1.
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Industrial Stormwater BenchmarksIndustrial Stormwater Benchmarks ––
Comparison of New Minnesota BenchmarkComparison of New Minnesota Benchmark
Concentrations To Historic IndustryConcentrations To Historic Industry--specificspecific
Testing ResultsTesting Results
Loren J. Larson
Managing Partner
Caltha LLP, Minneapolis
Conference on the Environment | Brooklyn Park, Minnesota | November 9, 2010

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OutlineOutlineOutline
• Background – Historical Context
• EPA Industrial Group Sampling
• Comparison of MPCA Benchmarks to Past
Data
• Projection of Future Results
• Summary

3.
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Key Historical EventsKey Historical EventsKey Historical Events
• 1972 Federal Water Pollution Control Act
» NPDES permit program
• 1977-85 NURP Studies
» Identified urban runoff as important factor impacting
water quality
• 1987 Amendment to Clean Water Act
» Required stormwater discharge be addressed under
NPDES permitting program

4.
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Key Historical EventsKey Historical EventsKey Historical Events
• 1990 - EPA promulgates stormwater permit rules
» Requires industrial facilities to be permitted by Sept. 1992
» Publishes Group Permit option
• 1990-1992 - General permits drafted by EPA
and States
» 1991 MPCA publishes general permit
• 1992 – Most facilities permitted under State or
EPA general permit

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Group Permit OptionGroup Permit OptionGroup Permit Option
• Offered option to be covered under permit
customized for industry sector
» Single company with multiple locations
» Multiple companies, trade associations
• EPA accepted Group Permit applications
• Applications required monitoring of
stormwater discharge from selected group
members
» Representative of group
» Wide geographic distribution

6.
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Typical Group Monitoring LocationTypical Group Monitoring LocationTypical Group Monitoring Location

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Typical Group Monitoring LocationTypical Group Monitoring LocationTypical Group Monitoring Location

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Typical Group Monitoring LocationTypical Group Monitoring LocationTypical Group Monitoring Location

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Typical Group Monitoring LocationTypical Group Monitoring LocationTypical Group Monitoring Location

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Typical Group Monitoring LocationTypical Group Monitoring LocationTypical Group Monitoring Location

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Group Permit OptionGroup Permit OptionGroup Permit Option
• Selected facilities conducted monitoring
and submitted data to EPA
• By 1992, most facilities in Groups had
State general permits available
» State permits were considerable less burdensome
• Ultimately Groups disbanded
• In Dec. 1995, EPA published Group data
and benchmarks in Federal Register

12.
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1995 Group Data1995 Group Data1995 Group Data
• Divided in similar sector designations (A–AC)
still used today
» More subsectors
• Some parameters represented by most sectors
» TSS, BOD, COD
• Some parameters represented by only a few
sectors
» TP, metals, toxic organics
• Range of number of results for each sector
» 10 – 20, up to hundreds

13.
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MPCA BenchmarksMPCA BenchmarksMPCA Benchmarks
• All industrial sectors have one or more
benchmarks
• All sectors have benchmark for Total
Suspended Solids (TSS)

14.
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MPCA BenchmarksMPCA BenchmarksMPCA Benchmarks
NC State standards
12TP
Median of NURP Results
0.680.68Nitrate
Many based on FAV in State
standards
Adjusted for hardness
Many based on FAV in State
standards
Adjusted for hardness
120
25
100
2010 MPCA
Benchmarks *
Sb, Ar, Cd,
Cu, Se,
Ag,
Al, Pb, Hg,
Ni, Zn
COD
BOD
TSS
Detection Limit x 3.18Detection Limit x 3.18
Ambient water quality criteriaAmbient water quality
criteria
4x BOD benchmark
120
POTW Secondary Treatment Standards
30
Median of NURP Results
100
Basis for EPA
Benchmark
1995 EPA
Benchmark
* Note: The same benchmarks are used by many States, therefore analysis and
conclusions apply across US

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Distribution of Data:Distribution of Data:Distribution of Data:

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Distribution of Data:
Normal Distribution
Distribution of Data:Distribution of Data:
Normal DistributionNormal Distribution

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Distribution of Data:
Log Normal Distribution
Distribution of Data:Distribution of Data:
Log Normal DistributionLog Normal Distribution

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Comparison of All Sector Data
To MPCA Benchmarks
Comparison of All Sector DataComparison of All Sector Data
To MPCA BenchmarksTo MPCA Benchmarks
• Combine all 1995 industrial sector
data
• Compare MEDIAN to Benchmark
Median (mg/L) / Benchmark (mg/L)

19.
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Comparison of All Sector Data
To MPCA Benchmarks
Comparison of All Sector DataComparison of All Sector Data
To MPCA BenchmarksTo MPCA Benchmarks
0
1
2
3
4
5
6
7
TSS TP BOD COD Cr Cd Ag Ni Al Fe Pb Cu Zn
Median/Benchmark
Median of all data = MPCA Benchmark
Insuff.
data
Insuff.
data
Insuff.
data
Insuff.
data

20.
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Individual Sector DataIndividual Sector DataIndividual Sector Data
• Conventional Wastewater Parameters
» TSS, BOD, COD, TP
• Heavy metals
» Fe, Zn
• Data expressed as “% of sector individual
results that exceed benchmark”
» e.g., probability that any one sample exceeds
benchmark
• Only presenting results for sectors that are
assigned that chemical benchmark

21.
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Total Suspended SolidsTotal Suspended SolidsTotal Suspended Solids
0%
10%
20%
30%
40%
50%
60%
70%
80%
A1 A3 B D E4 E1 F1 F3 G I K M O P2 P4 Q S U U U U V X Y AA AB
%ofSamplesExceedingMPCABenchmark

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Total Suspended SolidsTotal Suspended SolidsTotal Suspended Solids
0%
10%
20%
30%
40%
50%
60%
70%
80%
A1 A3 B D E4 E1 F1 F3 G I K M O P2 P4 Q S U U U U V X Y AA AB
%ofSamplesExceedingMPCABenchmark
A3.Log storage
& handling
D. Asphalt &
paving prod.
G. Mining
L. Landfills
U8. Fats/Oils
manufacturing

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Total Suspended SolidsTotal Suspended SolidsTotal Suspended Solids
0%
10%
20%
30%
40%
50%
60%
70%
80%
A1 A3 B D E4 E1 F1 F3 G I K M O P2 P4 Q S U U U U V X Y AA AB
%ofSamplesExceedingMPCABenchmark
USPS

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BODBODBOD
0%
20%
40%
60%
80%
A B D E E F F G I K M O P P Q S U U U U V X Y
AA AB
%ofSamplesExceedingMPCABenchmark

25.
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BODBODBOD
0%
20%
40%
60%
80%
A B D E E F F G I K M O P P Q S U U U U V X Y
AA AB
%ofSamplesExceedingMPCABenchmark
U8. Fats/Oils
manufacturing

26.
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CODCODCOD
0%
20%
40%
60%
80%
A C E E F H K N P P S U U U X Z AB
%ofSamplesExceedingMPCABenchmark

27.
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CODCODCOD
0%
20%
40%
60%
80%
A C E E F H K N P P S U U U X Z AB
%ofSamplesExceedingMPCABenchmark
U8. Fats/Oils
manufacturing
A1. Saw Mills

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Total PhosphorusTotal PhosphorusTotal Phosphorus
0%
20%
40%
60%
80%
A C E E F H K N P P S U U U X Z
AB
%ofSamplesExceedingMPCABenchmark

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Total ZincTotal ZincTotal Zinc
0%
20%
40%
60%
80%
A
1
A
3 B D E4 E1 F1 F3 G I K M O P2 P4 Q S U U U U V X Y A
A
AB
%ofSamplesExceedingMPCABenchmark

30.
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Total IronTotal IronTotal Iron
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
A C E E F H K N P P S U U U X Z AB
%ofSamplesExceedingMPCABenchmark

31.
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Total IronTotal IronTotal Iron
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
A C E E F H K N P P S U U U X Z AB
%ofSamplesExceedingMPCABenchmark
M. Auto salvage
F. Foundries

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Influence of On-Site MaterialsInfluence of OnInfluence of On--Site MaterialsSite Materials
Compared stormwater results within two
subgroups:
1. TRI Reporters – facilities that process
larger quantities of chemical
2. Non TRI Reporters - facilities that use
small quantities or none of chemical

33.
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Stormwater Results For TRI and
Non-TRI Facilities
Stormwater Results For TRI andStormwater Results For TRI and
NonNon--TRI FacilitiesTRI Facilities
50%
56%
21%
29%
40%
45%
51%
54%
0%
50%
100%
TRI Non TRI TRI Non TRI TRI Non TRI TRI Non TRI
%ofSamplesExceedingBenchmark
CopperCopper AluminumLead Zinc

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What is the probability a facility
will meet its benchmarks?
What is the probability a facilityWhat is the probability a facility
will meet its benchmarks?will meet its benchmarks?
• Benchmarks are not compared to single
values
• Minimum of four quarterly samples are
collected
• Mean of all samples is compared to
benchmark concentration

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Example 1995 Dataset: TSS
Sector P. Transportation/Warehousing
Example 1995 Dataset: TSSExample 1995 Dataset: TSS
Sector P. Transportation/WarehousingSector P. Transportation/WarehousingY-Axis

36.
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Two Possible TSS BenchmarksTwo Possible TSS BenchmarksTwo Possible TSS BenchmarksY-Axis
65 mg/L
Discharges to
wetlands
100 mg/L
All other discharges

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Monte Carlo Simulation
Randomly draw four samples, then calculate mean; 1,000 trials
Monte Carlo SimulationMonte Carlo Simulation
Randomly draw four samples, then calculate mean; 1,000 trialsRandomly draw four samples, then calculate mean; 1,000 trialsY-Axis
Sample #1
Sample #2
Sample #3
Sample #4
…then calculate mean

38.
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ResultsResultsResults
19%34%Existing data set
% facilities meeting
65 mg/L benchmark
% facilities meeting
100 mg/L benchmark

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Scenario 2: Cut-off High
Concentrations
Scenario 2: CutScenario 2: Cut--off Highoff High
ConcentrationsConcentrationsY-Axis
Limit
highest
values to
< 500
mg/L

40.
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ResultsResultsResults
33%44%Limit high values
(<500 mg/L)
19%34%Existing data set
% facilities meeting
65 mg/L benchmark
% facilities meeting
100 mg/L benchmark

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Scenario 3: Resample
Take two additional samples
Scenario 3: ResampleScenario 3: Resample
Take two additional samplesTake two additional samplesY-Axis
Sample #5
Sample #6

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ResultsResultsResults
21%36%Two extra samples
33%44%Limit high values
(<500 mg/L)
19%34%Existing data set
% facilities meeting
65 mg/L benchmark
% facilities meeting
100 mg/L benchmark

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ConclusionsConclusionsConclusions
• Benchmarks more likely to be exceeded
are:
» TSS
» Metals, especially iron
• All industrial sectors are equally likely to
exceed benchmarks for TSS
• For toxic chemicals and metals, volume
of use or storage on-site is not a good
predictor of meeting benchmarks

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ConclusionsConclusionsConclusions
• Certain industrial sectors are more likely to
exceed other benchmarks
» Food sector – COD, BOD
» Sawmills – COD
» Primary metals/foundries – Fe, Cu
» Concrete plants – Fe
» Salvage yards – Al, Fe
• Improving chance of meeting benchmarks
requires active prevention program
• Resampling without corrective action does not
improve chances of meeting benchmarks

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QuestionsQuestionsQuestions
Loren Larson
Caltha LLP
Minneapolis, Minnesota
llarson@calthacompany.com
(763) 208-6430