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
- Total phosphorus distribution map
- 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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