Use of GIS to Predict Pollution Contents

 in Surface Water Runoff

By

 

Muhammad Kashif Gill

kashif@cc.usu.edu

CEE 6440:  GIS in Water Resources

Department of Civil and Environmental Engineering

Utah State University

 

Table of Contents

Introduction

Objectives

Data Sources

Study Area

PLOAD

Processing

Results

Discussion

Future Work

References

 

Introduction

Pollution and contamination issues are very common these days because of more awareness about the environment in which we live, breath and eat. Pollution problem was always there but has become severe after industrial revolution. The urge to get more profit in less cost make people mean in a way that they stopped caring about the surrounding environment and the kept spoiling it. The problem is worse in third world where people are less aware about pollution and contaminant issues and where pollution controlling agencies are not much active.

 

Main Page

 

Various Pollution Sources

                                                                                                Figure-1

 

Figure-1 depicts various sources of pollution that ultimately make their way to the streams. Pollution in surface water comes from rain and snowmelt water carrying agricultural fertilizers, herbicides, pesticides, toxic chemicals, animal and residential wastes, industrial, construction and automobile wastes into the stream.

EPA is the agency responsible for monitoring environmental pollution and describing standards. Growing concern about increasing environmental problems led the amendment of Federal Water Pollution Control Act of 1972. The amended law called “Clean Water Act” was implemented and EPA was given the authority to implement water quality standards. Since then EPA is working to make water cleans but couldn’t get much success. According to a report by ENN (Environmental News Network) published in October 2002, “Four of five wastewater treatment plants and chemical and industrial facilities in US pollute waterways much more than what their federal permits allow”.

GIS helps in many ways to predict pollution contents. It not only provide layout maps showing the worse affected areas but can also be used to simulate various models and see the affect after varying different inputs. GIS BASINS package is one of the tools that can be used to identify affected streams in a watershed and applying some BMPs (Best Management Practices) to reduce contaminant load.

 

Objectives

The objectives are

Ø      To evaluate the extent of contaminants in area

Ø      To locate impaired reaches

First of all quantative analysis of contaminant loads is required that how much each sub-basin in the watershed contributes to the surface run-off. Secondly, identification of worse affected reaches is most important.

 

DATA Sources

BASINS (Better Assessment Science Integrating Point and Non-Point Sources) is a GIS based package developed by EPA. It contains data compatible with ARC view and ARC info for the whole USA. My main data source was BASINS database. It provides you liberty to download data for any specific state, HUC or county. Major data is about land use details, weather station data, River reach data, water quality data and mineral data. Besides that, it also allows to download data being in BASINS environment using utilities like NHD download tool, Grid projector and various lookup tables. Besides BASINS, USGS and EPA data sources were used to download required information like EMC table and other related data.

 

Study Area   

I selected “Middle Bear River Basin” as my study area (HUC # 16010202). Middle river basin is part of Bear River Basin, located at the border of Utah and Idaho, most of the area falls in Idaho (Figure-2). It has been a focus area of study for environmental engineers for long. Figure-3 depicts major land use in the watershed based on level2 classification.    

   

 

   

(Figure-2): Middle River Basin (HUC# 16010202)

 

                       

                        (Figure-3): Land use details-level2 classification

 

One can classify the watershed using Lucode, Level1 or Level2 classification criterion provided by Basins. I used level2 classification as it looks more detailed than others. Figure-3 illustrates that major area (nearly 70%) in the watershed consists of cropland and pasture. Remaining area mainly includes non-forested wetlands and shrub and brush rangeland. Based on the land use, it looks that there are many feedlots in the area that encourage cattle raising.

 

PLOAD

BASINS package include various models to calculate contaminants/pollutants. Few of them are listed here.

Ø      Qual2e

Ø      HSPF

Ø      PLOAD

Ø      SWAT

Each one has its own utilities. I used PLOAD to calculate non-point source pollution. It estimates non-point source pollution loads on annual average basis for any user defined pollutant. It may calculate TP, DP, NOX, NH3, BOD, COD, TDL, TSS, and TKN. PLOAD is very useful model based upon its wider applicability and user friendly interface that leads you step by step to calculate specified pollutant loads in watershed. PLOAD is also very popular because of its wider applicability and allowance to use BMPs (Best Management Practices) and to see their impact on watershed before physically doing something on ground.

PLOAD inputs are land use data, watershed data, EMC table, BMP (optional), pollutant loading rate and pollutant reduction BMPs tables (optional). I used EMC (Expected Mean Concentration based on experiments) table as an input to account for the pollution load that each land use contribute to the watershed. (Table-1). You can see the value for TP is 1.00 for agriculture land as compared to 0.26 for residential. Similarly, relative values for other pollutants based on land use are also there.

 

EMC

LUCODE

LEVEL2

BOD

COD

TSS

TDS

NOX

TKN

NH3

TP

DP

11

RESIDENTIAL

7

43

39

73

0.33

1.05

0.26

0.28

0.09

12

COMMERCIAL AND SERVICES

6

46

26

48

0.40

0.98

0.25

0.10

0.04

13

INDUSTRIAL

6

46

26

48

0.40

0.98

0.25

0.10

0.04

14

TRANS, COMM, UTIL

10

94

104

30

0.74

1.65

0.40

0.33

0.17

15

INDUST & COMMERC CMPLXS

6

46

26

48

0.40

0.98

0.25

0.10

0.04

16

MXD URBAN OR BUILT-UP

6

46

26

48

0.40

0.98

0.25

0.10

0.04

17

OTHER URBAN OR BUILT-UP

6

46

26

48

0.40

0.98

0.25

0.10

0.04

21

CROPLAND AND PASTURE

8

103

132

192

0.24

1.47

0.35

1.00

0.23

22

ORCH,GROV,VNYRD,NURS,ORN

8

103

132

192

0.24

1.47

0.35

1.00

0.23

23

CONFINED FEEDING OPS

8

103

132

192

0.24

1.47

0.35

1.00

0.23

24

OTHER AGRICULTURAL LAND

8

103

132

192

0.24

1.47

0.35

1.00

0.23

32

SHRUB & BRUSH RANGELAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

41

DECIDUOUS FOREST LAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

42

EVERGREEN FOREST LAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

43

MIXED FOREST LAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

51

STREAMS AND CANALS

3

22

26

0

0.60

0.60

0.18

0.03

0.01

52

LAKES

3

22

26

0

0.60

0.60

0.18

0.03

0.01

53

RESERVOIRS

3

22

26

0

0.60

0.60

0.18

0.03

0.01

61

FORESTED WETLAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

62

NONFORESTED WETLAND

8

45

78

30

0.61

1.08

0.26

0.14

0.03

74

BARE EXPOSED ROCK

8

45

78

30

0.61

1.08

0.26

0.14

0.03

75

STRIP MINES

8

45

78

30

0.61

1.08

0.26

0.14

0.03

76

TRANSITIONAL AREAS

8

45

78

30

0.61

1.08

0.26

0.14

0.03

                                                                                                                        Table-1

 

PLOAD calculates pollutant load using two approaches.

Ø      Export Coefficient Method

Ø      Simple Method

I used Export Coefficient Method.

                                   

                                                LP= ∑U ( LPU * AU )

                        Where

                                    LP   = Pollutant load ,lbs

                                    LPU = Pollutant loading rate

                                   For land use type u, lbs/acre/yr

                                    AU  = Area of land use type u, acres

Output includes total pollutant load in watershed and pollutant load on per acre area basis.

 

Processing

  1. Download data for the required HUC from BASIN CDs using BASIN data extraction tool.
  2. Projecting data into UTM-1983 and of course Middle Bear Basin is in UTM zone 12.
  3. BASIN project builder loaded the projected data in a project.
  4. Then I loaded DEM files of the Basin to know the topography of the area. (Figure-4)

 

 

 

                                                                        (Figure-4)

 

  1. NHD download tool was used to download NHD reaches in the watershed. (Figure-5)
  2. Then I delineated my watershed using automatic delineation tool. (Figure-6). Figure-6 shows layout for delineated watershed into 25 sub-basins with main stream and respective outlets.

 

 

                                                            (Figure-6)

 

                                                                        (Figure-7)

  1. Once delineation was completed PLOAD model was run to calculate pollutant loads that each sub-basin contributes to the stream. Details about PLOAD model simulation is provided as under.
    1. Loading the model from BASIN Extensions in the file menu.
    2. As the PLOAD is loaded from Model menu, a session manager window (Figure-8) will open and lead to various steps to run the model.
    3. “Create session” frame will lead to create a new session to run the model. Then I defined watershed boundary data set in frame 2.
    4. Then watershed identifier field selection was done. After that watershed selection was done.
    5. Similarly, land use details were provided and Level2 was selected in “Land use Filed Selection” window.
    6. I selected Export Coefficient Method in calculation setup menu.
    7. Then I loaded EMC table into the PLOAD model and selected the whole table.   

 

 

                                                                        (Figure-8)

 

    1. Then land use selection code was selected in “Land use Field selection” frame and pollutants were selected in “Pollutant Field Selection” frame.
    2. Since I didn’t applied BMPs in my model and calculated only non-point source pollutants, I selected radio buttons in frame 6, 7 and 8 to “No”.
    3. Finally, model was run.
    4. After completion output layouts and tables were produced for each pollutant.

 

Results

Layouts depicting pollutant loads in each sub-basin are shown below in Figure-9 & Figure-10. Table-2 & Table-3 are actually parts of a single output table that tells about pollution loads in lbs and lbs/acre basis. Darker spots in the layouts account for the heavy pollutant loads in a particular sub-basin that enters into the stream. Scanning the results and going through the tables show that sub-basins #1, 23 & 6 that contribute to into Bear River, Newton Creek and Battle Creek respectively are worse affected by Phosphates. Similarly, Bear River, Cottonwool Creek & Mink creek (Sub-basins 1, 3&8 respectively) are worse affected by Nitrates.  

 

                                                            (Figure-9)

 

                                                                        (Figure-10)

 

ID

GRIDCODE

SUBBASIN

LD_BOD

LD_COD

LD_TSS

LD_TDS

LD_NOX

LD_TKN

LD_NH3

LD_TP

LD_DP

1

2

2

235396.6710

1801382.5632

2739478.2251

2215819.5638

14904.3026

34987.8221

8390.9929

11196.2971

2528.5179

2

1

1

764402.8084

7187718.4281

10119420.8441

10917860.5477

39884.6262

122666.8266

29344.8684

56105.8362

12831.3271

4

4

4

307169.6640

2337999.8549

3562993.9951

2856155.5536

19529.2200

45570.7752

10929.8302

14422.8246

3255.9225

5

3

3

346499.0074

2252179.6412

3660444.9656

2145584.3921

24493.7880

48818.3682

11732.8638

10556.6157

2344.2964

6

5

5

379294.3134

3439846.1809

4909105.2395

5070558.3690

20627.8169

60020.3572

14367.7849

25997.8056

5928.2129

7

7

7

105427.4935

735206.8419

1155483.6166

781582.1209

7299.9182

15258.2064

3679.1161

3906.0147

876.9435

8

8

8

331455.2158

2353640.5069

3687155.0012

2609357.0039

22152.6702

48035.9354

11531.4056

13054.1944

2929.8705

9

6

6

381267.4296

3899082.7853

5346279.6980

6318997.7956

18036.9793

63332.6498

15143.3301

32639.7987

7471.6931

10

10

10

187851.3430

1956322.0820

2668926.7901

3216526.8639

8596.3844

31414.0945

7503.3896

16624.2531

3806.1541

11

9

9

113966.3611

1138532.6800

1569545.1205

1804858.1023

5703.2070

18806.7416

4511.1193

9320.6594

2133.4529

13

11

11

22476.4996

284074.3860

365917.9416

524603.0848

708.1726

4094.3479

975.1063

2730.8199

627.8872

15

12

12

152671.1477

1518789.2208

2099354.4621

2414301.3167

7448.6893

25061.8416

5991.7471

12439.8857

2845.7671

16

13

13

299550.4786

2115688.6867

3321629.7737

2326352.1155

20093.0449

43335.5167

10403.7449

11628.6305

2608.5463

17

15

15

114906.4269

1415634.1068

1820322.3812

2584406.1246

3830.8572

20723.4450

4942.8428

13395.0288

3087.9119

18

14

14

125374.5110

815456.4783

1323419.8476

777705.2762

8857.4025

17669.9290

4247.0256

3821.6007

849.3358

19

16

16

361483.5677

3355684.9943

4751456.6539

5049296.7804

19153.1701

57715.7337

13810.2669

25928.7264

5917.2434

20

18

18

27014.5538

310572.3198

404994.1888

544611.4172

1044.1150

4726.1738

1129.0843

2807.1610

647.2278

21

17

17

301866.3686

3261404.8486

4361097.5018

5504106.2428

13144.7043

51418.7638

12300.6235

28410.4145

6534.3708

22

19

19

250739.6766

3103536.4521

4017297.4912

5669938.3603

8313.6159

45242.6792

10779.4005

29484.4011

6778.6742

23

20

20

337752.7468

2807144.5386

4108742.7899

3821542.7133

19869.1972

51770.5144

12411.2163

19392.1730

4422.6279

24

21

21

149306.8255

1785005.8108

2326343.0791

3208473.5395

5311.7955

26534.2599

6326.8048

16646.5621

3830.7674

25

22

22

127338.7303

845272.3564

1333919.3128

857825.2302

8783.7007

18122.6165

4361.4772

4166.7458

947.9269

26

23

23

491733.6283

5314595.8506

7132353.3005

8949950.9662

21703.7623

83812.4971

20064.0482

46317.9221

10639.0764

27

24

24

73321.9002

889533.9938

1153494.6266

1595595.2397

2751.1915

13193.7637

3163.3680

8309.4210

1912.1692

28

25

25

45534.9251

548856.5043

710107.7491

968768.2541

1916.4532

8240.2798

1991.9834

5061.5413

1165.7765

                                                                        (Table-2)

ID

GRIDCODE

SUBBASIN

ACRES

AREA_GOOD

AR_BOD

AR_COD

AR_TSS

AR_TDS

AR_NOX

AR_TKN

AR_NH3

AR_TP

AR_DP

1

2

2

29789.8138

120555593.5614

7.90191817

60.46974900

91.96023324

74.38178629

0.50031540

1.17448945

0.28167322

0.37584314

0.08487861

2

1

1

98341.8536

397976992.7091

7.77291438

73.08910871

102.90044852

111.01947084

0.40557123

1.24735117

0.29839654

0.57051839

0.13047677

4

4

4

38418.7308

155475724.4644

7.99531004

60.85572860

92.74106460

74.34278786

0.50832549

1.18616035

0.28449222

0.37541127

0.08474831

5

3

3

46648.2348

188779481.9690

7.42791252

48.28006142

78.46909923

45.99497497

0.52507427

1.04652123

0.25151785

0.22630258

0.05025477

6

5

5

51522.8788

208506547.1572

7.36166771

66.76347015

95.28010379

98.41372390

0.40036227

1.16492631

0.27886223

0.50458760

0.11505981

7

7

7

13445.8368

54413594.0901

7.84090236

54.67914365

85.93616253

58.12818737

0.54291290

1.13479039

0.27362493

0.29049993

0.06522045

8

8

8

41431.9020

167669645.3831

8.00000000

56.80744531

88.99313870

62.97941629

0.53467664

1.15939489

0.27832190

0.31507591

0.07071533

9

6

6

48146.8322

194844114.9609

7.91884766

80.98316353

111.04115170

131.24431052

0.37462442

1.31540637

0.31452391

0.67792204

0.15518556

10

10

10

23499.8589

95100945.2802

7.99372217

83.24824801

113.57203469

136.87430540

0.36580579

1.33677801

0.31929509

0.70741927

0.16196498

11

9

9

15577.8991

63041779.1495

7.31590058

73.08640740

100.75460821

115.86017413

0.36610887

1.20727073

0.28958458

0.59832583

0.13695383

13

11

11

2939.6791

11896508.2137

7.64590244

96.63448844

124.47547135

178.45590180

0.24090133

1.39278736

0.33170502

0.92895170

0.21359039

15

12

12

20796.0940

84159151.3774

7.34133764

73.03242719

100.94946013

116.09397980

0.35817732

1.20512254

0.28811887

0.59818376

0.13684142

16

13

13

37480.9092

151680479.7250

7.99208144

56.44710152

88.62191032

62.06765431

0.53608745

1.15620239

0.27757451

0.31025476

0.06959667

17

15

15

15449.3402

62521517.7438

7.43762681

91.63071616

117.82525063

167.28262121

0.24796251

1.34138059

0.31993876

0.86702918

0.19987338

18

14

14

15680.9516

63458819.6289

7.99533818

52.00299695

84.39665407

49.59554089

0.56485108

1.12684035

0.27083979

0.24370974

0.05416354

19

16

16

48614.4741

196736602.6822

7.43571898

69.02645882

97.73748954

103.86406258

0.39398081

1.18721296

0.28407727

0.53335404

0.12171773

20

18

18

3409.3148

13797064.1126

7.92374873

91.09523116

118.79049386

159.74219136

0.30625362

1.38625327

0.33117631

0.82337982

0.18984102

21

17

17

38302.1019

155003742.0424

7.88119590

85.14950060

113.86052685

143.70245939

0.34318493

1.34245280

0.32114748

0.74174557

0.17060084

22

19

19

31475.3364

127376688.4756

7.96622706

98.60216941

127.63318683

180.13908694

0.26413112

1.43740097

0.34247134

0.93674618

0.21536463

23

20

20

46291.9037

187337455.6735

7.29615159

60.64007557

88.75726556

82.55315526

0.42921538

1.11834922

0.26810771

0.41891068

0.09553783

24

21

21

19344.3669

78284196.4410

7.71836195

92.27522514

120.25945802

165.86087082

0.27459133

1.37167890

0.32706187

0.86053796

0.19803013

25

22

22

17243.4549

69782072.3643

7.38475735

49.01989545

77.35800746

49.74787449

0.50939332

1.05098523

0.25293523

0.24164217

0.05497314

26

23

23

65315.6467

264324126.9628

7.52857322

81.36788226

109.19823443

137.02614026

0.33229040

1.28319172

0.30718594

0.70913976

0.16288710

27

24

24

9600.2892

38851151.0086

7.63746786

92.65699973

120.15207069

166.20283061

0.28657381

1.37430898

0.32950757

0.86553861

0.19917829

28

25

25

6176.5172

24995580.4985

7.37226557

88.86181104

114.96895841

156.84700985

0.31028056

1.33413047

0.32250916

0.81948145

0.18874334

                                                                        (Table-3)

 

Discussion

In my opinion Phosphates and Nitrates are the most important pollutants with respect to surface water point of view. Of course ammonia is also one of biggest threat to the water quality of stream but Phosphates and Nitrates contribute the most. Phosphates in surface water come from human and animal wastes, industrial wastes and agricultural runoff. Major nitrate contribution in surface results due to poor farming practices like more use of fertilizers than plants actually need.

Analyzing the result of study shows that major area in the watershed is affected by Phosphates and nitrates. These loads enter into the stream through surface runoff and contaminate the whole river. Since the water is later used for Irrigation or drinking purposes, it is a big threat to the human beings. Various BMPs like fencing across the affected streams may be applied to reduce the pollutant affect on the surface water run-off.

 

Future Work

Ø     BMPs should be applied first in the model to visualize the decrease in contaminant loads and then to implement such practices physically on ground.

Ø     Estimation of Pollutant loads using other BASIN models and predicting respective loads.

Ø     Estimation of pollutant loads in surface water for the whole Bear River Basin can give a better picture of problem.

 

References

·        BASIN CD package

·        http://protectingwater.com/index.html

·        http://ohioline.osu.edu/agf-fact/0204.html

·        http://www.hoover.k12.al.us/sphs/Science/TRickard/Phosphates.doc

·        http://www.epa.ie/

·        http://water.usgs.gov/

·        http://www.bearriverrcd.org/