NL1044306B1 - Method and network of devices for the monitoring of surface water quality - Google Patents
Method and network of devices for the monitoring of surface water quality Download PDFInfo
- Publication number
- NL1044306B1 NL1044306B1 NL1044306A NL1044306A NL1044306B1 NL 1044306 B1 NL1044306 B1 NL 1044306B1 NL 1044306 A NL1044306 A NL 1044306A NL 1044306 A NL1044306 A NL 1044306A NL 1044306 B1 NL1044306 B1 NL 1044306B1
- Authority
- NL
- Netherlands
- Prior art keywords
- sensor
- data
- sensors
- water quality
- surface water
- Prior art date
Links
- 239000002352 surface water Substances 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 12
- 238000012544 monitoring process Methods 0.000 title abstract description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 56
- 239000002689 soil Substances 0.000 claims abstract description 25
- 230000004927 fusion Effects 0.000 claims abstract description 23
- 238000007667 floating Methods 0.000 claims abstract description 12
- 235000015097 nutrients Nutrition 0.000 claims description 15
- 241000195628 Chlorophyta Species 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 7
- 241000195493 Cryptophyta Species 0.000 claims description 5
- 241000196324 Embryophyta Species 0.000 claims 1
- 238000005259 measurement Methods 0.000 claims 1
- 239000002699 waste material Substances 0.000 claims 1
- 238000012546 transfer Methods 0.000 abstract description 2
- 208000034699 Vitreous floaters Diseases 0.000 description 27
- 241000192700 Cyanobacteria Species 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 9
- 230000009182 swimming Effects 0.000 description 6
- 239000003814 drug Substances 0.000 description 5
- 238000003482 Pinner synthesis reaction Methods 0.000 description 4
- 229940079593 drug Drugs 0.000 description 4
- 239000000575 pesticide Substances 0.000 description 4
- 229920003023 plastic Polymers 0.000 description 4
- 239000004033 plastic Substances 0.000 description 4
- 230000005791 algae growth Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000001580 bacterial effect Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 239000003651 drinking water Substances 0.000 description 2
- 235000020188 drinking water Nutrition 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000013502 plastic waste Substances 0.000 description 2
- 230000005180 public health Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 230000036642 wellbeing Effects 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 241000588724 Escherichia coli Species 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- -1 ammonium ions Chemical class 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000031018 biological processes and functions Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 210000003608 fece Anatomy 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 238000002189 fluorescence spectrum Methods 0.000 description 1
- 238000011010 flushing procedure Methods 0.000 description 1
- 230000012010 growth Effects 0.000 description 1
- 239000005556 hormone Substances 0.000 description 1
- 229940088597 hormone Drugs 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000000968 intestinal effect Effects 0.000 description 1
- 239000010871 livestock manure Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 239000010841 municipal wastewater Substances 0.000 description 1
- 150000002823 nitrates Chemical class 0.000 description 1
- 239000010815 organic waste Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 description 1
- 229910001220 stainless steel Inorganic materials 0.000 description 1
- 239000010935 stainless steel Substances 0.000 description 1
- 229910052719 titanium Inorganic materials 0.000 description 1
- 239000010936 titanium Substances 0.000 description 1
- 238000004065 wastewater treatment Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
- G01N33/1886—Water using probes, e.g. submersible probes, buoys
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
- G01N33/246—Earth materials for water content
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Food Science & Technology (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Medicinal Chemistry (AREA)
- Pathology (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Remote Sensing (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The present invention relates to a method, network of devices, network of data and network of sensor fusion algorithms for the monitoring of surface water quality, characterized by at least a first sensor from the group of soil moisture sensors with at least two conductive 5 pins, both operatively connected to soil, at least a second sensor from the group of floating water quality sensors, operatively connected to surface water, means to transfer the sensor data to at least a first server and a first blockchain, a first sensor fusion algorithm translating soil moisture and surface water sensor data to a present and future surface water quality, a second sensor fusion algorithm translating the water quality data to a 10 surface water finger print, a first blockchain algorithm that automatically sends tokens existing on the first blockchain to sensor owners as a reward for uploading sensor data to said first server and said first blockchain and a publicly available map with an overview of the sensor locations and the surface water quality data so that public awareness is created on surface water quality. 15 The method and network of devices according to the present invention makes it possible to improve the quality of the surface water in our living environment through a community- driven ecosystem in which people are empowered to monitor and measure their local water quality and are rewarded with crypto tokens for contributing water quality data to the network. 1044306
Description
i
Method and network of devices for the monitoring of surface water quality
The present invention relates to a method, network of devices, network of data and network of sensor fusion algorithms for the monitoring of surface water quality, characterized by at least a first sensor from the group of soil moisture sensors with at least two conductive > ping, both operatively connected to soll, at least a second sensor from the group of floating water quality sensors, operatively connected to surface water, means to transfer the sensor data to at least a first server and a first blockchain, a first sensor fusion algorithm translating soil moisture and surface walter sensor data to a present and future surface water quality, a second sensor fusion algorithin translating the water quality data io a 19 surface water finger print, a first blockchain algorithm that automatically sends tokens existing on the first biockchain to sensor owners as a reward for uploading sensor data to said first server and said first blockchain and a publicly available map with an overview of the sensor locations and the surface water quality data 50 that public awareness is created on surface water quality.
The method and network of devices according to the present invention make it possible to improve the guality of the surface water in out living environment through a community- driven ecosystem in which people are empowered to monitor and measure thelr local water quality and are rewarded with crypto tokens for contributing water quality data © the network. introduction
The surface water in our living environment is vital for our health and wellbeing and provides essential habitat for various plants and animals,
We use our surface walter for recreation, such as swimming, sailing, and drinking water production. Unfortunately, the quality of our surface water is under continuous pressure, mainly because of our use of medicines, pesticides and plastics.
The primary source of medicines In our surface water originates from flushing our toilets.
The toilet water ends up in municipal wastewater treatment plants (MWT TPs), where the treatment plant biologically purifies the water before being discharged to the surface wader.
Unfortunately, the biological process at MWTTPs is nat sufficiently efficient to remove all medicine traces. As a result, the MWT TPs release many medicines Io the surface waler through the effluent,
The primary source of pesticides in our surface water is agriculture for crop protection.
Traces of these pesticides are extracted from the soll by rainwater and transported to the surface Waler
Our use of plastics and the resulting plastic waste in our environment results in mieroplastics in surface water, Micropiastics are small plastic particles originating from eroded and partially decomposed plastic waste,
Medicine traces, pesticides and plastics ave often referred to as micropoliutants since their presence is in the range of micrograms or even nanograms per litre of water.
Micropotiutants may seriously affect the metabolism of humans and other living organisms since a number of these components and thelr metabolites appear to interfere with, amongst others, hormone balances.
Past two decades, it became clear that we need to pay more attention to inproving the quality of our surface water since the micropoliutants in our surface water are only partially removed during the drinking water production process. As a result, increasing levels of micropofiutants affect our health.
The importance of suitable surface water quality is intermationally recognized, in 2000, the
EU adopted the so-called EU Water Framework Directive {WFD). The WFD comprises a timetable for the stepwise realization of water quality improvement steps to be executed by 2033.
Besides micropofiutants, high nutrient concentrations in the water, such as ammonium ions, phosphate ions and nitrates, may seriously affect water quality. These components originate from manure and fertilizer used In agriculture and other organic waste materials ending up in our surface water during periods of rain.
A nigh concentration of nutrients in surface water results In excessive growth of algae and cyanobacteria, often referred to as blue-green algae. As a result, the biological equilibrium in the water is disturbed, which may result in the excessive dying of fish and other aquatic organisms due to periods of low oxygen concentration in the water, Additionally, the blue- green algae may produce toxing and taste-and-odour compounds that may cause serious public health concerns and substantial economic damage in recreation areas since people 2h cannot recreate in the water during periods of algae bloom.
To sunumarize, it js essential for our health, wellbeing and environment that we safeguard the quality of our surface water, important points of attention are the micropoliutants and large amounts of nuirients that are currently accumulating in our environment and surface water due 10 our human footprint.
Al present, governments monitor surface water qualily periodically at a limited number of public swimming places, especially during the swimming season. In the EU, a directive for monitoring swimming weder quality Le, Directive 76/160/EEC, is in force since February 2008. In a nutshell, this directive describes how to ensure public health by periodic sampling and analyzing public surface water and by determining the concentration of
Escherichia coli and Intestinal enterococci in water as well as the risk of cyanobacterial {green-blue algae) bloom.
An important drawback of the presen! monitoring procedure are the relatively high labor cost Yor taking samples, preparing the samples Tot analysis and performing the anadyses.
Because of the high cost involved, monitoring is taking place at only a limited number of public swimming places and at a relatively low frequency. As a consequence, undesired changes in water guality are often not detected, or detected ton lata. 3 With the technology according to the present invention we aint to solve this problem by providing a community-driven monitoring system to monitor the surface water quality In our fiving environment The method and devices according to the present invention will make # possinks to rmonior surface water guality real-time and inline al a very large number of places i a very cost effective way, Changes in wader quality wil be detected by the network ata very fast rate. Al the same time, it will also be possible Io make water quality pradictions based on historical sensor data, historical weather data and the weather forecast. This approach opens possibilities for wader quality labs to selectively Increase the sampling in regions with high risks for bad water quality and, if necessary, to warn the government with an advice 10 take action.
Hence, the method and devices according te the present invention will be an important tog! and call to action Tor the community and the government to jointly improve the surface water quality in our living environment.
Technical description of the present invention
According to a first aspect, the present invention relates to at least one sensor from the group of piners Le, soil moisture sensors. Preferably, a soil moisture sensor is equipped with at least Z electrodes pins made of stainless steel or titanium which are placed in the ground. Preferably, the electrodes have a length of at least 20 om and are placed in the ground at a distance of about S ons from wach other, The pins are operatively connected to an AC source with a frequency of preferably 1 kHz. This frequency nan easily he generated by the PWM (pulsed width modulation) output of & first microcontroller, a NPN transistor as amplifier and a coupling capacitor to obtain an AC source. Preferably, the impedance of the wet sail between the electrodes is measured by the use of an analog to digital converter which is preferably connected to the first microcontrollers. IL ls noted that the impedance of the soll between the slectrodes depends on the moisture ity the soll and on the conductivity of the moisture in the soil. The conductivity of the moisture in the soil appears to be a good measure for the concentration of nutrients that are present in the soil. Summarizing, the soit moisture sensor measures the impedance of the soil between hath electrodes which contains information on the moisture content in the sof and the concentration of nutrients in the soil Stand alone application of the soll moisture sensor according to the present vention brings along several practical problems: 1. His not possible to discriminate between a high moisture content with low condustivity and a low maiative content with high conductivity since both moisture contents may resul In the same impedance of the soll belween the electudes, 2. His not possible to discriminate between a high moisture content in the upper part of the soli and a high moisture content in the lower part of the soil, since both maistare contents may result in the same impedance of the soll between the electrodes.
With the method according to the present invention, comprising sensor fusion, itis possible to take away both disadvantages. In order to take away the rst disadvantage, the soll sensor is preferably equipped with a alr humidity sensor, a temperalirs sensor sensor and an aly pressures sensor, These additional sensors collect dala on the weather conditions at the location where the soll sensor is placed jn the ground. By rezording a history file of weather data together with the sof impedance data and by application of an algorithm with the impedance data and the weather data as input, the inventors of the technology according to the present invention, furtheron referred to as “the inventors”, were able to 1% derive the concentration of nutrients in the soll mulsture and the sol nojsture content from the soil sensor data. The inventors also found that an additional VOC {volatile urganic compounds} Sensor, measuring the VOC content in the air close to the ground, adds aclditional information to the data, making the prediction of the concentration of nutrients in the soif even more accurate. In a preferred embodinient of a soil sensor according to the 200 present invention, the soil sensor consists of at least 2 electrodes, preferably with a length of at least 20 cm, that are operatively connected to the soll, means to determine the impedance of the soft between the electrodes using an AC source, a lemperatire sensor measuring the temperature of the alr and 7 or the soll, an air humidity sensor and a VOC sensor measuring the VOC concentration in the aly close to the ground, means for storing 35 the sensor data, resulting in a history Tie of the data and an algorithm translating the sensor data to a soil moisture content in the upper part of he sensor, a soit moisture content in the lower part of the sensor, a putrient concentration in the solf moisture in the upper part of the sensor and & nutrient concentration in the soil moisture In the lower part of the sensor,
According to a second sapect, the present invention relates to a sensor fusion algorithm converting the soil sensor data Into a "focal wash out of nutrients from the soll parameter”
Pwash. The value of Pwash al a certain location L is related to history Tile of temperature, air humidity, rainfall, soll humidity in the upper part of the soil, suit humidity in the lower part of the soil, conductivity in the upper part of the soft and conductivity In the lower part of the soil, all at location L. The value of Pwashi{l) Indicates the amount of nutrients that wilt be washed out from the sof and collect in the surface water near L. The values of Pwash{L} in a certain region combined with a weather forecast for that area make i possible to make a surface water quality forecast at focation L. Making surface water quality forecasts based on local Pwash(L) valùes and actual local surface water gualities, expressly makes part of the technology according to the present invention.
According to a third aspect, the present invention relates to at least one sensor from the group of Roaters Le, floating water quality sensors. Afirst type of floating water quality 5 sensors, furtheron referred 10 as "type one floaters”, preferably measures temperature of the water, electrical conductivity (EC) of the wader and light transmission at preferably 5 wavelengths in the UVA and visible regions and / or near infracead (MR) regions. A second type of floating water quality sensors, Le, type 2 floaters, measures temperature of the water, EC, light transmission at preferably 7 wavelengths and the fluorescence spectum id resulting from excitation wavelengths at each of these 7 wavelengths in the range between 300 nm and 850 nm, preferably with a resulotion of 300 measuring points,
The inventors have found that the first type of floaters provide important information on the surface water quality, Water temperatures above 20 degrees Celsius appear lo provide important information on the resuliing increased risk for bacterial and algal growth rates. An increase in electrical conductivity of the water appears to indicale an Increased concentration of nutrients in the water as well as an increased risk fot bacterial and algal growth, Finally, specific changes in the light transmission profile of the spectrophotometric sensors indicate bath an increased concentration of green algae ar blue green algae {cyanobacteria) and an increased concentration of nutrients. In the surface water,
Especially, an increased absorgtion of LVA light, blue light and red light as compared to green light are a strong Indication for algal growth. An increasing absorption with decreasing wavelength is a strong indication for an increase in the concentration of nutrients in the surface water
The inventors have found that the second type of floaters is able to measure the same information as the first type of floaters. On top of that, the second type of floaters is able to specifically detect green algae and blue-green algae. Since blue-green algae produce harmful ioxines, the blue-green algae concentration in the water is an important swimming water quality parameter. Because of this reason, the second type of floaters can be used in addition to the first type of floaters to provide extra information on water quality.
According to a fourth aspect, the present invention relates to a sensor fusion algorithm transtfating the information produced by type one floaters and / of type two floatersio a concentration of nulrients in the surface water and a concentration of green algae and / or blue-green algae In the surface water based on wader temperature, water conductivity and the transmission quotients of all transmissions measured with the type one floaters and for ype two floaters. In case type two floaters are applied, the surface water quality data can be further enriched by using the fluorescence spectum at the different LED wavelengths, le, al the different excitation wavelengths, of the type two floaters. The fluorescence spectra produced by the type two floaters can be used to quantitatively istermine the concentration of green algae and blue-green algae in the surface water, This means that the type two floater not only provides additional information on the surface water quality, but can also be used to validate the sensor fusion algorithms translating the vinner and floater 3 type one measwremants info surface water guality. fl is noted thd type two floater can also determine different species of green algae and blue-green algae since these species have different aborption and fiuurescence characteristics. Application of the type two floaters to (iaternyne the algae species present in the surface water and using the resulting information to Improve the sensor fusion algorithms exiwessly makes pan of the present invention.
According to a fifth aspect, the present invention relates To first means for connecting the pinners, type one floaters and type two floaters to a server and / or second means for connecting the pinners, type one floaters and type Iwo floaters © a blockchain, The first means and second means for connecting the ninners and floaters to a server and / or is blockehaln consist of hardware Trom the groups of Buetonth connectivity devices, wifi connectivity devices, LoRa connectivity devices, M2M connectivity devices, connectily devices using mobile technologies like SIM cards, connectivily devices using 5G technology, GPRS devies or connectivity devices using Hcerse-free radio communication bands. The server receiving data from the first means for connecting the sensors is zi equipped with software and hardware to fill and store a database containing time stamps of the data, the sensor data and the SPS coordinates of each sensor, Optionally, the datalzase is stored on a blockchain sn thal the data integrity is secured. Preferably, a hash of the data is stored on a blockehain instead of the data, This way, data integrity can be guaranteed without the need to burden the blockehain with large amounts of data. fl is also possible Io upload the data directly rom the sensor into to blockehain using second means for connection. These second means fr Connection are characterized by the previously mentioned hardware to connect the sensors increased with wallet software and biockehain transaction software in the sensor's Le in the ninners and floaters of type one and type two, An example of a blockehain that Is very feasible Tor this approach is the Algorand plockehain.
According to a sixth aspect, the technology of the present invention relates to A sensor fusion adgorittun aggregating data from different locations In a user defined geographical region G. The algorithm produces data conformity parameters Dim and Dt for each sensor in region G. The conformity parameters Dm and Dt of a sensor § are a measure for the momentary and time averaged deviations from sensor £ dada to all sensor data in region © respectively. In other words: high values of Dm and / or Dt of a sensor S indicate that, at the location where sensor S installed, unexpected and / or undesired changes In water quality are taking place.
According to an seventh aspect, the technology of the present invention relates to a sensor fusion algorithm raising sensor network alarms based on the Dm and / or Dt values of all sensors installed in the network. This approach cannot only be applied within a geographic region G but also io compare the sensor behavior between different geograpic regions K and L. The automatic comparison of differences in Dim and Dt behavior between different geographic regions K and L expressly makes part of the present invention.
According to a eighth aspect, the technology of the present invention relates to a graphical representation of sensor data on a map with GES coordinates characterized by means to express both the overall surface water quality as well as means to characterize the certainty of the surface water guality assessment. An example of such representation is a colored hexagon symbol on a map with a white circle in the middie of the hexagon: the color indicates water quality e.g., green is good quality and red is bad quality, and the white circle in the hexagon indicates the certainty of the data e.g., a small dot indicates a high certainty and a large dot indicates a low certainty of the data, It is noted that the graphical representation of water quality data on a char, combining water quality and certainty of the gata is essential for a good overview of the water quality in a geographic region. Therefor, it makes expressly part of the technology of the present invention.
According to & nineth aspect, the present invention relates to a reward system for sensor owners to share their data to the network. This reward system automatically rewards sensor owners with crypto tokens relative to the amount of data packets their sensors upload to the network, A non limiting example of a blockchaln that is feasible for such reward system is the Algorand blockehain.
According to a tenth aspect, the present invention relates to assessing a fingerprint of surface water by the use of the sensors and sensor fusion algorithms defined in this application. With fingerprint, the inventors mean in this application a series of water quality specific signals produced by the sensors combined with a series of water guality parameters that result from the sensor fusion algorithms,
Clauses 1. Network of surface water quality sensors characterized iy
® Aal least a first sensor from the group of soil moisture sensors measuring at least soil resistance between at least Iwo electrodes, air temperature, relative humidity of the af, alr pressure and VOC content in the air e al least a second sensor from the group of floating water quality sensors measuring at least water lemperalure, water conductivity, transmission of light through the water at five or more wavelengths in the UVA and / or visible and / of NIR regions
& al least one sensor fusion algorithm converting the soll moisture sensor data into a “Jocai wash out of nutrients from the soll” parameter Pwash
# atleast one sensor fusion algorithm translating the floating water quality sensar data 10 a concentration of nutrients in the surface water and a concentration of green algae and / or blue-green algae in the surface water
#2 first means for connecting the pinners, type one floaters and type two floaters to a server and / or second means for connecting the pinners, type one floaters and type two floaders directly or indirectly to a plockchain.
8 a server or blockehain receiving data from the sensor and storing the individual sensor dala in a database containing time stamps of the data, the sensor data and the GPS coordinates of each sensor e al least one sensor fusion algorithny producing data conformity parameters Dim and Dt for each sensor in al least one region CG.
e af least one graphical representation of sensor data on a map with GPS coordinates characterized by means to express both the overall surface water guality as well as means to characterize the certainty of the surface water guality assessment.
& areward system that automatically rewards sensor owners with crypto tokens relative to the amount of data packets thelr sensors upload to the network of surface water quality sensors.
2. A network according to clause 1, increased with at least one sensor from the group of type two floating water guality sensors. 3. Method for realizing a network of surface water quality sensors according to one of the previous clalises 1 and 2.
Claims (5)
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| NL1044306A NL1044306B1 (en) | 2022-04-29 | 2022-04-29 | Method and network of devices for the monitoring of surface water quality |
| PCT/NL2023/050228 WO2023211277A1 (en) | 2022-04-29 | 2023-04-28 | Network and method for the monitoring of soil and/or surface water quality |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| NL1044306A NL1044306B1 (en) | 2022-04-29 | 2022-04-29 | Method and network of devices for the monitoring of surface water quality |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| NL1044306B1 true NL1044306B1 (en) | 2023-11-13 |
Family
ID=82780871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| NL1044306A NL1044306B1 (en) | 2022-04-29 | 2022-04-29 | Method and network of devices for the monitoring of surface water quality |
Country Status (1)
| Country | Link |
|---|---|
| NL (1) | NL1044306B1 (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN204649234U (en) * | 2015-05-11 | 2015-09-16 | 深圳市鸿和达水利水环境有限公司 | A kind of multi-parameter water-quality Acquisition Instrument based on ARM and GPRS technology |
| CN106706876A (en) * | 2017-01-10 | 2017-05-24 | 南昌大学 | Bio-floating bed with on-line monitoring and intelligent remote alarming functions, and measurement and control method |
| US20200364456A1 (en) * | 2019-05-13 | 2020-11-19 | Bao Tran | Drone |
| KR102187336B1 (en) * | 2019-11-29 | 2020-12-07 | 이현찬 | System for water quality monitering |
| US20210140908A1 (en) * | 2019-11-04 | 2021-05-13 | Van Wall Equipment, Inc. | Soil moisture and nutrient sensor system |
| CN112798333A (en) * | 2021-03-31 | 2021-05-14 | 江西省生态环境科学研究与规划院 | Sampling drill bit for soil remediation, soil information system and information management method |
-
2022
- 2022-04-29 NL NL1044306A patent/NL1044306B1/en active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN204649234U (en) * | 2015-05-11 | 2015-09-16 | 深圳市鸿和达水利水环境有限公司 | A kind of multi-parameter water-quality Acquisition Instrument based on ARM and GPRS technology |
| CN106706876A (en) * | 2017-01-10 | 2017-05-24 | 南昌大学 | Bio-floating bed with on-line monitoring and intelligent remote alarming functions, and measurement and control method |
| US20200364456A1 (en) * | 2019-05-13 | 2020-11-19 | Bao Tran | Drone |
| US20210140908A1 (en) * | 2019-11-04 | 2021-05-13 | Van Wall Equipment, Inc. | Soil moisture and nutrient sensor system |
| KR102187336B1 (en) * | 2019-11-29 | 2020-12-07 | 이현찬 | System for water quality monitering |
| CN112798333A (en) * | 2021-03-31 | 2021-05-14 | 江西省生态环境科学研究与规划院 | Sampling drill bit for soil remediation, soil information system and information management method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Quinteiro et al. | Identification of methodological challenges remaining in the assessment of a water scarcity footprint: a review | |
| Gabriels et al. | Multimetric Macroinvertebrate Index Flanders (MMIF) for biological assessment of rivers and lakes in Flanders (Belgium) | |
| Zhang et al. | Decoupling analysis of water use and economic development in arid region of China–based on quantity and quality of water use | |
| Sayers et al. | Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing | |
| Herbst et al. | Interpreting the variations in atmospheric methane fluxes observed above a restored wetland | |
| Iriarte et al. | Influence of seasonal freshwater streamflow regimes on phytoplankton blooms in a Patagonian fjord | |
| Kritzberg et al. | Changes in Arctic marine bacterial carbon metabolism in response to increasing temperature | |
| Klante et al. | Brownification in Lake Bolmen, Sweden, and its relationship to natural and human-induced changes | |
| CN102487788A (en) | Intelligent spray irrigation discharge control system based on weather information services | |
| CN108830425A (en) | Prediction of Reservoir Water Quality system and method | |
| Stan et al. | Study on the evaporation and evapotranspiration measured on the Căldăruşani Lake (Romania) | |
| Gorski et al. | Hydrologic regimes drive nitrate export behavior in human-impacted watersheds | |
| CN102928569A (en) | Urban water body water bloom monitoring and early warning system and method | |
| Baban | Trophic classification and ecosystem checking of lakes using remotely sensed information | |
| Costa et al. | Interannual water quality changes at the head of a tropical estuary | |
| NL1044306B1 (en) | Method and network of devices for the monitoring of surface water quality | |
| Dąbrowska et al. | Phosphate dynamics in the drinking water catchment area of the Dobromierz Reservoir | |
| Garofalo et al. | Agronomic responses of grapevines to an irrigation scheduling approach based on continuous monitoring of soil water content | |
| Cox et al. | Water quality condition and trend in North Queensland waterways | |
| Hausmann et al. | Seasonal climate inferences from high-resolution modern diatom data along a climate gradient: a case study | |
| Takenaka et al. | Response of larch root development to annual changes of water conditions in eastern Siberia | |
| Coleman et al. | Limnology and diatom ecology of shallow lakes in a rapidly thawing discontinuous permafrost peatland | |
| Kimambo et al. | Harmful algae in aquaculture systems in Ngerengere Catchment, Morogoro, Tanzania: descriptive community structure and environmental concerns | |
| Axinte et al. | Adaptive management for sustainable development of Amara Lake (SE Romania). | |
| Ayaz et al. | An approach for determining the nutrient sensitive areas: a case study for Gediz River Basin, Turkey |