University of Texas at El PasoDigitalCommons@UTEP

Open Access Theses & Dissertations

2011-01-01

Residential Recycling StudyRichard AdamsUniversity of Texas at El Paso, adamsrx@mail.com

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RESIDENTIAL RECYCLING STUDY

RICHARD ADAMS

Department of Civil Engineering

APPROVED:

Charles Turner, Ph.D., Chair

John Walton, Ph.D.

John Hadjimarcou, Ph.D.

Patricia D. Witherspoon, Ph.D. Dean of the Graduate School

Copyright ©

by

Richard Adams

2011

One man’s trash is another man’s treasure Unknown

RESIDENTIAL RECYCLING STUDY

By

RICHARD ADAMS, B.S.I.E.

THESIS

Presented to the Faculty of the Graduate School of

The University of Texas at El Paso

in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF SCIENCE

Department of Civil Engineering

THE UNIVERSITY OF TEXAS AT EL PASO

MAY 2011

v

Acknowledgments

I would like to first thank the Solid Waste rate payers of El Paso, TX for informally funding

this study. Hopefully, they will get value for their investment. I would also like to thank the

Director of Environmental Services, Ellen A. Smyth, P.E., MPA, because without her authorization

this study would be landfilled. In addition, I would like to thank the following members of the

management team at Environmental Services for providing human resources and equipment:

Ginny Castaneda, Gloria Duran, Kurt Fenstermacher, Freddy Garcia, John Garza, P.E., Vernon

Greggerson, P.E., Valerie Joosten, P.E., Mike Parra, P.E., Brian Roderick, Raul Sanchez, Danny Soto,

and Debbie Strom, C.P.A.

The following employees helped in the sorting of material: Jorge Avitia, Cristian Benitez,

Michelle Brennan, Juan Camacho, Anh Do, Manny Grado, Ernesto Gutierrez, Doug McDonald,

David Perez, Jesus Ortega, Carlos Portillo, Eloisa Portillo-Morales, Mario Sandoval, Mark Sink,

Richard Telles, Jesse Valdez, and Rick Venegas.

Special Thanks goes out to the drivers who not only collected and inspected samples but

also helped sort material: Pete Carrillo and David Wilkins.

Dr. John Hadjimarcou, UTEP’s Department of Management and Marketing for taking on

the City’s Recycling program for his graduate Marketing class project and being on my thesis

committee.

Dr. John Walton, P.E., for being part of my thesis committee, calming my nerves and

encouraging me to “keep bugging Turner”. Dr. Walton was my instructor for Environmental

Processes and it was a wonderful learning experience.

Dr. Charles Turner, P.E., LEED, Department of Civil Engineering, my graduate advisor and

chair of my thesis committee. Thanks to you, I discovered a lot of tools that I can use in my

vi

environmental career. Dr. Turner was my instructor for most of my graduate classes and not

only was he concerned in my learning concepts but also on how I would apply them in the real

world.

Lastly to my family: Terri, Lydia, Sarah and Lily thanks for all the support and

encouragement.

vii

Table of Contents

Acknowledgements ............................................................................................................................................. iv

Table of Contents ................................................................................................................................................. vi

List of Tables .......................................................................................................................................................... ix

List of Figures .......................................................................................................................................................... x

Chapter 1 Introduction ....................................................................................................................................... 1

1.1 Overview....................................................................................................................................................... 1

Project Purpose & Background ............................................................................................................ 1

What is recycling? ...................................................................................................................................... 2

History of Recycling in El Paso, TX ...................................................................................................... 2

Understanding the City of El Paso’s Recycling Program ............................................................ 4

Study Methods ............................................................................................................................................ 5

1.2 Trial Run Findings .................................................................................................................................. 10

1.3 Key Sampling Findings .......................................................................................................................... 11

Chapter 2 Top Performing Areas .................................................................................................................. 13

2.1 Top Performing Recycling Routes .................................................................................................... 15

2.2 What the numbers really mean ......................................................................................................... 15

2.3 Heavy Recycling Routes and Good Behavior ................................................................................ 17

2.4 Conclusion and Recommendation .................................................................................................... 19

Chapter 3 Container Setout ............................................................................................................................. 20

3.1 Setout ........................................................................................................................................................... 20

3.2 Setout Comparison ................................................................................................................................. 21

3.3 Conclusion and Recommendation .................................................................................................... 21

Chapter 4 Participation .................................................................................................................................... 23

4.1 Definition .................................................................................................................................................... 23

4.2 Participation in Capital Cities ............................................................................................................. 24

4.3 Participation vs. Age of Program ....................................................................................................... 26

4.4 Conclusion and Recommendation .................................................................................................... 27

viii

Table of Contents

Chapter 5 Contamination ................................................................................................................................. 30

5.1 Contamination .......................................................................................................................................... 30

5.2 Percentage of Recycling Material by Weight ................................................................................ 31

5.3 Conclusion and Recommendation .................................................................................................... 32

Chapter 6 Diversion ........................................................................................................................................... 35

6.1 Introduction .............................................................................................................................................. 35

6.2 Understanding Diversion Rate ........................................................................................................... 37

6.3 San Francisco ............................................................................................................................................ 39

6.4 States Bans increase Diversion Rates .............................................................................................. 40

6.5 Diversion in Texas .................................................................................................................................. 41

6.6 National Diversion Rates ...................................................................................................................... 42

Mandated vs. Non-Mandated Programs ......................................................................................... 42

Frequency of Collection ......................................................................................................................... 44

6.7 Conclusion and Recommendation .................................................................................................... 45

Chapter 7 The Recycler .................................................................................................................................... 46

7.1 Characteristics of a Good Recycler ................................................................................................... 46

7.2 Marketing Strategies and the Psychology...................................................................................... 47

7.3 Survey Results .......................................................................................................................................... 51

Chapter 8 Final Analysis .................................................................................................................................. 60

8.1 The Profile .................................................................................................................................................. 60

8.2 Biweekly Collection? .............................................................................................................................. 65

Weight of Recycling Material .............................................................................................................. 65

Drive by Rate ............................................................................................................................................. 69

8.3 Final Comments ....................................................................................................................................... 71

ix

Table of Contents

References ............................................................................................................................................................. 74

Appendix A Recycling Inspection Form .................................................................................................... 77

Appendix B Contaminant Weight Log ........................................................................................................ 79

Appendix C Neighborhoods with High Recycling Weights ................................................................ 88

Appendix D Trash and Recyclables Collection Schedule ................................................................. 101

Appendix E Municipal Recycling Comparison ..................................................................................... 103

Appendix F Survey ......................................................................................................................................... 106

Curriculum Vita ................................................................................................................................................ 111

x

List of Tables

Table 1-1 Cost of Study ...................................................................................................................................... 8

Table 2-1 Top Five Performing Recycling Areas by Weight ............................................................. 16

Table 2-2 Routes with High Tonnage of Recycling Material ............................................................. 17

Table 2-3 Routes with Two or More Samples being 100% Compliant ......................................... 18

Table 3-1 Setout and Volume Percentages .............................................................................................. 20

Table 3-2 Setout Comparison........................................................................................................................ 21

Table 4-1 Participation Rates of Capital Cities in the U.S. .................................................................. 25

Table 6-1 Examples of Statewide Disposal Bans ................................................................................... 40

Table 6-2 Diversion Rates in North Central Texas ............................................................................... 42

Table 7-1 Survey Response Rate Comparisons ...................................................................................... 52

Table 8-1 Data for Regression Analysis .................................................................................................... 65

xi

List of Figures

Figure 1.1 Recycling Composition ................................................................................................................. 5

Figure 1.2 Sample Distribution among Zip Codes .................................................................................... 7

Figure 1.3 Sorting Material ............................................................................................................................... 8

Figure 1.4 Calibrated Weight Scale ............................................................................................................... 9

Figure 1.5 Containers used to Weigh Contaminant Material ............................................................ 11

Figure 2.1 Automated Sideloader Truck ................................................................................................... 13

Figure 2.2 Rearloader Truck ......................................................................................................................... 14

Figure 4.1 Participation vs. Age of Program ............................................................................................ 27

Figure 4.2 Participation ................................................................................................................................... 29

Figure 5.1 Contamination Profile ................................................................................................................ 30

Figure 5.2 Percent of Contaminant by Weight ....................................................................................... 32

Figure 5.3 Cardboard not Broken Down ................................................................................................... 33

Figure 5.4 Yard Waste in Container ............................................................................................................ 34

Figure 6.1 Waste Generation Cycle ............................................................................................................. 36

Figure 6.2 Diversion for the City of El Paso ............................................................................................. 37

Figure 6.3 North Central Texas Council of Governments ................................................................... 41

Figure 6.4 Diversion Rates for Mandated Recycling Programs ....................................................... 43

Figure 6.5 Diversion Rates for Non-Mandated Recycling Programs ............................................. 44

Figure 6.6 Diversion Rates for Biweekly vs. Weekly Collection Recycling Programs ............. 45

Figure 7.1 Level of Education ........................................................................................................................ 53

Figure 7.2 Recycling Information ................................................................................................................ 54

Figure 7.3 Kids in Household ........................................................................................................................ 55

Figure 7.4 Mode of Transportation ............................................................................................................. 56

Figure 7.5 Concern for the Environment .................................................................................................. 57

Figure 7.6 Recognition ....................................................................................................................................... 58

Figure 7.7 Level of Service ................................................................................................................................ 59

Figure 8.1a Demographics ................................................................................................................................ 61

xii

List of Figures

Figure 8.1b Demographics ............................................................................................................................. 62

Figure 8.2 Best Recycling Areas ................................................................................................................... 64

Figure 8.3 Scatterplot ....................................................................................................................................... 66

Figure 8.4 Regression Output ....................................................................................................................... 66

Figure 8.5 Fit Plot .............................................................................................................................................. 67

Figure 8.6 Equipment Demand for Biweekly Collection .................................................................... 68

Figure 8.7 Drive by Rate ................................................................................................................................... 69

Figure 8.8 Number of Trucks ......................................................................................................................... 70

1

Chapter 1 Introduction

1.1 Overview

Project Purpose & Background

The purpose of this study is to gain baseline performance figures and statistics for El

Paso’s curbside recycling program. With these baseline statistics, the effectiveness of changes

for promotion and outreach can be evaluated once implemented. In addition, operational

changes such as to every other week recycling collection rather than every week or a “pay as you

throw” system could be evaluated.

Last Fiscal Year (Sept 1, 2009 to August 31, 2010) the City of El Paso residents recycled

36,628 tons of material. What are the setout, diversion and participation rates for the City of El

Paso? Are residents comingling trash with recycling material? Do residents understand which

materials are not acceptable as recycling material? Are there outstanding neighborhoods which

are doing well in the recycling program? Of the 36,628 tons how much of it was contaminated?

The Environmental Services Department has never conducted a recycling study to answer these

questions. To help the department provide efficient and effective services, plan for future needs,

and track progress towards its recycling goals, this recycling study will:

• Categorize routes by weight of recycling material collected. This will provide a cursory

view of what areas are producing a high stream of recycling material based on weight.

• Correlate the areas that show a high recycling rate by weight with the results a random

sampling study that indicates areas with good recycling behavior, by not having

contaminants in their container.

2

• Determine container setout rate

• Determine customer participation rate

• Profile the contamination

• Determine diversion rate

• Profile the typical recycler

• Provide data for a Cost Analysis

• Make recommendations for management consideration

The Random Sample study collected recycling material from 400 randomly selected homes

over a four week period starting January 4, 2011. The material was collected on the customer’s

regularly scheduled day of collection.

What is recycling?

According to the U.S.E.P.A, “Recycling turns materials that would otherwise become waste

into valuable resources” and “Recycling includes collecting recyclable materials that would

otherwise be considered waste, sorting and processing recyclables into raw materials such as

fibers, manufacturing raw materials into new products, and purchasing recycled products.”

History of Recycling in El Paso, TX

The first recycling program implemented by the City of El Paso was in October 1991 by

the Sanitation department (Porras, 2010). It involved 5,600 randomly selected homes located in

all parts of the city: westside, northeast, lower valley, and eastside. A rearloader was sent to

these homes to collect their recyclable goods and then transported for sale to different

preapproved vendors within the city. The program lasted 6 months and was terminated. No data

or information could be found on the outcome of this program. The next program started in

1992 with drop-off sites located in area high schools with 6 cubic yard dumpsters: Andress,

3

Eastwood, Coronado, and Bel Air (Lira, 2010). The sites were open on Saturdays and citizens

would drop off their recycling goods. Workers at the site would sort out material into a truck

with various compartments for the different types of materials. In 1994 the department changed

its name to the Solid Waste Management Department and hired a Recycling Manager. The

program continued with five (5) Saturday-only drop-off sites: Franklin HS, Montwood HS,

Americas HS, Del Valle HS, and UTEP. In 2003, the department opened six permanent Citizen

Collection Stations (CCSs) that would not only accept recyclable material but garbage and

household hazardous waste material. In addition, drop-off sites were offered at: Coronado HS,

Eastwood HS, Franklin HS, Americas HS, El Paso Zoo, and Socorro. By 2005, the department

adopted another name change: Environmental Services.

In 2005, the City implemented a pilot curbside recycling program with about 5000

residents. Each City Council Representative selected 400 residents in their districts and the

Director of the department selected another 1000. Data was collected to determine participation

rate. In 2007, on Earth day April 22, a full scale voluntary curbside recycling program was

implemented. Every citizen was issued a blue 96 gallon container to be used for recycling, unless

they refused it. The program was and still is strictly voluntary.

One of the best features of this program is the single stream concept. Many other

communities provide their citizens with multiple bins for the different types of recycling

materials, for example, plastics, newspapers, glass, etc. In El Paso, you are allowed to comingle

all acceptable materials together. The Material Recycling Facility (MRF) will then sort the

material with either human capital or automated machinery. The Material Recycling Facility is

privately owned by, The Friedman Recycling Company, and won a bid to do the processing and

4

selling of recycling material in El Paso. The company signed a 15 year agreement to process and

profit-share with the City.

Understanding the City of El Paso’s Recycling Program

To manage its current recycling program effectively and to plan for the future,

Environmental Services needs to know who is and isn’t recycling, how often a container is put

out, and the scope of contamination. The diversion rate needs to be known in order to compare

itself with other communities or track progress towards goals. For the analysis of recycling

materials and customers, residents were selected at random using a computer random

generator. Such an analysis is useful in contract management, route design, establishing user

profiles, and public programs to reach target customers. In this study, recycling loads were

collected from the resident. The material was transported to a facility to sort, weigh, and classify

non-recycling material. Since the City of El Paso does not sell recycling material by specific

commodity or profit share by such, the profile of recycling material is of no value to the City at

this time. The City of El Paso did a composition study in 2007 at the MRF that included 16,000

customers. They examined two days of material and categorized the material. Figure 1.1 depicts

the outcome of this study. HDPE is High Density Polyethylene plastic, PETE is Polyethylene

Terephthalate plastic, and “Residual” is basically non-acceptable recycling material or

contamination.

5

Newspaper53%

Cardboard19%

Aluminum1%

HDPE Color (#2)2%

HDPE Clear (#2)1%

PETE (#1)3%

Rigid Plastic1%

Scrap Metal1%

Tin2%

Residual17%

Recycling CompositionNewspaper

Cardboard

Aluminum

HDPE Color (#2)HDPE Clear

(#2)PETE (#1)

Rigid Plastic

Scrap Metal

Tin

Fig. 1.1 Recycling Composition

Study Methods

This recycling study for the Environmental Services Department involved five major steps:

1. Gather and compile any existing data. The department collects daily tonnage data

from the Material Recycling Facility. The data is sent daily to Collections Supervisors in

the form of a spreadsheet. The supervisors then compile the data with respect to their

assigned regions. A region consists of a set number of clustered routes per day and

servicing a different area of the city each day. The data is entered into a spreadsheet by

route number and driver.

2. Sampling Study. The month of December is the ideal month to conduct the sampling

because traditionally it has the highest tonnage. However, due to availability of human

resources, holidays, and coordination, the study was conducted in January 2011.

6

Sampling Study Methodology

Using a sample size formula (Johnson, 1994):

SS = [(Z^2)*(p)*(1-p)]

C^2

Where:

Z = Z value (1.96 for 95% confidence level)

p = degree of variability, will use 0.5

(.5 is the most conservative)

c = precision level, expressed as decimal

(.05 for a +/- 5% margin of error)

The sample size (SS) was determined to be 385. Four hundred (400) residents were

sampled to be on the safe side and allow for error. The same four hundred samples were

be sampled every week. With no previous scientific study indicating how often a citizen

sets out his/her container, this study was performed over a 4 week period. This data will

provide a baseline for set out, participation, and contamination rates. The 400 randomly

selected customer counts per zip code are graphically shown on figure 1.1 in addition to

the proportion of the actual customers in the respective zip codes. This was done as

verification to ensure uniformity among the sampled proportion versus the total number

of customers.

7

Fig. 1.2 Sample Distribution among Zip Codes

Two drivers were selected to inspect, collect, and transport samples. The drivers were

dedicated to work on the recycling study full time for the four week period. Drivers were

trained how to inspect for contamination or non-recycling material. Drivers were given

the addresses and day to collect samples in advance in order for them to plan their

routing scheme for these samples. Samples were collected on the regular

garbage/recycling collection day. Drivers were trained on identifying non-recycling

material. Each container was inspected for contamination by going through the container

with a garden hoe or other similar hand tool. Observation was logged on a recycle

inspection form (see Appendix A). The accumulation of all samples were taken to the Clint

8

Landfill’s maintenance building for sorting, weighing and classifying of contaminants, see

figures 1.3.

Fig. 1.3 Sorting Material

The associated costs for the Sampling, Sorting and Weighing portion are: Table 1-1 Cost of Study

Material: Cost: Chemical

Resistant Gloves $27

Tyvek Suits $65 Disposable Dust

Masks $10

Portable Weight Scale Rental

$706

64 gallon containers

Provided by ESD

Fuel $800 Waste &

Recycling News 2011 Survey

$150

Postage for mail-out surveys

$90

Total Material Costs:

$1848

9

Labor: Cost: 6– Sorters 384 hrs $15/hr $5,760

2-Truck Driver 320 Hrs $13/hr $4,160 Project Manager 120Hrs $27/hr $3,240 Deputy Director 20 Hrs $41/hr $820

Total Labor Costs:

$13,980

Other: Cost: Appreciation luncheon for

employees who assisted

$175

Rebate for Survey $505 Total Other

Costs: $680

Total Project Cost:

$16,508

Weigh contaminant material on a calibrated scale. Contaminants were weighed on a

Weigh-Tronix, Model WI-125, S/N 054390, Calibration date 12/20/2010, rented from

Sherman Pease in El Paso, Texas, see Fig. 1.4. The weights of the contaminant material

were recorded on a contaminant weight log, see Appendix B.

Fig. 1.4 Calibrated Weight Scale

3. Compute Setout, Participation, and Contamination rates. Compile all data and

calculate rates. Setout will be calculated by counting all the containers setout for pickup

compared to the total of samples collected that week. Participation is calculated by

10

counting all the different customers who setout containers at least once during the four

week long study. Contamination is calculated by using the individual contaminant

weights and dividing by the total weight of contaminant material.

4. Develop Outreach and Recycling Program Strategy. Analyze and review the top

recycling neighborhoods and top recyclers determined from the sampling study. Use this

data to survey these neighborhoods and ascertain attitudes and behavior of these good

recycling stewards. Provide recommendations as to what type of outreach and behavior

change strategies should be implemented.

5. Mail-out Survey. In order to confirm or dispute characteristics of a good recycler a mail-

out survey was conducted. The survey was sent to residents of the random sample study

which are identified as demonstrating good recycling behavior. Good recycling behavior

is defined as having no contamination in their recycling container. Also, residents must

have setout their container at least two or more times during the study. Mail-out survey

was also used to propose some ideas from the City of El Paso regarding the recycling

program and to receive feedback. A postage paid envelope and a five dollar rebate off

their next solid waste monthly bill was offered to improve response rate. Response rates

will be compared to other surveys conducted in other communities on similar topic.

1.1 Trial Run Findings

On November 17, 2010 a trial run was performed to assess the feasibility of a driver being able

to collect all samples within the allotted time and establish the sorting procedures with sorters.

The trial run started at approximately 7:00 AM and lasted to approximately 2:20P.M. The one

driver was only able to collect 62 out of 92 samples. Although, the drivers work schedule is from

11

4:30 A.M. until 3:00 P.M. it was decided to use two drivers for the true study in January. One

would start on the Westside while the other would start on the Eastside of the city.

In addition, processes were established while doing the sorting of the samples. It was

determined that for efficiency purposes, all containers used for weighing would be weighed

empty ahead of time and marked with its tare weight, see Figure 1.5. Each container was marked

for specific contaminants. It was also determined that it would not be necessary to record

weight of acceptable individual recycling material since one of the goals of this study is to gain

knowledge of the quantity of contaminant material.

Fig. 1.5 Containers used to Weigh Contaminant Material

Twenty Seven (27) out of the Sixty Two (62) residents had their containers out for collection

which amounts to 43.5% setout rate. This is in line with department estimates on previous

cursory studies conducted. Also, in this trial period it was noted that yard waste, bathroom

waste, textile products, and construction debris were the most common type of contaminants

found. Weighing of contaminants was not conducted during the trial run.

1.2 Key Sampling Findings

12

Four hundred (400) random samples were to be collected as part of the study. Eleven samples

had to be eliminated for reasons unforeseeable. The following is a summary of the eleven

samples that were not counted:

• Commercial Property – 3

• House for Rent – 1

• Duplex – 4

• Abandoned Home – 2

• Quadplex – 2

Since the City of El Paso only provides recycling to residential homes and not businesses, the

three homes being used as a business were not used. The four duplexes and two quadplexes

were not used because it was too hard for the driver to determine which blue container belonged

to whom and only one unit was selected as part of the sample population. One house had a “For

Rent” sign out so it was assumed vacated. Two abandoned homes were identified because of

their dilapidated condition and no sign of anyone living in them. As mentioned previously, per

the sampling size formula only 385 samples were required hence the final sample size is 389.

13

Chapter 2 Top Performing Areas

There are 60 automation routes plus 7 manual routes collected daily for a total of 268

routes per week. An automation route is done with a garbage truck that has a mechanical arm on

the side and it lifts the container into the body hopper through hydraulic systems, see Fig 2.1.

This truck is referred to as an Automated Sideloader. The arm is controlled through a joystick

inside the cab of truck and driver is seated on the right hand side of the vehicle.

Figure 2.1 Automated Sideloader

Manual routes are performed by a Rearloader truck. A Rearloader truck, see Figure 2.2,

has containers emptied through the rear by “lifters”. A manual collection system requires a

Truck Driver and General Service Worker (GSW). The GSW is usually the employee who has to

get off the truck, take the container to the rear of truck to empty and then return container to its

usual placement.

14

Figure 2.2 Rearloader Truck

Prior to March 1st, the routing scheme for solid waste and recycling collections in

Environmental Services was different and evolved through manual mapping over the years by

supervisors and drivers. Routes were fragmented and unbalanced. On February 2, 2010 the

City began implementing new collection routes using an electronic routing software

(eroutelogistics) purchased by Environmental Services to optimize the routes and improve

operational efficiencies. Routes were balanced by day of week in a particular area and would be

serviced on a specific day (see Appendix D). The city was divided into two regions for the

purpose of optimization: east & west. Cotton Street was the dividing line. As the week

progresses the boundaries move further out. Collections of garbage and recycling material are

done by the same truck and driver in most routes. The department is currently conducting pilot

studies with all-recycling and all-garbage routes but these are minimal. Collections are done

Tuesday through Friday 5:00 A.M. to 3:00 P.M. daily.

15

2.1 Top Performing Recycling Routes

The top five routes with the highest recycling weight were identified for each day of

collection. These five routes equate to the top 10% customer population per day. Manual

Collections was not included since traditionally their weights have been low. Recycling weights

were compiled for 6 months, March 1, 2010 through August 31, 2010.

Table 2-1 is a reflection of the top 10% recycling household accounts serviced by

Environmental Services. There are approximately 160,000 accounts at the time of this study.

Ten percent equates to 16,000 divided by the four day schedule equates to 4, 000 homes per day.

Each automated route services approximately 635 homes while a manual route services

approximately 285.

2.2 What the Numbers Really Mean

Do these numbers really mean these are the best recycling neighborhoods in El Paso? No,

this data indicates that the recycling containers collected in these neighborhoods were the

heaviest for the 6 month period the data was analyzed. These areas could be heavy because

some people could be have used their recycling container as a second trash container or maybe

the drivers comingled trash and recycling for some homes, blocks, or even streets. Another

statistic to point out is route “E07” seems to be a statistical outlier. The reason is this is one of

those pilot routes mentioned earlier that the department is running as an all recycling route.

This route actually represents more than a typical trash/recycling route and a combination of

some routes.

16

This data can be useful in the identification of good recycling neighborhoods. By

correlating the top recyclers identified in the random study, we could establish neighborhoods

or routes that have both high recycling weights and sample(s) that demonstrated good recycling

behavior.

Table 2-1 Top Five Performing Recycling Areas by Weight

Day Route

No. Zip

Code Avg (lbs)/

Household Tues E29 79925 24.17 Tues E30 79925 22.93 Tues E28 79925 20.94 Tues E35 79925 22.02 Tues E24 79905 20.61 Weds E32 79924 29.15 Weds E33 79924 25.35 Weds W09 79912 22.24 Weds E36 79924 21.74 Weds E45 79904 20.83 Thurs E07 79936 34.14 Thurs E22 79915 23.67 Thurs E10 79935 21.70 Thurs E06 79935 20.63 Thurs E14 79915 20.62

Fri E07 79936 37.05 Fri E14 79936 22.69 Fri E22 79936 22.56 Fri E23 79936 21.53 Fri E31 79938 20.88

Average: 23.77 City Wide Avg 19.20

National Avg (EPA, 2008) 23.90 Note: Appendix C provides a description of the streets involved for

each of the above routes.

17

2.3 Heavy Recycling Routes and Good Recycling Behavior

Table 2-2 is a comparative analysis of routes with heavy recycling tonnage and samples

collected with good recycling compliance. Compliance refers to having only acceptable recycling

material in the container at the time the sample was collected.

Table 2-2 Routes with high tonnage of recycling material and compliant samples.

Street Name Zip Code Route TEJAS DR 79905 E24

BLUE WING DR 79924 E32 WAVERLY DR 79924 E33 CROSSON CIR 79924 E36

DEVONSHIRE DR 79925 E28 WH BURGES DR 79925 E29

GREGORY JARVIS DR 79936 E22 ARROW KNOLL CIR 79936 E7

18

Table 2-3 represents routes in which two or more of the samples demonstrated good recycling

compliance. These routes however, do not fall in the category of the top 10% of customers with

high tonnage of recycling weight.

Table 2-3 Routes with two or more samples being

compliant but not in high tonnage routes. Street Name Zip Code Route TARASCAS DR 79912 W1

DULCE TIERRA DR 79912 W1

BLUFF CANYON CIR 79912 W6

SOMERSET DR 79912 W6

BALSAM DR 79915 E20

PEARL LN 79915 E20

TANGERINE LN 79915 E20

GRANITE RD 79915 E20

WENDA DR 79915 E24

CADWALLADER DR 79915 E24

VISTA DEL MONTE RD 79922 W2

ROSINANTE RD 79922 W2

DULCINEA CT 79922 W2

BALLISTIC ST 79924 E30

GENIE DR 79924 E30

CARTWAY LN 79925 E32

BUCKWOOD AVE 79925 E32

PUEBLO ALEGRE DR 79936 E27

DEAN JONES ST 79936 E27

PAINTED QUAIL PL 79936 E28

FRANK CORDOVA CIR 79936 E28

A L GILL DR 79936 E29

DIANA NATALICIO DR 79936 E29

MISSY YVETTE DR 79936 E30

MISSY YVETTE DR 79936 E30

19

2.4 Conclusion and Recommendation In order to understand the good recycler and develop a profile on these residents, the

routes shown in Tables 2-2 and 2-3 need to be further evaluated. A random sample study along

with a questionnaire should be done targeting these neighborhoods. This author also

recommends performing random inspections on the loads coming from these neighborhoods to

ensure no signs of comingling by the driver. If Environmental Services could learn from these

residents, the department can concentrate its outreach to other similar neighborhoods and their

nearby schools, senior citizen centers, and other community organizations/events. By similar

neighborhoods, refer to other routes with high tonnages, compliant recyclers, and same

demographics. In the chapter on ‘The Recycler’, socio-demographics characteristics are

considered. Together with the information in this chapter a typical profile may be developed and

then targeted by the department.

20

Chapter 3 Container Setout

3.1 Setout

Container setout is defined as the number of residents putting out a container on

collection day compared to the total number of customers in the program:

S = [No. of residents putting out a container on collection day x 100% Total No. of residents in the program]

Setout is affected by many factors: single or multi stream program, collection frequency, number

of people living in the home, resident(s) on vacation, unoccupied home, abandoned home, home

for sale, and collection schedule. Setout rate is not really a good performance factor to determine

the effectiveness of a recycling program. For this study, it will be used to broach the issue of

changing to biweekly collection. Table 3-1 summarizes the setout percent for each week of the

study. It also shows data reflecting how full the 96 gallon container was at the time sample was

collected.

Table 3-1 Setout and Volume Percentages Margin of Error +/- 5%

Setout %

Percent 1/4 Full

Percent 1/2 Full

Percent 3/4 Full

Percent Full

Week 1 47.8 16.7 10.8 21.5 51.0 Week 2 41.1 13.7 24.4 27.5 34.4 Week 3 47.6 15.7 26.4 23.8 34.1 Week 4 44.0 22.8 25.1 24.0 28.1 Overall 45.1 17.2 21.5 24.1 37.2

21

3.2 Setout Comparison

According to MSW Management, The Journal for Municipal Solid Waste Professionals, there are

currently an estimated 7,700 curbside recycling programs. Yet, there is a lack of data for

performance benchmarks to compare. This author has extracted some data from an article by

MSW Management, to do a comparison with the City of El Paso (O’Brien, 2010). Keep in mind the

database is limited (only nine agencies have submitted data); data is collected through Solid

Waste Association of North America (SWANA). At the time the article was written the database

was not complete. Collection organizations are not named due to confidentiality of some private

companies and thereby, encouraging them to also participate in these surveys. Table 3-2

represents the most available data from the article.

Table 3-2 Setout Comparison

Collection Agency Service Provider

1 City of El

Paso Service Provider

2 Collection Method Single Stream Single Stream Single Stream Collection Vehicle Automated Automated Automated

Collection Frequency Weekly Weekly Biweekly

Setout Rate 54% 45.10% 75% Pounds per Setout 24.1 15.7 14.5

3.3 Conclusion and Recommendation

Although setout is not an indication of recycling performance from Table 3-1 we can

summarize that 38.7% out of the 45.1% containers setout weekly are half full or less. Planning

and Urban Design Standards refers to a figure of 40% for setout (American Planning Association,

2006).

22

To put it in perspective here’s a simplistic example:

100 Customers

45 Containers are setout

17 Containers are ½ full or less

28 Containers are ½ full or more

Environmental Services currently does not have a formal policy regarding setting out the

containers. They recommend not to put it out unless it is ½ full or more. If this guideline were to

become part of the City Ordinance, this author has to pose the question, “Is it cost effective to

have a truck travel a whole route to service 28% of the container every week?” Not according to

the U.S.E.P.A., changing collection frequency from weekly to biweekly can show a cost savings

(USEPA, 2011). According to Resource Recycling, to improve the economics of recyclables

collection you need to increase the household payload. This can be done by increasing the time

between setouts. If the time between setouts is increased then the number of stops decreases

and the quantity collected increases. This translates into an approximate 44% savings in

changing from weekly to biweekly (Anderson, 1994). This together with a Cost Analysis will

present a more robust argument to switch towards biweekly collection. It is also recommended

that the department participate in SWANA’s annual benchmarking survey to gain access to this

type of data.

23

Chapter 4 Participation

4.1 Definition

Participation is defined as setting out the container at least once in a given period, for this

study four weeks was selected, compared to the total number of customers in the program.

P = [No. of residents setting out a container for a given period x 100%

Total No. of customers in the program] = (320/389) x 100% = 82.3% For El Paso, the participation rate for the 4-week study indicated there is an 82.3% participation

rate with a margin of error of +/- 5%.

Participation rates or ratios are hard to compare because many factors affect

participation rates: mandatory vs. non-mandatory rules by local or state statues, type and

number of recycling materials that are acceptable, single stream or multi-stream collection,

collection frequency, intensity of outreach program or information made available to residents,

recycling infrastructure, and incentives. The author could not find any databases regarding

participation rates. As mentioned in the previous chapter, SWANA is trying to attract agencies

to participate in their annual benchmarking survey and if an agency does participate they will be

given access to the information. This is something the City of El Paso has not done.

Although 82% appears to be a good participation rate, how compliant are these

participants? The answer to this will be in the subsequent chapter on contamination.

4.2 Participation in Capital Cities

The only information that could be found and used for comparison purposes is the data

shown in table 4-1. This was generated by a person doing a thesis on “Factors Affecting

24

Participation in City Recycling Programs” (Lockhart, 2003). The study surveyed capital cities

from various states via email. I have inserted where El Paso would fall in the survey. Although

Lockhart mentions 16.7% of the cities are under mandatory recycling requirements, the author

does not identify those cities. Only 13.3% of the cities have voluntary recycling with incentive.

For example, the city of Boise offers a discount to garbage fees if a resident participates in

recycling. Cities which are voluntary with incentive are also not identified.

25

Table 4-1 Participation Rates of Capital Cities in the U.S.

CITY TYPE

AGE OF RECYCLING PROGRAM PARTICIPATION RATE (%)

Olympia Curbside 15 87.5 El Paso Curbside 18 82.3 Salem Curbside N/A 80

Columbia Curbside 12 80 Sacramento Curbside 13 75

Augusta Curbside 11 75 Boise Curbside 6 73.5

Saint Paul Curbside 17 70.9 Austin Curbside 14 70

Salt Lake City Curbside 5 65 Lansing Curbside 12 55

Nashville Curbside 12 52 Raleigh Curbside 12 50 Atlanta Curbside 11 50

Des Moines Curbside 9 46 Baton Rouge Curbside 12 42

Richmond Curbside 28 40 Tallahassee Curbside 15 39

Montgomery Curbside 14 38 Trenton Curbside 19 37

Springfield Curbside 11 33 Jackson Curbside 12 30

Charleston Curbside 8 28 Indianapolis Curbside 10 3.5

Pierre Drop off 3 45 Cheyenne Drop off 6 20

Topeka Drop off 11 4 AVG: 52.2

26

4.3 Participation vs. Age of Program

Does age or experience of a recycling program mean higher participation? Not according

to the data shown in figure 4.1 (Lockhart, 2003). This dataset does not show a correlation where

maturity of a recycling program will mean higher participation rates. It is worth mentioning

that, according to Lockhart, most of these cities started with pilot programs before any full scale

program was implemented, as did the City of El Paso. The clock for these programs did start at

the onset of any pilot program as in the case of the City of El Paso. The numbers are skewed for

the most part because they include pilot studies or programs.

27

Figure 4.1 Participation vs. Age of Program

4.4 Conclusion and Recommendation

Studies have been done in the United Kingdom on increasing recycling participation

(Tucker, 2002). Some of the results from these studies are: (1) using a rewards system does

increase participation but the improved results are short-lived; (2) persuasive messages

indicating how the environmental impacts may have a personal impact work well; (3) goal

setting and feedback systems don’t work well; (4) using a locally respected person as a

spokesman also helps. Using a combination of these measures is even more successful.

Method of delivery is also critical. TV ads work well for those who are not particularly

looking for recycling information. Personal contact works far better than leaflets or brochures.

Educational material should address:

28

• Lifestyles of the “too busy”

• Stimulating the extra effort - washing containers and cutting up cardboard,

etc.

• What to do if you are encountering personal difficulty – not enough space in

the kitchen

• Problems with the collection of the material - my neighbor’s car is always

blocking the container, the wind moves the container down the street, etc

• Effectiveness of the program

• Personal responsibility

• Dismiss any negative perceptions

Addressing the economic benefits or lack of, is of little relevance to someone driven by altruistic

motives. However, some people are only motivated through incentives or penalties.

Timing and frequency of promotional campaigns is important. There have been studies

that show campaign decay occurs after six months. Repeated or reinforced messaging should

occur on regular intervals.

In summary, in order to increase participation you need to increase the positive attitudes

and convenience of recycling. In the United Kingdom, making recycling mandatory showed an

increase in participation in the 90% range. (Tucker, 2002).

29

Figure 4.2 Participation

30

Chapter 5 Contamination

5.1 Contaminant Profile The following pie chart (Fig. 5.1) shows what the contaminant profile looked like after all the

samples were weighed and categorized. Percentage was calculated by:

= [Specific Contaminant Weight x 100% Total Contaminant Weight]

ewaste7% HHW

3% Textiles10%

Other Organics4%

Yard Waste10% Styrofoam

3%Food Waste

24%

C&D11%

Glass12%

Trash16%

City of El Paso Contaminant Profile

Fig. 5.1 Contaminant Profile

• Food Waste – Containers not cleaned out and containing residual food product,

food-soiled containers such as pizza boxes, actual food product

• Trash – Anything that didn’t fit into the other categories or contained a large

amount of various contaminant categories and was too time-consuming or tedious

to sort out

• Glass – Glass containers, mirrors, or any other type of glass articles

31

• C&D – Construction & Demolition, such as: tile, drywall, lumber, metal stud,

flashing, roofing material, etc.

• Textile – Clothing, shoes, belts, shower curtains, carpet, rugs, etc.

• Yard Waste – Grass and brush clippings, weeds, tree limbs, plants, etc.

• ewaste – Electrical products such as: cell phones, iPods, radios, motors, etc.

• Other Organics – Soiled diapers, tampons, animal feces, adult diapers, soiled toilet

paper, etc.

• Styrofoam – Includes “popcorn” packing material, foam packing material

• HHW (Household Hazardous Waste) – batteries, petroleum products, cleaning

solvents, house cleaning products, etc.

5.2 Percentage of Recycling Material by Weight

The overall contaminant percentage for the month long study was 17%, see Fig. 5.2. This figure

actually corresponds to the same number from the Recycling Composition study the City of El

Paso did in June of 2007 shown in figure 1.1. The percentage of acceptable recycling material

was calculated by:

= [Acceptable Recycling Material Weight x 100% Total Weight of Material Collected] = (1,848/11,057) x 100% = 16.7%

Another factor to consider is the MRF’s legal requirement of not accepting greater than

20% contamination by weight within a six month period. This is a deviation from the regulatory

requirement of 10%. The Freidman Company was able to obtain a deviation from the Texas

Commission on Environmental Quality (TCEQ) of an additional 10% in 2009.

32

Fig. 5.2 Percent of Contaminant by weight

5.3 Conclusion and Recommendation

During the sample study several significant things were uncovered. Speculation that

drivers were comingling trash and recycling is unfounded based on 17% contamination. This

author will not say that drivers are not doing this but to say they are doing it consistently and

intentionally would be an injustice to the drivers. Drivers have admitted to comingle, as an

example given, if they are on their recycling route and they get a call from a dispatcher or

supervisor for“missed garbage”. They are not going to drive all the way to the MRF to dump

their recycling load and then come back to pick up one garbage container. Another scenario is if

a regular route truck breaks down and a spare is used. Sometimes the spare truck contains

material unknown to the driver and if it is trash and he/she is collecting recycling you will have

contamination. Doing the math, 36,628 tons of recycling was collected in 2010 which would

mean 6,227 tons was contamination. This is equivalent to 1,557 trucks fully loaded.

33

The City of El Paso also received complaints from the MRF that garden hoses are a big

contaminant. During the study only two garden hoses were found. Garden hoses might be a

huge operational issue for the MRF which causes downtime to repair machinery but not a large

contaminant factor for the City.

Another issue is that of Styrofoam or foam material. This is a problem operationally for

the drivers since this type of material is not compactable. During the sorting of contaminants,

you could fill a 96 gallon container to the brim of Styrofoam and the material would only weigh 3

lbs. Styrofoam is not an acceptable recycling material and cannot be compacted.

Cardboard was problematic when it was not broken down, see figure 5.3. It can get stuck

in the container and not fall into the truck. It can get stuck in the hopper of the truck causing the

driver to have to stop and run the packing blade excessively, or can fall to the ground as the

container is being emptied.

Figure 5.3 Cardboard not broken down

Drivers have complained about a lot of yard waste being thrown in the recycling

container, see figure 5.4. In this study there was 10% yard waste. Of course the study was done

in January which is not really the season for yard waste.

34

Figure 5.4 Yard Waste in Recycling Container

The department needs to conduct a similar study during the summer months to get better

data on yard waste to confirm or deny complaints from drivers. This will also provide a baseline

comparison for two different seasons. Outreach needs to focus on food waste and cleaning of

food containers, cutting up cardboard, and Styrofoam.

35

Chapter 6 Diversion

6.1 Introduction

In order to conserve resources and reduce waste, the U.S.E.P.A. identifies three ways:

Reduce, Reuse, and Recycle (see figure 6.1). The most effective way to reduce waste is don’t make it.

Reduce and Reuse go hand and hand. For example, plastic bags, instead of throwing them away many

people will use them as a bag to put their lunch in and take to work, or a trash liner at home, or to store

other items. In El Paso you can recycle plastic bags; the MRF started accepting plastic bags in 2011.

According to the EPA in 2005, the national diversion average was 32.1% but this includes both

recycling and composting (City of El Paso does not have a composting program). Some of this is

attributed to the decrease in waste generation; hence diversion rate increases (Merrill, 2008).

More on the calculation of diversion rate below.

There is no true recycling measurement benchmark. Statistics such as setout rate,

participation rate, and capture weight are used throughout the United States but are inconsistent

due to the many variables involved in recycling programs, such as the manner of collection,

frequency of collections and materials collected. Some programs use single stream, which is

comingling all acceptable recycling material in one container. There is also multi-stream in

which different types of recycling material are sorted by the generator into different bins or bags.

Then there is recycling frequency; whether collection occurs weekly, biweekly, or monthly. Are

there drop-off sites available and what is their proximity? What types of material are accepted?

Glass and metal are heavy materials and thus, can skew capture weight.

36

Figure 6.1 Waste Generation Cycle

Figure 6.2 shows the diversion rate for the City of El Paso for the last three fiscal years (a

Fiscal Year is September 1 to August 31). This study is using residential curbside recycling

weight for this calculation. It does not include categories as what the solid waste industry refers

to as ‘ICI’ (Industrial, Commercial, and Institutional). The actual figure would be larger if

commercial recycling, recycling from the Citizen Collection Stations (CCS), Household Hazardous

Waste (HHW) from the CCSs, scrap metal recycling from the CCSs, scrap metal recovery from the

REDUCE

37

landfill, ewaste from the CCS and Construction & Demolition (C&D) deposits from the

commercial site were all taken into account. The rate is also skewed because the MRF removes

contamination from the curbside recycling and disposes it at the Clint landfill (City-owned

landfill). The problem with the amount of contaminant material disposed by the MRF at the

landfill is that the MRF does not distinguish between contamination from City residential

recycling material or from that of their ICI customers.

Figure 6.2 Diversion for the City of El Paso

6.2 Understanding Diversion Rate

Diversion rate is the same as recycling rate as defined by the EPA’s handbook “Measuring

Recycling: A Guide for State and Local Governments” which is basically the total amount of

material diverted from a landfill in proportion to the total waste generated.

Diversion rate seems to be the common rate in determining how effective and efficient a

recycling program is. But the figure can be misleading. Let’s look at an example. You and your

neighbor both have a 96 gallon recycling container. Both of you fill your recycling container to

38

the top with more or less the same type of material. However, your neighbor has two 96 gallon

garbage containers and you only have one. For illustration purposes let’s say you both put out

the same amount of garbage in each of the containers. Who has the better diversion rate?

Recycling container = 20 lbs each Garbage container = 40 lbs each

Your diversion rate is = 20______ = 0.333 or 33% 20+ 40

Your neighbor’s rate = 20__________ = 0.20 or 20% 20+40+40

But how can that be if both of you put out the same amount of recycling material? The obvious

point here is that if you generate a lot of waste and don’t increase your recycle portion, your

diversion rate will decrease. As far as diversion rates are concerned, weight matters and not

volume. Should the City of El Paso go after glass recycling? The diversion rate will increase but is

the recycling program really more effective? From one point of view, another product has been

added to the recycling program but it is hard to determine if the program will be more effective

until all the data has been analyzed. The above example is the gist of this study. Each community

sets its own benchmark and cannot readily be compared with other communities. And what

should the focus be on, waste reduction or gain in recycling weight?

6.3 San Francisco

The mayor of San Francisco boasts that “San Francisco Achieves 77% Landfill Diversion

rate, the Highest of Any U.S. City.” According to their city website (www.sfrecycling.com), San

Francisco has mandatory Recycling and Composting. They provide their citizens an outlet to

report their landlord, property manager, business owner or event sponsor for not providing

39

recycling or composting bins. San Francisco’s recycling website

(http://www.recologysf.com/residentialServices.php) contains an extensive list of acceptable

recycling, composting, and household hazardous items. In addition, San Francisco offers

compost and recycling to commercial businesses. The San Francisco landfill charges $140.76 per

ton (the City of El Paso charges $26 per ton) hence, San Francisco does not make it economical to

take things to the landfill. San Francisco also offers, for free, a “Recycle My Junk” pick up for large

items like: old TVs, sofas, chairs, furniture, etc. Small amounts of concrete, plaster, dirt, and

construction debris could also fall under the “Recycle My Junk” program. Looking at the stat

superficially, San Francisco’s 77% rate is much greater than El Paso’s 16%. San Francisco and El

Paso cannot be compared statistically by any of the parameters mentioned thus far in this study.

The 77% rate is San Francisco’s overall rate and the 16% for El Paso is residential curbside

collection.

6.4 State Bans increase Diversion Rates

States that have achieved 40% diversion or higher are: California, Maryland, Massachusetts,

Minnesota, North Carolina, Oregon, Washington and Wisconsin. These statistics are only as good

as the values used to calculate them. Some of these states count landfill daily cover as recycling

material, landfill gas recovery, and fines from Construction & Demolition. States with the most

successful recycling programs are backed by state level incentives, strategic plans, and

regulations (Mitchell, 2010). State Disposal Bans such as the one shown below in Table 6-1 help

improve diversion rates.

40

Table 6-1 Examples of Statewide Disposal Bans STATE BANNED MATERIALS

Massachusetts

• Aluminum, metal and glass containers

• Single polymer plastics, recyclable paper

• Asphalt pavement, brick, concrete, metal and wood

Michigan • Beverage containers 1 gallon or smaller

Minnesota • Telephone directories

North Carolina • Aluminum cans • Beverage containers consumed on

premises of ABC permit holders • Plastic bottles, wood pallets

Wisconsin

• Newspaper, corrugated cardboard and other containerboard, magazines, office paper, beverage and food containers (glass, aluminum, plastic #1 and #2, steel and bi-metal), foam polystyrene packing material

Note: Commonly banned materials such as yard waste, tires, white goods and batteries are not included.

6.5 Diversion in Texas

The North Central Texas Council of Governments (NCTCOG) had a study done on

benchmarking diversion rates in North Texas in 2004-05 by R.W. Beck. It is comprised of 16

counties all centered on the Dallas-Fort Worth metropolitan area. The Council of Governments

(COGs), as described by their website, is a “voluntary associations of local governments formed

under Texas law”. These associations deal with the problems and planning needs that cross the

boundaries of individual local governments or that require regional attention.” Figure 6.3

depicts what comprises the NTCOG. Table 6-2 shows some diversion rates for some of the major

41

cities in this region. Unfortunately, the study did not indicate the type of recycling programs in

each community thus, no suitable comparison can be made.

Figure 6.3 North Central Texas Council of Governments

42

Table 6-2 Diversion Rates in North Central Texas

City Population Waste

Generated Recycling Diversion Total NCTCOG Region 5,032,122 2,822,498 344,839 12.20% Arlington 362,805 149,132 24,324 16.30% Dallas 1,213,825 610,628 44,259 7.20% Fort Worth 624,067 261,285 53,015 20.30% Irving 193,649 69,096 8,632 12.50% Plano 250,096 109,656 38,598 35.20%

6.6 National Diversion Rates

One of the best surveys comes from Waste & Recycling News and it compares the 30

populous cities in the United States (Waste & Recycling News, 2011). This survey shows

recycling rate which is synonymous with diversion rate. The survey shows many parameters

about recycling programs in cities around the U.S.; see Appendix E for a snapshot look and how

El Paso compares with other cities of similar population.

Mandated vs. Non-Mandated Programs

Figure 6.4 shows the correlation between recycling programs that are mandated by either

local or state laws. In general, mandated programs show higher diversion rates in the categories

of residential collection and overall than non-mandated see figure 6.5. Mandated programs have

an average of 42.8% diversion overall and 31.1% for residential only. Also, there is no

correlation between the bigger cities versus the smaller ones in diversion performance.

43

0

10

20

30

40

50

60

70

31.1

Resi

dent

ial D

iver

sion

%

Mandated Programs

Figure 6.4 Diversion Rates for Mandated Recycling Programs

Non-mandated programs averaged 20.1% for residential diversion and 23.0 % for overall

diversion. Another observation, the diversion rates between residential and overall are further

dispersed in the mandated programs. This probably means that a greater focus is being placed

on ICI (Industrial, Commercial, and Institutional) entities which are taken into account in the

overall diversion rates.

44

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

20.116.7

Resi

dent

ial D

iver

sion

%

Non-Mandated Programs

Figure 6.5 Diversion Rates for Non-Mandated Recycling Programs

Frequency of Collection

Biweekly collection systems had a better diversion rate at 23.5% versus 18.9% compared

to weekly collection. Figure 6.6 shows the comparison. Austin, Houston and Denver are the

only three cities that provided data for biweekly collections. Cities in the blue bar graphs are

cities with weekly collection systems. As expected the frequency of collection does not impact

the fact that mandated programs in general will outperform non-mandated.

45

05

10152025303540

23.518.9 16.7

Resi

dent

ial D

iver

sion

%

Frequency of Collections

Yellow denotes biweeklyBlue denotes weekly

Figure 6.6 Diversion Rates for Biweekly vs. Weekly Collection Recycling Programs

6.7 Conclusion and Recommendation

One of the things the City of El Paso needs to do to improve on their diversion rate is to do

a Waste Characterization study on curbside residential collection trucks. The City needs to

know how much recycling material is still being disposed of at the landfill, and whether this

material is coming from neighborhoods categorized as good recycling neighborhoods, to be

discussed in the next chapter.

46

Chapter 7 The Recycler

7.1 Characteristics of a Good Recycler

There have been many studies done on this subject. The one commonality is that a recycler

of one material is a recycler of all materials. The most common characteristics of good recyclers

are (Tucker, 2001):

• People that own their homes

• Mature people, especially if they have children

• Retired people

• People showing a concern for the environment

• Well educated

• Higher Income

• Social Pressures (If everyone else is doing it. )

And who doesn’t recycle or drops out?

• Inconvenience, no space inside home or no time

• Not having a container

• Believing that they don’t have enough material to recycle

• Unaware of recycling guidelines

• If container is lost or damaged

• Bad service such as a missed pickup

• The non-realization of expected benefits (finding out drivers are treating recycling as

trash)

47

Studies have shown that it is very difficult to recruit and sustain new recyclers. This is why this

study focuses on the good recycler. Demographics is not an indicator for good recycling

behavior; however, the attitude in good recycling behavior is common in certain demographics

to be discussed in a later section. Overall, people of similar socio-demographics have similar

consumption habits and thus similar waste generation. There are three types of recyclers

(Tucker, 2001):

1. Those who already recycle and will always recycle.

2. Those who might switch their behavior.

3. Those who will never recycle.

7.2 Marketing Strategies and the Psychology

Three types of interventions have been tried in recycling: Antecedent, consequent, and

education (Shultz, 1995, Porter 1995), (Geller et al., 1990). Antecedent is based on removal of

barriers, setting goals, and prompting people. Consequent intervention is based solely on

reward and punishment. Education and communicative intervention is using a reward-based

system, information, advertising, and publicity.

According to studies in the U.K., participation drops when rewards are eliminated;

therefore, programs based totally on a reward system tend to be ineffective. In addition,

programs based on a rewards system tend to be administratively expensive (Jacobs and Bailey,

1982-83).

People that are pressured or told to change their behavior are resistant. It is better to

implement a program in steps (Oskamp, 1991). Start with a recycling survey, disseminate

48

information on recycling, ask people to recycle a few items only, and make it easy for them. This

is why the City of El Paso was successful in their implementation of curbside recycling in 2007.

The City had TV commercials on different channels in both English and Spanish with a local

comedian to reach a wide audience. The City also provided one container to all households to

allow them to comingle all recycling material and weekly collection.

Goal-setting has also been successful (McCaul and Kobb, 1982). For example, setting a

goal for participation, diversion rate, or captured weight. Publishing zip codes, neighborhoods,

or street names that have improved in recycling in the newspaper or website have proved to be

effective.

Werner did some studies on the differences between flyer only; telephone calls plus a

flyer; telephone call only; face-to-face plus flyer; and using a combination of all the latter.

Participation increased 9 to 14% using flyer only. A combination of the different treatments

increased anywhere from 15 to 36% (Werner et al., 1995). For the last couple of years the City of

El Paso has solely relied on mail-outs and the website to educate people. Brochures are provided

when a new container is put out. Brochures are also mailed out if a resident requires

clarification and also for all new customers. Wang and Katzev argue that face-to-face

communication will give participants the perception that they will be monitored and if they don’t

comply, may lead to disapproval by their peers and in some cases punitive action (Wang and

Katzev, 1990). This is why, if there are issues with non-compliance or ignorant customers it is

better to have a supervisor or the driver attempt to make personal contact with the customer

and explain the rules and guidelines. Werner also showed that verbal prompts by a recognized

person such as a neighborhood leader led to a higher participation (28% more) rather than

49

leaving a flyer, even if it is eye-catching (Werner et al., 1995). Reams and Ray also found that

making personal contact and eliciting a pledge from customers was more effective than just

leaving a brochure anonymously (Reams and Ray, 1992-93).

DeLeon and Fuqua found only 50% of the participants read the information distributed in

their study (DeLeon and Fuqua, 1995). Spaccarelli found that written information is effective

with higher income households only (Spaccarelli et al. 1989-90).

Intervention does not need to be expensive, recruiting the Boy and Girl Scouts of America

to distribute information and to talk to citizens can be successful (Katzev and Pardini, 1987-88).

Keller speaks of a 10 year old boy who inspired recycling in his neighborhood by writing

personal notes to each resident (Keller, 1991). Recruiting kids from other organizations such as,

school honor societies or science clubs, could be used as ambassadors for a recycling program.

Soliciting people of authority or environmentalists does not have an impact on participation. It is

better to use indigenous role models.

There are few studies on how successful a feedback system works. Some examples of

feedback in recycling could be; how much landfill space was saved, additional revenue to the

department, or any reduction in the carbon footprint. Feedback could be part of an arsenal of

education and outreach approaches. Goal setting is also not very effective unless there is a

persistent or prolonged strategy. It works well for short term events such as: a monthly goal

with constant intervention throughout the month to remind people of the goal.

Normative influence involves using block leaders or inserting artificial block leaders to

induce good recycling behavior. There has been mixed results in using this approach. This

50

approach tends to work better in the poorer socio-economic neighborhoods than affluent. The

reason is that in the poorer neighborhoods people feel a bond to a block leader, where as in the

affluent neighborhoods, households function more independently (Everett, and Pierce, 1993).

Recruiting leaders of neighborhood associations could serve as champions for their

neighborhood recycling.

The solution might well be market segmentation in which you treat different sub-groups

differently. The common factor among all interventions is some kind of personal contact. This

gives people the impression that someone cares, is interested, and is taking the time to make

contact.

Negative message delivery was found to be the most effective. Messages such as, “we are

destroying our planet” with a dirty landfill in the backdrop got the attention of the people in one

study (Engleberg et al. 1992). Negative messages that correlated a personal relevance are more

accepted (Davis, 1995). Children can be a positive influence on parents and make them more

pro-environment. By targeting children with environmental projects, presentations, and talks at

schools or by the mass media, children can re-socialize their family behavior towards recycling

(Easterling et al. 1995).

To reach an audience of higher educational levels or higher income, the newspaper is the

media of choice. For the lower income or less educated, the preferred media is television

(Vining and Ebreo, 1990). Printing information on the container or bin has proven to be

beneficial (Everett, 1994).

51

7.3 Survey Results

As part of this study a survey was sent out to 67 of the 389 residents randomly selected for the

recycling study. The 67 were chosen because they were the residents who put out their

recycling container at least 2 out of the 4 weeks that the study was conducted. These residents

were compliant with recycling guidelines and did not have contamination in their containers. As

mentioned previously, the focus of this study is on residents that recycle and comply with the

City of El Paso guidelines and learn from them. The other reason is to corroborate results from

other studies and determine if these residents fall under the same characteristics with these

studies. The residents were offered a five dollar rebate on their next solid waste bill to respond

to the survey and a postage paid envelope was provided. The response rate for the mail out

survey was 59.7%. To compare to other survey responses, Table 7-1 shows a comparison to

other surveys in the industry and the figures that follow show the outcome. The survey that was

mailed out is shown in Appendix F.

52

Table 7-1 Survey Response Rate Comparisons Date Type of Survey City, County, or

State

Conducted by With

Incentive

Response

Rate

2011 Curbside

Recycling

El Paso, Texas R. Adams Yes, $5 Rebate 59.7%

2010 Solid Waste

Customer

Service

St. Louis Park,

Minnesota

Public Works

Department

Yes,

Participate in

Drawing

39%

2010 Curbside

Recycling

Clearwater,

Kansas

City Staff No 32%

2002 Curbside

Recycling

40 Cities in

Western U.S.

(Tarnai & Miller,

2001)

No 27.5%

2005 Curbside

Recycling

Hillsborough

County, Fl

County Staff No 24.9%

2009 Trash and

Recycling

East Pikeland

Township,

Pennsylvania

Environmental

Advisory Council

No 7%

53

Figure 7.1 Recycling Information

Figure 7.1 shows the education level. Those with a college degree were 52.5%. Another

35% had some college. According to Peter Tucker’s Technical Monograph on Understanding

Recycling Behaviour, one of the characteristics of good recyclers is a higher level of education

(Tucker, 2001). This graph seems to substantiate that fact in El Paso.

54

Figure 7.2 Recycling Information

Figure 7.2 shows how information regarding the recycling program was obtained. As the

Vining and Ebreo study indicated, people with higher levels of education tend to get their

information from reading ; the survey results appear to confirm this observation with brochures

and newspapers accounting for 48.5%. It should be noted, that some respondents did check

more than one category and was taken into account.

55

Figure 7.3 Kids in Household

Children play a pivotal role in the sociology of a household. According to survey

responses, 40% have children in elementary or middle school and 20% in high school. Of these,

60% encourage or participate in recycling in the home. No kids in the household accounted for

40% of the responses. These are most probably senior citizens who also tend to recycle well.

56

14.6%

52.1%

18.8%

2.1% 4.2% 8.3%

0%10%20%30%40%50%60%

8 CYL 6 CYL 4 CYL HYB OTHER SUN METRO

Mode of Transportation

Mode of Transportation

Figure 7.4 Mode of Transportation

Figure 7.4 shows the mode of transportation which actually falls under the

category of concern with the environment. Although, the City of El Paso has room for

improvement in the public transportation business and hybrid vehicles are still cost prohibitive,

it is surprising to see the low figures in four cylinder vehicles and “Other”. It could be the more

educated have a higher income so they can afford to drive less fuel efficient vehicles. The “Other”

category represents motorcycles, bicycles, or people relying on someone else to transport them.

It is important to note here too, that some respondents did check more than one category.

57

35.0%

87.5%

12.5%

75.0%

2.5%0%

10%20%30%40%50%60%70%80%90%

100%

Concern for the Environment

Figure 7.5 Concern for the Environment

Part of the profile of a good recycler is their concern for the environment. This part of the

survey was an attempt to identify these attributes. There were respondents willing to pay a

“small fee” for recycling education, 35%. However, the survey did not provide a figure or range

and many questioned just how much “small” is. One of the questions was whether they wanted

another recycling containter, 12.5% answered they would. This is misleading because of the way

the questions were formatted. This is actually a subset of the question of going to biweekly

collection. In another words, 12.5% of the 87.5% of respondents are willing to go to biweekly

collection as long as they receive a second recycling container. A large majority of the

respondents, 75%, have installed compact flourescent light bulbs in their homes. However, is

this high percentage attributed to concern for the environment or savings to their electric bill?

58

5.0%

35.0%

47.5%

12.5%

0%10%20%30%40%50%

PUBL

IC

RECO

GNIT

ION

SMAL

LER

TRAS

H

BIN

/FEE

GOLD

LID

NON

E

Recognition

Recognition

Figure 7.6 Recognition

One of the questions was the type of recognition for being a good recycler and conforming

to the guidelines. Public recognition was defined as printing the name in the department’s

website or the local newspaper. Three of the respondents did not put a return address or fill-out

the form with their name meaning, they wanted no recognition, 12.5% total wanted no

recognition. Another question asked if they would take a smaller garbage container with a lower

fee, 15% said yes. An idea from the University of Texas at El Paso’s graduate marketing class was

to reward good recyclers with a gold lid. This option received the highest number of positive

responses.

59

90.0%

10.0% 0.0%12.5%

0%20%40%60%80%

100%

GOOD ACCEPTABLE POOR START RECYCL GLASS

Level of Service

Level of Service

Figure 7.7 Level of Service

The last part of the survey was to rate the recycling service in one of three categories.

There was also a comments section so that people could write in additional comments and

information. The most common request was for glass recycling, 12.5% . Most of the written

comments were praise for the recycling program along with; start composting, more recycling in

businesses, make it mandatory, and show proof of how it is benefitting the community.

60

Chapter 8 Final Analysis

8.1 The Profile

After reviewing all the supporting data on recycling, the profile of good recycling

neighborhoods in the City of El Paso was assembled. With the aid of demographic information

from the City’s Planning and Economic Development Department (Herrera, 2011), the data was

compiled, analyzed and sorted. From Chapter 2 and using tables 2-1, 2, & 3, and the

demographic data, routes with a high potential of having residents with good recycling behavior

was established. The Routes had to fall in one of the following categories:

(1) Routes are one of the top five heaviest routes on that day of collection. Heaviest is

defined as the average tonnage collected over a six month period as discussed in

chapter 2.

(2) Routes are both a top five heavy route and had at least one sample demonstrating

good recycling compliance (no contamination).

(3) Routes showed at least two or more samples with 100% recycling compliance but

not a top five heavy route.

City demographic data was analyzed and compiled by: home ownership, property taxes

paid, mature people, and higher education. Zip codes in the demographic tables were ranked

from 1 to 10. Rows highlighted in green are the rankings. The zip code with the highest

percentage or value of that category was given a “1” then the next highest was assigned a “2” etc.

Then all categories were summed and the zip code with the lowest sum was rated the best and

the rest were put in ascending order according to their sum.

61

Figure 8.1a Demographics

62

Figure 8.1b. Demographics

63

This is showed in figure 8.1b on the last row, “Total Ranking”. Figure 8.2 is the culmination of all

demographic data, recycling weight, and sampling observations and shows which routes are

potentially demonstrating the best recycling behavior. These are the routes that potentially have

citizens that are classified as “Those who might switch their behavior” in addition, to already

having good recyclers. Once these neighborhoods are optimized in participation and compliance,

the adjacent areas should be targeted.

64

Figure 8.2 Best Recycling Areas

65

8.2 Biweekly Collection?

The Director of Environmental Services has asked if the program should switch to

biweekly collection. One of the first things that need to be analyzed is how much more or less

recycling material will be collected? How will this impact the number of trucks and drivers

needed to perform this service? Another area that needs to be considered is what will the

drivers’ duties be on the off week when they are not collecting recycling material? Other

communities offset this with either bulk waste collection and/or yard waste pickup. Lastly, is

there a cost savings?

Weight of Recycling Material

In order to answer the other questions, need to predict how much gain or loss will be

attained in recycling material in switching to biweekly. A linear regression analysis can be

performed to predict the increase/decrease in recycling weight. Although there is limited data, a

regression analysis will be generated using data from Austin, Chicago, Denver, and Houston.

These communities are non-mandated and doing biweekly collection and have recycling

program attributes comparable to the City of El Paso. For a regression analysis, the first

requirement to determine is if there is a linear relationship among any of the variables. The

diversion rate will be considered the independent variable.

Table 8-1 Data for Regression Analysis

Diversion (Residential only) Weight

(tonnage) Population Households Chicago 6.90% 161,790 2,851,268 240,000 Houston 20.10% 226,720 2,257,926 205,000 Austin 37.30% 82,611 786,382 179,875 Denver 13% 31,000 610,345 170,000 El Paso ? ? 620,447 162,039

66

Figure 8.3 is the Scatterplot of population versus diversion rate and shows there is a linear

relationship between variables.

Figure 8.3 Scatterplot

Performing the regression analysis with this data produces the following results:

SUMMARY OUTPUT

Regression Statistics Multiple R 0.539111268 R Square 0.290640959 Standard Error 0.135556271 Observations 4

Coefficients Intercept 0.297902514 X Variable 1 -6.43429E-08

Observation Predicted Y 1 0.114443561 2 0.152620927 3 0.247304388 4 0.258631125

Figure 8.4 Regression Output

67

With this data output, the linear equation can be produced:

Y = (-6.4 x 10-8 )*X + 0.2979

Y = Diversion Rate

X = Population

Plugging El Paso’s data into the equation generates a diversion rate of 25.8%. See figure 8.5 for

the predicted values generated by the regression analysis fit plot. If the percentage holds, this

would be a 34.5% improvement in diversion and secondly, the City would meet its five year

Strategic Planning goal of 25% by 2013. The only problem with this value is the R2 factor is at

0.2906 which is relatively low considering the goal is to get it as close to 1.00 as possible. The R2

factor is the square of the correlation coefficient. This number tells you that the variability in one

variable, population, can be explained 29.06% of the time in the other. Using 25.8% diversion

rate means 46,515 tons of recycling material would have been diverted in FY 2010.

Figure 8.5 Fit Plot

68

Figure 8.6 shows staffing and equipment demand if the department elects to change to biweekly

collection based on the tonnage output from the regression analysis and using last fiscal year’s

waste total. This data is based on automated sideloader collection and does not take into

account manual collections. An increase in four trucks and four drivers would be needed for

biweekly collections.

Biweekly Collection: Trash Recycling Capacity of Trucks(CY) 31 31 Compaction Ratio (Lbs/CY) 750 275 Lbs / Truck Capacity 23250 8525 Tons/ Truck Capacity 11.625 4.2625 Predicted Collection Tonnage 180303 46514 Tons per week 3,467 895 Tons per day 867 224 Trips to Landfill/MRF per day 2 2 Number of Trucks required per day 37 26 Total Number of Trucks Required 64 Current Operations Require 60

Figure 8.6 Equipment Demand for Biweekly Collection

The department needs to ask what will be done with the surplus of drivers, 26 during the off

week. Some of the recommendations are: (1) improve morale by allowing more vacation time to

both trash and recycling truck drivers, (2) assign them to drive rolloff trucks for the Citizen

Collection Stations (this falls under working them below job specifications but on the other hand

the other division might not need to hire additional staff), (3)train them to use grapple trucks

and assign to the special collections group (same predicament as (2)).

69

Drive by Rate

Peter Anderson showed a unique way of calculating Equipment/Personnel demand using what he

called “drive by rate” (Anderson, 1994). Anderson formula:

DBR = 3600 (sec/hr) [(Setout %)*(Time at Stop(sec))*(Time between Stops(sec))]

This author has conducted previous studies on driver performance and has calculated averages

on Time at Stop and Time between Stops. The only variable that is estimated is the setout rate for

biweekly collection. Biweekly collection has never been done in El Paso and studies have not

been conducted to generate this number; therefore, the author will use the total participation

calculated at the end of two weeks during the sampling study. That number is 66.5%. Using all

the data the following analysis was generated, see figures 8.7 & 8.8.

Figure 8.7 Drive by Rate

70

Two different analysis with a slightly different outcome. Analysis by weight indicates minimum

number of trucks/drivers needed would be 26. Analysis by driver rate is 24. The conclusion is

that both methods involve some type of estimation; therefore, the department will need between

24 and 26 trucks/drivers for recycling routes done on biweekly collection.

Figure 8.8 Number of Trucks

71

8.3 Final Comments

To increase the accuracy of the study from +/-5% to +/- 2% a sample of 2,401 residents

or about 600 per day would have to be studied. This would get costly because of the greater

number of personnel that is needed to obtain samples, inspect, sort, and weigh. It was also

recommended by some in the literature review that studies should be 8 weeks long. With an 8

week study, the costs of study will also increase. The only gain in a longer term study is

improving on the participation figure. By doing a longer study, the issue of complacency,

boredom, or even laziness becomes a problem. Mapping out the pickup points is also time-

consuming and requires drivers to have some practice runs. Other issues encountered during

the sample study: (1) need two subject matter experts, one to oversee the grab samples and

inspecting of containers; and the other to supervise the sorting, classification, and weighing of

samples, (2) need to use the same sorters. During this study various personnel from the

department were shuffled week to week and on some occasions, day to day. This became a

problem because the sorters had to be retrained and the procedures reviewed each time.

In the mail-out survey, the issue of income and age was not addressed however, education

level is a surrogate. This becomes a problem since most people are going to be sensitive about

giving out such information. The mail-out survey should have also identified motorcycles and

bikes in the category of vehicle utilized.

The overall diversion rate for El Paso may be difficult to calculate and the residential

curbside rate is not totally accurate. There is a private landfill adjacent to the City of El Paso, the

Camino Real Landfill in Sunland Park, NM which is operated by the same company that collects

much of the Industrial, Commercial, and Institutional waste generated in El Paso. Unless, Flow

72

Control is enacted these figures might be difficult to obtain. Flow Control is basically a local or

state law requiring all waste generated within that City or County limits be disposed of in that

City or County operated landfill so that all waste generated can be accounted for.

Finally, MSW Management, The Journal for Municipal Solid Waste Professionals, published

an article on the “10 Steps for a Recycling Campaign” (Brown and Pasternak, 2010). Below is this

author’s assessment of where the City of El Paso’s program stands in regards to these steps.

(1) Decide Who Will Do the Work - Recently the department has assigned a Recycling

Manager, whereas before, it was being overseen on a part time basis by a Deputy Director. This

person will be the public face of the program. A new contract with the MRF is being proposed;

and was approved on April 12, 2011. The new contract would obligate the MRF to subsidize

some outreach and education. As recommended in the article, the new Recycling Manager

should start setting up a volunteer recycling committee.

(2) Establish Clear Goals – The department has not published any on its website but

has laid out a waste diversion goal of 25% by 2013 in their most recent five year strategic plan.

There is a new campaign coming out this year, but it is not clear to this author if any goals are

established in this campaign.

(3) Target Your Audience – This study has fulfilled this step in section 8.1 of this

chapter.

(4) Get to Know Your Audience – This study touched on this with the mail out survey

that was utilized. A similar survey needs to be sent to the targets mentioned in (3). The

department should provide webmail for feedback on recycling from its users.

73

(5) Develop Your Message – The “Why Should I Recycle?” is not really addressed by

the City and the “How do I recycle?” is addressed only through brochures that the department

published. As mentioned earlier, the 2007 campaign “Drop it in Blue” was a success. Studies

have shown that it is more important to focus on the “How to” then the “Why should I” (De

Young, 1989). There are plans for another campaign to come out this year, but as of the

publishing of this study, nothing can be found on the City’s website and only a few radio

announcements have been heard. The focus on the new campaign will be “Know What to Throw,

Recycle Right”

(6) Benchmarking – Using this study, the department now has legitimate setout,

participation, and curbside recycling diversion rates to measure operational changes or

educational outreach.

(7) Select an Educational Approach – This study has stipulated some strategies

towards various target audiences.

(8) Define Success for Your Program – With the benchmarking done in this study, any

new activities that are generated can be measured with follow up studies. It is recommended

that the City measure website hits in order to determine how often the website is being viewed

and the effectiveness of the website in educating the community.

(9) Develop a Feedback Loop - As mentioned in Chapter 7, there have been no studies

to substantiate how good a feedback system works. It is recommended that the information be

made available to the public. The department should develop a feedback system from the

customer to department and consider other avenues other than a telephone call.

(10)Evaluate Your Program - This study provides baseline rates. Any activities or

programs to be implemented in the future can therefore be compared for effectiveness.

References

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Anderson, Peter. Improving the Efficiency of Curbside Recycling Collection, Resource Recycling,

April 1994.

Brown, Katie, and Pasternak, Scott. 10 Steps for a Recycling Campaign, MSW Management, The Journal for Solid Waste Professionals. January/February 2010.

Davis, J.J. The Effects of Message Framing on Response to Environmental Communications.

Journalism and MassCommunication Quarterly, 1995. DeLeon, I.G. and Fuqua, R.W. The Effects of Public Commitment and Group Feedback on Curbside

Recycling. Environment and Behavior, 1995.

De Young, R. . Exploring the Difference Between Recyclers and Nonrecyclers: The Role of Information. J. Environmental Systems, 1989. Easterling, D., Miller, S., Weinberger, N. Environmental Consumerism: A Process of Children’s

Socialization and Families’ Resocialization. Psychology and Marketing, 1995. Engelberg, M., Pierson, R.M., Kashio, H. Applying Conjoint Analysis to Social Advertisements.

Advances in Consumer Research, 1992. Everett, J.W. and Pierce, J.J. Curbside Recycling in the USA: Convenience and Mandatory

Participation. Waste Management and Research, 1993.

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Geller, E.S., Rudd, J.R., Kalsher, M.J., Streff, F.M.,Lehman, G.R.A Conceptual Framework for

Developing and Evaluating Behavior Change Interventions for Injury Control. Health EducationResearch: Theory and Practice, 1990.

Herrera, Jessica. Department of Planning and Economic Development, City of El Paso, TX. Email correspondence, Subject: Recycling Study, March 2011. Jacobs, H.E. and Bailey J.S. Evaluating Participation in a Residential Recycling Program. J. Env.

Systems., 1982-83.

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References

Johnson, R. Miller & Freund’s Probability & Statistics For Engineers. 5th edition, 1994. Keller, J.J. The Recycling Solution: How I Increased Recycling on Dilworth Road. J. Appl. Behavior

Analysis, 1991. Wang T.H. and Katzev, R.D. (1990). Group Commitment and Resource Conservation: Two Field

Experiments on Promoting Recycling. J. Appl. Social Psychology, 1990. Katzev R.D. and Pardini A.U. The Comparative Effectiveness of Reward and Commitment

Approaches in Motivating Community Recycling. J. Env. Systems, 1987-88. Lockhart, Stacy M. Factors Affecting Participation in City Recycling Programs, Thesis, Louisiana

State University, 2003. Lira, Gerardo. Solid Waste Division Supervisor, Environmental Services Department, 1992-

present, verbal communication with author, November 2010. McCaul, K.D. and Kopp, J.T. Effects of Goal Setting and Commitment on Increased Metal Recycling.

J. Appl. Psychology, 67, 1982 Merill, Lynn. Recycling on the Move, MSW Management, The Journal for Solid Waste

Professionals, Elements Issue 2008. http://www.mswmanagement.com/elements-2008/recycling-diversion-continues-1.aspx

Mitchell, Robin. The Recycling Roadmap, Waste Age magazine, October 2010.

http://wasteage.com/Recycling_And_Processing/increase-state-recycling-rate-201010/index.html#

O’Brian, Jeremy K., P.E. The Benchmarking of Residential Recycling, MSW Management, The Journal for Solid Waste Professionals, July-August 2010, http://www.mswmanagement.com/july-august-2010/residential-recycling-

collection-1.aspx

Oskamp, S., Harrington, M., Edwards, T., Sherwood, P.L., Okuda, S.M., Swanson, D.L. Factors Influencing Household Recycling Behavior. Environment and Behavior, 1991.

Porras, Irma. Senior Office Assistant, Environmental Services Department, 1991 – present, verbal communication with author, November 2010. Porter, B.E., Leeming, F.C., Dwyer, W.O. (1995). Solid Waste Recovery: A Review of Behavioral

Programs to Increase Recycling. Environment and Behavior, 1995.

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References

Reams, M.A and Ray B.H. The Effect of Three Prompting Methods on Recycling Participation Rates: A Field Study. J. Env. Systems, 1992-93.

Schultz, P.W., Oskamp, S., Mainieri, T. Who Recycles and When? A Review of Personal and

Situational Factors. J. Environmental Psychology, 1995. Spaccarelli, S., Zolik, E. and Jason, L.A. Effects of Verbal Prompting and Block Characteristics on

Participation in Curbside Newspaper Recycling. Journal of Environmental Systems, 1989-90.

Tarnai, J. and Miller, K. Curbside Recycling Study. Data Report 02-25 Curb, Department of

Economics, Utah State University. August 2001. http://www.uwyo.edu/aadland/research/recycle/datareport.pdf

Tucker, Peter and David Speirs. Model Forecasts of Recycling Participation Rates and Material

Capture Rates for Possible Future Recycling Scenarios, University of Paisley Environmental Technology Group, July 2002.

Tucker, Peter. Understanding Recycling Behaviour, A Technical Monograph, University of

Paisley, 2001. U.S. Environmental Protection Agency, Wastes - Resource Conservation – Tools for Local Government Recycling Programs, at http://www.epa.gov/osw/conserve/tools/localgov/economics/collection.htm

Vining, J. and Ebreo, A. What Makes a Recycler? A Comparison of Recyclers and Nonrecyclers. Environment and Behavior, 1990.

Waste and Recycling News. Municipal Recycling Survey 2011. Data and Research center. http://www.wasterecyclingnews.com/ Werner, C.M., Turner, J., Shipman, K., Twitchell, S., Dickson, B.R., Brushke, G.V., Von Bismarck,

W.B. Commitment, Behavior and Attitude Change: An Analysis of Voluntary Recycling. J. Env. Psychology, 1995.

76

Appendix A

Recycling Inspection Form

77

Date:Name:

Day

of P

ick

up

Wai

ver (

Y/N)

Not

Out

For

Col

lect

ion

1/4

1/2

3/4

Full

Cont

amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

ywal

l, ca

rpet

, tile

)

Food

Was

teYa

rd W

aste

Diap

ers

Non

-Rec

ycla

ble

(Pla

stics

Gard

en H

oses

Text

iles

ewas

teTi

res

HHW

NO PREMISE ADDRESS ZIP CODE1109 SHAWNEE DR 79912910 S HILLS ST 799012206 E MILLS AVE 799011215 MYRTLE AVE 799011207 N BROWN ST 799022315 N OCTAVIA ST 799021341 MURCHISON DR 79902919 E UNIVERSITY AVE 79902611 NOBLE ST 79902920 N STANTON ST 79902700 W YANDELL DR 79902807 ARIZONA AVE 799021828 E CLIFF DR 799024333 N STANTON ST 799021221 PROSPECT ST 799024748 EXCALIBUR DR 799021411 W YANDELL DR 799024321 CAMBRIDGE AVE 799033001 DOUGLAS AVE 799031532 WEIGHTMAN CIR 799031981 PASEO COLINA PL 799035112 E YANDELL DR 79903834 SIERRA ST 799034801 TROWBRIDGE DR 799031228 HUCKLEBERRY ST 79903926 ARGENTINA ST 799034009 TULAROSA AVE 799034017 HUECO AVE 799034804 E YANDELL DR 799031628 SAINT JOHNS DR 799034668 CAPLES CIR 799035232 PIKES PEAK DR 799043512 SHEPPARD AVE 799048528 MOUNTAIN WILLOW DR 799044125 SNOWFLAKE CT 799043425 CLEARVIEW LN 799043907 TITANIC AVE 799044910 TETONS DR 799044921 BLUE RIDGE CIR 799045028 CATSKILL AVE 799043112 TITANIC AVE 799045520 HOMER CIR 799043917 FRED WILSON AVE 799043303 GABEL AVE 799043109 RIVERA AVE 799056023 TEJAS DR 799055913 MACIAS ST 79905374 S GLENWOOD ST 79905182 N AWBREY ST 799058430 INDEPENDENCE DR 799078124 BURNHAM DR 79907268 ROMERIA DR 799078371 WHITE RD 799071045 WYATT DR 79907374 ARVIN CIR 799079228 ROSEWAY DR 79907448 MOORELAND ST 79907106 LAS HADAS LN 79907850 BARANDAL DR 799078016 YERMOLAND DR 799079450 ARIEL RICO CT 799078574 LEE STARLING DR 79907445 LINK DR 79907513 ARAQUAIA WAY 799071057 MACADAMIA CIR 79907

78

Date:Name:

Day

of P

ick

up

Wai

ver (

Y/N)

Not

Out

For

Col

lect

ion

1/4

1/2

3/4

Full

Cont

amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

ywal

l, ca

rpet

, tile

)

Food

Was

teYa

rd W

aste

Diap

ers

Non

-Rec

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ble

(Pla

stics

Gard

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iles

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res

HHW

684 LOMALAND DR 799078868 DULCE CIR 799078629 INDEPENDENCE DR 799079139 BUR OAK CIR 799078203 ALAMEDA AVE 799079316 CHANTILLY DR 79907101 AMES CT 799071032 FRANCINE ST 799078040 MERAZ AVE 799079378 SCOOTER LN 799078398 BEVERLY PL 799078120 BURNHAM DR 79907240 MANUEL DR 799071080 FRANCINE ST 799078138 SAINT ANDREW LN 799078101 MARGARET LN 799078748 PLAINS DR 799079348 RICHARDSON DR 7990710336 ALCAN ST 79912837 TEPIC DR 799126668 TUSCANY RIDGE DR 79912245 VISTA BONITA ST 799126416 FRANKLIN RIDGE DR 79912624 WHITE CLIFFS DR 799121117 DESIERTO SECO DR 799121121 SOUTHWESTERN DR 799127169 EL CAJON DR 799124863 CUARTEL LN 799126852 ALTO REY AVE 799127336 DESIERTO AZUL DR 79912751 DE LEON DR 799126109 CAMINO ALEGRE DR 799121213 CAMBRIA COVE PL 79912747 COLCHESTER DR 799127651 MEDANO DR 799126916 MARBLE CANYON DR 799126744 VILLA HERMOSA DR 79912836 DULCE TIERRA DR 799125816 BEAUMONT PL 799127320 DESIERTO PAIS DR 799127109 DESERT JEWEL DR 79912529 COMICE LN 79912752 SOMERSET DR 799127301 GOLDEN HAWK DR 79912639 EL PARQUE DR 799126820 GRANERO DR 79912677 MARY STUART DR 79912810 DERRICKSON DR 799126913 ROCK CANYON DR 799127069 EL CAJON DR 799127363 GULF CREEK DR 799121608 PLAZA DEL SOL CT 79912309 S BALBOA RD 799127077 BLACK RIDGE DR 799121012 JAN ELLYN LN 799121801 E CLIFF DR 79912604 BLUFF CANYON CIR 799126841 GRANERO DR 799121476 CHEROKEE RIDGE DR 79912253 FERRARI CT 799126305 TARASCAS DR 79912925 CORTIJO DR 79912806 TEPIC DR 799127283 BALSAM DR 799157509 GLARDON CIR 799157872 MADRID WAY 79915

79

Date:Name:

Day

of P

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up

Wai

ver (

Y/N)

Not

Out

For

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ion

1/4

1/2

3/4

Full

Cont

amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

ywal

l, ca

rpet

, tile

)

Food

Was

teYa

rd W

aste

Diap

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Non

-Rec

ycla

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(Pla

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Gard

en H

oses

Text

iles

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res

HHW

7312 MOJAVE DR 799157800 PARRAL DR 79915421 LESA LN 799151119 DEL NORTE ST 79915109 MANNING WAY 799157675 BARTON ST 79915178 PEARL LN 79915232 BEN SWAIN DR 799157609 VERACRUZ AVE 799157056 BECKY LN 799157806 PAULA CT 799157153 TANGERINE LN 79915257 JENSEN AVE 799157186 GRANITE RD 799157957 CAMPO VERDE LN 799158520 INDEPENDENCE DR 79915212 EASTER WAY 799157709 MONTERREY DR 799157956 SUNNYFIELDS AVE 799157917 PARRAL DR 79915433 BERNADINE AVE 799157510 MONTERREY DR 799157780 VERACRUZ AVE 799158088 CARPENTER DR 799157720 HERMOSILLO DR 799157441 ALAMEDA AVE 799157727 WENDA DR 79915311 CADWALLADER DR 799154855 VISTA DEL MONTE RD 79922144 COURCHESNE RD 799224556 SKYLARK WAY 799224693 ROSINANTE RD 799224648 GLOBE WILLOW DR 79922808 DULCINEA CT 799229945 BALLISTIC ST 799249545 WAVERLY DR 799245504 RAYMOND TELLES DR 799249508 ROBERT HOLT DR 7992410172 TIVOLI ST 799244760 HARCOURT DR 799249553 WAVERLY DR 799249804 BLUE WING DR 799249329 SALISBURY DR 799244917 AJAX CT 7992410073 KEYSTONE DR 799249948 HONEY LOCUST LN 7992410745 CHERT ST 7992410432 BYZANTIUM LN 7992410828 AQUAMARINE ST 799245336 BIG HORN LN 7992410528 CRETE DR 799245775 DALHART DR 7992410833 TOURMALINE ST 7992410685 PLEASANT SAND DR 799245208 EDMONTON AVE 799248917 CROSSON CIR 799245121 LUBBOCK DR 7992410309 WINDSOR DR 799249581 ALBANY DR 799245320 DEBEERS DR 799245117 BALLINGER DR 7992410205 SHARP DR 799246042 SORRENTO ST 7992410744 PEARL SANDS DR 799244989 BLACK SANDS LN 799243413 WICKHAM AVE 79924

80

Date:Name:

Day

of P

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up

Wai

ver (

Y/N)

Not

Out

For

Col

lect

ion

1/4

1/2

3/4

Full

Cont

amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

ywal

l, ca

rpet

, tile

)

Food

Was

teYa

rd W

aste

Diap

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Non

-Rec

ycla

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(Pla

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Gard

en H

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Text

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res

HHW

9988 GENIE DR 799245133 DEBEERS DR 7992410798 KATELYN GRAY CIR 7992410400 ADONIS DR 799246688 TIGER EYE DR 7992410348 NEWPORT DR 7992410982 YOGI BERRA DR 7992410520 DYER ST 7992411220 HORSE RANCH ST 799244708 TROPICANA AVE 799249936 GENIE DR 799249147 MT ETNA DR 799241324 MYRTLE AVE 7992510301 ALLWAY DR 799259300 WH BURGES DR 799258401 MOYE DR 799253408 KIRKWALL ST 799259742 CARTWAY LN 799253303 CORK DR 799259258 WH BURGES DR 7992510417 SUGARBERRY DR 7992510552 GREENWAY AVE 799259517 ALBUM AVE 7992510200 ALLWAY DR 7992510171 HONOLULU DR 799253207 ORKNEY RD 799251500 DEVONSHIRE DR 7992510804 SAIGON DR 7992510168 SINGAPORE AVE 799257115 CIELO VISTA DR 799259301 MOYE DR 799256301 WIELAND WAY 799259909 BUCKWOOD AVE 799259716 BERMUDA AVE 799258340 TURRENTINE DR 799259501 FALKIRK AVE 799253300 SUFFOLK RD 79925141 ROMAN GABRIEL LN 799279859 ISAAC DR 79927308 CELAYA WAY 7992712429 PASEO ROJO DR 799273415 MEMPHIS AVE 799301410 MONTANA AVE 799302720 SILVER AVE 799303414 MOUNTAIN AVE 799302906 RICHMOND AVE 799303217 NATIONS AVE 799304018 TRUMAN AVE 799303815 HAMILTON AVE 799302204 TREMONT AVE 799303730 JEFFERSON AVE 799303427 NASHVILLE AVE 799303800 FRANKFORT AVE 799303031 FRANKFORT AVE 799302931 MONROE AVE 799302712 SACRAMENTO AVE 799303713 MONROE AVE 799302327 FEDERAL AVE 799302101 PITTSBURG AVE 799302728 BYRON ST 799302930 NASHVILLE AVE 799306154 TWILIGHT VIEW WAY 799327927 STARRY NIGHT DR 799321043 KIMBERLEY ST 79932956 TYLER SETH AVE 799326229 FABIAN ST 79932

81

Date:Name:

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of P

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ver (

Y/N)

Not

Out

For

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1/4

1/2

3/4

Full

Cont

amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

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l, ca

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, tile

)

Food

Was

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Diap

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Non

-Rec

ycla

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Text

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HHW

5949 OLEASTER DR 799325401 MONTOYA DR 79932897 DAKOTA RIVER AVE 79932244 ARISANO DR 79932651 COUNTRY OAKS DR 799325656 BETH VIEW DR 79932220 ARISANO DR 79932400 LINDBERGH AVE 799321092 LOS MOROS DR 799325394 CORY DR 799325461 ROGER MARIS DR 799345853 CLYDESDALE DR 799344601 LOMA GRANDE DR 7993411852 MESQUITE ROCK DR 7993410553 MCCOMBS ST 7993410917 NORTHVIEW DR 7993410908 BABE RUTH ST 7993410912 NORTHVIEW DR 799346937 JERICHO TREE DR 7993411008 MIDDLEDALE ST 7993411961 MESQUITE BUSH DR 799344365 LOMA TAURINA DR 7993411908 MESQUITE MIEL DR 799344444 LOMA DIAMANTE DR 799345784 RICK HUSBAND DR 799347213 JERICHO TREE DR 7993410909 DAVE MARR CT 799353325 BROOKROCK ST 7993510817 BROWNFIELD DR 799353625 ALHAMBRA LN 799351754 PICO ALTO DR 799352300 RUEWOOD PL 799352816 CABOT PL 799353401 DIAL ROCK LN 7993510620 ISLEROCK DR 7993511004 LAKEWOOD DR 7993510528 CARDIGAN DR 799352108 NOVIEMBRE DR 799351817 LARRY HINSON DR 7993611404 RANDY PETRI LN 799361280 A L GILL DR 799361000 BOLD RULER CT 799361669 PAUL TODD DR 7993612141 MISSY YVETTE DR 7993611620 BOB MITCHELL DR 799362908 GILBERTO AVILA ST 7993611412 JIM FERRIELL DR 7993611348 CRATER LAKE AVE 799362901 ERNESTO SERNA PL 799361574 COMMON DR 799361912 RALPH JANES PL 7993611743 GWEN EVANS LN 799362105 CHRIS ROARK PL 799361776 GREGORY JARVIS DR 7993612145 YVON RICHARDSON AVE 7993610919 ART WALL DR 799361301 CYNTHIA FARAH PL 799361837 TOM BOLT DR 799361821 DALE DOUGLAS DR 799363200 EAST GLEN DR 7993611940 PICASSO DR 799362401 TIRRES PL 799361312 MICHELANGELO DR 7993611906 VAN GOGH DR 7993610958 PELHEM RD 799363436 KILLEEN PL 79936

82

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of P

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Out

For

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amin

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n(Y/

N)

Glas

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D (lu

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r, dr

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, tile

)

Food

Was

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Diap

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Non

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ycla

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Gard

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HHW

3585 MIKE GODWIN DR 7993612245 DIANA NATALICIO DR 799363533 MIKE GODWIN DR 7993611309 MENLO AVE 799361780 DEAN JONES ST 7993612255 BRONCO BUSTER LN 799363421 BALMORHEA ST 799361772 DEAN JONES ST 799361783 POLLY HARRIS DR 7993611875 SCOTT SIMPSON DR 799361635 DALE DOUGLAS DR 7993611209 QUINTANA DR 799362117 EAST GLEN DR 7993611613 JOHN WEIR DR 7993611400 REX BAXTER DR 7993612114 FRANK CORDOVA CIR 799361618 DALE DOUGLAS DR 799362432 TIRRES PL 7993612239 VIA GRANADA DR 7993611104 LA QUINTA PL 799361914 LAKE OMEGA ST 799361315 OLGA MAPULA DR 799363117 ROCK WALL LN 799362344 ANISE DR 799361534 PETER HURD DR 799361800 PUEBLO ALEGRE DR 7993611749 BELL TOWER DR 7993612041 MICHELANGELO DR 799361757 PAINTED QUAIL PL 7993611345 LAKE ERIE DR 7993611936 BANNER RUN DR 7993611424 MENLO AVE 7993611620 MCAULIFFE DR 799362124 ROBERT WYNN ST 799361448 MISSY YVETTE DR 799361504 BERT GREEN DR 799361691 DONNA CAPONI LN 799363237 ITASCA ST 7993612500 TIERRA NORTE RD 799363332 MENARD LN 7993611400 BEACH FRONT DR 7993611989 ARROW KNOLL CIR 7993611963 FRANCIS SCOBEE DR 7993611201 IVANHOE DR 7993612044 JOSE CISNEROS DR 799363604 OASIS DR 799363509 RUNNING DEER DR 7993612208 SARAH LISA LN 7993612272 TIERRA INCA DR 7993812264 TIERRA CADENA DR 7993812432 TIERRA ALAMO DR 799384600 ADAN FUENTES ST 7993813021 FALLEN HERO LN 7993812848 TIERRA LINCE DR 7993812580 GILDED SUN DR 799382400 TIERRA GRIS WAY 7993812312 TIERRA PLATA DR 799383852 TIERRA ROCA PL 799383321 TIERRA JAZMIN LN 799383092 TIERRA BOWLES DR 799384084 TIERRA BRONCE DR 799383136 TIERRA CUERVO DR 7993812497 TIERRA REY CT 7993814232 SPANISH POINT DR 7993812720 TIERRA PUEBLO DR 7993812235 TIERRA ARROYO DR 79938

83

Date:Name:

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of P

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ver (

Y/N)

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Out

For

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lect

ion

1/4

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Full

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amin

atio

n(Y/

N)

Glas

sC&

D (lu

mbe

r, dr

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l, ca

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, tile

)

Food

Was

teYa

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Diap

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ycla

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12432 TIERRA CEBADA DR 7993812520 SOMBRA FUERTE DR 799383133 TIERRA LIMA RD 7993812464 TIERRA NOGAL DR 799384048 TIERRA BRONCE DR 79938

84

Appendix B

Contaminant Weight Log

85

WEE

K:D

AY

DA

TE

W0523195 (EWASTE)W0523150 (HHW)

W0523174 (TEXTILES)WW0522985 (GARDEN HOSE)W0523071 Recycling

W0523172 (Other Organics)W0523125 (Yard Waste

)W0522971 (Food Waste

) W0523113 (Glass)

WW0523105 (C&D, W

ood, Tile, Carpet) W0523029 (Trash)

W0523162 (Recycling)

W0523071 (Recycling)W0523040 (EWAST

TARE

WT

3029

2928

3129

2728

2830

3027

2728

GRO

SS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SSN

ETTA

RE W

T30

2929

2831

2927

2828

3030

2727

30G

ROSS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SSN

ETTA

RE W

T30

2929

2831

2927

2828

3030

2727

30G

ROSS

NET

TARE

WT

3029

2928

3129

2728

2830

3027

2730

GRO

SSN

ET

Tota

ls0

00

00

00

00

00

00

0

86

ASTE) W0523000 (HHW)W0523066 (TEXTILES) WW0523129 (Recycling) W0522966 (Styrofoam)

W0523028 (Yard Waste

)

97640004 (Food Waste)

W0523152 (Glass)WW0523065 (C&D, W

ood, Tile, Carpet) W0522966 (Recycling)

W0523009 (Recycling)

W22996 Recycling

W640021 Recycling

W0523119 Recycling

W0522993 (Recycling)

2928

3029

3130

3028

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

2929

2829

3028

3030

3031

2930

3025

00

00

00

00

00

00

00

87

Appendix C

Neighborhoods with High Recycling Weights

88

Day Rte_No House_No (start) House_No (end) Street ZipTUESDAY R24 6204 6259 ALGONQUIN RD 79905TUESDAY R24 6204 6264 ARAPAHO RD 79905TUESDAY R24 6204 6240 CIBOLO CT 79905TUESDAY R24 6101 6286 CLEVELAND AVE 79905TUESDAY R24 6106 6296 E YANDELL DR 79905TUESDAY R24 830 1032 GERONIMO DR 79905TUESDAY R24 6101 6251 GILA RD 79905TUESDAY R24 5900 6108 JEMEZ DR 79905TUESDAY R24 801 1132 MARLOW RD 79905TUESDAY R24 900 1151 N CLARK DR 79905TUESDAY R24 6204 6260 PAPAGO RD 79905TUESDAY R24 6191 6191 TAMPA AVE 79905TUESDAY R24 6100 6248 TAOS DR 79905TUESDAY R24 5900 6247 TEJAS DR 79905TUESDAY R24 6100 6241 TESUQUE DR 79905TUESDAY R24 6107 6301 TROWBRIDGE DR 79905TUESDAY R24 6200 6220 UTE LN 79905TUESDAY R24 1005 6243 VICTORIA LN 79905TUESDAY R28 1101 1419 APACHE ST 79925TUESDAY R28 6015 6571 AZTEC RD 79925TUESDAY R28 7012 7509 BELLROSE DR 79925TUESDAY R28 1100 1520 BROOKHAVEN DR 79925TUESDAY R28 1509 1529 CEDARDALE PL 79925TUESDAY R28 1100 6540 CHEYENNE TRL 79925TUESDAY R28 7019 7508 CIELO VISTA DR 79925TUESDAY R28 1200 1528 DEVONSHIRE DR 79925TUESDAY R28 7011 7505 EDGEMERE BLVD 79925TUESDAY R28 1200 1521 ELMHURST DR 79925TUESDAY R28 1201 1521 FAIRFIELD DR 79925TUESDAY R28 1420 1521 GREENWOOD CIR 79925TUESDAY R28 1100 1521 HONEYSUCKLE DR 79925TUESDAY R28 6400 6443 KIOWA CT 79925

Top Recycling Routes by Weight (Tuesday)

89

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Tuesday)

TUESDAY R28 1216 1400 MESCALERO DR 79925TUESDAY R28 6400 6562 MOHAWK AVE 79925TUESDAY R28 6110 6128 MONTANA AVE 79925TUESDAY R28 6104 6547 NAVAJO AVE 79925TUESDAY R28 6301 6341 OSAGE LN 79925TUESDAY R28 7100 7504 PARKLAND DR 79925TUESDAY R28 1404 1524 SIOUX DR 79925TUESDAY R28 1100 1309 ZUNI PL 79925TUESDAY R29 8801 8905 BASIL CT 79925TUESDAY R29 9200 9309 BREISH CT 79925TUESDAY R29 8500 9321 DARLINA DR 79925TUESDAY R29 8800 8921 DIRK CT 79925TUESDAY R29 8816 8916 GALLIC CT 79925TUESDAY R29 8600 8717 GAZELLE DR 79925TUESDAY R29 8616 8717 GROVER DR 79925TUESDAY R29 9300 9325 JACEY CT 79925TUESDAY R29 8801 9352 LAIT DR 79925TUESDAY R29 8904 9329 MCCABE DR 79925TUESDAY R29 8800 9328 MCFALL DR 79925TUESDAY R29 8800 9326 MOYE DR 79925TUESDAY R29 8800 8913 SAMPSON CT 79925TUESDAY R29 8800 9341 SHAVER DR 79925TUESDAY R29 8800 9337 TURRENTINE DR 79925TUESDAY R29 8900 8900 VISCOUNT BLVD 79925TUESDAY R29 8800 9332 WH BURGES DR 79925TUESDAY R29 9012 9108 WORTH CT 79925TUESDAY R30 1204 1501 CESSNA DR 79925TUESDAY R30 7608 8717 CIELO VISTA DR 79925TUESDAY R30 1205 1521 CLAUSEN DR 79925TUESDAY R30 1414 1521 DENNIS CIR 79925TUESDAY R30 7601 8717 EDGEMERE BLVD 79925TUESDAY R30 8300 8716 HOPEWELL DR 79925TUESDAY R30 1200 1521 IDLEWILDE DR 79925TUESDAY R30 1200 1521 LIKINS DR 79925

90

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Tuesday)

TUESDAY R30 1205 1525 MEADOWVIEW DR 79925TUESDAY R30 8300 8636 METTLER DR 79925TUESDAY R30 1404 1532 NUECES WAY 79925TUESDAY R30 1205 1521 OAKDALE ST 79925TUESDAY R30 8300 8416 PARADE LN 79925TUESDAY R30 7600 8717 PARKLAND DR 79925TUESDAY R30 1205 1521 PRAIRIE DR 79925TUESDAY R30 1300 1309 SUNBURST DR 79925TUESDAY R35 10200 10317 ALLWAY DR 79925TUESDAY R35 10540 10561 BREEZEWAY AVE 79925TUESDAY R35 10100 10240 BUCKWOOD AVE 79925TUESDAY R35 10201 10317 BYWAY DR 79925TUESDAY R35 10505 10533 CAUSEWAY DR 79925TUESDAY R35 10101 10493 CHINABERRY DR 79925TUESDAY R35 10427 10487 DAVWOOD LN 79925TUESDAY R35 10100 10128 DE ANZA CIR 79925TUESDAY R35 10200 10217 DONWAY PL 79925TUESDAY R35 10100 10328 GARWOOD CT 79925TUESDAY R35 10540 10561 GREENWAY AVE 79925TUESDAY R35 10426 10561 JANWAY DR 79925TUESDAY R35 10107 10324 MONTWOOD DR 79925TUESDAY R35 10200 10321 RIDGEWOOD DR 79925TUESDAY R35 10408 10420 SCHWOOD DR 79925TUESDAY R35 10428 10493 SEAWOOD DR 79925TUESDAY R35 10204 10553 SPRINGWOOD DR 79925TUESDAY R35 10117 10252 STONEWAY DR 79925TUESDAY R35 10300 10420 SUGARBERRY DR 79925TUESDAY R35 1505 1631 SUMAC DR 79925TUESDAY R35 10516 10553 TEXWOOD AVE 79925TUESDAY R35 10421 10562 TOMWOOD AVE 79925TUESDAY R35 1600 1741 TRAWOOD DR 79925TUESDAY R35 2061 2061 WINDROCK ST 79925TUESDAY R35 10100 10241 WOODWAY DR 79925

91

Day Rte_No House_No (start) House_No (end) Street ZipWEDNESDAY R32 9623 9652 ALBACORE LN 79924WEDNESDAY R32 5800 5841 BAGDAD WAY 79924WEDNESDAY R32 9600 9812 BLUE WING DR 79924WEDNESDAY R32 9800 9853 BOMARC ST 79924WEDNESDAY R32 5800 5905 DOLPHIN DR 79924WEDNESDAY R32 5801 5945 FLOUNDER DR 79924WEDNESDAY R32 9800 9845 GOBY ST 79924WEDNESDAY R32 5865 6021 MANILA DR 79924WEDNESDAY R32 5800 6005 MARLIN DR 79924WEDNESDAY R32 5872 5876 NIKE LN 79924WEDNESDAY R32 5800 9848 PICKEREL DR 79924WEDNESDAY R32 5800 5937 POMPANO AVE 79924WEDNESDAY R32 5800 6009 PORPOISE DR 79924WEDNESDAY R32 6005 6041 REDSTONE LN 79924WEDNESDAY R32 9800 9848 SIDEWINDER ST 79924WEDNESDAY R32 5800 5853 SNARK LN 79924WEDNESDAY R32 5800 5916 STURGEON DR 79924WEDNESDAY R32 5800 5913 TARPON DR 79924WEDNESDAY R32 5800 6017 TAUTOGA DR 79924WEDNESDAY R32 9800 9837 TITAN ST 79924WEDNESDAY R32 5800 5843 VANGUARD CT 79924WEDNESDAY R32 9623 9648 WAHOO LN 79924WEDNESDAY R33 9513 9581 ALBANY DR 79924WEDNESDAY R33 9300 9539 CHARLESTON ST 79924WEDNESDAY R33 5300 5412 DALTON AVE 79924WEDNESDAY R33 5320 5336 DOWNS CT 79924WEDNESDAY R33 9300 9537 FAIRFAX ST 79924WEDNESDAY R33 9303 9328 HOLLINGS ST 79924WEDNESDAY R33 9300 9426 MONTGOMERY DR 79924WEDNESDAY R33 5321 5337 OLAN CT 79924WEDNESDAY R33 9300 9528 RALEIGH DR 79924WEDNESDAY R33 5500 5520 RANCHITO AVE 79924WEDNESDAY R33 9300 9538 ROANOKE DR 79924

Top Recycling Routes by Weight (Wednesday)

92

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Wednesday)

WEDNESDAY R33 9500 9581 RUTLEDGE PL 79924WEDNESDAY R33 9301 9539 SALISBURY DR 79924WEDNESDAY R33 5325 5608 SANDERS AVE 79924WEDNESDAY R33 5333 5631 THREADGILL AVE 79924WEDNESDAY R33 9300 9581 VICKSBURG DR 79924WEDNESDAY R33 9500 9581 WAVERLY DR 79924WEDNESDAY R33 5507 5605 WAYCROSS AVE 79924WEDNESDAY R33 5508 5508 WREN AVE 79924WEDNESDAY W09 601 614 ALICANTE WAY 79912WEDNESDAY W09 7001 7229 ALTO REY AVE 79912WEDNESDAY W09 7100 7325 ARMISTAD AVE 79912WEDNESDAY W09 134 409 BELVIDERE ST 79912WEDNESDAY W09 304 309 BUENA SUERTE DR 79912WEDNESDAY W09 7100 7333 CERRO NEGRO DR 79912WEDNESDAY W09 7131 7316 CLEMENTE AVE 79912WEDNESDAY W09 7120 7324 GRAN VIDA DR 79912WEDNESDAY W09 304 309 LA SOMBRA WAY 79912WEDNESDAY W09 7001 7222 MAJORCA CT 79912WEDNESDAY W09 200 225 MELICENT ST 79912WEDNESDAY W09 200 225 N RESLER DR 79912WEDNESDAY W09 7101 7257 ORIZABA AVE 79912WEDNESDAY W09 7100 7138 PORTUGAL DR 79912WEDNESDAY W09 7100 7348 RAMADA DR 79912WEDNESDAY W09 200 296 THREE RIVERS DR 79912WEDNESDAY W09 7101 7325 TIERRA ALTA AVE 79912WEDNESDAY W09 138 300 VISTA BONITA ST 79912WEDNESDAY W09 200 277 VISTA RIO CIR 79912WEDNESDAY W09 7300 7372 WIND SONG DR 79912WEDNESDAY R36 4903 4972 AIKEN LN 79924WEDNESDAY R36 9109 9109 ALPS DR 79924WEDNESDAY R36 4908 4972 CAMDEN CIR 79924WEDNESDAY R36 8901 8917 CROSSON CIR 79904WEDNESDAY R36 8916 9210 DIANA DR 79904WEDNESDAY R36 5303 5321 GULFPORT DR 79924WEDNESDAY R36 5000 5321 JOE HERRERA DR 79924

93

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Wednesday)

WEDNESDAY R36 4904 5236 MARIE TOBIN DR 79924WEDNESDAY R36 9111 9151 MATTERHORN DR 79924WEDNESDAY R36 4892 4944 MAXWELL AVE 79904WEDNESDAY R36 4864 4944 MCGREGOR DR 79904WEDNESDAY R36 9108 9163 MT ETNA DR 79924WEDNESDAY R36 9107 9212 MT OLYMPUS DR 79924WEDNESDAY R36 9107 9135 MT RUSHMORE LN 79924WEDNESDAY R36 9107 9228 MT SAN BERDU DR 79924WEDNESDAY R36 9107 9212 MT SHASTA DR 79924WEDNESDAY R36 5100 5320 RAYMOND TELLES DR 79924WEDNESDAY R36 4935 5145 RUTHERFORD DR 79924WEDNESDAY R36 5300 9219 SALISBURY DR 79924WEDNESDAY R36 8900 8921 STRAND LN 79904WEDNESDAY R45 3401 4225 ATLAS AVE 79904WEDNESDAY R45 8305 8660 COMET ST 79904WEDNESDAY R45 4200 4217 DAWKINS CT 79904WEDNESDAY R45 3700 3824 DEVORE CT 79904WEDNESDAY R45 8202 8310 ECHO ST 79904WEDNESDAY R45 8120 8638 ECLIPSE ST 79904WEDNESDAY R45 3401 4219 EDGAR PARK AVE 79904WEDNESDAY R45 3400 4224 HERCULES AVE 79904WEDNESDAY R45 3700 3729 HUBBLE DR 79904WEDNESDAY R45 8120 8640 LEO ST 79904WEDNESDAY R45 8101 8309 MAGNETIC ST 79904WEDNESDAY R45 8122 8402 MERCURY ST 79904WEDNESDAY R45 8000 8325 NEPTUNE ST 79904WEDNESDAY R45 3400 4225 OLYMPIC AVE 79904WEDNESDAY R45 8308 8611 POLARIS ST 79904WEDNESDAY R45 3800 3821 QUILL CT 79904WEDNESDAY R45 8300 8309 SATURN PL 79904WEDNESDAY R45 8301 8309 SOLAR PL 79904WEDNESDAY R45 3400 4225 TITANIC AVE 79904WEDNESDAY R45 3400 4224 VOLCANIC AVE 79904

94

Day Rte_No House_No (start) House_No (end) Street ZipTHURSDAY R06 2100 2213 ABRIL DR 79935THURSDAY R06 10700 10745 ALTA LOMA DR 79935THURSDAY R06 1800 1848 ANDALUCIA DR 79935THURSDAY R06 1900 1913 ANISE DR 79935THURSDAY R06 1800 1849 ARNOLD PALMER DR 79935THURSDAY R06 10801 10929 BELLA VISTA DR 79935THURSDAY R06 1803 1825 BEN HOGAN DR 79935THURSDAY R06 10900 10909 BILL COLLINS CT 79935THURSDAY R06 10700 10729 CAMARO CT 79935THURSDAY R06 2100 2222 CUMBRE NEGRA ST 79935THURSDAY R06 1900 2133 DICIEMBRE DR 79935THURSDAY R06 2100 2213 ENERO DR 79935THURSDAY R06 2100 2319 ESCARPA DR 79935THURSDAY R06 2100 2213 FEBRERO DR 79935THURSDAY R06 1800 1854 JACK NICKLAUS DR 79935THURSDAY R06 10724 10784 JANWAY DR 79935THURSDAY R06 10700 10832 LA SUBIDA DR 79935THURSDAY R06 2000 2141 NOVIEMBRE DR 79935THURSDAY R06 1900 2149 OCTUBRE DR 79935THURSDAY R06 2100 2213 PACHECO DR 79935THURSDAY R06 10631 10757 PESCADOR DR 79935THURSDAY R06 1758 1791 PICO ALTO DR 79935THURSDAY R06 10801 10837 POZA RICA CT 79935THURSDAY R06 2000 2169 SEPTIEMBRE DR 79935THURSDAY R06 1900 1909 SOLANO DR 79935THURSDAY R06 2105 2221 TRAWOOD DR 79935THURSDAY R06 2100 2317 VILLA PLATA DR 79935THURSDAY R07 2600 2863 ANISE DR 79936THURSDAY R07 11204 11241 BEACH FRONT DR 79936THURSDAY R07 2100 2141 EAST GLEN DR 79936THURSDAY R07 11204 11241 KINGFISH CT 79936THURSDAY R07 2312 2349 MERMAID DR 79936THURSDAY R07 2100 2235 OCEAN SIDE DR 79936THURSDAY R07 2000 2032 PIER LN 79936THURSDAY R07 11101 11181 PINK CORAL DR 79936THURSDAY R07 2400 2617 RED SAILS DR 79936

Top Recycling Routes by Weight (Thursday)

95

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Thursday)

THURSDAY R07 2020 2245 ROBERT WYNN ST 79936THURSDAY R07 2500 2528 RUDDER PL 79936THURSDAY R07 11124 11199 SAM SNEAD DR 79936THURSDAY R07 11204 11240 SANDCASTLE CT 79936THURSDAY R07 11100 11125 SEA FOAM WAY 79936THURSDAY R07 2100 2259 SEA GULL DR 79936THURSDAY R07 11204 11252 SEA HORSE DR 79936THURSDAY R07 2201 2537 SEA PALM DR 79936THURSDAY R07 2300 2357 SEA SIDE DR 79936THURSDAY R07 11148 11281 SKIPPER DR 79936THURSDAY R07 11100 11116 STARBOARD LN 79936THURSDAY R07 11204 11241 STARFISH CT 79936THURSDAY R07 11100 11189 VOYAGER COVE DR 79936THURSDAY R10 1530 1594 BENGAL DR 79935THURSDAY R10 1514 1652 BESSEMER DR 79936THURSDAY R10 1530 1547 BOB GOALBY LN 79935THURSDAY R10 1500 1521 BOB LUNN PL 79935THURSDAY R10 1400 1414 BODEGA PL 79935THURSDAY R10 1701 1729 BRUCE DEVLIN DR 79935THURSDAY R10 1500 1521 BUD ALLIN PL 79935THURSDAY R10 11201 11221 CAMPESTRE LN 79936THURSDAY R10 1500 1517 CHARLES COODY LN 79935THURSDAY R10 1500 1641 CHARLES OWENS DR 79936THURSDAY R10 1530 1690 COMMON DR 79936THURSDAY R10 1400 1464 DALE DOUGLAS DR 79936THURSDAY R10 10900 10921 DAVE MARR CT 79935THURSDAY R10 10900 11033 DON JANUARY DR 79935THURSDAY R10 10901 11017 GARY PLAYER DR 79935THURSDAY R10 1500 1521 JERRY PATE PL 79935THURSDAY R10 1604 1608 KEN STILL LN 79935THURSDAY R10 11100 11109 LA QUINTA PL 79936THURSDAY R10 1504 1617 LARRY WADKINS DR 79936THURSDAY R10 11101 11101 LEO COLLINS DR 79936THURSDAY R10 1601 1719 LOMALAND DR 79935THURSDAY R10 1400 1469 MONTE NEGRO DR 79935THURSDAY R10 1500 1555 MONTE SANDERS LN 79935THURSDAY R10 2015 2021 MONTE SUR DR 79935THURSDAY R10 1501 1609 PAUL HARNEY DR 79936

96

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Thursday)

THURSDAY R10 1400 1547 PINTORESCO DR 79935THURSDAY R10 1500 1541 RANDY WOLFF PL 79935THURSDAY R10 1700 1708 ROD CURL LN 79935THURSDAY R10 10900 10972 SOMBRA VERDE DR 79935THURSDAY R10 1501 1553 VANDERBILT DR 79935THURSDAY R10 10904 11061 VISTA DEL SOL DR 79935THURSDAY R10 11100 11108 VISTA LAGO PL 79936THURSDAY R14 7201 7614 ALPINE DR 79915THURSDAY R14 1301 1618 ARLINGTON ST 79915THURSDAY R14 7200 7627 BENSON DR 79915THURSDAY R14 7201 7519 CUBA DR 79915THURSDAY R14 7200 7222 FLAGSTAFF CT 79915THURSDAY R14 1600 1620 GLOBE CIR 79915THURSDAY R14 1302 1817 HUNTER DR 79915THURSDAY R14 7200 7700 KINGMAN DR 79915THURSDAY R14 7300 7707 MOJAVE DR 79915THURSDAY R14 7200 7207 OTERO CT 79915THURSDAY R14 7200 7210 PIMA LN 79915THURSDAY R14 1806 7216 RATON DR 79915THURSDAY R14 1207 1903 ROSWELL RD 79915THURSDAY R14 7200 7219 SAFFORD CT 79915THURSDAY R14 7200 7427 WILCOX DR 79915THURSDAY R14 1207 1815 WINSLOW RD 79915THURSDAY R22 202 433 BERNADINE AVE 79915THURSDAY R22 400 437 BISSONET AVE 79915THURSDAY R22 7701 8335 BROADWAY ST 79915THURSDAY R22 7741 7741 CESAR CHAVEZ BORDER HWY 79915THURSDAY R22 201 433 CULLEN AVE 79915THURSDAY R22 201 457 GLADYS AVE 79915THURSDAY R22 7629 7848 HOCKNEY ST 79915THURSDAY R22 200 441 JENSEN AVE 79915THURSDAY R22 7804 8051 JERSEY ST 79915THURSDAY R22 200 437 KELVIN AVE 79915THURSDAY R22 113 449 MCCARTHY AVE 79915THURSDAY R22 8005 8049 ROMULUS PL 79915

97

Day Rte_No House_No (start) House_No (end) Street ZipFRIDAY R07 3121 3145 ABBEY WOODS WAY 79936FRIDAY R07 11916 11993 ARROW KNOLL CIR 79936FRIDAY R07 11916 12097 BANNER CREST DR 79936FRIDAY R07 3408 3469 BILLET HILL ST 79936FRIDAY R07 3300 3317 CROWN HILL PL 79936FRIDAY R07 11928 11957 CROWN OAKS CT 79936FRIDAY R07 12028 12045 CROWN WOODS CT 79936FRIDAY R07 11928 12057 DAVID FORTI DR 79936FRIDAY R07 3429 12064 DRAGON CREST DR 79936FRIDAY R07 6233 6233 GRACE MADRILES PL 79936FRIDAY R07 3124 3284 MANNY AGUILERA DR 79936FRIDAY R07 3209 3392 MIKE GODWIN DR 79936FRIDAY R07 11928 12024 REGAL BANNER LN 79936FRIDAY R07 3120 3198 ROYAL CREST ST 79936FRIDAY R07 3137 3333 ROYAL JEWEL ST 79936FRIDAY R07 11928 12057 SPIRE HILL DR 79936FRIDAY R07 11928 12061 SPIRE TERRACE DR 79936FRIDAY R07 3300 3321 TOWER ARMS DR 79936FRIDAY R07 3309 3329 TOWER WALL LN 79936FRIDAY R07 3300 3317 TREASURE HILL PL 79936FRIDAY R07 3405 12074 WATERSIDE DR 79936FRIDAY R14 2104 11473 BEACH FRONT DR 79936FRIDAY R14 11300 11389 CRATER LAKE AVE 79936FRIDAY R14 2100 2300 GEORGE DIETER DR 79936FRIDAY R14 11400 11401 LAKE ALICE DR 79936FRIDAY R14 11300 11457 LAKE GENEVA DR 79936FRIDAY R14 11330 11393 LAKE LOY DR 79936FRIDAY R14 2100 2113 LAKE MOSS PL 79936FRIDAY R14 11300 11461 LAKE NEMI DR 79936FRIDAY R14 1901 2221 LAKE OMEGA ST 79936FRIDAY R14 11304 11388 LAKE ONTARIO DR 79936FRIDAY R14 11300 11457 LAKE OZARKS DR 79936FRIDAY R14 11400 11517 LAKE TANA DR 79936FRIDAY R14 2116 2301 LAKE VICTORIA DR 79936FRIDAY R14 2100 2125 LAKE VOLTA PL 79936FRIDAY R14 2324 2449 ROBERT WYNN ST 79936

Top Recycling Routes by Weight (Friday)

98

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Friday)

FRIDAY R14 3355 3387 TRAWOOD DR 79936FRIDAY R22 1750 1763 ASTRONAUT PL 79936FRIDAY R22 11600 11741 BOB MITCHELL DR 79936FRIDAY R22 11629 11740 BUNKY HENRY LN 79936FRIDAY R22 1701 1741 CHALLENGER LN 79936FRIDAY R22 1743 1775 DEKE SLAYTON LN 79936FRIDAY R22 1640 1696 DENNIS BABJACK DR 79936FRIDAY R22 11627 11724 DICK MAYERS DR 79936FRIDAY R22 1801 1825 ED WHITE WAY 79936FRIDAY R22 11600 11816 FRANCIS SCOBEE DR 79936FRIDAY R22 11621 11673 GORDON COOPER LN 79936FRIDAY R22 1720 1789 GREGORY JARVIS DR 79936FRIDAY R22 1801 1821 GUS GRISSOM WAY 79936FRIDAY R22 1800 1876 JOHN GLENN DR 79936FRIDAY R22 1700 1791 JUDITH RESNIK DR 79936FRIDAY R22 1649 1751 LEROY BONSE DR 79936FRIDAY R22 1800 1831 MARLYS LARSON ST 79936FRIDAY R22 11620 11780 MCAULIFFE DR 79936FRIDAY R22 1720 1792 MICHAEL SMITH DR 79936FRIDAY R22 11700 11713 NASA WAY 79936FRIDAY R22 1800 1836 NEIL ARMSTRONG LN 79936FRIDAY R22 1720 1765 ONIZUKA DR 79936FRIDAY R22 11700 11747 ROGER CHAFFEE LN 79936FRIDAY R22 11700 11769 RONALD MCNAIR DR 79936FRIDAY R22 11631 11679 SPACE SHUTTLE LN 79936FRIDAY R23 1500 1613 CEZANNE CIR 79936FRIDAY R23 11940 11961 DALI WAY 79936FRIDAY R23 1501 12287 EL GRECO CIR 79936FRIDAY R23 12101 12160 GOYA CT 79936FRIDAY R23 1510 1531 HAL MARCUS PL 79936FRIDAY R23 12044 12089 JOSE CISNEROS DR 79936FRIDAY R23 1500 1561 KOLLIKER DR 79936FRIDAY R23 1500 1521 LINDA RUBY DR 79936FRIDAY R23 1530 12077 MICHELANGELO DR 79936FRIDAY R23 12100 12176 NOEL ESPINOZA CIR 79936FRIDAY R23 12032 12076 PAUL KLEE DR 79936FRIDAY R23 1580 1597 PETE FAULKNER PL 79936FRIDAY R23 1510 1560 PETER COOPER DR 79936

99

Day Rte_No House_No (start) House_No (end) Street ZipTop Recycling Routes by Weight (Friday)

FRIDAY R23 1501 1569 PETER HURD DR 79936FRIDAY R23 1461 1489 RUDY MONTOYA DR 79936FRIDAY R23 11900 12092 VAN GOGH DR 79936FRIDAY R23 12025 12037 VISTA DEL SOL DR 79936FRIDAY R31 3640 3657 AJA KOREN PL 79938FRIDAY R31 3640 3660 BREANN ISABELL PL 79938FRIDAY R31 3640 3657 KRISTA ILEE PL 79938FRIDAY R31 3640 3657 LUIS LARES PL 79938FRIDAY R31 3640 3657 MAYA LIZABETH PL 79938FRIDAY R31 3640 3661 PABLO SANCHEZ PL 79938FRIDAY R31 3640 3657 RACHEL CRYSTEL PL 79938FRIDAY R31 3640 3661 SAMMY REECE PL 79938FRIDAY R31 3328 3341 TIERRA ALMA LN 79938FRIDAY R31 12600 12680 TIERRA ALZADA DR 79938FRIDAY R31 3305 3537 TIERRA ANGEL DR 79938FRIDAY R31 12300 12381 TIERRA APACHE RD 79938FRIDAY R31 12221 12352 TIERRA AZTECA DR 79938FRIDAY R31 12617 12669 TIERRA CLARA RD 79938FRIDAY R31 3400 3468 TIERRA COBRE DR 79938FRIDAY R31 12644 12661 TIERRA CORAL CT 79938FRIDAY R31 3400 3445 TIERRA CROMO RD 79938FRIDAY R31 3528 3537 TIERRA FLOR PL 79938FRIDAY R31 12620 12661 TIERRA FRESA WAY 79938FRIDAY R31 12621 12633 TIERRA FUEGO CT 79938FRIDAY R31 12644 12661 TIERRA GEMA CT 79938FRIDAY R31 3640 12668 TIERRA INCA DR 79938FRIDAY R31 3500 3521 TIERRA LISA WAY 79938FRIDAY R31 3241 3345 TIERRA LUCERO LN 79938FRIDAY R31 3616 12280 TIERRA MAYA DR 79938FRIDAY R31 3600 3633 TIERRA MECA RD 79938FRIDAY R31 12620 12629 TIERRA PERA CT 79938FRIDAY R31 12616 12661 TIERRA PERLA CT 79938FRIDAY R31 12264 12280 TIERRA PEZ WAY 79938FRIDAY R31 3500 12364 TIERRA PLATA DR 79938FRIDAY R31 3600 3620 TIERRA REAL WAY 79938FRIDAY R31 3413 12288 TIERRA RUBY DR 79938FRIDAY R31 3400 3641 TIERRA ZAFIRO DR 79938

100

Appendix D

Trash and Recyclables Collection Schedule

101

Montwood

Sugarberry

City Limits

Cesar Chavez Memorial

Interstate 10

Pebble HillsGeorge Dieter

Yarbrough

Pendale

Patriot Freeway

Loma Real

Montana

Dale

Mesa

Redd

Borderland

Trans Mountain

Country Club

City Limits

Leroy Bonse

Hondo PassPa

triot

Free

way

Saul Kleinfeld

Railro

ad Dr

ive

Mountain Walk

Mesa

Piedras

City Limits

ArizonaCliff

City Limits

Interstate 10

City Limits

George DieterVista Del Sol

Interstate 10City of El Paso Environmental Services Department

Trash and Recyclables Collection Schedule

/

Legend

FridayManual Collections

Thursday

TuesdayWednesday

102

Appendix E

Municipal Recycling Comparison

103

Citi

esSA

N A

NT

ON

IOA

UST

INCO

LUM

BU

SFO

RT

WO

RT

HM

EMP

HIS

Pop

ula

tion

1,37

3,66

878

6,38

276

9,36

072

7,57

567

6,64

0

Rec

ycli

ng

rate

(%

)19

37.3

14.5

20.1

32

Rat

es b

y ca

tego

ry: R

esid

enti

al37

.312

.220

32

Tot

al T

onn

age

Coll

ecte

d:

105,

588

82,6

1160

,412

By

City

19N

A19

,584

NA

128,

516

Coll

ecti

on M

eth

ods:

Cu

rbsi

de

YES

YES

YES

YES

YES

F

req

uen

cyBI

WEE

KLY

BIW

EEKL

YW

EEKL

YW

EEKL

YW

EEKL

Y

N

um

ber

of h

ouse

hol

ds

338,

337

179,

785

11,6

6619

9,43

721

1,00

0

Is

pro

gram

man

dat

ory?

NO

NO

NO

NO

NO

H

ow m

ater

ials

col

lect

edCO

MM

ING

LED

COM

MIN

GLE

DCO

MM

ING

LED

COM

MIN

GLE

DCO

MM

ING

LED

Siz

e of

con

tain

ers

use

d48

& 9

6 G

AL

30,6

0,90

NA

NA

18 G

AL

Rec

ycli

ng

goal

s:

Non

-man

dat

ed g

oal

60%

BY

2020

NO

NE

35%

BY

2015

NO

NE

25%

WA

STE

RED

UCT

ION

BY

STA

TE

Rec

ycli

ng

bu

dge

t22

,485

,612

4,40

0,00

0N

AN

AN

A

Ove

rall

sol

id w

aste

bu

dge

t78

,924

,497

6,39

0,00

0N

A53

,713

.13

59,0

00,0

00.0

0

SOLI

D W

AST

E FE

E$8

.25

MO

NTL

Y FE

ESO

LID

WA

STE

FEE

SOLI

D W

AST

E FE

EPA

RT O

F SO

LID

WA

STE

FEE

How

are

res

iden

ts

char

ged

for

recy

clin

g

104

Citi

esP

opu

lati

onR

ecyc

lin

g ra

te (

%)

Rat

es b

y ca

tego

ry: R

esid

enti

alT

otal

Ton

nag

e Co

llec

ted

: B

y Ci

tyCo

llec

tion

Met

hod

s: C

urb

sid

e

Fre

qu

ency

N

um

ber

of h

ouse

hol

ds

Is

pro

gram

man

dat

ory?

H

ow m

ater

ials

col

lect

ed

Siz

e of

con

tain

ers

use

dR

ecyc

lin

g go

als:

Non

-man

dat

ed g

oal

Rec

ycli

ng

bu

dge

t O

vera

ll s

olid

was

te b

ud

get

How

are

res

iden

ts

char

ged

for

recy

clin

g

BA

LTIM

OR

EEL

PA

SOD

ENV

ERSE

AT

TLE

NA

SHV

ILLE

LOU

ISV

ILLE

637,

418

620,

447

620,

345

617,

334

605,

473

566,

503

18.1

1813

51.1

3023

18.1

1613

5830

27,3

4531

000

367,

735

56,8

1028

,476

YES

YES

YES

YES

YES

YES

WEE

KLY

WEE

KLY

BIW

EEKL

YBI

WEE

KLY

MO

NTH

LYW

EEKL

Y

210,

000

160,

000

170,

000

152,

309

123,

350

NA

NO

NO

NO

YES

NO

NO

COM

MIN

GLE

DCO

MM

ING

LED

COM

MIN

GLE

DCO

MM

ING

LED

COM

MIN

GLE

D

COM

MIN

GLE

D

NA

96 G

AL

65 G

AL

96 G

AL

+ VA

RYIN

G F

OR

ORG

AN

ICS

96 G

AL

18 G

AL

NO

NE

25%

/201

3N

ON

EST

ATE

25%

OVE

RN

ON

E

NA

NA

2,50

0,00

0N

A6,

300,

000

1,08

3, 4

00

NA

$12.

6 M

ILLI

ON

22,0

00,0

0015

8,48

9,72

424

,700

,000

19,7

46,0

00

TAXE

SN

O C

HA

RGE

TAXE

SIN

CL IN

SO

LID

WA

STE

FEE

TAXE

STA

XES

105

Appendix F

Survey

106

GET $5 OFF

Dear Resident,

We would like to offer you the opportunity to have $5.00 taken off an upcoming Solid Waste Bill for taking a five minute survey and mailing it back to us using the prepaid postage self address envelope (enclosed). The City of El Paso Environmental Services Department performed a Recycling Compliance Study throughout the month of January 2011 where 400 randomly selected homes were analyzed for compliance. This involved collecting and inspecting the Blue Recycling Containers for non-acceptable materials that contaminate the recycling stream. Your home was selected to participate in this survey because our study indicated that you show good recycling behavior and we would like to learn from you.

TELL US ABOUT YOU

1. How did you learn about the recycling program?

____Pamphlets/Brochures

____Newspaper

____Radio

____TV

____Word of mouth

____My kids

____Call to the City

2. Do you have kids in elementary /middle school? ____YES_____NO

a. Do your kids encourage recycling in your home? ____YES_____NO

107

3. What is your level of education?

____Less than High School

____High School

____Post HS education (some College)

____4 year Bachelor’s degree

____Master’s or Doctoral

4. What type of vehicle do you drive? ________ 8 Cylinder (gasoline) ________ 6 Cylinder (gasoline) _________ 4 Cylinder (gasoline) _________ Hybrid _________ Other

5. Do you use public transportation, Sun Metro buses?

____YES_____NO 6. How many trash (Gray) containers do you use?

____1 ____2 ____3 or more

7. How would you like to be recognized for being a good recycling citizen?

a. Publicly- such as my name posted on the City website, newspaper, or other media

b. Getting a smaller trash container and thus a smaller fee in your solid waste bill

c. Have your blue bin installed with a gold-colored lid to signify to others that you are a top performer in recycling

__________ (Choose one)

8. Do you have compact fluorescent light bulbs installed in your home?

____YES_____NO

9. Do you have Solar Panels installed in your home?

____YES_____NO 108

YOU’RE RECYCLING KNOWLEDGE

10. Are you aware that glass is not an acceptable recycling material in the City of El Paso? ____YES_____NO

11. Are you aware that pizza boxes are not an acceptable recycling material in the City of El Paso? ____YES_____NO

12. Are you aware that yard waste (grass clippings, brush, tree limbs, etc.) is not acceptable recycling material in the City of El Paso?

____YES_____NO

13. Are you aware Styrofoam/foam material is not acceptable recycling material in the City of El Paso?

____YES_____NO

14. Are you aware that you are supposed to cut up or fold up cardboard?

____YES_____NO

15. Are you aware that you are not allowed to bag recycling material?

____YES_____NO

IN YOUR OPINION

16. You currently are not being charged for recycling would you be willing to pay a small fee to cover education and outreach on recycling?

____YES_____NO

17. Do you have a problem if:

a. ___________we do not pick up recycling on windy days (40 mph or greater)- this causes bins to topple over and then material blows all over the neighborhood (Y/N);

b. ___________ we do not pick up recycling on days colder than 20 deg F – the diesel on trucks gels and thus causes difficulty starting and running, thus adding overtime and higher maintenance costs (Y/N);

c. ___________ we do not pick up recycling on days hotter than 110 deg F – trucks start overheating and break down, thus causing overtime and higher maintenance costs.

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18. Are you ok if the City collects recycling every other week (keep in mind the City is not charging you for recycling pickup)?

____YES_____NO

a. If No, would you be ok if we offered you another blue container with every other week pickup?

____YES_____NO

b. If still No, why not? ________________________________________________________________________ ________________________________________________________________________

19. Overall you feel Curbside recycling is:

___________Poor

___________Adequate

___________Good Service

20. Any other comments or concerns? ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

THANK YOU,

Your signature below signifies that we the City of El Paso can recognize you by name on our City website, local newspaper, or other media for being an exceptional recycling steward. Your address will not be published and your name will not be used for any other purpose. Please give the City 30 to 60 days to process your rebate.

___________________________________ _________________________

Print Name Date

___________________________________ _________________________

Signature Date

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Curriculum Vita

Richard Adams was born in El Paso, Texas. The only son of Stanley Adams and Lydia

Adams, graduated from Austin High School, El Paso, Texas. He entered the University of Texas at

El Paso on a Preview of Engineering Scholarship. He was in and out of school until May 1997

when he graduated with a bachelor’s degree in industrial engineering. He worked with several

companies while pursuing his bachelor’s including: Computer Systems Group, El Paso Water

Utilities, Banes General Contractor, and Solid Waste Management. In 2004 he started pursuing a

master’s degree from the University of Texas at El Paso in environmental engineering, while

pursing his master’s he was employed full time with Environmental Services with the City of El

Paso.

Permanent Address: 2915 Frankfort Ave El Paso, Texas 79930

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