Development and evaluation of an integrated approach to study in-bus exposure using data mining and artificial intelligence methods

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1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2012 Development and evaluation of an integrated approach to study in-bus exposure using data mining and artificial intelligence methods Akhil Kadiyala The University of Toledo Follow this and additional works at: Recommended Citation Kadiyala, Akhil, "Development and evaluation of an integrated approach to study in-bus exposure using data mining and artificial intelligence methods" (2012). Theses and Dissertations This Dissertation is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 A Dissertation entitled Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods by Akhil Kadiyala Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Engineering Dr. Ashok Kumar, Committee Chair Dr. Devinder Kaur, Committee Member Dr. Cyndee Gruden, Committee Member Dr. Defne Apul, Committee Member Dr. Farhang Akbar, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo August, 2012

3 Copyright 2012, Akhil Kadiyala This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

4 An Abstract of Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods by Akhil Kadiyala Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Engineering The University of Toledo August, 2012 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 (CO 2 ), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), micrometer (µm) sized particle numbers, µm sized particle numbers, particulate matter (PM) concentrations less than 1.0 µm (PM 1.0 ), PM concentrations less than 2.5 µm (PM 2.5 ), and PM concentrations less than 10.0 µm (PM 10.0 )) and the independent variables (meteorological variables, timerelated 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 iii

5 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 SO 2 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 NO 2 concentrations primarily varied with the month of the year. Passenger ridership and the month of the year mainly affected the in-bus CO 2 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 CO 2, CO, NO, SO 2, and PM 2.5 to be ppm (± 45.01), ppm (± 9.23), 5.23 ppm (± 4.49), 0.13 ppm (± 0.01), and µg/m 3 (± 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 CO 2 and PM 2.5. CO levels exceeded the TWA in some months. iv

6 I dedicate this dissertation to my parents, Mr. Satya Prasad Kadiyala and Mrs. Venkata Ramana Kadiyala: for your unconditional love and support with my studies, and giving me a chance to prove and improve myself through all walks of my life.

7 Acknowledgements This dissertation is an outcome of the support and well wishes of many individuals. I take this opportunity to acknowledge and thank all of them. To: Dr. Ashok Kumar: for providing continued guidance and support all through school, and giving the opportunity to work with you; Dissertation Committee Members: Dr. Devinder Kaur, Dr. Cyndee Gruden, Dr. Defne Apul, and Dr. Farhang Akbar: for providing your time and valuable inputs; the United States Department of Transportation (USDOT) and the Toledo Area Regional Transit Authority (TARTA): for funding the project with regards to data collection; TARTA Management and Employees: for their interest and involvement in this work during data collection. Special thanks to Mr. David Burnham and Mr. Kevin L. Russell for their swift management in helping me with the project related issues at TARTA; friends: who made my Doctoral program a memorable one. I am grateful to my sister, Charitha: for your love, support, and all the fun times; in-laws: for your love, patience, and confidence in me. Special thanks to my wife, Madhusha: for your constant love, support, patience, and encouragement in completing my PhD. And, above all to God Thank you. vi

8 Table of Contents Abstract... iii Acknowledgements... vi Table of Contents... vii List of Tables... xii List of Figures... xvi 1. Introduction and Research Study Background Research Study Background and Objectives Characterization of In-Bus Air Quality: Data Mining Introduction Methodology Experimental setup Database development A two-step approach to data analysis Results and Discussion Particulate matter concentrations (PM 1.0, PM 2.5, and PM 10.0 ) vii

9 Influence of the month on PM 1.0, PM 2.5, and PM 10.0 concentrations with varying ambient RH, ambient temperature, and visibility under different ventilation levels Sub-micron particle numbers Influence of the month/season on particles with varying indoor temperature with good ventilation Sulfur dioxide Influence of the indoor RH and the month under different ventilation levels Influence of the ambient temperature and the sky conditions under different ventilation levels Nitric oxide Influence of the indoor RH and the ambient RH under different ventilation levels Nitrogen dioxide Influence of the indoor RH and the indoor temperature under different ventilation levels Carbon dioxide Influence of the passenger ridership and the month under different ventilation levels Influence of the ambient temperature for low passenger ridership under different ventilation levels viii

10 Influence of the indoor RH with varying passenger ridership under different ventilation levels Influence of the ambient RH for medium passenger ridership under different ventilation levels Carbon monoxide Influence of the sky conditions during cold winter months Influence of the indoor temperature for low indoor RH with varying wind speed under different ventilation levels Influence of the wind speed for low indoor RH with varying indoor temperature under different ventilation levels Influence of the ambient RH with varying indoor RH under different ventilation levels Influence of the indoor RH for different months under different ventilation levels Influence of the light vehicles for different months under different ventilation levels Influence of the ambient temperature for different sky conditions with varying wind speeds under different ventilation levels Conclusions Operational Performance Measures Introduction Methodology ix

11 3.3 Ranked Operational Performance Measures Conclusions Artificial Intelligence Introduction Methodology Artificial neural network database development Classification and normalization of the data Artificial neural networks Back propagation network Radial basis function network Hybrid genetic algorithm based neural network Results and Discussion Conclusions Exposure Evaluation Introduction In-vehicle contaminant generation and health effects Indoor air quality guidelines Methodology Results and Discussion Conclusions Conclusions and Recommendations for Future Work x

12 6.1 Recommendations for Future Research References A. Abstracts from the Publications xi

13 List of Tables 1.1: Transit Agencies using Alternative Fuels and/or Advanced Technologies : Average Values of Independent Variables during Different Seasons and Ventilation Indicators : Classification of Independent Variables : Relative Importance of the Variables for In-Bus PM (PM 1.0, PM 2.5, and PM 10.0 ) Obtained from CART Runs : Sensitivity Results for In-Bus PM 1.0 Obtained from the ANOVA : Sensitivity Results for In-Bus PM 2.5 Obtained from the ANOVA : Sensitivity Results for In-Bus PM 10.0 Obtained from the ANOVA : Relative Importance of the Variables for In-Bus Particle Numbers ( µm, µm) Obtained from CART Runs : Sensitivity Results for In-Bus Particle Numbers ( µm) Obtained from the ANOVA : Sensitivity Results for In-Bus Particle Numbers ( µm) Obtained from the ANOVA : Relative Importance of the Variables for In-Bus SO 2 Obtained from CART Runs : Sensitivity Results for In-Bus SO 2 Obtained from the ANOVA : Relative Importance of the Variables for In-Bus NO Obtained from CART Runs. 31 xii

14 2.13: Sensitivity Results for In-Bus NO Obtained from the ANOVA : Relative Importance of the Variables for In-Bus NO 2 Obtained from CART Runs : Sensitivity Results for In-Bus NO 2 Obtained from the ANOVA : Relative Importance of the Variables for In-Bus CO 2 Obtained from CART Runs : Sensitivity Results for In-Bus CO 2 Obtained from the ANOVA : Relative Importance of the Variables for In-Bus CO Obtained from CART Runs : Sensitivity Results for In-Bus CO Obtained from the ANOVA : Summary of the Ranked Statistically Significant Variables Ranked Statistical Measures for Operational Evaluation of Indoor Air Quality Models Ranked Statistical Measures for Operational Evaluation of Outdoor Air Quality Models Prediction Results to Determine the Optimal Number of Neurons in the Single Hidden Layer of BPN-CO 2 -RT : Summary of the Optimal Conditions used for Running GA CO 2 Operational Performance Measures CO Operational Performance Measures NO Operational Performance Measures SO 2 Operational Performance Measures µm Particle Numbers Operational Performance Measures µm Particle Numbers Operational Performance Measures PM 1.0 Operational Performance Measures PM 2.5 Operational Performance Measures xiii

15 4.11. CO 2 Bootstrap 95% Confidence Limits CO Bootstrap 95% Confidence Limits NO Bootstrap 95% Confidence Limits SO 2 Bootstrap 95% Confidence Limits µm Sized Particle Numbers Bootstrap 95% Confidence Limits µm Sized Particle Numbers Bootstrap 95% Confidence Limits PM 1.0 Bootstrap 95% Confidence Limits PM 2.5 Bootstrap 95% Confidence Limits CO 2 Bootstrap 95% Confidence Limits Between GART and Other Models CO Bootstrap 95% Confidence Limits Between GART and Other Models NO Bootstrap 95% Confidence Limits Between GART and Other Models SO 2 Bootstrap 95% Confidence Limits Between GART and Other Models µm Sized Particle Numbers Bootstrap 95% Confidence Limits Between GART and Other Models µm Sized Particle Numbers Bootstrap 95% Confidence Limits Between GART and Other Models PM 1.0 Bootstrap 95% Confidence Limits Between GART and Other Models PM 2.5 Bootstrap 95% Confidence Limits Between GART and Other Models : Indoor Air Quality Guidelines : 8-hr TWA Contaminant Concentration Statistics : The ANOVA Results for Difference in CO 2 Exposure Levels : The ANOVA Results for Difference in CO Exposure Levels : The ANOVA Results for Difference in NO Exposure Levels xiv

16 5.6: The ANOVA Results for Difference in SO 2 Exposure Levels : The ANOVA Results for Difference in PM 2.5 Exposure Levels xv

17 List of Figures 1-1: Organization and integration of research disciplines : Map showing route # : Architecture of the BPN using TD : Architecture of the RBF network using TD : CO 2 scatter plots : CO scatter plots : NO scatter plots : SO 2 scatter plots : µm sized particle numbers scatter plots : µm sized particle numbers scatter plots : PM 1.0 scatter plots : PM 2.5 scatter plots : CO 2 Q-Q plots : CO Q-Q plots : NO Q-Q plots : SO 2 Q-Q plots : µm sized particle numbers Q-Q plots : µm sized particle numbers Q-Q plots xvi

18 4-17: PM 1.0 Q-Q plots : PM 2.5 Q-Q plots : TWA for CO : TWA for CO : TWA for NO : TWA for NO : TWA for SO : TWA for PM xvii

19 Chapter One Introduction and Research Study Background The average annual increase in population and road vehicle miles traveled between 2008 and 2035 in the United States (U.S.) were estimated to be 0.9% and 3.4% respectively [EIA Report, 2010]. The increase in vehicle miles traveled were mainly a result of the U.S. population s expected shift into suburban and exurban areas [CEP Report, 2008]. Over the years, vehicular usage also increased along with an increase in the population growth [National Transportation Statistics, 2011]. A combination of increasing population, vehicle miles traveled, and vehicular usage would increase vehicular pollution manifold, thereby, contributing to a significant increase in regional air pollution and climate change. The major air contaminants emitted from vehicle exhaust are carbon dioxide (CO 2 ), carbon monoxide (CO), nitrogen oxides (NO x ), particulate matter (PM), and volatile organic compounds (VOCs) mainly hydrocarbons (HC). The International Transport Forum (ITF) identified road transportation as a major area of concern due to its high global growth rate and increased contribution of 45% global CO 2 emissions between 1

20 1990 and Transportation sector (road transport predominance) accounts for 23% of the global CO 2 emissions and nearly 15% of the total greenhouse gas (GHG) emissions [ITF Report, 2010]. In the U.S., transportation sector is the second fastest growing energy consuming sector and accounts for one-third of all CO 2 emissions [EIA Report, 2010]. Highway vehicles in the U.S. contribute to about 50% of the total CO emissions, 32% of the total NO x emissions, 2% of the total particulate matter less than 2.5 micrometers (PM 2.5 ) emissions, 1% of the total particulate matter less than 10.0 micrometers (PM 10.0 ) emissions, 21% of the total VOC emissions, and 1% of the total sulfur dioxide (SO 2 ) emissions [EPA National Emissions Inventory Website, 2008]. These vehicular emissions have considerable effect on the human occupied indoor vehicle compartment [CTA Report, 2000; Peters et al., 2004] and communities (residential, offices, childcare centers, etc.) nearby to the roads [Fischer et al., 2000; Kim et al., 2004; Wichmann et al., 2005; Roosbroeck et al., 2006; Janssen et al., 2001]. Concerns over indoor air quality (IAQ) have escalated with the findings from National Human Activity Pattern Survey (NHAPS) that people spend about 87% of their time indoors and nearly 6% of their daily time commuting, mostly between their workplace and their residence [Klepeis et al., 2001]. People are exposed to higher levels of traffic contaminants when they drive in heavy traffic, stand near idling vehicles, and spend time at places near roads having high traffic, especially if the location is downwind of the road [OEHHA Report, 2004]. Considering the impacts of vehicular pollution on human occupied environments and climate change, the U.S. Environmental Protection Agency (EPA) aims at reducing 2

21 the emissions through technological advances in vehicle and engine design, together with cleaner and high quality fuels. Despite the increasing vehicular usage and miles traveled, the U.S. EPA succeeded in reducing the vehicular emissions to some extent by encouraging the use of alternative/clean fuels such as biodiesel (BD), electricity, ethanol (E), methanol (M), compressed natural gas (CNG), liquefied natural gas (LNG), and hydrogen (used specifically in fuel cell (FC) vehicles). One of the most visible applications of alternative fuels in the transportation sector is in public transit systems. Table 1.1 presents a summary of the various transit agencies using alternative fuels and/or advanced technologies in the U.S. There are nearly 75,000 transit buses operating across the nation that make up about 58% of the transit vehicle miles traveled [DOE Report, 2002] and the degree of exposure levels to contaminants is more for people commuting in a bus as compared to the levels of exposure occurring at bus stops or during loading and unloading times [CARB Factsheet, 2003]. Several studies observed in-vehicle contaminant concentrations to be much higher than those observed in ambient air [Shikiya et al., 1989; Ptak and Fallon, 1994; Lawryk et al., 1995; Rodes et al., 1998; Solomon et al., 2001; Wargo et al., 2002]. Therefore, it is important to study the IAQ in public transport buses more closely in order to understand the exposure levels and determine if the in-bus concentrations are within the permitted guidelines. 3

22 Table 1.1: Transit Agencies using Alternative Fuels and/or Advanced Technologies Transit Agency City Alternative Fuel (or) Advanced Technology Dallas Area Rapid Transit (DART) Dallas, TX LNG Bi-State Development Agency (Metro Transit) St. Louis, MO BD20 Metropolitan Atlanta Rapid Transit Authority (MARTA) Atlanta, GA CNG Houston Metro Houston, TX LNG Tri-Met Portland, OR LNG Metro Dade Transit Authority (MDTA) Miami, FL M100, CNG New York City Department of Transportation (NYCDOT) New York, NY M100, CNG Pierce Transit Tacoma, WA CNG Metropolitan Council of Transit Operations (MCTO) Minneapolis/St. Paul, MN E95 Greater Peoria (GP) Transit Peoria, IL E95/E93 Toledo Area Regional Transit Authority (TARTA) Toledo, OH BD20, ULSD New York City Transit (NYCT) New York, NY Electric- Hybrid Mass Transportation Authority (MTA) Flint, MI CNG GO Boulder Boulder, CO CNG Southwest Ohio Regional Transit Authority (SORTA) Cincinnati, OH CNG SunLine Transit Agency (STA) Thousand Palms, CA FC Connecticut Transit (CT Transit) Hartford, CT FC Bay Area Transit Consortia led by AC Transit Oakland, CA FC Alameda-Contra Costa Transit District led by AC Transit Oakland, CA FC Santa Clara Valley Transportation Authority (VTA) San Jose, CA FC San Mateo County Transit District (SMCTD) San Carlos, CA FC (Source: Compiled from National Renewable Energy Laboratory (NREL) Website) Note: There could be other transit agencies using alternative fuels and/or advanced technologies. 4

23 1.1 Research Study Background and Objectives This study aims at filling the knowledge gap in relation to the study of air quality inside a bus compartment operating on alternative fuels and was done as a part of the continuing research on the environmental impact assessment of alternative diesel fuels on the Toledo Area Regional Transit Authority (TARTA) public transport buses funded by the U.S. Department of Transportation. TARTA has been the Ride of Toledo since 1971 with about 40 routes in and around the Toledo metropolitan area carrying almost 5 million passengers each year. TARTA has more than 180 buses running throughout the day contributing to a significant percentage of total emissions in the region. Understanding the importance of cleaner environment, TARTA started using alternative diesels that included biodiesel (BD20-20% methyl ester bio-fuel + 80% ULSD), ultra low sulfur diesel (ULSD) containing less than 15 ppm sulfur content, and ultra low sulfur diesel supreme having some extra additives in June The following observations were made from a comprehensive review of the literature on in-vehicle air quality studies (refer to the Introduction sections of Chapter 3, Chapter 4, and Chapter 5 for more details). The contaminant concentration buildup within a transit microenvironment is predominantly influenced by the indoor sources (such as passenger density, emissions from different indoor components, etc.), ventilation settings, outdoor air quality (affected by the test vehicle and lead vehicular exhaust emissions, and the ambient contaminant background concentrations in relation to different meteorological 5

24 conditions), and time-related variables (such as the month/season of the year and time of the day). None of the prior in-vehicle air quality studies characterized and quantified the contaminant concentrations in relation to a set of identified statistically significant variables with data mining. None of the earlier studies provided a systematic approach to the use of ranked operational performance measures in validating air quality models. Modeling of air quality inside a vehicle compartment was limited to the use of regression, regression trees, and mass balance models; artificial intelligence methods were not investigated. None of the prior studies determined the occupant exposure to contaminant concentrations inside a vehicle cabin that operated on alternative fuels. The research objectives of this study are to characterize and quantify the air quality relationships inside a public transportation bus using data mining; to develop a comprehensive set of ranked operational performance measures for evaluation of air quality models; to develop advanced in-bus air quality models using artificial intelligence methods and evaluate their performance with the newly developed IAQ operational performance measures; and to determine the occupant exposure to in-bus contaminant concentrations by computing the time weighted average (TWA) and compare them with the corresponding IAQ guidelines. Figure 1-1 demonstrates the methodology adopted by the researcher to meet the objectives. Field study involved the collection of meteorological variables (ambient temperature (temp.), ambient relative humidity (RH), wind speed, sky condition, 6

25 visibility, weather type, precipitation), ambient PM 2.5 data, on-road variables (passenger density, light vehicles ahead, heavy vehicles ahead, ventilation settings indicated by the bus status and door position: run/close, idle/open, idle/close), in-bus comfort parameters (indoor temp., indoor RH), in-bus contaminant (gases: CO 2, CO, nitric oxide (NO), nitrogen dioxide (NO 2 ), SO 2 ; PM: particulate matter less than 1.0 µm (PM 1.0 ), PM 2.5, PM 10.0, and micrometer (µm) sized particle numbers) data, and designating the time-related variables (time of the day, month of the year, season of the year). Hourly averages of the six classes of observed data from the field study were put together to develop a primary database. In any experimental study, there are bound to be missing values. In the current study also, there were a lot of missing real-time on-road variable data, as monitoring these variables continuously for one year was not practically feasible considering the amount of time required for video monitoring and computerizing the monitored data. The primary database with no missing values for any of the monitored variables was then used to characterize and quantify the in-bus air quality relationships with development of a novel approach to data mining (using regression trees and the analysis of variance); and advanced in-bus prediction models were developed using artificial intelligence methods (back propagation network (BPN), radial basis function network (RBFN), hybrid genetic algorithm neural network or evolutionary neural network (ENN)). The performance of artificial intelligence methods were evaluated using a comprehensive set of newly established IAQ operational performance measures and the predictions were used to assess the in-bus exposure levels. 7

26 The following dissertation document is organized into five chapters: Chapter 2 presents the development of a new methodology to characterize in-bus air quality, i.e., data mining. Chapter 3 discusses the methodology associated with ranking of the operational performance measures for evaluating indoor and outdoor air quality models. Chapter 4 presents a novel methodology to predict in-bus contaminant concentrations using hybrid genetic algorithm neural networks (or ENNs), and evaluates the prediction capacity with reference to the traditional artificial intelligence methods of BPN and RBFN using a set of newly established IAQ operational performance measures summarized in Chapter 2. Chapter 5 provides a discussion on the occupant exposure to in-bus contaminants and determines if the in-bus contaminant concentrations are within the recommended IAQ guidelines. Chapter 6 concludes the work with a summary of the key findings and offers a list of recommended avenues for future work. 8

27 Figure 1-1: Organization and integration of research disciplines. 9

28 Chapter Two Characterization of In-Bus Air Quality: Data Mining 1,2,3 2.1 Introduction A study of exposure to PM 10.0, PM 2.5, metals, VOCs, CO, fine particle counts and black carbon (BC) identified driving lane, roadway type, congestion level, time of the day, and exhaust from lead vehicles as the significant factors affecting in-vehicle contaminants [Rodes et al., 1998]. Exposure to PM and CO contaminants inside a vehicle that operated along a standard route were influenced by time of the day, average speed, wind speed, and relative humidity [Alm et al., 1999]. Vehicle exhaust and self-intrusion were identified as the important factors affecting in-vehicle BC, particle-bound polycyclic aromatic hydrocarbons (PM-bound PAH), NO 2, particle counts, and PM 2.5 Parts of this chapter were published as 1 Kadiyala, A., & Kumar, A. (2012a). Development and application of a methodology to identify and rank the important factors affecting in-vehicle particulate matter. Journal of Hazardous Materials, , doi: /j.jhazmat Kadiyala, A., & Kumar, A. (2011c). Quantification of in-vehicle gaseous contaminants of carbon dioxide and carbon monoxide under varying climatic conditions. Air Quality, Atmosphere, and Health. doi: /s Kadiyala, A., & Kumar, A. (2012b). An examination of the sensitivity of sulfur dioxide, nitric oxide, and nitrogen dioxide concentrations to the important factors affecting air quality inside a public transportation bus. Atmosphere, 3(2), doi: /atmos

29 when the windows were closed, while ventilation settings played a major role when the windows were open [Fitz et al., 2003]. Road type, following distance between the lead vehicle and follow vehicle, and exhaust location of the lead vehicle affected vehicular BC, ultra-fine particles, NO x, CO, CO 2, PM 2.5, PM size distribution, and PM-bound PAH [Fruin, 2003]. The route travelled and the peak hours predominantly influenced in-bus CO 2, CO, SO 2, and PM [Vijayan and Kumar, 2008]. Outdoor concentrations and traffic had a significant impact on PM in Munich public transportation systems that included buses and trams [Praml and Schierl, 2000]. Lead vehicle and the type of bus for testing affected in-vehicle BC, PM-bound PAH, and NO 2 when windows were in open and closed conditions, respectively [Sabin et al., 2005]. CO concentrations inside the vehicle were influenced by the transport mode, the route selected, the monitoring period, and the season [Duci et al., 2003]. In-bus CO 2, CO, SO 2, NO, NO 2, and PM varied on a monthly and seasonal basis [Kadiyala and Kumar, 2011a]. Passenger exposure to emissions of benzene, toluene, xylene, and formaldehyde inside parked vehicles, in underground parking, revealed that emissions were higher in new vehicles compared to older ones [Zhang et al., 2008]. Similar observations were made in another study, on comparing the VOC concentrations in old and new vehicles [Fedoruk and Kerger, 2003]. The study also identified interior temperature, vehicle make, vehicle age, and the type of deodorizer used to play an important role in influencing invehicle VOCs. An exposure to about 24 gasoline-related VOCs, simultaneously with ozone, CO, and NO 2 recognized road type, driving time, and air conditioning to significantly influence the monitored contaminants [Chan et al., 1991]. In-vehicle PM

30 and ultra-fine particles were mainly influenced by the stop-and-go traffic predominantly found at signals [Diapouli et al., 2008]. Some studies observed mode of transport selected by the passenger to play a major role in influencing VOCs [Chan et al., 2003; Lau and Chan, 2003], PM, and CO concentrations [Chan et al., 2002a]. Low wind speed contributed to higher in-vehicle CO and PM 2.5 [Gomez-Perales et al., 2004]. A study of exposure to CO in three different commuting modes - bus, minibus, and taxi concluded that the exposure apart from being influenced by heavy traffic and street configuration was increased by two to three times in tunnel microenvironment compared to urban and sub-urban roads [Chan and Liu, 2001]. The study also reported vehicle height, size of the vehicle, and leakage and intake positions of ventilation systems to affect contaminant concentrations; much variation was not observed between air-conditioned and non-air-conditioned vehicles. In-vehicle CO concentrations were influenced by air conditioning [Clifford et al., 1997; Chan et al., 2002b], road lane used and vehicle size [Fernandez-Bremauntz and Ashmore, 1995]. Vehicle body position, intake point of ventilation, ventilation effect, transportation mode, road type, driving conditions, and relative distance from emission source affected the in-vehicle contaminants of CO, NO x, total hydro carbons, and ozone [Chan et al., 1999]. In-vehicle CO 2 concentrations were mainly influenced by passengers and not the driving environment [Chan, 2003]. Mean 8-hour exposure of occupants to CO 2 and SO 2 were significantly higher inside ULSD buses, compared to BD20 fueled buses; CO and NO concentrations were higher inside BD20 buses [Kadiyala et al., 2010a]. The study also observed exposure to NO 2 and PM 2.5 to be statistically similar for both the buses. 12

31 From the review of literature on in-vehicle studies, one can note that the contaminant concentration buildup within a vehicle is due to a combination of different factors and not a result of variation due to a single variable. The above discussion also indicates that there are a considerable number of vehicular studies that characterized IAQ in relation to the limited influential real-time on-road variables (e.g. passenger density, traffic, ventilation, etc.) using regression analysis. Yet, no single study had attempted to quantitatively characterize the IAQ in relation to the multiple influencing variables using advanced data mining techniques. This study attempts to fill the knowledge gap by quantitatively analyzing and characterizing the IAQ using a two-step approach. 2.2 Methodology Experimental setup A 20% grade biodiesel (BD20) Thermo King air-conditioned bus (ID: 506 with 106K engine miles acquired by TARTA in 2003) was selected from the 500 series Thomas built buses (acquired by Detroit Diesel) of the TARTA line up, with a Mercedes Benz MBE 900 engine; and run daily on a single pre-assigned route. The route selected for the study was Route #20 (refer to Figure 2-1), which runs between the TARTA garage (A) and Meijer (B) on the Central Avenue Strip. The route selected for the test-run is a standard two-lane (dual direction) asphalt urban road with a speed limit of 40 miles per hour, and predominantly has a stop-and-go traffic resulting from the combination of heavy traffic with traffic signals and bus stops. The BD20 test bus kept to the right lane for majority of the run and the variation of in-bus contaminants with driving lane, roadway type, commuting mode, vehicle size, and route selected were eliminated with 13

32 consistency in the test-run throughout the testing period. The locations of the bus, in transit, were identified by the GPS unit located inside the bus. Figure 2-1: Map showing route # 20 [TARTA Route, 2011]. In-bus PM (PM 1.0, PM 2.5, PM 10.0, and µm sized particle numbers) levels were monitored using the GRIMM Dustmonitor [Grimm Technologies, Inc., 2011], while in-bus gaseous contaminants of CO 2, CO, NO, NO 2, and SO 2 were monitored simultaneously with indoor temperature and indoor RH using the Yes Plus instrument [Critical Environmental Technologies, Inc., 2011]. Both the instruments drew power continuously from the adapters connected to the test bus and a wired mesh box safeguarded the instruments. The instruments were held in position within the wired mesh box using velcro attachments. More details on the experimental setup, instrument capabilities, and test protocol adopted by the researcher were documented elsewhere [Kadiyala et al., 2010a]. 14

33 2.2.2 Database development Data collection included downloading data from the instruments, obtaining meteorological data, designating monitoring period variables, and monitoring the on-road real-time variables. Data collected between 6:00 a.m. and 11:00 p.m., over a period of one year (April March 2008), were used in this study. The data downloaded from all the instruments were set for one-minute intervals that were averaged to one hour for analysis. Different factors that can possibly influence the vehicular IAQ, such as meteorological conditions (ambient temp., ambient RH, wind speed, sky condition, visibility, weather type, precipitation), monitoring periods (time of the day, month of the year, season of the year), and on-road real-time variables (passenger density, light vehicles (cars/suvs) ahead, heavy vehicles (buses/trucks) ahead, ventilation settings) were considered. Meteorological data were downloaded for the station at the Toledo Express Airport, from the National Climatic Data Center [NCDC, 2011] that was within a radius of 25 miles of the selected test route. Ambient PM 2.5 concentrations (used only for PM analysis) were obtained from U.S. EPA on request. The passenger density, light/heavy vehicles in front of the bus, and bus/door status that represented ventilation settings (idle/open, idle/close, run/close) were obtained by monitoring the hard drive that recorded the video during its run. Additional details on the instrument calibration, maintenance, and hard drive monitoring were documented elsewhere [Kadiyala et al., 2010a]. The database, referred to as the complete database in this chapter, included only the hourly data points with no missing values for any of the variables. Missing values were found predominantly in the on-road real-time hard drive monitored variables 15

34 category, as it was not possible to get the real-time on-road variable data for all the days on which in-bus contaminants were monitored. Missing variables were a result of camera error, hard disk problems, and the amount of time required to record the observations on one-minute intervals. The complete database had 1453 hourly gaseous data points, 189 hourly PM concentration data points, and 367 hourly sub-micron sized particle data points. The different seasons used in this study were defined as spring (April 2007 June 2007); summer (July 2007 September 2007); fall (October 2007 December 2007); and winter (January 2008 March 2008). Table 2.1 presents a summary of the average values for different independent variables and indicators for ventilation in different seasons. From Table 2.1, one can observe that ventilation indicator rankings were provided for different seasons (considering the ambient comfort parameters (temp. and RH) to be more or less equivalent to indoor comfort parameters, when there was sufficient ventilation or on the basis of idle/open conditions). There is good ventilation in the summer season; moderate ventilation in spring and fall seasons; and reduced ventilation in the winter season. To better understand the relationships between the monitored in-bus contaminants and the independent variables, some of the independent variables were further classified into three categories: low, medium, and high, as shown in Table

35 Table 2.1: Average Values of Independent Variables during Different Seasons and Ventilation Indicators Independent Variables Spring (Apr. 07 June 07) Summer (July 07 Sept. 07) Seasons Fall (Oct. 07 Dec. 07) Winter (Jan. 08 Mar. 08) Ambient PM2.5 (µg/ m 3 ) Indoor temp. (ºF) Indoor RH (%) Ambient temp. (ºF) Ambient RH (%) Wind speed (mph) Visibility (statute miles) Precipitation (inches) Passengers per 5-minutes (per hour) 5.75 (69) 5 (60) 6 (72) 4.91 (59) Light vehicles per minute (per hour) 0.23 (14) 0.27 (16) 0.35 (21) 0.28 (17) Heavy vehicles per minute (per hour) 0.14 (9) 0.14 (9) 0.16 (10) 0.16 (10) Run/Close (minutes per hour) Idle/Open (minutes per hour) Idle/Close (minutes per hour) Ventilation Indicators Difference between indoor temp. and ambient temp Difference between ambient RH and indoor RH Ventilation Indicator Ranking 2 (moderate) 1 (good) 3 (moderate) 4 (reduced) 17

36 Table 2.2: Classification of Independent Variables Independent Variables Low Medium High Passengers per 5-minutes <5 5-7 >7 Indoor temp. (ºF) < >72 Wind speed (mph) < >20 Indoor RH (%) < > A two-step approach to data analysis Firstly, a set of important factors affecting each monitored in-bus contaminant were identified by developing regression tree models using the CART software. Regression tree methods were observed to perform better than regression methods in identifying the important factors on comparing their performances for the monitored inbus contaminants of CO 2, CO, NO, NO 2, SO 2, and PM [Kadiyala and Kumar, 2008]. The importance of an input variable was indicated by whether it was selected as the basis for splitting the tree at the highest branches, and whether it had been selected at multiple levels of the regression tree to further subdivide the data. The partitioned data under different nodes of the same branch have significantly different mean values. No restriction was specified for the number of nodes in the regression tree, so that mean responses can account for all the variability in the output that can be captured by partitioning the dataset. Complete details of the developed regression tree models were documented elsewhere [Kadiyala and Kumar, 2011b]. Secondly, the identified important factors were ranked by performing the analysis of variance (ANOVA), as a complimentary sensitivity analysis to the regression tree results using SPSS software. The methodology of using the ANOVA as a complimentary sensitivity analysis to regression tree models was adopted from a study on food safety risk models [Frey et al., 2003]. As the sensitivity analysis results obtained 18

37 from the complementary analyses will be different for different nodal databases, the partitions of the original input data were based on the primary splitting criterion having considerable number of data points. The identified influential variables were then ranked based on the F-value of ANOVA results. Additionally, the inter-relationships between the ranked independent variables and the monitored in-bus contaminants were quantitatively analyzed on studying the regression tree models developed for each contaminant in the first step. This study provides environmental professionals with more information on the dependence of in-bus contaminants on multiple influential factors, and encourages future research based on regression tree analysis rather than regression analysis. 2.3 Results and Discussion Particulate matter concentrations (PM 1.0, PM 2.5, and PM 10.0 ) Table 2.3 presents the CART variable relative importance results obtained from the development of PM 1.0, PM 2.5, and PM 10.0 regression trees with the complete database. The term Score in Table 2.3 is defined as the relative importance of a variable in its role as a surrogate to the primary split. It can be observed from Table 2.3 that the important factors affecting in-bus PM 1.0 and PM 2.5 (strongly correlated, r = 0.97) are the same. From Table 2.3, one can observe the month and the ambient temperature to be consistently affecting all the monitored in-bus PM concentrations. Visibility did not have an impact on in-bus PM 10.0 (PM 10.0 was moderately correlated with PM 2.5 (r = 0.71) and weakly correlated with PM 1.0 (r = 0.51)). Ambient PM 2.5 aerosols are effective light 19

38 scatters that reduce the visibility [Sisler and Malm, 1994]; while PM 1.0 aerosols are the most efficient scatterers of visible light [Trijonis et al., 1991]. Table 2.3: Relative Importance of the Variables for In-Bus PM (PM 1.0, PM 2.5, and PM 10.0 ) Obtained from CART Runs PM 1.0 PM 2.5 PM 10.0 Variable Score Variable Score Variable Score Month 100 Month 100 Month 100 Visibility Visibility Ambient temp Ambient RH Ambient RH Ambient temp Ambient PM Ambient PM Ambient temp Month of the year was the most important factor in all three cases, as it was selected as the primary splitting criterion and that it was selected repeatedly throughout the lower nodes of the tree. Tables 2.4, 2.5, and 2.6 present the complimentary sensitivity analysis results obtained by using the ANOVA to rank inputs conditional on the month for PM 1.0, PM 2.5, and PM 10.0, respectively. Rankings for the ANOVA complimentary runs in Tables 2.4, 2.5, and 2.6 were presented based on the magnitude of F-value for statistically significant inputs (Sig.) < For PM 1.0 and PM 2.5 concentrations, the first dataset included data from the months April 2007 March 2008, excluding June 2007; and the second dataset contained data from the month of June For PM 10.0, the first dataset contained data from the months April 2007 March 2008, excluding May 2007 and June 2007; and the second dataset included data from the months of May 2007 and June From Table 2.4, one can observe in-bus PM 1.0 to be influenced by the ambient RH in both cases, while the visibility, the ambient PM 2.5, and the ambient temperature were influential only on the first dataset. Similar observations were made from Table 2.5, with the exception that the visibility was not found to be influencing either of the two datasets. From Table 2.6, one can note the ambient temperature to be significantly 20

39 influencing only the first dataset. At higher temperatures with lower humidity, there is a generation of secondary particles by atmospheric photochemical reaction; and there is a positive and negative relation of atmospheric PM with ambient temperature and ambient RH, respectively [Hien et al., 2002; Varadarajan and Kumar, 2007; Fang et al., 2007; Tecer et al., 2008; Galindo et al., 2011]. Cloudy sky conditions had shown a positive relation to atmospheric PM [Tecer et al., 2008]. Considering the complete database PM regression trees and results of the complimentary analysis, month, ambient RH, visibility, ambient PM 2.5, and ambient temperature were ranked as the five important factors, in ascending order that influenced in-bus PM 1.0 concentrations. Month, ambient RH, ambient temperature, and ambient PM 2.5 were ranked as the first, second, third, and fourth, respectively that influenced PM 2.5 concentrations inside the bus. Month and ambient temperature were ranked as the first and second important factors that influenced in-bus PM 10.0 concentrations. Table 2.4: Sensitivity Results for In-Bus PM 1.0 Obtained from the ANOVA Variable F Sig. Significant Rank Variable F Sig. Significant Rank Month = Apr. 07 May 07, July 07 Mar. 08 Month = June 07 Visibility < Yes 1 Visibility No Ambient RH Yes 2 Ambient RH < Yes 1 Ambient Ambient Yes 4 temp. temp No Ambient PM Yes 3 Ambient PM No

40 Table 2.5: Sensitivity Results for In-Bus PM 2.5 Obtained from the ANOVA Variable F Sig. Significant Rank Variable F Sig. Significant Rank Month = Apr. 07 May 07, July 07 Mar. 08 Month = June 07 Visibility No Visibility No Ambient RH Yes 2 Ambient RH < Yes 1 Ambient Yes 3 Ambient No PM 2.5 Ambient temp Yes 1 PM 2.5 Ambient temp No Table 2.6: Sensitivity Results for In-Bus PM 10.0 Obtained from the ANOVA Variable F Sig. Significant Rank Variable F Sig. Significant Rank Month = Apr. 07, July 07 Mar. 08 Month = May 07- June 07 Ambient temp Yes 1 Ambient temp No Based on the developed PM 1.0, PM 2.5, and PM 10.0 complete database regression trees, in-bus PM concentrations were categorized into three levels: low (< 20 µg/m 3 ), medium (20-45 µg/m 3 ), and high (> 45 µg/m 3 ), to better understand the consequences of different combinations of the influential factors Influence of the month on PM 1.0, PM 2.5, and PM 10.0 concentrations with varying ambient RH, ambient temperature, and visibility under different ventilation levels Medium levels of PM 1.0 were observed in the month of June 2007 and low levels of PM 1.0 were observed during the other months, with May 2007 having the second highest average PM 1.0 concentration. Similar trends were observed for the in-bus PM 2.5 concentrations. Medium levels of PM 10.0 were observed in the months of May 2007 and June 2007; and low levels of PM 10.0 were observed during all the other months. Relatively lower PM 1.0 concentrations were observed for (a) the month of June 2007 with (b) ambient RH > 60% as compared to the case of (b) ambient RH 60%. Similar trends 22

41 were observed for PM 2.5 concentrations inside the bus. Relatively higher PM 1.0 concentrations were observed (a) during the month of June 2007 with (b) ambient RH > 60% for (c) visibility 1.13, compared to the case with visibility > The following observations were made on the consideration of relatively higher ventilation during the summer months and considering visibility as a function of cloudiness: Medium levels of in-bus PM 1.0 and PM 2.5 concentrations were observed during the month of June Low levels were observed during all the other months. Medium levels of in-bus PM 10.0 concentrations were observed during the months of May 2007 and June Low levels were observed during all the other months. The relatively higher in-bus PM concentrations observed during May 2007 and June 2007, with moderate ventilating conditions, could be due to accumulation of the relatively higher outdoor ambient PM (more photochemical activity occurring on days having higher ambient temperatures with lower ambient RH in combination with lead vehicular traffic exhaust) indoors. In-bus PM levels were inversely proportional to ambient RH when there was moderate/good ventilation. PM levels inside the bus compartment were inversely proportional to visibility (or directly proportional to cloudiness) when there was sufficient ventilation. In-bus PM levels were mainly influenced by the ambient PM concentrations and PM trends inside the bus compartment were consistent with variations in atmospheric PM. 23