Project at a glance
- 80 percent of untreated sewage in developing countries is discharged into water bodies
- Seven water bodies are being monitored under this project
- 212 times sensors have been carried around on boats for in-situ measurements
- 1,100 time-stamped and geo-tagged data points on an average are available on water pollution in Ganga, Yamuna, Godavari, Brahmaputra, and three lakes in Bengaluru—Ulsoor, Nagvada, and Jakkur—at the end of each boat ride
Why this study
Rivers are the dumping grounds for pollutants from industry, agriculture, and human activities. Given the complexity of pollution sources, testing a water sample from a single location gives an incomplete picture of the overall health of a waterway. Access to data from continuous measurements is necessary to study the effects of each individual contributor.
Conventionally, government and non-government agencies collect data on river water quality parameters through analysis of lab samples. While this is the beginning of identifying hazardous pollutants, gathering continuous, time-stamped, and geo-tagged data through in-situ measurements reveals the full picture of complex pollutants in a dynamic water body.
The Water-to-Cloud technique used in this project was conceptualized by a team of researchers from the University of Chicago, US and IIT-BHU, Varanasi, India to demonstrate that scalable water quality mapping systems can detect and predict water contamination and thereby:
- Identify effective sanitation interventions and their outcomes
- Help control outbreak of infectious diseases in river valley populations
- Identify and pinpoint time-varying sources of pollution
- Raise awareness of water quality and contamination issues among public
Approach and Parameters
To gather data, boats are equipped with multiple submersible automated sensors and set on sail at different times of the day based on a pre-defined route. These sensors are chosen to measure parameters of interest based on the kind of pollution being monitored: industrial, agricultural, anthropogenic, etc. This time-stamped and geo-tagged data is then filtered and superimposed on geospatial maps to create two-dimensional area heat maps for ease of interpretation and predictive analysis.
Detailed lab analysis is done on a regular basis to support in-situ field measurements. This empowers researchers to assess the quality of river water using different parameters, in different seasons, and at different locations across the rivers. Following curation of the geospatial and temporal water quality data, this project applies mathematical tools to predict the spread of pollution, interpolate sparse data, and identify the specific sources of pollution.
The chemical characteristics of natural water are a reflection of the soils and rocks with which the latter has been in contact. In addition, agricultural and urban runoff, along with treated wastewater from municipal and industrial outlets, affects water quality. This is measured by several physical, chemical, and biological factors.
Parameters such as temperature, electrical conductivity, pH, dissolved oxygen and turbidity are measured using thermometric, electrometric and turbidometric techniques. These parameters are measured in-situ using the real-time data logging sensors.
Temperature influences biochemical reactions in water bodies. Temperature variability usually happens due to atmospheric interactions, except around thermal plants that may be responsible for temperature pollution. Electrical conductivity gives a measure of inorganic salts in the water. pH of water reveals its acidic or basic nature. Most streams have a neutral to slightly basic pH of 6.5 to 8.5. If stream water has a pH less than 5.5, it may be too acidic for fish to survive in, while stream water with a pH greater than 8.6 may be too basic.
Dissolved oxygen concentration indicates health of the water body. Its range varies from 0 mg/L (anoxic conditions) to 19 mg/L (supersaturated) and typically ranges between 3 and 9 mg/L. Water clarity is estimated in terms of turbidity, which is an optical parameter.
Microbial and chemical transformations affect the chemical characteristics of water which depend on inorganic and organic compounds. Inorganic compounds may dissociate into cations and anions to varying degrees. Major cations found in natural water include calcium, magnesium, sodium, and potassium, whereas major anions are chloride, sulfate, carbonate, bicarbonate, fluoride, and nitrate. Along with the major ions, heavy metals are the inorganic elements that may cause several diseases among humans, even though their levels are generally very low.
Organic compounds, which may be formed through natural processes or synthetic ones, affect water quality. Organic chemicals cause disagreeable taste and odor in drinking water. Dissolved oxygen levels may be depleted due to high levels of organic content in the water. To quantitatively assess concentration of organic materials, oxygen demand—both Chemical Oxygen Demand (COD) and Biochemical Oxygen Demand (BOD)—is used as an indicator. Total coliform and fecal coliform are crucial parameters to gauge the biological contaminants present in water.
Dynamic mapping of river water quality to understand how it changes with weather, pollution, fishing, and general use, can help pinpoint pollution sources accurately and ensure regulatory compliance.
To that end, the study is laying the groundwork for developing a scalable, low-cost real-time sensing network using mobile sensing platforms to obtain high frequency temporally and spatially varying water quality data, which traditional grab sampling method may have failed to capture.
The study also demonstrates the importance of detecting and anticipating pollutants that enter the river in the form of human waste, organic materials, and chemical contaminants.
On mapping the river chemistry, it emerged that the level of dissolved oxygen in two stretches of Ganga is often below the standards, and alarmingly low in the Yamuna.
When it comes to turbidity, which is a direct result of effluents from urbanized zones, large water bodies monitored in the study, do not fare well. While the Hudson River and Mississippi River in the US have a turbidity of 15 and 39 NTU (Nephelometric Turbidity Unit) respectively, some stretches of Godavari witness turbidity over 100 NTU. Ideally, turbidity level for surface water should be within 50. In case of Bengaluru lakes, the level is extremely high, hovering between 400 and 450 NTU.
All these water quality parameters are represented in easy-to-interpret heat maps, which are more intuitive and can help even laypersons decide which polluted stretches of the river they should avoid. As a next step, the researchers intend to create models to predict total coliform and fecal coliform in river water, which indicate the presence of sewage contamination of a waterway.