Experiences from the use of sensors for assessing water quality in rivers in Finland

Transcription

1 Experiences from the use of sensors for assessing water quality in rivers in Finland HELCOM workshop on total uncertainties in the input estimates, Uppsala, Sweden Sirkka Tattari Finnish Environment Institute (SYKE) Helsinki, Finland With contributions to my colleagues: Jari Koskiaho, Elina Röman, Jarmo Linjama

2 Why to use automatic water quality monitoring? New sensor technology allows high precision observations of multiple water quality variables. It provides high frequency data in a cost-efficient way (considering the number of measurements) that allows covering most of the peak events. High frequency water quality data allow also more accurate load estimates if precise flow data is available. It also provides more accurate data for modeling and model calibration and contributes to a better understanding of in-stream processes, flow pathways, and how effective different management actions and mitigation measures are. 2

3 Automatic measurements include chlorophyll-a, turbidity, nitrate-nitrogen, ph, DOC, electrical conductivity Turbidity: 20 stations Nitrate: 9 DOC: 2 ph: 7 EC: 6 Chlorophyll-a: 2 Before Rivers, lakes and small catchments After cleaning 3

4 Used water quality sensors Sensor type Manufacturer Finnish supplier OBS3+ s::can nitro::lyser Campbell Scientific, Inc. ( m) scan Messtechnik GmbH ( A-lab Oy ( Luode Consulting Oy (

5 Differences of the sensor types Functioning principle OBS3+ sensor works by emitting near-infrared light into the water, then measuring the light that bounces back from the suspended particles Functioning of s::can nitro::lyser is based on a continuous optical spectrum reaching from low ultraviolet to visible light, which makes it possible to measure NO 3 -N concentration simultaneously with turbidity Cleaning of the sensor lenses OBS3+ sensors were equipped with a battery-powered mechanical wiper brushes s::can sensor lenses were cleaned by bursts of compressed air generated by either electric-powered compressor or exchangeable bottle of pressurized air

6 HYDROTEMPO database for real-time automatic monitoring: Usage at one's own risk!!! 6

7 Turbidity: 3 stations in Finland Nitrate-N mg/l Agric. basin Vantaanjoki river Savijoki small catchment Raw data, collected continuously into the Hydrotempo database at 1 hour interval Tot. Org. carbon mg/l Vantaanjoki river 7

8 Water quality sensors Savijoki small catchment, since 06/2007 Nitrate-N, turbidity

9 Selection of location for monitoring is amongst the first things to be considered Sensors should be located deep enough in the water to prevent wrong measurements if too close to the bottom or due to damages by ice and intensive biofilm formation if too high. Formation of biofilm on the measuring sensors, especially in summer, is common as well as sedimentation to the sensors. Wrongly selected location of the device (e.g. in the middle of the stream) can make cleaning very challenging. 9

10 Pitkäkoski measurement station at the river Vantaanjoki Assembling the sensor in October 2010 Maintenance of sensor in winter 2011 s::can nitro::lyser sensor The hole in the ice remained unfrozen under the Finnfoam insulation plates

11 Good quality data can be produced only if proper maintenance procedures are followed. It includes periodic manual removal of organisms and sediments or automated cleaning of sensors/lenses with liquids, compressed air or mechanically by brush

12 Data flow and communication system with main data services of SoilWeather WSN Requests for maintenance Supervision of quality Control Status information Automatic alarm Quality control Control of procedure Data query Database for validated data Maintenance Data query Maintenance and control of sensor nodes a-lab sensor nodes Automatic warnings Message database Raw Data Database WEB interface Data query Data query User 1 User 2 User 3 User n Requests for maintenance Data query User 1 Luode sensor nodes Database Quality control and calibration WEB interface Data query Data query User 2 User 3

13 Turbidity FNU Nitrate-N mg/l Savijoki Calibration equations y = x R² = y = 1.048x R² = Turbidity [FTU]_autom. Nitrate-N [mg/l]_autom. Production of reliable data requires calibration of sensors that can be done by using samples from the studied water body. Each sensor is different and thus local calibration should be sensor specific. If the sensor is mounted into a new place or land use in the catchment area changes, local calibration has to be rearranged.

14 Total nitrogen mg/l Ptot, µg / l Savijoki Conversion equations (n=43) y = 1,12x + 0,76 R² = 0, y = 1,20x + 70,06 R² = 0, Nitrate-N mg/l Turbidity FNU

15 One example, how the different hydrological flow patterns are caughted

16 Load calculations, Aurajoki in south-western Finland Load calculated with Tot P load (kg/a) Tot P load (kg/ha/a) Tot N load (kg/a) Tot N load (kg/ha/a) Hertta, monthly mean concentrations multiplied with monthly Q Sensor, monthly mean concentrations multiplied with monthly Q Sensor, daily mean concentrations multiplied with daily Q High frequency water quality data allow also more accurate load estimates if precise flow data is available.

17 RIVER BASIN SCALE Location of 5 turb. mesurement stations in the Karjaanjoki River basin Calibration eqs. (r 2 ) Billnäs: y=2,71*x (0,94) Väänteenjoki: y=2,8+2,12*x (0,86) Häntäjoki: y=0,5+5,23*x (0,90) Olkkalanjoki: y=2,51*x (0,97) Vanjoki: y=1,08*x (0,93) Difference in material flux (total SS) in the Karjaanjoki basin in as calculated on the basis of (i) automatic monitoring and (ii) water sampling. Environ. Monit. Assess 187(2015) 17

18 Aerial image of Hovi wetland Wetland area: 0.6 ha (5% of the catchment) Locations of sensor

19 Turbidity (FTU) Sensor-detected turbidity in the Hovi CW during Winter break during Jan.-Apr Inflow 3000 Outflow Time

20 NO3-N (mg/l) Sensor-detected NO3-N concentration in the Hovi wetland during Winter break during Jan.-Apr Inflow Outflow Time

21 Total nitrogen mg/l Autom. Ntot, µg/l Model vs. measurements year Automat. totn VEMALA model y = 1,1x + 0,39 R² = 0, VEMALA- Ntot, µg/l

22 Total phosphorus μg/l Autom. Ptot, µg/l Model vs. measurements totp 300 TotP VEMALA TotP, autom y = 1x + 16 R² = 0, VEMALA- Ptot, µg/l

23 SUMMARY Quality of the collected data can vary a lot depending on selection of location for monitoring, maintenance of the devices and data handling. Therefore, reliability of the produced data series is not always sufficient. The existing measuring methods differ in their functioning principle, measurement range and accuracy. Only limited number of variables can be measured with the presently available sensors. Nitrates, which are often the major N fraction in agricultural runoff, can be measured directly. Turbidity is often highly correlated with suspended solids and total P concentrations, enabling load calculations of these substances 23

24 Kiitos! Thank you! 24