GSM & SMS Enabled Water Pollution Monitor

Via MKR GSM 1400, collate water quality data from resources over GPRS to train a Neuton model, run the model, and transmit results via SMS.

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In this project, I focused on developing an AI-driven budget-friendly device to collect water quality data from various water resources in the field and forecast water pollution levels based on oxidation-reduction potential (ORP), pH, total dissolved solids (TDS), and turbidity measurements.

📌 Why is water pollution a global issue?

Since we incessantly pollute our limited water resources, which are already drowning in chemicals, waste, plastic, and other contaminants, it is crucial to eschew water pollution before it is too late for relieving the plight of people lacking safe drinking water. Although there are international efforts and campaigns to plummet water contaminants, it is yet a pressing issue to track water pollution levels locally to get prescient warnings regarding possible health and environmental risks.

In addition to threatening terrestrial animals, plants, and marine life, water pollution is jeopardizing our health. Unsafe water kills more people each year than war and all other forms of violence combined. Meanwhile, our drinkable water sources are meager: Less than 1 percent of the Earth's clean (fresh) water is actually accessible to us. Without action, the challenges will only increase by 2050, when global demand for freshwater is inferred to be one-third greater than it is now[1]. We may even have less time than expected since water is uniquely susceptible to pollution. Water is widely described as the universal solvent since it can dissolve more substances than any other liquid on the Earth. Hence, harmful and toxic substances, mostly chemicals or microorganisms, can effortlessly contaminate a stream, river, lake, ocean, aquifer, or other body of water, degrading water quality and rendering it toxic to humans or the environment.

📌 Water quality measurements to detect water pollution

After perusing recent research papers on water pollution, I decided to utilize oxidation-reduction potential (ORP), pH, total dissolved solids (TDS), and turbidity measurements denoting water pollution (contamination) so as to create a budget-friendly device to forecast water pollution levels locally in the hope of obviating the need for expensive equipment to detect contamination and forfend perilous pollution effects.

Oxidation-reduction potential (ORP) is a measurement of ion exchange: Chemical substances with a negative ORP value can donate extra ions, but positive ORP values lead to ion absorption. When the ORP value is high in the water, bacteria decompose dead tissue and contaminants more efficiently. In general, the higher the ORP value, the healthier the body of water is. However, ORP values decrease closer to the bottom sediments, even in clean water resources, since many bacteria are working hard in the lower sediments to decompose dead tissue. Therefore, they use up most of the available oxygen. Since oxygen disappears quickly in the bottom mud (often within a centimeter or two), ORP values decrease quickly[2].

The pH is of importance in determining the corrosivity of water, but the relationship with several other parameters is complex. Natural waters contain gases, colloidal matter, and a variety of electrolyte and non-electrolyte material, and these, together with pH, determine the extent of corrosion in a system. However, in general, the lower the pH, the higher the potential level of corrosion. The pH of most drinking water lies within the range of 6.5–8.5. Natural waters can be of lower pH, for example, as a result of acid rain or higher pH in limestone areas[3].

Total dissolved solids (TDS) is the term used to describe the inorganic salts and small amounts of organic matter present in solution in water. The principal constituents are usually calcium, magnesium, sodium, and potassium cations and carbonate, hydrogencarbonate (bicarbonate), chloride, sulfate, and nitrate anions. TDS in water supplies originates from natural sources, sewage, urban and agricultural run-off, and industrial wastewater. Salts used for road de-icing can also contribute to the TDS loading of water supplies[4].

Turbidity in water is caused by suspended particles or colloidal matter that obstruct light transmission through the water. It may be caused by inorganic or organic matter or a combination of the...

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  • 1 × Arduino MKR GSM 1400
  • 1 × SIM Card
  • 1 × GSM / 3G Antenna
  • 1 × Raspberry Pi 3B+ or 4
  • 1 × DFRobot Analog ORP Sensor

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Glen Trudgett wrote 04/04/2022 at 01:11 point

Good to see this project and have been thinking about water quality measuring devices that can be remotely setup. I would like to perform the same sort of tests for sea water. To reconfirm this is for drinking water only correct?  Also, what is the required sample time analysis and is it a manual process (active) or passive process?

  Are you sure? yes | no

kutluhan_aktar wrote 04/07/2022 at 09:19 point

Thanks for your comment. Yes, this project is for testing drinking water only. After soaking the water quality sensors for 15 minutes in the sample, the sensors start generating accurate results.

It is a manual process: To send the collected water quality data and run the neural network model, the user needs to press the control buttons.  

  Are you sure? yes | no

kutluhan_aktar wrote 03/30/2022 at 14:38 point

Please feel free to leave a comment here if you have any questions or concerns regarding this project 😃

  Are you sure? yes | no

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