ME120 Conducts Mobile Total Phosphorus Survey to Assist Water Pollution Management | OceanAlpha

Location:        A river section of Taizhou city, Zhejiang Province

Date:                August 7th, 2019

Equipment:   ME120 Hydrographic Unmanned Surface Vehicle

                          Total phosphorus flow injection analyzer

Background:

To detect sudden water pollution outbreak of the city’s mother river in time, the government department of Taizhou, a coastal city of Zhejiang, China, introduced unmanned surface vehicle to their water monitoring routine to timely find out and restrain all illegal sewage discharge from companies alongside the riverbank.

According to the government officer, conventional water quality monitoring is well-developed regarding lad analysis, but the sampling frequency and sampling spot density of which is too low. Its disadvantages of spot location deviation and scattered data distribution also make it hard to present the evolution of water pollution comprehensively and timely.

Automatic sampling station widely built in recent years can only achieve full coverage in time, it can’t monitor the whole water space since the stations are built on fixed spots. This also lowers the accuracy and responding speed of water pollution monitoring.

 

Process

Deployed with a total phosphorus flow injection analyzer, a ME120 autonomous multi-purpose survey boat from OceanAlpha was utilized as a mobile on-line monitoring USV to monitor a designate river section.

From 10 am to 5 pm on August 7th, 2019, the USV conducted the mobile total phosphorus detection to the river section at the speed of 1 knot. The detection consisted of 84 monitoring spots and the total length is 3.5 kilometer. A water sampling report with sampling spot coordinates, time and sample amount was generated automatically during navigation.

Monitoring spots

 

Data sample

  1. Total phosphorus distribution map

  1. Total phosphorus monitoring summary table

Spot

Date & Time

Longitude

Altitude

Total phosphorus

mg/L

1

2019/8/7 10:05

28.8197810

121.1470710

0.315

2

2019/8/7 10:07

28.8199650

121.1473160

0.332

3

2019/8/7 10:09

28.8202310

121.1475220

0.320

4

2019/8/7 10:11

28.8205600

121.1476790

0.331

5

2019/8/7 10:13

28.8209633

121.1478286

0.320

6

2019/8/7 10:15

28.8212650

121.1478759

0.306

7

2019/8/7 10:17

28.8214677

121.1478786

0.292

8

2019/8/7 10:19

28.8218305

121.1478171

0.209

9

2019/8/7 10:21

28.8221637

121.1477168

0.191

10

2019/8/7 10:23

28.8223850

121.1476445

0.189

11

2019/8/7 10:25

28.8226732

121.1475720

0.194

12

2019/8/7 10:27

28.8229730

121.1474970

0.192

13

2019/8/7 10:29

28.8233440

121.1475020

0.193

14

2019/8/7 10:31

28.8236340

121.1476540

0.192

15

2019/8/7 10:33

28.8239870

121.1479230

0.194

16

2019/8/7 10:35

28.8242402

121.1483935

0.193

17

2019/8/7 10:37

28.8243375

121.1490532

0.192

18

2019/8/7 10:39

28.8243386

121.1494379

0.200

19

2019/8/7 10:41

28.8243675

121.1498449

0.189

20

2019/8/7 10:43

28.8244374

121.1502203

0.182

21

2019/8/7 10:45

28.8244839

121.1504931

0.184

22

2019/8/7 10:47

28.8245929

121.1508509

0.187

23

2019/8/7 10:49

28.8247383

121.1511932

0.173

24

2019/8/7 10:51

28.8249523

121.1514413

0.184

25

2019/8/7 10:53

28.8252755

121.1516243

0.186

26

2019/8/7 10:55

28.8256344

121.1517957

0.191

27

2019/8/7 10:57

28.8259987

121.1520519

0.170

28

2019/8/7 10:59

28.8262875

121.1523441

0.183

29

2019/8/7 14:14

28.8265345

121.1526678

0.263

30

2019/8/7 14:16

28.8267645

121.1530469

0.277

31

2019/8/7 14:18

28.8269950

121.1534229

0.263

32

2019/8/7 14:20

28.8272039

121.1537860

0.248

33

2019/8/7 14:24

28.8274536

121.1541006

0.224

34

2019/8/7 14:26

28.8278130

121.1543629

0.219

35

2019/8/7 14:28

28.8282048

121.1545241

0.194

36

2019/8/7 14:30

28.8287162

121.1546826

0.178

37

2019/8/7 14:32

28.8293973

121.1548532

0.187

38

2019/8/7 14:34

28.8299600

121.1549770

0.209

39

2019/8/7 14:36

28.8305147

121.1549493

0.214

40

2019/8/7 14:38

28.8310832

121.1549986

0.251

41

2019/8/7 14:40

28.8318317

121.1553450

0.221

42

2019/8/7 14:42

28.8322767

121.1559528

0.210

43

2019/8/7 14:44

28.8327237

121.1566546

0.179

44

2019/8/7 14:46

28.8333322

121.1571252

0.179

45

2019/8/7 14:48

28.8340610

121.1574187

0.180

46

2019/8/7 14:50

28.8345685

121.1577133

0.172

47

2019/8/7 14:52

28.8348925

121.1579799

0.168

48

2019/8/7 14:54

28.8352398

121.1582765

0.190

49

2019/8/7 14:56

28.8355744

121.1586075

0.183

50

2019/8/7 14:58

28.8358565

121.1588814

0.195

51

2019/8/7 15:00

28.8361614

121.1591758

0.194

52

2019/8/7 15:02

28.8364820

121.1594923

0.195

53

2019/8/7 15:04

28.8369314

121.1600349

0.180

54

2019/8/7 15:06

28.8371558

121.1603529

0.206

55

2019/8/7 15:08

28.8369748

121.1607395

0.317

56

2019/8/7 15:10

28.8363938

121.1597610

0.175

57

2019/8/7 15:12

28.8352173

121.1585764

0.163

58

2019/8/7 15:14

28.8339627

121.1576156

0.156

59

2019/8/7 15:16

28.8326636

121.1567979

0.170

60

2019/8/7 15:18

28.8316335

121.1554836

0.224

61

2019/8/7 15:20

28.8299880

121.1551690

0.220

62

2019/8/7 15:22

28.8287177

121.1548702

0.267

63

2019/8/7 15:24

28.8272541

121.1541904

0.340

64

2019/8/7 15:26

28.8262324

121.1528713

0.243

65

2019/8/7 15:28

28.8253233

121.1518537

0.267

66

2019/8/7 15:58

28.8199496

121.1461006

0.287

67

2019/8/7 16:00

28.8201101

121.1455995

0.281

68

2019/8/7 16:02

28.8201881

121.1452550

0.295

69

2019/8/7 16:04

28.8202373

121.1449194

0.234

70

2019/8/7 16:06

28.8199452

121.1445184

0.096

71

2019/8/7 16:08

28.8195179

121.1442227

0.084

72

2019/8/7 16:10

28.8188458

121.1442312

0.100

73

2019/8/7 16:12

28.8184767

121.1441746

0.079

74

2019/8/7 16:14

28.8179697

121.1441254

0.069

75

2019/8/7 16:16

28.8173282

121.1440252

0.056

76

2019/8/7 16:18

28.8168306

121.1440431

0.054

77

2019/8/7 16:20

28.8165877

121.1440803

0.062

78

2019/8/7 16:22

28.8163626

121.1439731

0.045

79

2019/8/7 16:24

28.8161720

121.1439570

0.044

80

2019/8/7 16:26

28.8159810

121.1439410

0.043

81

2019/8/7 16:28

28.8157540

121.1439160

0.050

82

2019/8/7 16:30

28.8153220

121.1438790

0.070

83

2019/8/7 16:34

28.8149920

121.1438680

0.062

84

2019/8/7 16:36

28.8146270

121.1441200

0.069

average value

0.193

Maximum value

0.340

Minimum value

0.043

Average difference

0.075

 

 Conclusion

Application of USV can monitor the full range of a designated water area with high efficiency and low cost. It can help environment managers to understand the water quality comprehensively in the shortest time.

As there is no human intervention in the data process, the accurate and authentic of the data is highly guaranteed. USV is proved to be an effective supplement to the traditional river cross-section monitoring methods.

 

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