# Presentation: 891761

## Industrial General Permit Study 

- Michael K.Stenstrom
- Haejin Lee
- Dept. of C&EE, UCLA
- December 7, 2004

**Industrial**
**General**
** ****Permit**

- From National Water Quality Monitoring Council home page

## Background(1)

- On November 19, 1991, the State Water Resource Control Board issued the statewide General Permit (GP) for discharges of stormwater  associated with Industrial activities.
- Permittees must collect water quality samples from two storms per year and analyze for four basic parameters (pH, SC, TSS, and Oil & Grease or TOC).
- Certain facilities must analyze for specific additional pollutants.

**Notes:**
On September 29, 1995, the US EPA issued the Multi-Sector Storm Water General Permit for discharges of stormwater  associated with Industrial activities

## Background(2)

- Currently, there are approximately 3,000 permittees within the Los Angeles County.
- Permittees collect approximately 25,000 water quality data points per year, incurring approximately $400,000 per year in lab costs.

**Notes:**
About 2,489 (84%) of our 2,962 permittees submitted their Annual Report on time for 2000-2001.

The price based on Los Angeles County Department of Agricultural Commissioner /Weight and measures Environmental Toxicology Laboratory
See  WER paper Table 4.

## Background(3)

- The original goal of the General Permit was
- To identify polluters and improve their pollution prevention behavior
- To create a database to help development of Total Maximum Daily Loads (TMDLs)

**Notes:**
TMDLs are the implementation of rules included in section 303 of the clean water act of 1972
Pollutant loads are calculated in mass per unit time discharged to a water body
For NPS such as urban stormwater, load allocations are often based on landuse types including urban runoff

## Objective of this study

- Evaluate the current GP monitoring
- Recommend a new, improved monitoring plan
- Estimate the potential burden or financial impact of new monitoring requirements

## Datasets
Relationship between WQ data and industrial activity
     - Discriminant analysis
     - Unsupervised NN model
     - Supervised NN model
Possible reasons
     - Sampling type
     - Parameter selection

- Datasets
- Relationship between WQ data and industrial activity
-      - Discriminant analysis
-      - Unsupervised NN model
-      - Supervised NN model
- Possible reasons
-      - Sampling type
-      - Parameter selection

## Additional study
    - Seasonal first flush phenomenon
    - Toxicity result by industrial type
    - National GP data
Suggested new permit requirements
     Parameters to monitor, sampling type, 
     when to sample, and SIC code, 
     web-based 	data entry?

- Additional study
-     - Seasonal first flush phenomenon
-     - Toxicity result by industrial type
-     - National GP data
- Suggested new permit requirements
-      Parameters to monitor, sampling type,
-      when to sample, and SIC code,
-      web-based 	data entry?

## Data sets

| Monitoring Area | Monitoring Year | Observed Parameter |
| --- | --- | --- |
| Los Angeles
County, CA | 1992-2001 | Basic WQ parameter,
Metals |
| Sacramento
County, CA | 1993-2001 | Basic WQ parameter,
Metals |
| Connecticut | 1995-2003 | Basic WQ parameter,
Metals, Toxicity |
| 15 other states | 1998-2003 | Basic WQ parameter,
Metals |

- LADPW monitoring data using composite samples provided for
- comparisons

**Notes:**
-For Connecticut, Metals, nutrients, and toxicity is mandatory parameter See Table 3.
- LACDPW industrial landuse monitoring during 1998-2001 wet season

## Relative Variability

## Analysis methods 

**used to determine relationships between various **
**industrial type and water quality results**

- Discriminant analysis first used to identify relationships
- Supervised Neural Network (NN) models were applied to the data to differentiate various industrial landuse activities based on SIC code
- Unsupervised NN model was applied to the data to see any possible distinctive class among the industries.

**Notes:**
Discriminant analysis is a preliminary classification approach.
The analysis gave us which parameter is more useful parameter to classifying among many parameter.
And also gave us rough classification results.
NN always gave better result.
NN is a computational tool that operates similarly to the biological progress of human brain.
NN is a black-box model.
These models do not require detailed knowledge of the internal functions of a system to recognize relationship between input and output.
For SNN, We have to give input (parameter) and output result to train NN model.
But USNN, We gave input and the model is trained to find a pattern (output) itself.

## An example of discriminant analysis

** **The data contains150 cases.
-  There are 3 classes;seriosa,
-    versicolour, virginic.
-  There are 4 parameters;
-    length of sepal, width of
-    sepal, length of petal, and
-    width of petal

**Canonical Scores Plot**

**-10**

**-5**

**0**

**5**

**10**

**FACTOR(1)**

**-10**

**-5**

**0**

**5**

**10**

**FACTOR(2)**

**3**

**2**

**1**

**SPECIES**

- Factor is a different linear
-  combination of four parameters

**Notes:**
98 % of data are correctly classified into three classes with four parameters.
Sepal length is least useful parameter among 4 parameter

## The selected eight industrial type

| Description | SIC code |
| --- | --- |
| Food and kindred products (FKP)  | 20 |
| Chemical and allied products (CAP)  | 28 |
| Primary metal industries (PMI)  | 33 |
| Fabricated metal products, except machinery and transportation equipment (FMP)  | 34 |
| Transportation equipment (TE)  | 37 |
| Motor freight transportation and warehousing (MFTW)  | 42 |
| Electric, gas, and sanitary services (EGSS)  | 49 |
| Wholesale trade-durable goods (WT)  | 50 |

**Notes:**
The selected industry type is major type in observation number.
See Report Table 5.

## Discriminant analysis

- using LA county GP data

**Notes:**
Factor is different combination of input parameters.
They all stick together.
I tried to reduce the target classes into two. There is no difference.

## Supervised NNs application

- We successfully applied NNs to the LACDPW
-       landuse monitoring to differentiate various
-       landuse activity using WQ data (composite
-       samples)
-  Next, three NNs (MLP, RBF, BN) were trained
-      using GP data.
-  The models were extensively trained with
-      various architectures.
-  The performance of all models was very poor.

**Notes:**
We obtained 93 % overall correct classifications using LACDPW landuse monitoring data1996-2001 wet seasons. There are 10 WQ parameter and 5 various landuse classes.
Multi- Layer Perceptron, Radial Basis Function, Bayesian network.

## Unsupervised Network

- Kohonen Network is a common unsupervised network
- The goal of Kohonen network is to map the spatial relationships among cluster of data points
- The purpose using the model was to see any possible distinctive class among the industries.
- When the model is trained successfully, it may be used to classify unknown data patterns.

**Notes:**
For unsupervised NN, We give input variable to train a NN model,  The model results gave a possible classes.

## A Kohonen network application

**1**

**2**

**3**

**6**

**8**

**4**

**5**

**7**

**9**

- Increased  -----Similarity----- decreased

- Activation map having 3*3 neuron
- In the Kohonen network

- Case number per node

## Grab sample variability
Parameter selection
Unbalanced cases

- Grab sample variability
- Parameter selection
- Unbalanced cases

## Observation number of parameters for LA industrial GP during 2000-2001 wet season

## Grab Sample Variability:Comparison of grab samples to composite samples

- TSS

- TOC

- SC

- G  : Grab sample from the GP monitoring
- C1: Composite samples from the industrial critical source monitoring by LACDPW
- C2: Composite samples from the industrial landuse monitoring by LACDPW

- G        C1     C2

- G        C1     C2

- G        C1     C2

**Notes:**
See figure 5. The three data are fro 1998-2001 wet seasons. 
Only 67 of the total area is from  Industrial activity. The Area is 2.77 km2.

## Grab Sample Variability:Comparison of grab samples to composite samples

- Cu

- Pb

- Zn

- G  : Grab sample from the GP monitoring
- C1: Composite samples from the industrial critical source monitoring by LACDPW
- C2: Composite samples from the industrial landuse monitoring by LACDPW

- G        C1     C2

- G        C1     C2

- G        C1     C2

## Parameter selection:Distribution of outside benchmark for Los Angeles industrial GP during 1998-2001 wet seasons

## Parameter Selection:Comparison of  Los Angeles GP data to Sacramento  and Connecticut GP data

## Parameter selection:Urban activity is a major source of metals

## Findings

- The GP monitoring data are unrelated to industry type.
- Data collected using grab samples have much higher variability than data from composite samplers.
- Metals are major pollutants in industrial landuse and metal concentrations frequently exceeded the US EPA benchmark levels.

## Additional study: Seasonal first flush

**Notes:**
Several big storm events were selected and used their median value.
For the Connecticut, figure 10.

## Monthly Average Rainfall in USA, 1971-2000

- Data source :http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmlprcp.html

## Additional study:Toxicity result by Industrial type

- Data source: Connecticut GP monitoring data

-  24hrLC50 = 50% means
-   that stormwater diluted
-   1:1 results In 50%
-   mortality in a 24 hr period
-  Lower LC50 value
-    means higher
-    acute aquatic toxicity

## Additional study:Multi-Sector GP data

**Notes:**
Y-axis indicates maximum concentration during certain observation periods.
To see case number for National GP, see Table 5,6, and 7.

## Additional study:Possible major source of metals in Industrial stormwater runoff

- Estimated contribution of various sources of metals
- in urban commercial stormwater runoff (Davis et al., 2001)

- Roof

- Siding

- Roof

- Roof

- Siding

- Lead

- Zinc

- Copper

**Notes:**
Metal concentration is probably susceptible to roof and siding material as well as their activity.

## Findings 

- Los Angeles County GP data have higher levels for many parameters than Sacramento County and Connecticut data.
- There is a strong positive seasonal first flush in the stormwater discharges associated with industrial activity
- Metal related industries have higher acute aquatic toxicity

## Proposed New Permit

- Goals
- Identify high dischargers
- Discriminate among different industry types
- Requirements
- Additional parameters
- QA/QC/Training
- Time of sampling guidelines
- Composite samples
- Web-based reporting
- Add pollutants identified in 303D listing
- Flow estimates for mass emissions
- Real-time availability of results

## Adding metals as a mandatory parameter to existing General permit.
It will add value to the resulting monitoring database.
Cost increases are probably inevitable, but off-sets maybe possible.

- Adding metals as a mandatory parameter to existing General permit.
- It will add value to the resulting monitoring database.
- Cost increases are probably inevitable, but off-sets maybe possible.

## QA/QC/Web based reporting

- Greater use of contract labs with trained personnel
- QA/QC plans for contract labs
- Web-based reporting
- Allows for real-time access to data
- Simple expert system can check for implausible data
- Improved credibility
- Lower cost

## Sampling Type

- Increased use of composite samples.
- A flow-weighted composite sample for a storm event is generally better than grab sample.
- Provide help in overcoming difficulties of collecting representative samples

## For a single grab sample, the best sampling time should considered.
Seasonal first flush should be considered
For example, the best time for sampling oil and grease from highway landuse is 
   ~ midway through the storm 

- For a single grab sample, the best sampling time should considered.
- Seasonal first flush should be considered
- For example, the best time for sampling oil and grease from highway landuse is
-    ~ midway through the storm

## When to sample – an example

## Suggestion:    Sample frequency( ongoing study)

- Leecaster et al.(2002), proposed that sampling seven storms is the most efficient method for attaining small confidence interval width for annual concentration.

## Abandon SIC codes? 
New categories tailored to stormwater management?

- Abandon SIC codes?
- New categories tailored to stormwater management?

**Notes:**
The current SIC code is useful for executive purpose.
does not tell specific relationships between WQ? 

## Cost Issues

- Sampling for metals and requiring composite samples will increase costs
- To off-set cost, staggered sampling can be considered – alternate years, random selections
- Create of a market for sampling companies may provide economy of scale
- Sampling “holiday” between permits

## Summary

- New permit to require
- More parameters, including metals when appropriate
- Professional samplers
- More use of composite sampling
- Attention to sample timing
- Real-time reporting based on web-entry which can flag implausible values
- Cost management by staggered sampling or group sampling

## This work was supported in part by a contract from the Los Angeles Regional Water Quality Control Board
www.seas.ucla.edu/stenstro
stenstro@seas.ucla.edu

- This work was supported in part by a contract from the Los Angeles Regional Water Quality Control Board
- _[www.seas.ucla.edu/stenstro](http://www.seas.ucla.edu/stenstro)_
- _[stenstro@seas.ucla.edu](mailto:Stenstro@seas.ucla.edu)_

## Thank you

**Thank you**

## Reference

- On September 29, 1995, the US EPA issued the Multi-Sector Storm Water General Permit for discharges of stormwater  associated with Industrial activities.

**Notes:**
On September 29, 1995, the US EPA issued the Multi-Sector Storm Water General Permit for discharges of stormwater  associated with Industrial activities.

## What is a Neural Network?

**Neural Network is **
**a computational tool **
**that operates similarly to the**
**biological processes of brain.**

**Neuron in Human brain**

**Processing Element **
**Or Node in ANN**

- From the cover of the Journal ”Neural Networks’

## Models

**Deterministic**

**Black Box**

**Grey Box**

**Artificial NN(ANN)**

**Supervised**

**Unsupervised**

- Multi-layer Perceptron,
- Radial Basis Function,
- Bayesian Network

- Kohonen network

## Structure of  a NN model

**Input**
**Layer**

**Hidden**
**Layer**

**Output**
**Layer**

- Water
- Quality
- parameters

- Industrial
- type