svEPA
         United States
         Environmental Protection
         Agency
           Office of Research and
           Development
           Washington DC 20460
EPA/600/R-95/130
August 1995
Development of Computer
Supported Information
System Shell for Measuring
Pollution Prevention
Progress

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                                      CONTACT
 Harry Freeman is the EPA contact for this report. He is presently with the newly organized
National Risk Management Research Laboratory's new Sustainable Technology Division in
Cincinnati, OH (formerly the Risk Reduction Engineering Laboratory). The National Risk
Management Research Laboratory is headquartered in Cincinnati, OH, and is now responsible for
research conducted by the Sustainable Technology Division in Cincinnati.

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                                               EPA/600/R-95/130
                                               August  1995
DEVELOPMENT OF COMPUTER SUPPORTED INFORMATION SYSTEM SHELL
          FOR MEASURING POLLUTION PREVENTION PROGRESS
                               Rada Olbina
                            Postdoctoral Fellow
                    Center for Environmental Management
                     Tuft University Medford, MA 02115

                                  and

                             Cynthia Flowers
                             Research Fellow
                    Science/Engineering Education Division
                 Oak Ridge Institute of Science and Education
                         Oak Ridge, TN 37831-0117
                              Project Officer

                              Harry Freeman
                    U.S. Environmental Protection Agency
               National Risk Management Research Laboratory
                           Cincinnati, OH 45268
       NATIONAL RISK MANAGEMENT RESEARCH LABORATORY
               OFFICE OF RESEARCH AND DEVELOPMENT
              U.S. ENVIRONMENTAL PROTECTION AGENCY
                         CINCINNATI, OH 45268
                                               Printed on Recycled Paper

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                                  DISCLAIMER
  The  information  in  this  document  has  been  funded wholly  or  in  part  by  the
United States Environmental Protection Agency.  It has been subjected to peer
and administrative review,  and it has been approved for publication as an EPA
document.  Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.

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                                   FOREWORD
   The U.S.  Environmental  Protection Agency is charged by Congress with
protecting the Nation's land, air,  and water  resources.  Under a mandate of
national environmental laws, the Agency strives to formulate and implement
actions leading to a compatible balance between human activities and the
ability of natural systems to support and nurture life.  To meet this mandate,
EPA's research program is providing data and  technical support for solving
environmental problems today and building a science knowledge base necessary
to manage our ecological resources wisely, understand how pollutants affect
our health, and prevent or reduce environmental risks in the future.

   The National  Risk  Management  Research  Laboratory  is the  Agency's center  for
investigation of technological and management approaches for reducing risks
from threats to human health and the environment. The focus of the
Laboratory's research program is on methods for the prevention and control  of
pollution to air, land, water, and  subsurface resources; protection of water
quality in public water systems; remediation  of contaminated sites and ground
water; and prevention and control of indoor air pollution.  The goal of this
research effort is to catalyze development and implementation of innovative,
cost-effective environmental technologies; develop scientific and engineering
information needed by EPA to support regulatory and policy decisions; and
provide technical support and information transfer to ensure effective
implementation of environmental  regulations and strategies.

   This publication has  been  produced  as  part  of  the  Laboratory's  strategic
long-term research plan.  It is published and made available by EPA's Office
of Research and Development to assist the user community and to link
researchers with their clients.
                         E. Timothy Oppelt, Director
                         National Risk Management Research Laboratory
                                      iii

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                                   ABSTRACT
   Basic  elements and concepts of information  systems  are  presented:
definition of the term "information", main elements of data and database
structure.  The report also deals with the information system and its
underlying theory and design.  Examples of the application of information
systems for solving primarily environmental problems are presented.

   Aspects of  measuring  pollution prevention progress  are  discussed.  The
application of system analysis and the definitions of system inputs and
outputs are analyzed.  Information system applications are considered as tools
for measuring pollution prevention.  Types of pollution prevention measurement
and the usage of normalization as well as financial and management issues are
briefly addressed.

   A  framework for an industrial  production and waste  generation  tracking
system is outlined.  A model of the system is presented and parameters  are
precisely defined.  A cost analysis of the system is carried out and examples
of cost analyses suggested for selection of industrial technologies are
discussed.

   Based  on the tracking model,  an information system  shell  has been  developed
and its application for waste minimization and for measuring pollution
prevention progress at an industrial facility is discussed.

   Finally,  the computer programming of the information  system  shell  using
commercially available database management systems is presented.
                                      iv

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                               TABLE OF CONTENTS
 Disclaimer	              ^
 Foreword	      	iji
 Abstract	-....'	„	                  ^v
 List  of Figures  .	.'.'!!'  ix
 List  of Tables   .	'•••.....!."!'!!.".'!.'!'!!   x
 Acknowledgements	xi

 Introduction	.  .  „	             j

 Conclusions    	  .....                 3


 1.  Identification of Concepts of  Information Systems.    	   4

   Definition of the Term Information 		   4
      Data and Potential Information Storage   .....  	   6
      Specification of the Information Application   	   6
      Main Elements of Database Structure  .	  	   6
            Data Classification .  .	   8
            Data Handling	   9
            Numerical Data Handling	9
                  Numerical Data Recording and Processing ......... 10
                  Evaluation of Numerical Data  	 .... 11

   Definitions of Information  Systems    ...  	 	12
      Information Paradigm	   13
      Systems Theory	! '! .' 14
      Information System Design as Problem Solving  	 18
      Available Information System Building Tools 	 ....... 19
      Information System Objectives and Design  	 ..... 20
References	.  .           27


2. Examples of Information  System Applications.   	 30

   Introduction	   30

   Water Management	    ^   30
      Strategies for Managing  Water Quality 	  	  .  ! !  30
      Management of Water Resources in  the Changing

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            Economic System in Russia	  32
      Groundwater Management  .  .  .  .	32
      Wellhead Protection Areas  and  Management of
            Environmental Resources  .	 ...  32
      Irrigation and Drainage Systems Rehabilitation  	  .....  34
      Water Quality Implications of  Nonpoint Source Pollution 	  34

  Wastewater  Management   	  	  34
      Storm Sewer System Management  	  34
      Wastewater Treatment Process Control   	  35
      Sewer Flows Forecast		35

  Waste Management	.36
      Waste Recycling, Source Reduction  and Disposal  	  36
      Waste Management - Cleanup Activities 	  37
      Site Characterization and  Remediation Activities	  38

  Environmental  Impact Assessment   .... 	 ........  38
      Nonpoint Pollution from Agriculture ..... 	  .  .  38
      Development of Emissions Inventories and Emission Modeling  	  40
      Environmental Impact Assessment for Gold Mining 	  41
      Oil  Spills Accidents  	  42

  Environmental  Management  	  .43
      UNEP Global Environment Monitoring Systems   	  43
      Decision-Making for Environmental  and Natural Resources 	  44
      Environmental Protection and Energy Conservation  	  45
      Environmental Quality and Land Use  .	  45
      Environmental Protection in Germany 	 	  46
      Environmental Information Systems  Inventory  	  46

  Industrial  Process  Control  and  Total  Quality Management   . i 	  47
      Utility Industry Application  . .  	  .....  47
      Development and Implementation of an Information
            System for Process Control at Inco's
            Copper Cliff Smelter Complex  	  48
      Offsites Management Systems in Oil Refineries 	  48
References	52


3. Aspects of Measuring Pollution Prevention Progress 	  55

  Introduction	  55
                                      vr

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      Application of System Analysis	55
      System  Inputs and Outputs Definition   ........  . -.  . -.'.  .  .  .55
      Database, Simulation and Information System Application  	.  . 57
      Pollution Prevention Measurement Types and Normalization   	   57
      Financing 		 58
      .Management Practice	58
References	59


4. Development of a Model of an Industrial Production and Waste
   Generation Tracking System	  .  .	53

   Introduction	  .	63
      Industrial Production and Waste Generation Tracking System  	 63
      Development of IPWGTS Model	64
      Cost Analysis of IPWGTS	.  .... 67

   Cases  of Industrial  Production  and  Waste Generation
   System for  Decision-Making	  . 70
      System Costs for Decision Making	  . 70
References	.		74


5. Development of Information System Shell for
   Measuring Pollution Progress ....	 75

   Introduction	  . 75
      Structure and Function of the  Information System Shell  	 .  . 75
      Use of Information System Shell  ................... 77


6. Computer Programming of the Information System  Shell  .	  .79

   System Overview	79
      Quantitative Analysis	. .  .  .	79
      Cost Analysis .  		 79
   Information System Shell  .......  	  ..... 	 81
      Goals and Requirements	 81
      Implementation	 81
      Data Entry	82
            Data Entry -  Bulk Information  	  ............ 83
                  Data Format	83
                  Calculation with Data Entry .	84

                                     vii

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               Automatic Data  Entry	84
               Existing Data Entry	86
               Module Design 	 86
               Data Entry  - Specific  Information 	 89
               Input Material  Information	89
               Cost Analysis Information 	 89
               Waste Streams	90
Data Storage and  Manipulation   	91
    Data Storage  - Archive  Database  	 91
    Module Design	93
    DM Database Design		   94
    Data Queries   . ,	.95
         Module  Design	  . 95
    User Interface Design 	 98
         Forms and Controls	98
               Overall System   	  100
         Common  User Activities   	  101
    Using the  ISS System	103
    Development of Dynamic  Forms   	  104
         Constant Forms  	  104
         Quantitative Analysis 	  104
         Cost Analysis	105

    Future Work	,	105
                                   vm

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                          LIST OF FIGURES
 Figure  1.   Concepts of information theory and definitions
            of the term "data/information"   	   4

 Figure  2    Physical structure of a database in term
            of logics and semantics	   7

 Figure  3.   Open system concept	15

 Figure  4.   A methodological approach to information
            system design	21

 Figure  5.   Schematic presentation of system analysis
            application to  industrial production process  	  56

 Figure  6.   Industrial production system's inputs and outputs ....  57

 Figure  7.   Industrial production and waste generation
            tracking system	  64

 Figure  8.   Cost analysis of an industrial production
            and waste generation tracking system  	  67

 Figure  9.   Scheme of an information system shell 	 .76

 Figure  10.  Schematic presentation of the ISS databases 	 77

Figure  11.  Control  flow graph of the ISS system	99

Figure  12.  Screen transition  graph - quantitative analysis
            display functions	  100

Figure 13.  Screen transition  graph - cost analysis
            display functions	  100
                                IX

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                          LIST OF TABLES
TABLE 1.    Summary of information system applications  .......  49

TABLE 2.    Definition of IPWGTS model parameters 	  66

TABLE 3.    Costs and revenues of industrial production
            and waste generation tracking system  	  68

TABLE 4.    Summary of calculation requirements and results of
            data entry	86

TABLE 5.    Queries created dynamically by DM database at
            start-up	95

TABLE 6.    Queries created dynamically by DM database
            for cost analysis	96

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                                ACKNOWLEDGMENT
    We thank the U.S. Environmental Protection Agency's National Risk Management
Research Laboratory, Cincinnati, OH for sponsoring the project.

    Special  thanks to our colleagues Harry Freeman, Jim Bridges, Ivars Licis and
to Pollution Prevention Research Branch for their understanding and support.
                                      xi

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                                 INTRODUCTION
    The growing importance of pollution prevention (P2) as an environmental
strategy has increased pressure to develop systems for measuring P2
achievements.  The U.S. Environmental Protection Agency defines P2 as the use
of materials, processes, or practices that reduce or eliminate the creation of
pollutants or waste at the source.  It includes practices that reduce the use
of hazardous materials, energy, water, or other resources and practices that
protect natural resources through conservation or more efficient use.  The
idea underlying the promotion of P2 is that it makes far more sense not to
generate waste than to develop extensive processing/treatment schemes to
insure that the waste poses no threat to the quality of the environment1.   The
Pollution Prevention Act2 charges the U.S.  EPA with responsibility for
developing national P2 goals, and for devising a scheme for measuring national
P2 progress.  Industrial firms are under pressure, in part from the public
disclosure of toxic release information required under Susperfund Amendment
and Reauthorization Act Title III, to practice P2 and to communicate successes
to the public.  All of these activities must occur within a recognized frame
of reference or measurement scheme to be meaningful3.   In the Report to
Congress, Pollution Prevention Strategy4,  the  EPA states that establishing
clear and measurable indicators of progress in P2 serves a number a purposes,
such as helping to focus the efforts of each sector of society, and making it
easier for the public and Congress to understand and track progress in
reducing pollution.  The EPA seeks continually to improve the quality of the
data gathered under the Resource Conservation and Recovery Act and the Toxic
Release Inventory, to improve the effectiveness of both programs in tracking
progress in preventing pollution at industrial facilities.  In addition, work
is being carried out to:

    (i)     develop a more comprehensive database to measure pollution
            prevention in sectors other than manufacturing;

    (ii)    identify the most useful indicators and units for measuring
            pollution prevention at the source;

    (iii)   determine whether the same indicators and units can be used across
            different sectors; and

    (iv)    identify relationships between plant-level  measurements of
            pollution prevention and combined  effects of prevention by
            multiple sectors at regional  and national  levels.

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    The Chemical Manufacturers Association's Pollution Prevention Code of
Management Practices5 states that each company should strive for annual
reductions, recognizing that changes in production rates, new operations, and
other factors might result  in increases.  The goal is to establish a long-
term, substantial downward  trend in the amount of waste generated and
contaminants and pollutants released.  Quantitative reduction goals will be
established that give priority to those pollutants, contaminants and waste of
greatest health and environmental concern.  The Code asks each member company
to establish a P2 program that.includes the following tasks:

        developing a quantitative inventory at each facility of waste
        generated and releases to the air, water, and land, measured or
        estimated at the point of generation or release.

        measuring progress  at each facility in reducing the generation of
        waste and in reducing releases to the air, water, and land, by
        updating the quantitative inventory.

    To implement P2 programs, industrial facilities must first measure the
environmental impacts of their facilities.  The industrial facility should
set up boundaries of the observed system (unit operation, process, facility,
etc.); identify and quantify system parameters (inputs and outputs toxicity
and quantities) and their relationships; then collect, process and evaluate
data, and make cost analyses.  Finally, facility managers will encourage the
implementation of P2 programs that will improve efficiency and reduce impacts
on the environment; and reduce costs of facility operation and maintenance.

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                                 CONCLUSIONS
    The analysis of available literature shows that the number of information
system applications for solving environmental  problems has significantly
increased in the last three years.  However,  these applications are still
concentrating on "end-of-the-pipe treatment"  technologies rather than on the
improvement of industrial  operations responsible for generating environmental
pollution in the first place.  Furthermore,  these applications proved to be
very complex and site specific, and most of them incorporate graphic
presentation of environmental impacts resulting in so called geographic
information systems (GIS)  which require teams  of experts in different fields,
as well as sophisticated software and hardware.  Data collection, processing
and evaluation remain the  biggest challenge in widespread use of information
systems.

    The main aspects in dealing with pollution prevention measurement are
applications of system analyses (including system inputs and outputs
definition), databases, computer simulation and information systems, pollution
prevention measurement types and normalization, financing and management
practice.  However, to introduce pollution prevention and to be able to
measure its progress in any industrial  process that generates waste, the
quantity of waste at the point of its generation and the costs of generating
waste have to be identified.

    Tracking system for quantification  of parameters in industrial  production
and waste generation system is presented based on the basic theoretical
approach for the system understanding.   Cost  analysis of such a system is
carried out.  A scheme of an information system shell for measuring pollution
prevention progress in an  industrial facility  is developed.  Cost analysis is
a part of the information  tracking system.  It is intended to help facility
managers to measure and track quantities of waste generated, managed,
released, processed, recycled and disposed.   Knowing the cost of their
operations, facility managers could recognize  the opportunity to introduce
pollution prevention measures and improve efficiency of their facility.

    The information system shell has been programmed based on the idea of
generality.  It has been designed incorporating the common Windows interface.
It consists of three main  sections: data entry, data storage and manipulation,
and display functions.  The display functions  are created and enabled based on
the input of the user.  In addition, the same  system can be used to study more
than one process; the design of the system is  not based on the characteristics
of the first data sets entered.

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                                    SECTION 1
             IDENTIFICATION  OF .BASIC  CONCEPTS OF INFORMATION SYSTEMS
 DEFINITION OF THE TERM INFORMATION
     The term "information" is defined by two main theories6 -- semiotic
 (information theory of symbols and characters which further includes
 syntactic, semantic and pragmatic theories) and statistic  (information theory
 of signals transmission).  Figure 1 shows schematic presentation of the
 relationship among information theories and the definition of term
 information.
                                                                                DEHNmOKSOF
                                                                               DATA / WFORUATION
 THEORY OF
INFORMATION
          Figure 1. Concepts of information theory  and definitions of
                           the  term "data/information"

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    The terms data and information are not always separately defined with
different meanings; usually they are used as synonyms.  However, in this
report, the following definitions are adopted:  data are sets of messages
which have a certain meaning, while information is a set of data which
receivers use for removal or reduction of uncertainty, as well as the basis
for undertaking definite actions6.

    Data consist of a collection of symbols or characters arranged in some
orderly way to serve as a vehicle for information.  Information is the meaning
derived from data and represents the semantics - the relationship between a
symbol and the actual object or condition that is symbolized.  The impact of
the objects or conditions on the receiver represents the pragmatic level of
information.  It deals with the relationship of information and the user.
Introducing parameters such as value and usefulness of information, this
theory increases the capability for prediction and/or anticipation of future
events.  According to these theories, data/information is defined as follows:

(1)   relationships among characters which respond to certain rules in order
      to provide meaningful information7;

(2)   collections of symbols or characters arranged in some orderly way to
      serve as the vehicle for information8;

(3)   data which have been recorded, classified, organized, related or
 (4)

 (5)


 (6)

 (7)


 (8)

 (9)
      interpreted within the framework so that meaning emerges ;
                                  10
the enlargement of knowledge

the extent to which one's knowledge is enhanced upon receiving the
          .11.
information  ;

communication of facts or instructions11;

the part of a sentence or news, which has news value for the receiver
and which enables problem solving7;

event that reduces the amount of uncertainty of the system10;

numerical quantity that measures uncertainty in the outcome of an
experiment to be performed12;
 (10)   the  amount,  in  the  simplest cases, to be measured  by  the  logarithm of
                                      12.
       the  number of available  choices  ;

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 (11)  the quantity that  is transported between two or more objects or  systems
      when the objective  is not to transport energy, mass, momentum, or charge
      (or any other conserved quantity of physics)
                                                  13
Data and Potential Information Storage
    In the process of applying  information, all relevant information  is not
used simultaneously.  For example, a decision-maker usually does not  gather
all the information necessary to make a decision.  Potential information is
gathered which requires that some form of intermediate storage be used.  Data
covering long periods of time may be necessary to undertake some manipulations
and decisions.  Therefore, data must be stored until needed14.
Specification of the Information Application

    The specification of application is necessary because information has
meaning only when associated with any kind of action to be taken, such as15:

    - decision making,
    - planning,
    - monitoring (as performance or quality control indicator),
    - problem solving,
    - knowledge creation.

    These elements also implicitly include the specification of events to be
observed, and the. time and place of observation14.
Main Elements of Database Structure

    A database,  according to Birplla et al .6 is  a  selected  set of data
relevant to a specific field of application, stored in some common computer
memory and organized in a such a way that its elements (data) may be accessed
by users with simple user-friendly dialogues (Figure 2).

    According to the Mcmillan Dictionary of Information Technology16,  the
database structure is independent of programs that use the data and a common
control approach is employed in adding, deleting or modifying the data.

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     Data  models  specify  how  separate pieces of information are related  in a
database.   Information is broken down  into data structures.   The  most basic  of
these  structures  are  records and fields.   A record can be simply  defined as  a
collection of fields  or  attributes.   At the logical  (file) level,  records are
the basic units  of data  storage.   Fields  are  the  elementary data  items.   A
field  can be  defined  both in terms  of  its:
                                                                   DATABASE
                                                                   The highest unl of the physbal
                                                                   structure of data, which include*
                                                                   bgc and semantic*.
                                                                   RLE Ofl SCOPE

                                                                   The common element of th« logical,
                                                                   semantic, and physical structure of
                                                                   data, which relates to the more
                                                                   specific field of application.
                                                                   DATA BLOCK OR RECORD
                                                                   The specific element of the physical
                                                                   structure of data adapted to the
                                                                   characteristics of the machine
                                                                   processirg the data.
                                                                   DATA SYLLABLE
                                                                   The specific element of the logical
                                                                   and semantic structure of data which
                                                                   represents the data relationship
                                                                   concerring the application of an
                                                                   indvUua) object suitable to user
                                                                   cognition.
                                                                   DATA RELD OR CONCEPT
                                                                   The specifc ebmant of the logical
                                                                   and semantic structure of data
                                                                   which defines particular basic data
                                                                   or concepts.


                                                                   SYMBOL OR BYTE
                                                                   The common element of logical.
                                                                   semantic and physical structure
                                                                   of da!a which idantliet the location
                                                                   in the memory where one symbol
                                                                   can be placed.


                                                                   BIT
                                                                    The simplest unit of the informed ion
                                                                    in the physical structure of the data:
                                                                    an element
                       Figure 2.  Physical  structure of a database
                               in  term of  logics  and  semantics

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        syntax  -- the  information that a field contains which conforms to a
        certain type,  such as real number, an integer, a character or a
        string;

        semantics -- the meaning of information within a fields which
        corresponds to the concept represented by the field.  Thus, if the
        field refers to a quantity of some measurement, the number in that
        field should be a realistic representation of that quantity.

    Although complex data models can be defined simply, in the essence of
every data model lie structures that are equivalent to records and fields,
whether or not they are referred to by those names".

    The approach in the organization and structuring of formatted databases
needs to encompass two contradictory requirements:

    -  user needs or the way users define and use data,

    -  machine (computer) requirements or the way data are memorized in order
      to optimize machine processing.

    On the  basis  of  these  requirements, two different  structures of the same
database may be distinguished:

    -  logical-semantic structure (user aspect) which completely ignores the
      way in which data are physically stored in the computer memory,  giving
      attention only to the meaning and the logic connection dictated  by user
      need;

    -  physical structure (machine aspect) which ignores the meaning of the
      data and takes into consideration only the formal characteristics such
      as data length,  type of symbols or codes, etc.
Data Classification--
    In  order to  reduce  the  large  amount of different types of data to a
manageable order, the classification of data, and formation of general  ideas
about groups of facts  (or concepts) is applied.

    Data generation17 requires four steps  to  be useful  beyond the moment  of
observation.  The steps involve:

    -  classification of data - the basic problem of relating observation to

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      anticipated situations;

   -  establishment of procedures for recording data in a manner that will
      allow retrieval, yet be sufficiently simple to enable the operation to
      be repeated;

   -  summarization of classified and recorded data;

   -  specification of the collection procedure of the system.

   Classification reduces the complexity of the available materials, provides
the basis for identification by grouping similar facts together, provides a
record of experience, and relates classes of events14.

   Three major characteristics of any classification system17 are:

   -  classes must not overlap - i.e., they must be mutually exclusive;

   -  the classification system must be exhaustive - requiring that each item
      be classified and  placed in some distinct category;

   -  the basis for classification must be significant (oriented towards
      specific goals) and in accordance with some previously determined
      pattern.
Data Handling--
   Since data are past-oriented, manipulation is necessary to adapt them into
information relevant to the present and future.   Based on the works of
                            1ft
Johnson, Kast and Rosenzweig  ,  it  can  be  stated that  the  system connecting
the information and its application must be designed to gather relevant facts
and screen out unwanted or unusual  data.  Selected data may become information
for decision making.  However,  it is more likely that additional  processing
is necessary before meaningful information emerges.
Numerical Data Handling--
   Numerical data, as usually understood in physical sciences such as physics
and chemistry, are numerical representations of the magnitudes of various
quantities19.

   Data  in science and technology are usually the result of experiments or
observations carried out by researchers.  In some instances the primary

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objective of the research is to obtain the data, but more frequently the data
are generated for some other purpose (e.g., as a base for the establishment of
legislation and regulations of  a specific field of human activity).  As a
result, some potentially valuable data are not published at all.  It is,
however, desirable that such data be submitted to, and stored in, appropriate
databases to facilitate later utilization
20
   Data handling is taken to include all of the steps of intellectual and
physical manipulation involved in:

   -  recording the results of observation (in the laboratory, pilot-scale
      level, industrial production or field),

   -  interpreting and refining those results,

   -  publishing and disseminating the report, and improving accessibility of
      that report,

   -  re-analyzing and evaluating the results where necessary, and compiling
      them,

   -  delivering the data to a user for final application in solving some
      problems, or in decision making, planning, and in creation of new
      knowledge19.
Numerical Data Recording and Processinq--
   The trend  today  is to obtain data  in digital form, thereby facilitating
the extensive capabilities of modern computers and telecommunication systems.
As computer technology advances, there is a growing tendency toward the
automation and computerization of data, including the handling, collection,
storage, editing, retrieval, and dissemination of data.

   The analysis  and interpretation of  data  is becoming diffused throughout
every part of science and technology.  It includes the need to:

   -  assign  a correctly assessed error to  a single datum derived by
      measurement,

   -  determine  the  best function and  parameters to represent a relationship
      between variables,

   -  detect  and eliminate bias and false-effects introduced by the
                                       10

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      instrumentation or the method of performing an experiment,

   -, test hypotheses and statistical inference.

   An important  function of the analysis of data  is to provide  a good
estimate of quantities on which the observed data depend.  Powerful
statistical methods have been developed to perform this task. Periodically it-
becomes, necessary to evaluate all  the new data available and to ensure that
any adjustments made to data sets are consistent with the operational
characteristics of the system which is subjected to errors of measurement.
Statistics is used to represent the interdependence of the measurement and to
connect the previous data set with new measurements, in order to derive a new
data set of new characteristics.
Evaluation of Numerical Data--
   The critical  evaluation of data refers to those processes  involved in
assuring that retrieved data meet certain standards for accuracy and
dependability.   Kieffer21  states  that  a  short definition  of reliable  data
would be data which are presented with error bars which were chosen so that
the probability  of the 'true' value lying outside of these limits of error is
extremely small.  Branscomb22 suggests that  in  carrying out a  critical
evaluation, a meaningful quantitative statement can be made about the probable
presence of systematic errors in the data.  This statement must be based on a
set of objective criteria for assessing the likely presence and effect of
systematic errors.
   The processes  of data evaluation procedures can be summarized23 as:  (1)
examination and appraisal of the data, and assessment of experimental
techniques with associated errors, (2) re-analysis and recalculation of
derived results,  and (3) selection of 'best' values.

   The proper  assessment of experimental techniques is a fundamental factor
in the evaluation process, involving:  (1) a study of the experimental design,
(2) the way in which instruments have been used,  and (3) the problem of
systematic errors.

   The end result of any evaluation process should be the presentation of
'best' or recommended values together with quantitative estimates of the
uncertainties  in  these values.  The 'best' values derived by the critical
evaluation process are only the best values at the time reviewed. Any
selection of recommended values is subject to change in the light of improved
measurement or evaluation24.
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DEFINITIONS OF INFORMATION SYSTEMS
   There are many definitions of the term information system.  The following
refer to the definitions of a computer-supported information system:

  •   a set of hardware, software, and informational  facilities that permit
      the accumulation, classification, storage and retrieval of a large
      amount of information.  An information system not only stores data, but
      also provides facilities for assigning meaning  to it, and hence,
      provides information. The information system consists of three major
      components:

      -  a large repository for data called a database,
         a means of accessing the data,
         a means of processing the data for analysis and reports25;

 •    any means for communicating knowledge from one  person to another,  such
      as by simple verbal communication,  or by completely computerized methods
      of storing,  searching, and retrieving information9;

 •    systems concerned with collection,  storage,  processing, transmission,
      distribution, retrieval  and utilization of information26;

 •    a system whose goal is to provide information and information services
      for its environment.  This definition implies that an information system
      must encompass at least two subsystems,  one  consisting of the system's
      collection of information,  the other providing  the information
      services27;

 •    an application of the computer that provides for  the routine processing
      of data.  It is made of databases,  application  programs and manuals,  and
      machine procedures.  The databases  store files  (subject of the  system).
      The application programs provide the data entry,  updating,  query and
      report processing of the system.  The manual procedures document how  to
      obtain the necessary data for input into the system and how to
      distribute the system's reports and forms.   The machine  procedures
      instruct the computer how to perform the system's processing activities
      in which the output of one program  is fed as input to the next
      program28;

 •    a system designed to solve a variety of data, information,  and
      knowledge-based problems.   Information systems  provide analytical
                                      12

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      support to users.  They help allocate and evaluate resources, plan and
      simulate large events and processes.  This extremely important
      distinction defines the range of application of modern information
      systems.  Recently their applications have been expanded from data-
      oriented to analytical computing (search, identification,
      classification, categorization, planning, evaluation, prioritization and
      decision-making)29.

 According  to Nichols14, the general  function of information systems is to
determine user needs, to select pertinent data from the infinite variety
available in an'organization's environment (internal and external), to create
information by applying the appropriate tools to the data selected, and to
communicate the generated information to the user.

 A  special  type of  information system  is  the geographic information system
(GIS) which can be defined as an electronic data storage system or database
connected to graphic tools for mapping and illustrating data that they
      .30
31
contain  .   According  to  Bruckman  et  al.   a GIS is a specialized data
management system designed for the entry, analysis, management, and display of
data commonly found on maps.  The GIS is an effective tool for determining the
relationships among demographic, natural resource, land use, and air and water
quality objectives.  For example, a GIS can overlay spatial data of various
types and compute populations affected by air quality, or it can map emission
estimates and land cover for a visual relationship, analyze them spatially,
and report the resulting statistical  relationships.  A GIS can provide maps
and tabular summaries of results (e.g. areas of implementation, results, ratio
of cost to effectiveness) or maps of progress to date compared to
environmental goals.
Information Paradigm

 A possible way to  look at information  systems  is through the so-called
information paradigm27.  In the  information  paradigm  real systems are
distinguish as parts or aspects of reality to be investigated as a whole, in
order to know or eventually control them; information systems describe the
real  systems in the past,  present or future for the purpose of knowing and
controlling.  Information  system functions then include collection, storage,
processing, retrieval, transmission and distribution of data by people and
machines.  An important aspect of the information paradigm is the so-called
recusion principle, which  specifies that the information paradigm also holds
for the sub-systems, i.e., for all real systems and information systems within
the system considered, and that it does so at all levels.  This view has
                                      13

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important consequences for modelling information systems, and designing models
of information systems or real systems.  The information system in this view
must always be considered in combination with the real system of which it is a
representation, and special attention is to be paid to the dynamic behavior of
this combination.

 The system under manipulation  becomes  more  and  more  complex.   Therefore, the
behavior of information systems, and their interaction with the real systems,
becomes harder and harder to predict.  Nevertheless, that is exactly the
intention:  the task is to achieve certain goals in a real system (e.g., just-
in-time delivery  in a transportation chain, or 100% of the airplane-seats sold
without having to reject any customers), to try to develop information
systems, modify the real systems, to define the interfaces between these two,
and to achieve these goals.  In order to understand the objectives, and
subgoals derived  from them, and comprehend the dynamic behavior of both the
current and alternative systems, the discipline of information systems is to
offer new approaches and new techniques.
Systems Theory

  Systems  theory as an approach to studying  organizations  and their behavior
has its roots in physics and biology27.   Its application to the study and
development of  computer-based  systems gained particular importance and
attention with  the development  of cybernetics, defined  as  a  science of
communication and  control concerned especially with comparative study of
automatic control  systems.  Many  papers have been published  describing systems
and their fundamental  properties, characteristics, environments, components,
relationships,  and regulatory  capabilities.

  According  to Nordbotten27, the most basic concept is that of a system,  which
can be initially defined as "a  group of units so combined  as to form a whole
and to operate  in  unison".  A  system is composed of a group  of units.  These
units can be  considered as subsystems.  The system is also a component of some
supersystem.  In addition to the  system under consideration, the supersystem
is composed of  one of more units  (subsystems) that form the  environment for
the system.
                                                          t
  When analyzing a  particular  system,  it  is  necessary  to fix attention on that
system and then to identify27:

      1.  its supersystem,  i.e., that system of which  it is a component  unit,
      2.  the environment with which the  system interacts, and
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      3.  its component subsystems.   i-

   Subsystems 'so combine as to form a whole'.   There must be a system
structure that relates  the  component subsystems  into  an organized whole: the
system.  The system  structure  is defined by the  relationships  that exist among
the subsystems.  The relationships  among the system components define order,
sequence, interdependency,  and time relationships.  The relationships define
the sequence  of actions and activities within the system.  An important task
of a systems analysis  is to identify and evaluate the performance of these
actions.

   The  primary goal  for the system defines  its  reason for  existence,  that  end
toward which the system strives.  Further, when  the subsystems are goal-
fulfilling at their  subgoal level, the system's  goals can be greater than the
sum of the subgoals.  This axiom is central to the interest in determining the
primary goal(s) for  the system before considering the subsystems goals.
Consequently, the system can be defined as a structured, interrelated set of
components (subsystems) whose combined goals act to achieve the primary goal.

   Systems are  of two types:  closed  and  open.   A  closed  system  is  one  that  is
entirely self-sufficient, with no necessary interaction with its environment.
However, information systems exist to provide information services for their
environment and thus can not be considered closed system.   On the contrary, an
open system is one that is dependent on its interaction and relationships,
with its environment.  The primary goals of the  system are defined in relation
to this interaction.  These environmental  interactions or relationships fall
into three classes (Figure 3):

   1)  input,  consisting of stimuli, which can be taken from resources,
      directives, queries and/or information directed toward the system,  and
      which can be used in some fashion by the system;

   2)  output, consisting of the system's reactions to the  input stimuli,  which
      may take the form of results, products,  and/or system state information
      available to the environment.  Outputs are released  to the environment,
      possibly initiating some feedback.

   3)  feedback, consisting of messages and reactions to the outputs  from  the
      system.  The feedback commonly  generates new or modified input to  the
      system, and as such is a special  type of input relation.

   In analyzing an open  system, defining the primary system goal(s), the
system may be considered as a 'black box'  or undefined entity,  and the

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attention can be concentrated on the environment's interactions with the
system.

  The  topic  of  this  text  is  concentrated  on  the  following:

  1)  open systems,
  2)  goal-directed systems,
  3)  the interaction of systems with their environment, and
  4)  a structured set of interrelated subsystems which systems are composed
      of.

  The  lowest-level  subsystems  are  considered to  be the  atomic  elements  if the
system. The atomic elements of a system are either elementary processes or
system objects.   Elementary processes perform some action on the system
objects and can be combined to form the action(s) or task of the subsystem.
             system boundaries
                                  "black box"
                 INPUT
                                  SYSTEM
OUTPUT
                                -\  Feedback \-
                         Figure 3.  Open system concept
   The three basic system components important for understanding  of
information  system concepts are:

       1)  processes,  or actions performed on objects  as  required  by the  system
          goals,

       2)  objects, or things of interest to the system (synonym for the  system
          object  is entity), and

       3)  relationships, which exist between objects  and processes.
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   System processes are the actions of the system.  Systems objects or
entities are of  interest  to the  system, as defined by the  system goals.
are described by characteristics or attributes.   There  are two  types  of
attributes:
They
      1}  descriptive attributes,  whose values give descriptive or identifying
          properties of an individual  object,  and

      2)  associative attributes,  whose values identify a relationship between
          two separate objects.

   System objects can be grouped into  classes, where a class is defined as a
group of  individuals  having some common attribute(s) of  interest  to the
system.

   The third basic system component is the relationship set.  Relationships
exist between system  processes, between system objects,  and between processes
and objects.  Nordbotten27 recognizes  that there  are two  kinds of relationship
relevant  to  information  system concept:

      1)  structural relationships,  which  define  the organization or  structure
          of the system processes  and  objects,  giving groupings and sequences,
          and establishing the place,  within the  whole  system, of each of  the
          subsystems,  and

      2)  communicative relationships,  which define the interactions  between
          the system and its environment and among  the  system's subsystems.

   The structural  relationships  commonly coincide with  the movement of
objects,  such as material resource flow through the system and in this way,
representing the system's external relationships.  Communicative relationships
exist between the system  and its environment and between the  system's
subsystems.  They define  the system's paths for exchange of information in
terms of  the inputs to each unit and the expected or required outputs.  It is
among the communicative relationships that the system goals can be found.
Further,  the set of communicative relationships defines the information
transfer  of the system.   This information flow can be changed over time in
response  to the changes in the external system goals.  Consequently,  the major
task of system analysis is to identify these relationships and evaluate them
with respect to the current or anticipated goal set.
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Information System Design as Problem Solving

  Designing and building of information  systems can be regarded as a process
of solving an ill-structured problem.  In most cases the  information system is
developed to learn about or control the real system.  According to
Nordbqtten27, to solve  a problem of information system design, the process of
creating an information system is divided into the subprocesses of problem
perception, conceptualization, specification,  development of proposed
solutions, solution choice and implementation.

  Developing information systems  and problem  solving are closely related
activities.  In problem solving and, therefore, in information systems design,
conceptualization plays an important role.  When a problem solving case is
looked at, the structure of the decision making process can be used as a
framework.  This process consists of the following steps: (1) intelligence,
and the analysis of possible courses of action, (2) choice, (3) selection of
an alternative course of action from those available,  and (4) implementation,
efficiency and effectiveness of the choice.

  An  information  systems designer tries  to solve  problems  in  the real  system
that is being controlled.  A problem can be thought of as a state in which the
problem owner is in doubt how to  identify one or more desired outcomes.  A
problem is defined as a situation meeting the following conditions:

      (i)   there is a decision maker;

      (ii)  the decision maker has a desired result (a goal);

      (iii) there are two or more  alternatives to achieve that goal.  The
            alternatives have differing efficiencies;

      (iv)  the decision maker is  in doubt as to choose from the alternatives;
            and

      (v)   the environment that  cannot be controlled by the decision maker
            has an influence on the  result of the decision.

   Problems may be considered as  well-structured  or ill-structured.   A  problem
is defined as well-structured when  it meets the following requirements, and as
ill-structured if it fails  to meet one of  the requirements:

      1.    the set of action alternatives  is finite and identifiable;
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       2.
       3.
the solution is consistently derived from a model that shows a
good correspondence; and
                                                             !
the effectiveness or the efficiency of the action alternatives can
be numerically evaluated.
    In problem solving two more types of models,  known as  models as-is and
 models  to-be  can  be  identified.  Analysis  of  the  existing  situation  results  in
 a  model  as-is.  The  construction of  the model  as-is  is achieved by going
 through  the sub-cycle  problem  as perceived -  (conceptual model-empirical
 model),  as often  as  needed to  reach  a degree  of correspondence  that  will
 suffice.  The construction of  models to-be is  achieved by  going through the?
 sub-cycle conceptual model-empirical model  solution  as often as needed to
 reach a  satisfactory degree of validity.             •                     ,.
Available  Information System Building Tools

    In  order  to  structure the ideas  about what  an  information system  should
do, diagraming  techniques were introduced27.   Data-flow  diagrams,  entity-  .
relationship diagrams, activity graphs, and precedence diagrams are some of
the multitude of techniques that were advocated.  Essential in most of these
diagramming techniques is the idea of decomposition:  the aim is to cut up an
information system into manageable and understandable pieces.  These pieces,
or components, may be of a very high level of abstraction.

   One major problem of these diagramming techniques is the need of different
techniques in order to get a somewhat complete view on a particular system.
An additional problem is that the different techniques harmonize badly, which
makes consistency-checks extremely difficult.  A weakness is that they mostly
give only a static representation of the information system under
consideration.

   In the growing interest in information systems design, a great number of
automated tools have appeared on the market to support the design process,
although the first attempts in  this field originate from the 1950s.   New tools
usually support a particular design methodology.  A tool that is able to
create and maintain a dynamic representation of systems  is simulation.   A
number of available simulation  computer languages have a powerful  animation
(picture) function.   In that way they are capable of giving insight  in  the
dynamic aspect of the empirical  model that is being studied.
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Information System Objectives and Design

   Holloway32 stated that the objectives of an information system can be
summarized as follows:

   -  clear communication with the user concerning the objectives and role of
      the information system in the field of application, and commitment to
      apply the appropriate resource once priorities have been set;

   -  a clear sense of direction based on the above objectives and information
      system role;

   -  information system functions organized so as to support growing needs
      such as user computing, data resource control and application
      architecture; and

   -  appropriate levels of skills and resources, as well as adequate
      planning, to meet demands.

   Figure 4  shows a methodological approach to  information system design32:

   -  first level (Level 1) assists in "application/construction" and database
      implementation. If a methodology has only these two components, it can
      be said that it facilitates the efficient translation of an application
      design into the software program having capabilities to integrate
      application design and database design. This suggests another level is
      needed to aid design;

   -  second level  (Level 2) suggests that there is an application design and
      database design, and it can be said that  it facilitates the efficient
      translation of user requirements into a high-capability software program
      connected  with  powerful database management system. This suggests that
      before a design is started, it is necessary to understand user
      requirements;

   -  third  level  (Level 3) helps identify and clarify user requirements; and

   -  fourth level  (Level 4) determines user requirements which consist of
      information systems analysis and conceptual data modelling. Information
      system analysis considers the processes and the data used by the
      processes, by prototyping the requirements with the user.
                                      20

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                                         METHODOLOGY
           LEVEL 4
           LEVEL 3
           LEVEL 2
           LEVEL 1
                                          W FORMATION
                                        RESOURCE PlANffiNO
                                     PRODUCT EFFECTIVENESS
                      Figure 4.  A methodological  approach
                          to information system design
   When a methodology has all four levels, it can be said that it fosters
product effectiveness in  supporting interrelated applications which share
common data.  In modeling a system, its dynamic composition needs to be
described by the way  it accomplishes  work,  not just its static structure.
dynamic composition of a  system can be described in-terms of activities,
processes and events.  This applies to virtually all  systems, especially
information systems,  whose accomplishment  of work depends on the interaction
of computer, software and human activities.   These interactions are analyzed,
designed,  implemented and maintained.   The  following  analysis and design
approaches can provide advantages  to  the system during  information system
development  :
                                                                     The
   1.
   2.
   3.
more realistic  representation  of real  world behavior leading to
improved  information  system analysis  and  design;

inclusion of  important  system  evaluation  measures such as cost,
productivity  and  reliability as  components  of the modeling tool;

more rapid development  of information  system projects by
identification  of feasible alternatives through  simulation;
   4.  improved maintenance of current operating information systems through
                                      21

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       simulation of proposed changes;  and

   5.  personnel and hardware/software  productivity  improvements achieved  by
       extensive sensitivity analysis of important variables.

   The construction of a dynamic model  of a real-world information system
includes the specification of the operational parameters and behavioral
relations of the information system, the assignment of probability or other
distributions to necessary input variables, and the simulation of the
alternative system design.

   The performance-related variables might include the following:

   Costs
        Development
        Implementation
        Operations
            Personnel
            Hardware
            Software
            Supplies, etc.

   Physical  Productivity
        Total  capacity                    .     ,
        Per unit processing time
        Personnel  utilization time
        Hardware utilization time        :
        Database activity rate

   Reliability
        Error rates of hardware, software and personnel
        System downtime rate

   Organizations  confronted with building an information system  usually have
 a  staggering  amount of  information  for  planning,  implementation  and
 development  of this technology.

   According to Flagg34, in general, the form of  an information system follows
 an understanding of the  organization's  functional objectives.  The following
 seven steps  are recognized as  important to the  success  of any  complex
 information  system building.  According to the  author,  the steps follow a
 logical  progression, but the distinction becomes less clear as the information
 system building process  matures.

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 1)   Orientation of Decision-Makers and Staff.   This step involves a formal
     orientation and introduction to an information system,  and includes:

 •  discussion of various information system uses and functions;
 •  implications to an agency's organizational structure;
 •  operation, staffing, cost and personnel requirements;
 •  performance expectations and general implementation time frames;
 •  questions raised by the technology  (legal, political, economic,
   scientific);
 •  relational and object-oriented data model information; and
 «  a hardware/software demonstration.

 2)   Organizational  Assessment.   This step involves identifying existing
     operations,  funding mechanisms,  functions  and  missions,  and includes:

 •  determining the types of graphic-based applications conducted and
   documenting how the graphic information is used;
 •  identifying existing graphics data support personnel;
 •  understanding what, how and how well information flows between sections,
   departments and other agencies;
 •  determining data formats,  scales and media;
 •  identifying how an information system could  assist specific projects and
   programs;
 •  identifying various automation solutions (both software and hardware)
   that may be appropriate;
 •  identifying and considering various  organizational scenarios for
   information system placement,  including possible system designs, service
   levels and staffing;  and
 •  analyzing information system costs and benefits.

3)   Functional Objectives  and Implementation Plan.  This step  involves
     creating and documenting specific  information  system objectives and
     functions, and  includes:

•  establishing automation  priorities and time  frames related to programs
   and projects;
•  developing a plan to  establish and further information system processes
   both internally and externally;
•  designing the general  process  for information system decision-making and
   production;
•  establishing a training  plan;
•  formalizing  data development  and maintenance  agreements with outside
   organizations;  and
                                   23

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•  providing answers to questions such as  how the  agency  will  handle  data
   requests, how the agency will  work with other agencies,  whether the
   agency will  seek to collect revenue,  how legal  issues  will  be resolved,
   who will  have access to the data and how data integrity  will  be
   maintained;

4)  System  Design.  This  step  involves designing the system specifications
    that will  support  an  organization's program and project goals, and
    includes:

•  designing hardware and software environments and schematics;
•  describing required interfaces;
•  developing benchmarks for hardware and  software purchases;
•  establishing a plan and chronology for  procuring necessary  hardware and
   software;
•  describing the user interface;
•  designing information access levels and security; and
•  describing the databases.

5)  Project Requirements.   This  step involves  selecting  a  pilot project
    and  evaluating the data and  funding required  to complete  it,  and
    includes:

 •  identifying project goals and the primary project impetus,  e.g.,
   scientific, political, etc.;
 •  determining data quality objectives, constraints and critical decision
   pathways;
 •  identifying the  information and base themes needed to complete the
   project;
 •  assessing existing  data  formats;
 •  determining who  will be  responsible for the  information;
 •  assessing whether the  information  exists or developing additional
   information;
 •  procuring necessary funding;
   identifying  a  project  team;
   defining the roles  and duties  of  each  team member;
   determining  if extra training  or  outside  help  is needed;
   deciding if additional  hardware or software  is  needed;
 • establishing quality assurance/quality control  procedures; and
 • setting  checkpoints and target dates for  completion.

 6)  Detailed Database Design.  This step  involves the design, development
     and output of the application information, and includes:
                                    24

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7)
      evaluating program/project heeds;
      reviewing available software;
      designing data layers;
      designing and defining  coding schemes, global variables and conventions
      for object names;
      creating data tables and related tables, as well as data and project
      directory structures and documentation for all project-related work;
      defining data characteristics and relationships;
      designing processes for data input and conversion, updates, maintenance
      and archival; and
      adding to data documentation.

       Application  and Development.   This step  involves  the  design,
       development  and output of  the  application  information, and  includes:

      assessing specific  user needs;
      designing and testing macro programs and user-interface menus to most
      efficiently meet project goals;
      developing a user-friendly interface;
      establishing hierarchical  structure charts;
      creating logical data query and  analysis screens;
      reconciliating spatial  and data  inconsistencies for all data layers;
      eliminating redundancy;
      fixing problems;
      reviewing problem  solutions;
      re-evaluating project goals and  accepting feedback form project
      manager(s);
   •  automating and outputting all data sets, including digitizing, entering
      and importing files;
   •  creating and outputting graphic  files;
   •  developing a prototype;
   •  creating test plots and a trial  run of the acceptance test;
   •  checking, adjusting and cleaning outputs using quality assurance/quality
      control procedures;
   •  running the acceptance  test;
   •  documenting and reviewing all procedures;
   •  maintaining all data;
   •  procuring additional funding; and
   •  obtaining, finally, bliss.

   According to Essnik35,  an  information  system is a system that  is  created to
support a larger system  of which it is a component.  That larger system is to
be studied, as seen from a set of specific perspectives, in order to delineate
                                   25

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and structure a variety of informatory and decision making support to a number
of user groups.  The development of information system is, in essence, the
transformation of a model of a part of reality into a model of the target
information system.  For this reason the abstract model is the representation
('what is relevant to be depicted within the information system') and
embedding specification ('who will use the system for what') of the target
information system.  Most of the methods do not make it clear how they
perceive these relationships, although such a. treatment is essential to
understand the way of modeling and the perspectives that are encompassed (or
rather that are disclosed).

   As an effect, three basic 'axioms' are to be incorporated in the
information system development strategy:

   1.   the  information system  is  part of  the observed  system  ('the  component
        paradigm'),  an abstract model of the reality ('the model  paradigm'),
        and  support system  for  the observed system  ('the controlled  system
        paradigm');

   2.   the  information system  can be seen as a  simulation instrument  of the
        observed  system in  order to contribute to the steering of the  behavior
        within the  observed system.  Because the observed  system  itself is a
        dynamic system, the information  system model  should reflect  the
        dynamic properties  of the  observed system;

   3.   the  problem of information system  development is to be seen  as the
        evolving  generation of  a specific  and partial real-world  model, and
        the  transformation  of that model into an information system  model.
        The  generation of the evolving model of  information system is  a cyclic
        process in  which  the information system  is  seen at different levels of
        abstraction.
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'                                REFERENCES

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 2.  Pollution  Prevention Act of 1990  (Enacted by Public Law  101-508,
     November 5, 1990), 1990.  3 pp.

 3.  Measuring  Pollution Prevention  Progress, Proceedings,  EPA/600/R-
     93/151, U.S. EPA, Risk Reduction  Engineering Laboratory,  Office of
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 4.  Pollution  Prevention Strategy,  Report to Congress, U.S.  EPA,  [Part II,
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 5.  Pollution  Prevention Code>of Management Practice, Chemical
     Manufacturers Association (CMA),  1991.  6pp.

 6.  Birolla; H., FeriSak, V., Fischer, D., Kliment, A., Panian,  Z., Rusan,
     I., Srida, V. and Skoro, I.  Basics of Information Science [Osnove
     informatike], Informator, Zagreb, 1989.  398 pp.

 7.  Han, S., Balaban, N.  Basics of Information Theory [Osnovi
     informatike]* Belgrade, 1981.  440 pp.

 8.  Galliers,  R;  Information Analysis - Selected Readings, Addison-Wesley
     Publishing Company, 1987.  285 pp.

 9.  McGraw-Hill Directory of Scientific and Technical Terms.  1984.

 10.  Dobrinic, S., Jurisic,  D.,  Krsmanovic, S.  and Mesic,  D.   Information
     Systems [Informacijski  sistemi], Savremena administracija, Belgrade,
     1982.  300 pp.

 11.  Parasaye, K., Chignell, M., Khoshafian,  S.  and Wong,  H.   Intelligent
     Databases, Object-Oriented, Deductive, Hypermedia Technologies, John
     Wiley & Sons, 1989.  479 pp.

 12.  Webster's Third International  Dictionary.  G&C Merriam Company. 1979.

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13. Rony, P.R.  Microcomputer Architecture in Chemical Research  . In:
    Physical Methods in Chemistry, Volume I. Components of Scientific
    Instruments and applications of Computers to Chemical Research, B.W.
    Rossiter,  J.F. Hamilton, eds. 1986.  pp. 489-686.

14. Nichols, G.E.  On the Nature of Management  Information,  Management
    Accounting, 1985.  159 pp.

15. Jurs, P.C.  Computer Software Application in Chemistry, Wiley-
    Interscience Publication, John Wiley & Sons, 1986.  253 pp.

16. Mcmillan Dictionary of Information Technology, Second Edition, The
    Mcmillan Press, Ltd., 1985.  790  pp.

17. Giese,  1962. In: Information Analysis -  Selected  Readings,   R.
    Galliers, Addison-Wesley  Publishing Co., 1987.  285pp.

18. Johnson, Kast  and Rozednweig,  1963. In:  Information Analysis -
    Selected Readings, R. Galliers, Addis.on-Wesley Publishing Co., 1987.
    285  pp.

19. Rossmassler, S.A.  Presentation of Data  in  the Primary Literature.  In:
    Data Handling  for Science and  Technology -  an Overview and  Sourcebook,
    S.A. Rossmassler, D.G. Watson, eds. North-Holland Publishing Company,
    UNESCO  and CODATA, 1980.  pp.   65-73.

20. Kizawa, M.  Data Generation.  In:  Data  Handling for Science and
    Technology - an Overview  and  Sourcebook,  S.A. Rossmassler,  D.G.
    Watson, eds. North-Holland  Publishing Company, UNESCO  and CODATA,
    1980. pp.  9-21.

21. Kieffer,  L.J.  The Reliability of Property  Data or Whose guess Shall
    We Use?, J. Chem. Doc.  9: 167-74, 1969.

22. Branscomb, L.M.  Is the  Literature Worth Retrieving? Sci. Res. 3(49),
     1968.

23. Watson, D.G.   Compilation and Evaluation of Data   In:  Data Handling
     for Science  and  Technology  -  an  Overview and Sourcebook,  S.A.
     Rossmassler, D.G. Watson, eds. North-Holland Publishing  Company,
     UNESCO  and CODATA,  1980.  pp.   83-93.

24.  Goudsmit,  S.A.  Is  the  Literature Worth Retrieving?,  Phys.  Today,

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    19(6), 1966.

25. Katzan, H., Jr.  Introduction to Computer Science,  Petrocelli/Charter
    Publishers, Inc., 1975.  500 pp.

26. Master of Science Course in Information System Design, A brochure,
    Teessiede Polytechnic, UK, 1995.  5 pp.

27. Nordbotten, J.C.  The Analysis and Design of  Computer-Based
    Information Systems, Houghton Mifflin Company, One  Beacon Street,
    Boston, MA 02108, 1985.  345 pp.

28. Freedman, A.  The Computer Glossary, Fourth Edition, The Computer
    Language Company, Inc., 1989.  540 pp.

29. McGraw-Hill Encyclopedia of Science & Technology, 7th  Edition, McGraw-
    Hill,  Inc., Vol. 9,  1992. pp.  148-57.

30. Valleur, M.   Integrate Offsites Management with  Information  Systems,
    TECHNIP, Paris  France, Hydrocarbon Processing, November, 1993, 63-6.

31. Bruckman, L., Dickson, R.J., Wilkinson, J.G.  and Ivey, W.A.  The Use
    of  GIS Software in the Development of Emissions  Inventories  and
    Emission Modeling. In: Air & Waste Management Association, 84th Annual
    Meeting & Exhibition, Vancouver, British Columbia,  Canada, June 16r21,
    1991.  pp.  91.-91.8.

32. Holloway, S.  Methodology Handbook for  Information  Managers, Gower
    Technical, 1989.  300 pp.

33. Eddins, W.R., Sutherland  II, D.E. and Crosslin,  R.L.   Using  Modeling
    and Simulation  in the Analysis and Design of  Information Systems.   In:
    Dynamic Modeling of  Information Systems, H.G. Sol,  K.M. van  Hee, eds.
    Elsevier Science Publishers B.V.  (North-Holland, 1991.  pp.  61-88.
    1991.

34. Flagg, A.W.   Follow  a Seven-Step  Path to GIS  Nirvana,  GIS World.
    September  1993, 48-9.

35. Essnik, L.  Dynamic  Modeling: An  Example of an  Event-Driven  Approach.
    In:  Dynamic  Modeling of  Information Systems, H.G.  Sol, K.M. van Hee,
    eds.  Elsevier  Science Publishers B.V.  (North-Holland,  1991.  pp.   89-
    119.
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                                   SECTION 2
                  EXAMPLES OF INFORMATION SYSTEM  APPLICATIONS,
INTRODUCTION
   The application of elements of  information science  (databases, simulation,
expert systems artd information systems) as a tool for planning, decision-
making, monitoring and problem solving has steadily grown as more capable and
user-friendly computer software programs become available on the market!

   Below are summaries of available published literature explaining the
application of information systems for solving of environmental and industrial
production problems.  As can be seen, the majority of information systems use
geographical mapping system as an  integral part, so that geographical
information systems (GIS) are predominant in this review.

   In order to discuss the application  of information systems, environmental
protection and industrial process  areas are divided in the following
categories:         r

   -  water management,
   -  wastewater management,
   -  waste management,
   -  environmental impact assessment,
   -  environmental management, and
   -  industrial process control  and total quality management.
WATER MANAGEMENT
Strategies for Managing Water Quality

   Nonpoint source load data generated for a recent project funded by the
Galveston Bay National Estuary Program (GBNEP)  was used to develop strategies
for managing Galveston Bay's water quality1.
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   The project involved defining the drainage areas contributing runoff to
the bay and developing a current land use map for those areas.  A GIS
hydrological model was developed to calculate runoff from the drainage areas,
and a water quality model was designed to calculate the total load of a given
water quality constituent entering the bay.  Nonpoint sources include a wide
array of diffused pollutant types and sources from land drainage, human
activity and major storm water outfalls.  Pollutants include sediments,
nutrients (total  phosphorous and total nitrogen), BOD, oil and grease, heavy
metals, synthetic organics and fecal coliforms.

   ARC/INFO, a GIS software developed by Environmental Systems Research
Institute, Inc.,  served as the fundamental tool for the entire Galveston Bay
nonpoint source assessment.  The CIS allowed researchers to store,  manipulate
and process several hundred megabytes of electronic data required for the
n'onpoint source calculation.  Hydro!ogic and nonpoint source load models were
incorporated into the system so flow and water quality calculations could be
attributed to different geographic regions.

   Three main GIS mapping types were developed:

   1) GIS watershed/subwatershed mapping; 2) GIS soils mapping; and 3) GIS
land use mapping.

   The first dealt with mapping of two drainage delineations: watershed and
subwatershed.  For this study the area was divided into watershed boundaries
which were digitized into a GIS database from maps.  The second, mapping
concerned soil  types within the project area and these were mapped using the
county soil surveys published by the Soil Conservation Service (SCS).  The
third mapping is the product of a land use database which was developed from
interpreted Landsat satellite imagery.

   For GIS nonpoint source modeling purposes, each pixel in the land use
database was associated with a subwatershed and a soil map.  A soil
type/watershed composite polygon map was obtained by overlaying the soils maps
and the subwatershed map layers in the GIS.  The soil  type/subwatershed
composited polygons were transformed to pixels through an ARC/INFO
transformation process.  A software utility was developed to overlay the soil
type/subwatershed pixels and the land use pixels, and to output data
aggregated by the land use category, subwatershed and soil type attributes of
each pixel in the study area.

   A GIS runoff and water quality modeling was tested based on raingage data.
A GIS model for calculating runoff from the study area was developed using the

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Soil Conservation Service's method.  The runoff calculation model was used to
calculate the runoff from the whole basin.  For each land use class, typical
concentrations - Event Mean Concentrations (EMCs) - of each constituent were
estimated from available nonpoint source data.  A nonpoint source load
calculation model also was developed.  The load model requires calculated
runoff volumes and EMC values for each pollution parameter based on land use.

   The project's GIS mapping are expected to  be the  foundation for  future bay
projects that require intensive mapping effort.


Management of Water Resources in the Changing Economic System in Russia

    Belyaeva  et  al.2 describes the cooperation between the Water Problem
Institute of the Russian Academy of Science and the  Tennessee Valley Authority
to develop a joint project demonstrating the  use of  GIS  in managing water
resources under the changing economic system  in Russia.  The purpose of the
project  is to improve decisions by better organizing, analyzing and presenting
water resource data and management options.   Results to  date include
development  of a conceptual approach and review of existing data.   The project
area includes the Upper Volga River Basin which encompasses the Moscow
metropolitan area.  Data are being managed at three  levels.  Initial
conclusions  indicate a great potential  for this technology application, but
many social  and economic obstacles due  to the current political  situation are
also apparent.
 Groundwater Management

    Hall  and Zidar3 explain  the  cost-effective microcomputer  technology for
 groundwater data management,  data  analysis  and  the  delineation  of wellhead
 protection zone.  The data  was  designed  to  be transported to a  UNIX  platform
 and integrated in the GIS.


 Wellhead Protection Areas and Management of Environmental Resources

    According to Rifai et al.4,  the article  exemplifies one of many potential
 links between ground-water models  and GIS.   An  example demonstrates  through a
 GIS user interface for delineating wellhead protection areas (WHPAs) the
 tremendous opportunities presented by GIS for management of  environmental
 resources.
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   The project developed a GIS database and modeling interface to implement
ground-water protection strategies by state arid local  government and
regulatory agencies.  The specific objectives were to:

   -  collect and incorporate the data relevant to developing a wellhead
      protection program from the various federal, state and local agencies
      into  the GIS database;

   -  develop an automated linkage between the GIS database and a ground-water
      model to delineate WHPAs around public water-supply wells for the City
      of Houston;

   -  evaluate the various WHPA model parameters to address the effects of
      parameter input on the size and shape of delineated WHPAs;

   -  show how GIS helps delineate WHPAs; and

   -  develop a prototype ground-water management system {using the
      interactive GIS/modeling capabilities). This management system allows
      area! enlargement, incorporation of more data, or use in other areas
      which are developing a similar ground-water protection program.
                                                  %
   A geographic  information  system promotes efficient and effective
management of ground-water resources,  the GIS database combines data from
numerous sources into one system and spatially relates the data to enhance the
decision-making process of wellhead protection programs.

   The main  advantages  of this type  of  system  are:

    (1)  the data are contained in one*database  and accessed  from one system
        and are therefore easily  transferable;

    (2)  the system can be used by several  governmental  or regulatory agencies,
        promoting data sharing and interaction  between  the different agencies;
    (3)  any change in the public water-supply wells or the sources of
        contamination and land-use patterns can be assessed quickly; and

    (4)  decision-makers are able to spatially conceptualize and easily relate
        all the available data.  All these factors allow for more informed
        decisions regarding ground-water protection and management strategies.
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Irrigation and .Drainage Systems Rehabilitation

   Allen5 presented the application of CIS for solving of water irrigation and
drainage problems.  Topics include the planning and rehabilitation of
irrigation and drainage systems, managing conflicts between irrigation and
drainage systems and urban development, urban-agricultural transfers and
exchanges, and economic and regulatory influences on conservation.  Other uses
include:  managing conflicts between wetland resources and irrigation
projects, developments in surface irrigation, groundwater management,
hydrology and management of intermountain groundwater basins, aquifers,
evaporation ponds, watershed hydrology, and the potential effects of climate
change on water resources.
Water Quality Implications of NonpointSource Pollution

   Heidtke and Auer6 explain the magnitude and water quality implications of
non-point source phosphorus loadings to Owasco Lake in New York, and they use
data supplied by GIS to establish the specific land use, soil texture and
surface slope attributes within each of the hydrologic sub-basins comprising
the overall watershed.  Data are evaluated through the application of a
methodology which links geographic characteristics, long-term average runoff
loads and a set of critical lakewide water quality response parameters.  The
GIS-generated attribute matrices provide a much more accurate depiction of
critical geographic characteristics known to impact nonpoint source runoff
loadings, thereby improving the reliability of current and projected
phosphorus loads to Owasco Lake.
WASTEWATER MANAGEMENT
Storm Sewer System Management

   The City and County of Denver's Wastewater Management Division  (WMD)
realized that an adequate funding base was needed to maintain its
deteriorating storm sewer system7.  A revenue system existed for the sanitary
sewer system but not for the storm sewer system.  City Council approved a
revenue system that required property owners to pay an equitable share for
maintaining the storm sewer system.  A method was needed that reasonably
predicted the amount of water runoff from any given property.  A billing
system was devised that determined a charge by taking the percentage (or
ratio) of impervious area to the total parcel area.  Initially, a group of

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field inspectors measured impervious areas on properties using a hand-held.
wheel.  However, it was estimated that it would take approximately 80 years to
measure the entire city.  In 1986 HMD solicited information from technical
contractors on the development of a computerized information system that uses
aerial photography.                                         ,

   A  system was designed and developed that requires individuals to collect
data on a workstation that provide on-screen digitizing capabilities.  The
workstation uses specialized hardware and software that combines the
technologies of photogrammetry, image processing and GIS.  .The system
initially used the Kork Geographic Information System (KGIS) and an ORACLE
relational database management system. Later the system was converted to
ARC/INFO, a GIS software developed by Environmental Systems Research
Institute, Inc., Redlands, CA, and the workstation was upgraded to Microvax
3800.                                       .   .

   The collection  system operates by converting a  portion of a black-and-
white aerial photography to an image that appears on a high-resolution color
monitor as a 256-level gray-tone image.  The GIS calculates the area of the
features instantaneously, providing ,a basis for determining the ratio of a
particular land parcel bill. The calculations are converted into a
transpprt.able ASCII file and moved across the ETHERNET network for billing.

   WMD is correcting  the GIS databases for future  billing.  The process
includes registering  photographs, modifying line and annotation information,
and verifying data in the billing database. ,
Wastewater Treatment Process Control

   Arnold8 presents process control with consideration of environmental
aspects or so-called "phase model of production".  It is introduced as a
semantic tool  for  a structured visualization of complex processes and
exemplified  in wastewater treatment.  Based on the elaborated information
model, various engineering applications that determine the plant's
instrumentation  or requirements of field communication are illustrated. ,
Prospects for  process control in relation to the environment are presented.
Sewer Flows  Forecast

    Shamsi  and Scheinder9 discuss the applicability,of GIS to help forecast
sewer flows  and  determine whether a watershed has the ability to meet the
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county needs (Allegheny County, PA, USA).  The watershed is expected to
undergo significant development in the future.  The main issue appears to be
the concern about the watershed hydraulic capacity to convey future flows.

   An analysis of the terrain, hydrography, soil associations, land use,
census properties, and locations for major trunk and interceptor sewers was
conducted to develop the watershed GIS.  The software programs used in the
project were primarily ARC/INFO, and ERDAS, an image processing and raster-
based GIS from ERDAS, Inc., Atlanta, GA, USA.  Additional programs were
written whenever needed for data format changes or for creation of a product
for which the methodology was not available in either of the commercial
packages.

   The final GIS analyses were performed on raster information layers.  The
color, raster maps were produced: (1) existing service areas, (2) present land
use, (3) census tracts and (4) soil associations.

   The present land  use map shows that despite the existing developments, a
substantial watershed area is still undeveloped.  Future subareas outlined are
based on the assumption that each parcel of land ultimately will be developed.
The U.S. Environmental Protection Agency's Storm Water Management Model (SWMM)
simulates dry weather (sanitary) flow and wet weather (storm water) flow on
the basis of land use, demographic conditions, the hydraulic conditions  in
the watershed, meteorological inputs and conveyance/treatment
characterizations.   Based on these, SWMM can be used to predict combined
sewage flow quantity and quality values.  SWMM is well suited to this  study
and is the model of  choice for most combined sewer overflow feasibility
studies.  The capacity analysis was performed by comparing the maximum
simulated flows in various sewer segments on the interceptor to their full
flow capacities, defined as the maximum  flows that can be conveyed  by sewers
without surcharging, estimated from sewer size, slope and roughness.
WASTE MANAGEMENT
Haste Recycling. Source  Reduction  and  Disposal

    Portland's  Metro is a regional  government agency that serves three Oregon
counties.  The  agency  is charged with  responding  to a  population of 1.4
million on a variety of  regional and environmental  issues.   In  recent years,
the public's interest  and participation  in  recycling has grown  to challenge
Metro's staff10.  One area directly impacted is the agency's Recycling
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Information Program.  A GIS-based call  response system is one way the district
is managing its response.

   Metro's Recycling Information Program is a regional clearing house for
recycling, waste reduction and disposal information.  The program offers a
telephone hotline, which receives more  than 80,000 calls annually, and
provides information  to businesses, governmental  agencies, schools and the
public.  The typical information requested is the name, location and hours of
a recycling or solid waste disposal facility that handles a specific material;
directions to a facility;  or instructions for preparing recyclable materials.

   Thus, the information has to be retrieved quickly to respond to caller
questions.  Often this involves locating the appropriate facility nearest the
caller from among the area's more than  280 recycling facilities.  This is done
by the help of fully integrated computer system.

   In order to improve the efficiency of its operations, Metro has
incorporated a call response system.  The system developed is based on
ARC/INFO GIS software and is compatible with the Regional Land Information
System (RLIS) used by Metro's Transportation Department.

   Basically, the  system links Topologically Integrated Geographic Encoding
and Referencing (TIGER) files and the base maps of Metro's RLIS with a series
of databases containing recycling information.   The various databases store
information about  individual recyclers and haulers, curbside recycling,
business recycling, yard debris recycling and processing, household hazardous
waste disposal, and general recycling data.

   The system supplies information  for referrals t,o other  government agencies
and community resources, records complaints and referrals, and provides maps
of ZIP code areas  and main streets.  The system allows Metro staff to compile
a variety of reports: monthly reports can be generated by ZIP code, by county,
and by type of request to help review and monitor the program's activities.
Waste Management - Cleanup Activities

   Salzmann11 reported on GIS applications in the environmental field
particularly in waste management.  According to the author there has never
been such a wide-scale effort in subsurface  investigations, from a corner gas
station underground storage tank (UST) removal (tank yank), to Superfund
cleanup efforts across the country.  The environmental  industry also is
changing its purpose, shifting away from Remediation Investigation (RI) and

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moving into actual clean-up operations.

   The role of GIS technology is proportional to the scale of the clean-up
efforts.  For example, a UST job by itself does not call  for a GIS
application.  However, a GIS is essential  if many of these operations occur
within a municipality and the local government needs to monitor cleanup
activities.  Increasingly, the technology is employed to detail where a
responsible party's contamination is located, and how much contamination can
be defined legally on that property.

   Geological software alone could be directly applied to the hazardous waste
business.  A true GIS solution would manage the automated mapping/facilities
management (AM/FM) infrastructure needs of the site's residents; store,
display and model the contaminant; and forecast the contaminant's
transportation.
Site Characterization and Remediation Activities

   Ganter and Cole12 discuss the establishment of a Facility for Information
Management Analysis and Display (FIMAD) to support site characterization and
remediation activities of the Department of Energy's Environmental Restoration
Programs (ER).  An important first step for these programs is establishment of
necessary  infrastructure to support the massive clean-up efforts that will be
required.  FIMAD is designed to support management, research and documentation
of all ER activities, through a series of workstations available to task
leaders.  Within  the workstation environment, GIS and Relational Database
Management System (RDBMS) tools are currently provided.
ENVIRONMENTAL IMPACT ASSESSMENT
Nonpoint Pollution from Agriculture

   According  to  Mertz13,  each year more than 1 billion tons of sediment wash
from agricultural lands into U.S. waterways.  With the runoffs and sediment
come pesticide residues and nitrogen and phosphorus from fertilizers,  further
deteriorating water quality.  Actually, agriculture is one of the biggest
contributors  to  nonpoint pollution.

   Advances  in combining GIS with modeling  capabilities  offer a  powerful,
efficient opportunity to target regions creating the  most  nonpoint pollution.
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This technology is being used in Pennsylvania and the Chesapeake Bay drainage
basin.  Farms located in Pennsylvania's Susquehanna River Basin contribute
nearly one-third of the nitrogen and one-quarter of the phosphorus pouring
into the Bay.  States participating in the federal Chesapeake Bay program are
committed to reducing  nitrogen and phosphorus input 40 percent by the year
2000.

   Researchers working in this program spent a year defining watersheds in
Pennsylvania and identifying the processes and parameters that contribute to
nonpoint pollution.  Ultimately, their GIS-basecl model ranked the nonpoint
pollution potential of 104 watersheds in Pennsylvania, providing a map for
targeting funds.

   The project's one-year time frame forced the researchers to use existing
data.  Seven data layers were scaled and subdivided into 1 hectare grid-cells.
These layers were adapted from a number of existing data sets:

   1.  Watershed boundaries
   2.  Land use data
   3.  Animal densities (N and P were calculated by developing nutrient
       loadings)
   4.  Topography (latitude/longitude)
   5.  Soils data (types and percentages of individual soils)
   6.  Precipitation layers (daily precipitation)
   7.  Rainfall-runoff (22-year average)

   An Agricultural  Pollution  Potential  Index  (APPI)   was  developed  from  the
data layers  to rank the 104 relative nonpoint pollution potential.   Runoff
Index, Chemical Use  Index, Sediment Production  Index,  and Animal  Loading  Index
were developed also.

   The  104 watersheds  were  ranked  according  to  their potential  for  nonpoint
pollution.   Only the pollution potential  of agricultural  lands were  assessed.
Results  showed that watersheds with the greatest  agricultural  pollution
potential  were located in the  southwestern and  northeastern  sections of
Pennsylvania, despite  the fact that agricultural  production  was  conducted on
less than  40 percent of the  land within those watersheds.

   The  GIS-based computer model  is accurate  on  a statewide  basis but it  is
not  reliable if  the  attention  is focused  on  the small,  individual watershed.
Further, the watersheds were  divided  with the highest nonpoint pollution
potential  into  subwatersheds.   Extra  details  provided a clearer picture  of
watershed  nonpoint pollution  potential.   The  GIS-based model  may widely  affect

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management of nonpoint pollution in the entire Chesapeake Bay drainage basin.
The model was also used to evaluate agricultural  nonpoint pollution potential
in the Chesapeake Bay drainage region.

   GIS will remain an essential tool for decision makers.  More and more
local people are starting to realize that CIS is an indispensable tool for
setting priorities and concentrating programs.  GIS-based computer models
provide scientists with pictures,  an important communication tool.  According
to the researchers, one of the real values of GIS is the visual  mapping so
that people can easily see problems.

   An additional layer is already  in the works.  Groundwater information is
being prepared for Pennsylvania.  Other layers that would be incorporated into
the sy:tem include detailed pesticide/chemical use information,  urban
pollution potential, and mining effects.
Development of Emissions Inventories and Emission Modeling

   Many studies have been performed to address the ozone nonattainment
problem in the United States31.  More recently, many  of these  investigations
have relied upon the use of advanced photochemical models.  Pollutant emission
rates are a key input to these models.   Four different types of emissions
estimates are required:

   - base year emission estimates,
   - periodic annual updates to the base year emissions estimates,
   - reasonable further progress projected emission  estimates, and
   - emission estimates for input to photochemical and other models.

   The emission database needed for photochemical modeling  involves the
preparation of spatially and temporally resolved emission estimates.  The
development of an emissions modeling database requires  the spatial management
of large quantities of emissions data and emissions  estimates.  GIS have been
used to develop a more flexible, user-friendly emissions modeling system.

   An emission model is an integrated collection of  equations and other
computational procedures that are encoded for computer-based calculation of
emission estimates.  Emissions data refer to the information, typically stored
in a database and commonly referred to as an emission inventory, that is input
to an emissions model to produce emission estimates, by source category or
source within given classification.
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   An  emission modeling  system  {a group  of  emissions models executed  in ..
specified sequence) further processes the emission estimates and generates
speciated, spatially and temporally resolved values for input to photochemical
and other regional air quality models.

   GIS can be used to facilitate the development, of modeling of emissions
data,  emission estimates and the quality assurance of.these emissions-related
information.  This paper presents several different techniques that illustrate
how GIS can be used to help:

   - develop emission data,
   - process emission data to produce emission estimates, and
   - perform quality assurance  functions on emissions data and estimates.
Environmental Impact Assessment for Gold Mining

   One of the largest gold.mining companies  in the USA,  Independence Mining  ,
Co. (IMC), is looking to expand its exploration and development operations ori
3,000 additional acres in Jerritt Canyon, Elko County, Nevada14.   To develop
an environmental impact statement to substantiate its expansion plans for
rigorous federal requirements, IMC is merging global  positioning system (GPS)
and GIS technologies, and hiring numerous experts in mapping, air and water
quality, soils,  vegetation and wildlife biology.

   According to the author, the IMC is trying to extract minerals in an
environmentally sound manner.  Mill, roads and disposal, site were designed to
minimize the mine's impact on designated threatened species.  The IMC has been
accumulating more data about the impact of its operations.  In cooperation
with U.S. Forest Service, the IMC is working to create a detailed Cumulative
Effects Analysis (CEA) as a tool to try to quantify cumulative effects and to
move away from qualitative assessments.  As a part of EPA's EIS requirements,
CEA will provide a baseline for environmental norms before the mine expansion
and carefully monitor changes in the total environment during,mine operations.
The refined information in IMC's CEA will be entered into a GIS for storage,
analysis and cartographic display.  The transfer of survey data into the GIS
also would be. time-consuming, requiring manual digitizing and separate entry.
Meanwhile, the GIS would be only as useful as the field data supporting it.

   After surveying the options, IMC chose GepLink, a computerized GPS/GIS
mapping system,  to provide the missing link between field'data collection and.
GIS map creation.   The system basically quantifies ecological and other mine
information and  puts it into a GIS.
                                      .41

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011 Spills Accidents

   The applicability of CIS to deal with oil spills in general and
particularly in Florida's most ecologically sensitive habitats and popular
beaches near St. Petersburg is discussed
15
   Florida Department of Environmental Protection (DEP) has been designing a
GIS application to help manage spills; the accident occurring in August 1993
near the mouth of Tampa Bay provided the test of the application's design.

   The  U.S.  Geological Survey  (USGS) topographic maps are  annotated with
Environmental Sensitivity Index  (ESI) for shoreline types, wildlife-resources
areas and strategies.  The ESI ranking of shorelines is critical because it
cartographically indicates the vulnerability of specific shorelines to an oil
spill.

   The  existing  ESI  are  updated  and the  information  integrated  into a  GIS to
facilitate more  frequent updates and real-time analyses.   One of the tasks
also was to  provide  the DEP Office  of Coastal Protection with the  capability
and technical support to facilitate oil-spill contingency  planning, response
and damage-assessment responsibilities.

   A  Florida Marine  Spill Analysis  System (FMSAS) was  initiated.   The
principal goal of  the project  is to design  an application  that  integrates a
variety of  information  (digital  maps,  images and tabular data)  with targeted
analytical  routines  needed to  implement  an  oil-spill response  strategy focused
on resource protection.  Additional requirements are to implement  a selected
set of  these conditions  for  pilot study  area and in  Florida Keys  to develop  a
strategy for expanding  the prototype to  a state-wide,  operational  system.

    The functional  requirements of and basic format  for the FMSAS database
design  were tested.   In  addition, the needs assessment and database  and
application designs  determine  data requirements  and  guide  a thorough  inventory
and evaluation  of coastal data available in Florida.  The  FMSAS prototype is
used  with 10 different  databases (including data on  marinas,  habitats, and
threatened and  endangered  species)  to generate  a resource-at-risk report.
Experience gained from this  and other exercises  are  used  to further refine  the
evolving FMSAS design and  prototype.

    Many data resources were combined to provide maps for  simultaneous
 evaluation and monitoring  aspects of the response  efforts.  To meet those
 requirements, additional data have to be acquired  and  integrated in GIS.  GPS
 receivers are.  used to record locations of the vessels and the changing
                                       42

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perimeter of the spill.  The GPS files are imported immediately into CIS and
incorporated into maps.  The maps includes information such as road networks,
navigational aids and the locations of temporary rescue headquarters.  Scanned
USGS quadrangle images are rectified and used as base maps to provide maximum
annotation quickly.  The various databases and images are combined to create
different maps: those showing the changing locations of spill boundaries and
resources at risk are used by command center and media and field workers,
while those showing information for determining environmental sampling
strategies are used to create response and damage assessment.

   The question of whether GIS can contribute to oil spill management was
answered in the Tampa Bay accident.  The conceptual design and physical
characteristics of the system are further refined.  The plan for further
incremental development prioritizes key databases.  The challenge is to
assemble and automate the data for each region of the state before a spill
occurs there.  DEP is exploring the possibility of cooperative agreements with
other agencies and organizations to foster a collective investment so FMSAS
can be shared and improved without redundant expenditures.  The long term goal
is to continue FMSAS development to provide .greater protection for Florida's
natural resources.
ENVIRONMENTAL MANAGEMENT
UNEP Global Environment Monitoring Systems

   UNEP (United Nations Environmental Programme) has established GEMS (Global
Environment Monitoring Systems) to assess the state, trends and problems of
the environment.  This task requires a well-coordinated, high-quality data
collection program.  The task of assembling,  storing and disseminating data in
geographic form was given to the GRID (Gl.obal Resource Information Database).
Both  GEMS and GRID are elements of Earthwatch,  the environmental  assessment
side of UNEP and UN.  According to Hebin and  Witt16, conventional methods of
data collection, distribution and analysis are not suitable for large-scale
multi/interdisciplinary studies.  So GRID initially undertook a pilot study
during which a variety of activities were performed.  The most prominent were
global data set collection and a series of case studies in the developing
world, as well as the development of a comprehensive environmental  database in
digital form for the African continent.

   GRID'S task-oriented activities and objectives include database
management, GIS/IP (Geographical Information  System/Image Processing)

                                      43

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technology transfer and GRID system development in response to the
requirements of the UN Conference on Environment and Development's "Agenda
21," which called for strengthened GRID to promote decision-making
information.

   UNEP/GRID's long-term goals  are to  facilitate access to all major existing
global and regional environmental databases; to make available to all UN
agencies and intergovernmental  organizations compatible GISs; and to promote
the use of databases and GIS/IP technology in national environmental
assessment and management.

   There  are two  UNEP-funded GRID centers: GRID-Nairobi and  GRID-Geneva.
There are several nationally funded  GRID  centers  (Arendal  for Norway, Tsukuba
for Japan, Warsaw for Poland, then  in  Nepal, Fiji,  and in  Brazil) and some are
in process of being established (Denmark, Germany,  Russia  and Ukraine).   The
idea  is for the whole to form  an interconnected network for data exchange and
data management.  The intention is  to  connect  all  GRID centers with      ,
communication links to  be  able  to transmit data to and from  them.

    To fulfill  GRID'S  task, data are collected from a wide  variety of sources
such  as statistical tables,  satellite  images,  and  digital  data.   The data are
stored  in the computer  systems  after being  ingested,  reformatted and
georeferenced to  a standard coordinate system and projection.   Data processing
 includes  use  of GIS and IP analysis systems  from  many public and private
 sources.

    GRID also uses a wide variety of software packages including ARC/INFO,
 which is  used primarily for digitizing and GIS functionality; NASA's Earth
 Resources Laboratory Applications Software (ELAS) and Land Analysis System
 (LAS);  IBM's Image Analysis Executive (IAX);. ERDAS', image processing software
 ERDAS;  Clark University's IDRISI program for both GIS and IP capability;
 TYDAC's SPANS for modeling  purposes;  and various database management, graphics
 and word processing systems.
 Decision-Making for Environmental  and Natural Resources

    Gracia  and  Hecht17 report on a program prepared to develop an effective
 decision-support  tool for  the  Naval  Undersea Warfare Center  (NUWC) at Keyport,
 Washington and its Environmental  and Natural Resources Division.  The program
 goals are  twofold: (1)  to  improve the data  assimilation  or data  fusion
 function that  must be performed  by the  Center's  environmental managers  through
 use of graphic GIS-based site  characterization;  and  (2)  to  improve the  overall

                                        44

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 environmental data management  process.

    Common to all  hazardous waste reclamation programs is a requirement to
 follow  the CERCLA (Comprehensive Environmental  Response,  Compensation  and
 Liability Act) which  requires  sites  to  be  screened by applying  a  Preliminary
 Assessment and Site Inspection evaluation.   Sites  meeting predefined criteria
 are  subject to a  detailed  Remedial  Investigation/Feasibility Study  (RI/FS)  to
 gather  information to support  a selection  of clean-up alternatives  and cost-
 effective remedial actions.  Authors of NUWC believe GIS  offers a marked
 improvement in the way restoration  is typically conducted and managed.

    Initial project results indicate  that GIS will  provide solutions to the
 complex data management problems associated  with environmental restoration
 programs.   The early  results indicate that the  spatial  analysis and display
 capabilities of GIS can improve the  ability  of  environmental  restoration
 managers .to visualize and  evaluate site  conditions and  the  scope  of their
 sampling  plans and programs.   The research will  continue  to address site
 characterization, data fusion  and data management  functions.  Near-term
 project emphasis  is being  focused on two areas:  (1)  applying  predictive
 modeling  techniques to support  the risk  assessment process  and subsequent
 decision-making activity,  and  (2) integrating GIS  with  other  data systems and
 analytical  tools  to enhance decision-support use.
Environmental Protection and Energy Conservation

   Cassitto   describes the use of information and communication technology as
being increasingly applied to enhance environmental protection and energy
conservation.  Automation and control systems allow energy recovery schemes to
be used in the most effective manner, while intelligent buildings use
computerized information systems to regulate energy use and the internal
environment.  The use of telemanagement systems to monitor and optimize
natural gas supply in cities and water supplies and wastewater treatment is
also described.  Information systems are being applied to air quality
monitoring networks, to improve environmental pollution control, and to manage
traffic problems worldwide.  Efforts to apply communication technology to
improve energy and environmental services in Europe (Italy, Norway, Germany),
Asia (Turkey) and New Zealand are highlighted.
Environmental Quality and Land Use

   An unidentified source from Israel19 describes  the implementation  of

                                      45

-------
policies concerning environmental quality in land use and the application of
Information science.  In the past 15 years, environmental protection has been
achieved through policies the have been incorporated into the land-use
planning system by environmental advisors to national, district, and local
planning authorities, by environmental impact statements (EIS), and through
the use of computer technology.  The EIS system is the formal means by which
environmental considerations are incorporated into planning decisions.  The
EIS is a statutory system which deals with a restricted number of major
development schemes such as airports, industrial plants, and solid waste
disposal sites.

   The development  of the GIS  allows  the analysis of  geographic data  for
environmental purposes.  GIS can be used to identify  areas of  environmental
impact and to choose alternative sites.


Environmental Protection in Germany

    Page20 presents the review of the state of data processing  and applied
informatics  in  environmental protection  in West  Germany  (FRG). The main
results of an empirical  study  of environmental computing by  the FRG's
Environmental Federal Office are cited.  Subject and  technically  oriented
aspects are  discussed,  and  a final  evaluation  of the  development  state of
environmental software  is presented.   The  informatics approaches  discussed are
mainly  non-standard database applications, interactive ecological  simulators,
and  expert system approaches.


Environmental  Information  Systems  Inventory

    Kokoszka21 discusses  an  attempt  to automate environmental  information   in
order to  develop an extensive  network of databases.  The U.S. EPA for example
 has established an Information Systems Inventory (ISI) that consists of more
than 600  EPA (regional  and headquarters) systems, databases and models.  A
 brief description  of the basic application of each database  is included.  The
 databases are arranged for discussion according to the following categories:

        *  Air
        *  Contractors
        *  Emergency Response
        *  Facilities
        *  Hazardous Waste/Solid Waste
        *  Laboratory QA/QC
                                        46

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        * Physical/Chemical  Properties
        * Superfund/CERCLA
        * Toxic Substances/TSCA
        * Toxicity
        * Treatment
        * Water
 INDUSTRIAL PROCESS CONTROL AND TOTAL QUALITY MANAGEMENT
 Utility Industry Application

    Epner and Parmenter22 describe the application of GIS in the utility
 industry.   This industry  has embarked  on  management  and structural
 transformations aimed  at  providing  higher quality  service  at  a competitive
 price to keep old customers  and  win new ones.   Under these conditions,  quality
 management  programs  are fast becoming  the norm in  the  utility industry.   These
 programs come under  a  variety of names, but  total  quality  management (TQM) is
 best  known.

    For utilities, accurate,  up-to-date and easily  accessible  information
 provide the  foundation for delivering  quality  service  to the  customer.   As the
 amount  and the  complexity of information  grows, more and more utilities  are
 converting their information sources to integrated AM/FM/GIS  (Automated
 Mapping/Facilities Management/Geographic  Information System)  databases.
 However, the  critical  need for database accuracy and the high cost  of data
 conversion present enormous  hurdles to overcome before  the  benefits  of
 AM/FM/GIS can be realized.

    The  philosophy behind TQM is  the  following:  by  improving the process  of
 production or service  delivery using well-defined tools, long-term
 improvements  in  quality and  productivity  can go hand-in-hand.  TQM transforms
 the approach to  quality by focusing on process.  According  to  the authors,  80
 percent of quality problems  are designed  into the process and  thus are
management issues, while only 20 percent  are due to individual job
performance.  Thus, 80 percent of quality problems can be eliminated  by
developing a better process,  which at the same time eliminates the inspection
and rework required to correct the problems.

   Without good,  detailed and objective data, one cannot assess process
performance.  With good data, facts replace options,  conflicts can be
minimized, real problems can  be identified and solutions can be monitored.

                                      47

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   Process data may come from a variety of sources.  Computers can provide
data by measuring time or automatically tallying errors found in automated
inspections.  Whatever the data source, management must make the source's use
clear to everyone involved.  First,  there must be a well-defined purpose for
collecting data.  More data are not necessarily better;  they may not show
anything new and they will take time and effort to collect.   Second,
management must reassure team members that the data are for process analysis,
not individual performance evaluation.  To ensure the optimal use of data for
process analysis, data should be kept in computer spreadsheet programs.  An
efficient, accurate database must be in place and operational.  TQM,  applied
from the beginning of AM/FM/GIS development, brings users and conversion
specialists together to create a smarter conversion process.  Using TQM
analysis tools and sharing data and brainpower, this process can be made even
more efficient as it proceeds.
De\
/el opment
jind
Implementation
of an
Information
Svstem
for
Process
Control at
 Inco's Copper  Cliff  Smelter Complex

    In recent years,  Inco has  implemented  major productivity programs  with  the
 purpose of  decreasing  unit costs.  Key  activities  at  the Smelter Complex   have
 dealt with  improvements  in operating  and  maintenance  practices, energy
 conservation,  redefinition of administrative  structures and employee
 involvement.  Metallurgical process control has played an  important role in
 the development of more  efficient  operating practices with high specific
 throughput. This has  permitted a  reduction in the number  of operating  units,
 allowed the removal  of redundant pieces of equipment  and has led to a
 reduction of maintenance and  operating  costs.  Landolt et  al.  discuss the
 strategy  and approach  followed in  the development and implementation  of
 information systems  for process control.   It  includes descriptions of process
 assessment work in the plant, modelling,  implementation  of improved practices,
 and development of information system for operators and  supervisors.


 Offsites  Management Systems in Oil Refineries

    Computerized  offsites management  systems  in  oil refineries offer  a  unique
 opportunity to integrate advanced technology  into a  coherent refinery    ^
 information system that contributes to benefits-driven optimal operations  .
 According  to  Valleur, the information  system  can  improve  oil refineries by
 improving  business opportunities, oil  movement  and advanced  technology, and
 project  scoping  and sizing.
                                       48

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    The following business opportunities  have  been  recognized by applying
 information  systems:

    •   increased complexity of day-to-day refinery operations with more
       products and more complex commercial specifications;

    •   recognition that offsites must not deoptimize what advanced process
       control has generally achieved on process units;

    •   efficient short-term scheduling requiring flexibility on the offsites;
       and

    •   availability of new equipment such as. low cost, reliable on-line
       analyzers and emergence of commercial  software packages to monitor,
       control, and schedule oil  movement operations.

    Advanced  instrumentation and computing technology is used in  conjunction
with information systems to improve:

    •   mixing techniques and tank status,

    •   accuracy and reliability of on-line analyzers, and

    •   expert systems to help operators in selecting options and diagnosing
       incidents.

    Project scoping and sizing the system's engineering effort are difficult
because of their combinational nature and integration requirements with other
systems such as laboratory, oil accounting and scheduling.  Applying
cost/benefit analysis and thorough economic evaluation based on information
system relationships would select the optimal  investment.
             TABLE  1. Summary of  information system applications
No.
1
2
Area of
Application
Water Management
Water Management
Research Objective
Water quality monitoring
Decision-making improvement
IS Type
GIS
hydrogeo logical
model
GIS managing
water resources
Year of
Development
1993
1993
Author(s)
Rifai et al.
Belyaeva et al.
                                      49

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Area of
Application
Water Management
Water Management
Water Management
Water Management
Uastewater
Research Objective
Cost-effective groundwater
data management
Water quality monitoring
Irrigation/ drainage system
management
Water quality monitoring
Cost-effective storm sewer
IS Type
GIS
GIS
GIS
GIS
GIS
Year of
Development
1993
1993
1993
1993
1993
Author (s)
Hall & Zidar
	 . 	
Rifai et al.
Allen
Heidtke & Auer
Smith
Water Management
Wastewater
[ Management j
Wastewater
Management 	
Wastewater
Management 	
1 Waste Management
II 	
Water quality monitoring
Cost-effective storm sewer
system maintenance
Wastewater treatment
process control
Wastewater flow forecast
Recycling information
program
GIS
GIS
IS
GIS
GIS-based call
responsive ,
system
1993
1993
1993
1993
1993
Heidtke & Auer
Smith
Arnold et al. '
F1 — —
Shams i &
Scheinder
Himes ,
10
 15
 16
       Impact
       Impact
       Impact
       Impact
       Assessment

anagement
ianagement
imental
nent
n mental
nnent
nmental
ment
nmental
program
Superfund cleanup
Superfund cleanup
Nonpoint pollution
potential ranking
Ozone no-attainment p
Gold mining environme
impact
Oil spill management
                                                        GIS
                                                        GIS
                                                                          1994
                                                                          1991
                                                        GIS
                                                                          1993
                                                        GIS emission
                                                        model
                                                                          1991
                                                        GIS
                                                                          1993
                                                         GIS
 17    Environmental
                    Global  environmental
                    monitoring  system
                                                         GIS
                                                                                        Salzmann
                                                                                        Ganter & Cole
                                                                                        Mertz
                                                                                        Bruckman
                                                                                         Rodbell

1993
1993

Friel'et al.
Hebin & Witt
 18
  19
  20
Environmental
Management
Environmental
Management
Decision-making  for
environmental  and natural
resources
                                                         GIS
Environmental
Management
Environmental protection
and energy conservation
IS energy
consumption
Environmental quality and
land use
                                                         GIS
                                                                           1993
                                                                           1990
                                                                            1989
                                                                                         Gracia & Hecht
                                                                                          Cassito
                                                                                          Unv.,  Israel
                                                    50

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No.

21

22

23




24




25




Area of
Application
Environmental
Management
Environmental
Management
Industrial
Process Control
and Total
Quality
Management
Industrial
Process Control
and Total
Quality
Management
Industrial
Process Control
and Total
Quality
Management
Research Objective

Environmental protection

Environmental protection

Total quality management
(TQM)



Process control




Oil refineries off-sites
management systems -
benefits driven optimal
operations

IS Type

IS

IS

GI:S




6IS




IS




Year of
Development
1988

1992

1993




1988




1993




Author(s)

Page

Kokoszka

Epner &
Parmenter



Landolt et al.




Valleur




51

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                               REFERENCES

1.  Rifai, H.S., Newell, C.J. and Bedient, P.B. GIS Enhances Water Quality
    Modeling, GIS World, August 1993. pp.  52-5.

2.  Belyaeva, T., Higgins, J.M., Kirpichnikova, N., Lanzova, I. and
    Hagerman, J.R. GIS Application to Water Quality Management in the
    Upper Volga  River Basin: Joint TVA/Russia  Project.  In:  Proceedings of
    the  IAWQ  1st International Conference  on Diffuse  (Nonpoint) Pollution:
    Sources,  Prevention, Impact, Abatement, Water  Science  and Technology,
    Vol. 28,  No.S-5, 1993.  pp.  119-27.

3.  Hall,  P.  and Zidar, M.   Use of Geographic  Information  Systems and
    Models  on Personal  Computer and  Workstation Platforms  in the
    Development  of Wellhead  Protection  Program, Salinas Valley,
    California.   In:   Proceeding of  the Symposium on  Engineering
    Hydrology,  1993.  pp.   377-82.

4.  Rifai,  H.S., Hendricks,  L.A.,  Kilborn, K.  and Bedient, P.B.   A
    Geographic Information System  (GIS) User Interface for Delineating
    Wellhead Protection Areas, Ground Water 31(3): 480-8,  1993.

5.  Allen, R.G.  Management of Irrigation and Drainage Systems: Integrated
     Perspective. ASCE.  1993.  1204 pp.

6.   Heidtke T.M. and Auer, M.T.  Application of CIS-Based Nonpoint Source
     Nutrient Loading Model for Assessment of Land Development Scenarios
     and Water Quality in Owasco Lake, New York, Water Science Technology,
     28(3-5):595-604, 1993.

 7.   Smith, L.   Wastewater Management Project  Provides Timely Revenue, GIS
     World, August 1993. pp.  38-40.

 8.   Arnold,  M., Ingendahl,  N. and Polke,  M.   Process Control with
     Consideration of Environmental  Aspects,  (Techno!ogiepark Herzogenrath,
     Rheinisch-Westfaal. Tech. Hochsch., D-52134  Aachen, Germany). CherrL.
     Tech.  (Leipzig), 45(5):363-74,  1993.

 9.  Shamsi,  U.M. and Schneider, A.A.   GIS Forecasts  Sewer Flows, GJS
     World, March 1993.  pp.   60-4.

  10. Himes,  D.O. and Fadhl,  M.   GIS  Facilitates Regional Recycling  Efforts,
     GIS World,  September  1993.  pp.  37-9.

                                     52

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 11.  Salzmann,  A.   Environmental  Industry Needs Hybrid CIS,  GIS World.
     February 1993.  pp.   18.

 12.  Ganter,  J.H.  and Cole,  G.L.   Geo-Based Integrated Data  Analysis for
     Environmental  Restoration Programs,  Cartography and GIS/LIS Tech Pap.
     In:   91  ACSM  ASPRS  Annu  Conv Technical Papers - ACSM-ASPRS Annual
     Convention, Vol.2.,  1991. pp.   122.

 13.  Mertz, T.  GIS  Targets Agricultural  Nonpoint Pollution, GIS World.
     April  1993. pp.   41-6.

 14.  Rodbell, S. GPS/GIS Mapping  Accurately Assesses Environmental  Impact,
     GIS  World. December 1993. pp.   54-6.

 15.  Friel, C., Leary, T., Norris,  H.,  Warford,  R. and Sargent,  B.   GIS
     Tackles  Oil Spill in Tampa Bay,  GIS  World.  November 1993.  pp.   30-3.

 16.  Hebin, G.  Witt,  P.V. Global  Environment  Monitoring System (GEMS),
     United Nations  Environmental  Programme,  1993. pp.  34.

 17.  Gracia,  J.M.  and Hecht,  L.G.,  Jr.  GIS Improve Visualization,
     Evaluation Capabilities  in Superfund  Cleanup, GIS World.  February
     1993. pp.  37-41.

 18.  Cassitto,  L.  Use of Information and  Communication Technologies in  the
     Field of Environment and Energy  Conservation, OECD/et all  Cities &  New
     Technology Conference, November  1990.  pp.   201-18.

 19.  Unv.  Environmental  Planning,Israel  Environment B, 12(4):2-8,  1989.

20.  Page, B.   Environmental  Computing  Status  and Research Perspectives,
     Computer Techniques  in Environmental  Studies.  In: '88  2nd
     International Conference,  1988.  pp.   597-608.

21.  Kokoszka,  L.C.   The  Guide to  Federal  Environmental .Databases,
     Pollution  Engineering, 24(3),  1992. pp.   8.

22.  Epner, M,., Parmenter, B.,  1993,  Competitive  Utility Environments
     Require Total Quality Management Techniques,  GIS World, March  1993.
     pp.  38-41.

23.  Landolt, C.A., Steenburgh, W.M.  and Hloang, T.  Development  of  a
     Process Control  and  Information  System at  Inco's Copper Smelter

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    Complex, Copper '87, (Univ. Chile, Fac. Cienc. Fis. Mat., Santiago,
    Chile), Vol. 4, 1988. pp.  603-23.

24. Valleur, M. Integrate Offsites Management with Information Systems,
    TECHNIP, Paris, France, Hydrocarbon Processing, November 1993, pp.
    63-6.
                                     54

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                                   SECTION 3
              ASPECTS OF MEASURING POLLUTION PREVENTION PROGRESS
 INTRODUCTION
   Measuring pollution prevention  (P2) is'a significant  challenge.   The
 difficulties  are  conceptual--how do  you  measure waste that has not yet been
 created  and practical--what  units to use,  and  whether to track money saved or
 waste  avoided?  Another  question involves  the  use of measurement data: is it
 desirable  or  even possible to  compare P2 results of different companies  and
 different  industries ?

   Further, why should P2 be measured?  How should  accomplishments  be
 measured?  How can  pollution prevention  measures be incorporated into  the
 larger environmental data reporting  scene?  Are we trying to  measure overall
 national progress in reducing  waste  or merely  local  progress  in reducing
 discharges to local air, water,  and  landfills;  to measure the physical amount
 of waste reduced  or reductions in  toxicity and  other adverse  environmental
 effects; to measure the  efficiency of a  single  industrial  plant or to  be able
 to compare across plants, products,  or economic sectors2?

   An analysis of available literature2'3'4'5'6'7'8'9-10 shows that the main
 aspects in dealing  with  P2 measurement as summarized  by  researchers  from
 industry, academia  and R&D institutions  are:

   (1)    application of system analysis;
   (2)    system inputs and outputs definition,
   (3)    database, simulation and  information system  application;
   (4)    pollution prevention measurement types and normalization application;
   (5)    financing; and
   (6)    management practice.
(1) Application of System Analysis

   Research  carried out  in  industry  has  shown  that efforts are concentrated on
measuring releases before and after some waste and/or wastewater minimization
actions have been applied.   Thus, system analysis is a commonly recognized

                                      55

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technique in dealing with industrial production systems (Figure 5).  Detailed
process diagrams10,  a simple  block flow diagram11 or process flow diagrams  •
are applied to analyze the system under consideration.  Boundaries (unit
operation10,  product unit  definition12 or facility boundaries14)  of  this
dynamic system14  are determined  ("black box"8 approach).  Furthermore, time of
system observation  is considered.  Mass balance12'13'15'16 appears to be the
usual approach in quantifying system parameter relationships.


jjstta bounds
(unit optralioH, p
MASS IN
MASS
"itact box"

"*" , dynamic tjitim
roetss) ' '
FLOW DIAGRAM ASD
MATERIAL BALANCE
Industrial ,
Production
' Process" _':
MASS OUT

5 IN = MASS OUT
\
\
\
\
\
\
Timeframe '
 Figure 5. Schematic presentation of system analysis application to  industrial
                               production process
     System Inputs  and  Outputs Definition

   System  inputs17  (raw and other materials)  entering into the industrial
 production processes as well  as system outputs17  (products,  by-products  ,
 process losses10, waste16,  emissions7,  discharges8, nonproduct outputs  ,
 releases18) are in most cases precisely  identified and quantified1 .  The terms
 describing products  and by-products are commonly used and their meaning widely
 understood.  However,  it seems there are many different definitions concerning
 the amount that is generated, lost, emitted, and/or released into the
 environment,  as shown  in Figure 6. It appears that considerable work on
 standardization of these terms is needed to clarify precise meanings.
                                       56

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INPUTS
' OUTPUTS
1
1

RAW . . .^ plj
MATERIALS r fl


OTHER
MATERIALS

S'DUSTRIAL 1
PROCESS j
1
|
1
1
1
t


->


PRODUCTS

BY-PRODUCTS



PROCESS
LOSSES,
WASTE,
EMISSION'S,
DISCHARGES,
NONPRODUCT
OUTPUTS,
RELEASES
(TO AIR, WATER,
LAND)
          Figure 6. Industrial production system's  inputs  and  outputs
 (3)  Database.  Simulation  and  Information  System Application

   Computer-supported databases15'16'19'20 are used to organize and process data
 on inputs- and  outputs  that  characterize any industrial  production system.
 These databases  allow  establishment  of tracking matrix19 and/or loss tracking
 system1 for bill of materials, shop production, and product routing15.   A
 computerized information  system  is applied  to  provide  material  accounting22,
 inventory  control  (for purchasing minimal quantities  of raw materials23) as
 well as computerized integrated  manufacturing  (CIM)21.  Computer-supported
 simulation   is used for direct observation of system behavior under a variety
 of conditions, while optimization24 has proven to be a powerful tool for the
 scheduling of  resources such  that the  overall  efficiency of the  system is
 achieved.
(41 P2 Measurement Types and Normalization

   According  to some  theoretical  research12'15'15'16 a material balance is the
most commonly used approach for measuring P2 progress  in  industry.   Terms such
as material accounting system10'13, inventory of production and emissions6,
purchase records, material inventory, product records  and specifications'"
22
   are
                                      57

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also being used.  Some companies have developed tracking matrices19,  while
others use input, by-product and emission reduction indexes12,  as well  as so
called point loss measurement.  Normalization of data to present more
meaningful P2 measurement has been used in many different ways.  Some
practical application showed industries compare data on system inputs  and
outputs with previous year data and adjust for production5, while others use
material use efficiency expressed in percentage or economic efficiency21.
Absolute and normalized pollution levels25,  as well  as  absolute and normalized
pollution reduction25 are also being  used  in  practice.   Independent
variables12,  regression  theory12, and autocorrelation12  approaches are applied.
The quantity of waste generated is presented  per unit of product22.
Production index21  and normalization  per unit of product at each step of
production15,  is used.
(5) Financing

   The financial aspects of P2 are considered to be very important7'17'21'22.
Cost  analysis22 serves as a basis for P2 project selection in industry.
Activity based  costs  (ABC)23 and/or economic feasibility analysis7 are
applied.   In industry,  the  placement  of environmental  costs  are recommended to
be made into separate accounts26 to serve as a powerful tool  for resource use
optimization.
    Management  Practice
                                                            .21
                                                                         20
                                                                         27
   Total  Quality Management-- zero losses or 100% efficiency"'  --is  only  a
different term for the  introduction of  P2 practice  into  industrial  production
based on material balance.   So called Total  Quality Management Structure
and/or Total Quality Management  Program (TQMP)  are  also  being  introduced'
Measuring effectiveness7 by the ratio of waste reduction and production  is
also being used.  Important  considerations  for  industrial  management
concerning introduction of P2 measurement are time  frame28, costs28,
priorities28,  and implementation period29.   Material accounting could  therefore
be considered as a standard  management  procedure  for  P2  measurement.
                                       58

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                                REFERENCES
1.    Freeman, H., Harten, T.,  Springer, J., Randall, P., Curran, M.A.,
      Stone, K.  Industrial Pollution  Prevention: A Critical  Review,
      Pollution  Prevention Research Branch, Risk Reduction  Engineering
      Laboratory, U.S.  EPA. J.  Air Waste Manage. Assoc. 42(5):  618-56,
      1992.

2.    Rappaport, A., Management Tools to Support Pollution  Prevention, A
      Project Proposal,  Tufts University, Center for Environmental
      Management, Boston MA and U.S.  EPA, Risk Reduction  Engineering
      Laboratory, Cincinnati, OH, 1992.  10 pp.

3.    Measuring  Pollution Prevention  Progress, Proceedings,  EPA/600/R-
      93/151, U.S. EPA,  Office  of Research and development,  Risk  Reduction
      Engineering Laboratory, Cincinnati, OH 45268, 1993.   97 pp.

4.    Evaluation of Measures Used to  Assess Progress in Pollution
      Prevention, U.S.  EPA, Pollution Prevention Office,  Pollution
      Prevention Division, Washington, D.C., Center for   Economic Research,
      Research Triangle Institute, Research Triangle Park,  NC,  1990.  52
      pp.

5.    Craig, J., Baker,  R.D., Warren, J.L., Evaluation of Measures,Used  to
      Assess Pollution   Prevention Progress in Industrial Sector, Final
      Report, EPA Contract No.  68-W8-0038, RTI Project No.  233U-4633-1FR,
      1991.  58  pp.

6.    Harper, P.O., Application of Systems to Measure Pollution Prevention,
      Pollution  Prevention Review, 145-53, 1991.

7.    Raftelis,  G.A.,  Financial and Accounting Measures as  Part of
      Pollution  Prevention Assessment, Environmental Finance, 129-50, 1991.

8.    Willis,  D.G.,  Pollution  Prevention Plans  -- A  Practical Approach,
      Pollution  Prevention Review, 347-55, 1991.

9.    Facility Pollution Prevention Guide, EPA/600/R-92/088,  U.S. EPA, Office
      of Research and  Development, Washington, D.C. 20460,  1992.  143  pp.

10.   Pojasek,  R.B.,  Cali,  L.J.,  Measuring Pollution Prevention,  Pollution
      Prevention Review, 1991.  pp.  119-29.
                                    59

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11.   Gemar,  C.,  How to  Conduct a  Waste Minimization  Study?,  Pollution
      Prevention Review,  1992. pp.  277-86.

12.   Greiner,  T., Measuring  Toxic  Use  Reduction  at the  Production-Unit
      Level,  Massachusetts Office of Technical Assistance.   In:   Measuring
      Pollution Prevention Progress,  Proceedings, EPA/600/R-93/151, U.S. EPA,
      Risk   Reduction  Engineering  Laboratory,  Office  of   Research  and
      Development,  Cincinnati, OH 45268,  1993.   pp.  79.

13.   Hearne,  S.A., Aucott, M.,   Source Reduction versus  Release Reduction:
      Why the TRI  Cannot Measure  Pollution Prevention?, Pollution Prevention
      Review,  Winter  1991-92.  pp.   3-17.

14.   Hirschhorn,   J.S.,   Waste  Reduction  Measurement   Project   at  IBM,
      Hirschhorn & Associates,  Inc.    In:   Measuring  Pollution Prevention
      Progress,  Proceedings,  EPA/600/R-93/151,  U.S.   EPA,  Risk  Reduction
      Engineering  Laboratory, Office of Research and Development, Cincinnati,
      OH 45268,  1993.  pp.  29-30.

15.   Saminathan,   M.,  Sekutoeski, J.,  Williams,  G., The  Use of  Systems
      Analysis  for   Measuring   Pollution   Prevention  Progress   in  the
      Manufacturing of Electronic Components.   In:   Proceeding of the IEEE
       International Symposium on  Electronics and the Environment, Arlington,
      VA, May 10-12,  1993. pp  1-6.

16.    Roberts, N., Andersen, D.,  Deal, R., Caret,  M.,  Shaffer, W.  Computer
       Simulation,  Addison-Wesley Publishing Company, 1983.  562 pp.

17.    Consoli, F.J.,  Life-Cycle Assessment (LCA):  Current Perspectives,
       Presentation, SCOTT Paper Company.   In: Southern States Conference on
       Hazardous Waste Minimization,  Pollution Prevention and Environmental
       Regulations Mississippi, Coast Coliseum Center,  Biloxi, MS,  September
       22-24, 1992.

18.    Industrial Pollution Prevention Opportunities for the 1990s, EPA
       600/8-91/052, U.S.  EPA, Office of Research and Development,
       Washington, D.C. 20460, 1991.  60 pp.

19.    Price, R.,  Implementing and Tracking Waste Reduction Progress at
       DuPont, Waste Reduction Programs, DuPont Corporation  In: Measuring
       Pollution Prevention Progress, Proceedings, EPA/600/R-93/151, U.S.
       EPA, Risk Reduction  Engineering Laboratory, Office of Research  and
       Development, Cincinnati, OH 45268,  1993. pp.  52-3.

                                     60

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20.   Zosel, T.,  3M  Corporation's  Pollution  Prevention Measurement  Program,
      3M Corporation.   In: Measuring  Pollution  Prevention Progress,
      Proceedings, EPA/600/R-93/151,  U.S.  EPA,  Risk Reduction  Engineering
      Laboratory, Office  of  Research  and Development, Cincinnati, OH 45268,
      1993. pp.   64-70.

21.   Pojasek, R.B., Production  Efficiency as a Measure of  Pollution
      Prevention, GEI Associates.   In: Measuring Pollution  Prevention
      Progress, Proceedings,  EPA/600/R-93/151,  U.S. EPA, Risk  Reduction
      Engineering Laboratory, Office  of Research and Development,
      Cincinnati, OH 45268,  1993.  pp.  50-1.

22.   Hawes, T.,  Implementing Pollution Prevention Measurement:  Practical
      Consideration, Polaroid Corporation  In:  Measuring Pollution
      Prevention  Progress, Proceedings, EPA/600/R-93/151, U.S.  EPA, Risk
      Reduction Engineering  Laboratory, Office  of Research  and Development,
      Cincinnati, OH 45268,  1993.  pp.  23-6.

23.   Nagle, G.,  Pollution Prevention Systems and Issues at Bristol-Myers
      Squibb Corporation, Bristol-Myers Squibb  Corporation   In:  Measuring
      Pollution Prevention Progress,  Proceedings, EPA/600/R-93/151, U.S.
      EPA, Risk Reduction Engineering Laboratory, Office of Research and
      Development, Cincinnati, OH  45268, 1993.  pp.  49.

24.   Ciric, A.R., A Proposal for  Synthesizing  Waste Minimal Chemical
      Processes Using Multi-Objective Optimization Methods,  Department of
      Chemical Engineering,  University of  Cincinnati, Cincinnati, OH 45221-
      0171, 1991.  17 pp.

25.   Karam, J.G., Craig, J.W.,  Curry, G.W., Targeting Pollution Prevention
      Opportunities  Using the Toxics  Release Inventory, Pollution
      Prevention  Review,  1991. pp.  131-43.

26.   Lizzote, R.P., Financial Planning in Pollution Prevention,  Texas
      Instruments Incorporated.  In:  Measuring Pollution Prevention
      Progress, Proceedings,  EPA/600/R-93/151,  U.S. EPA, Risk  Reduction
      Engineering Laboratory, Office  of Research and Development,
      Cincinnati, OH 45268,  1993.  pp.  33-4.

27.   Mannion, D., Integrating Source Reduction with Total  Quality
      Management, Foxboro Corporation. In: Measuring Pollution Prevention
      Progress, Proceedings,  EPA/600/R-93/151,  U.S. EPA, Risk  Reduction
      Engineering Laboratory, Office  of Research and Development,
                                    61

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      Cincinnati, OH 45268, 1993. pp.  35-46.

28.   McEntee, D., Pollution  Prevention Assessment  at  Simpson Tacoma Kraft,
      Simpson Tacoma Kraft.   In: Measuring Pollution Prevention Progress,
      Proceedings, EPA/600/R-93/151, U.S. EPA,  Risk Reduction Engineering
      Laboratory, Office of Research and Development,  Cincinnati, OH 45268,
      1993. pp.  47-8.
                                     62

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                                   SECTION  4
            DEVELOPMENT OF  A MODEL  OF AN  INDUSTRIAL  PRODUCTION  AND
                   WASTE GENERATION TRACKING SYSTEM  (IPWGTS)
INTRODUCTION
   Waste  characteristics  and  quantities  of waste generated,  managed  and/or
released in the environment in'a given time have to be determined to implement
waste minimization and/or pollution prevention measures in any industrial
production process generating waste.  Cost analysis of such a system could
enable one to estimate waste generation and management costs and could help
production managers to evaluate the system and to introduce measures to reduce
or minimize waste at the point of its generation,  or to prevent release of
pollutants into the environment.


Industrial Production and Waste Generation Tracking System

   A  framework  for  the determination  of  the main parameters  of  an  industrial
production and waste generation tracking system1'2 was defined (Figure 7). It
is based on the following main production process  variables:

         1)  raw materials (rm),
         2)  other materials entering production process (v),
         3)  produced products (P),
         4)  generated waste (y).

   This generated waste may  be:

         a)  managed (g)  by applying waste management,
         b)  released (z)  into the environment,  causing environmental
            pollution.

   Managed waste  (g)  may  be  further  processed to be:

         c)  used as secondary raw material  and/or  energy (s),
         d)  finally disposed of as  processed  waste (residues)  in a special  (or
            secure) landfill  (d).
                                      63

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                                        = Ur
RAW AND
OTHER
MATERIALS


PRODUCTION
PROCESS
  r = rm + v
                                      y =
                              SECONDARY
                                 RAW
                               MATERIALS
                             AND/OR ENERGY
                             REUSE AND/OR
                               RECOVERY
                             s = R M (1-U) r
                                                    PRODUCTS
                                                      WASTE
                                                    GENERATION
                     RELEASE
                     (EMISSION,
                    DISCHARGE
                   AND/OR SPILL),
   WASTE
MANAGEMENT
g = M (1-U) r
                                                     PROCESSED
                                                      WASTE
                                                       FINAL
                                                     DISPOSAL
              d = M (1-R) (1-U) r
     Figure 7. Industrial production and waste generation tracking system
Development of IPWGTS Model


   Based On the works of Baetz et al.3,  a  model,  enabling calculation of
quantities of waste generated in an industrial production was developed  and
defined in Table 2.

   During an industrial  process at time t, a production factor U, correlating
quantity of raw materials r  (r - rm + v, where v represents  "other  materials"
not defined as raw materials entering production process.  An analysis  of
large data set on raw materials consumed, products  produced  and  waste
generated in different  industrial production  sites* enabled  recognition  of
additional parameters including "other  materials" entering production in time
t. For example, paints  and lacquers in  "white goods"  and furniture  production
- those materials are usually not defined as  raw materials)  and  products (P)
has the value 0 < U  £ 1  is defined as:


            U • P/r

   The quantity of products then may be expressed as:
                                       64

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            P « U r

while quantity of waste generated is

           • y - (1 - U) r.

   Managed waste is further quantified by a waste management factor M which is
defined as the ratio between waste managed and waste generated:

            M - g/y

and can have a value   0 * M * 1. The quantity of waste managed by storage,
collection, transportation, processing and final disposal is then

            g * M  (1 - U) r

while the quantity of waste released  (emitted, discharged and/or spilled)  into
the environment is

            z « (1 - M) (1 - U) r

   If waste is further processed by physical, chemical, thermal and/or
biological processing to recover secondary raw materials and/or energy,   then
processed  waste  is  determined by  the waste  recycling  factor  R:

            R.- s/g

having  the value  0 < R <,  1. The quantity of waste  recycled  into secondary raw
materials  and/or  energy by waste processing  is

            s « M R  (1 -  U) r

while  the  quantity of waste to  be  finally disposed is

            d « M (1 - R)  (1  -  U)  r.

   Knowing quantities of raw and other materials  (rm +  v) entering  the
 observed system and  quantities  of  products (P)  produced,  quantities of waste
 generated (y)  could  be  calculated.   If the quantity of waste managed (g), by
 the  waste generator  is  known,  it  is possible to predict quantities of material
 lost (z) through  release  (emission, discharge,  and/or spill).   Finally, if the
 waste  generator recycles  managed waste into secondary raw materials and/or
 energy (s),  then  the quantity of waste to be disposed (d)  can be determined.

                                       65

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TABLE 2. Definition of IPWGTS model parameters
Parameter
t
rm
V
r « rm + v
U - P/r
P - U r
y - (1-U) r
H *
g/y - g/d-u) r
g « H (1-U) r
z
R -
s
d •
- (l-M) (1-U) r
s/g - s/M (1-U) r
-MR (1-U) r
H (1-R) (1-U) r
Definition
industrial production time in which system was
observed;
quantity of raw material entering industrial
production in time t;
quantity of other materials (not defined as raw
materials) entering industrial production in time
t;
quantity of raw and other materials entering
industrial production in time t;
production factor after time t. U = 1 represents
total production, while U = 0 represents zero
level of production and therefore, total waste
generation;
quantity of products produced in time t;
quantity of solid, liquid and/or gaseous waste
generated in time t;
waste management factor after time t. M = 1
represents total waste management while M 
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                             RAW
                           MATERIALS
                             COST.
                              c,
                            INDUSTRIAL
                            PRODUCTION
                                                    PRODUCTS
                                                      SALE
                                                    REVENUE,

                                                      PR,
                                                     WASTE
                                                  GENERATION
                          SECONDARY RAW
                          MATERIALS AND/OR
                           ENERGY REUSE
                            AND/OR SALE
                             REVENUE,

                           PR8  PR,
                       RELEASE COST
                         (EMISSION,
                      DISCHARGE AND/OR
                          SPILL),
    WASTE
MANAGEMENT COST,
     c.
                                                  PROCESSES WASTE
                                                   FINAL DISPOSAL
                                                      COST.

                                                       C7
              Figure 8. Cost analysis  of an industrial production
                      and waste generation tracking system
Cost Analysis  of IPWGTS


   A cost analysis  of an industrial productionrand waste  generation tracking
system  is defined2  in Table 3 and general  scheme presented  in Figure 8.  The
cost analysis  discussed is an example but does not necessarily include all
costs associated with environmental  issues and/or industrial production
process.  However,  the  idea of  approaching the problems of assessing costs  at
the facility level  of industrial  production is shown.
                                        67

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TABLE 3. Costs and revenues of industrial  production
        and waste generation  tracking system
COSTS
ri» r2» r3» •*• rn
Pr1» Pr2» Pr3> ••• Prn
1*1 * Pr1>/2* Pr2' r3 * Pr3>
• * • > rn Prn
Ci
Ip Ij* ^3' "• ^n
W.j > Wg > Wj , • > « > WR
1 * u 1 * w l*w
1 1* 2 2' 3 3' **"'
ln*"n
C2
m,,, m2, nij, . .. mn
Pm1> Pm2» Pm3> ' ' * Pmn
^1» ^2» ^3> **• ^n
«"l * P«1 * 51' m2 * P.1 * 52> %
* PnS* «3» "- mn* P«* 5n
C3
°1> °2» °3' •" °n
Pol* Po2> Po3» ••• Pon
°1 * Pol* °2 * PoH' °3 * Po3>
•••» °n * Pon
c*
quantity of raw materials (by type)
unit price of raw materials (by type)
cost of raw materials (by type)
total cost of raw materials
number of employees by employment
classification
wages per employees by employment
classification
labor cost per employment classification
total labor cost
number of machines (by type)
unit price of machine (by type)
depreciation rate of machine (by type)
cost of machine depreciations
total depreciation
number of miscellaneous production
factors (by type) (e.g. commercial and/or
financial, insurance activities,
transport, energy, etc.)
unit price of miscellaneous production
factors (by type)
cost of miscellaneous production factors
(by type)
total cost of miscellaneous production
factors
                          68

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COSTS
Z1» Z2» Z3» ' " Zn
Pr1» Pr2» Pr3» ••' Prn
Z1 * Pr1> Z2 * Pr2> Z3 * Pr3»
7 * n '
' •* ' > zn Prn
C5
9l» 92» 93> ••• 9n
Pg1» Pg2> Pg3» ••• Pgn
91 * P9V 92 * Pg2> 93 * Pg3'
••" 9n * PQn
C6
dr, d2, d3, .. . dn
Pd1> Pd2> Pd3» •'• Pdn
d1 * Pd1> d2 * Pd2' d3 * Pd3>
••" dn * Pdn
C7
quantity of gaseous, liquid and/or solid
raw materials lost by release to air, ,
water and/or soil/land
unit price of raw materials (by type)
cost of raw materials loss (by type)
total cost of raw materials loss
quantity of gaseous, liquid, and/or solid
waste managed (by type)
unit cost of each increment of waste
management (.temporary storage,
collection, transportation and
processing)
cost of each increment of waste
management .
total cost of each increment of waste
management
quantity of processed waste for final
disposal (by type)
unit cost for processed waste final
disposal (by type)
cost for processed waste final disposal
(by type)
total cost for processed waste final
disposal (by type)
REVENUES
XT > Xg> Xj, .... . Xn
P'xl* Px2> Px3' ••' Pxn
X1 * Px1'^X2 * Px2» X3 * Px3'
' ' ; » Xn Pxn
PR,
quantity of products (by type)
sale price of products (by type)
revenues on sale of products (by type)
total revenues on sale of products
69

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REVENUES
s^» s2> s3> ... sn
Ps1> Ps2> Ps3' •'• Psn
S1 * P81»^S2 * Ps2> S3 * Ps3»
• • • » Sn Psn
PR2
&1> 6g, 65, ... 6n
Pe1» Pe2> Pe3» '"Pen
61 * Pel* P2 * Pe2> e3*Pe3»
•••> en*Pen
PR3
quantity of secondary raw materials (by
type) recovered by waste processing
sale price of secondary raw materials (by
type) recovered by waste processing
revenues realized on reuse or sale of
secondary raw materials (by type)
recovered by waste processing
total revenues realized on reuse or sale
of secondary raw materials (by type)
recovered by waste processing
quantity of energy recovered by waste
processing
sale price of energy recovered by waste
processing
revenues realized on reuse or sale of
energy recovered by waste processing
total revenues realized on reuse or sale
of energy recovered by waste processing
Note:    The assumption that n raw material types, n employee classifications or n machine types-,  etc. are
        involved In the industrial production process  is not correct.  There are actually n raw material
        types, k employee classification types and f machine types, etc.  This notation, however,
        complicates parameter identifications; consequently, n does not represent the same number in every
        case.
CASES OF INDUSTRIAL PRODUCTION AND WASTE GENERATION  SYSTEM COSTS FOR  DECISION-
MAKING
System Costs for  Decision-Making

   Costs defined C1?  C2,  C3, C4,  (Figure 8)  are usual costs in any  type of
industrial  production  and they can vary,  but  they are  inevitable.   Revenue
realized on production sale,  PR,,  has to  cover all  these costs  and provide a
profit for  successful  continuation of the production.
                                            70

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   However, the costs caused by waste generation, C5, C6 and  C7, are rarely
 precisely  defined,  quantified  and  evaluated  in  terms of price  in  the
 production process.   Little  is known  or  appreciated  about  true  costs
 associated with generating waste  in industry5.

   For example, costs C6 depend on the level  of legislation and regulations of
 the country where an  industrial production is under  consideration.  However,
 these costs have to be  added to one of the basic cost of industrial
 production, C5, caused by material release (and thus material loss) to the
 environment.   Therefore, even  if legislation does not define permissible
 levels of  waste releases to air, water and/or soil/land  and does  not  include
 taxes on waste generators, a manager  of  an industrial production  can  still
 calculate  these costs.

   Roney  approaches the same problem  from a  total  production  quality
 management point of view;  he  stated  that hazardous  waste  is a  result of bad
 quality and is a significant part  of  manufacturing costs.  He suggested that
 high class manufacturers have  to eliminate it for these  reasons alone -- they
 have to cut their emissions to be  allowed to keep their  factories open while
 environmental  pressures close  down the others.

   A similar situation exists with revenues obtained on  reuse or sale  of
 secondary  raw  materials and/or energy recovered by waste processing.  Cost
 analysis can identify profitability of waste processing  for recovering
 secondary  raw  materials and/or energy, or for finally disposing of processed
waste into landfill.  This analysis did help .environmental  regulators to
 encourage  waste recycling by introducing high taxes on waste to be finally
disposed of in a landfill.

   Finally, the industrial  production  and waste  generation  system  under
consideration can be generally viewed according to the following three cases:
Case I                             -          ,

   In countries where legal  sanctions against environmental  pollution  are
absent, industrial technologies (production processes) are operated and/or
typically selected maximizing profit, i.e., the difference between revenue and
cost, as it is shown in the following expression (i):
(i)  max {PR,  -  (C,
C2 + C3
                                          C4)}
                                      71

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Case II

   In countries where sanctions against environmental  pollution have been ,
legislated, industrial technologies are operated and/or selected taking  into
account:

         (i)  max (PR, -  (C, +  C2+ C3 + C4)}

as well as additional technologies for reduction of waste generation  (by
processing waste into secondary raw materials and/or energy), maximizing the
difference between revenues and costs, as shown in (ii):

         (ii)  max {(PR,  + PR2  + PR3) - (C5 + C6  + C7)}.
Case III

   However, an overall approach to the solution of environmental problems
caused by  industrial  productions generating waste  is suggested  by operating
existing and/or  selecting  industrial  processes through:

   1.  industrial  production  technology,

   2.  technology for  reduction  of waste generation  (waste  processing  into
      secondary  raw materials and/or  energy), and

   3.  improvement/optimization  of industrial production by introducing
      pollution  prevention measures,

which maximize the total profit:

      (iii)   max {(PR, + PR2 + PR3)  -  (C, + C2 + C3 + C4 + C5 + C6 + C7 + C8)}.
   In the expression (iii) C8 (total  costs of activities related to
 improvement/optimization  of industrial  production  for  environmental
 production)  is  incorporated.   These  pollution  prevention  costs  should  enable
 optimal  industrial  production  under  reduction  of waste generation.   In this
 case  costs  of materials loss,  C5, as well as waste management and processed
 waste final  disposal  costs,  C6 and C7, could be minimized or even eliminated.

   Using this or similar cost analysis could help industrial
 managers/operators  to evaluate their industrial  production  processes and  to

                                       72

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concentrate on improving/optimizing production processes, instead of
concentrating on managing waste and/or wastewaters generated by these
processes.  In fact,  one dollar spent on improvement of'the processes
efficiency means in a long run, one dollar more of revenue realized on sale of
products.  Finally, for industrial managers/operators,  applying pollution
prevention strategies means observing their processes from different points of
view which could result in improved efficiency and reduced costs.
                                     73

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                               REFERENCES
1. Olbina, R., Cizerle, A., Krnel, D.  Specialized  Bibliographic Database
   on Waste  and Wastewater Management  (COS/ISIS  Information Storage  and
   Retrieval  System),  on-line  IZUM. University of Ljubljana,  Faculty of
   Science and Technology, 1991.  4000 documents.

2. Olbina, R. Computer Supported  Modelling  of Hazardous Waste Management,
   Dissertation,  University of Ljubljana, Faculty of  Sciences and
   Technology, 1991.   251 pp.

3. Baetz, B.W., Pas,  E.I., Vesilind,  P.A. Planning  Hazardous  Waste
   Reduction and  Treatment Strategies: An Optimization Approach. Waste
   Management & Research, 7(2):  153-63,  1989.

4. Glazar, S.A.,  Dolnicar, D., Olbina, R. Computerized Information System
   for  Special Waste  Management.  Module  5:  Factual  Database for Waste
   Generation Quantities  in Slovenia.  University of Ljubljana,  Faculty of
   Science and Technology,  1990.  930 records.

5.  ICF  Consulting Associates  Inc. Economic  Incentives for the Reduction  of
   Hazardous Wastes.  707  Wilshire Boulevard,  Los Angeles, CA  90017,  1985.
    79 pp.

6.  Rooney, C.,  Reducing Waste, American  Paint and Coating Journal, Jan 9,
    1989. pp. 36-40.
                                    74

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                                   SECTION 5
       DEVELOPMENT OF INFORMATION SYSTEM SHELL FOR MEASURING P2 PROGRESS
INTRODUCTION
   The objective of this  part  of the research was to build a generic
computerized information system shell (ISS)  for measuring pollution prevention
progress in any industrial facility generating waste.  It comprises a real
data model of an industrial production and waste generation tracking system
(IPWGTS).  The  IPWGTS model precisely defines the observed system parameters
and their relationships (Section 4).

   The essential part  of  the information  system  shell  is  a model  of  an  IPWGTS.
An IPWGTS model was tested in  selected industrial production processes of an
industrial sector, and real data were used for ISS building, model testing and
improvement.

   An  ISS could  provide industry,  research  organizations,  and  governmental
institutions with a powerful  tool for implementation of pollution prevention
strategies and  introduction of efficient waste reduction/minimization
practices by calculating  quantities of waste and/or wastewater generated in
industrial sectors and by applying cost analysis. After introducing process
optimization while minimizing  waste and wastewater generation in production
processes, ISS provides measurement of P2 progress.

   Commercially  available database management systems  (DBMS) for  information
system shell building applicable on IBM PC are analyzed and appropriate DBMS
are selected.
Structure and Function of the Information System Shell

  The  information  system shell  includes  databases on the following data
(Figure 9):

  (i)       codes,  types,  quantities and cost of raw and other materials
            entering production process;
  (ii)      codes,  types,  quantities and revenues of products;
                                      75

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  (ill)     codes, types, quantities and cost of waste  and  wastewater
            generated by the process;
  (iv)      codes, types, quantities and cost of waste  and  wastewater
            management system;
  (v)       codes, types, quantities and cost of material loss  through
            emission, discharge and spill;
  (vi)      codes, types, quantities and cost/revenues  of secondary raw
            materials/energy recovered by waste processing  and/or wastewater
            treatment processes; and
  (vii)     codes, types, quantities and cost of processed  waste final
            disposal.

  Using  developed parameter relationships of IPWGTS it is  possible to
calculate the quantities of waste  and wastewaters  generated by  the observed
industrial process in a selected time interval.  Data  evaluation could  be
carried out by using the U.S.  EPA's Toxic Release  Inventory data as well as
direct measuring  and monitoring of releases.
                 8-M*(1-U)*r
Databast
on raw and
other materials
quantites
i
Datab.
wastage
quart

iseon
•derated
tities
«"f

Comparison

Database
on products
quantites
P-U*r
Database on
actualy
measured
released
quantities
y-(1-U)*r .^
^^^ • 1 r
Database on
was!*
managed
quantities

MX —
y
Database on
waste released
quantities
"VsV. z-(1-M)*(1-(J)*r
_ t^
Database on
secondary
material and/or
•nergy recovered
quantities
H±g
Database on
processed
waste disposed
quantities
                          *-M«R*(1-U)*r
d-M*(1-R)*(1-U)*f
                Figure 9. Scheme of  an  information  system shell
                                       76

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   The IPWGTS model  provides among others the following data:

   (1)  production factor determines the overall production process
        efficiency;
   (2)  waste management factor determines the level of waste and wastewaters
        management and estimates the waste and wastewater management system
        efficiency;
   (3)  waste recycling factor determines the level of recyclability  and
        estimates the secondary material and/or energy recovery efficiency;
Use of Information System Shell

   ISS  is  programmed  in a way that allows the use of existing data in any
industrial facility.  A  facility  manager can  select  a  unit  operation,  several
unit operations, a part  of facility and/or  a  whole facility as  the  IPWGTS  for
testing.  A manual for the use of ISS  is available describing data  preparation
and import into the system,  as well as the  steps  of  using the ISS.   Once data
are collected and imported,  all calculations  are  provided by the  ISS.  Further,
quantitative and cost analyses and the determination of the efficiency of  the
observed system can be done  quickly and precisely.   The ISS provides daily,
weekly, monthly, quarterly and yearly time  frames (Figure 10).
                      MCUSmuu. SECTOR
                             ISC|    I DATE; WEEK |
                        RAW MATE mil
                                                                 • Us
                                                           Ui
            Figure 10. Schematic presentation of the ISS databases
                                      77

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  Finally,  ISS  allows  schematic, tabular  and graphic presentation of the
system parameter relationships.  After the estimation of the facility
efficiency, the management team could decide which waste minimization and/or
pollution prevention measures should be applied, if any.  If improvements have
been selected and implemented, the ISS can be used again and measure pollution
prevention achieved.
                                        78

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                                   SECTION 6
              COMPUTER PROGRAMMING OF THE INFORMATION SYSTEM SHELL
 SYSTEM OVERVIEW

    The structure of the ISS is based on the parameters and relationships of an
 IPWGTS discussed in  Section  4.   The ISS  has  been  developed using  Microsoft
 windows and  requires  MS Access 2.0 and  MS Excel  to run.   For fast performance,
 it  is recommended  that ISS be used  on a 486DX computer  with around 66  MHz!
 However,  a 386  computer with  Windows will  also  work.


 Quantitative Analysis

    The information  used within  the  ISS can be broken  into seven categories:  raw
 materials  input, other  materials  input,  products,  waste  generated, measured
 releases,  and  managed  waste  (Figure 7).   Each of  these  categories contains
 measurements of a specific  parameter quantity taken  on a daily basis.  The  ISS
 is based on the  relationships that exist  among these seven categories through the
 model.

    There are three  factors used to correlate quantities within this  system:  the
 production factor, the waste management factor, and the recycling factor (Section
 4).  Each of  these can be computed within the ISS on the basis of available data.

    By implementing  these  formulas,  the  ISS  is able  to  calculate parameters
 quantities.  For example,  in an industrial  production  process where only  the
 inputs and the product are closely measured, the total amount of waste generated
 can be predicted but the management and  recycling factors cannot.  In addition,
 these formulas could aid in determining if the methods of parameter calculations
 are producing correct results.
Cost Analysis

   In  addition  to  calculations  of the  quantities of  input  and output  of
materials in a  process,  this  ISS also provides the capability  to  make a cost
analysis.  The cost analysis  incorporated  in the  ISS is  based on the  model  of
IPWGTS and its parameter relationships and their unit prices.  There are eight

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cost types recognized within the ISS (Section 4):

   Cl:   Total  cost of  raw materials: 2(r,.  p5),  where r  = quantity  of raw
         materials  by type  and p = unit price of raw materials by type;

   C2:   Total labor cost:  2(1, \a.),  where 1  = number of employees by
         employment classification and w  = wages per employee by  employment
         classification;
   C3:
    C4:
    C5:
    C6:
    C7:
    C8
Total depreciation: 2(111,. £,  pf), where m
type), p » unit price of machine (by type), S
machine (by type);
number of machines (by
      depreciation rate of
Total cost of miscellaneous factors: 2(0, p,-),  where o = number of
miscellaneous production factors (by type) (e.g., commercial and/or
financial, insurance activities, transport, etc.) and  p = unit  price
of miscellaneous production factors (by type);

Total cost of activities related to improvement/optimization of
production: 2(i, p,.),  where i  = number of activities related to
improvement/optimization of production for environmental protection
and p = cost of activity related to improvement/optimization of
production for environmental  protection;

Total cost of raw  materials loss: 2(z, p,),  where z - quantity of
gaseous,  liquid, and/or solid raw materials lost  by releases to
environment  and p  = unit price of raw materials;

Total cost of waste management: 2(g, p,-), where g = quantity of
gaseous,  liquid and/or solid  waste  managed  (by type)  and p  = unit
price of  each  increment of waste management;

Total cost of  processed waste final disposal:  2(d,-  PJ), where d =
quantity  of  processed waste  for final  disposal (by  type) and p =
unit cost for  processed waste final disposal  (by type).
    As data are collected, the user will be able to input new data as it
 arrives.  At all  times, the ISS will determine, from the available data, if
 any other types of quantities can be predicted, thereby offering the user an
 improved view into the industrial production process under study.
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 INFORMATION SYSTEM  SHELL
    The  structure of the ISS  can  be divided  into  three  sections:  data entry,
data storage/manipulation, and data display.  These sections are  bound
together through  the user interface.
Goals and Requirements

    One  of the  requirements  the  ISS must  fulfill  is  to be  user  friendly.   By
being user friendly, the ISS is responsible for the following:

    - organization  of data in a  format that allows easy comprehension  by  user;
    - the display of correct values throughout  system;
    - quick and timely responses; and
    - a  system  that is easy  for  the users to learn.

    In addition to  being user friendly, the ISS is expected to  be  applied to a
wide range of  industrial processes and domains.  In order to meet this
requirement, the system has been designed in a shell-like fashion.  The  core
of the  system  consists of table definitions and functions that are used  for
data input, storage, manipulation,  and display.  Depending on the data
entered, the core  system will  produce different storage and displays specific
to the  process being studied at that time.

   Another requirement of this  ISS is that it functions successfully not only
for the ideal  situation, (having all  data at hand from the beginning), but
also for the bare minimum,  which in this case means only data on the raw
materials,  and other materials,  and products to the system.   The next
requirement is that the system should be able to handle not only new data, but
also new data of different  types.   This will  not only lead to more storage
types,  but also to new areas within the user interface.
Implementation

   The ISS is being developed on a PC machine and it entails two software
packages:  Excel 5.0 (A Microsoft spreadsheet package)  and Access 2.0
(Microsoft DBMS).  In order to form a more coherent system,  Visual  Basic 3.0
(Microsoft) has been used to connect these packages.
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   The data entry section of this system has been set up as an Excel
application.  The design has taken the liberty of assuming that the majority
of users by this time have implemented storage of data on industrial processes
within spreadsheet packages.  Excel has been used because of its conversion
capabilities, its general user friendliness, and its compatibility with other
software packages.  In addition to data entry, the majority of calculations
needed by this system are done within Excel due to its speed.  The
calculations are usually made up of multiplications and division operators.
The cost calculations used within this system are not calculated within Excel.

   Access  2.0  is  a database management system (DBMS)  package produced  by
Microsoft.   In the ISS system, it  is  used  for data storage and manipulation.
This  ISS has been based  on  two databases:  Archive and DM databases.  The
Archive database  is used strictly  for data storage.  The DM database is used
for data manipulation and is dependent on  the Archive database.  The system
has been split  into two  databases  in  order to conserve storage  space and to
allow,  in  future  implementations,  the choice  of  different Archives  as  the
basis for  the  DM  database (i.e.,  allow the user  to choose the  production
process archive to use  at a given  time).   The Archive has been  set  up  as the
primary storage  facility of the  system,  therefore, each  unique  process studied
will  have  its  own Archive database.   The DM database  references the data in
the Archive database.   The  ISS will  contain only one  DM  database that  is
 independent of the type of  process studied.  The DM database is made  up of
pieces of code (SQL)  that create information  by organizing  data relationships
 in different ways.   This new  information is stored  as tables known as  queries.
These queries, rather than  the original  tables,  are  used by the user
 interface.

    The user interface has  been designed and implementation has begun  using
 Visual Basic.  This  has been done to enable an easier transition between  the
 software packages and a more coherent display.  In addition, Visual Basic
 offers more control  than interfaces  designed and implemented in either Excel
 or Access.  Visual  Basic blends well with these application packages  because
 all  use similar data structures and  objects within the programming language.
 Data  Entry

     The data entry for the ISS can be divided  according  to  the  amount  of data
 that  they accept.  The bulk  of the  information needed for  the  system  to
 operate is  entered using  an  Excel  application.  This application accepts and
 formats the daily measurements of all  materials that course through the
 system.  In addition,  the Excel  macro is  responsible for calculating  the

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predictive values used within the system.  However, in addition to this bulk
information, the system also requires specific information about each
parameter and outside source that may affect the parameter relationships
inside the process.  The amount of this type of data is usually not as large
as the primary data and can therefore be entered into the ISS by the user
through the user interface.
Data Entry - Bulk Information--
   Bulk information is used to refer to the daily data collected throughout
the system.  These data contain information on quantities of material flowing
through the industrial production process.  Because the structures of the ISS
are dependent on the user input,  the data entry section of the system is made
up of macros and functions written in Excel Basic (similar to Visual Basic) to
accept formatted input and perform the needed calculations,  and extra
formatting procedures to allow easy transition of data between Excel
and Access.
Data Format—In order for the system to be successful,  data entry must follow
certain formatting guidelines. It has been assumed that  each type of data is
stored in a separate file which conforms to the following guidelines:

   1.  all data on parameters quantities will  be given on a daily basis;

   2.  all data conversions will been performed before data enter into the
       system;

   3.  the first row in each column in a table will  contain a unique title
       for that column.  The title can be a parameter name or some type of
       representation symbolizing that parameter,  and

   4.  the incoming spreadsheets will  be formatted in the following ways:
         - the first column  in  every sheet contains the date;
         - no overall  daily  totals are given on any sheet;
         - for parameters named  (managed waste, measured releases, managed
           releases, and secondary raw materials) the sheets will contain
           four columns: date, waste, wastewater contamination, and air
           emission.   If these titles are not supplied,  the system will
           automatically assign them in that order.
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   As the user is entering the needed files, the application will be
performing calculations and additional formatting based on the type of data
being entered.  For example, when the sheet containing the amounts of raw
materials is entered, the application will compute the daily totals and assign
variables needed for transfer into the archive database.
Calculation with Data Entry--When the user has completed entering data, the
application will develop additional worksheets based on the types of entered
data.  Based on the user inputs, the system will determine what extra types of
calculations can be made.  The minimum required types of data for the ISS to
function are:  raw and other materials (inputs), and products.  The user will
not be allowed to continue the system until these three types of data are
entered into the system.  If a system is given only these three types of data,
the application will create a worksheet containing the predicted amounts of
waste generated based on the production factor computed from the inputs and
the products quantities.

   As  for  the  rest  of the data  types, the  user  is responsible for entering as
much information as possible.   It is hoped that through the use of this
system, in addition to improving the efficiency of the observed production
process, the user will take the initiative to improve techniques needed to
measure, collect and handle these types of data.  Based on this second goal,
in future  sessions with the information system, the user will have the ability
to incorporate new types of data that may not have existed at the beginning of
the  ISS application.  In adding new types of data to the system, the data
entry will be  able to perform different calculations used within the system.
For  example, given the quantities of waste generated and waste managed, it
will be able to predict the quantities  of waste released using the previously
calculated managed waste quantity.
Automatic  Data  Entry—The  user  is  sent  directly  into the  Excel application
from the Setup  window in Visual  Basic.   The  user is given a choice between
manual  and automatic  data  entry.   Manual  data  entry allows the user to enter
data directly from the keyboard.   This  option  will probably not be viable due
to  the  size of  data files.   Therefore,  the data  entry  application will not
begin until  the user  presses the "Automatic" button on the top screen.
Automatic  data  entry  will  prompt the  user through the  process by asking  for
the type of data and  the file where that data  is stored.

    Once data entry begins, the  automatic window, known as a  dialog  frame in
Excel,  will  remain on top  of spreadsheets throughout the  data entry stage.   It

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contains a set of click buttons which represent different data types.  As the
user loads these different data types, the dialog frame will disable the data
type click buttons that have already been used.  In this way, the dialog frame
facilitates the ease and correctness of data entry by keeping track of what
type of data has already been entered, and keeping track of what types of data
are yet needed.  Since certain types of data require different computations,
calculation errors made by the application can be limited by specifying the
type of data entered. In addition to highlighting a click button, the user is
prompted for the full path name of the file containing the data.  After these
two inputs are given, the user can load the file by pressing the "load" button
on the dialog box. Once this button is pressed, the background of the screen
will display the file being loaded into Excel and the resulting calculations
that the application will perform on the given sheet.
     N
The following steps represent the summary of data entry procedure:

    1.  press  "Automatic" Button on Top Form. (Dialog Frame will appear);

    2.  click  on type of data to be entered;

    3.  enter  full path name of file containing that type of data;

    4.  press  the  "Load" button: the file will be loaded and required
       calculations will be performed  (e.g., total columns for raw material
       input, other material input, products, and releases);

    5.  repeat steps two through four  until all available data or at least the
       minimum required data are entered;

    6.  press  "Complete" button signaling completion of data entry.
       Application will perform predictive calculations based on the data
       given;  and

    7.  press  "Back to main" button on  the tool bar within Excel to return to
       user  interface.

    After  entering data  spreadsheets,  the user continues the  Excel application
by  pressing the "complete" button on the dialog frame (step 6).  This button
will signal to the application that data entry has been completed and based on
the types  of  data that have been entered, additional spreadsheets are created
by  the application (Table 4).  At the  conclusion of these calculations, a
message box  stating that the data entry has been concluded will appear.  At
this point, the user is ready to exit  the data entry application.
                                      85

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    TABLE 4. Summary of calculation  requirements  and  results  of  data  entry
Given:
Raw material Inputs,
products
Raw material Inputs,
products [releases] ,
Raw material Inputs,
products [releases] ,
other material inputs,
other material inputs,
managed waste
other material inputs,
managed releases
Raw material Inputs, other material inputs,
products [releases], {managed waste/releases},
secondary raw materials
Output :
Waste calculation
Waste calculation, production
releases, management factors
Waste calculation, production
management factors.
factors, managed
factors, managed waste,
Waste calculation, production factors, managed
waste/releases, management factors, final disposal and
recycling factors.
Exiting Data Entry--After completion of  extra  calculations  performed  and
entering the available data types,  control will  return  to the  user, and the
user will be responsible for exiting the application  by pressing  a toolbar
button. The toolbar button will  perform  the  data transfer between Excel and
the Archive database in the background.  Exiting  the data entry application in
this manner allows the user who  may be familiar  with  the capabilities of  Excel
to "play" with the data, i.e., explore different tabular and graphical
functions within Excel using the data created  by the  application. To  get  a
full understanding of this data  however,  the user is  referred  to  the
discussion on the formulas used  in  the calculations section.
Module Design—The following  are the  basic divisions of the modules  used
within the data entry section.  Only  the major  procedures  and  functions have
been included in this listing:
Data Entry:


AUTO BUTTON CLICKO:



HAN BUTTON CLICKO:



CANCEL_BUTTONO:


BACKTO_ MAI HOI
controls the user screen in general cases;

sets up "Automatic" dialog box and displays it to user,
called from "Top" sheet;

sets up "Manual" dialog box and displays it for user.
Also called from "Top" sheet;

cancels the Excel application;

assigned to a box on a tool bar. It is used for the
exiting data entry section after data entry is completed
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Automatic Input:
LOAD BUTTON_CLICKO:
DETERMINE, ADATAO:
COMPLETE_BUTTOM_CLICK()I
by user. BACKTO_MAIN will  saved  the entered data, and
then call and Access function  based in the Archive
database to transfer the data  into the database.

controls the automatic data  entry;

loads the user defined file  into the workbook.  It also
calls functions to perform calculations based on  the
type of data that the new  worksheet contains.  Finally,
it keeps track of what types of  data have been  entered
and adjusts the dialog box accordingly;

determines what, if any, types of additional  data have
been entered into the system.  Waste managed,  releases,
and managed releases are considered as additional data;

signals completion of data entry.   It first checks for
the three minimum data types required;  if all three are
not present, it issues a warning to the user and  does
not allow the user to exit the data entry section. It
then transfers control over  to the control_calc
procedure;
Manual Input:
controls the manual data entry  and  dialog  box.
to the automatic input module described  above;
Similar
Total Calculation:    computes the daily totals for all sheets;
COMPUTE TOTALS<):
WASTE_CALCULAT10NS<):
CONTROL_CALC(>:
computes daily totals for all sheets  and  adds
information needed for data transfer. Waste  Calculation:
used to compute the various predictive calculations;

creates a new worksheet containing calculated  waste
values.  These include the production factor,  total
inputs, and total waste using data from raw  materials,
other inputs, and products worksheets.  It is  called
every time new data is entered  into the system;

using the information from the  automatic  entry and  the
additional data determination function, it controls the
calculation of further predictive calculations dealing
with waste management;
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Managed Haste  and Releases:    computes the predicted values  of waste managed
                               and released.  It will create  at least two new
                               tables depending on given  input.  Only called
                               when either managed releases or managed waste
                               values are given;

cALc_MFACTORSo/cALc_HFACTORS2o:  calculates management factors.  One is  used for
                           calculations when managed waste values  are given
                           and the other is used when managed  releases are
                           given;
CALC_MRELEASES<>:
CALC MUASTEO:
CALC_HANAGED_RELEASES()I
CALC MANAGED WASTEO:
calculates managed release  amounts when managed waste  is
given;

calculates managed waste  amounts when managed releases
are given;

controls the calculation  and creation of two new sheets:
managed releases  and  the  management factors used;

controls the calculation  and creation of two new sheets:
managed waste and the management factors used;
Calculate secondary material
and final disposal:   computes  all  of the needed information for  secondary raw
                       materials and final disposal amounts.  It will  create
                       two  new tables: one containing final disposal  amounts
                       according to  types of waste, and one containing
                       recycling factors used in this calculation.  Can only be
                       called when either managed wastes or managed releases
                       are  input to  the system;
 RECYCLING FACTORSQ:
 D!SPOSAL_FORMULASO:
 RFACTOR_TABLE<):
 DVALUE TABLEO:
enters the  formulas  needed for computing recycling
factors;

enters the  formulas  needed for computing final disposal
amounts;

takes computed  values and moves them into a  separate
table;

takes completed disposal  values and moves them into a
separate  table; and
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CREATE_NEW_VALUES<>:
controls the creation of two new worksheets: d_rfactors
and d_final_disposal.
Data Entry - Specific Information--The data entry implemented through the user
interface is responsible for accepting a relatively small amount of needed
data.  The specific information collected by this system includes: information
on individual materials input used within the process, costs associated with
labor, machinery,  miscellaneous factors, and measurements of different waste
streams (Section 4).   Within the system, only the material  input information
must be entered before the analysis screens can be viewed.   The other
information can be entered into the system either at the beginning or during
the ISS testing.  Like the bulk information, the ISS system can successfully
be used without entering all types of available data.
Input Material Information—The specific information on the input materials
within the process include the following:
   *  name - filled in by system according to bulk data entry;
   *  inventory code - code used by company or organization for the given raw
      or other input materials;
   *  unit of measurement - unit from daily measurement;
   *  section of process where material  occurs - the section of the process
      where the material was measured and filled in by the system; and
   *  unit price - unit price of the material for a single unit.

   In order to enter the analysis display section of ISS, the information
must be entered into system.  The data entry screen is found through the setup
screen.  For a new process, this data can only be entered after the bulk data
entry is completed.  However, if a previous process is being tested, this
information can be updated once the user enters the setup screen.  However,
there is a warning: only one unit cost can be used for calculations.  That is,
if the unit cost of a certain parameter goes up or down over time, the ISS
cannot accept more than one cost per parameter. The cost analysis cannot take
into account parameter price changes over time.
Cost Analysis Information—In order to form a. complete cost analysis study,
extra information about the process must be taken into account.   This extra
information includes data on labor, machinery,  and miscellaneous factors
Section 4).  This information is not required for the ISS to successfully run;
however, it will  offer the user a more complete view of the process under
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study.  These data entry screens follow  basically the same  format:  the user is
allowed to update the information contained within the tables  if desired,  but
is then required  to specifically tell the  system that a new piece of
information  is  going to be entered.  The following is a listing  of the
information  collected for each of these  types of data:

Labor/Employee:  Description of job type.
            Job type number.
            Number  of individuals employed at this  type of job.
            Salary  for this type of job.

Machinery Depreciation: Description of machine type.
                Machine type number.
                Number of machines of this  type used  in process.
                Cost for one machine of this type.
                Depreciation rate of machine.

Miscellaneous Factors:  Description of activity.
                   Type of activity.
                   Number of activities of this type.
                   Unit cost of the activity.

   The format of the data  entry windows for this type of  information is
prepared.  The  buttons on the bottom of  the screen include  arrows to view and
update all records within the table.  An update button, when pressed will
clear  the  text  boxes on the screen  allowing for new  information  to be entered.
The update button must be pressed only  after the new information is entered
into the  system to record the new pieces of data.  The cancel  button is used
to close  the window and return  to the  system.
Waste  Streams—Finally, the last  type of information that  can  be entered into
the  system deals with measured quantities of specific waste  streams.  This
type of information has been  separated from the other data entry because in
many systems this type of  information may not be available at  such a specific
level.   If this type of information  is available, it can be  used to form a
more complete cost analysis of the process and potentially offer more
opportunities to apply pollution  prevention strategies.

    ISS accepts waste streams  into three main groups  (names are chosen to be
easily distinguished): vent flow,  liquid waste stream,  and waste stream.  The
data entry for specific information  pertaining to the contents of these three
waste  streams expects the  measurements for each type of waste  stream to be
stored in its own Excel spreadsheet  file maintaining the same  format as the
data types entered through data  entry for bulk information.  The data entry
screen for this type of information  is also prepared.   The user is asked to
type the full path name of the data  file and the type of waste stream that the

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file contains.  When the ok button is pressed, the ISS will automatically find
these files and enter them into the archive database and perform the needed
data manipulation.
DATA STORAGE AND MANIPULATION
   Data storage and manipulation in  ISS is accomplished using two separate
database files: the Archive and the Data Manipulation (DM) database.  The
Archive database is only used for storage of the information needed to study
the process.  No manipulation is done within this database.  The DM database
contains the necessary functions and queries to allow the user to study the
observed process.   The DM is a read-only file; it uses attached tables from
the Archive database as its source of data.  This has been done to limit the
possibilities of data corruption.
Data Storage - Archive Database
   The data storage function in  ISS is accomplished using a single database
file.  This file is referred to as the Archive database.  It is the foundation
of the Information System Shell.  The Archive database contains only the data
tables and the macros used to control  the acceptance of data from the data
entry sections of the system.  The user will never directly access this
database.  Once information is entered into the system, it is saved by the
Archive database.  This information can then not be changed or deleted by the
user.  This limitation has been implemented to ensure data integrity and
eliminate potential errors by users.  The Archive database is the only data
file saved at the end of a testing session in order to be used in another
testing session of the ISS.

   An empty Archive database contains 13 tables.  These tables are empty in
the beginning;  only their data dictionary is defined.  Depending on the
number of different types of data entered into the system, the tables
corresponding to these types of data will be filled.  The system, based on the
user input, will determine which tables are to filled.  In addition to filling
the pre-defined tables the Archive database creates three new tables each time
a new process is to be studied.  These three tables, containing raw material
inputs,  other material inputs,  and products quantities are defined
dynamically, since they are created at run time.  The following is a list of
the tables defined and created by the Archive database:
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   1.    Daily raw material inputs - created dynamically
   2.    Daily other material  inputs - created dynamically
   3.    Daily amounts of products - created dynamically
   4.    Daily amounts of measured releases
   5.    Daily amounts of calculated wastes
   6.    Daily amounts of managed waste
   7.    Daily amounts of managed releases
   8.    Daily amounts of secondary raw materials
   9.    Daily amounts of final disposal
   10.   Daily values of management factors according to type of waste
   11.   Daily values of recycling factors according to type of waste
   12.   Material Information
   13.   Current name of industrial process and company using this system
   14.   Labor/Employee information
   15.   Machine Depreciation information
   16.   Applied Pollution Prevention Techniques
   17.   Miscellaneous Production factors
   18.   Waste Stream information: Vent Flow
   19.   Waste Stream information: Waste Stream
   20.   Waste Stream information: Liquid waste stream
   For the data types numbered 1 through 10, the user will enter data into
this database indirectly through Excel.  By pressing the "back to main"
button, the Excel application has been programmed to open the Archive database
and then transfer the data into the Archive.  This process has been automated
to eliminate potential user errors and increase the system's user
friendliness. For data formats that remain constant throughout different
processes (e.g. waste, managed waste, managed releases, management factors,
etc), the Archive database simply appends the information into the existing
table.  If however, the data is of type numbered 1, 2, or 3, that may change
or be dynamic among different processes.  The Archive will first check to
determine if a table already exists.  If a table does not exist, the
application will create a new table and transfer this data into the table. If
the table does exist, the application will append the data into the table.

   For the data types numbered 11 through 20, data will  be entered  into these
tables through the user interface using the methods for  specific information
(described in section Data entry).  Data table number 12, material information
is a special case.  The contents of this table are based on the information
entered in the Excel macro as well as the information collected from the user
directly.  The Archive database is responsible for taking the bulk information
and extracting chemical names and data types to enter into the material

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information table.  The rest of the required data is entered by the user
through the interface of the system.  The material  information table has been
designed in this way to improve the quality of data by enforcing consistency
throughout the data and reducing the data entry performed by the user, thereby
reducing the possibilities of error and data corruption.

   The following describes the  actions  that the Archive  database takes
to develop a file for a given process:

   1.  Open Archive  database.
   2.  Determine if  all  needed tables  exist.
   3.  If all  needed tables  do not exist:
       -  Create needed tables;
       -  Transfer data from  Excel  into Archive;
       -  Fill  chemical  information table;
   4.  If the  tables do exist:
       -  Transfer data from  Excel  into Archive;
   5.  Close Archive.
Module Design
Control:
contains the driver program for the Archive database;
CONTROL_ARCHIVEO:   determines  if tables  exist  and  takes the  necessary  actions
                depending on the result of determination.
Get Names:


FILL_CHEMICALO:


GET_NAMES<>:
fills the input table with chemical information;

contains the code to fill the input table successfully;.

inputs the  field  names of the  imported tables containing  raw
material inputs, other material inputs, and products, and enters
them as records  in  the input table under the  chem-name field.
In addition, this procedure will also enter the original  table
name where the given material is originally located;
Input Archive:  enters  the  user  data  into the  appropriate tables  either by
                creating  new tables  or  appending the  information  into the
                already existing tables;

                returns the  number  of  tables  within the Archive database at a
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APPENO_ARCHIVE<):
APPEND_CALCO:
CREATE_TABLES():
                given  moment.
                created;
                 Used  to determine  if  all  tables  have  been
used when tables already exist within the database and data must
be appended into the tables;

enters data into  the  tables containing "calculated"  values  as
well  as   tables  with  consistent  format  among  processes.  It
appends the data into the existing tables.

creates the needed  tables  for  loaded data.   These  tables  will
not remain constant  between  different types of processes and are
therefore considered to be dynamic.
DM Database Design
   The DM database  is  responsible  for  the manipulation of the parameter  tables
in order to emphasize  informative  relationships occurring within the data. The
DM is dependent on the Archive database for its data only.   It is not dependent
on a  given  process and  the  specific contents of the  data  represented  by the
Archive.  Different Archive databases can be  "plugged  into"  the DM and  still
provide a successful system operation.

   In  order to  maintain data integrity,  the user  never directly views this
database.  All  creation  requirements have  been automated so that the  user may
simply press a button  and wait for the system  to complete.
The following is a  summary of DM database creation:

      1.  attach  tables from Archive  database;
      2.  create  Queries  using attached tables;
      3.  close Database  and return control to  user.

   The informative  tables,  known as  queries,  that are  created within this
database are based  on  data referenced  in the Archive database.  These
references are read-only, thus reducing the chance of data corruption.
Information is created through these tables in DM rather than within the  user
interface in order  to  take advantage of the speed of the DBMS package.   By
creating these queries within DM,  while there may be a lag time at the
beginning of execution while the queries are formed, the remaining system will
run smoothly and at a  higher speed.
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Data Queries
   Data queries are only developed once during a testing session with  ISS.
These queries must be recreated every time ISS is run in order to maintain a
DM that matches the Archive database used.  The user is able to change the
Archive used at the beginning of the system,  but is not given such an option
for the DM database.  Recreating the queries  in the DM does not take long and
reduces the amount of memory needed for the system.

   The DM database maintains 59 queries.  The DM contains queries that
organize the data into tables according to time frame requirements:  there is a
separate table for each type of data according to time frames: daily, weekly,
monthly, and quarterly.  While this may seem  to take up much space,  it allows
a speedup in the interface by having the data in a format ready to be
displayed rather than forcing the interface to both prepare and display the
data, slowing the system down.   In addition to time requirements, these
queries are used to organize data for the display functions.  For example, in
displays containing all input material  information, a join on the raw input
material and other input material  must be created.

   Not all of these queries are created dynamically because certain data
types have been carefully laid out and formatted so the contents are always
constant, regardless of the data type.   Measured quantities of release is one
example of this type of data.   All tables containing release data will be made
up of five attributes:  date,  air emission, wastewater, solid waste, and total
waste.  However, certain data types are not constant throughout uses, i.e.,
for given processes, the contents of the data types vary.  The dynamic data
types that force queries to be created dynamically are raw input material,
other input material, and product material. These tables can consist of a
varied number of attributes because each attribute represents a material.
Table 5 gives a listing of all  queries created dynamically at start-up.

        TABLE  5. Queries  created dynamically by DM  database  at  start-up
Basic Table
daily raw material input
daily other material
input
daily product
daily raw input material
and daily other input
material
Daily Query
da i ly_raw
daily_other
daily_product
daily_inputs
Weekly Query
w raw
w_other
w_products
w-inputs
Monthly Query
m raw
mjDther
m_products
m_inputs
Quarterly Query
q raw
q_other
q products
q_ inputs
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   In addition to these queries  if the  user  enters  specific information on
materials within one of the three waste streams, the  DM  data  base  will  create
twelve queries in order to allow the user to view information according to
time frame requirements (Table 6).  This is  based on  the same reasoning used
in the creation of the queries for input and products materials, that each
process may contain a different  amount  of materials within  each  of these waste
streams.
     TABLE 6.  Queries created dynamically by DM database for cost analysis
Basic Table
daily vent waste
flow
daily liquid waste
flow
daily solid waste
flow
Daily Query
daily_ventflow
daily_1iquidwaste
daily_solidwaste
Weekly Query
w_ventf low
w_liquidwaste
w_solidwaste
Monthly Query
m_ventf low
m_liquidwaste
m_so1idwaste
Quarterly Query
q_ventf low
q_liquidwaste
q_solidwaste
 Module  Design


 Attach  Tables:  determines what tables are to be  referenced  from Archive
                database;

 ATTACH_TABLESO:    checks if Archive database is referenced  for correct  data.   If
                not, it will create the reference from DM to the Archive
                database;

 TABLE_ATTACHEDO:   determines whether a  specified  table  is already attached  by
                trying to select it in the database window.  It  is  called  from
                Attach Table;
 Starting
runs all needed query creation procedures during start-up;
 DYNAHIC_QUERIESO:   functions  as  the driving  program for creating the needed
                 queries.

 Time frame      provides the  individual functions that are used to create the
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DARY_QUERYO:
INPUT_QUERYO:
MONTHJJUERYO:
QUARTERJHJERYO:
WEEK_QUERY(>:
Cost Ana1ysis2
COST_SQL< ):


FIND_PRJCEO:

DAILY_LOST AMTSO:




WEEK_LOSTO:


MONTHJ.OSTO :


QUARTER LOSTCK
dynamic queries;

creates the SQL code  for  queries  containing daily information.
These new tables contain  an  updated format to be compatible
with the other time based queries for the same data material;

creates the SQL code  for  queries  containing input information,
which combines data from  raw material  input tables and other
material input tables;

creates the SQL code  for  queries  containing monthly sums for
the materials used within the process;

creates the SQL code  for  queries  containing quarterly sums for
the materials used within the process;

creates the SQL code  for  queries  containing weekly sums for
the materials used within the process.

   creates  the queries containing specific  information  about
   materials within the specified waste stream.   Queries  then
   used as  a basis  for the time frame queries  and cost
   analysis display.

creates the SQL for queries  containing  cost information for
materials lost during production;

determines the price  of a given material;

creates the SQL code  for  queries  containing the daily sums for
the materials measured within the waste streams.  It is
created to improve compatibility.

creates the SQL code  for  queries  containing the weekly sums
for the materials measured within the waste streams;

creates the SQL code  for  queries  containing the monthly sums
for the materials measured within the waste streams;

creates the SQL code for  queries  containing the quarterly sums
for the materials measured within the waste streams.
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User Interface Design

   ISS is a windows program created using Visual Basic for  its user
interface. Visual Basic has been used because of its compatibility with Access
and Excel and ease of use.  The interface serves as a unifying front end for
the two applications being used.
Forms and Controls--
   A form is an  individual window  used  to  either display  system  output  or
accept user input.  Controls are text boxes, buttons, click boxes, etc., that
appear on forms.  These items accept user  input.  Each time the  system  is
used, it is called a session.

    In order to develop  an  overall  idea  for the  entire system,  Figure  11 is
given.  This diagram is a  control  flow  graph of the  ISS system.  By following
the transfer of  control, users can determine where they are within the  system.
The dotted boxes around the Access modules  represent hidden systems
programming used to create information  system.  The  user  will  never directly
see these modules.  Finally, display functions represent  the hierarchical
structure of the screen transition diagrams shown in Figures 12  and 13.
Overall System--The  system  is  based  on  user  input.  Therefore,  the development
of the  user  interface  has been based on the  fact  that  some  of the forms used
for display  will  be  dynamic.   The  only  forms that are  dynamic correspond to
the dynamic  queries;   all forms dealing with the  display  of raw input
materials, other  input materials,  products,  or  any combination  of these three
must  be created dynamically.   To accomplish  this  dynamic  property within the
user  interface, grid controls  have been implemented in all  forms.  These grid
controls  are similar to spreadsheet  pages.   By  using grid controls,  multiple
columns and  rows  of  data can  be developed based on the user input.   In
addition  to  these grid controls, code modules have been developed to extract
the needed data from the database  and fill  these  grids.

       Data  entry  in this system,  as discussed  above,   is  split  between an
Excel  spreadsheet application  and  the forms  within the user interface.  These
data  entry functions have been placed within the  system at  the  beginning of
the flow  of  control.  It is hoped  that  the user will enter  in  all relevant
data  at one  time, at the beginning of the session.
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                        (^  Start System  J
                              Start TJvough
Figure  11. Control  flow graph of the ISS system
                         99

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       I           I
         OftwMsUrW

       I    ln(X*
RjwM**n*t
                   Product*
                                                          Mdwtal*
                          1
                                                                                              Product v».
     Figure  12. Screen transition  graph -  quantitative analysis display functions
                                                  Cost Analysis
         ._J	

          Depreciation
LaborCost
  ZLH
Raw Material Cost
                                                                         i:
                                                                      Cost of Acllvt et
                                                                        related to
                                                                       Improvement   I
                      1
                                    L_
                                     Other Production
                                        Costs
[Waste Management
_±!_J
          Figure 13.  Screen transition graph - cost  analysis display funcions
                                               100

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    The user interface has  been  designed to. allow as much freedom as possible
 in  determining what to  study while maintaining  some control  over the  flow of
 events.   The  following  lists some events  that must  be controlled and  ordered
 in  a  particular way:
   1.
   2.
   3.
   4.
   5.
Data entry of bulk information must be done before the archive database
can be created, as well as the creation of the DM database.

Material information can only be entered after the bulk information has
been stored within the archive database.

DM database can only be accessed after the bulk information has been
stored.

Time frame requirements can only be set after the queries within the DM
database.have been created.

Analysis display functions cannot be accessed until  all  costs within
material information have been completed.
    This  control  has  been  enforced  by  enabling  and disabling certain  control
buttons  at given points within the execution.  This type of enforcement is
also used within the display functions to limit the user to the displays
containing information to be viewed.


Common User Activities--Throuahout this ISS system, a set of activities exists
that the user will expect to be able to complete.  These activities relate to
studying the available data and forming some conclusion based on the apparent
trends.  When attempting to study trends, it is sometimes beneficial for the
user to  be able to summarize or break the data into different sets according
to elapsed time (i.e. a trend may become clearer when data is viewed monthly
rather than simply daily).  Therefore, within this system, these activities
have been implemented using common controls and menu bars on each form.

Moving through the System: in order to move through the system, from whatever
form has the current focus, the user can specify the next display by clicking
on hot spots or control buttons located on the form itself.   Another way to
move from form to form within the system is to use the menu  bars located on
each form.  Within the pull down menus, the user is offered  the data type to
view, and based on what type the user chooses,  the display for that data type
is brought up.
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Grouping Data According to Time:  when the user is faced with attempting to
determine trends within the data, it may be easier to view the data according
to a different time frame.  The time frames within this system include:
daily, weekly, monthly, and quarterly.  This option has been implemented on
these forms in a control button known as the time period button, and the View
pull down menu.  The time period button located on the form will always
display the current time frame (the default is Daily).  If the user decides to
change the time frame, if using the time period button, the system will change
the time frame according to the order daily, weekly, monthly, and quarterly.
If the user is interested in viewing quarterly information, and does not want
to spend the time going through the other time frames, the user may go
directly to the quarterly time frame by using the View pull down menu.  When
using the pull down menu, the current time frame will appear with a check
beside its listing.  When either the time period button or View menu is used,
both are updated to display the current time frame in use.

    In addition to  these  common  activities,  the menu  bars  implemented on these
forms allow the users to perform additional activities.  The File pull down
menu offers the user two additional choices other than Close:  Print to print
the current form, and Exit to quit the system entirely.  The Graph pull down
menu enables the user to go to a common graphing form where the user is given
the choice of what types of materials to graph, based on the form that opened
the graphing capability.  Each menu contains a Help pull down menu.

Closing Forms: when moving around the system, the user will be forced to close
certain forms when the screen becomes too cluttered.  The cancel button and
Close menu option under the File pull down menu can be used in this case.  The
cancel button on a form, when pressed, will close the form and open the form
that appears above it in the tree structure of the transition diagram (Figures
12 and 13).  The Close option on the other hand, will only close the current
form and not open another form.

Studying Individual Pieces of Data:  while the display forms will show only
one data set at a time,  it becomes obvious that in order to make any type of
decision based on these data collections, all of the data must be able to be
viewed by the user.  Because this system is based on databases, two arrow
buttons have been  implemented on these forms to be used in traversing the
data.  When each button  is pressed, the form will display either the next data
set or the previous data set to the user.  These buttons have been programmed
to sound a beep if the user attempts to move to a data set that does not exist
either because the user  has reached the last data set in the table, or has
reached the first data set in the table.
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Using the ISS  system--

Starting ISS:   in order  to begin a  session with  ISS, double click the ISS
icon.   The first form that will appear deals with the Archive database.  This
window offers  the choice to either  use a Previously developed Archive file,  or
create a new Archive.  Assuming that  this is the first  time the  user is
running ISS, a new Archive is chosen.   If. a previous Archive database were  -
chosen, the summary found below would still be  followed,  but rather than
offering the user the ability to add  new data  in step 2,  the button will
appear with a  label "Add Data".  This button,  as well as  the "Material
Information" and "Work System" button will be  enabled,  allowing  the user the
choice of either continuing with the  current Archive as  is, or adding new data
to  It. •                                                                     .'-..',

       The following is a summary of the actions  that must  be taken  by the user
in  order to set up a study for a new  process:

    1.  From Setup menu form, click on grey factory button. This will lead to the setup form.

    '2.  Enter bulk  information through Excel application by pressing the "New Data" button.

    3.  User will be in Excel application.  Complete data entry within Excel application  and return to
       interface.

    4.  Wait until .computer has stopped working.

    5.  At this point, the "Material Information" button and the "Work System" button .will be enabled (i.e.,
       the user will be allowed  to click on these  buttons and receive a response). Enter material
       information by clicking on respective button.

    6.  Once the material information has been entered, click on the "Work System" button to create the DM
       database.  Wait until computer has stopped  working.

    7.  At this point, the Date Range box will appear on screen. The user is to choose the date range for
       the current session.

    8.  The setup form has now been completed, to continue on to display functions or specific information
       data entry press the "OK" button.

    At Step 7,  the user is asked to  determine a date range for use  within this
session.  This  date range  will  serve as  a filter  for the rest of the system;
only  data falling  within  that  date  range will be displayed  for the  user.   The
user  is allowed only to  set  the date  range at this time.  The date  range form
is  prepared.   It is made  up  of two  list boxes: the first one,  the start list
box will  contain a listing of dates within the system.   When the user
highlights and  clicks on  a given date,  the end list box will  contain all  dates
following the  start date.   This has been done to eliminate  the possibility of
the user entering  an ending  date that  may come before the start date.   If the

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user chooses to do so, all data can be viewed by highlighting the check box
located on the form.  This form will not disappear until the user enters
either a starting and ending date or checks the all data box.  At this point,
the system will continue on to the display functions.

Concluding a session with ISS:  the system can be terminated only through one
form, the Exit form.  This form is reached through the setup menu.  The Exit
form will ask the user for a full path name to save the current Archive
database.  The current Archive must be saved under a new name in order to run
the system again.  Once the Archive database is stored under a new name it can
be used in future sessions of ISS.
Development of Dynamic Forms--

    In this  system, most of the forms can be designed and created before run
time.  However, as in the DM database, those forms which are based on raw
material inputs, other material inputs, and products must be created at run
time.  This has been implemented in order to make the system able to
accommodate a variety of processes.  The user interface creates these screens
based on usage. If the user does not reference forms, the forms are not
created.  The forms display the chemical name, the amount of the chemical, the
unit of measurement, the unit cost, and then the total  price of the amount of
specific chemical.  The number of rows displayed is determined at run time.
ConstantForms--

   All  other  forms  not  based  on  raw material  input, other material  input,
products data can be developed during design time.  These forms deal with
waste and release amounts.  Appendix ISS - Visual Basic Interface gives a
diagram of each form and the modules that control it.
and
Quantitative Analvsis--

    The  quantitative  analysis  section  is  based  on  the diagram  in  Figure  7.
This figure represents the relationships among all types of data involved
within the process.  This diagram has been implemented with "hot spots" that
allow a user to position the  cursor on top of an  area and when the mouse is
clicked, a new form will open for the user. "Hot  spots" are located on top of
each box describing a type of data material.  By  clicking on one of these
boxes, the user will be sent  to the corresponding display form.  In addition
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 to these "hot spots",  there is  a listing  of common  comparisons  made with this
 type of data.  The user is free to  view those  display forms  by  double clicking
 on the desired description.   The appearance of this form will change according
 to the amount of different types of material entered into the system.   The
 "hot spots"  corresponding  to material  data  that has not  been supplied or
 calculated for the current session  will be  disable-appearing grey to the user
 and performing no action when clicked  upon.  This has been implemented in
 order to aid user in testing and limit the  possible choices  that  could be made
 by the user.  The hierarchical  structure  of the system is shown in Figure 7.
 This type of structure has been implemented in order to  facilitate ease of
 learning and high retention  rate for the  user.
 Cost  Analvsis--

    The cost analysis of this system will  follow the  same pattern  found  in  the
 quantitative  analysis.   The  basis for this section is shown in Figure 8.   It
 is  similar  to the form  used  by the  quantitative  analysis  in its use of  "hot
 spots"  and  the limitation of user choices according to available data,  but
 cost  analysis contains  different types of information. However, it does not
 offer the user the ability to view  common relationships among the different
 types of data.  Within  the cost analysis however, data entry screens can be
 brought up  to update  information dealing with labor and employees, machinery,
 miscellaneous factors,  and applied  pollution prevention techniques.


 Future Work
   With  the completion of this  ISS, the true test of any software package is
its acceptance by the users.  The system will be distributed to interested
users in order to get feedback.  It is hoped that this system will  be valuable
to the user.  The following is a listing of subject areas and potential
extensions of this work.

       Data Entry - A major concern throughout this project has dealt with the
measurements of materials within different parts of the system.  Because this-
system has been designed  to deal with  many data types,  not all  of which are
required by the system to successfully execute, formatting requirements have
been enforced.  Future work in this area can include:

   *  A better way of formatting data  coming into the  system.   This  method
      should be developed and  improved upon based on user feedback.

                                      105

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   *  A better way of determining and defining the types of data coming into
      the system.   A prime example is in the waste streams: for some
      processes,  all three types of waste may be measured, allowing the system
      to simply create a total for these three wastes.  Other organizations
      will measure only the total amount of waste coming out of the process
      rather than separating it into the three types of waste.  Not only does
      this affect the methods of accepting this data into the system, it also
      plays a role in calculating the predictive formulas based on these
      measurements.

   Output of System - At  this  time,  the  only  output  that  ISS  produces is  seen
in its display functions.  In the future, a report capability could be added
to this system.  At this time, the report function could be implemented as a
call to the DM database.  This would involve  creating dynamic reports within
the DM database.  It is not clear how much time the  creation of these reports
may require.

   Display  Function - Through  actual  use of this  system,  it  can  be improved
by adding display forms for relationships among the  data that may  not have
been included in the system.  These  relationships may be specific  to  a process
or organization, but can  be included within this  system if needed.

   Updating the System  -  As mentioned  above,  display functions  could be  added
to the system if appropriate.  However,  as an extension to this  system, a
"create your own relation" could  be  added.  This  would allow  the  user to
create a  useful relationship within  the  system.   This would allow the system
to grow according to the  needs and requirements of the organization.
                                       106

                                              *U.S. GOVERNMENT PRINTING OFFICE: 1995-650-006722069

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