The objective of this research was to develop and evaluate an integrated approach to model the occupant exposure to in-bus contaminants using the advanced methods of data mining and artificial intelligence. The research objective was accomplished by executing the following steps. Firstly, an experimental field program was implemented to develop a comprehensive one-year database of the hourly averaged in-bus air contaminants (carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), 0.3-0.4 micrometer (¿¿¿¿m) sized particle numbers, 0.4-0.5 ¿¿¿¿m sized particle numbers, particulate matter (PM) concentrations less than 1.0 ¿¿¿¿m (PM1.0), PM concentrations less than 2.5 ¿¿¿¿m (PM2.5), and PM concentrations less than 10.0 ¿¿¿¿m (PM10.0)) and the independent variables (meteorological variables, time-related variables, indoor sources, on-road variables, ventilation settings, and ambient concentrations) that can affect indoor air quality (IAQ). Secondly, a novel approach to characterize in-bus air quality was developed with data mining techniques that incorporated the use of regression trees and the analysis of variance. Thirdly, a new approach to modeling in-bus air quality was established with the development of hybrid genetic algorithm based neural networks (or evolutionary neural networks) with input variables optimized from using the data mining techniques, referred to as the GART approach. Next, the prediction results from the GART approach were evaluated using a comprehensive set of newly developed IAQ operational performance measures. Finally, the occupant exposure to in-bus contaminants was determined by computing the time weighted average (TWA) and comparing them with the recommended IAQ guidelines.
In-bus PM concentrations and sub-micron particle numbers were predominantly influenced by the month/season of the year. In-bus SO2 concentrations were mainly affected by indoor relative humidity (RH) and the month of the year. NO concentrations inside the bus cabin were largely influenced by the indoor RH, while NO2 concentrations primarily varied with the month of the year. Passenger ridership and the month of the year mainly affected the in-bus CO2 concentrations; while the month and sky conditions had a significant impact on CO concentrations within the bus compartment.
The hybrid GART models captured majority of the variance in in-bus contaminant concentrations and performed much better than the traditional artificial neural networks methods of back propagation and radial basis function networks.
Exposure results indicated the average 8-hr. exposure of biodiesel bus occupants to CO2, CO, NO, SO2, and PM2.5 to be 559.67 ppm (¿¿¿¿ 45.01), 18.33 ppm (¿¿¿¿ 9.23), 5.23 ppm (¿¿¿¿ 4.49), 0.13 ppm (¿¿¿¿ 0.01), and 13.75 ¿¿¿¿g/m3 (¿¿¿¿ 4.24), respectively. The statistical significance of the difference in exposure levels to in-bus contaminants were compared during morning, afternoon, and evening/night time periods. There was statistically significant difference only between the morning (driver 1) and the evening/night (driver 3) exposure levels for CO2 and PM2.5. CO levels exceeded the TWA in some months.