Personal computers and environmental engineering Part II - Applications
Raymond P.cpnele Univemity of Michigan AM Arbor, Mich. 48109
Martin T.Auer Michigan Bchnobgicdl University Hwhton, Miah. 49931 Dramatic advances have been made in micro or personal computer technology in recent years (1).Most envirimnmtal engineers now have direct ~cceggto such computers and are using them increasingly because of their low cost and ever*xpandbg apabilities. prrsonal compltrrs also me portable and easily programmable for graphic and animated displays. Them are, howevec significant que+ tiom reganling the role of personal compltrrs for large-scalewater quality managwnent applications. For examPk 'cal operations and data bandling processes are slower on personal computws than on traditional mainframe computer system. If this limitation of the personal computer is not too serious, we may be able to increase s i g n i 6 d y our abiity to CommunicatcUre~tSOftechnicald~s% to decision makers. However, if the charaaeristics of personal computers prove too limiting, we may be unable to simulation of a variety of manageperform calculations efficiently. m n t options aimed at cost-efkdve The objective of this is to proimprovement of water quality, and vide an example of the application of e W v e communicationof technical personal computers for water quality concepts and management recommanagement in Green Bay, w is.The mendations to decision makers and capbiitim of the personal computer public interest groups. can help decision makers determuLe 'the The Green Bay system level of rcmedlatl ' 'onnecessarytom speci6ed Watersuality goals rhrough Green Bay, a large gulf on the norQdevelopment and use of complex west comer of Lake Michigan, has mahnatical models for water qnal- ' been cited BS one of the major problem ity pamuwen, such BS phosphotus areas for water quality in the Great Lakes.The bay's length along its major and OXY€+ organrumon and manipulation or the northeast-southwest axis is 160 km,its large amounts of k l d data r q k d mean width is 22 km,its mean depth is for model calibration and v d c a - 15.8 m, and it$ mean hydraulic m i tion. dence time is six years (2). The four
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938 Envimn. Sd.Rohnol..Vol.21. NO.10,1887
major tributarim to the bay are the Fox, oconto,F'eshtigo, and Menaninee rivers. The Fox River, which is the largest source of water and pollutants, contributes 45% of the major tributary flow 'and63% of the tributary biochemical oxygen demand to Green Bay. It also discharges 78% of the total phosphorus and 87% of the suspended solids loads into the bay. Mark& longitudinal gradients in water quality and trophic state OCCUT along the major axis of the bay in response to pollutant loadings from the Fox River. The southern end of Green Bay is hypenmtrophic: It Contains high levels of turbidity, chlorophyll, and phosphorus and is not saturated with dis-
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solved oxygen, despite its shallow depth. Water quality improves with distance fromthe mouth of the Fox River, approaching an oligotmpbic state Bp pmximately1oohtotheaorth,autr the bay's junction with LaLC MWgwf. Historically,indugtrial@ralpBBd~, per), municipal, and a g r b b a l discharges of oxygen-denw@g. .substances and nutrients: algal growth have depletion of dissolved the wmmer in the low
.
southern Green Bay. In wigter,,'oxyge~~ depletion has Mxwred *&e i& to 8ouIce loads of oxygen
stances during the past (le42a&-led'
toSO~improvement.inwater'quauty in the Fox River and southern Green Bay (4). Nevertheless, significant residual water quality problem remain. These problems ate related to poiht and nonpoint SwICes of phosphorus and sediment as well as internal production of
oxygendemanding materials. Green
r
The computer program was designed to: orient the user and communicate the overall features of the study site and approach; provide the capability to store and retrieve large amounts of information, including field data, model coefficients, and forcing functions; compute interactively pollutant concentrations on both a steadystate and dynamic basis in all parts of the bay; and provide an interactive management analysis tool for decision makers to permit convenient evaluation of alternative pollution abatement plans.
Definitions of symbols in Equation 1
4
= area of the interface between i and the adjacent cell j Ci = the oxygen concentration in cell i Ci = the oxygen concentration in adjacent cell j C, = the saturation oxygen concentration Ei = the coefficienttor dispersion across the boundary of cell K, = the atmospheric oxygen exchange coefficientfor cell i 4 = the distance between the centers of cells i and Pi = photosynthetic production of oxygen in cell i Qi = the flow leaving cell i Q, = the flow entering from cell j R, = water wlumn respiration in cell i Si = sediment respiration in cell i
t = time Vi = the volume of cell i
la.
.
In tbis article, we will dtscribe a m&XlWidmodeldesigncdto~
TOMphosphorus
c s u s b e f f e a r e l a t i ~ ~ ~Total - organic c a h n lutant loads and e n v b m m t a l 4Dissolvedoxygen tim in Green Bay. The model is wed to simulate the impact of remedial
actions on water quality. Ibemasbalsneemodel Water quality umditions in Green Bay are simulated using a mass balance model, The bay is divided into 19 wntml volumas or model cells (12 surface cells--one for each surfaoe cell that exhibits thermal shati6catioband7 bot-
tomcells).Amassbalaacecomputation is performedoneachmodelcell for all the variables of intereet, such a s d e ride, total phosphorus, total Mganic carbon, and dissolved oxygen. The maaSbollanceincludesexchengeamong adjacent model cells (horhnt.4 and vertical mass transport) and all sou~ces andsinks ofmaterial @ble 1). Thelast tbree variables in m l e 1, p h m p h , organic carbon, and oxygen, are clcaely related. The phosj&w conceatration controls algal activity and the intemal pl.odwtion of organic carbon. The breakdown of organic carbon
ogram
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ment respiration (S) was calculated from suriicial sediment characteristics and laboratory measurements of sediment oxygen demand (7). Re-aeration was calculated as the product of the atmospheric exchange coefficient (K) and the surface water oxygen deficit (8.9). Net horizontal advectlve mass transport (Q)was calculated as throughput using tributary flow data (USGS). Dispersive mass transport (E) was estimated from chloride profiles (honzontally) and from temperature profiles (vertically) (10). Data for overall model develop ment were collected weekly at 34 hay and tributary stations from May to Sep ternher 1982. The model of the Green Bay system has several components and mclndes complex nonlinear interactions and processes. The overall model contains 114 differential equations with timevariable forcing funaions. These eqnations must he solved numerically. The field data set for model calibration contains approximately 8OOO individual items of information. We need to be able to manipulate these data easily and quickly. Finally, we would lke to perform simulations rapidly and interactively to encourage application of the model for decision making. Computer software that meets all of these needs has been developed for thii study and is described below.
Applying the models The complexity of the water quamy problems and the management issues facmg deciiion makers in Green Bay must he addressed thmugh the development and application of mathematical models. The models described here are based on an extensive field-monitoring program and analyses of the key physical and biochemical processes that influence water quality. The potential of the personal computer for communicating results and faciitathg the decision making process was examined using a comprehensive computer program written for the lBM PC in the BASIC language. The p r o w is menu-driven to facilitate use and provide flexibility of operation. The main menu for the model software is shown in Screen 1. This, along with other color figures in this paper, are photographic mages of the actual computer screen. The main menu allows the user to select any of 10 options, ranging from an orientation and outline of the study approach to management applications. Thns all of the components of a comprehensive water quality management tool are at the fingertips of the user in a single personal computer program. System familiarization. The first five options from the main menu 938 Envlmn Sci Technol , W 21, NO 10.lW-I
SCREEN 1
Main menu for Green Bay ecosystem model soflware
Ill
Orientation Study tipproaoh System Monitorins C 4 l Model Components I 5 1 Hodel C e l l s I 6 1 Mechanism Studies 171 System Lordins c81 Model Input Data 191 -del Slnulations ti01 #hnas.mnt Applioatl I21 I33
SCREEN 2
Listingof m e water qual and site locatlon for Green
reen Ba' e Michi,
- High nutrients
-
-
High algae Poor clarity LOW oxysen
SCREEN 3
Key componentsand processes in the model' '
Settl
f
I Total
Phosphorus
1
Organ i c Carbon
I Dissolved oxusen
I
I J Reaerat ior Sediment
(Screen with system SCREEN^ .. .1) are concerned farmluulzation. They provide the user physical, chemical, andbbbglcaipmcesws in each model ceiP with an idea of the general nature of water quality problems in Green Bay, theshucaveandcomponeiotPoftbe m0de1,andthelOcationof~ statim. Screen 2 shows a _ I I
I
i
the interest of users. This is c*leofthe chief advantages of using personal rather than lnfhhme eomplbers. Option 4 from the main menu (Screen 1) gives a submrm that allows the user to view the o v e d htem&om among the model cornpanems and examine the details of any single variable in the model. Screen 3 shows a amputer-gmerated color graphic mnwentationofthekeyco+anhinBractions in the model. Different colors areusedtodistinguishbetwmdepen-
SCREEN5
dent variables, internally computed variables, forcing functions, kinetic pmcessas,andmass~mecbanisms.DependentViUi&leS~shown I in red, dculated variables in flow; and loadine. kinetic. end mBp8 aansfer
CUT in a single model
I
nir I
id.This figure is
coastructed by the wmputer with color SCREEN grsphics and u9e8 a O h % i O U to illW , m a p @ m B a y , b trate turbulent exchange among model cells. Animation is acunnplished by slowly revolving the mmcqtnc - . Circlei
rows move to represent the masstr88% Port P.Screen 6 shows a wmputex grapllie of the model cells superinpod m a map ofthe Green Bay, dmg with pling stations in the b y and in its m i taries. ThesetvDes of htmIim fad.
Envlmn.Sci.Technol..MI.21, NO. 10. i s 7
ose
discussion explains how the personal SCREEN^ computer can be used to organize and Computerdrawn pie chart showing of a large- COntributiOn by tributaries to total phosphorus load illustrate the comwmn~ scale water quality management project. The personal computer also can be Mean TP Loa used to store, display, and rehieve data on an interactive basis. Opfion 6 from the main menu (Screen 1) containsdata from experiments that dehe the major kinetic mechanisms in the model. Oprion Tallows the user to display systemloading data in a convenient manner. As an example, Screen 7 shows a pie chart for the total phosphorus loading I into the bay fromthe four major tribuPcshtir taries. It summarim vast quantities of data regarding the magnitude of phosOconto p h o r ~loading ~ from each tributary. (5%) Such illustrations are easy to develop with BASIC and color graphics commands available on -many personal computers. Option 8 from the main menu SCREENB (ScIYsXI 1) presents the user with a subTotalphosphorus concentration@rc,d menu for model input data. AU of the model coefficients, forcing functions, and calibration data can be interactively input, retained, and later retrieved from the computer using this submenu. The ability to handle data in an interactive maOner is an important advantage of the personal computer in largescale water quality management projects. Model simulations. The personal computer can be used to compute the steady-state or time-variable concentration of pollutants in various sections of Green Bay. The steady-state concentrations are computed by solving a system of linear algebraic equations. In our case, 19 equations represem the concentrations of each variable in each model cell. There are six model varia- .St8ady.*le m u m of distme ,,, -, o~F~~ nlvsr bles for a total of 114 equations. These % summer average totsl 01 phosphorw -tions can be solved by a of zng;nrpet$zy$ standard numerical techniques, We ‘Une reprmntsmncenlralnrnCSlCYlaled Imm rncdel have found that the Gauss-Seidel iterative technique. (11)is well suited for OUT SCREEN 9 equations when measured concentra- Timevariable total phosphorus concentration+b tions are used as initial guesses. Convergence to within O. 1% is usually obtained in 20-10 iterations and rakes less than one min of execution time when compiled BASIC programs are run with a standard IBM PC. Option 9 from the main menu (Screen 1) gives a model simulation submenu. The submenu allows the user to select the variable to be computed, steady-state or time-variable concentrations. and gra~hicor tabular outuut. Soreen 8 LOGSa computer-gene& plot of measured summer average phosphorus data (repmented by the small boxes) versus concentrations calculated by the model (shown as the lie). Time-variable concentrations are
0
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940 Envimn. Sei. Technol..MI. 21. NO.10. 1987
l l y l l l llluyll I
The equations have time-variable coefficients and forcing functions because t e m p e m and tributary loading vary throughout the season. The differential equations can be solved using various numerical techniques. M have found that a second-order Ralston technique (11) is simple to program and is .¶5ciently accuraSeailmiusing a step-size of 0.5 day. Screen- 9 shows a compum-gem ated plot of the 8e85on81 variation of phosphorus for the h t three model cells lakeward from the mouth of the Fox River The vertical axis represents phosphorus concentrations in mg/L; the horizontal axis representsthe. day of the year. M m able to compute the seasonal variation of all model variables in about 6 nun using canpfied BASIC programs and a standard IBM PC. Although this is satisfactory for most applications, the computation time cwld be rednced considerably by using 11 a math-processing chip, a more effi- SCREEN P r o l i b ofsteady4ate dlssohredoxygen concentratIoWb cient language, or more powerful hardware. Higher order mathematical techniques then may be employed with a larger step size, but thm are more diffidttoprogram. Weelectedtousethe moBt bask appmachbecause of its simplicity and widespread availabii.
applications. option 10 from the main menu (Screen 1) concerns management applications. This selection allows the user to employ the personal computer to investigate interactively various management stmtegies or pollution abatemeat alternatives. The user defines new lo&dsfor the variable8 of interest and then l e hlrns to the main menu (Screen 1) to recompute the resultrmt concentmion profiles. New profiles can be compared with prolilea wmputed under current conditions to evaluate the impact of changes in system loading. For example, using option 10, the total phosphorus loading from the upper Fox River was suMantiaUy reduced, simulating a hypothetical phosphorus management program; the software allowed newcollcentrah'onstobecomputedand corn@ with old concentrations. Screen 10 shows the comparisonon a plot consttllcted by the personal computer. The top curve represents a profile of phosphorus concentrations before a major phosphorus management program was undertaken; the bottom curve shows this pro* after the program came into being. The model also may be used to evaluate the relative importance of individual biochemical rate processes by omitting the contribution of specific components in the mass balance equations. For example, when option 8 is chosen from the main menu, the impact of sediment oxygen demand on dissolved oxygen
concentration can be examined by changing the appropriate coefficient Values. Screen 11 shows the calculated increase in dissolved oxygen (top curve) following a significant reduction in Fox River total phosphorus loads and the attendant reduction of the sediment oxygen demand to zero. The bottom m e shows the oxygen concentrations prior to the improvements. Historically, oxygen deplerion pro& lems in Green Bay have been linked to tributary loads of oxygendemanding substances. The simulations presented here stmngly suggest that internally produced organic matter and sediment oxygen demand a E the primary CBuSes of dissolved oxygen depkion in Green Bay. They indicate that remedial measures to control total phosphorus . . O& Chargesandreducea t the greatest oppoflrnity for significant
imp-inwaterquality. These computations can be performed easily with the personal canputer and the program developed for this project. The personal computer fa-
cilitatestheconsiderationofmanymanagemem alternatives on an interactive basii and enables decision makaB to evaluate. readily the impact of various abatement strategies on water quality.
New appkdhm Thepersonalcomputerisbecoming increasingly advantageous for modeling large systems such as Green Bay. More research will be devcxed to the development ofnatural system models that have fewer computetionsldemands and ale m r e accwate and reliable. S i l e models may be more reliable rhan needlessly complex ones, because of the Unceliainties inwith an excessive number of model coeffiEmimn. K I.Technd.. MI. 21. No. 10,1987 Mi
ley: Nnv York, 1979; pp. 368-73. (11) Cha a S C C d e , R. P. N u m e r i d M e d f A r k g i e m with Peraonal Cantpvtrr Applications; McQraw-Hill: New York, 1985, pp. 241,542.
cients. Monte Carlo techniques can be
used to evaluate the PMforWlnce and
reliability of models of dsemll mplexity 0). The convenience of persaasl annpmand the usc of siaipk attdeglmt models will fscitate the coasidcrstion of mom alternatives during thc &&n and evaluation phases of I a r g e d e modeling pmjccrs. This will more creative and holistic analym and
c e-
managanent activities. % will be ablc to communicate the d t s of our anelyses to decision makers and to tbc public mon effectively using graphics, animation, and the portability featurts of
personal computers. Overall, our experience with Gn?cn Bay and~personalc=JmpaermY suggests that water qudity modcling will play a gnatcr role in decision making and management. In the long run, we expect to see rhesc applications expand to larger system as complur
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hardware becomes more p W d and as we becomemore sophisticated inour apprcach to model design.
(5) Bulb. A. I(. M.S. Thsir, Michiv REb.ological U d V C r a i ,M.y 1984. 16)
Y
b.M.T:Kher.%.S.:C.nalc.R.I! a n dA&C Science
C&&I &~G 1% 43(2), 379-88.
O)GnrdincrR.D.;A~,,M.T.;C.nsle,R. I! In Envirohrncnrm En tneenn Pmceedin@ of the 1981 &cia&
cm&nce; pirbunri, M.; Divinny, 1. S., Us.; A m h Scci# of Civil Engineers: New Yo&. 19W pp. 514-29. (8) Dilbm, D. M.; cooeolly, 1. P. “Maihcrmpicsl Models of wptpr Qudity in Luge L.Lca. prrt 2. Lakc me,” BpAdoo/3-80aar; mw Dulutb, Minn.. 1980; pp. 124-
Fkld s(udies and model devehfmenl were wpponed by grants From Ems Othaof Gram and Centers, Washhgm, D.C. (orant NO. R809.521) and EPA’s IWhrnmental Research Laboratory. Duloth.
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042 Emimn.Sd. Techd.. MI. 21, No. 10.1887
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Ikayaod I! C u d # &) is a professor in the Depamnmt of Civil Engineering a rhe University of Michigan. He hpr published
mensively m subjects relored to mathenoriml modcling of w e r quality in naiuml systems. He is also the author OJSW era1 book and software packages on capvtcr appliaorions in engineering.
Miaii~I: Avar ( E ) is an associate prqfessor ofcivil engineering and an adjunct as-
sociate p t q k o r oJbiologinJ m‘ences01 Michigan &hnologiral University He receivcdhisB.S. inLwlogyfromtheSIaIe University oJNm lbnt Courge of Envimnmntal Science and Forestry. his M.S. in Syracuse University, civil engineering and his Ph D. in m e r resources science from he University of Michigan. Avpr has published widely on o variety of ropics reloring m I d e s , including algd ecology. nwrimt dynamics, and ewmphimtion.
from
