Ergonomic & Environmental Study of Solid Waste Collection. Final Report

Transcription

1 Ergonomic & Environmental Study of Solid Waste Collection Final Report Pamela McCauley Bush, Principal Investigator*, Debra Reinhart, Co-Principal Investigator,**, Mousa Maimoun**& Fatina Gammoh* *Department of Industrial Engineering and Management Systems, * & **Department of Civil, Environmental, and Construction Engineering, University of Central Florida November 8, 2012

2 Executive Summary The growing municipal solid waste generation rates have necessitated more efficient, optimized waste collection facilities. This research is an in-depth ergonomicenvironmental analysis of the task of waste collection with consideration for the type of waste collection. The first objective of this research was to conduct a comprehensive ergonomics study of waste collection tasks of three different types of collection including manual, semi-automated and automated. The study utilized observational analysis, site visits and a review of historical data. Laboratory analysis was also performed to assess ergonomic and biomechanics aspects of task performance. Moreover, two surveys were conducted of solid waste collectors and safety personnel at different waste companies to understand the factors affecting waste collectors safety. Detailed analysis of injury data and industry statistics collected by the Bureau of Labor Statistics was performed to evaluate the injuries trend in the waste collection industry; it was noticed that the waste collection field has the highest rate of non-fatal injuries among fields of waste management industry from 2003 through Also it was observed that manual waste collectors are exposed to severe occupational injuries more than the automated and semiautomated haulers due to lifting, heavy load handling, repetition and awkward postures. The majority of the US collection fleet is composed of diesel-fueled vehicles which contribute significant atmospheric emissions including greenhouse gases. In order to reduce emissions to the atmosphere, more haulers are investigating alternative fuel technologies such as natural gas, biofuels (bio-gas and bio-diesel), and hybrid electric technology. The second objective of this study was to perform an environmental analysis of potential alternative fuel technologies for waste collection vehicles. Life-cycle emissions, cost, fuel and energy consumption were evaluated for a wide range of fossil and bio-fuel technologies. The energy consumption and the tail-pipe emissions of dieselfueled waste collection vehicles were estimated using MOVES 2010a software. Emission factors were calculated for a typical waste collection driving cycle as well as constant speed. Finally, the selection of fuel type by the waste collection industry requires consideration of environmental, security, financial, operational, and safety issues. In this study, a qualitative comparison of alternative fuels was performed; a multifactorial assessment of these factors was conducted taking into account the opinion of the waste collection industry of the importance of each factor. The study concluded that liquidpetroleum fuels have higher life-cycle emissions compared to natural gas and landfill natural gas has the lowest life-cycle emissions compared to all other fuel categories. Compressed natural gas waste collection vehicles have the lowest fuel cost per collection vehicle mile travel compared to other fuel categories. The actual driving cycle of waste collection vehicles consists of repetitive stops and starts during waste collection; this generates more emissions than constant speed driving. Finally, the multifactorial assessment indicates that natural gas and landfill gas have better environmental, economic, and energy security performance than current liquid-petroleum fuels. This study established a foundation for additional research and recommendations for mitigating risks at all levels of task performance. Evidence of this is the presentation of the research at four conferences, three conference publications, the submission of two journal articles and one book chapter. Additionally, the research was featured in the industry publication, Waste Age Magazine in January 2012 ii

3 ACKNOWLEDGEMENTS The authors would like to acknowledge the financial support of the Environmental Research and Educational Foundation (EREF) for funding this study and for the consistent support throughout the project. This report could not have been completed without the support and cooperation of several public and private waste collection facilities; invaluable information along with technical support was provided at all levels of employment. The authors would also like to thank all anonymous haulers, safety personnel and industry professionals who took the time to complete our surveys. iii

4 Table of Contents Executive Summary... ii LIST OF TABLES... viii LIST OF ABBREVIATIONS... ix I. Study Overview... 1 II. Ergonomics Study Introduction Significance of study Background Study methodology and discussion Define Phase Measure Phase Analysis Phase Improve and Control Phases Conclusions Future Work III. Environmental Study Introduction Background History of Waste Collection Vehicles Strategies to reduce GHG emission of Waste Collection Vehicles Waste Collection Vehicle Actual Driving Cycles Alternative Fuels Life-cycle Emissions Operating Conditions for Diesel-Fueled Waste Collection Vehicles Introduction Methodology and Assumptions Results and Discussion Alternative Fuel Comparison Introduction Methodology Alternative Fuels iv

5 5.4 Alternative Fuel Survey Future Fuel for Waste Collection vehicles Conclusions Future Work References List of Publications resulting from Research APPENDIX A: Surveys Appendix B: Laboratory Analysis for Younger Subjects Appendix C: Borg Scale for Rating of Perceived Exertion (RPE) Appendix D: Fuel Selection Criteria v

6 LIST OF FIGURES Figure I-1: Study Overview of Solid Waste Collection Figure II-1: BLS Data of fatalities and injuries Figure II-2: Fatalities injuries from by: (a) age (left); (b) affected body parts (right) Figure II-3: Simulated Waste Collection Task in Lab Figure II-4: Fish Bone Analysis of waste collectors injuries Figure II-5: Survey results of waste collection and safety section: (a) Number of stops collected on average route day (left); (b) estimated average container weight (right) Figure II-6: Musculoskeletal injuries/pain: (a) Number of waste collectors who experienced injuries or pain in each body part at least once per week; (b) Lower back injuries occurrence according to the type of truck Figure II-7: Safety supervisor survey results: (a) Tasks that present the greatest risk to waste (left); (b) Injuries types experienced by waste collectors (right) Figure II-8:Lifting Posture: (a) Photo of the interviewed waste collector; (b) 3DSSPP TM virtual figure; (c) Orthogonal-view in 3DSSPPTM (Side view) Figure II- 9: JACK RULA Score Figure II- 10: Jack Simulation Lifting Posture Figure II- 11: JACK Simulation Dumping Posture Figure II-12: Chart of Heart Rate v RPE Figure II- 13: Lifting Posture in 3DSSPP Figure II- 14: Dumping Posture in 3DSSPP Figure II- 15: Observed Back-Arched Dumping Posture Figure II- 16: Observed Back-Arched Dumping Posture in 3DSSPP Figure II- 17: Observed One- Handed Lifting Posture Figure II- 18: Observed One-Handed Lift in 3DSSPP Figure II-19: Lifting Posture with 41.8 lb. (19 kg) load Figure II-20: Dumping Posture with 41.8 lb. (19 kg) load Figure II-21: Observed Back-Arched Dumping Posture at 41.8 lb Figure II-22: Observed One-Handed Lifting Posture at 41.8 lb Figure II-23: Lifting Posture: (a) Photo of the interviewed waste collector; (b) JACK virtual figure; (c) 3DSSPP virtual figure Figure III-1: Fuel Life-Cycle GHG Emissions Figure III-2: Cost Trend for Waste Collection Vehicle Travel Figure III- 3: Waste Collection Vehicle EFs at Constant Average Speed Figure III- 4: Waste Collection Vehicle EFs vs. Road Grade Figure III- 5: Comparison of Waste Collection Vehicle EFs and Energy Consumption Assuming Typical Waste Collection Cycle Figure III- 6: Breakdown of Issues Related to Fuel Selection Criteria Figure III-7: US Petroleum Consumption, Production, and Import Trends (US Energy Information Administration, 2010) vi

7 Figure III- 8: Dosimeters locations with respect to waste collection vehicle Figure III- 9: Noise Level Associated with Diesel-fueled Manual and Automated Waste Collection Vehicles Figure III- 10: Comparison between CNG and Diesel Refuse Trucks Noise Levels (Modified From NGVA, 2010) Figure III-11: Boxplot for Fuel Selection Criteria Survey Responses Figure III-12: 95% Confident Interval for the Selection Criteria Survey Responses Figure III-13: Heat Map for Fuel Selection Criteria Figure III-14: Heat Map for the Weighted Fuel Selection Criteria Figure III-15: Alternative Fuel Performance Relative to LFG vii

8 LIST OF TABLES Table II-1: WISHA checklist results of manual and semi-automated collection Table II-2: Strength capability of the body joints for each task Table II-3: REBA and RULA Scores Table II- 4: Comparison of JACK and 3DSSPP L4/L5 Forces for Subject 1, Task Table II- 5: Comparison of JACK and 3DSSPP Strength Capability Summary for Subject 1, Task Table III-1: LDVs Specifications and Energy Requirement for Different Fuel Technologies Table III-2: Equivalent Energy Conversion Factors (EECFs) for Different Alternative Fuel Categories, using LDV Table III-3: Estimated Alternative Fuels Waste Collection Vehicles Energy Requirement and Mileage Data (Car Data) Table III-4: Equivalent Energy Conversion Factors (EECFs) for Different Alternative Fuel Categories, using Bus Data Table III-5: Estimated Alternative Fueled Waste Collection Vehicles Mileage Data (Bus Data) Table III-6: Comparison between Modeled mpdeg and Field Measurements Table III-7: NYGTC EFs Approach 1-Using Travel Time and Idling Time Table III-8: NYGTC EFs Using Approach 2-MOVESa Link Driver Schedule Table III-9: NYGTC EFs Approach 3-Constant Average Speed (2.4mph) Table III-10: GHG Life-cycle Emissions Relative to Gasoline Table III-11: Water Consumption for Fuel Production (Cizek, 2010) Table III-12: Properties of Alternative Fuels (Murphy, 1994) Table III-13: Descriptive Statistics for the Survey Responses Regarding Importance of Criteria (0-10, 10 most significant) viii

9 LIST OF ABBREVIATIONS DMAIC 3DSSPP BLS NSWMA WASTEC WISHA RULA REBA WMSD C&D CNG CVMT EF GHG GREET Transportation HDV HLA LCA LDV LFG LNG MOVES Mpg MSW MSWM NYGTC OIGAI OSHA PTW R&D REBA RULA TTI USEPA VMT WTP WISHA WTW Define, Measure, Analyze, Improve and Control phases 3D Static Strength Prediction Program TM Bureau of Labor Statistics National Solid Wastes Management Association Waste Equipment Technology Association Washington Industrial Safety and Health Act Rapid upper limb assessment Rapid Entire Body Assessment Work-related Musculoskeletal Disorder Construction and Demolition Compressed Natural Gas Collection Vehicle Mile Traveled Emission Factor Greenhouse Gases Greenhouse Gas, Regulated Emissions, and Energy Use in Heavy-Duty Vehicles Hydraulic Launch Assist Life-cycle Analysis Light Duty Vehicle Landfill Gas Liquefied Natural Gas MOtor Vehicle Emission Simulator Mile per Gallon Municipal Solid Waste Municipal Solid Waste Management New York Garbage Truck Cycle Operate In Gear At Idle Occupational Safety & Health Administration Pump-to-Wheel Research and Development Rapid Entire Body Assessment Tool Rapid Upper Limb Assessment Tool Texas Transportation Institute United States Environmental Protection Agency Vehicle Mile Travel Well-to-Pump Washington Industrial Safety and Health Act Well-to-Wheel ix

10 I. Study Overview Municipal solid waste (MSW) is generated by daily activities at homes, hospitals, schools, businesses, and industries. MSW collection is a necessary activity to remove waste from its origin to a location for processing and disposal. MSW collection is associated with occupational injuries due to ergonomic risk factors including lifting, heavy load handling, awkward postures, long task durations and high levels of repetition. In the past, waste has been collected manually from customers resulting in frequent injuries. Technological development has introduced automated and semiautomated collection systems. This report intorduces a comprehensive study that assesses and compares the ergonomic and biomechanic issues associated with waste collection at varying levels of automation including manual, semi-automatic and automatic. Closely tied to ergonomic issues of waste collection are the effects of changes in fuel technologies on collection efficiencies in terms of vehicle use and fuel consumption, as well as vehicle emissions. Increasingly stringent USEPA emission standards and escalating diesel issues have led to the pursuit of alternative fuel sources, reduced road time, and equipment optimization. These ergonomic and environmental aspects will be incorporated in this study, as illustrated in Figure I-1. Waste Collection Vehicles Environmental Study Ergonomic Study Alternative Fuel Technologies Financial Issues Other Factors Loacation Vehicle Design Injuries & Ergonomic issues Associated with: Environmental Issues Operational Issues Strategic Issues Safety Issues Collection Speed Idling Time Fully Automated Collectors Semiautomated Collectors Manual Waste Collectors Figure I-1: Study Overview of Solid Waste Collection. 1

11 II. Ergonomics Study 1. Introduction Municipal solid waste (MSW) collection is an important task that is required globally but is associated with occupational injuries due to ergonomic risk factors including lifting, heavy load handling, awkward postures, long task durations and high levels of repetition. In the past, waste has been collected manually from customers resulting in frequent injuries. Technological development has introduced automated and semi-automated collection systems that, according to manufacturers claims, enhance worker safety and collection productivity and reduce worker compensation claims. Such advantages can balance increases in equipment cost; however capital, operating and maintenance costs of automated collection are higher than manual collection. Six Sigma methodology was applied in this study by following the Define, Measure, Analyze, Improve and Control phases (DMAIC) to identify and analyze the ergonomic risk factors and injuries associated with waste collection tasks (Brady and Allen, 2006). Observational analysis, site visits, laboratory analysis and a review of historical data were performed; in addition, surveys were conducted of haulers and safety personnel to better understand the factors affecting waste collectors safety. The focus of the study was on the type of waste collection tasks performed in residential communities. Comprehensive ergonomics analyses were conducted using the 3D Static Strength Prediction Program TM (3DSSPP) from the University of Michigan to evaluate the physical demands of waste collection tasks by analyzing the spinal compression forces and the static strength requirements for these tasks and comparing the data to NIOSH guidelines (University of Michigan 2010). In addition to the 3DSSPP Program, the research team utilized the Rapid Upper Limb Assessment Tool (RULA) and the Rapid Entire Body Assessment Tool (REBA) to further evaluate the waste collection tasks. A detailed analysis of the injury data collected by the Bureau of Labor Statistics (BLS) was also performed to evaluate the nature and frequency of injury incidents over time in solid waste collection field. 2. Significance of study Waste collection is a physically intensive task that has been associated with severe occupational and musculoskeletal injuries. From the published literature, it was noted that relatively little research has been published on ergonomics and safety in the waste collection industry from a musculoskeletal injury perspective. The field is lacking a comprehensive study that assesses and compares the ergonomic and biomechanics issues associated with the three different types of waste collection approaches commonly used in residential environments, including manual, semi-automated and automated. This study fills this research gap by providing an ergonomic and biomechanics assessment of 2

12 the impact waste collection has on haulers during the three primary approaches to waste collection. 3. Background Solid waste collectors are exposed to safety and ergonomics risk factors including lifting, heavy load handling, long task durations, awkward postures and high levels of repetition. Such risk factors can lead to musculoskeletal disorders and injuries. Rogoff et al. (2010) reported that manual collection crews lift, on average, over six tons per worker per day; the repetition of this heavy lifting throughout a work shift increases the risk of musculoskeletal injury. Verbeek (1991) found that the incidence rate of disability for work among waste collectors in the capital of the Netherlands was about four times higher than the rate among office workers within the same waste collection company. A similar comparison was made by Yang et al. (2001) for waste collectors in Taiwan. The result of the study is that alternative methods have been introduced to waste collection in the organization. For example, pushing and pulling the waste containers to perform has replaced some of the lifting, as the lifting of the containers introduces a greater risk than does pushing or pulling the load. According to Schibye et al. (2001), pulling and pushing waste containers will cause less stress and compression forces on the lower back which should never exceed the acceptable NIOSH limits. Among waste collection employees in the United States it has been reported that musculoskeletal injuries make up about 80% of workers compensation costs (Dorevitch and Marder 2001). A study conducted in Denmark showed that solid waste workers are 5.6 times more at risk of occupational injuries than the general workforce (Poulsen 1995). Also, a study in the Netherlands by Verbeek et al. (1993 cited by An et al. 1999), reported that 15 out of 1,000 waste collection workers were disabled from their work. In Florida, musculoskeletal injuries such as strains or sprains accounted for 47.7% of all reported injuries for municipal solid waste workers from 1993 to 1997 (An et al. 1999). Robazzi et al. (1997) analyzed occupational accidents that occurred among garbage collectors. They concluded that the major cause of the accidents was improper garbage wrapping and the body parts most often injured were the legs, followed by the arms; the early days of the week seemed to favor a higher frequency of occupational accidents than other days. The first four hours of work seemed to favor a higher occurrence of occupational accidents. Choi et al. (2011) identified the characteristics of occupational accidents by work type among municipal sanitation workers. They stressed the most common types of occupational accident were as follows: slips and trips, falls, musculoskeletal disorders (i.e. back injuries and soft tissue injuries), traffic accident, collision, amputation, cut and puncture, crush injuries, strenuous movement, and musculoskeletal disorders showed the highest incidence in large waste collection. Similarly, Ivens et al. (1998) analyzed and described risk circumstances associated with 3

13 injuries among waste collectors. They concluded that better education of the haulers might lower the injury rate as well as a reduction in the working speed. Since many lifting and carrying tasks have been replaced by pushing and pulling tasks in order to reduce the load on the workers, Schibye et al. (2001) compared the mechanical load on the low back and shoulders during pushing and pulling a twowheeled container to the load experienced during lifting and carrying with the same amount of waste. They observed that the torques at the low back and the shoulders are lower during pushing and pulling. A number of factors have been shown to contribute to the likelihood of an injury in the waste collection industry. In a study on the size of the solid waste industry in the US, 53% of the solid waste facilities were owned by the private sector (Beck, 2001). Bunn et al. (2011) compared injuries among solid waste collectors in the private versus public sectors. They concluded that solid waste collectors in the private sector were more likely to have injuries that resulted in a workers compensation reports of injury or claims with awarded benefits than those in the public sector. Assessment of energy expenditure during work and its physical demand is important for the fields of work physiology and worker health, because it provides useful information for determining the physiological workload (Rodahl, 1989). Anjos et al. (2007) reported the physiological assessment of domestic garbage collection conducted in the field. They concluded total energy expenditure was higher on Mondays and Tuesdays than on Wednesdays and Thursdays, probably because of longer working hours resulting from greater amounts of household garbage produced on weekends. These injuries not only have a negative effect on employees but also on the companies who employ them. The cost of treating back injuries varies from B USD annually. Additionally, approximately 93 million work days are lost each year in the United States due to back pain (McGinn, 2005). Furthermore according to the Washington State Department of Labor and Industries, from 2000 to 2004, solid waste collection workers had 640 serious overexertion injuries causing over 71,000 lost workdays. Almost half of them involved lifting, throwing, or carrying garbage containers, whereas 4% of overexertion injuries were caused by pushing or pulling waste containers. In Lakeland, Florida (USA), worker compensation claims of manual collection increased from 2005 to The majority of the claims were related to knee, ankle, wrist and back injuries, where the total compensation cost reached over 1.2 M USD (Rogoff et al. 2010). 4

14 4. Study methodology and discussion The methodology design created a systematic and repeatable approach to accomplish the ergonomic and biomechanical analysis goals. This study implemented the DMAIC phases of Six Sigma methodology as follows: 4.1 Define Phase In the first phase, the problem and the scope of the project were defined and the implementation schedule was developed. Also in this phase a detailed review of the existing literature was conducted on the ergonomics factors and musculoskeletal injuries among solid waste workers. 4.2 Measure Phase The second phase was partitioned into the following tasks in order to achieve the study goals and objectives Statistical Analysis of Industry Injury Data Fatal and non-fatal injury data were collected from the BLS to identify injury trends, as well as the nature and type of these injuries in the solid waste collection industry. The data were statistically analyzed using Excel. As shown in Figure II-1a, the waste collection field had the highest rate of non-fatal injuries among the other fields of waste management industry from 2003 to Figure II-1b shows the trend of fatal injuries in the solid waste collection industry from 2003 to 2010.The fatalities increased by 18% in 2010 as compared to the previous year; as a result the solid waste collection was ranked by BLS report as the seventh most dangerous in the United States. The National Solid Wastes Management Association (NSWMA) will be investigating the reasons behind the increase in solid waste collection fatalities, and they will publish an updated safety manual in 2012 in hopes of reducing fatal accidents (Waste Age 2011). As per the nonfatal injury data for solid waste collection workers, the BLS reports indicated that in 2009 the injury and illness rate per 100 full time employees decreased by 21% as compared to the previous year (Figure II-1c). The National Solid Wastes Management Association (NSWMA) and the Waste Equipment Technology Association (WASTEC) and their member companies initiated several programs to reduce the injuries and improve safety performance. For example; a training video (Be Safe, Be Proud), was developed to highlight the hazards posed by residential waste collection. Also NSWMA has established a Manual of Recommended Safety Practices to help companies deal with the significant safety issues in the waste service industry. Safety training programs were also developed for refuse vehicle drivers such as Coaching the Refuse Driver II (National Solid Wastes Management Association 2011). In addition to recognizing the development of programs to reduce injuries and improve safety, the research team reviewed the level of waste generation for the industry. 5

15 An analysis of waste generation data revealed that there was a decrease in the amount of waste generated over the past several years. The reduction in waste generated may be a factor in the overall reduction of injury since the number of waste collection employees remained constant. 6

16 (a) (b) (c) Figure II-1: BLS Data of fatalities and injuries: (a) Non-Fatal Injuries Rate In Waste management and remediation services; (b) Trend of Fatal Injuries in Solid Waste Collection; (c) Incidence rates of nonfatal occupational injuries and illness in Solid Waste Collection. 7

17 The BLS data of injuries and fatalities were not classified according to the type of waste collection; BLS reports categorize the fatal injuries in solid waste collection according to worker characteristics such as age and gender. From 2003 to 2009, all the injured solid waste collectors were men. The fatal injuries were analyzed according to worker age along with the affected body parts (Figure II-2). On average, 50% of the fatal injuries affected workers years old and 39% affected multiple body parts, however the BLS data do not identify which parts. According to the occupational injury and illness manual developed by the BLS, Multiple Body Parts include two or more divisions of the body (i.e. dislocated shoulder and fractured upper arm). The next highest percentage of fatal injuries involved the head and trunk parts (Bureau of Labor Statistics 2010). Figure II-2: Fatalities injuries from by: (a) age (left); (b) affected body parts (right) Surveys In order to understand the factors affecting hauler safety, two survey forms were designed for distribution to collectors and safety personnel. The objective of the surveys was to obtain details on the waste collectors experience, physical comfort, safety practices and injury history according to different types of waste collection practice. Permission to interview solid waste workers and safety personnel was obtained from the participating waste companies and the Institutional Review Board (IRB) at the University of Central Florida approved the protocol for conducting the study surveys. The hauler survey consisted of multiple choice questions arranged in three sections: Participant demographics, including job description, age, gender and years of residential waste collection experience, Waste collection and safety, consists mainly of the waste trucks types being used, route day duration, number of collection stops, estimated container weight and frequency of safety training programs, and Musculoskeletal injuries, includes questions on frequency and location of injuries/pain. 8

18 The survey for the safety personnel consisted of 19 multiple choice and openended questions including six for addressing the organization profile and 13 for health and safety issues such as the safety and health programs at the company, personal protective equipment, number of injured workers per year, and type of injuries being experienced by waste collectors. The surveys were distributed by to one of the companies, while the rest of the companies were visited by the research team, where the workers and safety personnel met face to face to describe the purpose of the surveys and the study. Overall, the surveys were distributed anonymously to 83 solid waste collectors and four safety supervisors Site Visits The investigators of this research conducted several site visits to observe the waste collectors during their regular working hours in order to identify the potential risks and hazards. Data collection check sheets were prepared; the team used the Washington Industrial Safety and Health Act (WISHA) checklist to assess the risk level for each type of waste collection (Washington State Department of Labor and Industries 2011). The team videotaped the haulers during waste collection and interviewed them to identify their experience in waste collection Two-phase laboratory Study An initial laboratory analysis was used to establish baseline data for the evaluation of waste collection tasks. To obtain details to simulate in the lab, the research team interviewed and videotaped waste collection workers from different views during manual and semi-automated waste collection. Next, the video was imported into Windows Live Movie Maker to view the video frame by frame, capture snapshots of any awkward postures and save them as still photos. For the manual waste collection, the team selected postures of lifting and dumping the waste container, while for the semi-automated waste collection, pulling and pushing poses were selected for the simulation. The information obtained was used to define the physical task the subjects would perform in the laboratory experiment Equipment Equipment utilized in this experiment included a trashcan, a goniometer, a Timex Ironman Heart rate Monitor, Borg scale for Ratings of Perceived Exertion, 3D Static Strength Predictor Program (3D SSPP) and JACK Ergonomic and Biomechanics simulation software. The goniometer was used to measure the various joint angles needed to perform the tasks and determine the inputs required for a RULA and REBA) analysis. The software program, JACK was also used to calculate a RULA score. A heart rate monitor was used during the simulated waste collection task to record participant heart rates in one-minute intervals. Additionally, the Borg scale was used to assess the subjective measure of task difficulty or the rating of perceived exertion (RPE). 3D SSPP was used to analyze the task requirements. Summaries of 3D SSPP, RULA/REBA and JACK follow. 9

19 3D Static Strength Predictor Program (3D SSPP) 3D SSPP is software used to predict static strength requirements for tasks such as lifts, presses, pushes, and pulls. It is applicable to worker motions in threedimensional space. The program provides an approximate job simulation that includes posture data, force parameters and male/female anthropometry. Output includes the percentage of men and women who have the strength to perform the described job, spinal compression forces, and data comparisons to NIOSH guidelines. It is most useful for analyzing slow movements involved with heavy material handling tasks. This is due to assumptions that the effects of acceleration and momentum are negligible. RULA/REBA RULA and REBA are useful tools for quickly determining the general musculoskeletal risk involved in a specific task. They provide an easy observational postural analysis by noting the locations of various parts of the worker s body, such as upper arms, lower arms, neck, trunk, and legs, and taking into account task specific information such as static/dynamic lifts, task frequency, and load. Ultimately, they produce a numeric score where a lower score indicates low risk and a higher score represents a high risk (Marras and Karwowski, 2006). JACK JACK is a comprehensive software system used for human modeling and simulation in ergonomics, biomechanics and physiology. This product was developed by Siemens Corporation and has various modules available to use the program in manufacturing, service and other industries where physical task performance is to be assessed. JACK allows the users to build an environment in which they can create virtual humans and design a work area as well as the associated tasks. The user can assign a single task, or a series of tasks, for the human to complete and then perform an analysis of the outcomes. JACK is particularly useful for ergonomic evaluations and can help to improve product design and workplace tasks. For the purpose of this research, JACK was used to obtain a RULA score to assess the impact of the waste collection task on the upper extremities, head and neck (Simulation Solutions, 2012). Borg Scale for Rating of Perceived Exertion The Borg Rating of Perceived Exertion (RPE) is a subjective method for measuring physical activity intensity level. Perceived exertion refers to a scale of 1 (minimal exertion) to 10 (strong exertion) for how hard one feels their body is working. It is based on the sensations a person experiences during physical activity, including increased heart rate, increased respiration or breathing rate, increased sweating, and muscle fatigue. Although this is a subjective measure, a person's exertion rating have been shown in research studies to have linear relationship with the level of exertion and resulting heart rate during physical activity (cdc.gov, 1998). 10

20 Participants Participants of interest in this research were males and females. Participants were categorized into two groups (group one) based on their age; ages below 30 were referred to as younger subjects, whereas those above 30 were the older subjects (group two). Group one was recruited through engineering classes at the university and contained four subjects. The second group consisted of university employees and individuals in the community; this group included six subjects Method The experiment was partitioned into three stages. The first stage was preparatory and orientation consisting of: Briefing the participant as to the purpose, method and risks of the experiment, Obtaining informed consent from the subject, Equipping the participant with a heart rate monitor, and Recording subject age, height, weight, gender, resting heart rate, frequency of exercise and smoking habits After the paperwork was complete, the REBA assessment portion of the experiment began. The procedure was first demonstrated by the researchers so that participants would understand what was required. The process involved: Lifting the empty trash can into an initial lifting position, Researchers using the goniometer to measure joint angles relating to the neck, trunk, legs, arms, and hands, Taking picture of subjects doing a simulated waste collection task for comparison with the actual waste collectors observed and photographed doing the waste collection task, Lifting the empty trash can into the dumping position, Researchers using the goniometer to measure joint angles relating to the neck, trunk, legs, arms, and hands, and Taking pictures for comparison with the waste collection task The final step in the experiment was conducting the simulated waste collection task (Figure II-3). Prior to running the task, the participants were familiarized with the Borg scale of perceived exertion, the trash can was loaded to 20.9 lbs., approximately half the weight of the average trash can. This average weight was determined from the survey data that were collected in previous stages of the research project. A table was set up to simulate the height of the area on the garbage truck where the waste is deposited for manual collection. The average weight was reduced by half in order to maintain safety in the lab setting. The task consisted of multiple steps: Walking from the table to the trash can location, Pulling the can back to the table, Lifting the trash can, Dumping the contents onto the table, Placing the can back on the ground, Pulling the trash can back to the starting location, and 11

21 Returning to the table. This process was done once each minute for 10 minutes, during which photographs were taken of the subject. After each repetition, participant heart rate and RPE were recorded. Heart rate was also recorded every minute after the task concluded until it returned to within 5% of resting. Figure II-3: Simulated Waste Collection Task in Lab 4.3 Analysis Phase Observational Analysis The WISHA Caution/Hazard Checklist was used for manual and semi-automated collection tasks observed during actual waste collection. The checklist is divided into four body zones; low back, hands and wrists, neck and shoulder, and the knee. Each of these body zones were evaluated according to specific risk factors including posture, weight/force, pinch/grasp grip, repetitive motion, (for computer keyboard entry tasks) and hand/arm vibration. For each of these factors caution and hazard conditions are presented. For the manual collection tasks as shown in Table II-1, the hazard boxes were checked for all body zones except the knee; this means that a work-related musculoskeletal disorder (WMSD) hazard exists and actions should be taken to reduce 12

22 the risk (Washington State Department of Labor and Industries, 2011). Based on the analysis, the hazard box was checked in the low back region because objects were lifted weighing more than 25 kilogram more than 20 times per day. The hazard boxes that were checked in the hands and wrists region were: Gripping with a force of 4.5 or more kilograms per hand more than four hours total per day, and Repeating the same motion with the elbows, wrists, or hands (excluding keying activities) with little or no variation every few seconds more than six hours total per day. As per the neck and shoulder region, the hazard box was checked because the hand is repeatedly raised above the head or the elbow above the shoulder more than once per minute and more than four hours total per day. The hazard and caution boxes were not checked for the knee region because none of the conditions presented in the WISHA check list occurred, such as kneeling and squatting or using the knee as a hammer more than two hours per day. For the semi-automated tasks, the caution box was checked only for the hands and wrists due to the repetitive motion with the elbows, wrists and hands during pulling and pushing the waste container (Table II-1). Table II-1: WISHA checklist results of manual and semi-automated collection. Manual Collection Semi-automated Collection Body Zone Overall Evaluation None Caution Hazard None Caution Hazard Low Back X X Hands and Wrists X X Neck and Shoulder X X Knee X X Survey Analysis The survey data were analyzed using Microsoft Excel and Minitab software. Quantitative and qualitative analyses were applied; also chi-squared test of independence was conducted to determine whether the injuries frequency and waste truck type are independent Waste Collectors Survey Demography Section: This survey was filled out by a total of 83 workers from different waste companies; the majority of the workers were men (96%). The workers were divided into three different age groups: less than 30 years old (22%), between 31 and 50 years (65%) and above 50 years (13%). Almost 47% of the participants work as waste collectors only, 28% as drivers, and 42% as both. As per their experience in waste collection, 30% have been working for more than 10 years and 47% have 2-10 years of experience. 13

23 Waste Collection and Safety Section: Based on the interviews conducted with the waste collectors and safety personnel along with the site visits, the research team conducted simple fish bone analysis to understand the major root causes of the injuries and disorders. As shown in Figure II-4, the root causes were mainly classified into five categories: people, material, machine, methods and workplace conditions. Figure II-4: Fish Bone Analysis of waste collectors injuries. For each of these root causes, the detailed causes that had excessive effect are summarized below: People and Methods: The waste collectors were observed to be careless and unconcerned with significant safety issues, although 57% of the participants stated that they receive safety training on monthly basis and 23% on quarterly basis. However, the research team noticed during the site visits that waste collectors were not following the proper safety techniques during waste collection. For example they were riding on the running step of the truck, collecting waste from both sides of the street which creates other vehicles impact hazard, and using wrong techniques in lifting or dumping the waste container that lead to awkward postures. Waste collectors need frequent reminders to improve their awareness and ensure they use safe handling and lifting techniques. Safety personnel need to observe collection periodically to detect early signs of occupational disorders. 14

24 Machine: The analysis of the survey showed that the type of waste truck plays a major role in the occupational injuries. Approximately 64% of the workers were using manual trucks, 29% automated trucks and 7% semi-automated trucks. Through the interviews, the majority of the waste collectors mentioned that they prefer automated trucks since they do not need to manually lift the waste container. Material and workplace conditions: The survey data indicated that several risk factors and working conditions affected waste collector safety. During the site visits, it was observed that not all workers were wearing protective equipment; safety supervisors should ensure the availability and accessibility of proper personal protective equipment (PPE) and that all workers are using them. As shown in Figure II-5a, 63% of the workers reported that the number of stops collected on average route day were more than 800. Also 54% estimated that the average container weight is kg. As per the average route day, 69% of the workers mentioned that they work more than eight hours per day, whereas 31% work eight hours or less. High repetition of tasks, long work duration and frequent lifting of heavy loads are significant factors that could result in chronic injuries. In addition to the musculoskeletal risks, working outdoors can cause other occupational disorders due to weather conditions, air and noise pollution, and traffic. According to the BLS, 62% of the solid waste collection fatalities in 2010 were caused by transportation incidents. Figure II-5: Survey results of waste collection and safety section: (a) Number of stops collected on average route day (left); (b) estimated average container weight (right). Musculoskeletal injuries Section: Waste collection tasks could be associated with severe musculoskeletal injuries if proper safety techniques are not taken into consideration. In this section, waste collectors were asked to identify how often they experienced injuries, pain or discomfort during the last 12 months. The injuries frequency was categorized for 22 body parts as never, rarely, sometimes, often (at least once a week) and daily. Twenty seven percent of the workers reported that they have not 15

25 experienced pain or injuries in any body part during the last 12 months. Figure II-6a presents the number of waste collectors who declared experiencing injuries or pain at least once per week in each body part listed. The lower back was the most affected body part due to torso twisting and heavy lifting of waste containers which exposes the spine to compression forces and produces low back pain as well as an increased risk of injury. Forearms, upper arms and upper back were the next body parts that were most frequently affected. It was noticed that during lifting or dumping the container, waste collectors were working with hands above the shoulder, which represents a risk factor for musculoskeletal disorder. 16

26 (a) (b) Figure II-6: Musculoskeletal injuries/pain: (a) Number of waste collectors who experienced injuries or pain in each body part at least once per week; (b) Lower back injuries occurrence according to the type of truck. 17

27 Figure II-6b presents the lower back pain occurrences to waste collectors of automated and manual trucks. In order to increase anonymity among individuals in responses, one of the companies chose to provide summary data rather than individual survey response. The majority of waste collectors who were never exposed to lower back pain were working on automated trucks; in contrast, most of workers who experienced lower back pain were working on manual trucks. The same conclusion was found for the forearms; upper arms and upper back. Semi-automated trucks were not analyzed, since none of the surveyed workers worked only on semi-automated trucks. Furthermore, in order to understand the relationship between low back pain/injuries and the truck type, a Chi-squared test of independence was conducted. The assumed null hypothesis (H 0 ) stated that lower back pain/injuries frequency and waste truck type are independent. The results showed that the P value is significant (p <0.05) which means that at a 95% confidence interval, lower back injuries frequency and waste trucks type are not independent Safety Supervisors Survey A Safety Supervisor survey was developed and initial data collection in this area was performed. Four safety supervisors were surveyed. When they were asked which task represented the greatest risk to waste collectors, 34% of the collected answers were for lifting tasks and 25% for contact with objects and equipment (Figure II-7a). Figure II-7: Safety supervisor survey results: (a) Tasks that present the greatest risk to waste (left); (b) Injuries types experienced by waste collectors (right). Three safety supervisors stated that the average number of injured workers per year according to OSHA300 logs, ranged from 1-10 workers. As per the type of injuries experienced by waste collectors, 62% of the answers were equally distributed among strain/sprain and laceration as shown in Figure II-7b. 18

28 The information collected from the safety supervisors provides a different perspective and additional insight into the ergonomics issues in waste collection, however due to the small sample size the results cannot be considered conclusive. Additional data collection from safety supervisors is suggested Preliminary Laboratory Analyses The research team used the 3D Static Strength Prediction Program TM (3DSSPP) version developed by the Center for Ergonomics at the University of Michigan. The research team interviewed and videotaped the workers from different views during manual and semi-automated waste collection. Next, the video was imported into Windows Live Movie Maker to view the video frame by frame, capture snapshots of any awkward postures and save them as still photos. For the manual waste collection, the team selected postures of lifting and dumping the waste container, while for the semiautomated waste collection, pulling and pushing poses were selected for the simulation. While a participant performed the above tasks, the research team measured the joint angle inputs using goniometer and entered them along with the anthropometric data in the 3DSSPP software, and then a virtual human with the above data was created in the software. Some of the limb orientations and postures of the virtual figure had to be manually manipulated to attain a similar pose. The following analysis was completed for each posture: Low Back Analysis (forces on L4/L5): 3DSSPP calculates the compression forces on the L4/L5 vertebral disc and compares to the NIOSH Back Compression Design Limit (BCDL) of 3400 N and the Back Compression Upper Limit (BCUL) of 6400 N (University of Michigan, 2010), Static Strength Prediction Percentage Capable (% Capable): 3DSSPP used the NIOSH recommended limits for the percent of the population with sufficient strength to perform the specified task. The Strength Limits in the program are named Strength Design Limit (SDL) and Strength Upper Limit (SUL) and they match the NIOSH Action Limit (AL) and Maximum Permissible Limit (MPL). The SDL is set at 99% for men or 75% for women, while the SUL is set at 25% for men or 1% for women (University of Michigan 2010). 19

29 Manual Collection results Figure II-8:Lifting Posture: (a) Photo of the interviewed waste collector; (b) 3DSSPP TM virtual figure; (c) Orthogonal-view in 3DSSPPTM (Side view) Table II-2: Strength capability of the body joints for each task. Joint Lifting Task (% Capable) Dumping Task (% Pulling Task (% Capable) 20 Pushing Task (% Capable) Capable) Wrist Elbow Shoulder Torso Hip Knee Ankle Lifting the waste container: Table II-2 shows the strength capability (% Capable) of the body joints for each task. Figure II-8 shows an example of a waste collector during lifting the waste container. In this pose, the waste collector was not bending his/her torso so it did not require high flexion to lift the waste container; the weight of the waste container that was used in the simulation was around 41.8 lbs. The low back compression force was calculated as 2645 N. This value is within the acceptable level according to NIOSH standards. The compression force will be higher if the waste collector lifts a heavier container and bends his/her torso. The percent of the population capable of performing this task with the necessary posture ranges from 41-99%. The wrist area exhibits the most strain (41% of population capable of performing this task) due to the awkward position while lifting the container. The percent capable of all body areas (except the elbow) are below the NIOSH action limit value. The ankle area requires

30 significant attention as well, since it was located in an awkward pose for this posture. Repeating this task several times during the day may cause musculoskeletal disorders. Dumping full waste container: Dumping the waste container was the riskiest task due to the way the worker lifts the garbage container. The low back compression force is calculated as 3491 N. This exceeds the NIOSH Back Compression Design Limit of 3400 N. Workers should avoid twisting postures while dumping the waste container to limit awkward postures of the body joints. The percent of the population capable of performing this task ranges from 11-99%. The torso area exhibits the most strain; according to the analysis, only 11% of the population is capable of performing this task, falling below the NIOSH Upper Limit Value Semi-Automated Collection Pulling the waste container: The compression force L4/L5 for pulling the waste container is calculated as 600 N representing only a slight risk of low back injury. The percent of the population capable of performing this task ranges from %. The wrist, shoulder and hip are slightly below the action limit. Seventy-five percent of the population is capable of performing this task with respect to the ankle joint due to uneven footing. Pushing the waste container: For this posture, the worker bends his/her torso to push the waste container and returns it back to its original place. The low back compression force is calculated as 1577 N which is within the acceptable level according to NIOSH standards. The percent of the population capable of performing this task ranges from 79 to100%. The torso, knee and ankle are slightly below the action limit. Seventynine of the population would be able to perform this task with respect to the hip joint Advanced Laboratory Analyses The preliminary analysis only utilized 3DSSPP while the Advanced Laboratory Analysis was designed to get more in depth results, thus the REBA and RULA methods were added to the analysis. REBA, as the name describes, allows assessment of the entire body while RULA analyses are focused on the upper extremities. The advanced laboratory analysis also evaluated two different waste collectors also demographics; younger subjects (participants below the age of 30) and older subjects (participants above the age of 30). Using 3D-SSPP ergonomics analysis software the research team analyzed the lifting and dumping postures observed in the experiment. This package was also used to evaluate the strength requirements and the impact of the different tasks on the lower back. As stated, both the REBA and RULA techniques (with the JACK Software and by hand) were used to assess the risks associated with the task. The results of these outcomes was compared to assess the differences in the results from each software. The research team also assessed the usability, or ease use, for each software package. These results are being 21

31 evaluated as an aspect of a different study. The goniometer was used to measure the various joint angles needed to complete RULA and REBA. Furthermore, RULA score was obtained with using JACK software for comparison with the hand calculated scores Methods The experiment was broken up into three stages. The first stage was preparatory and consisted of: Briefing the participant as to the purpose, method and risks of the experiment, Obtaining informed consent from the subject, Equipping the participant with a heart rate monitor, and Recording subject age, height, weight, gender, resting heart rate, frequency of exercise and smoking habits. After the paperwork was complete, the RULA/REBA assessment portion of the experiment began. The procedure was first demonstrated by the researchers so that participants would understand what was required. The process that the research team followed included the following: Moving the empty trash can into an initial lifting position, Researchers using the goniometer to measure joint angles relating to the neck, trunk, legs, arms, and hands, Taking pictures of the subjects during the simulated task for comparison with the waste collection task analyzed when observing actual waste collectors. Lifting the empty trash can into the dumping position, Researchers using the goniometer to measure joint angles relating to the neck, trunk, legs, arms, and hands, and Taking pictures of the subjects during the simulated task for comparison with the waste collection task analyzed when observing actual waste collectors. The final step in the experiment was simulating the waste collection task (Figure II-3). Prior to running the task, the participants were familiarized with the Borg scale of perceived exertion, the trash can was loaded to 41.8 lbs. for the younger subjects (average weight for a trash can), however 20.9-lbs trash cans were used for older subjects to maintain safety in the lab setting. Finally, a table was set up to simulate the height of the garbage truck. The task consisted of the subjects following the previously described in the process. The specific activities for the subjects included the following steps: Walking from the table to the trash can location, Pulling the can back to the table, Lifting the trash can, Dumping the contents onto the table, Placing the can back on the ground, Pulling the trash can back to the starting location, and Returning to the table. 22

32 This process was done once a minute for 10 minutes, during which pictures were taken. After each repetition, the participant s heart rate and RPE were recorded. Heart rate was also recorded every minute after the task concluded until it returned to within 5% of resting Results and Discussion Younger Subjects The younger subject consisted of four subjects The ages ranged from age 22 to 29 years. The same procedure utilized in the Preliminary Analysis was used for this large group of subjects in the younger group. A subject was selected to perform the above tasks and the research team measured the joint angle inputs using goniometer and entered them along with the anthropometric data in the 3DSSPP software, and then virtual human was created in the software. Some of the limb orientations and postures of the virtual figure had to be manually manipulated to attain a similar pose. The results of the laboratory analysis are provided in Appendix B Older Subjects The participants were males and females above the age of 30. Four men, ages 30, 46, 60 and 65, and two women, ages 38 and 48, took part in this study. RULA/REBA Measurements for RULA and REBA were taken for the six participants, four male and two female. The results of the analyses are shown in Table II-3 which depicts the individual scores for each subject. For REBA, the following applies: A indicates the score associated with the neck, trunk and legs, B indicated the score associated with the arms and wrists; C is a composite score obtained from a reference table (contained in the software or available separately) on the A and B scores, the Activity score is based on static/dynamic lifts and frequency of lifts, and Final indicates the overall REBA score for the task (the sum of C and Activity). Similarly, for RULA, the following applies A represents the arms and wrists score, B: represents the neck, trunk and leg score, and C is the overall RULA score associated with the task. Results that indicated low risk (1-3 for REBA, 1-2 for RULA) were displayed in green, medium risk (4-7 for REBA, 3-4 for RULA) in yellow and high risk (>7 for REBA, >4 for RULA) in red. The results for the lifting posture were consistent for both sides of the body. The lowest REBA score was a 5 which occurred based on data taken from the lifting side of the body. This score indicates that there is a moderate amount of risk associated with this posture, it should be investigated further and perhaps changed. The highest REBA score 23

33 was a 9, obtained on the left side of the body. It value for REBA represents high risk and the task should be changed immediately. RULA produced scores of 6 and 7 for all four participants. These are also high risk scores. There was more variability in the dumping scores based on which side of the body was observed. Low risk REBA scores (2, 2, 2 and 2) were obtained for the left side of all participants, but moderate risk scores were obtained for the right side (4, 5, 6 and 7). RULA produced scores associated with moderate risk for the left side of the body (3, 3, 4, and 4) and moderate to high risk for the right side (4, 6, 6 and 6). A RULA score was also computed with the aid of the JACK simulation software. This score, listed in Figure II-9, was based on a simulation of the entire task, as opposed to the individual postures. JACK reported a RULA score of 3, which indicates a moderate risk associated with the task. This score is lower than expected. Possible reason for the inconsistency will be discussed later. Table II-3: REBA and RULA Scores. REBA RULA Lift Left Side A A B B C C Activity Final Lift Right Side A A B B C C Activity Final Dump Left Side A A B B C C Activity Final Dump Right Side A A B B C C Activity Final

34 Figure II- 9: JACK RULA Score The results obtained using RULA and REBA indicated that the lifting and dumping postures are in need of change as soon as possible. There was some variability in the scores depending on which side of the body the measurements were taken. However, the lower scores, such as those obtained on the left side of the dumping posture, are negated by the higher ones because the higher scores represented the worst case scenario. For the lifting posture, both the RULA and REBA scores indicated that the most improvement could be gained by adjusting the neck, trunk, and legs. The trunk was the most notable contributor to risk of the three. The best way then to reduce the RULA/REBA score would be to reduce the amount of bending at the trunk. The arms and wrists scores were also high but it would be difficult to change the arm angles due to the location of the handles on the trash can. For the dumping posture, the arms and wrists scores were the main contributors. Again it would be difficult to improve these scores due to the locations of the handles on the can. The best solution may be to use a different trash can with handles that are closer together. Outside of the laboratory setting however, this solution would not be practical because people are able to purchase a large variety of garbage cans. It may also be important to note that the scores seen in the results section would have been higher had the weight not been reduce to half that of the average weight can. The RULA results obtained through the use of JACK may not be reliable as there was a challenge in fully replicating the task in the system. Thus the simulation that was created did not ideally reflect the manner in which a normal human would lift and empty the trashcan in the waste collection task. Specifically, the simulated human bent his left 25

35 arm in an manner that is impossible for humans to do when grasping the top of the can (represented by the solid object, Figure II-10). The dumping posture was also inconsistent with actual human movement because the model s left hand moved closer to the middle of the trashcan, instead of grasping the top, and the right hand came completely off of the can (Figure II-11). These inconsistencies may have affected the results of the RULA computations in JACK and therefore these results cannot be relied on for an accurate comparison with the RULA values obtained through hand calculations. These inconsistencies were attributed to necessary updates needed for the software. These updates have since been installed in the JACK software. However, the overall results of the RULA and REBA analysis can be considered reliable as all measures were also calculated by hand and using Microsoft Excel. Figure II- 10: Jack Simulation Lifting Posture. 26

36 Figure II- 11: JACK Simulation Dumping Posture. Heart Rate/RPE The correlation between heart rate and RPE was also examined. A scatter plot (Figure II- 12) was used to graph the participants heart rates (x-axis) against their RPE (y-axis). The results displayed an overall positive relationship between the two variables. This relationship can be interpreted to mean that as heart rate increased, RPE had a tendency to increase as well. 7 B o r g R P E Heart Rate (Bpm) Figure II-12: Chart of Heart Rate v RPE The positive correlation between the variables was expected and logical in that as a person exerts himself his heart rate should increase as should his perception of that exertion. If the full 41.8 lbs. load had been used, it would be expected that the heart rate 27

37 and RPE values would have been higher, but the same positive relationship would have been seen. For this reason, the results obtained in the lab setting are an accurate reflection of those one would expect to see out in the field. In other words, for the task activities that were expected to be more demanding, the study reveals a higher RPE with an increased heart rate thereby validated the hypothesis that the task is intensive when performing these actions 3DSSPP 3DSSPP was used to analyze the lifting and dumping postures observed in the experiment. Joint angles collected for the RULA/REBA analysis were used to create the postures seen in Figures II-13 and II-14. Figure II-13 represents the general lifting posture that participants used. 3DSSPP determined that only 86% of the population would be able to lift 20.9 lb. in this posture. The limiting body part in this pose was the hip. Additionally, the program reported the compression on the L4/L5 to be 936 lbs. Figure II-14 models the dumping position. This posture was significantly better than the lifting as 96% of the population would be capable of performing it. The compression of the L4/L5 vertebra was also greatly reduced to 286 lbs. Figure II- 13: Lifting Posture in 3DSSPP 28

38 Figure II- 14: Dumping Posture in 3DSSPP. Additional models were created in 3DSSPP based on postures that differed from those measured for the RULA/REBA assessment. The postures were observed while participants performed the timed portion of the experiment and picture and/or videos were taken to document them. The first of these was a dumping pose where the subject leaned backwards (Figure II-15). The 3DSSPP analysis (Figure II-16) revealed that 95% of the population would be able to dump the trash can in this posture. The limiting factor was the model s wrist. Furthermore there were 203 lbs. of compressive force on the L4/L5. 29

39 Figure II- 15: Observed Back-Arched Dumping Posture 30

40 Figure II- 16: Observed Back-Arched Dumping Posture in 3DSSPP. The second unique posture observed was a lift where the subject used only one hand to lift the trash can to about chest height, seen in Figure II-17. The 3DSSPP model predicted that only 65% of the population would be able to perform the lift in this fashion due to the requirements on the wrist (Figure II-18). The reported L4/L5 compressive force was 537 lbs. 31

41 Figure II- 17: Observed One- Handed Lifting Posture. Figure II- 18: Observed One-Handed Lift in 3DSSPP The 3DSSPP analysis of the two postures (lifting and dumping) where the joint angles were measured revealed that only 86% of the population would be able to perform the task given that these two postures were used. This value should ideally be in 32

42 the high nineties thus 3DSSPP indicates that the task needs to be changed, which supports the results from the RULA/REBA analysis. The compressive force of the L4/L5 also supports the conclusion that task needs improvement. It is also important to realize that only 20.9 lb., half of the average container load, was used in the analysis. When the load was increased to 41.8 lbs, only 57% of the population would be able to perform the lifting posture (Figure II-19) while 70% would be able to do the dumping posture (Figure II-20). Overall, 57% of people would be able to perform the task using the two postures measured. The compressive force of the task increased to 1232 lbs. Reducing the amount of bending in the trunk, as suggested in the RULA/REBA section, would also improve the 3DSSPP results. Therefore it is suggested that, waste collection employees should minimize bending at the trunk whenever possible. However, completely eliminating bending may not be a viable solution as will be demonstrated below. Figure II-19: Lifting Posture with 41.8 lb. (19 kg) load. 33

43 Figure II-20: Dumping Posture with 41.8 lb. (19 kg) load. The two other postures were also reanalyzed with a 41.8 lb. load. The first, the dumping posture with an arched back (Figure II-20), could be performable by only 78% of the population but would only have 221 lbs. of compressive force on the L4/L5. The compressive force result is a bit surprising because the back-arching would assumedly use the back to support more of the load. Despite this, it is still not an optimal posture and is not recommended for waste collectors. The second posture, lifting with only the left hand (Figure II-22), would be one solution to minimize the amount of bending done at the trunk. However, the lift was not performable by any of the population. The program computed that the load placed on the wrist and shoulder would be more than a human could hold while maintaining the given posture. It is important to note that a lift done in this fashion would be performed with a quick burst of strength, and the posture would not be held statically at all. The ability to analyze the momentary impact of sudden movements is limited in 3DSSPP (mentioned in the methodology section). As a result, the load is correct as calculated by 3DSSPP however, the fact that the activity is performed for such a short time must be considered in the interpretation of the results. In other words, the impact may not be as significant as the load would suggest since the task is performed for such a short duration. 34

44 Figure II-21: Observed Back-Arched Dumping Posture at 41.8 lb. Figure II-22: Observed One-Handed Lifting Posture at 41.8 lb. 35

45 4.4 Improve and Control Phases The information and analysis obtained from the measure and the analyze phases provided insight into the overall ergonomics risks of waste collection. In order to improve waste collector health and safety and to control the ergonomics risk factors, several recommendations were developed by the research team based on the statistical and laboratory analysis. Periodic surveillance should be conducted for haulers to detect early signs of occupational musculoskeletal disorders. Periodic health tests can also be done for this purpose. Safety personnel should interact with all workers on a regular basis by distributing questionnaires or conducting regular meetings concerning the work load, physical demands and work-related complaints. Job rotation should be considered between waste collection and driving the waste truck. Efforts should be made during training to promote postures that are less strenuous in physically intensive task activities. Waste collectors should take frequent breaks during the day. Education on safety and ergonomics should be provided for waste collectors and safety personnel. Waste collectors need to be educated on the basics of ergonomics and biomechanics so they will be able to keep the body in an ergonomically neutral posture. 5. Comparison of JACK and 3DSSPP Although the young subject pool included a group of 25 participants, the results for two subjects is presented in the discussion of the specific details associated with the comparison of 3DSSPP and JACK. Although 3DSSPP and JACK indicated that the compression force L4/L5 for this pose acceptable; the force is high and close to the Back Compression Action Limit. In 3DSSPP the force was 2566 N while in JACK it was higher by 26%. For the percent capable, similar to the previous pose, it was noticed that there is a significant difference between both packages, for the population strength in the shoulder joint; JACK indicated that only 2 % of the population will be able to perform this pose, while 3DSSPP indicated that 89% of population will perform this pose. Analysis Recommendations: The low back compression force of is below the NIOSH Back Compression Action Limit of 3400 N, representing a nominal risk of low back injury for most healthy workers. 36

46 Figure II-23: Lifting Posture: (a) Photo of the interviewed waste collector; (b) JACK virtual figure; (c) 3DSSPP virtual figure Table II- 4: Comparison of JACK and 3DSSPP L4/L5 Forces for Subject 1, Task 2 JACK 3DSSPP L4/L5 Compression Force (N) Table II- 5: Comparison of JACK and 3DSSPP Strength Capability Summary for Subject 1, Task 2 JACK 3DSSPP Joint (% Capable) (% Capable) Wrist Elbow Shoulder Torso Hip Knee Ankle

47 The results of both simulations concurred that dumping the waste container was the riskiest task for the waste collection workers due not only to the excessive load but also because of the way the worker is lifting the garbage container. The low back compression force in 3DSSPP and JACK exceeds the NIOSH Back Compression Design Limit of 3400N. Workers should avoid twisting while dumping the waste container to avoid awkward postures of the body joints. The JACK low back analysis report suggests the following ways to reduce the back compressive forces: Reducing the weight of the load. Changing the job environment such that the worker does not need to stoop to lift the load (avoid having to bend over). Ensuring the load is small, such that it can be held close to the body. Avoiding asymmetric (twisted) postures. The percent of the population capable of performing this posture ranges from 11-99% according to 3DSSPP. The torso area exhibits the most strain; 11% only of population is capable of performing this task, this percent falls below the NIOSH Upper Limit Value. On the other hand, JACK indicated that 92% of the population will be able to perform this task with respect to the torso joint. As per the knee joint 100 % of the population will be able to perform this pose, while 3DSSPP indicated that 70% of population will perform this pose. Also, it was noticed that there is a significant difference between both packages for the wrist joint; JACK indicates that 98% of population will perform this pose while according to 3DSSPP only 42% of the population will be able to perform the dumping task. This difference is due the degrees of freedom in manipulating the joints in 3DSSPP. For the other joints, both packages indicated that they would fall within the yellow zone which indicates a moderate risk level. 5. Conclusions In conclusion, from the review of the literature, survey analysis, observation analysis and laboratory experiments, the collection of waste using automated and semi-automated vehicles results in substantially less force and risks to the musculoskeletal system than performing the task with a manually operated waste collection vehicle. 5.1 Survey Analysis Musculoskeletal injuries Section: Waste collection tasks are extremely precarious and could be associated with severe musculoskeletal injuries if proper safety techniques were not taken into consideration. The majority of waste collectors who were never exposed to lower back pain were working on automated trucks; in contrast, most of workers who experienced lower back pain were working on manual trucks. The same conclusion was found for the forearms; upper arms and upper back. Semi-automated trucks were not analyzed, since none of the surveyed workers were working on semi-automated trucks only. However, given the known risk factors, the hypothesis is that works performing the tasks on a semi-automated truck would experience less musculoskeletal risk than those performing the task with a manual waste collection vehicle. 38

48 5.2 Observation Analysis The conclusion of the observation analysis indicates the presence of various musculoskeletal risk factors to the lower back, upper extremities and hands. According to the observational analysis and the videos, manual waste collection requires lifting the garbage container by elevating the shoulder and upper arms at high distance, representing an awkward posture and excessive compressive forces. Thus, this phase of the research indicates risks that can lead to musculoskeletal injuries. 5.3 Laboratory Experiment The laboratory study conclusions from Groups one and two demonstrate presence of musculoskeletal injury risks to the lower back (L4/L5), upper extremities and as well as the hands and wrist. The research found that the waste collection task is in need of improvement from an ergonomics and biomechanics perspective. The RULA and REBA scores calculated from all sources indicated serious risk and a need for immediate change in task design. The 3DSSPP results also supported the need for improvement. The values for RULA and REBA analysis using JACK in the in the laboratory study resulted in risk levels which would represent a risk of musculoskeletal injuries. Major differences in the 3DSSPP results were observed when the 20.9 lb. load was increased to 41.8 lb. Specifically, the risk on the musculoskeletal system (back and arms) was notably more serious with the use of the 41.8 lb. load. Improvements in risk of injury for these areas can result from implementing the following task modification approaches; Changing the height/handle of containers in order to reduce the amount of bending in the trunk Reducing the level of repetition Modifying the handle locations Reducing the weight of the containers Increasing recovery periods by introducing additional work breaks throughout the task These conclusions are consistent with what was found in the literature for similar levels of physical exertion, lifting and awkward posture. Overall, the musculoskeletal risks associated with task performance for manual waste collection is significant and automated devices as well as task redesign should be considered to reduce risks of musculoskeletal injury. Suggestions for mitigating risk in addition to task redesign should include periodic surveillance of waste collectors to determine early signs of musculoskeletal disorders and the incorporation of ergonomic specific training in the safety training for waste collectors. 39

49 6. Future Work According to the results of this project, there is a need to expand the research to include more in-depth biomechanical analysis of the waste collection tasks, including gaining access to semi-automated vehicles for extensive analysis. A detailed biomechanical analysis is needed to further understanding through the use of the JACK software to perform different analyses that were beyond of the current research scope; this will include additional postures and a rating associated with the duration of task performance. Additional analysis should include Metabolic Energy Expenditure, NIOSH Lifting Analysis to assess impact of duration and repetition, Predetermined Time Analysis and Fatigue/Recovery Time Analysis. Furthermore, JACK software is enabled with a Motion Capture Module that allows direct input of 3D motion data of an actual human subject. The future analysis is expected to provide additional insights into the risks as well as lead to the establishment of ergonomics-based guidelines for enhancing task performance, safety and productivity (Siemens PLM Software, 2011). Moreover, the research team realized the need to highlight an important area in waste collection field; which is the ergonomic issues and occupational risks during disaster debris management. Management of debris is a concern after any major disaster and debris removal is needed to facilitate the recovery of the region. While it presents many ergonomic problems and risks, a literature search revealed that relatively few publications exist on ergonomics and safety in the waste collection industry during disaster management. 40

50 III. Environmental Study 1. Introduction MSW is generated by daily activities at homes, hospitals, schools, businesses, and industries primarily from two sources, residential (55-65%) and commercial (35-45%) (Bueno, 2011). The Municipal Solid Waste Management (MSWM) industry is affected by multiple factors including changes in population, waste generation rates, technology, consumer behavior, and the state of the economy. The United States Environmental Protection Agency (USEPA) estimated that over 250 million tons of MSW were generated in 2010 (USEPA, 2012). This rate has been increasing steadily during the last three decades as a result of rising per capita waste generation rate (from 2.68 lb./capita/day in 1960 to a maximum of 4.72 lb./capita/day in 2000) and the growth of the US population. In addition to demands imposed by increasing population and waste generation rates, collection services have expanded the frequency and type of waste collection. Now, most collection services include three services lines for each household - collection of trash, recyclables, and yard trimmings. Such a schedule requires waste collection vehicles to pay up to four visits to each household per week, compared to two visits that were scheduled in previous decades, resulting in at least a two-fold increase in fuel consumption and multiplying both the cost and the environmental impact of solid waste collection. Consequently, it is imperative to increase collection efficiency and decrease vehicle emissions in order to reduce this environmental impact. Waste collection, the most fuel-intensive step in waste management, accounts for 40-60% of the total MSWM budget (Bueno, 2011). The majority of the US collection fleet is composed of diesel-fueled vehicles which contribute significant atmospheric emissions, including greenhouse gases (GHG). Approximately 91% of the 136,000 waste collection trucks, 12,000 transfer vehicles and 31,000 dedicated recycling vehicles are diesel-fueled, and 40% are more than 10 years old. Each truck travels an average of 25,000 miles (40,000 km) annually, with fuel efficiency averaging less than three miles per gallon (mpg, 1.3 km per liter) (Gordon et al., 2003). This fuel efficiency is considered to be low even when compared to other heavy-duty vehicles which average 7 mpg (3 km per liter) (Huai et al., 2006). In 2009, less than 1% of the US collection fleet was using alternative fuels (Rogoff et al., 2009). Clearly, the waste collection industry has a significant carbon footprint. Moreover, more stringent emission standards as well as diesel price stability, national energy security policies, and pollution concerns have led to the investigation and testing of alternative fuels, reductions in road time, and optimization of equipment. Manufacturers claim that switching to alternative fuel technologies saves fuel costs compared to diesel although such performance is not always measured under actual operating conditions. The main objective of this part of the study, therefore, was to develop a better understanding of the environmental impact of alternative fuel use by waste collection vehicles. Moreover, factors associated with alternative fuel technologies such as security, financial, operational, and safety issues were considered. 2. Background Prior to metropolitan organization, humans disposed of their garbage by placing it in nearby piles. Later, humans understood the need for moving wastes away from their cities which necessitated the use of waste collection vehicles. In 500 BC, the Greek city of Athens established the first ever refuse removal system. The Athenians consolidated 41

51 their garbage in a dump that was located at a distance of one mile from the city walls (Hadingham et al., 1990). 2.1 History of Waste Collection Vehicles Two centuries ago, Western garbage hauling vehicles consisted of two-wheeled, horsedrawn carts which were used to collect waste from consolidation barrels. Waste collectors emptied these barrels into a wagon bed in order to move waste away from the city. The first motorized collection trucks appeared in The open-top trucks were not ideal for hauling garbage since they attracted insects and emitted malodorous fumes. Later in the 1920 s, Britain manufactured the first covered-body, motorized truck specially designed for hauling garbage. This model is considered to be the first prototype for today s waste collection vehicles (Montville, 2001). Post-World War II America s strong economic growth and increased population led to a significant escalation in waste production and a concomitant burden on municipal governments to remove greater volumes of waste. To accommodate this increased volume, the garbage truck industry incorporated hydraulic rams on its vehicles to compress trash as it was collected. Also during this era, the US collection fleet completely modernized, converting the fleet of the horse drawn carts into motorized collection trucks (Montville, 2001). Up to this time, municipal waste had been manually collected from commercial and residential clients, causing frequent injuries to the collection workers. In the 1970s, the waste management industry developed automated and semi-automated collection systems that were designed to enhance worker safety and reduce injury, while simultaneously improving collection efficiency by facilitating high-speed collection (Montville, 2001). The city of Phoenix was the first to introduce automated side-loader collection vehicles in their fleet in the 1970s. The move was an attempt to minimize the back-breaking nature of waste collection (Rogoff et al., 2010). Despite the great improvement in waste collection vehicles, the industry continues to rely almost completely on diesel fuel; and, in fact, for the last century, diesel-fueled refuse trucks were considered the backbone of the waste management industry. However, the aging US fleet and their once-tolerable operational specifications have become unacceptable by present-day standards (Gordon et al., 2003). These trucks are now notorious for their adverse impact on the environment, worker safety, and public health. In addition, the Occupational Safety & Health Administration (OSHA) standards specify that the maximum permissible noise level for eight hours of exposure (the average daily route) is 90db (US Department of Labor, 2012). However, diesel-fueled waste collection vehicles can easily exceed theses noise levels and potentially cause hearing problems (Gordon et al., 2003). Today, the US government is considering a cross-cutting strategy which modifies carbon pricing to reduce GHG emissions; this can be accomplished by adopting a carbon cap and trade system, carbon tax system, or a higher motor fuels tax. This system provides an incentive for waste collection companies to consider alternative fuel technologies (US Department of Transportation, 2010). Hybrid electric vehicles are 42

52 available and technological developments in energy storage, drive trains, and engine management will lead to their increased use. Compressed Natural Gas (CNG), Liquefied Natural Gas (LNG), and bio-fuel (bio-gas and bio-diesel) waste collection vehicles are becoming part of the US national fleet. 2.2 Strategies to reduce GHG emission of Waste Collection Vehicles The primary GHG produced by waste collection vehicles are CO 2 (carbon dioxide), CH 4 (methane), and N 2 O (nitrous oxide). Waste collection operations primarily use heavyduty diesel trucks which are highly regulated by the USEPA. In order for conventional diesel to meet the 2007 emission standards, manufacturers have had to install highefficiency catalytic exhaust control devices that limit emission of particulates, NO x, and non-methane hydrocarbons. The use of low-sulfur diesel fuel has further reduced emissions at greater fuel cost. These technologies reduce emissions to the atmosphere, but ironically add considerable weight and increase the cost and fuel demand of the collection vehicles. A study done by the US Department of Transportation (2010) recommended the following strategies to reduce GHG emissions from waste collection vehicles: Introduce low-carbon fuels (alternative fuels): The introduction of low-carbon alternative fuels into the waste collection industry would generate fewer GHG emissions to the atmosphere. Examples of alternative fuels that could be adopted by waste collection vehicles include: CNG, LNG, bio-diesel, and bio-gas (such as landfill gas (LFG) which is considered a sustainable, clean, and cheap source of natural gas which can be used in CNG and LNG waste collection vehicles), hydrogen, and dimethyl ether (DME). Increase waste collection vehicle efficiency: Strategies for increasing waste collection efficiency include adopting advanced engine models, advanced transmission designs which will Operate In Gear At Idle (the OIGAI concept was developed with the intention of lowering operating revolution per minutes and therefore reducing operating noise and fuel usage); automated manual transmission (AMT) such as the Volvo I-shift which uses microprocessors that continuously monitor the vehicle speed, acceleration, and torque demand as well as operating conditions such as road grade and air resistance selecting the right gear at the right time, thus improving fuel efficiency (Volvo Group, 2012); lighter-weight vehicle construction materials; and Hydraulic Launch Assist (HLA). HLA is capable of capturing and storing energy during braking which can be used to initiate the next acceleration of the vehicle, improve the vehicle efficiency and reduce wear on brake pads at the same time (Hall, 2010). Improve the waste collection route: Planning of individual collection routes can minimize unnecessary mileage, thereby reducing fuel consumption and the associated GHG emissions caused by waste collection activities. Routing has been the subject of many studies (Beltrami and Bodin, 1974; Chang and Lu, 1997; Kim et al., 2006). 2.3 Waste Collection Vehicle Actual Driving Cycles Researchers analyzed the New York Garbage Truck Cycle (NYGTC) by recording the characteristics of their operation (Clark et al., 1998). The NYGTC consists of repetitive stop and starts typical of waste collection practices. The NYGTC operational pattern 43

53 highlights the opportunity for waste collection vehicles to operate under OIGAI, and at the same time, demonstrates potential for HLA in these vehicles. Moreover, Renova (2006) showed that while collecting waste from highly populated areas, typical collection vehicles spend 60% of their time at a standstill, half of which is spent compacting, while the rest is spent idling as waste collectors manually load contents of waste containers. One study by Farzaneh et al., (2009) set a baseline for diesel refuse truck emissions under actual operation conditions. Two portable emissions measurement systems (PEMS) were used to measure gaseous and particulate matter (PM) emissions from in-use waste collection vehicles. Four different operation modes for waste collection vehicles were investigated including (1) urban driving, (2) trash collection, (3) freeway driving, and (4) landfill activities. The study concluded that the repetitive stop and starts during trash collection and landfill activities lead to higher fuel consumption and emissions levels than driving at constant speed (Farzaneh et al., 2009). Because the collection vehicle engine is designed to move the vehicles at higher speed, the inefficient use of the engine at collection and disposal points contributes to unnecessary emissions and noise levels (Renova, 2006). The industry would benefit from reanalyzing the consequences of the actual driving cycle of waste collection vehicles in order to optimize their operation. 3. Alternative Fuels Life-cycle Emissions 3.1 Introduction The literature is missing a comprehensive study that evaluates the life-cycle emissions for waste collection vehicles using alternative fuel technologies. Fuel life-cycle analysis (LCA) facilitates evaluation of the potential impact of fuel technologies. A fuel LCA can be used to evaluate pollutants, GHG emissions, water impact, or cost of a fuel over all stages of production and use. This study focused on a GHG emission LCA for waste collection vehicles. The GHG emission LCA covers the fuel extraction well emissions through production line processing emissions, transport of the fuel, and the final fuel burning emissions (Ruether et al, 2005). The first two stages involve fuel feedstock and fuel distribution. Emissions from these two stages are together known as well-to-pump (WTP) emissions. The third and the last stage of the fuel cycle generates vehicle operation emissions, herein referred to as pump-to-wheel (PTW) emissions. For any fuel life-cycle, the summation of the WTP emissions and PTW emission is referred to as wellto-wheel (WTW) emissions which are equivalent to the total fuel life-cycle emissions. 3.2 Methodology In this study, a quantitative comparison of alternative fuel technologies used by waste collection vehicles was performed. An evaluation of alternative fuel life-cycles was carried out using the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Fleet Footprint Calculator 1.1a to estimate the life-cycle emissions for alternative fuel technologies (US Department of Energy, 2007). This LCA of GHG emissions is reported as equivalent gram of CO 2 per collection vehicle mile traveled (CVMT) for each alternative fuel category. The estimated CO 2eq is based on fuel life-cycle CO 2, CH 4 and N 2 O emissions. The model can be used either to calculate the petroleum and GHG footprint of a certain fleet or to compare GHG emissions from onroad or off-road vehicles using alternative fuel technologies. The on-road vehicles 44

54 include medium-duty vehicles (MDV), heavy-duty vehicles (HDV), buses, and vans, as well as a specific category for waste haulers (US Department of Energy, 2007). Accordingly, waste collection vehicles, or waste haulers, were considered to be on-road mobile emission sources; the number of gallons (or cubic feet) of each fuel required to move the vehicle one mile (herein referred to as mileage) was used as input for the GREET Calculator. It was not possible to obtain the fuel consumption data for all fuel alternatives considered, as some of these alternative fuels are still in the research and development phase and are not yet available for assessment in the market (e.g., hydrogen and dimethyl ether). However, the mileage for diesel, CNG, LNG and hybrid waste collection vehicles was obtained from the technical literature; these values were used for model validation as described below. CNG and LNG may be either petroleum-based or produced during waste degradation (e.g. LFG). 3.3 Analysis Approach In order to assess various fuel technologies, it was assumed that for a waste collection vehicle to move one mile, the same amount of energy is required regardless of the fuel type. In the analysis, all alternative fuel waste collection vehicles were assumed to have the same weight. This assumption implies that they will have the same friction while traveling at the same speed on similar roads. Consequently, a waste collection truck using any fuel requires the same amount of energy per one mile traveled. Diesel-fueled waste collection vehicles are the baseline for this model. From the literature, diesel-fueled waste collection vehicle mileage average 2.8 mpg (Gordon et al., 2003). According to the GREET Model (2007), one gallon of diesel contains 129,488 Btu. Therefore, in order to move a diesel truck a distance of one mile, it requires 46,200 Btu. The calculated energy requirement (Btu/mile) was adjusted by energy efficiencies inherent to each fuel, represented by the ratio of the energy required by an alternative-fueled vehicle to travel one mile to that of a diesel-fueled vehicle. Again, energy efficiencies associated with different fuel types were unavailable from the literature for waste vehicles. Therefore, the USEPA fuel economy guide for the year 2011 was used to compare energy efficiencies for light-duty vehicles (LDVs) operating on various fuels which were then applied to waste collection vehicles. LDVs were selected because they operate on a widerange of alternative fuel technologies. In this comparison, similar LDVs (VW-Jetta, Honda Civic and Buick Regal) running on gasoline, diesel, CNG, hybrid, hydrogen fuel, and E85 were assumed to have the same weight. For each LDV, energy requirement per mile travel was calculated based on the estimated mileage for each fuel technology as shown in Table III-1. The energy required for similar LDVs was used to calculate equivalent energy conversion factors (EECFs), which represent the ratio of the energy required by x-fueled LDV to travel one mile to that of a y-fueled LDV. Table III-2 lists the equivalent energy conversion factors for these fuel categories. Finally, EECFs were applied to collection vehicles to determine mileage, assuming parallels between LDV engines and collection vehicles engines running on the same fuel technology. 45

55 Table III-1: LDVs Specifications and Energy Requirement for Different Fuel Technologies. Car Type Fuel Type Engine Size/Cylinders MPG (City/Highway) Combined Mileage (MPG) Fuel Lower Heating Value (LHV) a (Btu/gal) Energy b ( Btu/mile) VW-Jetta Diesel 2.0/4 30/ ,488 3,320 VW-Jetta Gasoline 2.0/4 23/ ,090 4,300 Honda Civic Gasoline 1.8/4 23.5/ ,090 4,070 Honda Civic CNG 1.8/4 24.3/34.2 c ,090 3,730 Honda Civic Hybrid 1.3/4 40/43 c ,090 2,760 Honda FCX Clarity Hydrogen Fuel 288V Li-Ion 60/60 d ,090 1,930 Regal E85 2.0/4 13/ ,294 4,420 Regal Gasoline 2.0/4 18/ ,090 4,640 a GREET Fleet Footprint Calculator, 2007 b Energy Required (Btu/mile) =Fuel LHV (Btu/gal)/Combined Mileage (mpg) c Mile per Gasoline Gallon Equivalent (MPGGe) d Mile per Kg Hydrogen (MPkg), where the Energy in 1 kg of Hydrogen is equal to 1 gal gasoline Table III-2: Equivalent Energy Conversion Factors (EECFs) for Different Alternative Fuel Categories, using LDV. Fuel Conversions (y to x) Applied Car Model Equivalent Energy Conversion Factor (EECF) a Diesel to Gasoline VW-Jetta 1.29 Gasoline to CNG Honda Civic 0.92 Gasoline to Hybrid Honda Civic 0.68 Gasoline to Hydrogen Fuel Honda Civic 0.48 Gasoline to E85 Regal 0.95 a Ratio of the energy required by x-fueled LDV to travel one mile to that of y-fueled LDV Two alternative fuels could not be evaluated by the above approach, LNG and biodiesel. LNG has not been considered by car manufacturers as an alternative fuel option, and therefore it has not been compared in matched car tests. However, it is a valid option for waste collection vehicles. Waste collection vehicles using LNG travel an average of 2.14 miles per diesel equivalent gallon (mpdeg) as measured during field tests (Chandler et al., 2001). Accordingly, the EECF of diesel to LNG was calculated to be Biodiesel was assumed to have the same energy required per mile travel as diesel, based on its similar chemical properties, therefore, the EECF for biodiesel was 1. Table III-3 provides the energy requirement, mileage, and mileage per diesel equivalent gallon (mpdeg) for alternative fuel technologies, using the EECFs from Table III-2. 46

56 Table III-3: Estimated Alternative Fuels Waste Collection Vehicles Energy Requirement and Mileage Data (Car Data). Fuel LHV b (Btu/gal) EECF Energy Required by Collection Vehicle e (x10 4 Btu/mile) Average Mileage (Mile per Gallon or ft³) Calculated Average Mileage (mpdeg) Conv. Gasoline 116, Diesel 129, d Natural Gas (per ft 3 ) Gaseous Hydrogen (per ft 3 ) Hybrid 129, E85 a 82, Biodiesel 119, LNG 74, c a 85% Ethanol and 15% Gasoline b GREET Model (2011) c Chandler et al. (2001) d Gordon et al. (2003) e Baseline Diesel Energy Requirement (46,200 Btu) X EECF Data from a study of alternative fuels used by buses were used to validate the LDV-based model (NREL, 1996). Fuel mileage for buses, measured by USDOE, running on CNG, LNG, biodiesel-20 (B20) and diesel was used to calculate EECFs, in a similar approach to the LDV (Table III-4). EECFs from the bus tests were then used to estimate the mileage of waste collection vehicles running on CNG, LNG and B20 (Table III-5), which was within ±4% of mileage based on LDV data, suggesting the methodology is valid. Table III-4: Equivalent Energy Conversion Factors (EECFs) for Different Alternative Fuel Categories, using Bus Data. Fuel Conversion (y to x) Bus Mileage a,b (mpdeg) EECF c Diesel to LNG Diesel to CNG Diesel to BD a Source: US Department of Energy (NREL, 1996) b Diesel Control Mileage (mpdeg); LNG Test 3.85, CNG Test 4.65, BD20 Test 4 c Ratio of the energy required by alternative-fueled bus to travel one mile to that of diesel-fueled bus Table III-5: Estimated Alternative Fueled Waste Collection Vehicles Mileage Data (Bus Data). Fuel EECF Energy Required by Collection Vehicle a (x10 4 Btu/mile) 47 Collection Vehicle Mileage (mpdeq) [Bus Data] Collection Vehicle Mileage (mpdeg) [car Data] % Error LNG % Natural Gas (per ft 3 ) % Biodiesel % a Baseline Diesel Energy Requirement (46,200 Btu) X EECF

57 Finally, estimated mileage for gasoline, CNG, and hybrid waste collection vehicles were compared to field measurements for waste collection vehicles. These values are tabulated in Table III-6, where field and calculated mileages were within ±20%. This comparison further demonstrates the validity of the fuel comparison model described above. Table III-6: Comparison between Modeled mpdeg and Field Measurements. Fuel Calculated Average (mpdeg) Field Measured (mpdeg) Source Conv. Gasoline (US Department of Energy, 2011) Natural Gas (per ft 3 ) (Johnson, 2010) Hybrid According to the EPA, the use of the HLA system for hybrid refuse trucks has a potential fuel savings of up to 30%. Therefore, if a diesel-fueled refuse trucks truck travels 2.8mpg, a hybrid refuse truck would travel 4mpg (Hall, 2011). 3.4 Results and discussion The estimated fuel mileage was used to generate two useful outputs for the waste collection industry; first, the life-cycle emissions were estimated for each fuel category. Second, using the estimated mileage together with the USDOE projections for fuel prices (2011), future fuel cost per CVMT was estimated for each fuel category CO 2eq Emissions per Collection Vehicle Mile Travel In order to calculate CO 2eq emissions, estimated volume (gallon or ft 3 ) consumption for each fuel category was used as an input to the GREET Fleet Footprint Calculator 1.1a. The fuel consumption was estimated for one mile travel so the model calculates the total CO 2eq emissions per CVMT on a WTW basis for each fuel category. Figure III-1 illustrates the fuel life-cycle emissions for alternative fuels based on the fuel source. 48

58 Figure III-1: Fuel Life-Cycle GHG Emissions. As seen in Figure III-1, a quantitative life-cycle analysis was developed for alternative fuels used by waste collection vehicles. The WTW emissions were calculated for alternative fuel options, and total emissions were reported as g CO 2eq per mile of travel for waste collection vehicle. Based on the analysis, liquid-petroleum fuels have higher life-cycle emissions compared to natural gas (CNG and LNG). LNG must be kept cold to remain liquid; this is the main reason for higher life-cycle emissions compared to CNG. Landfills (LFG) are considered to be an even better source for natural gas for waste collection vehicles; the fact that LFG is a biogenic gas reduces the total life-cycle emission by around 90% for CNG and LNG compared to petroleum-based fuels. LFG has the lowest life-cycle emissions compared to all fuel alternatives. Other fuels also had favorable WTW emissions compared to diesel. For example, biodiesel and ethanol are biogenic alternatives for diesel and gasoline, respectively. Biodiesel in particular is a sustainable, clean future fuel for waste collection vehicles. The use of a hybrid waste collection vehicle has a possible 30% reduction of life-cycle emissions (Hall, 2011) compared to diesel. Currently, hydrogen-fueled waste collection 49

59 vehicles are unavailable. However, the analysis indicates a potential reduction of more than 40% in the total life-cycle emissions if gaseous hydrogen were used as a fuel for waste collection vehicles. In order to use hydrogen as a fuel for waste collection vehicles, it must be compressed into liquid hydrogen under low temperatures and high pressure conditions. At this time, liquefaction of hydrogen involves an enormous energy input, thus greater WTW emissions and travel cost are predicted for liquefied hydrogen than gaseous hydrogen Fuel Cost per Collection Vehicle Mile Travel Fuel cost and price stability are considered to be significant factors in selecting an alternative fuel. In order to compare these factors, the estimated average volume (gallon or ft 3 ) per CVMT was used together with the USDOE (2011) Reference Case Scenario for fuel prices to predict the cost trend for waste collection vehicle travel for the next 25 years. The Reference Case scenario assumed a baseline economic growth of 2.7% per year from 2009 through 2035, taking into account the global oil price (USEIA, 2011). The prices were estimated to include Federal and State taxes while excluding county and local taxes (USEIA, 2011). The USDOE predicted cost for CNG (US dollars per Btu) was used to estimate the travel cost for LNG vehicles based on energy requirements for these vehicles. The results are illustrated in Figure III-2. Figure III-2: Cost Trend for Waste Collection Vehicle Travel. As seen in Figure III-2, CNG was found to have the lowest fuel cost per CVMT compared to all the other fuel categories, while LNG had the second lowest fuel cost. The price of natural gas is predicted to be relatively constant over the next three decades compared to all other categories which gives CNG economic advantages over other fuel 50

60 categories. In order to use LFG for waste collection vehicles, it must be converted to the methane quality of pipeline natural gas (Messics, 2001). The pipeline natural gas is then converted to CNG or LNG at the filling station. The final cost of processing LFG to high Btu gas ranges between $5 and 8 per MBtu depending on the season and the weather (Hesson, 2008). This makes LFG even less expensive than natural gas which is expected to range from $ per MBtu over the next three decades (not shown in Figure III-2). The fuel cost associated with ethanol (E85) is high compared to other fuel alternatives. This observation can be linked to the low energy content of this fuel alternative. Hybrid waste collection vehicles have a potential fuel savings of 30% compared to the regular diesel-fueled waste collection vehicles, however, this study did not consider the currently high cost of periodic battery replacement. 4 Operating Conditions for Diesel-Fueled Waste Collection Vehicles 4.1 Introduction As diesel-fueled vehicles operate, they release CO 2, CO, H 2 O, NO x, HC, and PM as combustion by-products. The MOtor Vehicle Emission Simulator (MOVES) 2010a software was developed by the USEPA to model on-road mobile pollution sources (USEPA, 2011). In this study, MOVESa was used to estimate emissions and energy consumption by diesel-fueled waste collection vehicles under both constant and actual operating conditions. The amount of pollutant emitted by a waste collection vehicle is represented by its emission factor (EF) in units of g per CVMT. These EFs are estimated during two operational modes; cold start and running exhaust emissions. Cold start occurs within the first few minutes of starting the vehicle; it is accompanied by higher levels of emissions as the engine is not running under its optimum operating temperature (Blaikley et al., 2001). Running exhaust emissions are pollutants generated during normal driving and idling, once the engine reaches its optimum operating temperature. The EFs calculated by MOVESa account for both cold start and running exhaust emissions. 4.2 Methodology and Assumptions MOVESa can be used to determine specific emission profiles for various modeled scenarios (USEPA, 2011). In order to run MOVESa, the user prepares an input file which defines the place, time, vehicle, road, fuel, emission producing process, and pollutant parameters (USEPA, 2011). MOVESa can be used either to calculate the quantity of emissions generated and/or energy consumed within a region and time span using the inventory approach, or to calculate the EFs using the emission rates approach (USEPA, 2011). In this analysis, the inventory approach was used to model waste collection vehicles at a US county level. Diesel-fueled waste collection vehicles were modeled under either constant or variable speed scenarios. In the constant speed scenario, an inventory was prepared for one waste collection vehicle traveling one mile on an urban unrestricted road using MOVESa Link Average Speed Importer. In the variable speed scenario, the MOVESa Link Driver Schedule was used to define the precise speed and grade as a function of time (seconds) on an urban unrestricted road (USEPA, 2011). 51

61 MOVESa was used to estimate the effect of operating conditions, such as speed and road grade, on diesel-fueled waste collection vehicle energy consumption and emissions. The New York Garbage Truck Cycle (NYGTC) data were used as representative of a typical waste collection vehicle driving cycle. In the NYGTC analysis, three different approaches for emissions estimates were used, (1) the NYGTC data were split into driving time and idling time, where emissions were summed (2) the NYGTC data were analyzed using the MOVESa Link Driver Schedule, and (3) the NYGTC data were averaged and a constant speed was assumed. To perform a comparison of the EFs of waste collection vehicles under different operating condition, EFs were calculated for all scenarios based on the following assumptions: All modeled waste collection vehicles were diesel-fueled, Collection vehicles operated in Orange County, Florida, USA, The modeled year was 2011, The national age distribution applied to waste collection vehicles was used, The garbage trucks travelled on urban unrestricted roads, Collection runs lasted one hour, 6am to 7am, on weekdays, and The collection occurred under August temperatures. MOVESa was used to estimate energy consumption as KJ per CVMT, and EFs for CO 2eq, CO, and NO x emissions as g per CVMT for all scenarios. 4.3 Results and Discussion Sensitivity Analysis for Speed and Road Grade In this section, the effect of speed and road grade on diesel-fueled waste collection vehicle energy consumption and emissions was evaluated using MOVESa. EFs of study pollutants were estimated for waste collection vehicles operating at constant speeds on level grade. Figure III-3 illustrates energy consumption and EFs for this analysis. Figure III- 3: Waste Collection Vehicle EFs at Constant Average Speed. 52

62 As the average speed increases, the energy consumption and EF decreases. However, waste collection vehicles usually travel at relatively low average speeds while collecting waste, i.e. below 10 mph. This travel scenario will be explored in detail in the next section. EFs and total energy consumption were also estimated for a waste collection vehicle traveling at a constant speed of 25 mph as a function of road grade. The results are illustrated in Figure III-4. EFs for waste collection vehicles travelling downhill, as well as on level grade, are almost the same for any case. However, EFs rise as these vehicles move uphill. Figure III- 4: Waste Collection Vehicle EFs vs. Road Grade New York Garbage Truck Cycle (NYGTC) Data Analysis In this analysis, the NYGTC data (Clark et al., 1997) were used as representation of a typical waste collection driving cycle. Three different approaches were used in analyzing the NYGTC. In Approach 1, the overall NYGT EFs were estimated separately for idling times and traveling times. The NYGTC data consist of constant speed travel between collection points; EFs for these constant speeds were estimated by MOVESa, however, any travel at non-constant speed was assigned idling EFs calculated by MOVESa. These EFs were used to calculate the weighted average NYGT EFs as shown in Table III-7, assuming total travel of one mile. Table III-7: NYGTC EFs Approach 1-Using Travel Time and Idling Time. Pollutant EF at 5.3mph Speed (g/mile ) EF at 12.6mp h Speed (g/mile) EF at 20 mph Speed (g/mile ) Idling EF (g/hr) Total Emission/Energ y in 600sec cycle a Weighted Average EF b (g/mile ) Carbon Monoxide Oxides of Nitrogen Carbon Dioxide 3,500 2,440 2,080 7,930 1,720 4,290 Total Energy 108,00 47,800 33,300 28,300 Consumption 0 23,500 58,500 CO 2 Equivalent 3,500 2,440 2,080 7,930 1,720 4,300 53

63 a Calculated based on 0.1 miles, 0.07 miles, 0.05 miles, and 527 Seconds for 20 mph speed, 12.6 mph, 5.3 mph, and total idling time, respectively. b Calculated by dividing the total emission for 600 seconds travel by mile (total distance travelled). In Approach 2, the speed of the NYGTC at different times was used as input to the MOVESa Link Driver Schedule. MOVESa converts this schedule into different operating modes, however, unlike Approach 1, this technique takes into account acceleration and deceleration EFs (Table III-8), rather than assigning idling EFs to these activities. Finally, in Approach 3, EFs were estimated for collection vehicles traveling at NYGT average constant speed of 2.4 mph (600 sec for 0.40 miles), provided in Table III-9. Table III-8: NYGTC EFs Using Approach 2-MOVESa Link Driver Schedule. Pollutant Emissions for the NYGTC (g per 0.4 Overall EFs a mile) (g/mile) Carbon Monoxide Oxides of Nitrogen Carbon Dioxide 5,180 12,900 Total Energy Consumption 70, ,000 CO 2 Equivalent 5,180 12,900 a Calculated by dividing the total emission for 600 seconds travel by mile (distance traveled). Table III-9: NYGTC EFs Approach 3-Constant Average Speed (2.4mph). Pollutant EFs (g/mile) Carbon Monoxide 18.9 Oxides of Nitrogen 59.1 Carbon Dioxide 6,070 Total Energy Consumption 82,800 CO 2 Equivalent 6,070 A quantitative comparison of the EFs for the NYGT using the three different approaches is shown in Figure III-5. The actual driving cycle of waste collection vehicles consists of repetitive stops and starts during waste collection, which generates more emissions than constant speed driving. This driving pattern should be considered in calculating the EFs of these vehicles. The New York study tested diesel-fueled collection vehicles for NO x emissions. The average actual NO x emissions (112 g/mile) for the NYGTC measured by Clark et al. (1998) were 30% higher than the MOVESa estimate (87 g/mile) for the same driving cycle. 54

64 EF (g per CVMT) Carbon Monoxide Oxides of Nitrogen NYGT Average Speed 2.4 mph NYGT Approach 1 NYGT Approach 2 Figure III- 5: Comparison of Waste Collection Vehicle EFs and Energy Consumption Assuming Typical Waste Collection Cycle. Also, by comparing MOVESa estimates with field measurements for waste collection vehicles during waste collection (Farzaneh et al., 2009), it is noted that the field EFs are higher for almost all the pollutants compared to MOVESa estimates. The Texas Transport Institute (TTI) waste collection vehicle average speed was 17.5 mph while NYGTC was 2.4 mph. The analysis for speed shows that increasing the average speed travel reduced the EFs. However, the TTI EFs were higher than the NYGTC measured and modeled EFs. These results suggest that MOVESa underestimates waste collection EFs. Factors which affect this comparison include location, time of the year, road types, and the age of waste collection vehicles. 5 Alternative Fuel Comparison 5.1 Introduction The recent increase in fuel costs and waste generation rates have tested solid waste collection management, whose financial goal is to ensure sound bottom lines. The waste collection industry is driven by the need to reduce costs and emissions while increasing operating efficiency. These challenges encourage the collection industry to explore alternative fuel technologies; however, investing in alternative fuel technologies is still novel for much of the collection industry. Moreover, the field is lacking a comprehensive study that accounts for the different factors affecting fuel selection. 5.2 Methodology The selection of fuel type by the waste collection industry requires consideration of environmental, security, financial, operational, and safety issues. Previously the GHG emissions and cost issues were explored for fuel alternatives; this section will assess less quantitative factors with respect to fuel technologies. These issues will be analyzed and described, and the significance of these factors in the selection process will be evaluated by a survey of randomly sampled waste industry professionals. A multifactorial assessment will be provided for fuel technologies taking into account the collection industry opinions regarding each factor. EF (g or KJ per CVMT) 1.E+05 1.E+05 8.E+04 6.E+04 4.E+04 2.E+04 0.E+00 NYGT Average Speed 2.25mph NYGT Approach 1 NYGT Approach 2 55

65 5.3 Alternative Fuels Figure III-6 divides major selection criteria into subcomponent factors. In this section, a comprehensive discussion of these factors will be presented based on the technical literature and on new findings from this study. Fuel Selection Criteria Environmental Issues Security Issues Operational Issues Financial Issues Safety Issues Air Pollution & Toxins Emitted US Fuel Vehicle Weight Capital Cost Vehicle Noise Life-cycle Emissions Renewable Fuel Operational Range Running Cost Occupational Safety Impact on Water Resources Community Acceptance Infrastructure Cost Fuel Hazards Figure III- 6: Breakdown of Issues Related to Fuel Selection Criteria Environmental Issues Fuel Life-cycle Emissions. In section III-3, life-cycle emissions were evaluated for alternative fuel technologies. Table III-10 lists the GHG life-cycle emissions for each fuel category relative to gasoline. 56

66 Table III-10: GHG Life-cycle Emissions Relative to Gasoline. Fuel Category GHG Emissions Relative to Gasoline (%) Gasoline 100 Diesel 84 Biodiesel (B100) 20 Biodiesel (B20) 76 Compressed Natural Gas (North America 75 Source) Compressed Natural Gas (Non-North America 85 Source) Compressed Natural Gas (Landfill Gas Source) 2 Liquefied Natural Gas (North America Source) 88 Liquefied Natural Gas (Non-North America 91 Source) Liquefied Natural Gas (Landfill Gas Source) 4 Liquefied Petroleum Gas 83 Hybrid 71 Gaseous Hydrogen (Refueling Station on-site) 58 Ethanol (E85) 79 All biogenic fuels (LFG, biodiesel and ethanol (E85)) have lower GHG life-cycle emissions compared to fossil derived fuel categories. Biodiesel and ethanol are considered to be sustainable biogenic alternatives for gasoline and diesel, respectively. Ethanol was found to have too low an energy content for waste collection vehicles. Air pollution and Toxics Emitted by Fuel. The tail-pipe emissions of waste collection vehicles include emissions of several pollutants in addition to GHGs. The use of alternative fuel technologies usually provides relative reduction for these emissions as well as GHGs. Analysis of these emissions reduction is beyond the scope of this study. However, according to Natural Gas Vehicles for America (NGVA), switching a dieselfueled waste collection vehicle to a new natural gas vehicle reduces tail-pipe emissions as follows: Carbon Monoxide by 70-90%, Non-methane Organic Gas by 50-75%, Nitrogen Oxides by 75-95%., and Carbon Dioxide by 20-30%. According to the EPA, The use of the HLA system for hybrid waste collection vehicles has a potential fuel savings up to 30%. This reduces the carbon dioxide emissions of a refuse truck by 40%. Moreover, the use of HLA system reduces the brake wear because the technology uses hydraulic breaking system (Hall, 2010). Currently, there is no commercially available hydrogen-fueled (fuel cell) waste collection vehicle. However, in 2010, Heliocentris and FAUN developed a German prototype waste collection vehicle that has an onboard fuel cell (Gosztonyi, 2010). The waste collection vehicle runs on hydrogen, diesel or a backup hybrid battery. The 57

67 onboard fuel cell allows the main diesel engine to switch off during collection. This approach saves up to 3 liters of diesel an hour, reducing the total fuel consumption by 30%, as well as reducing CO2, NOx and PM emissions (Gosztonyi, 2010). E85 has fewer evaporation emissions as well as CO and CO2 emissions, however NOx emissions are almost the same as liquid-petroleum based fuels (JCB, 2009). Impact on Water Resources: the water footprint associated with fuel production is listed in Table III-11. According to the US Department of Energy, ethanol production is considered to have the highest water footprint of fuel sources (Cizek, 2010), while natural gas was found to have the lowest water footprint. In general, oil refining has an average to moderate impact on water resources. The water consumption of converting LFG to CNG was not addressed in the literature. Table III-11: Water Consumption for Fuel Production (Cizek, 2010). Fuel Type Water Consumption (gal/ MMBTU) Oil Refining 7-20 Natural Gas Extraction/ Processing 2-3 Grain Ethanol Processing Corn Irrigation for Ethanol Oil Sands Security Issues American Fuel. Diesel and gasoline waste collection vehicles rely on imported oil. This reliance creates national energy security risks by increasing national dependence on foreign sources (Gordon et al., 2003). Figure III-7 shows the distorted consumption and production pattern of petroleum in the US. The growing gap between fuel consumption and local production has an inverse effect on the national energy security risk. Figure III-7: US Petroleum Consumption, Production, and Import Trends (US Energy Information Administration, 2010). 58

68 On the other hand, the US has enormous quantities of natural gas. According to 2010 Annual Energy Outlook published by the Energy Information Administration (EIA), there are 2,119 trillion ft 3 of technically recoverable natural gas in the US (EIA, 2010). The US Potential Gas Committee projected that these supplies would last 100 years based on an annual consumption rate of 22 trillion ft 3 (NaturalGas.org, 2011). All biogenic fuels can be produced domestically, reducing US dependence on other countries. The main sources of hydrogen fuel are natural gas, coal, landfill gas and water (USEPA, 2011), all readily available in the US; consequently, the use of hydrogen fuel reduces US energy security risk but using the present available technology, it takes more energy to produce hydrogen than the hydrogen has in it. Fuel World Supply. The duration of world liquid-petroleum supplies are correlated to the consumption rate. Before the word economic crisis in 2008, BP estimated that the world supplies of recoverable oil will last 40 years (Howden, 2007). After 2008, the global supplies of oil are estimated to last longer due to lower demand. The oil used today was discovered 40 years ago. However, there is a growing gap between discoveries and oil production rate (ASPO, 2004).This scenario indicates that the global supplies of oil will not last and the remaining oil will not be as cheap as today s supply. All biogenic fuels are considered to be renewable, sustainable sources of energy. Hydrogen fuel can be manufactured from water which is considered a renewable supply (USEPA, 2011), but as stated above the parasitic energy consumption makes this fuel currently unsustainable. Community Acceptance. In general, traditional fuels such as liquid-petroleum based are more accepted by consumers as fuel source. However, CNG and LNG are finding their way into the market. The use of fuel cell and hybrid technology is still considered risky by consumers (USEPA, 2011). is Operational Issues Vehicle Weight. The weight of CNG/LNG waste collection vehicle is only 500 lbs. greater than a similar equipped diesel-fueled vehicle (Vocational Energy, 2010). Hybrid waste collection vehicles are also heavier than the diesel-fueled vehicles. This slight weight disadvantage limits the environmental benefits of hybrid waste collection vehicles (Waste Management World, 2010). Operational Range. For the same driving range as diesel, CNG waste collection vehicles require four times the tank space, while the LNG vehicles require twice the tank space (Vocational Energy, 2010) Financial Issues Fuel Cost and Price Stability. In section III-3.4.2, it was clear that CNG waste collection vehicles using LFG have the lowest travel cost. On the other hand, CNG waste collection vehicles using natural gas has the second lowest travel cost and its considered to be relatively constant over the next three decades compared to all other categories. Hybrid waste collection vehicles provide fuel savings of 30% (Hall, 2010) compared to 59

69 diesel-fueled vehicles. Gasoline, LPG, and ethanol are considered to be non-cost effective fuels for waste collection vehicles; this is mainly because of the low energy content of these fuels. Vehicles Cost. The initial cost of CNG waste collection vehicle is $200,000 to $250,000; Diesel-fueled collection vehicles are only 15-25% less (Inform, 2006). Hybrid waste collection vehicles cost about $100,000 more than the traditional diesel-fueled vehicles (Danna, 2011). However, the use of the HLA system by hybrid waste collection vehicles saves 30% on fuel consumption. Also, the hydraulic braking technology reduces the expensive brake replacement to less than once per year (Hall, 2010). Diesel-fueled waste collection vehicles can operate at any biodiesel blend (B20 and B100) with few or no adjustments (JEB, 2008). Infrastructure Cost. The main disadvantage of CNG/LNG fueled waste collection vehicles is the infrastructure cost. A typical natural gas fueling station costs around $1.5 million (Vocational Energy, 2010). Other liquid alternative fuels could be made available at existing fuel station. However, hydrogen fuel requires new infrastructure for production, transport, and fueling stations (USEPA, 2011) Safety Issues Noise Levels. The use of diesel-fueled waste collection vehicles imposes potentially harmful noise levels on waste workers, and may be a considerable nuisance for residents. In this study, noise levels were measured for manual and automated diesel-fueled waste collection vehicles. Sources of noise coming from the collection vehicles included the diesel engine noise, hydraulic compactors and automated arms. A noise dosimeter was used to measure the noise level at different locations, behind and ahead of two similar sized waste collection vehicles; one manual, the other automated. Figure III-7 illustrates the different locations dosimeters placement as a function of distance from manual or automated waste collection vehicles. Figure III- 8: Dosimeters locations with respect to waste collection vehicle. The measured noise levels associated with manual and automated waste collection vehicles are illustrated in Figure III-8. At early all locations, the noise level associated with automated solid waste collection was lower than manual collection vehicles. The 60

70 noise level recorded at position three represents the solid waste collector s daily working environment. The average daily collection route is eight hours. Thus, laborers working with diesel-fueled manual and automated waste collection vehicles are subject to 89db and 85db respectively, which is considered to be the limit of the maximum eight hour permissible noise exposures level. On the other hand, local residents were exposed by (77-84db) at 60 feet, and (70-72db) at 100 feet. Figure III- 9: Noise Level Associated with Diesel-fueled Manual and Automated Waste Collection Vehicles. According to a study by the European NGVA, CNG and LNG waste collection vehicles were found to have lower noise levels than diesel-fueled waste collection vehicles (Manuel, 2010). Figure III-9 illustrates the average noise level associated with CNG and diesel waste collection vehicles. Hybrid truck manufacturers, Volvo and Autocar claim that the use of hybrid waste collection vehicles reduces noise levels associated with waste collection, however there are no studies that support such claims. Figure III- 10: Comparison between CNG and Diesel Refuse Trucks Noise Levels (Modified From NGVA, 2010). Occupational Safety. The improper use of energy sources such as gasoline, diesel, hydrogen and natural gas can be dangerous. According to Table III-12, natural gas has 61

71 higher ignition energy in comparison with gasoline and diesel. Moreover, natural gas and hydrogen are lighter than air which means they dissipate quickly when released accidentally. At standard atmospheric temperature, natural gas is only flammable when present at 5 to15% by volume in mixtures with air. The gas mixture does not ignite below or above this mixture (Murphy, 1994); however hydrogen gas is flammable over a wider range 4 to 75%. Hydrogen has a higher auto ignition temperature compared to natural gas, gasoline and diesel, but its minimum ignition energy is the smallest among them (Btu). Such an ignition energy is considered extremely low and even an invisible spark or static electricity discharge could cause ignition (College of the Desert, 2001). E85 is denser than air but ethanol vapor disperses quickly. E85 is seasonally adjusted; gasoline percentage is increased up to 30% in colder months. This changes the flammability limits and the auto ignition temperature. In general at lower temperature, E85 is more flammable than gasoline; however at higher temperature E85 is less flammable due to the higher auto ignition temperature (JEB, 2008). Table III-12: Properties of Alternative Fuels (Murphy, 1994). Property Natural Gas 1 Gasoline 1 Hydrogen Diesel 1 Flammability Limits (Volume % in air) Auto ignition Temperature ( F) Minimum Ignition Energy in Air (10-6 Btu) Peak Flame Temperature ( F) Source: Murphy, 2004 Source: College of the Desert, 2001 Source: Calvert, 2008 Fuel Toxicity. Liquid-petroleum fuels are considered to be toxic compounds to humans and wildlife. On the other hand, natural gas is considered to be non-toxic unless it displaces O 2 and has no threat to water sources (Murphy, 1994). Hydrogen is not soluble in water and considered to be non-toxic unless it displaces O 2 (College of the Desert, 2001). E85 will mix with water, however at high water concentrations, ethanol and gasoline will separate. E85 is less toxic than gasoline but still considered carcinogenic due to the presence of gasoline (JCB, 2008). 5.4 Alternative Fuel Survey A survey was used to evaluate the significance of the aforementioned criteria in the fuel selection. The survey is attached in Appendix D. The fuel survey was completed by 35 professionals from the waste management industry. Respondents were asked to quantify the significance of each criteria on a scale of 0-10, where 10 is most significant. Table III-13 provides descriptive statistics for the survey responses. The following criteria were found to have the highest arithmetic average ( ) compared to the other criteria: air toxics emitted, capital cost, running cost, renewable source and American fuel. Also, the standard deviation for these criteria was lower than for other categories. 62

72 Table III-13: Descriptive Statistics for the Survey Responses Regarding Importance of Criteria (0-10, 10 most significant). Factors Mean Standard Minimum Median Maximum Range Deviation Air Toxics emitted Fuel Life-cycle Impact on Water Sources American Fuel Global Supplies Renewable Supplies Community Acceptance Capital Cost Running Cost Fuel Price Stability Fuel Infrastructure Cost Vehicle Weight Operational Range Vehicle Noise Occupational Safety Fuel Toxics A boxplot diagram was used to represent the survey output in Figure III-10. Almost all of the criteria were scored above 5. However, some criteria such as environmental (air toxics emitted), American fuel source, and financial considerations (capital and running cost) were significantly more important than other criteria such as operational and safety issues. 63

73 10 8 Significance Air Toxics emitted Fuel Life-cycle Impact on Water Sources American Fuel Global Supplies Renewable Supplies Community Acceptance Capital Cost Running Cost Price Stability Infrastructure Cost Vehicle Weight Operational Range Vehicle Noise Occupational Safety Fuel Toxicity Figure III-11: Boxplot for Fuel Selection Criteria Survey Responses. Figure III-11 illustrates the 95% confident interval for the factors arithmetic mean. The figure clarifies the initial findings that the aforementioned factors, air pollution emitted, American fuel source, global supplies, renewable supplies, capital and running costs are more significant in the selection criteria compared to other factors Significance Air Toxics emitted Fuel Life-cycle Impact on Water Sources American Fuel Global Supplies Renewable Supplies Community Acceptance 64 Capital Cost Running Cos t Price Stability Infrastructure Cos t Vehicle Weight Operational Range Vehicle Noise Occupational Safety Fuel Toxicity Figure III-12: 95% Confident Interval for the Selection Criteria Survey Responses.

74 5.5 Multi-factor Assessment of Alternative Fuels A multifactorial assessment was used to evaluate the fuel selection criteria. Based on study findings and the technical published literature, all environmental, financial, security, operational, and safety issues related to different fuels were scored from 0 to 10, where 10 represents the most positive impact of using the fuel. This analysis was represented by a heat map as shown in Figure III-12. This heat map is a graphical representation which reflects better fuel performance by darker shades. 65

75 Fuel Alternatives Gasoline Diesel CNG LNG Hybrid LFG Biodiesel E85 Hydrogen gas Environmental Air Toxics Emitted Fuel Lifecycle Impact on Water Resources American Fuel Security Global Supplies Renewable Supplies Community Acceptance Issues Financial Capital Cost Running Cost Fuel Price Stability Infrastructure Cost Operational Vehicle Weight Operational Range Vehicle Noise Safety Occupational Safety Fuel Toxicity Fuel Performance Figure III-13: Heat Map for Fuel Selection Criteria. In addition, the arithmetic mean for each survey response, was used to weight the fuel selection criteria represented in the heat map in Figure III

76 Fuel Alternatives Gasoline Diesel CNG LNG Hybrid LFG Biodiesel E85 Hydrogen gas Environmental Air Toxics Emitted Fuel Lifecycle Impact on Water Resources American Fuel Security Global Supplies Renewable Supplies Community Acceptance Issues Financial Capital Cost Running Cost Fuel Price Stability Infrastructure Cost Operational Vehicle Weight Operational Range Vehicle Noise Safety Occupational Safety Fuel Toxicity Fuel Performance Figure III-14: Heat Map for the Weighted Fuel Selection Criteria. 67

77 5.6 Future Fuel for Waste Collection vehicles Based on the weighted results, fuels can be compared by summing the total scores for each criteria. The higher the fuel score, the better the overall performance with respect to all the aforementioned criteria. According to this analysis, LFG has the best performance relative to other fuel categories. Figure III-14 illustrates alternative fuel performance relative to LFG. Figure III-15: Alternative Fuel Performance Relative to LFG. In conclusion, CNG, LNG and LFG are the best available alternative fuels for waste collection vehicles. These alternatives have better environmental, economical, and energy-security performance than current liquid-petroleum fuels. 6. Conclusions CNG, LNG and hybrid waste collection vehicles are considered better alternatives to traditional diesel-fueled vehicles. The use of LFG to fuel CNG and LNG waste collection vehicles has a reduced environmental and economic impact compared to fossil fuels. Natural gas has a lower travel cost compared to other fuels and the USDOE suggests this will continue over the next two decades. The use of hydrogen as a fuel for waste collection vehicles is still a novel idea which should be explored further since the use of gaseous hydrogen provides a significant reduction in GHG life-cycle emissions. Hybrid waste collection vehicles have advantages in GHG emissions with respect to dieselfueled waste collection vehicles as well. Additional fuel efficiencies can be achieved by 68

78 adopting advanced engine models, advanced transmission designs, and proper planning of collection routes and schedules. Emissions from diesel-fueled waste collection vehicles were evaluated under typical operating conditions, and compared to emissions generated under constant operation. MOVESa was used to evaluate the Emission Factors (EFs) for waste collection vehicles. The use of MOVESa Link Driver Schedule, which accounts for emissions while idling, decelerating, and acceleration, provides a better estimate for waste collection vehicles EFs compared to calculating EFs based on average speed or driving time and idling time approaches. However from a comparison of these emissions with actual field measurements, it appears that MOVESa still underestimates these emissions. Further, a qualitative comparison of alternative fuels was performed. The main purpose of such a comparison was to assess different factors that affect the selection of a future fuel for waste collection vehicles. The analysis consists of a multifactorial assessment of environmental, financial, operational, safety, and security issues associated with the use of alternative fuel technologies. In order to complete the analysis, the selection criteria were evaluated based on the technical literature and the study findings. Moreover, a survey measuring the significance of these criteria to industry professionals was conducted. According to the analysis, CNG, LNG and LFG were found to be the best available alternative fuels for waste collection vehicles. In conclusion, CNG, LNG and hybrid waste collection vehicles are considered better alternatives to traditional diesel-fueled vehicles. Moreover, LFG is a sustainable source of natural gas. The use of LFG to fuel CNG and LNG waste collection vehicles has a reduced environmental impact compared to fossil fuel. Natural gas had a lower travel cost compared to other fuels and the US Department of Energy suggest this will continue over the next two decades. However, the initial infrastructure cost need for CNG/LNG fueling stations is the main disadvantage. The use of hydrogen as a fuel for waste collection vehicles is still a novel idea which should be explored further since the use of gaseous hydrogen provides a significant reduction in GHG life-cycle emissions. Hybrid waste collection vehicles have advantages in GHG emissions with respect to diesel-fueled waste collection vehicles as well. However, the initial high cost of these vehicles compared to the traditional diesel-fueled is currently considered the main disadvantage for this alternative technology. 7. Future Work This study examined waste collection vehicles during waste collection. Waste collection vehicles operate under four different operation modes: (a) urban driving, (b) waste collection, (c) freeway driving, and (d) landfill activities. Each operational mode has different speed, idling time, emissions, and breaking profiles. Texas Transportation Institute at Texas A&M University established a baseline for diesel-fueled waste collection vehicles emissions under actual operation conditions (Farzaneh et al., 2009). Two portable emissions measurement systems (PEMS) were used to measure the gaseous state emission and PM emissions from in-use refuse trucks during the four different operational modes. In a future study, MOVESa will be used to model Texas Sate collection vehicles to find the expected gaseous and Particulate Matter (PM) emissions 69

79 under the four different operational and compare them to real data emissions. This analysis would be useful to validate the MOVESa results as compared to actual emissions. In a future study, on-board GPS devices will be used to track waste collection vehicles during their daily operational activities. GPS devices could be monitored remotely to collect driving cycle information of waste collection vehicles at different times and locations. In this approach, the speed vs. time will be generated for waste collection vehicles at different locations. Simultaneously, atmospheric conditions such as temperature will be obtained from the local weather station. Accordingly, MOVESa will be used to estimate emissions for diesel-fueled waste collection vehicles using the collected data as an input. In this study, natural gas (CNG/LNG), biodiesel, hybrid, and hydrogen fuels have a potential to be a future fuel for waste collection vehicles. However, so far MOVESa does not provide data regarding emissions and fuel consumption for waste collection vehicles using these fuels. A future study will be conducted to build a database using MOVES for these alternative fuel technologies. The literature provides a good source for such data. Several examples include Texas Transportation Institute study, which investigated the emissions from Liquefied Natural Gas (LNG) and Compressed Natural Gas (CNG) waste collection vehicles under actual operating conditions (Farzaneh et al, 2009), Waste Management Inc. also conducted tests on CNG/LNG waste collection vehicles (Chandler, 2001). Several field tests were performed on the Autcar E3 hybrid refuse truck as well (Parker Corporation, 2010). This future study will implement a national model for waste collection vehicles. The model will serves as a tool to estimate the fuel consumption, emissions, and travel cost for diesel-fueled waste collection vehicles as well as alternative fuels vehicles under different operating conditions. 70

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82 NGVAMERICA. (2011). Natural Gas Vehicles. Retrieved from Natural Gas Vehicles for America: OSHA. (2011). Occupational Safety & Health Administration. Retrieved from US Department of Labor: p_id=9735 Poulsen, O.M., Breum, N.O., Ebbehøj, N., Hansen, Å.M., Ivens, U.I., van Lelieveld, D., Malmros, P., Matthiasen, L., Nielsen, B.H. and Nielsen, E.M.,. (1995). Collection of domestic waste. Review of occupational health problems and their possible causes. Science of the Total Environment, 170(1-2), Renova. (2006). Cleanova-electric-hybrid technology for more environment-friendly waste collection. Goteborg, Sweden: Renova. Rogoff, M. J., Lilyquist, R. E., Ross, D., & Wood, J. L. (2010). AUTOMATED WASTE COLLECTION HOW TO MAKE SURE IT MAKES SENSE. APWA Reporter. Retrieved from Rogoff, M., Trulock, A., & Clark, B. (2009). Solid WAste Collection Programs. MSW Management Elements, Ruether, J., Ramezan, M., & Grol, E. (2005). Life-Cycle Analysis of Greenhouse Gas Emissions for Hydrogen Fuel Production in the United States from LNG and Coal. Department of Energy and National Energy Technology Laboratory. Retrieved from Schibye, B., Søgaard, K., Martinsen, D. and Klausen, K.,. (2001). Mechanical load on the low back and shoulders during pushing and pulling of two-wheeled waste containers compared with lifting and carrying of bags and bins. Clinical Biomechanics, 16(7), University of Michigan. (2010, University of Michigan). 3D Static Strength Prediction Program user s manual. Retrieved July 20, 2011, from US Departement of Energy. (2011). GREET Model. Retrieved from Transportation Technology R&D Ceneter: US Department of Energy Information Administration. (n.d.). Energy Prices by Sector and Source, United States. Retrieved from EIA : AEO2011&table=3-AEO2011&region=1-0&cases=hm2011-d020911a,ref2011- d020911a US Department of Energy; USEPA. (2011). Fuel Economy Guide. Office of Energy Efficiency and Renewable Energy. US Department of Energy and USEPA. Retrieved from US Department of Transportation. (2010). Transporation's Role in Reducing US Greenhouse Gas Emissions. Washington DC: US Department of Transporation. Retrieved from _April_2010_-_Volume_1_and_2.pdf 73

83 US Energy Information Administration. (2010, August). How Dependence are we on foreign oil? Retrieved from US Energy Information Administration: USEPA. (2011, August 22). Modeling and Inventories (MOVES). Retrieved from USEPA: USEPA. (2011, July 26). US Minicipal Solid Waste. Retrieved from USEPA: USEPA and US Department of Energy. (2011, October 5). Fuel Cell Vehicles. Retrieved from Energy Efficiency and Renewable Energy: Vocatioanal Energy. (2010). Natural Gas Truck Presentation. Vocatioanal Energy. Retrieved from Volvo Group. (2012, May 22). I-Shift. Retrieved from Vovlo Trucks United States: Washington State Department of Labor and Industries. (2006). Waste Collection Overexertion Injuries. Retrieved January 10, 2011, from Washington State Department of Labor and Industries: Washington State Department of Labor and Industries. (n.d.). Hazard/Caution Zone Checklist. Retrieved July 20, 2011, from Washington State Department of Labor and Industries: Waste Age. (2011). Federal Report Indicates Growth in Solid Waste Collection Fatalities. Retrieved August 2011, 2011, from Waste360: Waste Management World. (n.d.). Driving the future. Waste Management World. Retrieved from 74

84 List of Publications resulting from Research a) Poster Presentations: Ergonomic Study of Solid Waste Collection. Florida A&WMA Annual Conference (2010). Clearwater, Fl. Environmental Study of Solid Waste Collection. Florida A&WMA Annual Conference (2010). Clearwater, Fl. Ergonomic Study of Solid Waste Collection. A&WMA 104 th Annual Conference (2011). Orlando, Fl. Environmental Study of Solid Waste Collection. A&WMA 104 th Annual Conference (2011). Orlando, Fl. b) Conference Papers: Ergonomics Evaluation of Solid Waste Collection, Global Waste Management Symposium, Phoenix, Arizona. October 1, 2012 Ergonomic Study of Solid Waste Collection. A&WMA 104 th Annual Conference (2011). Orlando, Fl. Environmental Study of Solid Waste Collection. A&WMA 104 th Annual Conference (2011). Orlando, Fl. c) Technical Papers (submitted): Ergonomic Study of Solid Waste Collection. o Submitted to Journal of Applied Ergonomics, September 2012 Environmental Study of Solid Waste Collection, o Submitted to Waste Management, accepted with revisions October d) Master s Thesis: Environmental Study of Solid Waste Collection. University of Central Florida (2011). Orlando, FL. 75

85 APPENDIX A: Surveys 76

86 Ergonomic Study of Solid Waste Collection Funded by the Environmental Research and Education Foundation Waste Helpers (Collectors) Survey Research Team: Dr. Pamela McCauley Bush: Associate Professor Dr. Debra R. Reinhart: Assistant Vice President for Research Mousa Maimoun: Graduate Research Assistance Fatina T. Gammoh: Graduate Research Assistance Objectives of the survey: Researchers at the University of Central Florida (UCF) are currently working on Environmental Research and Education Foundation (EREF) sponsored project intended to gain an understanding of the factors affecting waste collectors safety and how to reduce injuries and compensations costs. To help us achieve this goal, we would like to invite you to complete this survey. All answers are anonymous. There are no anticipated risks or direct benefits to you if you decide to participate. There is no penalty if you decide not to participate. You can end your participation at any time and you do not have to answer any questions that you do not want to answer. The survey will take about minutes of your time. You can respond using this questionnaire or on-line using the website link < IRB contact about your rights in the study or to report a complaint: Research at the University of Central Florida involving human participants is carried out under the oversight of the Institutional Review Board (UCF IRB). This research has been reviewed and approved by the IRB. For information about the rights of people who take part in research, please contact: Institutional Review Board, University of Central Florida, Office of Research & Commercialization, Research Parkway, Suite 501, Orlando, FL or by telephone at (407) Name of Respondent (Optional) Company: Date: 77

87 Interview Questions for Waste Collectors This Survey contains multiple choice and open ended questions: Participants Demographics 1. Job Description 2. Sex a) Waste Collector(Helper) b) Driver c) Both a) Male b) Female 3. Age a) Less than 30 years b) years c)41 50 years d) Greater than 50 years 4. Years of Residential Waste Collection Experience a) 0-2 years b) 2 5 years c) 5-10 Years d) More than 10 years Collection and Safety Questions 1. Current Type of Truck being Used Please provide the Lift Mechanism per each type. a) Rear Loader Manual loading Semi-automated b) Side Loader Manual loading Automated Semi-automated c) Residential Front Loader 78

88 2. How many workers (including driver) are there in a crew? a) One b) Two c) Three d) More than three 3. Are you allowed to collect both sides of the street at once? a) No, never b) Yes, always c) Yes, but only on side streets d) Yes, but in specific conditions e) N/A automated truck used. 4. Number of stops you collect on an average route day a) Less than 400 b) c) d) e) f) More than What personal protective equipment are you required to wear? (Select all that apply.) a) None required b) Work boots c) Visibility vest or clothing d) Gloves e) Eye protection f) Hearing protection g) Back Belt h) Others(Please Specify): 6. How long is your average route day? a) 8 hours or less b) 8-9 hours c) 9-10 hours d) 10+ hours 79

89 7. Are you allowed to go home after your route is completed, or must you work the entire shift? a) Yes; I leave work whenever my route is done. b) Yes; but if a truck is down, I may help on that route before leaving c) No; I must work the entire shift. 8. When are you allowed to use the riding steps on a rear loader? a) Never traveling in reverse b) When traveling less than 10 MPH c) When traveling less than 2/10ths of a mile d) All of the above 9. Estimated Average Container weight( For waste collectors) a) Less than 20 pounds b) c) d) Greater than 60 pounds 10. During the last 12 months, how often did you receive Safety training/instructions (For example, training in proper lifting and carrying techniques, awareness sessions on the potential hazards/injuries of solid waste collection)? a) Never b) Monthly c) Quarterly d) Semi-annually 80

90 Musculoskeletal Injuries/pain?(Places where your body hurts) In this section the waste collector should identify in which body part he has a trouble (If any Body Parts During the last 12 months, how often did you experience pain or discomfort due to work-related activities? Actual Days Off during the last 12 months due to Injuries, Pain or discomfort? Neck Shoulder Upper Back Upper Arm Lower Back Elbow Forearm Wrist Hip/Buttocks Thigh Knee Lower Leg Foot Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days Never Rarely Sometimes Often (at least once a week) Daily 1-5 Days 6-10 Days More than 10 Days 81

91 Appendix B: Laboratory Analysis for Younger Subjects Rapid Entire Body Assessment (REBA) REBA analyses were conducted for four males to assess postural risks of waste collection tasks including manual and semi-automated. For the manual waste collection, the lifting and dumping tasks were selected for the assessment while for the semi-automated waste collection; pulling and pushing postures were evaluated. REBA assessment gives a quick and systematic assessment of the complete body postural risks to a worker, it divides the body into two groups, group A includes the trunk, neck and legs and group B includes the upper limbs (arm, forearm and wrist). REBA takes into account of joint analysis, body position, and other factors, such as load handled, to determine the level of risk. Equipment/tools Utilized A waste container of 40 pounds The Borg scale to rate the perceived exertion (RPE) during task performance. A polar monitor to measure the heart rate while performing the task. A goniometer to measure the limb angles. REBA assessment work sheet to measure the final risk score. A camera to capture photos and videos while performing the task. Resting heart rate was taken prior to beginning task. Then the participants began performing the waste collection tasks, each task was completed four times and data concerning the participants heart rate and RPE (Rate of Perceived Exertion) were taken after each task. REBA method classifies the final score into 5 ranges as shown below where each range corresponds to specific Action Level Final Score Action Level Level of Risk Performance 1 0 Priceless No action is required Low Action may be necessary Means Action is required High Action is required soon Very High Action is required Immediately Overall, the lifting and dumping tasks got higher REBA scores as compared to pushing and pulling; the dumping task got the highest score due to the unstable postures adopted while performing the task, on average the lifting and dumping tasks are at an action level of 4, or very high, which requires immediate action. The pushing and pulling task scores are at an action level of 3, or high, and require action in the near future. 82

92 Subject 1 (24 years old male): Lifting Dumping Pushing Pulling Table #1. Younger Subject #1 - Rating of perceived Exertion and Heart Rate Data Task HR RPE Rest 90 Lifting # Lifting # Lifting # Lifting # Dumping # Dumping # Dumping # Dumping # Pushing # Pushing # Pushing # Pushing # Pulling # Pulling # Pulling # Pulling # Table 2. Young Subject #1 : REBA Analysis Scores Task REBA Score Dumping 13 Lifting 12 Pulling 9 Pushing 10 83

93 Younger Subject 2 (26 years old male): Lifting Dumping Pushing Pulling Table 4. Younger Subject #2 - Rating of perceived Exertion and Heart Rate Data Task HR RPE Rest 91 Lifting # Lifting # Lifting # Lifting # Dumping # Dumping # Dumping # Dumping # Pushing # Pushing # Pushing # Pushing # Pulling # Pulling # Pulling # Pulling # Table 3. Young Subject #2 : REBA Analysis Scores Task REBA Score Dumping 12 Lifting 13 Pulling 10 Pushing 10 84

94 Subject 3(26 years old male): Lifting Dumping Pushing Pulling Table 5. Younger Subject #3 - Rating of perceived Exertion and Heart Rate Task HR RPE Rest 120 Lifting # Lifting # Lifting # Lifting # Dumping # Dumping # Dumping # Dumping # Pushing # Pushing # Pushing # Pushing # Pulling # Pulling # Pulling # Pulling # Table 6. Young Subject #3 : REBA Analysis Scores Task REBA Score Dumping 12 Lifting 11 Pulling 10 Pushing 9 85

95 Subject 4: Lifting Dumping Pushing Pulling Table 7. Younger Subject #4 - Rating of perceived Exertion and Heart Task HR RPE Rest 78 Lifting # Lifting # Lifting # Lifting # Dumping # Dumping # Dumping # Dumping # Pushing # Pushing # Pushing # Pushing # Pulling # Pulling # Pulling # Pulling # Table 8. Young Subject #4 : REBA Analysis Scores Task REBA Score Dumping 12 Lifting 12 Pulling 8 Pushing 9 86