HERO Digital Data Sets
Data Set Types: Tabular
1. Data Set Overview:
Data Set Title: Idling Emissions
Data Set Description: The data set is a list of automobile idling times during half
hour periods as recorded at the McDonald’s restaurant at 995 Main St. in Worcester.
Cars and trucks are distinguished.
Objective/Purpose: The purpose of this data is to find out how much air pollution
is attributable to automobiles idling at drive-thru windows.
Summary of Parameters: Automobile types- passenger cars and trucks (pick-ups,
vans, minivans, and sports utility vehicles), length of idling time (minutes), time of day,
day of the week, and amount of annual emissions for Worcester County.
Discussion: NA
Related Data Sets: NA
2. HERO Data Set Investigators
Name: Emily Shusas
Type of Investigation: Reorganization of data from class project for the HERO
project.
Time of Investigation: Spring 2000
3. Data Set Origin:
Source Data Title: Drive-thru Idling Emissions
Source Organization/Agency/Person: Students of ES 180, Fall 1999
Name: NA
Address: NA
Source Data Investigators:
Name: Daniel Niles
Address: Graduate School of Geography, Clark University
950 Main St., Worcester, Massachusetts 01610
Phone: NA
Fax: NA
Email: DNILES99@yahoo.com, dniles@clarku.edu
WWW: NA
Name: Emily Shusas
Address: Clark University P.O. Box 1835
950 Main St., Worcester, Massachusetts 01610
Phone: (508) 795-6142
Fax: NA
Email: emshu@hotmail.com, eshusas@clarku.edu
WWW: NA
Data Acquisition Method: Field investigation, including observation of
automobiles at McDonald’s drive thru window; also, investigation of websites
Original Data Format: handwritten notes
4. Data Description:
Data Format: Microsoft Excel dataset
Data Structure: Microsoft Excel dataset
Spatial Characteristics: McDonald’s Restaurant at 995 Main St. in Worcester, MA
Spatial Coverage: NA
min. X:
max. X:
min. Y:
max. Y:
Spatial Coverage Map: NA
Spatial Resolution/Scale: NA
Projection: NA
Grid Description: NA
Cols:
Rows:
Resolution:
Temporal Characteristics: NA
Temporal Coverage: Late October-early November, 1999
Temporal Resolution: minutes of idling
Data Characteristics:
Parameter/Variable: Automobile types- passenger cars and trucks (pickups, vans, minivans, and sports utility vehicles), length of idling time (minutes), time of
day, day of the week, and amount of annual emissions for Worcester County.
Variable Description/Definition: car emissions include volatile organic
compounds (VOCs), nitrogen oxides (NOx), and carbon dioxide (CO).
Data Set Completeness: NA
Data Set Consistency: NA
5. Data Manipulations:
Formulae: factor conversions of EPA data (see processing steps below)
Derivation Techniques and Algorithms: NA
Processing Steps: The average number of idling minutes per day (from 8:30 a.m.
to 6:30 p.m.) for cars and trucks was calculated from the collected data. These numbers
were then multiplied by information provided by the EPA website of grams of VOCs,
CO, and NOx released by cars and trucks per minute. These figures (in g/day) were then
multiplied by 365 to give the grams of these compounds that are released in one year.
The annual emissions figures were further converted from grams to pounds, and summed
to give total emissions.
Processing Changes: NA
Calculations: NA
Special Corrections/Adjustments: NA
Calculated Variables: annual idling emissions
Graphs and Plots: Distribution of Time Sessions
Average Daily Fluctuations in Emissions
6. Errors:
Sources of Error: imprecise timing of vehicles, discrepancies in emissions
between cars based on age and type, differing numbers of observation sessions at
particular hours of the day, absence of observation sessions during certain hours
Quality Assessment: NA
Data Validation by Source: NA
Confidence Level/Accuracy Judgement: NA
Measurement Error for Parameters: NA
Additional Quality Assessments: NA
Data Verification by HERO project:
7. Notes:
Limitations of the Data:
The random scattering of data collection times could lead to discrepancies in the
data. It should also be noted that this particular McDonald’s is known to be especially
busy, so its use as an average fast food establishment may lead to estimations of pollution
amounts that are too high. However, this high estimate may have been counterbalance by
the effect of only observing the drive-thru between 8:30 a.m. and 9:30 p.m. The
relatively small amount of data collected may also lead to bias in the data, as could
seasonal fluctuation in fast food business (since all of our data was collected over a two
week period in the fall).
Known Problems with the Data: NA (or see above)
Usage Guidance: NA
Any Other Relevant Information about the Study:
8. Application of the Data Set: The data set was used for air pollution assessments.
9. Future Modifications and Plans: Possibly more observation time, and application of
data to include other types of establishments that have drive-thru window operations.
10. Software:
Software Description: Microsoft Excel
Software Access: computer in Cofert Lab or website
11. Data Access:
Contact Information:
Address:
Phone:
Fax:
Email:
Procedures for Obtaining Data:
12. References:
The published references for the source data are: EPA website, www.epa.gov
Other references used in metadata preparation: NA
13. Glossary of Terms:
idling- Not in use or operation; in this study, idling refers to vehicles that remain
running but immobile as their occupants wait for food.