Processing
the Emissions data for MapEcos was a major undertaking. We tried to be as unbiased and informative as possible within the
constraints set by the size of the location point bubbles.
We tried also to be careful and accurate, but we are certain
that some errors remain. This page provides more detailed
information about how we processed these reported data.
Corporate Ownership Information
Firms
report the identity of their corporate ownership to the TRI by
reporting a name and a Dun and Bradstreet Identifier.
Unfortunately, the name of the facility is often inconsistently
reported and the ID is invalid or does not match the name of the
reported corporate
owner. To identify the corporate owner of each facility,
we used a computerized matching program
to match each facility to the National Establishment Time-Series
(Nets) Database database.
We report
our estimates for the most recently released TRI year – 2005.
Location
We
obtained location data from the Council on Environment
Cooperation, the EPA, and automated requests to Google to
geocode street addresses. We attempted to identify the most
accurate location information by comparing data from multiple
sources, but location errors undoubtedly persist. If you would
like to report such an error, please click on the “report
inaccurate information” link.
Emissions
Emissions
data were calculated from the 2007 release (2005 reporting year) of the EPA’s Toxic
Release Inventory.
Hazard
Hazard
score in the “summary” tab are calculated as:
|
Oral
Hazard |
= RSEI* scores for oral toxicity X lbs emitted to water,
|
|
Air Hazard |
= RSEI* scores for inhalation toxicity X lbs emitted to air,
|
|
Hazard
|
=
Oral Hazard + Air Hazard
|
*
Risk-Screening Environmental Indicators
Note, we
recentered the RSEI scores so that the average chemical in the
TRI has a toxicity of 1. Thus, if a firm has a lower hazard
score than its lbs emitted, it is emitting less toxic
chemicals.
Dot
color and level
Most
facilities in the US have low emissions, while a few facilities
have enormous emissions. In calculating our levels and in
setting the color of the facility marker dot, we wanted to take
this into account. As a result, we created an exponential
level. In our level scheme, the bottom 50% of the firms are in
level 0, the next 25% in level 1, and so on. Despite the
reducing number of firms in each level, the total emissions from
all the facilities in each level actually increase with the
level (e.g. emissions from all facilities in level 9 > emissions
from facilities in level 8).
|
Level |
Emissions |
|
0 |
< 1
lb |
|
1 |
1 to 10 |
|
2 |
. to100 |
|
3 |
. to1000 |
|
4 |
. to10000 |
|
5 |
. to100000 |
|
6 |
. to1000000 |
|
7 |
. to10000000 |
|
8 |
. to100000000 |
|
9 |
>
100000000 lbs |
Line
graphs
To create
the line graphs, we first estimated the average for that
industry (based on 2 digit Standard Industry Classification
(SIC) codes. We then logged this value to make graphing
easier. The graphs then report this logged “average” facility
relative to the focal facility.
Histograms
To
calculate the histogram graphs, we first calculated the log
value of emissions for the facility in the comparison group
(state, county, industry) with the most emissions. We divide
this number into 6 groups and calculate the number of firms in
each group.
|