Industrial General Permit Study Michael K.Stenstrom Haejin Lee Dept. of C&EE, UCLA December 7, 2004...
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Industrial General Industrial General Permit Study Permit Study Michael Michael K.Stenstrom K.Stenstrom Haejin Lee Haejin Lee Dept. of C&EE, Dept. of C&EE, UCLA UCLA December 7, 2004 December 7, 2004 Industrial General Permit From National Water Quality Monitoring Council home page
Industrial General Permit Study Michael K.Stenstrom Haejin Lee Dept. of C&EE, UCLA December 7, 2004 Industrial General Permit From National Water Quality
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
Slide 2
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.
Slide 3
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.
Slide 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)
Slide 5
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
Slide 6
Outline(1) Datasets Relationship between WQ data and industrial
activity - Discriminant analysis - Discriminant analysis -
Unsupervised NN model - Unsupervised NN model - Supervised NN model
- Supervised NN model Possible reasons - Sampling type - Sampling
type - Parameter selection - Parameter selection
Slide 7
Outline(2) Additional study - Seasonal first flush phenomenon -
Seasonal first flush phenomenon - Toxicity result by industrial
type - Toxicity result by industrial type - National GP data -
National GP data Suggested new permit requirements Parameters to
monitor, sampling type, Parameters to monitor, sampling type, when
to sample, and SIC code, when to sample, and SIC code, web-based
data entry? web-based data entry?
Slide 8
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
Connecticut1995-2003 Metals, Toxicity 15 other states 1998-2003
Basic WQ parameter, Metals LADPW monitoring data using composite
samples provided for comparisons
Slide 9
Relative Variability
Slide 10
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.
Slide 11
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 -10-50510 FACTOR(1)
-10 -5 0 5 10 FACTOR(2) 3 2 1 SPECIES Factor is a different linear
combination of four parameters
Slide 12
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 The selected
eight industrial type
Slide 13
Discriminant analysis using LA county GP data
Slide 14
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.
Slide 15
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.
Slide 16
A Kohonen network application 1 2 3 6 8 45 7 9 Increased
-----Similarity----- decreased Activation map having 3*3 neuron In
the Kohonen network Case number per node
Slide 17
Reasons for failure to show relationships Grab sample
variability Parameter selection Unbalanced cases
Slide 18
Observation number of parameters for LA industrial GP during
2000-2001 wet season
Slide 19
Grab Sample Variability: Comparison of grab samples to
composite samples TSS TOCSC 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
Slide 20
Grab Sample Variability: Comparison of grab samples to
composite samples CuPbZn 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
Slide 21
Parameter selection: Distribution of outside benchmark for Los
Angeles industrial GP during 1998-2001 wet seasons
Slide 22
Parameter Selection: Comparison of Los Angeles GP data to
Sacramento and Connecticut GP data
Slide 23
Parameter selection: Urban activity is a major source of
metals
Slide 24
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.
Slide 25
Additional study : Seasonal first flush
Slide 26
Monthly Average Rainfall in USA, 1971-2000 Data source
:http://www.ncdc.noaa.gov/oa/climate/online/ccd/nrmlprcp.html
Slide 27
Additional study: Toxicity result by Industrial type Data
source: Connecticut GP monitoring data 24hrLC 50 = 50% means that
stormwater diluted 1:1 results In 50% mortality in a 24 hr period
Lower LC 50 value means higher acute aquatic toxicity
Slide 28
Additional study: Multi-Sector GP data
Slide 29
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 SidingRoof Siding LeadZincCopper
Slide 30
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
Slide 31
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
Slide 32
Suggestions: Parameters to monitor 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.
Slide 33
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
Slide 34
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
Slide 35
Suggestion : When to sample 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 ~ midway through
the storm
Slide 36
When to sample an example
Slide 37
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.
Slide 38
Suggestion: Different industrial classification? Abandon SIC
codes? New categories tailored to stormwater management?
Slide 39
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
Slide 40
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
Slide 41
Acknowledgements This work was supported in part by a contract
from the Los Angeles Regional Water Quality Control Board
www.seas.ucla.edu/stenstro www.seas.ucla.edu/stenstro
[email protected][email protected]
Slide 42
Thank you
Slide 43
Reference On September 29, 1995, the US EPA issued the
Multi-Sector Storm Water General Permit for discharges of
stormwater associated with Industrial activities.
Slide 44
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
