WP9: System Incubation and deployment, Kristnam Technologies

Objective:

This work-package deals with incubation of the complete prototype and development of multiple modules of the NSVS to cover a significant area for observation.In what follows is a brief description of the challenges related to the process and the outline of the work.

Data collection:

The presently available techniques for water quality measurements are as follows:
1. Laboratory methods involving trained professionals. These are high accuracy methods but require sample collection and incur high costs.
2. In-situ automated base stations that can continuously monitor a wide range of water quality parameters. These have very good accuracy, very high cost, high power consumption, and require continuous maintenance.
3. Economic in-situ water quality sensors. These are generally moderate accuracy, low-cost,low-power and low-maintenance sensors.
Basing on the type of the technique that is adopted for water quality analysis, a two-way communication solution has to be chosen for real-time transmission and automation. The popular GSM/GPRS based solutions requires high power and high installation costs and is preferred in In-situ base stations with continuous power supply and very well arranged instrument security. These base stations are too costly for multiple deployments across the river basin. While the low-power wide area networks based solutions require minimal power and has less installation costs and is best suited for integrating with low-cost low-power consuming water quality sensors that can be deployed in multiple places across the river basin.

In case of high precision and high accuracy requirements, real-time automated sample collection can be a more suitable solution. The sample collection can either be triggered manually by an operator
using the web interface or can be event-based automatic trigger. The sample will later be analysed in the laboratory.

In order to scientifically address the water quality issues that are prevalent in India, it is critical to establish a systematic network of cost-effective real-time water quality measurement stations across the landscape of the river basin. Considering the market prices of the available methods and data quality requirements by scientific community, the project proposes the following cost-effective framework for the data collection which uses a distributed network of highly accurate in-situ methods, automatic samplers and low-cost sensors.

Temporary fine but spatially not-dense: The high cost high-accurate in-situ base stations along with automatic water samplers can be deployed only at few selected critical locations across the basin such as confluence of steams, Industrial discharge sites, agricultural outlets, urban sewage discharge, urban human settlements etc. The data can be transmitted every 15 mins or more. The data collection frequency can be altered from the web interface itself. The data is temporally fine and high quality.

Temporary not-fine but spatially dense: The automatic portable samplers and portable high cost and high accurate instruments can be used to collect data at multiple sites across the basin but will be carried out only periodically such as once in month or a twice in a month. This process is spatially dense and high quality.

Temporary fine and spatially dense: The economic sensors when integrated with LPWAN technology will be cost-effective and suitable for multiple spatial deployments across the basin. The measurements can be transmitted every 30 mins or more. The low-power requirements make the entire set-up capable of un-attended continuous real-time monitoring for long durations. The accuracy of the measurements will be lower than the above two methods but the system can be integrated with auto-sampler for later laboratory validation whenever the permissible limits are crossed. Other than being just cost-effective these LPWAN based water quality stations can be distributed within few tens of square kilometers, which is typically the range of established networks.

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