Study of Metallic Pollutants in the Northern River of India | Chapter 2 | Research Perspective on Biological Science Vol. 2

Metals load in the riverine system reflects the concentration of toxicants in the water body that acts as a gutter for industrial effluents in and around the Sirsa tributary of the river Sutlej, the lifeline of North India. The inductively coupled plasma mass spectroscopy (ICPMS) was used to detect the concentration of metal load at IIT Delhi and IDMA laboratory, Panchkula. ICPMS is based on the coupling of inductively coupled plasma, as a method of producing ions (ionizations), with a mass spectrometer as a method of separating and detecting ions. ICPMS is a highly sensitive and modern technique for the determination of a range of metals and several non-metals at concentrations below one part per trillion. The application of Metal Index (MI) can be performed to find out the impact of individual metal ions on river water quality and correlate it with health hazards. The Concentration of Metals (Na, Mg, Ca, Zn, Cu, Mn, Fe, Mo, Al, Se) reported at S₁, S₂, S₃ were within permissible Limit of WHO and BIS, but the value of Boron, Mercury and Thallium were exceeded these guidelines constantly at site S₂ of study area. The metal index values at S₁, S₂, and S₃ were 1.55, 5.80, and 2.57, respectively. The site S₂ was strongly affected due to the presence of elemental dust and untreated wastewater discharged by chemical industrial units of the Baddi region. This site (S2) was nearer to CETP on the bank of the Sirsa river and found to be unsuitable at all with a higher value of metal index. It is to devise indices to estimate drinking water quality with respect to the concentration of metal elements after an equal interval of time; as there may be seasonal changes in the load of debris due to the rate of pollutant dumping in the riverine ecosystem, which is directly proportional to the amount of metal ions.

 

Author (s) Details

 

Bhagat Singh
Department of Zoology, Niilm University Kaithal, Haryana – 136027, India.

 

Ram Naresh Tyagi
Department of Zoology, Niilm University Kaithal, Haryana – 136027, India.

 

Anil Jindal
Department of Zoology, RKSD College, Kaithal, Haryana – 136027, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rpbs/v2/4861

Aqua Kit: A Kit for Efficient Freshwater Fish Farming – A Comprehensive Sensor-Based Approach | Chapter 13 | Leading the Charge: A Guide to Management, Entrepreneurship and Technology in the Dynamic Business Landscape Edition 1

Fish farming and aquaculture have been practiced for hundreds of years, and Asia’s country has shared approximately 55% of global production.  In recent years, the authorization body has noticed a significant drop in fish farming in fresh water, like ponds, and lakes, and also several unclarities among people on how to take care of fish in artificial aquatic resources. The main causes of that include inadequate optimization a poor understanding of and culture surrounding fisheries a weak approach to fishing a shortage of personnel and a dearth of reliable databases pertaining to aquatic and fisheries resources, weak approach to fisheries, inadequate manpower, water pollution, poor optimization, and not adequate good fisheries knowledge and culture. The integration of Internet of Things (IoT) technology has revolutionized the realm of freshwater fish farming by providing a sophisticated and real-time monitoring system. This paper proposes an innovative approach to enhancing the aquaculture industry through the utilization of various sensors and IoT devices. By continuously monitoring water quality parameters and the well-being of aquatic life, this approach aims to optimize the farming process, mitigate risks, and ensure a sustainable environment for freshwater fish. The proposed solution may cover all of the above aspects to build a healthy fisheries culture and easily connect aqua research and development to continue monitoring to the ground level, it will also help to maintain a proper database to build better aquaculture, maintain the sustainability of nature and aquaculture. The system will be a smart machine learning (deep learning) and Internet of Things (IoT) based system that can detect oxygen, temperature, Ph level, ammonia, water overflow, fish disease, and many more and store the data as (big data) also manage to have user-friendly communication with as well as higher authorities.

 

Author (s) Details

 

Sumit Singha Chowdhury
Department of MCA, Acharya Institute of Technology, Bangalore, India.

 

Ratnakirti Roy
Department of MCA, Acharya Institute of Technology, Bangalore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-98-3/CH13