (CMB 8.2) model has been extensively used in order to determine source contribution for particulate matters (size diameters less than 10 and 2.5 ?m) in air quality analysis. A comparison of the source contribution estimated from three CMB models was realized through optimization techniques, such as 'fmincon' (CMB-fmincon) and genetic algorithm (CMB-GA) using MATLAB. The proposed approach was validated using a San Joaquin Valley Air Quality Study (SJVAQS) California Fresno and Bakersfield PM10 and PM2.5 followed with Oregon PM10 data. The source contribution estimated from CMB-GA was better in source interpretation in comparison with CMB 8.2 and CMB-fmincon. The performance accuracies of three CMB approaches were validated using R2, reduced X2 and percentage mass tests. The R2 (0.90, 0.67 and 0.81, 0.83), X2 (0.36, 0.66 and 0.65, 0.43) and percentage mass (67.36, 55.03 and 94.24 %, 74.85 %) of CMB-GA showed high correlation for PM10, PM2.5, Fresno and Bakersfield data, respectively. To make a complete decision, the proposed methodology was bench marked with Portland, Oregon PM10 data with the best fit with R2 (0.99), X2 (1.6) and percentage mass (94.4 %) from CMB-GA. Therefore, the study revealed that CMB with genetic algorithm optimization method exhibiting better stability in determining the source contributions.