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 µm) 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 PM 10 and PM 2.5 followed with Oregon PM 10 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 R 2 , reduced χ 2 and percentage mass tests. The R 2 (0.90, 0.67 and 0.81, 0.83), χ 2 (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 PM 10 , PM 2.5 , Fresno and Bakersfield data, respectively. To make a complete decision, the proposed methodology was bench marked with Portland, Oregon PM 10 data with the best fit with R 2 (0.99), χ 2 (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.

Characterization and Source Identification of Poly Cyclic Aromatic Hydrocarbons (Pahs) for Coastal Industrial City Mangalore, India

“…Various studies concluded that PM 10 concentration is a strong indicator for several health effects both in India (Gupta, Karar, & Srivastava, 2007) and in other parts of the world (Braga et al, 2001). Numerous Indian researchers' works on optimizing the receptor model technique to find a holistic approach for source apportionment of particulate matter (Anu, Rangabhashiyam, Rahul, & Selvaraju, 2015;Selvaraju, Pushpavanam, & Anu, 2013). Therefore studies on these particulates is necessary for obtaining better knowledge and exposure on their behaviour and composition for improvising the monitoring and control of particulate matter.…”

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

“…It is difficult to obtain accurate temporal and spatial distribution characteristics of PM2.5 pollution; model simulation is the most efficient way to solve the problems of spatial analysis and prediction. PM2.5 concentration prediction methods include multivariate regression methods [ 7 , 8 , 9 ], genetic algorithms [ 10 , 11 ], grey [ 12 ] and Markov models [ 13 , 14 ], and artificial neural network models (ANN) [ 15 , 16 ]. An artificial neural network model is a network composed of a large number of neurons; compared with other forecasting models, the back propagation (BP) artificial neural network (BP-ANN) model is widely used to predict air pollution levels because of its high accuracy and ability to accurately map complex nonlinear problems [ 17 , 18 , 19 ].…”