JISU Journal of Multidisciplinary Research (JISUJMR)
JISU Journal of Multidisciplinary Research (JISUJMR)
A PEER REVIEWED REFEREED BI-ANNUAL INTERNATIONAL JOURNAL
Published by JIS University 81, Nilgunj Rd, Jagarata Pally, Deshpriya Nagar, Agarpara, Kolkata, West Bengal 700109
Volume 1, Issue 1 i December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
Chief Patron
Sardar Taranjit Singh, Chancellor, JIS University, Kolkata 700 109, West Bengal
Chief Publication Adviser
Sardar Simarpreet Singh, Director, JIS University, Kolkata 700 109, West Bengal
Patrons
1. Prof. Bimal Chandra Mal, Vice Chancellor, JIS University, Kolkata 700 109 2. Padma Shri Prof Ajoy Kumar Ray, Director, JISIASR, Kolkata – 700 091 3. Dr. Tapan Kumar Saha, Director Affiairs, JIS University, Kolkata 700 109 4. Prof. Tapan Kumar Chatterjee, Dean, JIS University, Kolkata 700 109
Editorial Board
1. Prof. Himangshu Sekhar Maji Department of Pharmaceutical Technology, JIS University, Kolkata 700 109 Email: [email protected]
2. Prof. R. R. Paul Department of Oral and Dental Sciences, JIS University, Kolkata 700 109 Email: [email protected]
3. Prof. Shibnath Banerjee Department of Management Studies, JIS University, Kolkata 700 109 Email: [email protected]
4. Prof. Rina Bhattacharya Department of Physics, JIS University, Kolkata 700 109 Email: [email protected]
5. Dr. Atanu Kotal Department of Chemistry, JIS University, Kolkata 700 109 Email: [email protected]
6. Dr. Sanhita Banerjee Chattaraj Department of Mathematics, JIS University, Kolkata 700 109 Email: [email protected]
7. Dr. Souvik Chatterji Department of Juridical Sciences, JIS University, Kolkata 700 109 Email: [email protected]
8. Dr. Saikat Maity Department of Computer Science and Engineering, JIS University, Kolkata 700 109 Email: [email protected]
Volume 1, Issue 1 ii December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
9. Prof. Swades Ranjan Samanta Department of Education, JIS University, Kolkata 700 109 Email: [email protected]
10. Dr. Mainak Mukhopadhyay Department of Biotechnology, JIS University, Kolkata 700 109 Email:[email protected]
11. Dr. Manua Banerjee Department of Earth Science, JIS University, Kolkata 700 109 Email: [email protected]
12. Dr. Sateyendra Singh Tomar, Assistant Registrar JIS University, Kolkata 700 109 Email: [email protected]
13. Prof. J. K. Mandal Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal Email: [email protected]
14. Dr. N. C. Mondal Ex-Editor, Indian J. Radio Sp. Physics, CSIR, New Delhi Email: [email protected]
15. Prof. Radharaman Pal Vidyasagar University, West Bengal Email: [email protected]
16. Prof. Sourangshu Mukhopadhyay University of Burdwan, Burdwan, West Bengal Email: [email protected]
17. Prof. Uday Chakraborty Department of mathematics and Computer Science, University of Missouri, USA Email: [email protected]
18. Prof. Jasjit S. Suri Atheropoint LLC, USA Email:[email protected]
19. Prof. Jeffrey M. Lichtman Founder Emeritus of the Society of Radio Astronomers, Texas, USA Email: [email protected]
20. Prof. John C. Mannone Department of Physics, East Tennessee area University, USA Email: [email protected];
Volume 1, Issue 1 iii December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
Editor-in-Chief
Prof. A. B. Bhattacharya, Pro-Vice Chancellor, JIS University, Kolkata 700 109
Editors
Dr. Mainak Biswas, Department of Computer Science and Engineering, JIS University, Kolkata 700 109 Dr. Dipankar Ghosh, Department of Biotechnology, JIS University, Kolkata 700 109 Ms. Moumala Bhattacharjee, Department of Juridical Sciences, JIS University, Kolkata 700 109
Editorial Secretary
Mr. Gaurav Majumder, Assistant Registrar, JIS University, Kolkata 700 109
Editorial Support
Dr. Satyendra Singh Tomar, Assistant Registrar, JIS University, Kolkata 700 109
Journal Design
Dr. Saikat Maity, Department of Computer Science & Engineering, JIS University
Volume 1, Issue 1 iv December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
Subjects Covered
Basic and Applied Sciences, Engineering, Health Sciences, Management and Law
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Volume 1, Issue 1 v December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
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Volume 1, Issue 1 viii December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR)
CONTENTS
Vice Chancellor’s Message 1 From Editor-in-Chief 2 Design and Development of Perforated Tray Aerator (PTA) by Subha 3 Manash Roy and Bimal Chandra Mal Searching for some irreversible inhibitors as drugs of Human Cathepsin B by Computer Aided Drug Design (CADD) Method by Indrani Sarkar 12 Some Preliminary Reports on Vegetation Indices using Operational Land Imager and Thermal Infrared Sensor by Simran Parvin 17 Single Axis Solar Tracking System using Arduino Uno Controller by 27 Suparna Pal Arsenic Contamination of Groundwater in West Bengal: A Human Health 38 Threat by M. Roy A novel artificial intelligence method and angular distribution function for 47 characterization of breast cancer by Mainak Biswas, Saikat Maity, Shubhro Chakrabartty Impressions of High Frequency Radio-Waves from Cell Phone Towers on Birds: A Base-Line Study by Sauvik Bose, Rajeshwari Roy, Urbi Chakraborti, Risha Samanta, Sipra Jana, Tanusree Mondal, Soumini 54 Chaudhuryand Rina Bhattacharya Thermal Stress Analysis for Indian Metro Cities during Summer Months by Ahana Mitra, Gourab Biswas, Joyeesha Manna, Arkadip Nandan , Soumi 63 Bose, Soumini Chaudhury and Rina Bhattacharya Bioelectricity Generation from Waste Water using Microbial Fuel Cell: A Literature Survey by Dipankar Ghosh, Shrestha Debnath and Pritha Mondal 71 Physical and Electrical properties of a Tropical Cyclone as derived from Satellite Imageries and Cyclone Detection Doppler Weather Radar: A Case Study by Hazer R. D. Warjri, Hasina Paslein, Mebashongdor Kharkongor, 77 Dipankar Dam, Zahidul Islam, and A. B. Bhattacharya Effect of Pre-Monsoon Lightning Activity on Surface Nox & O3 over GWB by Arpita Das, Sujay Pal, Subrata Kumar Midya 91 Air Pollution Status: A case study in West Bengal by Debanjali Roy, Subhangi Chakraborty, Koustav Bhowmik, Sourav Mandal, Priyadarshini 101 Ghosh, Amitlal Bhattacharya, Soumini Chaudhury and Rina Bhattacharya Exploration of dietary Habits of IVF Children – A Comparative Evaluation by Sudipta Kar, T. K. Pal, S. L. Seal and Gautam Kumar Kundu 110 Frequency and Amplitude Spectra Analysis of the Sound of Indian Folk Musical Instruments by Sudipta Pal , Rinku Sarkar and Sushmita Saha 115
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JISU Journal of Multidisciplinary Research (JISUJMR)
Vice Chancellor’s Message
JIS University was established through the Legislative Act of the Government of West Bengal, viz., JIS University Act, 2014. The University is recognised and enlisted as a Private University by the University Grants Commission as competent to award degrees as specified by UGC under Section 22 of the Act. The Legal Science courses of JIS University are recognised and approved by Bar Council of India. The AICTE informed that the University does not require its approval for conducting Technical courses. The Pharmacy Council of India accorded approval for running of Bachelor of Pharmacy (B. Pharm.) and Diploma of Pharmacy (D. Pharm) Courses. The National Council of Teachers‘ Education approved running of B. Ed. course. With an objective to enhance the spread and growth of Higher Education, Research, Entrepreneurship and Skill Development blending traditional pedagogy with modern technology enabled Teaching Learning Practices and R&D, JIS University provides various degree course programmes at Graduate, Post-graduate and Ph.D. levels in disciplines such as Physics, Chemistry, Biotechnology, Geology, BBA-LLB; LLB, B. Pharm, D. Pharm, BBA, MBA, B. Ed., Hotel Operation, International Culinary Arts, International Hospitality and Hotel Administration, Hospitality and Hotel Administration, Oral and Dental Sciences, Computer Science and Engineering, Remote Sensing and GIS. JIS University follows Continuous Evaluation Process through the Semester system of Examination. The University arranges a Seminar Presentation of its students. The University also organizes a unique Exhibition event in all the semesters to further the cause of Innovation and Entrepreneurial Development for the students which has been names as ‗SKILLX‘. The exhibition is visited by the students and teachers of different schools and colleges, dignitaries from industries, academic and research organizations. The School students also participate with the University students in the SKILLX programme.
The University is not lagging behind in research activities. The University admitted more than 50 research scholars for undertaking doctoral research in disciplines like Management, Earth Sciences, Biotechnology, Computer Science and Engineering, Pharmacy, Law, Oral and Dental Sciences and Mathematics. The faculty of the University received Research Grants from DST and other private organization. At newly established JIS University, we make continuous efforts to see that all-round development of the students takes place. Young-spirited, industry experienced and qualified faculty members not only teach the subjects as per the course curricula but also make sure to build a strong foundation for the students that will aid them to overcome life-hurdles in future. They play a huge role in grooming the young people and molding their knowledge-hungry minds into matured and innovation-oriented adults. We already published one research journal last year. I am happy that the University is going to publish its second research journal. It is hoped that the faculty and scholars of the University will contribute quality research papers for publication in the journal. It is also expected that academicians from other organizations will find it a useful medium for publication of their papers. I wish that the JIS University Journal of Multidisciplinary Research provides an excellent opportunity to all the faculty and students, especially the research scholars to develop a knowledge base for carrying out their research activities.
Prof. (Dr.) B. C. Mal Vice Chancellor
Volume 1, Issue 1 1 December, 2019
JISU Journal of Multidisciplinary Research (JISUJMR)
From Editor-in-Chief
It is our pleasure to present to the readers our scientific journal ―JISU Journal of Multidisciplinary Research‖ published by the university. The Journal is a multi-disciplinary one, published bi- annually by JIS University covering quality papers on all aspects of Basic and Applied Sciences, Engineering, Health Sciences, Management and Law. It is the outcome of the cohesive, integrated force of the entire teaching staff and scholars of the JIS University and different colleges of the JIS group whose contribution reflects in this quality research journal of its own. We have also published a number of papers in the Journal received from other Universities and Institutes. Besides interdisciplinary interest it caters specialized interest as well. The Journal publishes review articles, original contributions, research notes and short contributions.
In this volume, as before, we continue to publish results of interdisciplinary scientific researches in many aspects. We are glad to have authors from different Universities and Institutes covering a wide area of research. Most of the articles presented in the Journal are original research works oriented towards the possibility of implementation in many states. The character of our edition allows us to investigate basic and applied problems from different points of view and to get new ideas and new knowledge within the frames of wide scientific discussion.
We believe that our academic publication will be important and useful among researchers, post- graduates, specialists and students at different parts of the country and also outside. We will continue to strive for its inclusion in prominent scientific research databases. The Editors acknowledge the Authors, the Editorial Board Members and the Designer, the staff of the Publishing Office and everyone who have contributed to the publication of the journal in different ways. Special thanks are for our future readers and contributors and in this opportunity we like to invite them for collaboration with our scientists in a more meaningful way for working together in various multidisciplinary areas.
Prof. (Dr.) A. B. Bhattacharya Editor-in-Chief and Pro Vice Chancellor
Volume 1, Issue 1 2 December, 2019
JISU Journal of Multidisciplinary Research (JISUJMR)
Evaluation of Aeration Efficiency of Perforated Tray Aerator aSubha Manash Roy and *bBimal Chandra Mal aAgricultural and Food Engineering Department Indian Institute of Technology Kharagpur, West Bengal, India, Pin -721 302 E-mail - [email protected]
*bVice Chancellor, JIS University, Agarpara, Kolkata, West Bengal, India, Pin - 700 109 E-Mail: [email protected]
Abstract: One perforated tray aerator (PTA) was designed, developed and its suitability for use in intensive aquaculture pond was evaluated. Aeration experiments were conducted in a tank of dimensions 4.0 × 4.0 ×1.5 m. Initially experiments were conducted with different numbers of trays (n): 1, 2, 3 and 4, keeping the perforated hole diameter d: (2 mm) and flow rate: Q (10 L/s) as constants. The result showed that the optimum numbers of trays should be three. After that keeping the numbers of trays in the PTA as constant (3), the response surface methodology (RSM) and central composite rotatable design (CCRD) were applied for conducting experiments to determine the optimum values of d and Q. The experimental results showed that the maximum standard oxygen transfer rate (SOTR) and standard aeration efficiency (SAE) of the developed PTA aerators are 0.463 kgO2/h and 1.382 kgO2/kWh respectively at which the optimum values of the d and Q are 1 mm and 19 L/s respectively.
Keywords: Perforated tray aerator, Aquaculture, Standard oxygen transfer rate, Standard aeration efficiency
1 Introduction
Dissolved oxygen (DO) is an important variable regulating post aeration system water treatment. DO refer to the mass of oxygen that is contained in water. The concentration of DO is an essential indicator of the water quality in a terrestrial or aquatic environment. As the DO concentration drops below 5.0 mg/L in water, the aquatic life is put under stress and if the concentration remains below 1–2 mg/L for a few hours, the chances of mortality of the species is high [1,2]. The intensive aquaculture system possesses very high stocking density. Therefore, in order to maintain the water quality with an appropriate level of dissolved oxygen, artificial aeration through aerators becomes essential. Aerators increase the DO level in the water body, thus enhancing the oxygen transfer rate and simultaneously providing water circulation which prevents stratification in the water body [3].
The perforated tray aerator (PTA) is generally made up of a number of trays (n) arranged vertically one above the other at a constant vertical spacing (SP) between the trays. The tray is made from a flat sheet with a number of holes drilled on it. The variation in number of trays (n) and perforated hole diameter (d) are important parameters which may affect the oxygen transfer rate and the aeration efficiency. Water is pumped over the topmost tray and it falls vertically
Volume 1, Issue 1 3 December, 2019
JISU Journal of Multidisciplinary Research (JISUJMR) through the perforations of the trays. The fall through the small diameter holes helps the water drops to form fine sprays. The fine spray increases the water-air surface contact area and thus helps to increase the aeration efficiency of perforated tray aerator [4]. When water is allowed to fall over the consecutive trays (trays are square in shape), large area to water volume ratio in contact with the air is obtained. Therefore, the present study was taken up to optimize the different operating parameters of perforated tray aerator for achieving the maximum SOTR and SAE.
2 Theoretical Considerations
In this section, different relationships for overall oxygen transfer coefficient (KLa), standard oxygen- transfer rate (SOTR) and standard aeration efficiency (SAE) are included.
2.1 Overall oxygen transfer coefficient (KLa)
The standard model for oxygen transfer is formulated as a mass balance equation for variation of dissolved oxygen concentration in the water with time and is given by Equation 1 [5].
∗ = KLaT (C ∞ - C0) …. (1)
∗ Where C (mg/l) is the concentration of oxygen at time t, C ∞ (mg/l) is the equilibrium liquid phase -1 oxygen concentration, C0 (mg/l) is the initial DO concentration, KLaT (h ) is the overall oxygen transfer coefficient at T ° C.
By assuming KLaT constant, the solution of the Equation 1 can be as follows:
∗ ∗ C = C ∞ - (C ∞ - C0) × Exp [- KLaT × t] …. (2)
According to ASCE (2007), the Gauss-Newton method is used to fit experimental data to estimate ∗ KLaT and C ∞. The KLaT is then converted to KLa20 and expressed as:
KLa20= (KLa)T/ …. (3)
-1 Where KLa20 is the overall oxygen transfer coefficient at 20°C (h ) and is the correction factor of temperature = 1.024 for clean water.
2.2 Standard oxygen-transfer rate (SOTR) and Standard aeration efficiency (SAE)
To evaluate the performance of an aerator, the two performance measures i.e., standard oxygen transfer rate (SOTR) and standard aeration efficiency (SAE) are generally used. These parameters are defined as follows:
The standard oxygen-transfer rate (SOTR) of an aeration system is defined as the oxygen transfer per unit time to a water body under standard conditions (water temperature: 20°C, initial DO concentration = 0 mg/L, one atmospheric pressure and clean tap water) [5,6] and is given by Equation 4.
-3 SOTR = KLa20 × (C* - C0) × V = KLa20 × 9.07 × V × 10 …. (4)
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JISU Journal of Multidisciplinary Research (JISUJMR)
Where SOTR is the standard oxygen-transfer rate (kg O2/h), C* is the DO saturation value (mg/L), 3 -3 C0 is the initial DO concentration (mg/l), V is the volume of water in the tank, m and 10 is the factor for converting g to kg.
Standard aeration efficiency (SAE) is a better comparative performance parameter than SOTR [7] which is defined as the standard oxygen-transfer rate (SOTR) per unit of power input to the aerator and given by Equation 5.
SAE (kgO2/kWh) = SOTR/P …. (5)
Where, P denotes the input power to the aerator (kW).
The power consumption (P) may be denoted as break power. Break power (kW) to aerator is calculated [7] by Equation 6
P = (9810 × H × Q) / η …. (6)
Where H = head against which the pump works (m), Q = flow rate (m3/s), and ɳ = efficiency of the pump. The mechanical efficiency of the pump was assumed as 50% for estimation of brake power [7]. In the present study, to achieve the proper mixing of DO throughout the water, the water volume in aeration tank was decided based on the condition stated by Elliott (1969) as given in Equation 7.
P/V = 0.1 kW/ m3 …. (7)
Where P = aerator power (kW) and V = water volume under aeration (m3).
3 Materials and Methods
Perforated tray aerator (Fig. 1 & 2) consists of an open submersible pump of 3.7 kW (5 HP) with a control valve and a structure to support the trays. A rising pipe of 100 mm internal diameter was attached with the pump. The pump was setup at the bottom of the tank. The water discharge through the outlet of the pump was measured using an Electromagnetic Flow Meter. A number of perforated trays were welded and bolted to this central pipe as shown in Fig. 2. Central rising pipe made of galvanized iron was coupled to the pump. A factory made perforated mild steel sheet of uniform hole diameter and thickness was cut in the size of 800 mm x 800 mm, fitted to an outer supporting frame and finally joined to the central pipe. The specification of perforated tray used of different hole diameters is presented in Table. 1.
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JISU Journal of Multidisciplinary Research (JISUJMR)
Figure 1 Schematic diagram of perforated tray aerator (PTA)
Figure 2 Experimental setup for testing of perforated tray aerator
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JISU Journal of Multidisciplinary Research (JISUJMR)
Table 1 Specification of perforated tray used for experiment Perforated Mild Steel Numbers of holes Total Tray Area Ratio of total hole Sheet hole diameter per Square cm (cm2) area to area of (d, mm) tray (K) 1 52 0.103 2 20 0.156 3 5 80 × 80 0.329 4 2 0.526 5 1 0.394
Unsteady state reaeration tests were conducted on a brick masonry tank. Initially, the tank was filled up with clean tap water up to a depth of 1 m. Effort was made to maintain the standard conditions as far as possible (20°C temperature and 760 mm Hg pressure APHA, 1980). Then the tap water was deoxygenated with 0.1 mg/L of cobalt chloride and 10 mg /L of sodium sulphite for each litre of water and 0.1 mg/L of dissolved oxygen present in water [2]. After that the aerator was run until the DO in the water body approached saturation, typically greater than 96% of saturation [8]. The DO concentrations in water was measured at different corners of the tank by inserting the probes of dissolved oxygen (DO) meters to a depth of approximately 0.20 m [9]. At least 45 DO readings at equal time intervals were taken. The temperatures and atmospheric pressures were also recorded along with the DO readings.
4 Experimental Designs
Aeration tests (Table 2) were conducted keeping Q (10 L/s), d (2 mm), K (0.526) as constants. For optimizing the number of trays, different numbers (n): 1, 2, 3 and 4 were used for the experiment. The vertical interval between two successive trays was 30 cm and the bottommost tray was also at a height of 30 cm from the top of the water surface to allow a falling height of 30 cm. The volume of water to be used for testing of aerator was found out by maintaining the condition of power- volume ratio i.e. P/V ≤ 0.1 kW/m3 (Elliot, 1969).
Table 2 Aeration experiments to optimize the numbers of trays (n) No. of trays (n) Constant parameters 4 Q = 10 L/s , d = 2 mm, and K = 0.156
Response surface methodology (RSM) is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize the response. A central composite rotatable design (CCRD) was used to examine the combined effects of d and Q levels frequencies on2 the response of SOTR and SAE. The range of independent variables d (1-5 mm) and Q (7 to 19 L/s) were selected based on preliminary trials to maximize the SOTR and SAE.
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JISU Journal of Multidisciplinary Research (JISUJMR)
5 Results and Discussion
In this section, determination of optimum numbers (n) of trays of PTA; optimization of perforated hole diameter (d) and flow rate (Q) are included.
5.1 Determination of Optimum Numbers of Trays (n) of PTA
In this set of experiments the effect of numbers of trays was evaluated keeping the values of d and Q as constants and the results are presented in Table 3. It is observed from Table 3 that SOTR increases with the increase in number of trays and the SAE initially increases and attains the maximum value at n = 3. Then the SAE decreases at n = 4. This is due to the fact that although oxygen transfer increases with the increase in number of trays, but the power consumption also increases as the total lift becomes more with more number of trays. The increase in oxygen transfer is not commensurate with the increase in power which results in the reduction of SAE.
Table 3 Performance of the perforated tray aerator with different numbers of trays (n)
N SOTR (kg O2/h) Power (kW) SAE (kgO2/kWh)
1 0.0654 0.1177 0.556 2 0.174 0.2943 0.591 3 0.316 0.5297 0.596 4 0.397 0.7063 0.562
0.605 SAE = -0.0175 n2 + 0.0901 n + 0.4825 0.6 R² = 0.996 0.595 0.59
0.585 /kWh) 2 0.58 0.575 0.57 0.565 SAE SAE O (kg 0.56 0.555 0.55 0 1 2 3 4 5 No. of trays (n) Figure 3 Variation of SAE with numbers of trays (n) The variation of SAE with number of trays is presented in Fig 3. The relationship can be expressed by a second order polynomial equation of the following form: SAE = - 0.0175 × n2 + 0.0901 × n + 0.4825 …. (8)
Differentiating the above equation, dSAE / dn = - 0.035 × n + 0.0901 At, dSAE / dn = 0, n = 2.58 and d2SAE / dn2 = - 0.0901 (˂ 0) Therefore, SAE attains its maximum value at n = 3. Volume 1, Issue 1 8 December, 2019
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At n = 3, the maximum value of SAE is obtained as 0.596 kgO2/kWh. Therefore, from the relationships developed by using the experimental data, it may be concluded that the optimum number of trays for efficient aeration is 3. 5.2 Optimization of perforated hole diameter (d) and flow rate (Q)
Three numbers of trays in which the maximum SAE was obtained was considered as the optimum number and was fixed for conducting further experiments with different diameters of hole (d) and flow rates (Q). The experimental results for different diameters of hole and flow rates are presented in Table 4. It can be seen from the table that the maximum SOTR of 0.463 kg O2/h and the maximum SAE of 1.382 kg O2/kWh are obtained at 1 mm diameter of hole and 19 L/s of discharge. Table 4 Experimental results for optimisation of geometric parameters -1 Sl No. d (mm) Q (L/s) KLa20 (h ) SOTR Power SAE (kgO2/h) (kW) (kgO2/kWh) 1 1.00 7.00 0.4101 0.0529 0.1236 0.4282 2 1.00 13.00 0.4101 0.1892 0.1236 0.8242 3 1.00 19.00 3.5937 0.4638 0.3355 1.3825 4 3.00 7.00 0.2587 0.0333 0.1236 0.2701 5 3.00 13.00 0.9259 0.1195 0.2295 0.5206 6 3.00 13.00 0.9259 0.1195 0.2295 0.5206 7 3.00 13.00 0.9259 0.1195 0.2295 0.5206 8 3.00 13.00 0.9259 0.1195 0.2295 0.5206 9 3.00 13.00 0.9259 0.1195 0.2295 0.5206 10 3.00 19.00 1.4727 0.3356 0.3355 1.0004 11 5.00 7.00 0.2067 0.0266 0.1236 0.2158 12 5.00 13.00 0.6410 0.0827 0.3604 0.3604 13 5.00 19.00 1.4727 0.1900 0.3355 0.5665 Summary of ANOVA of the experimental results for SOTR and SAE is presented in Tables 5 and 6 respectively. It can be seen from the tables that the models have F-values of 277.30 and 318.35 implying that the models are highly significant (p value probability> F = 0.0001).
Table 5 ANOVA for Response Surface Quadratic Model SOTR Source Sum of Squares df Mean Square F Value p-value Prob > F Model 0.18 5 0.037 277.30 < 0.0001 significant D 0.028 1 0.028 209.15 < 0.0001 Q 0.13 1 0.13 972.70 < 0.0001 d × Q 0.015 1 0.015 116.32 < 0.0001 d2 1.753 × 10-4 1 1.753× 10-4 1.33 0.2865 Q2 8.821× 10-3 1 8.821× 10-3 67.00 < 0.0001 Residual 9.216 × 10-4 7 1.317× 10-4 Lack of Fit 9.216 × 10-4 3 3.072× 10-4 Cor Total 0.18 12
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Coefficient of determination (R2) = 0.995; adjusted R2 = 0.991; standard deviation (S.D.) = 0.011; coefficient of variation (C.V.) = 7.56%; mean = 0.15
From multi-variable regression, the following expression for SOTR is obtained. SOTR = - 0.010846 + 0.021210 ×d - 9.98292 × 10-4 × Q - 5.15613 × 10-4 × d × Q + 1.99146 × 10- 3 × d2 + 1.56982 × 10-3× Q2 .... (9) Table 6 ANOVA for Response Surface Quadratic Model SAE Source Sum of df Mean Square F Value p-value Prob > Squares F Model 1.19 5 0.24 318.35 < 0.0001 significant d 0.37 1 0.37 495.55 < 0.0001 Q 0.69 1 0.69 921.97 < 0.0001 d × Q 0.091 1 0.091 121.62 < 0.0001 d2 5.196 × 10-3 1 5.196 × 10-3 6.94 0.0337 Q2 0.021 1 0.021 27.49 0.0012 Residual 5.241× 10-3 7 7.488× 10-4 Lack of Fit 5.241× 10-3 3 1.747 × 10-3 Cor Total 1.20 12 Coefficient of determination (R2) = 0.995; adjusted R2 = 0.992; standard deviation (S.D.) = 0.027; coefficient of variation (C.V.) = 4.65%; mean = 0.59
From multi-variable regression, the following expression for SAE is obtained. SAE = 0.17929 - 0.025939 × d + 0.031904 × Q - 0.012574 × d × Q + 0.010843 × d2 + 2.39809 × 10-3 × Q2 .... (10)
After knowing the possible design criteria to maximize the SOTR and SAE, optimization was carried out using ―Point Optimization‖ technique of Design Expert software. The optimum values of different variables (d and Q) for the maximum SOTR and SAE are presented in Table 7. Table 7 Optimum values of variables for PTA
Sl. No. d, mm Q, L/s SOTR (kgO2/h) SAE (kgO2/kWh) 1 2.57 12.03 0.112 0.525 2 1.19 17.71 0.383 1.215 3 1.28 17.42 0.363 1.167
6 Conclusions From the present study, the aeration characteristics of the perforated tray aerator could be evaluated which depends upon different variables (n, d and Q). Based on the above results the following conclusions are drawn.
I. Optimum number of trays in a perforated tray aerator should be 3 to achieve the maximum SAE. II. Tray hole diameter of about 1 mm and discharge of about 19 L/s result in the maximum
SOTR and SAE values of 0.463 kgO2 /h and 1.382 kgO2/kWh respectively.
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III. Finally, it may be said that the developed RSM/CCRD model is an effective tool for deciding the optimized variables of perforated tray aerator.
References
1. Boyd, C. E., 1998. Pond water aeration systems. Aquacultural Engineering, 18 (1): 9-40. 2. Boyd, C. E. and Tucker, C. S., 1998. Ecology of aquaculture ponds. Pond aquaculture water quality management, US: 8–86. 3. Boyd, C.E., & Martinson, D.J. (1984). Evaluation of propeller-aspirator-pump aerators. Aquaculture, 36(3): 283 – 292. 4. El-Zahaby, A.M., El-Gendy, A.S., 2016. Passive aeration of wastewater treated by an anaerobic process—A design approach. Journal of Environmental Chemical Engineering, 4(4): 4565-4573. 5. American Society of Civil Engineers 2007. Measurement of Oxygen Transfer in Clean Water; ASCE/EWRI 2-06; American Society of Civil Engineers: Reston, Virginia. 6. APHA, 1992. American Water Works Association, and Pollution Control Federal (16th ed.), APHA, Washington, DC, 1268. 7. Lawson, T. B. and Merry, G. E., 1993. Procedures for Evaluating Low-Power Surface Aerators under Field Conditions. In Techniques for Modern Aquaculture (Ed. Wang, J.K.), Proceedings of an Aquacultural Engineering Conference, ASAE, Michigan (USA), 21– 23. 8. Jiang, P. and Stenstrom, M.K. 2011. Oxygen transfer parameter estimation: impact of methodology. Journal of Environmental Engineering, 138(2): 137-142. 9. Baylar, A., Hanbay, D., Ozpolat, E., 2007. Modeling Aeration Efficiency of Stepped Cascades by Using ANFIS. CLEAN–Soil, Air, Water, 35(2):186-192.
Volume 1, Issue 1 11 December, 2019
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Searching for some irreversible inhibitors as drugs of Human Cathepsin B by Computer Aided Drug Design (CADD) Method
Indrani Sarkar Department of Basic Science and Humanities, Narula Institute of Technology Agarpara, Kolkata E-mail: [email protected]
Abstract: Human Cathepsin B is an important lysosomal cysteine protease, the elevated level of which causes various diseases. This is a potential target for the development of drugs to treat those diseases. Various efforts have been made in the past to develop irreversible inhibitors targeting cathepsin B. The first irreversible inhibitor was isolated from Aspergillus japonicus, identified as N-(L-3.trans-carboxyoxirane-2-carbonyl)-L-leucine-4-guanidinobutylamide popularly known as E-64. It inhibits many cysteine proteases like cathepsins B and L, papain and calpains. So, E-64 and its derivatives cannot be used as specific drugs only for cathepsins B. We need specific inhibitors for various cysteine proteinases to treat specific diseases caused by them. The aim of this study is to build a mathematical model by Quantitative Structure Activity Relationship (QSAR) methods to correlate molecular properties of the inhibitors with their biological activities. The biological activity IC 50 is taken as the dependent variable and nine molecular descriptors are taken as independent variables to build the model. The potential inhibitors are subsequently studied using molecular modelling and dynamics simulation to identify active compounds from inactive compounds.
Keywords: Quantitative Structure Activity Relationship, Human Cathepsin B, inhibitors, Molecular Descriptors
1 Introduction
Cathepsin B belongs to a family of lysosomal papain like cysteine proteases [1]. It plays an important role in intracellular proteolysis. Its role has been found in initiation, growth, tumor cell proliferation, angiogenesis, invasion and metastasis of murine pancreatic and mammary carcinomas. Cathepsin B enhances the activity of other proteases like matrix metalloproteinase, and cathepsin D and thus it plays an essential part in the proteolysis of extracellular matrix components. Wide arrays of diseases result in elevated levels of cathepsin B causing cell death, inflammation, and production of toxic peptides. It causes a significant amount of the apoptotic cell death inducing epilepsy. It is responsible for causing neuromuscular dysfunction, memory loss, and neuronal cell death. Cathepsin B also seems to cause Alzheimer's symptoms. The biological function of cathepsin B is also important during viral infection and replication for several viruses, such as Ebola, SARS (Severe Acute Respiratory Syndrome) in human cells. Due to its important involvement in many human diseases, cathepsin B has been chosen as a drug development target.
An example of structure-based design of cathepsin B-specific inhibitors is described [2]. The first inhibitor was isolated from Aspergillus japonicus, identified as N-(L-3. trans-carboxyoxirane-2- Volume 1, Issue 1 12 December, 2019
JISU Journal of Multidisciplinary Research (JISUJMR) carbonyl)-L-leucine-4-guanidinobutylamide, and popularly known as E-64 . It inhibits cathepsins B and L, papain and calpains specifically. But E-64 and its derivatives are not, however, selective inhibitors of cathepsins B and L either in vitro or in vivo. As specific inhibitors are required for understanding individual roles of these cysteine proteinases an attempt is made to find a specific inhibitor of cathepsin B.
2 Materials and Methods
Statistical methods in QSAR (The Quantitative-Structure-Activity-Method) presented here deals with compounds of small molecules. Computational chemistry generates molecular data or descriptors including geometries, energies and associated properties from a molecular structure.
Table 1 Compund and Cathespin details
Compound R Cathepsin B (IC50) I. EtO.tES-Ile.Pro.OH 2.28 2 EtO.tES-Pro.Pro.OH 25.0 3 EtO.tES-Thr.Ile.OH 13.5
4. HO.IES.Leu.t.iAA 3.36 5. COOH 2.28 4 CONH2 5600 6 COOMe 4100 7 CH2OH 6300 8 H 2500 9. H 30.4 10 Me 20.0 11 Et 2.28
12 t.Pr 1.45 13 t-Bu 1.41 14 c.Hex 1.11 15 EtzN 2080 16 EtNH 6.88 17 i.PrNH 4.64 18 n-PrHN 2.24 19 i-BuNH 1.78 20 n.BuNFI 2.26
21 i-AmNH 2.40
22 n.AmNH 3.16 23 n-HexNH 3.92 24 PhCH2NH 5.48 25 PhNH 12.2 26 c.HexNH 2.20 tES, L-trans-epoxysuccinyl; IAA, isoamnylamide, tES, L-truns.epoxysuccinyl, I.-Pr, |isopropyl, i-Bu, isobutyl; c-Hex, cyclolohexyl; i-Pr, isopropyl; i-Bu, isobutyl; n.Pr, n-propyl; n-Bu, n-butyl; i-Am, isoamyl; n-Am, n-amyl; n.Hex, n-hexyl; Ph,phenyl; c-Hex, cyclohexyl
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The biological activity of the molecules is measured by logIC50 (50% inhibitory concentration). The molecular descriptors are used to build statistical or computational neural network models to predict the property or activity of interest. Relationships between the variations in the values of molecular properties and the biological activity for a series of compounds are studied so that these models can be used to evaluate new chemical compounds. A QSAR generally takes the form of a linear equation:
Biological Activity = Constant + (c 1 ×P 1) + (c 2 ×P 2 ) + (c 3 ×P 3 ) + ...
Where the parameters P 1 through P n are computed for each molecule in the series and coefficients c 1 through c n are calculated by fitting variations in the parameters and biological activity. The main steps of QSAR study are: Structure entry and molecular modelling -----> Descriptor generation -----> Feature selection -----> Construct Model MLRA or CNN -----> Model validation.
2.1 Molecular Modelling
The molecular structures for 26 molecules (Table 1) are searched in the chemical database by pharmacophore selection (7). The 3D models of structures are generated by the software Avogadro. The structures are subjected to energy minimization with Mopac. This energy minimized three dimensional molecular structures are used to generate descriptors by using their topological and geometrical representations. As the structures of organic compounds can be represented as graphs, theorems of graph theory can be applied to generate graph invariants which are known as topological descriptors
2.2 Descriptor calculation
Over 250 descriptors are available with MOE QSAR module. Initial selection for the descriptors is attempted empirically based on the nature of the descriptors. Fifty descriptors of physicochemical and molecular properties are selected and calculated [3, 4, 5, 6].
2.3 Genetic algorithm for descriptor selection
After calculation of the descriptors a reduced set of descriptors are selected which are rich in information but smallest in size. The feature selection process is done by the Genetic Algorithm variable selection technique. A subset of descriptors is selected to relate with IC 50 values. MLR (Multiple Linear Regression) method determines the root mean-square error of cross validation (RMSECV) when using only that subset of variables in a regression model. The nine descriptors that demonstrated an apparent correlation with the observed biological activity are chosen to build QSAR models. Among these descriptors, N acc+don are the sum of the counts of hydrogen acceptor and donor. Apol, MR and Dipole represent atomic polarizabilities, molar refractivity, and the dipole moment of a molecule calculated from the partial charges of the molecule respectively. SA and SA pol measure the water accessible surface areas (WASA) of whole molecule and the hydrophilic part of WASA calculated using a radius of 1.4 Å for the probe, respectively. Vol, E sol, and logP (o/w) represent the molecular volume, the empirical solvation energy calculated based on OPLS force field, and the log value of octanol/water partition coefficient of a compound,
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JISU Journal of Multidisciplinary Research (JISUJMR) respectively. These descriptors mostly describe molecular solvation properties. Combination of various computational methods would be a better choice to estimate the partition property of the molecules which have considerable effect on its biological activities.
3 Result and Discussion
The biological activity IC 50 is taken as the dependent variable and the nine as independent variables to build the model. The result shows that the molecular descriptors geometrical molecular descriptors when taken for univariate correlation are insufficient to describe the structure activity relationship. The low correlation values (R= 0.5) are not taken. The best statistics is shown by Apol. For bivariate correlation best statistics and was shown by MR and Esol. For trivariate correlation best solution was shown by Apol, MR, Esol,. For tetravariate Apol, MR, Esol and logP(o/w) were the best combination. For pentavariate Apol, MR, Esol, logP(o/w) and Dipole ; for hexavariate Apol, MR, Esol, logP(o/w), Dipole and SApol show best statistics. The mathematical model obtained from above variables is given by
IC 50 = 3.07Apol + 3.4 MR + 0.45 Esol + 0.34 logP -0.21Dipol +0.5 I2SApol + 12.03
The above equation is validated for the experimental value of logIC50.
4 Conclusions
A computer- simulated study of epoxysuccinyl peptides shows that all inhibitors bind to S subsites of cathepsin B. The common structures of strong inhibitors suggest that the carboxyl group is necessary for inhibition of cathepsin B. (Ref 7) The presence of a bulkier ester g r o u p in trans epoxysuccinic acid is necessary for specific inhibition of cathepsin B. T h e amide derivatives have weaker inhibitory activities than the corresponding ester derivatives, but considerably higher specificities for cathepsin B. The c o m p u t e r simulation study shows the presence o f a large hydrophobic pocket around the active thiol g r o u p o f the active site of cathepsin B. The present paper attempts to correlate the selective inhibitory activities of epoxysuccinyl peptides on cathepsin B in vitro from the viewpoint of QSAR.
Acknowledgements The author is thankful to the University Grants Commission, India for providing necessary facilities.
References
1. Mason, R.W., Johnson, D.A., Barrett, A.J. and Chapman, H.A., 1986. Elastinolytic activity of human cathepsin L. Biochemical Journal, 233(3): 925-92. 2. Hanada, K., Tamai, M., Yamagishi, M., Ohmura, S., Sawada, J. and Tanaka, I., 1978. Isolation and characterization of E–64, a new thiol protease inhibitor. Agricultural and Biological Chemistry, 42(3): 523-528. 3. Ivanciuc, O. and Balaban, A.T., 1996. Design of Topological Indices. Part 6. 1, 2 A New Topological Parameter for the Steric Effect of Alkyl Substituents. Croatica chemica acta, 69(1): 75-83.
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4. Mohar, B., Babic, D. and Trinajstic, N., 1993. A novel definition of the Wiener index for trees. Journal of chemical information and computer sciences, 33(1): 153-154. 5. Randić, M., 1993. Comparative regression analysis. Regressions based on a single descriptor. Croatica Chemica Acta, 66(2): 289-312. 6. Schultz, H.P., Schultz, E.B. and Schultz, T.P., 1990. Topological organic chemistry. 2. Graph theory, matrix determinants and eigenvalues, and topological indexes of alkanes. Journal of chemical information and computer sciences, 30(1): 27-29. 7. Murata, M., Miyashita, S., Yokoo, C., Tamai, M., Hanada, K., Hatayama, K., Towatari, T., Nikawa, T. and Katunuma, N., 1991. Novel epoxysuccinyl peptides Selective inhibitors of cathepsin B, in vitro. FEBS letters, 280(2): 307-310.
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Some Preliminary Reports on Vegetation Indices using Operational Land Imager and Thermal Infrared Sensor
Simran Parvin Department of Remote Sensing and GIS, JIS University, Agarpara, Kolkata 700109 [email protected]
Abstract: The paper reports some preliminary analysis of vegetation indices as derived from Operational Land Imager and Thermal Infrared Sensor. Three types of vegetation indices have examined considering normalized different vegetation index, soil adjusted vegetation index and enhanced vegetation index emphasizing the combination of surface reflectance at two or more wavelengths to find any typical vegetation property as an application of remote sensing. Dominant features of the two sensors and the related flow chart used for a successful implementation of the method are discussed at length with new possibilities.
Keywords: Vegetation Indices, Land Imager, Infrared Sensor, Vegetation property Remote sensing
1 Introduction
Healthy vegetation reflects more near-infrared (NIR) and green light in comparison to other wavelengths while it absorbs more red and blue light. Normalized Difference Vegetation Index (NDVI) is an index to quantify vegetation by taking a difference between near-infrared and red light. NDVI is thus a measurement of surface reflectance to provide a quantitative estimation of vegetation growth and biomass. The physical properties of soil are affected by plants and their roots e.g., infiltration rate, aggregate stability, moisture content etc. and all these have a vital role in soil conservation [1, 2]. The magnitude of NDVI is related to the level of photosynthetic activity in the observed vegetation [3]. In general, higher values of NDVI indicate greater vigor and amounts of vegetation [4]. Soil-Adjusted Vegetation Index (SAVI), on the other hand, is structured similar to the NDVI but demands the addition of a 'Soil brightness correction factor', where reflectance value of the near infrared band, the reflectance of the red band, and the soil brightness correction factor are to be taken into account [5-9]. Empirically derived NDVI products have been found to be unstable, varying with soil color, soil moisture, and saturation effects from high density vegetation. With a view to get further information and to improve NDVI, we have attempted to examine the vegetation indices that accounted for the differential red and near-infrared extinction through the vegetation canopy.
2 Vegetation Indices
Vegetation Indices are combination of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation. They are essentially derived Volume 1, Issue 1 17 December, 2019
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utilizing the reflectance properties of vegetation. Each of the Vegetation Indices is designed to accentuate a particular vegetation property [10-14]. The different Vegetation Indices are termed as Normalized Different Vegetation Index (NDVI), Soil Adjusted Vegetation index (SAVI), and Enhanced Vegetation index (EVI).
2.1 Normalized Difference Vegetation Index (NDVI)
The normalized difference vegetation index (NDVI) is a simple type of graphical indicator that can be used for analyzing remote sensing measurements, typically, but not necessarily, from a space platform, and assess whether the target being observed contains live green vegetation or not. NDVI is a powerful indicator to monitor the vegetation cover of wide areas, and also for detecting the frequent occurrence and persistence of droughts [15-17]. NDVI is a vegetation index which indicates the healthy and dense vegetation. NDVI can be computed from satellite imagery using spectral radiance in red and near infrared reflectance. NDVI is calculated by this formula:
NDVI= (NIR−RED)/ (NIR+RED),
Where, NIR= near infrared band, R= Red band; here red and NIR stand for the spectral reflectance measurements acquired in the red (visible) and near-infrared regions, respectively [18, 19]. Spectral reflectance is the ratio of the reflected over the incoming radiation in each spectral band individually and so they take on values between 0.0 and 1.0. By design, the NDVI itself varies between -1.0 and +1.0.
2.2 Enhance Vegetation Index (EVI)
The enhanced vegetation index (EVI) is used to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de- coupling of the canopy background signal and a reduction in atmosphere influences [20-22]. The EVI has thus been considered a modified NDVI with improved sensitivity to high biomass regions and improved vegetation monitoring capability through a de-coupling of the canopy background signal and a reduction in atmospheric influences.
EVI is calculated by the following formula:
EVI = G * ((NIR - RED) / (NIR + C1 * RED – C2 * BLUE + L),
Where, L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5.
EVI is similar to Normalized Difference Vegetation Index (NDVI) and can be used to quantify vegetation greenness. However, EVI corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. It incorporates an ―L‖ value to adjust for canopy background, ―C‖ values as coefficients for atmospheric resistance, and values from the blue band (B).
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2.3 Soil Adjusted Vegetation index (SAVI)
SAVI is calculated by the following formula: SAVI = (1+L)(NIR−RED)/(NIR+RED+L),
where L is a canopy background adjustment factor. An L value of 0.5 in reflectance space was found to minimize soil brightness variations and eliminate the need for additional calibration for different soils. The transformation was found to nearly eliminate soil-induced variations in vegetation indices [17, 23].
3 Instruments and Methodology
The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are two instruments on board the Landsat 8 satellite Figure 1 shows the overview of OLI and TIRS while Figure 2 presents the View of the spacecraft bus and the solar panel including the positions of mounting of TIRS and OLI.
Figure 1 Overview of the two instruments (a) Operational Land Imager (OLI), (b) Thermal Infrared Sensor (TIRS)
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Figure 2 View of the spacecraft bus and the solar panel including the position of mounting of TIRS and OLI Wavelength, spectral region and spatial resolution of the sensors OLI and TIRS corresponding to different bands have presented in Table 1. The methodological flow chart is given in Figure 3. For the present study we used GIS Software ArcGIS 10.3.1, ERDAS Imagine 2014 and QGIS 3.6. Table 1 Some particulars of the two sensors OLI and TIRS Sensor Band Wavelength in Spectral region Spatial Number micrometre resolution (m) OLI 1 0.43-0.45 Deep Blue 30 2 0.45-0.51 Blue 3 0.53-0.59 Green 4 0.64-0.67 Red 5 0.85-0.88 Near Infrared 6 1.57-1.65 SWIR 1 7 2.11-2.19 SWIR 2 8 0.50-0.68 Panchromatic 15 9 1.36-1.38 SWIR 3 30 TIRS 10 10.60-11.19 Thermal 100 Infrared 1 11 11.50-12.51 Thermal Infrared 2
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Figure 3 Flow chart followed for implementing the method 4 Results and Discussion From the NDVI value, it is possible to assess vegetation healthiness. From the NDVI study, it is clearly observed that, NDVI value -0.99 to -0.01 indicates the healthiest condition of vegetation. On the other hand, NDVI value 0.16 to 1 exhibits the unhealthy condition of the vegetation. The spatial distribution of NDVI was indicates that precipitation is a key factor for vegetation growth. From the L value of the SAVI, which is indicates the brightness of the soil. It has been conclude that, SAVI value -1.49 to -0.02 show the high soil adjusted vegetation and 0.01 to 0.24 is less soil adjusted vegetation. Landsat OLI 8 and TIRS satellite image is presented in Figure 4. The figure shows the Landsat8 satellite image with the help of this image the vegetation indices have been derived.
In Figure 5 and Figure 6 we have shown the normalized vegetation index and the enhanced vegetation index respectively. In Figure 5 it has been observed that the part of the southern, northern portion and some parts of north-western portion are covered with dense vegetation which indicates the healthy vegetation. Similarly, the part of the western section and some
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parts of south-eastern section are covered with less vegetation. From Figure 6 it can be inferred that the parts of the western section and some parts of the north- eastern section are covered with moderately less vegetation which indicates the unhealthy vegetation due the biomass and canopy background noise. When compared the vegetation index values for the
normalized and enhanced conditions, we note very prominent and interesting features.
Figure 4 Landsat OLI 8 and TIRS satellite image
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Figure 5 Normalized vegetation index
Figure 6 Enhanced vegetation index
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EVI corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. ―L‖ value to adjust for canopy background, ―C‖ values as coefficients for atmospheric resistance, and values from the blue band (B). From this EVI study, it is clearly observed that, EVI value in the range 0 to 61 indicates the most greenness of vegetation; on the other hand the value 574 to 3114 indicates least greenness of the vegetation.
4. Conclusions
Our present study of NDVI can be used not only for accurate description of vegetation vigor, vegetation classification and continental land cover but is also effective for monitoring rainfall and drought, estimating net primary production of vegetation, crop growth conditions and crop yields, detecting weather impacts and other events important for agriculture, ecology and economics. The index is a transformation technique which minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths [21, 23].
Acknowledgements
We are thankful to the JIS University authorities for getting the facilities of work through the Department of remote Sensing and GIS.
References 1. Ramesh, P., Singh, P., Roy, F. and Kogan, F. 2003, Vegetation and temperature condition indices from NOAA-AVHRR data for drought monitoring over India. International Journal of Remote sensing, 24:4393-4402. 2. Hazra, S., Roy, S. and Mitra, S. 2017, Enhancing Adaptive Capacity and Increasing Resilience of Small and Marginal Farmers of Purulia and Bankura Districts, West Bengal to Climate Change. 3. Suhad M. Al-Hedny, Ahmad S. Muhaimeed, Suhad M. Al-Hedny, Ahmad S. Muhaimeed. 30 August 2019, Drought Monitoring for Northern Part of Iraq Using Temporal NDVI and Rainfall Indices. Environmental Remote Sensing and GIS in Iraq 301- 331. 4. G. Gyssels, J. Poesen, Esther Bochet and Yong Li. June 2005, Impact of plant roots on the resistance of soils to erosion by water: A review. Progress in Physical Geography 29(2):189-217.
5. WouterVannoppen, ,Matthias Vanmaercke, Sarah De Baets, J. Poesen. September
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2015, A review of the mechanical effects of plant roots on concentrated flow erosion rates. Earth- Science Reviews, DOI: 10.1016/j.earscirev.2015.08.011.
6. Almamalachy YS, Al-Quraishi AMF, Moradkhani H. 2019, Agricultural drought monitoring over Iraq utilizing MODIS products. In: Al-Quraishi AMF, Negm AM (eds) Environmental Remote Sensing and GIS in Iraq. Springer Water.
7. Al-Quraishi AMF, Qader SH, Wu W. 2019, Drought monitoring using spectral and meteorological based indices combination: a case study in Sulaimaniyah, Kurdistan region of Iraq. In: Al-Quraishi AMF, Negm AM (eds) Environmental Remote Sensing and GIS in Iraq. Springer Water.
8. Boken VK, Cracknell AP, Heathcote RL (eds). 2005, Monitoring and predicting agricultural drought. Oxford University Press, Oxford, 472
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10. Fadhil AM .2011, Drought mapping using geoinformation technology for some sites in the Iraqi Kurdistan region. Int J Digital Earth 4(3):239–257
11. Fadhil AM ,2013,Sand dunes monitoring using remote sensing and GIS techniques for some sites in Iraq. In: Proceedings SPIE 8762, PIAGENG 2013: intelligent information, control, and communication technology for agricultural engineering, p 876206. https://doi.org/10.1117/12.2019735.
12. Fern RR, Elliott AF, Andrea B, Michael LM. 2018, Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. EcolInd 94:16–21.
13. Hueso S, Brunetti G, Senesi N, Farrag K, Hernandez T, Garcia C. 2012, Semi-arid soils submitted to severe drought stress: influence on humic acid characteristics in organic- amended soils. J Soil Sediments 12:503–512.
14. Jain SK, Keshri R, Goswami A, Sarkar A, Chaudhry A. 2009,Identification of drought vulnerable areas using NOAA-AVHRR data. Int J Remote Sens 30(10):2653– 2668.
15. Jain SK, Keshri R, Goswami A, Sarkar A. 2010, Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards 54:643–656.
16. Ji L, Peter AJ. 2003, Assessing vegetation response to drought in the northern
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Great Plains using vegetation and drought indices. Remote Sens Environ 87(1):85– 98. 17. Murthy CS, Seshasai MVR, Chandrasekar K, Roy PS. 2009, Spatial and temporal responses of different crop-growing environments to agricultural drought: a study in Haryana state, India using NOAA AVHRR data. Int J Remote Sens 30:2897–2914.
18. Quiring SM, Ganesh S. 2010, Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150:330– 339.
19. Reich P, Eswaran H. 2004, Soil and trouble. Science 304:1614–1615.
20. Shahabfar A, Eitzinger J. 2011, Agricultural drought monitoring in semi-arid and arid areas using MODIS data. J AgricSci 149:403–414.
21. Singh RP, Roy S, Kogan F. 2003, Vegetation and temperature condition indices from NOAA-AVHRR data for drought monitoring over India. Int J Remote Sens 24(22):4393– 4402.
22. Tabrizi AA, Khalili D, Kamgar-Haghighi AA, Zand-Parsa SH 2010, Utilization of time-based meteorological droughts to investigate occurrence of streamflow droughts. Water Resource Manage 24:4287–4306.
23. Zhang Z, Kang H, Yao Y, Fadhil AM, Zhang Y, Jia K. 2017, Spatial and decadal variations in satellite-based terrestrial evapotranspiration and drought over inner Mongolia autonomous region of China during 1982–2009. J Earth Syst. Sci 126(8).
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Single Axis Solar Tracking System using Arduino Uno Controller
Suparna Pal Department of Electrical Engineering, JIS College of Engineering, Kalayani, Nadia [email protected]
Abstract: Solar energy is the most abundant out of different energy sources and also it can be conveniently converted to electrical energy. Utilizing solar panel, energy originating from the Sun can be converted to electrical energy but due to transition of the Sun from east to west the fixed solar panel may generate optimum energy. In the present paper we have proposed a system which can be implemented suitably by some simple arrangement for the solar panel to track the Sun. In the system the solar panel is to be coupled to a dc motor to track the Sun with a view to getting maximum sun light upon the panel at any given time. We have constructed the code using C++ programming language and targeted to Arduino UNO controller for capturing the highest amount of sunlight at any time of the year.
Keywords: Advantages of Renewable Energy, solar tracker, circuit description, advantages, future scope
1 Introduction
As power demands are increasing day by day, so fulfilment of the requirement of power we are increasing our energy generation by fossil fuel (CHP generation).But increasing CHP power environment get polluted and fossil fuel storage is decreasing day by day. It is heard that only 150 years fossil fuel are storage in world, after this stipulated time period we cannot produce bulk power through our conventional system. It is the time now to increase our generation by renewable energy. If we are not focusing our power generation by renewable sources, we will not survive. For that reasons every corner of the planet concentrate on solar power mainly because it is clean, green energy. But solar energy totally depends upon solar radiation which is uncertainty of everywhere. Though high end research we are not generate bulk way power from solar due to its low efficiency. To increase efficiency of solar panel radiation must be concentrate on plates but it is impossible to trap same radiation whole day time. We know that Sun position is not same whole day long, for maximum trapping of solar energy; solar panel must be rotate according to position of sun.
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2 Objective of Solar Tracking System
It is known that the angle of incidence lies between -90degrees after sunrise and 90 degrees before sunset passing zero degrees at noon. This makes the solar radiations to be 0% during sunrise and sunset and 100% during noon. This variation causes solar panel to lose more than 40% of the collected energy. At any time of the month or a day, the position of the sun is decided by two angles in the spherical co-ordinate system. The fifth angle of the projected position of the sun is in the horizontal plane.
3 Methodologies
Proposed Model
Solar tracking is a widely-applied proven technology that increases energy production by directing or concentrated the photovoltaic to track the sun on its path from dawn until dusk. Instantaneous solar radiation collected by the photovoltaic modules, assembled in a tracking system, is higher than the critical irradiance level for a longer number of hours than in fixed systems. There are numerous types of solar trackers, of varying costs, performance and sophistication. One of those is:
Single Axis Trackers
Single axis trackers have one degree of freedom that acts as an axis of rotation. The axis of rotation of single axis trackers is typically aligned along a true North meridian. It is possible to align them in any cardinal direction with advanced tracking algorithms. Their types are
1. Horizontal Single Axis Tracker (HSAT) 2. Vertical Single Axis Tracker (VSAT) 3. Tilted Single Axis Tracker (TSAT)
Main Components of Solar Tracking System
The Solar tracking system consists of two main parts:
1. Circuit for sensing and controlling the microcontroller (Arduino UNO) and motor driver. 2. The circuit required for solar panel.
Hardware Requirements
1. Solar panel 2. Sensors (2 LDRs) Volume 1, Issue 1 28 December, 2019
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3. Potentiometer 4. DC motor 5. Motor driver 6. Microcontroller (Arduino UNO) 7. D.C POWER SUPPLY 8. RECHARGEABLE BATTERY
Working Principle In general the electricity generated by the PV panels is influenced the solar radiation and ambient temperature .To generate the maximum electric power PV must be controlled so that the position of PV is always perpendicular to the sunlight to track the maximum intensity of light .The proposed model of single axis solar tracking system offers optimal energy conversion process of solar energy.
STEP 1: First we connect the solar panel to the rechargeable Battery STEP-2 – We use the microcontroller named Arduino Uno which is connected with an external source in which the program is installed. STEP-3 – The arduino which is connected with the LDR will sense an analog input & gives a digital output to the motor STEP-4- In this step the two LDR‘s (Light dependent Resistor) which are connected with the panel will come into action. STEP-5 – As the sunlight travels from east to west the intensity of light also changes. STEP-6 – As the intensity of light changes the LDR which senses more intensity will instruct the arduino to rotate the motor in that side with the help of motor driver to move the panel to that side. STEP-7 – The output of rechargeable battery which is charged by the solar panel, will supply a power to run the motor. STEP-8 – When the sun comes over head means in the middle of the day both the sensor will sense the same intensity then the panel will become stationery.
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Figure 1 Flow Chart
Specification of Hardware Components
L HARDWARE RATINGS NAME NO. (VOLT ,WATT) 1. SOLAR PANEL 9 V,3 W 2. SENSORS 3V,5V,12V 3. POTENTIOMETER 1K,5K,10K 4. D,C MOTOR 12V,10 R.P.M 5. MOTOR DRIVER --- 6. ARDUINO UNO 5V 7. D.C POWER 230V TO 12V SUPPLY 12V TO 5V 8. RECHARGEABLE 6V ,5AH BATTERY
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Experimentation
Snapshots of Project Entitled ―Automated Single Axis Solar Tracking System‖ are given below.
Figure 2 Snapshot of sensor circuit
Figure 3 Snapshot of Arduino Uno circuit along with both the sensor circuits
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Figure 4 Snapshot of power supply circuit
Arduino Programming const int east = 2; const int west = 3; const int motorpin1 = 4; //Pin 2 of L293D; const int motorpin2 = 5; //Pin 7 of L293D; const int en = 6; //Pin 1 of L293D; int a = 0; int b = 0; void setup() { // put your setup code here, to run once: pinMode(east, INPUT); pinMode(west, INPUT); pinMode(motorpin1, OUTPUT); pinMode(motorpin2, OUTPUT); pinMode(en, OUTPUT);
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digitalWrite(east,LOW); digitalWrite(west,LOW); digitalWrite(en,LOW); digitalWrite(motorpin1,LOW); digitalWrite(motorpin2,LOW); } void loop() { // put your main code here, to run repeatedly: a=digitalRead(east); b=digitalRead(west); digitalWrite(en,HIGH); if(a>b) { digitalWrite(motorpin1,HIGH); digitalWrite(motorpin2,LOW); delay(3000); digitalWrite(motorpin1,LOW); digitalWrite(motorpin2,LOW); } else if(a Final Setup of “Automated Single Axis Solar Tracking System” After Integrating all the hardware components and optimizing the designs through software analysis our final setup is made and the screenshot is also attached with it. Volume 1, Issue 1 33 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 5 Left Side View Figure 6 Right Side View Volume 1, Issue 1 34 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 7 Top View 4 Advantages The conservation of non-renewable energy resources Photovoltaic (PV) solar power eases the usage of diminishing natural resources such as oil, coal and gas. Today, we live in an exceptionally demanding environment where the use of energy is growing at an alarming rate. It is vital to preserve the earth‘s fossil fuels and other natural resources, not only for a healthier environment but also for the ability of future generations to meet their own needs Lower amount of Waste and Pollution PV solar power systems minimize the amount of waste production. For example, the entire process of converting coal to electricity produces a lot of dust, discarded solid waste, spillages of toxins and harmful emissions, as well as wasting energy, heat, land and water. Pollution from non-renewable fuels is inevitable. Emissions such as Sulphur Dioxide, Nitrogen Oxide and Carbon Dioxide all can have a negative effect on farming, people‘s health and water. Ecosystems are also at risk of being destroyed. Furthermore, pollutants from kerosene used for lighting purposes is reduced with the use of solar power systems, as well as the decrease in use of diesel generators for the production of electricity. Volume 1, Issue 1 35 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Offsetting Green House Gases PV Solar power systems produce electricity without giving off carbon dioxide. One PV Solar system can offset approximately six tons of CO2 emissions over a twenty year life span. Limiting the use of conventional energy sources Solar power improves energy efficiency and is therefore it is beneficial to us. Use of solar energy for generation of electricity reduces the consumption of conventional power for built up cities. It is cheaper and hence can be used for industrial and commercial purposes to run various operations. Thus, the use of photovoltaic systems to generate power is one of the most efficient ones ways of generating power. Universal application The versatility of the solar tracker is that, it can be used for various applications and can be implemented in various parts of world except for Polar Regions. Generating efficiency Over 40% increase in radiation reception from sun comparing with fixed installation. With dual axis tracker, additional over 45% increase in radiation reception from sun will be gained. Independent control The important factor concerning the system is that, it can be installed anywhere, where no manual operation is involved. LDR sensors play a vital role in making the system automated by sensing the intensity resulting in generation of pulse, thus making the system independent. 4.1 Limitations 1) When there is cloudy atmosphere it is difficult to tracking the sun. 2) Panel rotations require an extra power from outside of power used that produce by panel itself. 3) Fixing arrangement of LDR at perpendicular to sun light is somewhat problematic 4) LDRs are very sensitive elements and so may get damaged in extreme climatic conditions. 5 Conclusions A solar tracker is designed employing the new principle of using smaller cells to function as self- adjusting light sensors, providing a variable indication of their relative angle to the sun by detecting their voltage output. By using this method, the solar tracker was successful in maintaining a solar array at a sufficiently perpendicular angle to the sun. The power increase Volume 1, Issue 1 36 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) gained over a fixed horizontal array was in excess of 30%.The solar trackers are a mean of controlling a sun tracking array with an embedded microprocessor system. Specifically it demonstrates a working software solution for maximizing solar cell output by positioning a solar array at the point of maximum light intensity. The electronics needed to activate the motors are simple and the system can be applied to any electromechanical configuration. With minor adjustments it can be used with various types of collectors including flat-plate, compound- parabolic, evacuated tube, parabolic trough, Fresnel lenses, parabolic dish and heliostat field collectors. In Future the conventional energy is not sufficient for use so there is need of use non- conventional energy sources .This Project is very useful for power supply in rural areas where we can use high sensitive solar panels which can work in mild sun light also and by connecting number of solar tracker assemblies we will able to produce sufficient large quantity of power which will be able to supply power to medium size village. We can make use of solar panels in our day to day life for street lighting, in mobile phone chargers, water heaters, etc. References 1. Www. youtube.com 2. www.slideshare.com 3 .www.electronicshub.com 4. www.en.wikipedia.org 5. www.arduino.com 6. MD Khan, S M Shahrer Tanzil,SM Shfiul Alam, 2010, Design and Construction of an Automatic Solar Tracking System Conference: 6th International Conference on Electrical and Computer Engineering ICECE 2010, December, Volume 1, Issue 1 37 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Arsenic Contamination of Groundwater in West Bengal: A Human Health Threat M. Roy Centre for Environmental Studies, Vidyasagar University, Midnapur, West Bengal 721102 E mail: [email protected] Abstract: Arsenic contamination in groundwater in the Ganga-Brahmaputra fluvial plains in India and its consequences to human health have been reported as one of the world‘s biggest natural calamities. In India, seven states namely- West Bengal, Jharkhand, Bihar, Uttar Pradesh in the flood plain of the Ganga River; Assam and Manipur in the flood plain of the Brahmaputra and Imphal rivers and Rajnandga on village in Chhattisgarh state have so far been reported affected by arsenic contamination in groundwater above the permissible limit of 10 μg/L. People in these affected states have chronically been exposed to drinking Arsenic contaminated hand tube-wells water. Arsenic toxicity is a great threat in human civilization especially in West Bengal, India. Bio-accumulation of arsenic is occurring from the food grains as irrigated with arsenic-contaminated water. Arsenic contamination of groundwater in West Bengal leads to adverse effects on human health. Chronic exposure to arsenic can cause skin cancer and other health effects. To combat the arsenic crisis in West Bengal it desperately needs to increase awareness and educate the people about the problem. Keywords: Arsenicosis; toxicity; cancer; ground water; drinking water contamination 1 Introduction The rapid development of industries and successive overexploitation of water has led to water pollution. Water pollution has become a serious worldwide health hazard [1]. The main source of freshwater in several parts of the globe for meeting the requirements of everyday purposes and agriculture is groundwater. Large areas of the world experience huge contamination in groundwater due to the mixing of different toxic, minerals and heavy metals either naturally or anthropologically [2, 3]. Arsenic is one of the most important and fatal among these minerals and its presence is considered as one of the hazardous elements in the environment and exposure of it causes serious health issues like cardiovascular, neurological, hematological, renal, and respiratory problems. The high concentrations of arsenic (As) in drinking water in inorganic form cause skin, liver, lungs and other organs damages [4]. The contamination of arsenic in soil occurs through polluted groundwater through irrigation, which is then taken by several edible parts of plants and consequently accumulates in other food chains. The arsenic is one of the most important and fatal among these metals. In India, the groundwater arsenic contamination was Volume 1, Issue 1 38 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) first surfaced from West-Bengal in 1983, a number of other States, namely; Jharkhand, Bihar, Uttar Pradesh in flood plain of the Ganga River; Assam and Manipur in flood plain of the Brahmaputra and Imphal rivers, and Rajnandga, village in Chhattisgarh state have chronically been exposed to drinking arsenic-contaminated hand tube-wells water above permissible limit of 50 μg/L. Many more North-Eastern Hill States in the flood plains are also suspected to have the possibility of arsenic in groundwater. Arsenic contamination was first noticed in and around localized compartments in a few districts of West Bengal (India) [5]. In West Bengal, around 79 blocks in 8 districts among 26 districts were reported to be contaminated with arsenic in groundwater exceeded 50μg/L. According to Das, 2015, this problem is expanding rapidly and in 2006 almost 3235 villages were affected including North 24 Parganas, South 24 Parganas, Nadia, Murshidabad and Burdwan, Howrah, Hooghly, and Maldah districts. The severely affected districts are Murshidabad, Maldah, Nadia, North and South 24 Parganas, Burdwan, Howrah, and Hooghly. There are many alluvial aquifers of this state that carry arsenic in drinking water beyond the permissible limit of WHO, 2017 (0.01 mg/L) [6]. West Bengal lies within the Ganga–Brahmaputra delta basin have high contamination of arsenic mainly in groundwater [7]. West Bengal can be divided into three zones i.e. highly affected regions cover the eastern side of Bhagirathi River; mildly affected regions include the northern part of the Bhagirathi river and the unaffected region carries western part of the state [8]. This study reveals the condition of arsenic toxicity in West Bengal, a state of India and the fatal effect on human health due to arsenic toxicity. 2 Origin of Arsenic in the Environment There are various natural activities that release arsenic in the environment. Most significant are the volcanic eruption, erosion of rocks and forest fires. Arsenic is widely distributed in 320 minerals. The most common arsenic minerals are Arsenopyrite (FeAsS); Orpiment (As2S3); Realgar (As2S2); Pyrite (FeS2). Apart from this the widespread use of arsenic in insecticides and pesticides are also responsible for traces of arsenic in natural water. Arsenic enriched pesticides are Monosodium methane arsenate (MSMA)- HAsO3CH3Na; Disodium methane arsenate (DSMA)-Na2ASO3CH3; Dimethylarsinic acid (Cacodylic acid) - (CH3)2 AsO2H:Arsenic acid- H3AsO4. 2.1 Arsenic Toxicity Arsenic pollution has become a significant environmental problem nowadays. Recent epidemiological studies have reported noteworthy detrimental effects of As on humans due to its high toxicity. The World Health Organization (WHO) suggested that the arsenic concentration in the drinking water should not increase 10 µg/L. Arsenic (As) is a trace inorganic element. It is found as a crystal lattice of Arsenic minerals. It enters the water system through the traces of Volume 1, Issue 1 39 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) arsenic minerals absorption. The rate of interaction between water and arsenic minerals depends on the biogeochemical conditions such as redox potential, speciation, the concentration of metals in the fluid, ionic strength, microbial activity, pH and the temperature. Once it is released it can be absorbed, precipitate, dissolved and bio integrate according to the surrounding environmental conditions. Skin Disease: The specific skin diseases are pigmentation and keratosis, which are caused by chronic arsenic toxicity. Respiratory Disease: Non-malignant lung diseases are caused due to long exposure to arsenic- contaminated drinking water (800 mg/L). Chronic lung diseases were common in the exposure of chronic arsenic toxicity through arsenic-contaminated drinking water. Gastrointestinal Disease Dyspepsia is one of the most common gastrointestinal syndrome for chronic arsenic toxicity. Gastroenteritis also caused by chronic arsenic through the drinking of arsenic-contaminated water with a concentration greater than 50 mg/L. Diseases of Nervous System Peripheral neuropathy results from chronic exposure of arsenic through drinking water [9-12]. Several reports also revealed an increased incidence of cerebrovascular disease in patients suffering from chronic arsenicosis [13, 14]. There are also other neural complications such as peripheral neuritis, sleep disturbances, weakness, and cognitive and memory impairment reported. Cardiovascular Disease The important complications of chronic arsenic toxicity are Blackfoot disease (BFD), a peripheral vascular disease. Another peripheral vascular disorder such as Raynaud‘s syndrome and acrocyanosis with varying degrees of severity has also been reported in arsenic exposed peoples. Hematological Effects Acute and chronic arsenic poison lead to anemia, leukopenia, thrombocytopenia and other hematological abnormalities. West Bengal carries an average 50% anemia caused in the exposure to arsenic-contaminated groundwater (200-2,000 mg/L) [15]. Diabetes Cumulative arsenic exposure and prevalence of diabetes mellitus have shown a dose-response relationship in arsenic endemic areas [16]. In Bangladesh, diabetes mellitus prevalence also increased significantly where arsenic-contaminated water was taken as drinking water [17]. Volume 1, Issue 1 40 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Arsenicosis and Cancer Exposure to arsenic causes symptoms of carcinogenicity in humans. This carcinogenicity is principally responsible in skin, urinary bladder, and lungs, around 4.35% of skin cancer and 0.78% of internal cancers were detected in arsenic-affected villages [18]. Genotoxic effects Long-term exposure of arsenic through drinking water expresses genotoxic effects which include increased rate of chromosomal aberrations and micronuclei formation in buccal and urothelial cells. The frequencies of the formation of micronuclei were reported to be significantly high in peripheral lymphocytes, oral mucosa and urothelial cells of arsenic exposed patients [19-21]. 3 Methodologies The study of ground water quality assessment was conducted in Nadia district, W.B. In Nadia district W.B arsenic polluted ground waters occur in shallow unconfined aquifer; both As (III) and As (V) occur in ground water. Ground water in the study area occurs in shallow water unconfined aquifer in which reducing agent viz. organic matter is abundant. Concentration of iron, bicarbonate, trivalent arsenic is high. As a consequence ferric iron oxides are reduced and their load of arsenic is released to ground water. The ground water samples were collected from 34 different sampling locations (Table 1). Collections of Samples: The water samples from different tube wells were collected after pumping for a few minutes without filtration in 10 ml polyethylene bottles prewashed with nitric acid and water (1:1). After collection, 1 drop of concentrated nitric acid: water (1:1) per 10 m of water sample was added as preservative. In addition to arsenic contamination, iron and pH were also detected to evaluate the quality of water. pH is estimated by electrometric method; Iron is estimated by photometric method and arsenic is analysed by SDDC method [22]. Table 1 Detail of Study Area Locations Sample No. Location Sample No. Location W1 Karimpur-I W18 Krishnaganj W2 Karimpur-I W19 Haringhata W3 Karimpur-II W20 Haringhata W4 Karimpur-II W21 Chakdaha W5 Nakashipara W22 Chakdaha W6 Nakashipara W23 Santipur W7 Tehatta-I W24 Santipur W8 Tehatta-I W25 Chapra W9 Tehatta-II W26 Chapra W10 Tehatta-II W27 Ranaghat-I Volume 1, Issue 1 41 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) W11 Kaligunj W28 Ranaghat-I W12 Kaligunj W29 Ranaghat-II W13 Nabadwip W30 Ranaghat-II W14 Nabadwip W31 Krishnanagar-I W15 Hanskhali W32 Krishnanagar-I W16 Hanskhali W33 Krishnanagar-II W17 Krishnaganj W34 Krishnanagar-II 4 Results and Discussions The pH is the intensity of the acidity or alkalinity and measures the hydrogen ion concentration in water. It has no direct adverse effect on health. However a low pH (≤4) will produce sour taste and higher values (≥8.5) shows alkaline taste. The WHO permissible limit of pH in drinking water varies from 6.03 mg/l to 8.5mg/l. All the samples were well within the permissible limit (Table 2). The WHO permissible limit of iron for drinking water is 0.3 mg/l. All the samples were detected with Iron range much higher than the permissible limit. The arsenic standard range for drinking water is 0.01 mg/l to 0.1 mg/l. The result shows that all the samples range between 0.02 to 0.24 mg/l hence well within the permissible limit. Table 2 Concentration of pH, Iron and arsenic in water samples Sample pH Iron Arsenic Sample pH Arsenic Iron No. (mg/l) (mg/l) No. (mg/l) (mg/l) W1 6.58 2.4 0.09 W18 6.37 0.06 3.1 W2 6.61 3.7 0.06 W19 7.13 0.09 2.5 W3 6.72 4.1 0.24 W20 7.10 0.11 3.7 W4 6.87 4.9 0.22 W21 6.97 0.07 3.4 W5 6.49 7.3 0.19 W22 6.53 0.06 4.2 W6 7.01 8.5 0.08 W23 7.03 0.09 4.0 W7 6.78 4.4 0.11 W24 7.12 0.25 5.3 W8 6.92 4.9 0.10 W25 7.21 0.04 4.19 W9 6.95 3.9 0.07 W26 7.10 0.02 3.27 W10 6.67 4.2 0.10 W27 6.49 0.04 3.6 W11 7.00 3.8 0.07 W28 6.86 0.06 3.9 W12 7.05 3.1 0.09 W29 7.13 0.09 4.8 W13 6.59 4.5 0.06 W30 7.00 0.17 5.0 W14 6.83 4.9 0.11 W31 6.52 0.06 3.4 W15 6.48 4.2 0.07 W32 7.04 0.04 3.8 W16 7.09 4.9 0.05 W33 7.01 0.09 4.2 W17 6.45 2.37 0.04 W34 6.97 0.14 4.5 Volume 1, Issue 1 42 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 5 Epidemiological Studies The epidemiological study was carried out by carrying house to house survey of arsenic affected villages of Nadia. Villages considered for sampling are those which have evidence of arsenic contaminated public tube well (Table 3). Around 17 blocks in the study area were surveyed and the report reveals that around 7662 tube wells were reported to have arsenic contamination above 50 µg/L and around 1600 patients were found with arsenic lesions. Table 3 Details of the Epidemiological Studies Parameters Total Total Area 8927 Total Population 4604827 Total Blocks 17 No. of Villages having minimum one T.W. contaminated with As 1011 Total no. of population of blocks 3625308 Population of Villages having As contamination (>50 µg/L) 3220182 % of Exposure (% of Arsenic contaminated public tube wells in the blocks) 6.36-45.96 Total population of villages exposed to arsenic 839675 No. of Villages studied 37 Total population in the study villages 635728 No. of house hold surveyed 2297 Total no. of public tube wells in the district 29640 Total no. of public tube well water above 50 µg/L 7662 No. of water samples contaminated with arsenic more than 50 µg/L 809 No. of persons examined 10469 No. of patients with arsenical skin lesion 1616 Population derived as probable affected with arseniocosis 141592 Source: PHED, Gov. of W.B 6 Remediation Technological options to combat the arsenic menace, in groundwater, to ensure supply of arsenic-free water, in the affected areas, can be one of the followings or a combination of all: I. In-situ remediation of arsenic from aquifer system, II. Ex-situ remediation of arsenic from tapped groundwater by arsenic removal technologies like chemical coagulation, reverse osmosis, membrane filtration, etc. III. Use of surface water source as an alternative to the contaminated groundwater source, IV. Tapping alternate safe aquifers for the supply of arsenic-free groundwater, Volume 1, Issue 1 43 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) V. Innovative technologies, such as permeable reactive barriers, phytoremediation, biological treatment, and electro kinetic treatment are also being used to treat arsenic- water, wastewater, and soil. Removal of Arsenic from Drinking Water: Oxidation Coagulation Absorption Ion Exchange Membrane techniques and Reverse Osmosis Oxidation Techniques: Oxidation involves the conversion of soluble arsenite to arsenate. For anoxic groundwater, oxidation is an important step since arsenite is a prevalent form of arsenic at near-neutral pH. Nowadays many chemicals, as well as bacteria, are used to directly oxidize the arsenic Coagulation: Coagulation and filtration with metal salts followed by filtration is the most documented method of arsenic removal. Adsorption is the electrostatic binding of soluble arsenic to external surfaces of the insoluble metal hydroxide. Coagulation is achieved using alum, ferric chloride or ferric sulfates that are effective at removing arsenic. Ion exchange: The ion exchange process is not dependent on the pH of the water. Pre oxidation of As(III) to As(IV) is required for removal of arenite by the ion exchange process, but the excess of oxidant needs to be removed. Ion exchange resins can be easily regenerated by washing them with a NaCl solution. Membranes are utilized to remove many contaminants from water including pathogen, salts, ions, etc. generally two types of membranes are used. 7 Conclusions Arsenic Contamination of groundwater is an alarming problem in West Bengal. If arsenic safe water cannot be provided, the concentration of arsenic should be removed. Many peoples are affected by arsenic contamination in groundwater. An alternative water source is not possible everywhere, hence arsenic removal techniques can be implemented in those places. Rainwater harvesting and groundwater recharge techniques can be implemented in some places. It is necessary to create awareness among the people about arsenic contamination. Location-specific research, combined with a combination of technology, incentive, and self-protection policies could be used to address the problem of arsenic contamination worldwide. Arsenic sources and effects are multiple and diffused in nature and it requires detailed assessment and policy. To reduce arsenic in water resources, incentive policies such as taxing and subsidizing can be used to reduce arsenic levels in point sources through the creation of appropriate incentives. Creating awareness among the people in the arsenic affected states in India will decrease the contamination level in water resources. Volume 1, Issue 1 44 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) References 1. Aina, M P., Kpondjo, N M, Adounkpe, J., Chougourou, D., Moudachirou, M. 2012, Study of the Purification Efficiencies of three Floating Macrophytes in Wastewater Treatment. I Res J Environ. Sci., 1(3): 37-43. 2. Ravenscroft, P., Brammer, H., Richards, K. S. 2009, Arsenic Pollution: A Global Synthesis. Blackwell-Wiley, USA. 3. Onodera, J., Takahashi. K., Jordan, R.W. 2008, Eocene silicoflagellate and ebridian paleoceanography in the central Arctic Ocean. Paleoceanography 23(1): 1-9. 4. Smith, A. H., Hopenhayn Rich, C., Bates, M.N., Goeden, H.M., Hertz Picciotto, I., et al. 1992, Cancer risks from arsenic in drinking water. Environ Health Perspect 97: 259-267 5. Majumdar, P.K., Ghosh, N.C, Chakravorty, B., 2002, Analysis of arsenic-contaminated groundwater domain in the Nadia district of West Bengal (India). Hydrological sciences journal 47(1): S55-S66. 6. Yu, W.H, Harvey, C.M, Harvey, C.F. 2003, Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies. Water Resour. Res 39(6): 1146. 7. Mukherjee, A., Fryar, A.E, Rowe, H.D. 2008, Regional-scale stable isotopic signatures of recharge and deep groundwater in the arsenic affected areas of West Bengal, India. Journal of Hydrology 334: 151-161. 8. Borgono, J.M, Vicent, P., Venturino, H. Infante, A. 1977, Arsenic in the drinking water of the city of Antofagasta: epidemiological and clinical study before and after the installation of a treatment plant. Environ Health Perspect 19:103-105. 9. Ahmad, S.A, Sayed, M.H.S.U, Hadi S.A., Faruquee, M.H., Jalil, M.A., Ahmed, A., Khan, W. 1999, Arsenicosis in a village in Bangladesh. Int J Environ Health Res 9(3): 187-195. 10. Cebrian, M.E., Albores, A., Aguilar, M., Blakely, E., 1983, Chronic arsenic poisoning in the north of Mexico. Hum Toxicol. 2(1): 121-133. 11. Saha, K.C., 1984, Melanokeratosis from arsenic contaminated tube well water. Indian J Dermatol 29(4): 37-46. 12. Hotta, N., 1989, Clinical aspects of chronic arsenic poisoning due to environmental and occupational pollution in and around a small refining spot. Nippon Taishitsugaku Zasshi 53: 49- 70. 13. Kilburn, K.H., 1997 Neurobehavioral impairment from long-term residential arsenic exposure. In: Abernathy CO & Calderon RL (Eds.), Arsenic exposure and health effects, UK, : 159-177. 14. Chen, C.J., Chiou, H.Y., Huang, W.I., Chen, S.Y, Hsueh, Y.M., Tseng C.H., Lin, L.J., Shyu, M.P., Lai, M.S, 1997, Systemic non-carcinogenic effects and developmental toxicity of inorganic arsenic. In: Abernathy CO & Calderon RL (Eds.), Arsenic exposure and health effects, UK: 124- 134. Volume 1, Issue 1 45 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 15.Guha Mazumder, D.N., Chakraborty, A.K., Ghosh, A., Gupta, J.D., Chakraborty, D.P., Dey, S.B., Chattoadhyay, N.1988, Chronic arsenic toxicity from drinking tube-well water in rural West Bengal. Bull World Health Organ 66(4): 499-506. 16. Lai, M.S., Hsueh, Y.M., Chen, C.J., Shyu. M.P, Chen, S.Y., 1994, Ingested inorganic arsenic and prevalence of diabetes mellitus. Am J Epidemiol 13995: 484-492. 17. Rahman, M., Tondel, M., Ahmad, S.A., Axelson, O.,1998, Diabetes mellitus associated with arsenic exposure in Bangladesh. Am J Epidemiol 14892): 198-203. 18. Saha, K C., 2003. Saha‘s grading of arsenicosis progression and treatment. In: Chappell WR & Abernathy CO (Eds.), Arsenic exposure and health effects, Oxford, UK: 391-414. 19. Warner, M.L., Moor, L.E., Smith, M.T., Kalman, D.A., Fanning, E., 1994, Increased micronuclei in exfoliated bladder cells of individuals who chronically ingest arsenic contaminated water in Nevada. Cancer Epidemiol Biomarkers Prev 3(7): 583-590. 20. Gonsebatt, M.E., Vega, L., Salazar, A.M., Montero, R., Guzmán, M.E., 1997, Cytogenetic effects in human exposure to arsenic. Mutat Res 386(3): 219-228. 21. Basu A, Ghosh P, Das, J.K., Banerjee, A., Ray, K., 2004, Micronuclei as biomarkers of carcinogen exposure in populations exposed to arsenic through drinking water in West Bengal, India: a comparative study in 3 cell types. Cancer Epidemiol Biomarkers Prev 13(5): 820-827. 22. I.C.M.R,1975, Manual of standard of quality of drinking water supplies, Spl Report Series No. 44, Volume 1, Issue 1 46 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) A novel artificial intelligence method and angular distribution function for characterization of breast cancer Mainak Biswas1, Saikat Maity1, Shubhro Chakrabartty2 1Deparment of Computer Science and Engineering JIS University, Kolkata-700109, India 2 Department of Nanoscience and Engineering Inje university, Gimhae, 50834, Republic of Korea Abstract: Breast cancer kills nearly 42,000 women and men every year. The detection of breast cancer can ensure earlier treatment and saving of thousands of lives. In this regard, we present an artificial intelligence (AI)-based breast cancer detection from 699 instances of data. Further, we propose a novel Angular Distribution Function for Classification (ADFC) for classification of breast cancer patients. The ADFC compares the length of arcs each test instance makes with central points of each class cluster. It associates the test instance with the class having smallest arc angle using K10 cross-validation (nine parts for training and one part for testing). The performance of ADFC is compared with standard algorithms such as general KNN algorithm and PLDL algorithm. The algorithm was also validated using standard datasets such as Iris. The results clearly showed that ADFC performed better with contemporary algorithms. Keywords: Classification, supervised learning, breast cancer. 1 Introduction It‘s seen that a greater number of women are diagnosed with breast cancer than any other forms of cancer [1]. In the US alone, 331,530 women are diagnosed with breast cancer. Another 2,670 men are also diagnosed with breast cancer. The total fatality rate due to breast cancer is approximately 42, 260 deaths per year including 41, 760 women and 500 men. Breast cancer develops from tissues in the breast [2]. The breast is made up of lobes and ducts. The lobes are further divided into lobules which end in tiny bulbs. Each of these parts are connected by ducts. The detailed physiology of breast is shown in Figure 1. Cancer generally forms in one of these several parts of breast. There are several causes of cancer formation i.e., family history, inherited Volume 1, Issue 1 47 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) gene mutations, obesity, alcohol abuse etc. The breast cancer is generally characterized by formation of lumps. The gold standard of diagnosis of breast cancer is manual, making the entire process tedious, error-prone. Over the years semi-automated and automated techniques have made their mark in computer aided diagnosis process with the advancement of hardware and software. Artificial intelligence (AI) techniques [3] mimic human thinking and memory in understanding and solving different computational problems. Characterization of human diseases from different sickness parameters is one of the several problems where AI can be used. Various AI techniques for diagnosis of diseases have been developed till date. Wu et al. [4] used Artificial Neural Network (ANN) on 43 image features for distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve. In another work, NG et al. [5] used ANN for a different set of data and achieved 61.54% accuracy. In this regard, we propose a novel angular distribution function for classification (ADFC)-based K-Nearest Neighbor (KNN) algorithm for breast cancer characterization. ADFC is inspired from our earlier work in label distribution learning [6, 7]. ADFC computes the length of the arc joining test instance with central points of each class cluster. Based on the number of K-Nearest neighbors all the arc lengths relative to each training points are computed. Based on arc lengths, orientation of each test point to each class is computed. A cosine approximation of normal distribution is applied to each orientation. Based on the frequency of closest orientation, the class is assigned to the test instance. It‘s seen that the current methodology gives better performance than previous contemporary methods. The paper is arranged in the following manner: section 2 discusses the data acquisition, section 3 describes the methodology, section 4 discusses the results and discussion and finally the paper concludes in section 5. 2 Data Acquisition Data has been collected from 669 patients. There are a total 30 features describing different characteristics of the cell nuclei present in the image. These features are computed from the digitized image of a fine needle aspirate (FNA) of a breast mass [8]. Volume 1, Issue 1 48 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 1 Physiology of breast 3 Methodologies In this section, we present the ADFC-based KNN algorithm for characterizing breast cancer data into benign and malignant groups. This function works on the following presumption. Supposedly, there are ― ‖ training instances and ― ―classes. In each of these ― ‖ clusters the central point is computed using averaging. Let us call them points. If there are ― ‖ nearest neighbors, a Euclidean distance matrix is computed for a given test instance t, to all and nearest neighbors. | | | | (1) | | After all the distances are found, the angle between training instance and central points and the test instances are found out. A representative image can be found in Figure 2, where theta is found out based on the distance between test instance and training and central points respectively. The mathematical expression, is given as: (2) Where, = Distance between k and y or ky, . Volume 1, Issue 1 49 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 2 Representation of hypothesis A modified cosine approximation of normal distribution [7] is applied to each orientation for k- nearest neighbors. The cosine approximation is given as: ( ) [ { ( )}] (3) ( ) represents the orientation of how much the test instance is closer to a class, based on the number of -nearest neighbors. The results and discussion are given in the next section. 3 Results and Discussion The experiment is run using K-10 cross validation. In K-10 cross-validation, the dataset is divided into ten parts where nine parts are used for training and one part is used for testing. The accuracy chart for breast cancer is shown in Figure 3. It is clearly seen that ADFC gives better results for all the folds. A validation is done using well-known Iris dataset. The accuracy chart is shown in Figure 4. The results clearly showed that ADFC performs better exclusively for all datasets. Volume 1, Issue 1 50 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 0.7 0.6 0.5 0.4 Accuracy 0.3 0.2 0.1 0 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 KNN PLDL ADFC Figure 3 Accuracy chart for breast cancer 4 Conclusions ADFC is a new approach of looking at characterization algorithms. ADFC classified instances based on the orientation of each test instance towards a class. ADFC is used on breast cancer dataset and results prove that ADFC gives better results than contemporary algorithms. ADFC was also used on Iris dataset for validation. The results again prove the generalization ability of ADFC algorithm. Volume 1, Issue 1 51 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 1 0.9 0.8 0.7 0.6 0.5 Accuracy 0.4 0.3 0.2 0.1 0 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 6 Fold 7 Fold 8 Fold 9 Fold 10 KNN PLDL ADFC Figure 4 Accuracy chart for Iris dataset References 1. Available Online. https://www.cancer.net/cancer-types/breast-cancer/statistics. 2. Available Online. https://www.cancer.gov/types/breast/patient/adult/breast-treatment- pdq. 3. Russell, S. J., & Norvig, P. 2016, Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited. 4. Wu, Y., Giger, M. L., Doi, K., Vyborny, C. J., Schmidt, R. A., & Metz, C. E. 1993, Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer, Radiology, 187(1): 81-87. 5. Ng, E. K., Fok, S. C., Peh, Y. C., Ng, F. C., & Sim, L. S. J., 2002, Computerized detection of breast cancer with artificial intelligence and thermograms, Journal of medical engineering & technology, 26(4): 152-157. Volume 1, Issue 1 52 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 6. Biswas, M., Kuppili, V., & Edla, D. R., 2019, ALDL: a novel method for label distribution learning. Sādhanā, 44(3): 53. 7. Kuppili, V., Biswas, M & Edla, D. R., 2019, PLDL: a novel method for label distribution learning. IAJIT 16(6): 1021-1027. 8. O.L. Mangasarian, W.N. Street and W.H. Wolberg. (July-August 1995), Breast cancer diagnosis and prognosis via linear programming, Operations Research, 43(4): 570-577. Volume 1, Issue 1 53 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Impressions of High Frequency Radio-Waves from Cell Phone Towers on Birds: A Base-Line Study Sauvik Bose1, Rajeshwari Roy2, Urbi Chakraborti1, Risha Samanta1, Sipra Jana1, Tanusree Mondal1, Soumini Chaudhury1and Rina Bhattacharya1 1Department of Physics, JIS University, Kolkata, West Bengal, 700109 2Department of Environmental Studies,Rabindra Bharati University, Kolkata,West Bengal, 700007 E-mail: [email protected] Abstract: The enhanced use of wireless telephony and internet services is indispensible need of the hour to keep up economy and thus meet the global pace. But amongst all of the much studied deadly environmental pollutions, electro-magnetic radiation (EMR) is the newer one and global concern. Although studies regarding its ill impacts on human health are the major concern; a number of results also reveal the impacts on other creatures too. An adverse relation between EMR and common birds is concluded several times. A comparative baseline bird survey between an EMR exposed location and an EMR free location; with identical weather conditions has been documented in the current investigation. The experimental fields are located in sub urban, rural; agriculture and forest areas of Arambagh subdivision (22.88°N, 87.78°E) of West Bengal, India and the studies were carried out in the month of September of this year. Only 28.08% birds are found near the EMR exposed zones however occurrence of small birds are very few compared to large birds. Key Words: Wireless telephony, Habitat, Environmental Pollution, Bird Survey. 1 Introduction If we walk along the memory lane of the worldwide civilizations and communications, we observe a popular pattern of using pigeons as messengers. It was around the 12th century BC, when these birds were used to deliver mails, documents and many more [1]. By the 18th century, the newspaper came and successively became common, though it was not a way for one to one communication [2]. The newspaper was followed by the introduction of the telegraph, and finally telephones appeared in 1876. In the later part of 1800s, the concept of wireless telegraphy emerged. Since then, through several inventions and modifications, computer, wireless telephony, email system, instant messenger, widespread internet access, smart phones, social media and video communications using mobile applications have evolved. Volume 1, Issue 1 54 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) World Economic Forum stated that the cell phone industry is now under abrupt growth globally having a steadily growing market for data traffic. According to the available statistics, the count of mobile subscribers has already surpassed the world population [3]. Our country has got the second largest telecommunication market in the world. Moreover, the second highest internet using country (in terms of users) is India. The sum of telephone connections per 100 individuals (or tele density) has grown from 18.3% in financial year 2007 to 92.84% in financial year 2018. Further assumptions reveal that the figure of internet subscriptions is going to get doubled by 2021 (up to a value of 829 million). The report adds that over a period three months in 2018, India turned out to be the most rapidly expanding market for cell phone applications [4]. It was reported that in developing countries that with the 10% increase of mobile and broadband connectivity, the GDP per capita will be increased by 0.81% and 1.38% respectively [5].The exponential progress on Telecom Sector in the country has contributed to the economic as well as societal progress of India [6, 7]. To combat the present need of communication of more than 50 crore cell phone users, about 4.4 lakh mobile base stations are installed. The frequency band transmitted by different antenna is given in Table 1. Moreover, in few cities 4G has been used for which the transmitted frequency is in the range 1900-2025 MHz and 2110-2200 MHz [8]. Table 1 Antenna and transmitted frequency Antenna CDMA GSM900 GSM1800 Frequency range 869 - 894 935 - 960 1810 – 1880 (MHz) But every good thing has a dark side. Mobile telephony is of course not the exceptional one. While a large part of the nation is busy with the improvisation of telecommunications for a better economic future, a group of researchers has concluded the deadly effects of continuous exposures in the EMR field. Adversities due to cell phone radiations have been reported worldwide [9]. Even certain studies claim it to be carcinogenic to human [10]. If we put aside human health, these radiations have huge detrimental effects on ecosystem too. A number of studies proved the direct effect of cell phones and cell phone tower emissions on several organisms [11]. Birds are accounted to be highly troubled due to exposures to tele radiations. Since radiation may have both thermal and non-thermal effects on living tissues, it is highly able to hamper their nesting, egg laying, roosting, feeding and associated behaviours [12]. There are ample evidences that birds avoid high radiation zones, especially, the cell phone masts [13, 14]. Hence, the modern communication system deliberately hurts the avian; not only the pigeons, the pioneer of all the communication systems, but other avian varieties too. Here, we have conducted a comparative documentation of bird occurrences, between a radiation exposed zone and a radiation free zone in the Arambagh subdivision of Hooghly district. Volume 1, Issue 1 55 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 2 Materials and Methods [A] Study areas Four different habitats viz suburban, rural, agriculture and forest are selected in and around Arambagh ( 22.88°N, 87.78°E) city in Hooghly district of West Bengal, India as shown in Fig 1. This locality has a population density of 4224/sq-km. This is a rice and potato producing agricultural area with several rice mills and cold storages which can attract birds of diverse variety. The climate is tropical in nature. The temperature rises to 42 °C during summer and falls to as low as 8 °C in winter and the average annual rainfall is 1,600 mm [15]. The survey is conducted in four habitats as shown in Figure 1. I. II. III. IV. Figure 1 Study area: (I) Sub urban habitat, (II) Rural habitat, (III) Agriculture habitat and (IV) Forest habitat Volume 1, Issue 1 56 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) I. Sub urban ( ): This is situated in the Arambagh Municipality beside the Link Road. There are a good number of trees within the location and 50m away there is a pond. A private hospital is situated near the habitat and the noise-level is little bit high (68dB-75dB) in the locality. II. Rural ( ) : This habitat is within the Arambagh Block 150 m away from the Ahilyabai-Holkar Road. The area is green due to the presence of variety of trees viz. bamboo (Bambusa aridinarifolia), black plum (Syzgium cumini), banyan (Ficus benghalensis) etc. The noise-level is about 60 - 72 dB. III. Agriculture ( ): This habitat (paddy field) is located in Arambagh Municipality 500 m away from Arambagh-Bardhaman road and there is a river called ‗Dwarakeswar‘ 400 m away from the habitat. The noise level is about 60dB. IV. Forest ( ) : This habitat (named Chandur) is also under Aramabgh Municipality 10 m away from the Arambagh-Bardhaman road. There is no mobile tower within 500 m from the location. There is a river ‗Dwarakeswar‘ 500 m from the location. The noise level is very low (about 50dB). This location is fully covered with Sal trees (Shorea robusta). [B] Field visit and Data collection Field surveys are done during morning hours from 6:00 hrs to 9:30 hrs on each day during the month of September, 2019 using point count method in case of rural and agriculture habitat and line transect method in sub urban and forest habitat [16].The presence of birds are counted both from visual observation and accounting the chirps of birds followed by snap shot using NIKON D5500 camera. [C] Mathematical Tool Species diversity in a community is computed by using Shannon diversity index (H) for both abundance and evenness of the species present [17]. It is defined as: Shannon Index (H) = - --- (1) th where Pi is the proportion of individuals of the i species out of total number of individuals and S is the total number of species. Volume 1, Issue 1 57 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Table 2 Characteristics of birds Name Identifying character Breeding time Common Myna Colour: Yellow bill, leg and bare eyes; brown with a October-March (Acridotheres black head tristis) Avg. size: 24cm. Dove Size: Small head compared to their body; Long tail with September-October (Spilopelia distinct colour pattern; chinensis) Colour: Having 35 colours, Most commonly white, brown, grey and pitch. Tailor Bird Colour: Brightly coloured bird with bright green upper February - May (Orthotomus part and creamy lower part; Size range: 10-14cm; sutorius) Having short round wings and long tails Bulbul Colour: Upper wing and mantle are mid brown, the February-July (Pycnonotus rump is white, tail is long and blackish with prominent cafer) white tip, eyes and legs are dark; Length: 13-19 cm; Size: concave and short wings; These are singing birds. Wood Pecker Tough pointed beak; Tip of their bill is chisel shaped; April – July (Dendrocopos They have sharp claws on their toes; Tongue is 4 inchs. macei) Long. Pigeon Shape: spindle or fusiform body; Length: 33cm; Mid July - Mid (Columba livia) Colour: eyes and feet are pink, body is salty grey with October glistening metallic green and purple sheen on the breast and around the neck, the wing has two black bars. Pied Myna Colour: strikingly marked in black and white, yellowish March - September (Gracupica bill with reddish bill base. the upper body, throat & contra) breast are black while the cheek, lores, wings converts and rump are contrastingly white Crow Size: 47cm (16 inchs.); Robustly build bird with tails March-June (Corvus that are short or medium length; Tail is stiff; Bills varies splendens) from species to species Crane Height: 1.8m (5.9 ft.); Tall birds with large wings, long April – June (Bubulcus ibis) legs, and graceful necks; Colour: black and white or grey, Often there are bright patches of bare red skin that are shown in threat & dance display Jungle bubbler Length: 16cm; Noisy birds; Shape: short rounded wings October- November (Turdoides and weak flight; Colour: grey shades, brown coloured striata) body lower part Rose ringed Length: 38-42cm; Colour: yellow, green plumage, long December -June parakeet graduated tail and broad, rounded and hooked pinkish- (Psittacula red bill. krameri) Volume 1, Issue 1 58 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 3 Results and Discussion All together we have found 11 types of birds in different habitats. Their identifying characters [18] and breeding time are presented in Table 2. Figure 2 Variation of power density with distance from tower base There are two mobile towers in the sub urban and rural habitat. The variation of power density with distance as shown in Figure 2 ranges from 75.1μW/m2 to 433.51μW/m2 in case of tower 1 in rural habitat and from 620.31μW/m2 to 4054.1μW/m2 in case of tower 2 in sub urban habitat. The distribution of birds in various habitats is presented in Table 3. Depending on the land use pattern the population varies from 3.00% to 52.98%. Common Myna is the most dominant species in all habitats except agriculture field. It is interesting to note that diversity is highest in Forest Habitat. Alpha, beta and gamma diversity indices as depicted in Table 4 are analyzed to compare the diversity between habitats. Two significant parameters Shannon diversity index and evenness of the bird diversity are computed using equation (1). Table 3 Distribution of birds (%) in different habitats Name I II III IV Common Myna 23.07 25.43 9.27 24.46 Dove - 11.86 5.29 2.57 Tailor Bird 8.79 - 3.97 - Bulbul 6.59 - - - Wood Pecker - - - 6.43 Pigeon 14.28 - - Pied Myna 8.79 22.03 23.84 9.44 Crow 24.17 27.11 - 3.00 Crane - - 52.98 21.45 Jungle bubbler 14.28 13.55 4.63 20.17 Rose ringed parakeet - - - 12.44 Volume 1, Issue 1 59 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) From Table 3 it is observed that the diversity and evenness of sub urban habitat are higher than agriculture area (paddy field). Forest doesn‘t only takes in a substantial number of species present, the individual birds in the community are uniformly distributed. Table 4 Alpha, Beta and Gamma diversities Alpha 7 5 6 8 diversity (sub urban) (Rural) (agriculture) (Forest) Beta diversity I II III IV I - 4 4 4 II 4 - 4 5 III 4 4 - 5 IV 4 5 5 - Gamma 11(eleven) diversity Table 5 Shannon Diversity Index and Evenness Habitat Diversity Index Evenness I 1.87 0.96 II 1.56 0.97 III 1.32 0.74 IV 1.85 0.89 In the forest habitat there are 8 species and over 60% of the individuals belong to three species. Common myna and crow are the most common species of suburban habitat which makes up about 47% of the community. 4 Conclusions Different levels of stress have different impact on avian diversity. Habitat type, proportion of vegetation cover, exposure to electromagnetic radiation are the possible potential root of stress in this particular instance. We observed that occurrence of small birds near the tower are less in number. The presence of birds is significant about 150 m away from mobile towers though the diversity indices are comparable in all habitats. Moreover the occurrence of birds in the vicinity of EMR exposed zones and non exposed zones are respectively 28.08% and 71.91% Volume 1, Issue 1 60 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) respectively. The preliminary survey has been carried on for a short duration of one month, so observations based on various habitats for longer period is highly desired to obtain a definite result regarding the impact of EMR exposure to avian community. Acknowledgements We are thankful to JIS University for their all kinds of supports. We also acknowledge Arambagh Municipality for their cooperation as and when required. Local people in the vicinity of different habitats for their cooperation during field visit.. References 1. Blechman, A. 2007, Pigeons-The fascinating saga of the world's most revered and reviled bird. St Lucia, Queensland: University of Queensland Press. 2. Wax, B. 1964, Newspaper Collections and History. Library Trends, 13: 254-271. 3. Mitra, R. and Pattanayak, S. 2018, Mobile phone and tower radiation: a challenge to all living entities. Explor. Anim Med Res, 8(1): 5-10. 4. Gopika, G. G. 2014, Growth and Developement of Telecom Sector in India- An Overview. IOSR Journal of Business and Management, 16(9): 25-36. 5. Kumar, K. and Kumar, K. 2017, Evolution of Telecom Sector in India. Int. J. Eng. Res. Dev., 13(9): 1-4. 6. Baruah, P. and Baruah, R. 2015, Growth and Developement of Telecom Sector: A Comprehensive Study of Assam Telecom Circle. Ind J. Com. Mag. Stud, 6(1): 71-77. 7. Baruah, P. and Barua, R. 2014, Telecom Sector in India: Past, Present and Future. International Journal of Humanities & Social Science Studies, 1(3): 147-156. 8. Kumar, G. 2010, Report on cell tower radiation, Department of Technology (DOT), Delhi. 9. Kuss, D. J., Kanjo, E., Ramsey, M. C., Kibowski, F., Wang, G. Y. and Sumich, A. 2018, Problematic Mobile Phone Use and Addiction Across Generations: the Roles of Psychopathological Symptoms and Smartphone Use. Journal of Technology in Behavioral Science, 3(3): 141-149. 10. Desai, N. R., Kesari, K. K. and Agarwal, A. 2009, Pathophysiology of cell phone radiation: oxidative stress and carcinogenesis with focus on male reproductive system. Reprod Biol Endocrinol, 7: 114. 11. Rafiqui, S. I., Kumar, S., Chaudhary, R., Farooq, U. B. and Krithika, P. 2016, Mobile Phone Radiations and Its Impact on Birds, Animals and Human Beings. Trends in Veterinary and Animal Sciences, 3: 24-27. Volume 1, Issue 1 61 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 12. Bhattacharya, R. and Roy, R. 2013, Impacts of Communication Towers on Avians: A Review. International Journal of Electronics & Communication Technology, 4(1): 137 - 139. 13. Kumar, M. and Singh, R. K. 2018, Effect Of Mobile Tower Radiation On Birds, Rural And Urban Area Of Durg District (C.G.). World Journal of Pharmacy and Pharmaceutical Sciences, 7(6): 1330-1338. 14. Balmori, A. 2009, Electromagnetic pollution from phone masts: Effects on wildlife. Pathophysiology, 16(2-3): 191-9. 15. Directorate of Census Operations, West Bengal, 2011. District Census Hand Book, Series 20 , Part XII B. 16. Wilson, R. R., Daniel, J. T. and Blaine A. E. 2000, Comparison of line transects and point counts for monitoring spring migration in forested wetlands. J. of Field Ornithology, 71(2): 345-355. 17. Carol, I., Richard, G.and Tim, I. 2011, Birds of the Indian Subcontinent . Oxford University Press, 480. 18. Spellerberg, I. F., and Fedor, P. J. 2003, A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‗Shannon – Wiener‘ Index. Global Ecology and Biogeography, 12: 177–179. Volume 1, Issue 1 62 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Thermal Stress Analysis for Indian Metro Cities during Summer Months Ahana Mitra1, Gourab Biswas2, Joyeesha Manna1, Arkadip Nandan1 , Soumi Bose1, 1 1 Soumini Chaudhury and Rina Bhattacharya 1Department of Physics, JIS University, and Kolkata, West Bengal, 700109 2Department of Physiology, Krisnath College, Behrampore, West Bengal, 742101 E-mail: [email protected] Abstract: It is widely accepted that the earth's climate is changing in a rapid pace, with already documented adverse effects on living world and the environment. Heat is severe hazard for humans in an occupational setting due to the rise of thermal discomfortability day by day and may be worsening with global warming. For a tropical country like India, the effect worsens particularly during summer months. April, May and June. In this work, heat stress over four metro cities - Delhi, Kolkata, Mumbai, and Chennai located at different geographical locations in India have been analysed for the months April, May and June. Delhi has been found to be the warmest place over a span of last one decade. The number of hot days is maximum in the month of May and more than 75 % of the summer days belong to severe stress days for all the four cities. Effective work-rest cycle, improved regulations and enforcement should be adopted to fight against heat stress. Key words: Discomfortability; Heat stress; Global warming; Work-rest cycle 1 Introduction In the last few decades, global mean temperature has increased due to climate change. Increased air pollution, fast urbanization and industrialization leads to increase the surrounding air temperature as well as heat stress during summer seasons [1, 2, 3, 4]. Summer months (April to Jun) are the most stressful period particularly for the unorganized sector workers who are engaged in outdoor activities. Soldiers, farmers, athletes, travellers, as well as regular office goers are also affected by thermal discomfortability [5, 6]. They often suffer from different heat related disorders such as heat rash, heat cramp, and heat exhaustion and most severe heat stroke. Several investigators have reported that the degree of thermal stress depends on different factors such as ambient air temperature, relative humidity, heat exchange capacity and total metabolic rate of the body, air flow, global radiation and also clothing insulation of the individual. The thermal comfort zone ranges from 23ºC to 27ºC and 20ºC to 25ºC during summer and winter seasons respectively [7, 8, 9, 10, 11, 12, 13]. According to World Meteorological Organization (WMO) thermal discomfortability starts from 24ºC with relative humidity of 100%, a heat wave can occur when the maximum air temperature exceeds 5ºC above the normal temperature [14]. Volume 1, Issue 1 63 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) India being a tropical country, summer months is very much stressful for the workers engaged in construction industries, agricultural field, power plant, pottery industries etc. Since last few decades, heat stress researches are going on to estimate the thermal load in different climatic zones [15, 16, 17, 18]. Pai et al., in 2013 has reported about a significant decadal increase of heat wave over India [19]. In this study, an attempt is made to estimate the heat stress and thermal discomfortability of four major cities of India to execute proper guidelines and work plan to minimize the heat related illness. 2 Materials and Methods [A] Study areas Delhi, Mumbai, Kolkata and Chennai the four metro cities of India as shown in Figure 1 are selected for our present study. The climate of these cities is as follows [20]: Figure 1 Study areas Delhi (28º36'N, 77º13'E, 200 m above msl), is the national capital, lies under humid subtropical and semi-arid climatic zone. Its annual temperature ranges from 7ºC to 41ºC. Summer season starts from early April and lasts till the last week of June. Maximum temperature found in the month of May. The average rainfall for the summer months is 18.67 mm. Volume 1, Issue 1 64 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Kolkata (22º57'N, 88º36'E, 9 m above msl), is the capital city of West Bengal, lies under the tropical wet and dry climatic zone with annual mean temperature of 24.8 ºC (ranges from 13ºC to 36ºC) and average rainfall for the summer months is 159.34 mm. Maximum temperature often exceeds 40ºC in the month of May and June. Mumbai (18º58'N, 72º49'E, 14 m above msl), is the economic capital of India, lies under the tropical wet and dry climatic zone with an average temperature of 27.2ºC. Summer season starts from April and the temperature reaches the maximum in the month of May. The average rainfall for the summer months is 178.77 mm. Chennai (13º5'N, 80º16'E, 6m above msl), is the capital of Tamil Nadu, is located on the Coromandel coastal region of Bay of Bengal. It lies under tropical climatic zone with an average annual temperature of 28.6ºC. May is the warmest month with an average temperature of 33ºC. The rainfall during summer months is too small about 35.84 mm. [B] Data Source and Mathematical Tools: Radiosonde data [https://ruc.noaa.gov/raobs] during the period 2009 to 2019 are used to analyze the heat stress of the four major cities of India. Two significant heat stress indices (i) thermohygrometric index (THI) and (ii) Wet Bulb Globe Temperature (WBGT) are used to compute heat stress [21, 22]. THI = 0.72 (Ta + Tw) + 40.6 (1) where, Ta and Tw are the air temperature and wet bulb temperature in °C respectively. Wet Bulb Globe Temperature (WBGT) is the most widely used heat stress index which was developed by WBGT = 0.1 Ta + 0.7 Tw + 0.2 Tg (2) where, Ta, Tw and Tg are the air temperature, wet bulb temperature and globe temperature in °C respectively. Tg may be replaced by Ta in case of indoor environment. The degree of stress is quantified by the five graded heat stress scales of THI and WBGT as given in Table 1. The scale was developed after critical survey among huge number of populations and at different climatic conditions [23, 24] Table 1 Five graded discomfort scale Score Thermal sensation Comfort sensation THI WBGT 1 Natural Comfortable <60 25.6-27.7 2 Slightly warm Slightly uncomfortable 60 ≤ to <70 27.8-29.4 3 Warm Uncomfortable 70 ≤ to <80 29.5-31.0 4 Hot Severe stress 80 ≤ to <90 31.1-32.1 5 Very hot Very severe stress ≥ 90 >32.2 Volume 1, Issue 1 65 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 3 Results and Discussion Average weather parameters i.e., atmospheric pressure, dry bulb temperature, dew point temperature and wet bulb temperature of four major cities during 2009 to 2019 are given in Table 2. It is observed that summer temperature ranges from 22.8°C to 46.2°C, 25.4°C to 36.6°C, 22.0°C to 38.6°C and 26.0°C to 41.2°C during 2009 to 2019 over Delhi, Mumbai, Kolkata and Chennai respectively. Mean summer temperatures of the respective cities are found to be 37.5°C, 32.2°C, 33.4°C and 34.3°C. It is interesting to note that Delhi is the warmest place among the four metro cities of India in this study period. Frequency distribution of THI and WBGT of four metro cities are given in Figure 2 and 3. It is observed that 76.0%, 92.5%, 89.7% and 97.5% days of Delhi, Mumbai, Kolkata and Chennai fall in category 4 i.e., hot or severe stress category according to THI. It is also found that 8.7% day of summer months over Delhi are fall in the category 5 i.e., very hot or very severe stress category. It is observed that 26.5% and 18.7%, 57.7% and 7.6%, 38.2% and 37.8%, 52.5% and 33.5% days of Delhi, Mumbai, Kolkata and Chennai respectively are fall in category 2 and 3 i.e., slightly warm and warm category according to WBGT index during summer months. From this WBGT index, we have also found that 6.9% and 5.6% summer days of Delhi and Kolkata fall in category 4 whereas 2.7% days of Delhi are in very severe stress condition. Table 2 Mean weather parameters of four metro cities Month Atmospheric Dry bulb Dew point Wet bulb pressure temperature temperature temperature (hPa) (⁰C) (⁰C) (⁰C) April 973.9±2.7 35.6±2.7 10.5±4.3 21.7±1.7 Delhi May 969.8±2.9 38.9±4.1 13.3±5.4 23.9±1.9 June 966.1±2.4 37.9±4.6 19.4±4.5 25.6±1.9 April 1005.1±1.7 32.4±1.2 22.4±2.4 25.4±1.2 Mumbai May 1004.1±1.9 32.9±1.1 24.5±1.3 26.7±1.1 June 1001.3±1.9 31.1±2.4 25.3±1.0 26.8±1.4 April 1003.0±2.1 34.4±2.5 23.8±3.1 26.8±1.8 Kolkata May 1001.0±3.1 33.7±3.4 24.5±2.0 26.9±1.7 June 998.9±2.3 31.7±2.8 26.1±1.4 27.4±1.3 April 1004.4±1.6 33.9±1.4 24.2±2.0 26.9±1.2 Chennai May 1002.2±2.7 34.9±2.6 23.7±1.6 26.8±1.0 June 1001.2±1.4 34.2±2.5 22.9±2.3 26.2±1.3 Volume 1, Issue 1 66 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 2 Frequency distribution of THI Figure 3 Frequency distribution of WBGT Volume 1, Issue 1 67 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 4 Conclusions From the present study it is noted that during summer season, number of hot days is found to be maximum in the month of May for all four cities of India and also the thermal stress during 2009 to 2019. So, heat related hazards are more probable among the people who are engaged in outdoor activities during summer months. Heat illness or disorders not only affects individual‘s health status but at the same time it overall reduces performance, efficiency, working hours, increases the wages cost of the manpower hence it has a negative effect on productivity and economy. Sufficient air movement in working places, adequate fluid intake, proper clothing, recommended work-rest cycle and periodic health check-up can subsequently combat the thermal stress of an individual during summer days [25]. One of the main constraints is to obtain permission to investigate occupational heat stress related issues at work place in different sectors in India. Hence, to establish a definite relation between health and thermal stress is highly needed. Acknowledgements Authors are grateful to JIS University for providing us all kinds of facilities and opportunities for carrying out the work and thankful to the National Oceanic and Atmospheric Administration (NOAA) for using some of the relevant data. References 1. Tjaša, P., Casanueva, A., Katja, K. K., Urša, C. , Igor, B. M. , Lučk,a K. B. and Zalika, C. 2018, The effect of hot days on occupational heat stress in the manufacturing industry: implications for workers‘ well-being and productivity. Int J biometeorol, 62(7): 1251– 1264. 2. Dash, S.K.and Kjellstrom, T. 2011, Workplace heat stress in the context of rising temperature in India. Curr Sci,101(4): 496–503. 3. Bhattacharya, R., Pal, S., Biswas, G., Karmakar, S. and Banik, R. 2012, An estimation of heat stress in tropics. International Journal of Engineering Science and Technology, 4(10): 4302-4307. 4. Bhattacharya, R., Pal, S., Biswas, G., Karmakar, S. and Saha, G. 2013, Seasonal distribution of comfortability: A regional based study over Kalyani, West Bengal, India. International Journal of Innovative Research in Science, Engineering and Technology, 2(7): 2856-2862. 5. Arnell, W. N., Lowe, A. J., Challinor, J. A. and Osborn, J. T. 2019, Global and regional impacts of climate change at different levels of global temperature increase. Climatic Change, 155(3): 377–391. Volume 1, Issue 1 68 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 6. Prasad, S. K. and Power, B. C. 1982, Discomfort over Bombay during winter. Vayumandal, 12: 53-54. 7. Nag, P.K., Nag, A. and Ashtekar, S.P. 2007, Thermal limits of men in moderate to heavy work in tropical farming. Ind Health, 45(1): 107–17. 8. Al-Bouwarthan, M., Quinn, M.M., Kriebel, D. and Wegman, D.H. 2019, Annals of Work Exposures and Health. Ann Work Expo Health , 63(5): 505-520. 9. Feifei, W., Xiaohua, Y. and Zhenyao, S. 2019, Regional and seasonal variations of outdoor thermal comfort in China from 1966 to 2016. Sci Total Environ, 665: 1003-1016. 10. Fanger, P.O. 1970,Thermal comfort. Analysis and applications in environmental engineering. Copenhagen: Danish Technical Press. 11. Chakrabarty, K. K. 1982, An usual cold day in Calcutta in the third week of April. Vayumandal, 12: 29-31. 12. Epstein, Y. and Moran, D. S. 2006, Thermal comfort and heat stress indices. Ind Health, 44(3): 388-398. 13. Kumar, S. 2018, Air Pollution and Climate Change: Case Study National Capital Territory of Delhi. International Journal Of Scientific & Engineering Research, 9(6): 844-848. 14. Landsberg (Ed.), H. E. 1972, The assessment of human bio climatic- A limited review of physical parameters. WMO Technical note, 123: 2-16. 15. Miller, V. and Bates, G. 2007, Hydration of outdoor workers in northwest Australia. Journal of Occupational Health and Safety – Australia and New Zealand, 23(1): 79-87. 16. Bhattacharya, R. and Biswas, G. 10th March 2010, Physiological stress during hot weather months over Kolkata, West Bengal. Proc. National Seminar on Biodiversity, Water Resources and Climate Change Issues, Kalyani University, 88-93. 17. Bhattacharya, R., Biswas, G., Guha, R., Pal, S. and Dey, S. S. 2010, On the variation of summer thermal stress over Kolkata from 1995 to 2009. Vayumondal, 36: 16-21. 18. Biswas, G., Bhattacharya, A., Ali, M. and Bhattacharya, R. 2016, Assessment of Heat Stress on Open Field Workers at Four Indian Coastal Stations. International Journal of Innovative Research in Science, Engineering and Technology, 5(3): 2998-3003. 19. Pai D. S., Nair, S .A. and Ramanathan, A. N. 2013, Long term climatology and trends of heat waves over India during the recent 50 years (1961-2010). MAUSAM, 64 (4): 585-604. 20. Attri, S.D. and Tyagi, A. 2010, Climate profile of India, Met Monograph No. Environment Meteorology-01. India Meteorological Department, New Delhi. 21. Thom, E. C. 1959, The discomfort index. Weatherwise, 12: 57-60. 22. Yaglou, C. P. and Minard, D. 1957, Control of heat casualties at military training centers. AMA Arch Ind Health, 16(4): 302-316. Volume 1, Issue 1 69 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 23. Roghanchi, P. and Kocsis , C. K. 2018, Challenges in Selecting an Appropriate Heat Stress Index to Protect Workers in Hot and Humid Underground Mines. Saf Health Work, 9(1): 10- 16. 24. de Freitas, C. R. and Grigorieva , E. A. 2015, A comprehensive catalogue and classification of human thermal climate indices. Int J biometeorol, 59(1): 109-120. 25. Srinivasan, K., Maruthy, K. N., Venugopal, I.V. and Ramaswamy, P. 2016, Research in occupational heat stress in India: Challenges and opportunities. Indian J Occup Environ Med, 20(2): 73-78. Volume 1, Issue 1 70 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Bioelectricity Generation from Waste Water using Microbial Fuel Cell: A Literature Survey Dipankar Ghosh1, Shrestha Debnath1 and Pritha Mondal1 1Microbial Engineering & Algal Biotechnology Laboratory, Department of Biotechnology JIS University, 81 Nilgunj Road, Agarpara Kolkata 700 109 [email protected] Abstract: Bioelectricity generation using waste water treatment is a big industrial dream project. Microbial fuel cell technology is one of the most promising sustainable approaches to attain this goal. Major objective of waste water treatment are water purification or desalination, value added energy generations, environmental protection and human health. In this context, microbial fuel cells can efficiently degrade organic and inorganic constituents of waste water towards value added process development likely bioelectricity generation. However, existing microbial fuel cell platform for waste water recycling and electricity generation are in infancy. It requires lots of research work to make this technology industrially viable. To this end, current article briefly discusses general discussion about microbial fuel cell configurations, waste water utilization as potential feedstock and major limitation of exiting microbial fuel cell technology for waste water purification. Keywords: Bioelectricity, Current density, Exoelectrogens, Power density, Waste water recycling 1 Introduction Bioelectricity generation is an alternative sustainable approach towards utilization of waste biomass using microbial biocatalysts. Microbial Fuel Cell (MFC) is one of the promising platforms to generate such bioelectricity from organic/ or inorganic waste biomass using diverse microbial regime through anaerobic fermentation. The concept of bioelectricity production was first time coined by M.C Potter in 1911 [1]. A typical MFC design consists of anode compartment, cathode compartment and proton exchange membrane or proton exchange salt bridge. In principle, microbial catalysts degrade organic/ or inorganic wastes (complex substrate) into simple end metabolites and releases electron, protons, CO2 under anaerobic environment. Released electrons pass through external wire circuits from anode chamber. Whereas, generated proton in anode chamber will move to cathode compartment through proton exchange membrane or proton exchange salt bridge. In cathode section, protons and electrons combine to produce water molecule in presence of oxygen [2,3]. The current literature survey emphasizes on following points likely a. type of MFCs; b. waste biomass utilization as potential feedstock for MFCs operations; c. existing challenges of MFCs. Volume 1, Issue 1 71 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 2 Description of microbial fuel cells 2.1 Type of MFCs The main purpose for developing different types of MFCs design is to increase the power densities or current densities, columbic efficiencies and mass utilization of waste biomass using potential biocatalysts. There are several kinds of MFC designs are available to attain this goal including Cube reactor design, Air Cathode two chamber design, Bottle Reactor design, Single chamber MFC, Flat Plate Type MFC, U Tube MFC, Tubular MFC and membrane bioreactor etc. A very typically simple MFC set up is depicted in Figure 1. In MFCs, anode and cathode constituents need to have following features likely higher electronic conductivity, biocompatibility, chemical stability, higher specific surface areas, and higher porosity for enhancing power density and over all bioelectricity generations. Most commonly used anode materials are graphite plates or rods. Sometimes, Mn (IV) and Fe (III) and covalently linked neutral red/ methylene blue usage ameliorate the overall performance of anode through enhanced electron transfer [4, 5]. Moreover, cathode contains materials likely carbon coated with platinum catalyst, carbon without platinum catalyst, plain carbon, metals other than platinum (MnO2, CNT, TiO2) and bio cathodes [6, 7, 8]. On the other hand, salt bridge or ion exchange membrane needs to have following criteria improving power density and over all bioelectricity efficiencies i.e. excellent thermal, chemical & mechanical stability, higher ionic conductivity, lower cost and lower degradation etc. Most commonly used ion exchange membrane includes Ralex, Ultrex, Fumatech, PEO, PEG, and Nafion membrane etc [9, 10, 11]. Power density or current density is the most promising unit to represent the MFCs efficiency. Power density in Microbial fuel cell depends on the following factors likely nature of substrate, mediator type, type of exoelectrogens, reactor configuration, nature of anode material and cathode material and physical condition like temperature, pH value etc. Figure 1 Diagrammatic Depiction of a typical Microbial Fuel Cell set up Volume 1, Issue 1 72 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 2.2 MFCs useful tool for waste water recycling Waste water disposal and recycling towards clearer water are the two major concerns in current scenario. The main purpose of treatment of waste water recycling plants are water purification through desalination, restoration of environment and ecosystem through recovery of valuable resources likely nutrients and purified water for human health. There are several approaches have been applied likely activated sludge process, waste stabilization ponds, lagoon system, oxidation ditch, membrane bioreactor, extended aeration, rotating biological contactor and trickling filters [12]. However current conventional strategies are highly expensive having lower overall heat & energy conversion efficiencies. Moreover, biological nutrient recycling approaches (sequential nitrification and denitrification) are highly energy intensive process and accelerate higher sludge disposal concerns. To this end a paradigm shift requires to establish an energetically and chemically favorable platform with higher water purification and nutrient recovery efficiency towards sustainable output i.e. bioelectricity generation. In this current scenario bioelectrochemical platform represent a sustainable basement towards energy positive treatment of waste water. Microbial fuel cell (MFC) is one of the most promising examples of bioelectrochemical system which accelerates waste water recycling and bioelectricity production through electron generating exoelectrogenic microbial regimes. Moreover, there are several alternative technologies have been applied time to time to conserve the energy, improve bioprocess efficiency, and nutrient recovery which makes the overall process energy positive and energy neutral for waste water treatment. These advanced approaches are namely combined heat & power Systems [13], Biogas Generation & anaerobic digester [14], Microalgae Growth for Biofuels generations [15], biodigesters configuration & innovative design [16, 17]. However, these novel avenues do not meet the industrial needs towards effective waste water recycling. MFCs have shown very extensive potential anaerobic digestion of waste water to produce environmentally benign bioelectricity utilizing organic and inorganic constituents of waste water. There are certain parameters which determine the efficiencies of MFC overall performances likely carbon removal (chemical oxygen demand & biological oxygen demand etc), nutrient removal, energy efficiency, current density, power density, open circuit voltages, energy payback time, and energy recovery concept [18, 19, 20, 21, 22, 23] 2.3 Major challenges of MFCs There are several issues in MFC operations. These issues include likely poor electrochemical kinetics of exoelectrogens, poor interaction between electrodes and bacteria, inefficient electron acceptors [24]. Electron acceptors help to alleviate power generations in MFCs by minimizing electron loss in electrodes. There are several alternative electron acceptors have been used i.e. nitrate, nitrobenzene, metal ions, perchlorate and azo dyes towards better waste remediation and bioelectricity generations [25]. Different cheaper chemical catalysts are in use to maximize the oxygen reduction kinetics and reduce electrode activation which includes lead dioxide [26], Volume 1, Issue 1 73 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Fe/Fe2O3 [27], cobalt [28], manganese dioxide [29], and activated charcoal [30]. Moreover, few experimental studies suggest that metallic oxidants (e.g., U, Cd, Cr, Cu) can also be applied to improve the waste recycling and power generation. Electrode chemical constituents are another major independent variable which determine over all power density i.e. carbon cloth, carbon brush, carbon rod, carbon mesh, carbon veil, carbon paper, carbon felt, granular activated carbon, granular graphite, carbonized cardboard, graphite plate, reticulated vitreous carbon, and many other types of electrode materials (graphite, graphite felt, platinum black). Metal based electrodes are also applicable including stainless steel plate, stainless steel mesh, stainless steel scrubber, silver sheet, nickel sheet, copper sheet, gold sheet, and titanium plate. However, these MFC applications suffer due to lower surface area, lower reactor volume and poor electron transfer kinetics. 3 Conclusion and future outlook In over all, current brief literature survey shows that there are several approaches are made in MFC technical field. However, current MFC technology requires further enormous research activities to make it industrially sustainable approach. The major factor which needs to be improved further i.e. finding novel microbial exoelectrogens having higher metabolic efficiencies to shuttle higher numbers of electrons; advanced MFC design; process control development; resource recovery; large scale production; integrated approaches and implementation of biorefinery concept in near future. As far economic stand point, MFC technology still remains prohibitive which requires serious attentions via synthesizing low cost alterative electrode materials, MFC reactors and electron parts. Even current MFC technology requires extensive pilot scale manifestation to ensure confidence on practical implementation and large scale commercialization of MFC towards waste water recycling and renewable bioelectricity generations. Acknowledgements Authors would like to thank JIS Group Educational Initiative and JIS University Agarpara for financial support to conduct this work. References 1. Gude V, G., 2015, Energy and water autarky of wastewater treatment and power generation systems, Renewable and Sustainable Energy Reviews, 45: 52–68. 2. Gude V, G., 2015, Energy positive wastewater treatment and sludge management, Journal of Waste Management (Edorium), 1: 10–15. 3. Gude V. G., 2016, Wastewater treatment in microbial fuel cells–an overview, Journal of Cleaner Production, 122: 287–307. Volume 1, Issue 1 74 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 4. Zhou M., Wang H., Hassett. D.J., Gu T., 2013, Recent advances in microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) for wastewater treatment, bioenergy and bioproducts, Journal of Chemical Technology and Biotechnology, 88(4): 508–518. 5. Logan B.E., 2010, Scaling up microbial fuel cells and other bioelectrochemical systems, Applied Microbiology and Biotechnology, 85(6): 1665–1671. 6. He C.S. et al, 2015, Electron acceptors for energy generation in microbial fuel cells fed with wastewaters: a mini-review, Chemosphere, 140: 12–17. 7. Feng Y. et al., 2014, A horizontal plug flow and stackable pilot microbial fuel cell for municipal wastewater treatment, Bioresource Technology, 156: 132–138. 8. Yuan H., He Z., 2015, Integrating membrane filtration into bioelectrochemical systems as next generation energy efficient wastewater treatment technologies for water reclamation: a review, Bioresource Technology, 195: 202–209. 9. Zhang F., Brastad K.S., He Z., 2011, Integrating forward osmosis into microbial fuel cells for wastewater treatment, water extraction and bioelectricity generation, Environmental Science and Technology, 45(15): 6690–6696. 10. Zhang F. et al., 2013, In situ investigation of tubular microbial fuel cells deployed in an aeration tank at a municipal wastewater treatment plant, Bioresource Technology, 136: 316–321. 11. Wang A., et al, 2011, Integrated hydrogen production process from cellulose by combining dark fermentation, microbial fuel cells, and a microbial electrolysis cell, Bioresource Technology, 102(5): 4137–4143. 12. Singh P., Kansal A., Carliell-Marquet C., 2016, Energy and carbon footprints of sewage treatment methods, Journal of Environmental Management, 165: 22–30. 13. USEPA, 2012, Evaluation of Combined Heat and Power Technologies for Wastewater Treatment Facilities, EPA 832-R-10-006. 14. Stillwell A.S. et al, 2011, The energy-water nexus in Texas, Ecology and Sociology, 16(1): 2. 15. Beal C.M. et al, 2012, Energy return on Investment for algal biofuel production coupled with wastewater treatment. Water Environment Resources, 84: 692–710. 16. Siegrist H., et al, 2008, Anammox brings WWTP closer to energy autarky due to increased biogas production and reduced aeration energy for N-removal, Water Science and Technology, 57: 383–388. 17. Shoener B.D. et al, 2014, Energy positive domestic wastewater treatment: the roles of anaerobic and photorophic technologies, Environmental Science Processes Impacts 16: 1204–1222. 18. Rabaey K et al, 2004, Biofuel cells select for microbial consortia that self-mediate electron transfer, Applied Environmental Microbiology, 70: 5373–5382. Volume 1, Issue 1 75 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 19. Fang C., Min B., Angelidaki I., 2011, Nitrate as an oxidant in the cathode chamber of a microbial fuel cell for both power generation and nutrient removal purposes, Applied Biochemistry and Biotechnology, 1(4): 464–474. 20. Huggins T. et al,2013, Energy and performance comparison of microbial fuel cell and conventional aeration treating of wastewater. Journal of Microbiology Biochemistry and Technology, S 6: 2. 21. Ge Z. et al, 2013, Recovery of electrical energy in microbial fuel cells: brief review, Environmental Science and Technology Letter, 1(2): 137–141. 22. Ge Z., He Z., 2016, Long-term performance of a 200 liter modularized microbial fuel cell system treating municipal wastewater: treatment, energy, and cost, Environmental Science Water Research, 2(2): 274–281. 23. Tommasi T., Lombardelli G., 2017, Energy sustainability of microbial fuel cell (MFC): a case study, Journal of Power Sources, 356: 438–447. 24. Santoro C., et al, 2017, Microbial fuel cells: from fundamentals to applications: A review, Journal of Power Sources, 356: 225–244. 25. Liu X.W., Li W.W., Yu H.Q., 2014, Cathodic catalysts in bioelectrochemical systems for energy recovery from wastewater, Chemical Society Reviews, 43(22): 7718–7745. 26. Morris J.M., et al, 2007, Lead dioxide as an alternative catalyst to platinum in microbial fuel cells, Electrochemical Communications, 9(7): 1730–1734. 27. Zhuang L., et al,. 2010, In situ Fenton-enhanced cathodic reaction for sustainable increased electricity generation in microbial fuel cells, Journal of Power Sources, 195(5): 1379–1382. 28. Lefebvre O. et al, 2009, Optimization of a Pt-free cathodesuitable for practical applications of microbial fuel cells, Bioresource Technology, 100(20): 4907–4910. 29. Lu M., Li S.F., 2012, Cathode reactions and applications in microbial fuel cells: a review, Crit. Rev. Environmental Science and Technology, 42(23): 2504–2525. 30. Pant D. et al, 2010, Use of novel permeable membrane and air cathodes in acetate microbial fuel cells, Electrochima Acta, 55(26): 7710–7716. Volume 1, Issue 1 76 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Physical and Electrical properties of a Tropical Cyclone as derived from Satellite Imageries and Cyclone Detection Doppler Weather Radar: A Case Study Hazer R. D. Warjri, Hasina Paslein, Mebashongdor Kharkongor, Dipankar Dam, Zahidul Islam and A. B. Bhattacharya Department of Remote Sensing and GIS, JIS University, Agarpara, Kolkata 700109 [email protected] Abstract: Tropical cyclones and the accompanied lightning are one of the most destructive and recurrent natural hazards in the tropics with widespread impacts. In recent years some sophisticated high resolution global circulation models have been developed for predicting the development and movement of the system as an interactive process within certain limit. In the present investigation we have used the satellite INSAT-3D enhanced imageries and the cyclone detection Doppler Weather Radar (DWR) for studying the physical and electrical properties of tropical cyclone. We have emphasized the track and the behavioural changes of the tropical storm MORA that damaged largely with packing winds of up to 117 kilometres per hour and moved towards India's Northeast. We have demonstrated some prominent INSAT-3D imageries in association with CS MORA. The associated convection revealed curved band pattern with well-marked wrapping into the centre from eastern sector. Broken low to medium clouds associated with intense to very intense convection covered over BoB between latitude 11.0º- 19.0ºN and longitude 84.0º- 91.0ºE. During analysis we have examined typical Kalpana-1 imageries of cloud top temperature obtained by using VHR sensor with 8000 m. DWR imageries of some typical selected Max (Z) as recorded by IMD, Kolkata centre using cyclone detection radar when examined provide some interesting properties. The study reveals that the said tropical cyclone is a combination of consequences of hazardous natural or social phenomenon and environmental condition at the place of occurrence. The outcome of the study supports strongly the finances to the government and public as well affecting both fishing and tourism seriously. The study gave an understanding of key environmental issues with localization, tracking, threat identification and characterization of cyclone and associated lightning and precipitation which can serve largely to the society and environmental management in course of severe cyclones at local and regional levels. Keywords: Tropical Cyclones, Lightning, Natural Hazards, Tropics, Global Circulation Models, INSAT-3D, Doppler Weather Radar Volume 1, Issue 1 77 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 1 Introduction Investigations on tropical cyclones in the north Indian Ocean has made through various phases in the last 150 years and progress was made with the advancement of technology. Up to the middle of the 20th century, the only way of knowing about the formation and intensification of this disastrous phenomenon was through ship observations and so the climatology of the cyclones, their surface structure, movement, and the rules to avoid the damage to shipping at sea were considered in a majority of the observations in India till 1960s. Introduction of new technologies through weather Doppler Radars, upper air soundings, weather satellites and computers have brought a remarkable change in tropical cyclone research in different countries during the 1950s to 1980s. During these three decades time, besides data analysis some breakthrough in theoretical studies and the development of computer models to simulate the complete genesis cycle of tropical cyclones are considered important. Predicting the track of tropical cyclone was also attempted during this time. During the last 20 years‘ time some sophisticated high resolution global circulation models have been developed in many parts of the world for predicting the development and movement of the system as an interactive process. In India also some attempts for the developments of the interactive processes can be seen within certain limit. Inter-decadal variation of tropical cyclones in the north Indian Ocean has been investigated and the frequency of their formations has shown drastic decrease since 1980s. No connection has been reported between the warm/cold ENSO events in the Indian Ocean basin and tropical cyclone frequency in the basin. Observational and theoretical approaches with computer simulations have brought a convergence of views related to the nature of large-scale conditions essential for development and movement of severe tropical cyclones. Some suggested further study for directing special attention toward the north Indian Ocean basin. The cyclone Mora was the name suggested by Thailand, came from a Thai word, meaning "star of the sea". In this paper we have examined characteristic changes of tropical cyclone Mora experienced during the pre-monsoon month May 2017, and identified some interesting features using a combination of observations from satellite imagery and data of cyclone detection Doppler Weather Radar. The analysis has been carried out by dividing the life cycle of the TC into various stages of intensification and weakening. 2 Some Important Findings An analysis of historical TC data during the satellite period (1981–2006) showed that the intensity of the strongest TCs had increased substantially over the northern Indian Ocean [1-3]. In the analysis, however, TCs in the Bay of Bengal and the Arabian Sea were grouped together. A subsequent observation reported that Bay of Bengal TCs in the post monsoon season increased in intensity over the past 30 years and noted large‐scale changes in ocean‐atmosphere conditions that were responsible [4, 5]. It was found that TCs in the pre-monsoon month of May exhibits an enhanced intensity and attributed changes in TC activity to anthropogenic aerosols and greenhouse gas forcing. But the role of natural climate variability in the reported changes in TC Volume 1, Issue 1 78 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) activity was not systematically considered in the study [6-8]. Moreover a good number of early studies of inter annual variability of Bay of Bengal TCs emphasized on the post monsoon season. It is because most of the climate phenomena that impact global climate at inter annual timescales, e.g., the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole, tend to manifest more strongly during boreal late fall and winter. A significantly large‐scale ocean‐ atmosphere state was noted to be more favourable for TC development in the post monsoon Bay of Bengal during La Niña and during the negative phase of the Indian Ocean Dipole [9, 10]. But the environmental impact on pre-monsoon TCs at inter annual timescales is not clearer. It was proposed that ENSO may not have a significant influence on pre-monsoon Bay of Bengal TC activity [11-14]. Though the impact of ENSO is higher during boreal late fall and winter months, its influence over the large‐scale ocean‐atmosphere system in the northern Indian Ocean may persist until May–June. It was established that ENSO revealed an important role in the withdrawal phase of the Indian summer monsoon in addition to its onset phase. This large‐scale climate variability outside the Bay of Bengal may affect pre-monsoon atmospheric conditions and TC activity in the Bay of Bengal [15-18]. An investigation of Bay of Bengal tropical cyclone (TC) track data for the months of May–June during 1979–2014 reveals a meridional dipole in TC intensification [19-22]. It was reported that the TC intensification rates were increased largely in the northern region and decreased in the southern region. The dipole is consistent with the variations in the large‐scale TC environment as estimated using the Genesis Potential Index (GPI) for the same period [23-26]. While an increase in lower troposphere cyclonic vortices and mid troposphere humidity in the northern Bay of Bengal made the environment more favourable for TC intensification, increased vertical wind shear in the southern Bay of Bengal reduce the TC development. These environmental variations are assumed to be associated with a strengthening of the monsoon circulation driven by a La Niña‐like shift and associated tropical wave dynamics [27-30]. Emanuel [10] collected data for a long period to calculate the TC power dissipation index (PDI) and the intensification tendencies. The PDI for a season and for a given TC strength category (e.g., tropical storm, tropical cyclone, major tropical cyclone) is estimated as the sum of the cubes of the maximum wind speed at each 6‐hourly TC location during the months of October– November, wherever the maximum wind speed of the storm is within the range defined for that category [31-33]. The intensification tendency for a storm at a location was estimated as the linear regression coefficient of the maximum wind speed over the current and five subsequent 6‐ hourly snapshots [23]. It was reported that during 1981–1995, eight storms obtained TC strength or higher out of a total of 27 storms. On the other hand, 10 out of 24 storms attained TC strength or higher between 1996 and 2010, causing a higher conversion rate of about 42%. An interesting characteristic of Volume 1, Issue 1 79 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) the BoB TC formation is the zonally asymmetric nature. It was found that all seven MTCs that occurred in the BoB formed east of the 90°E longitude. This geographical dependence of TCs can be attributed to the longer time spent over the warm ocean for storms that form to the east of 90°E [2]. It was further noted that the frequency of storms during the two 15 year periods, 1981– 1995 and 1996–2010, was statistically identical. Further investigations showed that the total storm days was nearly the same for the two periods. Also, the mean duration spent by each MTC in MTC‐phase during the 15 year period 1996–2010, despite an increase, was statistically indifferent from that during the 15 year period 1981–1995. Besides storm frequency and longevity, the other factor that can play a vital role in its power dissipation is the maximum intensity attained. It was observed that the mean maximum intensity sustained by storms during MTC phase has been increasing with time. On average, the maximum wind speed of storms during MTC phase for the period 1981–1995 was about 54 m s−1, which corresponded to category 3. On the other hand, the mean maximum wind speed for storms during MTC phase for the period 1996–2010 was about 62 m s−1, which corresponded to category 4. Based on this evidence, the intensity of post monsoon BoB storms has indeed been noted to be increasing. Rayner et al. [25] and separately Reynolds et al. [26] used early data to evaluate changes and trends in SST. Monthly mean oceanic temperature data were considered by Chang et al. [4] while European Centre for Medium‐Range Weather Forecasts' (ECMWF's) ocean reanalysis system 4 was examined by Balmaseda et al.[8]. They used the data with a view to estimate changes and trends in the depth of the 23°C isotherm. The upper ocean heat content (OHC) was calculated as the temperature averaged vertically from the surface to a depth of 100 m [24]. This method was used to represent the upper ocean energy available for extraction by a TC better than the traditional tropical cyclone heat potential [24]. Monthly mean sea surface height anomaly data for the period 1981–2010 was analysed by Behringer and Xue [1] to compute changes in dynamic sea surface topography. National Centres for Environmental Prediction (NCEP)‐ Department of Energy (DOE) reanalyzed [12] monthly mean air temperature, relative humidity, and geo-potential height data to calculate changes and trends in moist static energy (MSE) and changes in equivalent potential temperature [34]. The MSE is averaged between the 700 and 925 hPa pressure levels. Corresponding ERA‐INTERIM data [34, 5] for the same period are also obtained from ECMWF to evaluate changes and trends in MSE. 3 Techniques and Instruments Employed Two major equipment provided data for the present investigation. The first one is the satellite INSAT-3D enhanced imageries and the second one is the real time data as derived from the cyclone detection Doppler radar. Volume 1, Issue 1 80 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 4 Storm Path of the Cyclone MORA Under favourable atmospheric conditions, convective area developed over the Bay of Bengal (BoB). It causes advancement of a circulation and low pressure area (LPA) over southeast and adjoining central BoB on May 27, 2017 [35- 37]. It is then concentrated into a depression over the central Bay of Bengal at 0000 UTC on May 28, 2017 and rapidly reinforced into a deep depression on the same day. During the early hours of May 29, the storm attained the intensity of the cyclonic storm and named as MORA which followed a NNE track parallel to Burma coast, reaching its maximum strength as a severe cyclonic storm with the wind speed of 110 km/h and lowest mid pressure of 978 hPa during 0300 UTC on May 30. We have shown the Mora tracker in Figure 1, showing the storm path when entered into Bangladesh, touching Dhaka and Chittagong. The tropical cyclone MORA made landfall in Bangladesh in the morning of May 30, 2017 accompanied by heavy rains and winds estimated at 117 km/h (73mph). Figure 1 MORA Tracker showing the storm path when entered into Bangladesh (Courtesy: IMD) Volume 1, Issue 1 81 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) After the land interaction, the cyclone weakened gradually to be lost into a distinct LPA over Nagaland and its surroundings at 0000 UTC on May 31, 2017 [38]. The low-lying areas of the coastal districts and their offshore islands and chars of Bangladesh and Myanmar are inundated by the storm surge of the cyclone. The well-marked LPA cantered at 0000 UTC on May 28 over SE and adjoining areas of central Bay of Bengal, which then concentrated into a depression and moved along NE and lay cantered at 1200 UTC on May 28, over EC BoB near (15.7ºN, 90.7ºE). Continuing its movement the system slowly weakened into a DD and lay cantered at 1200 UTC on May 30 over Tripura and surrounding areas (24.2ºN, 92.2ºE) and into a well- marked LPA over Nagaland and neighbourhood at 0000 UTC on May 31. According to the report of India Meteorological Department, on May 30, 2017 between local time 7:30 AM and 9:30 AM the cyclone MORA made landfall along the SE coastal area of Bangladesh in the vicinity of Kutubdia Island between Cox's Bazar and Chittagong [39]. The track and the behavioural changes of the tropical storm MORA as of 30 May 2017, 12:30 PM is presented in Figure 2. Figure 2 Tropical Storm MORA as of 30 May 2017 at 12:30 PM (Courtesy: Myanmartime Myanmar Information Management Unit) Volume 1, Issue 1 82 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 5. Satellite and Radar Data As pointed out before, the cyclone Mora made landfall in the morning of May 30, 2017 as a Category 1 cyclone between Chittagong and Cox's Bazar. The powerful cyclone damaged largely with packing winds of up to 117 kilometres per hour and moved towards India's Northeast. Satellite image of the cyclone Mora is shown in Figure 3. Figure 3 Satellite image of Cyclone Mora (Credit: IMD) Volume 1, Issue 1 83 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) As a result of this severe cyclone, heavy rainfall occurred to lash the states of Mizoram, Tripura, Arunachal Pradesh, Meghalaya, Assam and Nagaland on May 30 and 31. Some prominent INSAT-3D imageries in association with CS MORA have shown in Figure 4. The convection experienced during May 28, 2017 exhibits curved band pattern with well-marked wrapping into the centre from eastern sector. Broken low to medium clouds accompanied by intense to very intense convection covered over BoB between latitude 11.0º- 19.0ºN and longitude 84.0º- 91.0ºE. Figure 4 INSAT-3D enhanced coloured imageries associated with CS MORA during May 29 and May 31, 2017 [source: IMD] Volume 1, Issue 1 84 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) In Figure 5, we have shown Typical Kalpana-1 imageries of cloud top temperature obtained by using VHR sensor with 8000 m resolution at 0445 UTC on May 30, 2017 in association with CS MORA. Figure 5 Kalpana–1 satellite imageries of cloud top temperature using VHR sensor with 8000 m resolution at 0445 UTC on May 30, 2017 in association with CS MORA [source :IMD] Volume 1, Issue 1 85 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Doppler Weather Radar (DWR) imageries of some typical selected Max (Z) as recorded by IMD, Kolkata centre using cyclone detection radar during May 29 and May 31, 2017 associated with cyclone Mora are presented in Figure 6. Figure 6(a), (b) & (c): Doppler Weather Radar (DWR) imageries of some typical selected Max (Z) as recorded by IMD, Kolkata center using cyclone detection radar during May 29 and May 31, 2017[source : IMD, Kolkata] Volume 1, Issue 1 86 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 6 Discussions Tropical cyclones and associated lightning are the most destructive and recurrent natural hazards in the tropics and subtropics areas of the globe with widespread impacts. A significant number of the deadliest cyclones, like the cyclone Mora reported here, have occurred in the Bay of Bengal affecting widely the state of West Bengal and Bangladesh [40]. A combination of factors like a flat coastal terrain and high population density of surrounding nations causes cyclones in the Bay of Bengal to have devastating consequences upon landfall. The annual cycle of tropical cyclones (TCs) in the northern Indian Ocean exhibits a clear bimodal structure. The season starts from April when the sea surface temperatures increase and continues to intensify through May. By the second week of June, the monsoon sets in and the accompanied strong vertical wind shear and unfavourable atmospheric vortices largely limit the formation of TCs during the monsoon from June to September. In October, the TC activity increases further, getting a second peak during the month of November. While more TCs form during the post monsoon months, the most intense storms are found to form during the pre-monsoon period. Large ocean heat content and strong variability of northward propagating intra seasonal oscillations during April–May are mainly responsible for the formation of intense TCs during pre-monsoon months. The said research problem is associated with disaster study which is a combination of consequences of hazardous natural or social phenomenon and environmental condition at the place of occurrence. As a consequence of a devastating tropical cyclone both fishing and tourism are seriously affected. The outcome of the study thus supports strongly the finances to the government and public as well. This interdisciplinary program may promote an understanding of key environmental issues with localization, tracking, threat identification and characterization of cyclone and associated lightning and precipitation which can serve largely to the society and environmental management in course of severe cyclones in West Bengal. Similar study will impart knowledge on environmental issues at local and regional levels [33, 38]. Percentage frequency distribution, radial profile as well as quadrant-wise mean rain rates is required to determine stage-wise for each TC. Further, variations in the rainfall asymmetry is needed to examine from higher to lower rain rate side when the TC passes from intensification to weakening stages using Fourier analysis by computing the first order wave number-1 asymmetry around the TC centre. Tropical cyclonic rainfall distribution to be elaborately considered by environmental factors such as wind shear, sea surface temperature (SST), moisture distribution etc. as well as TC specific factors like intensity, location and translational speed. Volume 1, Issue 1 87 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Acknowledgements We are grateful to the Indian Space Research Organization (ISRO) for sharing their pearls of wisdom with us during the course of preparing the paper. We are also immensely grateful to India Meteorological Department for sharing their data and comments on an earlier version of the manuscript. References 1. Behringer, D., and Y. Xue (2004), Evaluation of the global ocean data assimilation system at NCEP: The pacific ocean, in Proc. Eighth Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Wash. 2. 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Sengupta, D., Goddalehundi, B. R. and Anitha, D. (2008), Cyclone‐induced mixing does not cool SST in the post‐monsoon north Bay of Bengal. Atmos. Sci. Lett., 9(1), 1–6. 34. Singh, O., Khan, T. M. A. and Rahman, M. S. (2001), Has the frequency of intense tropical cyclones increased in the north Indian Ocean?. Curr. Sci., 80(4), 575–580. 35. Roy Chowdhury, Biswas, M., Das, S., Bhattacharya, A. B., Lichtman, Jeffery M., A study on the spectral pattern of sferics as derived from ELF/VLF radio signal at Techno India University Campus, Kolkata during cyclone ‗Roanu‘. IEEE Xplorer, 2017. DOI: 10.1109/ICCECE.2016.8009562. 36. Joint Typhoon Warning Centre (May 26, 2017). (2017). 37. Joint Typhoon Warning Center (May 27, 2017). (2017). 38. RSMC bulletin (IMD) – MORA (2017)http://www.rsmcnewdelhi.imd.gov.in 39. J Erdman. (2017) https://weather.com/storms/hurricane/news/deadliest-cyclone-history- bangladesh-20130605#/2 40. PTI. (2017) http://www.dnaindia.com/world/report-cyclone-mora-hits-bangladesh- hundreds-of-thousands-evacuated-2455330 Volume 1, Issue 1 90 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Effect of Pre-Monsoon Lightning Activity on Surface Nox & O3 over GWB Arpita Das, Sujay Pal, Subrata Kumar Midya Department of Atmospheric Sciences, Calcutta University, 51/2 Hazra Road, Kolkata-700019 [email protected] Abstract: Lightning is an important source of tropospheric reactive nitrogen oxide species (NOx) which affect regional air chemistry and directly influence the abundance of tropospheric ozone. The major oxidant O3 and NO2 produced at the upper troposphere due to lightning activity influence the surface concentration of NO2 and O3. Excess surface concentration of O3 can damage forests, crops and can irritate human lungs. For last few years, West Bengal is getting highly affected due to lightning activities. In the present work we have tried to find quantitatively the effects of lightning activity on surface NO2 and O3 at different places of GWB during the pre-monsoon Nor‘wester days (April to early June) for the first-time using ground based lightning network data considering CG, IC and total lightning events. Lightning flash rates for IC, CG and total lightning are analyzed at different radial distances (5 km, 10 km, 15 km, and 25 km) around the stations to check the correlations of surface NO2 and O3 with IC, CG and total lightning counts. Keywords: Lightning flashes; Nor‘wester; Surface ozone; Thunderstorm activity 1 Introduction The nitrogen oxides NO and NO2 (collectively known as NOx) are important trace gases in tropospheric chemistry as it facilitates chemical reactions in the troposphere and stratosphere. NOx determines the concentrations of ozone (O3) [5] in the atmosphere which further impacts the oxidising capacity of the atmosphere. NOx has severe impact on oxidation of hydroxyl (OH) which in turn contributes to acid rain [16]. Natural nitrogen is continuously cycled by many different processes (Xu et al., 2012). NOx, has a very short lifetime of several hours near the ground, mainly originates from anthropogenic combustion of fossil fuel, biomass burning and biogenic soil emissions. These sources primarily influence the lower troposphere. The residence time of NOx get increased in the upper tropospheric region [17]. Lightning is an important source of NOx, particularly for the upper troposphere [20]. At least, 50% of the initial NOx in the upper troposphere is induced by lightning, and only 20% of NOx originates from upward transport from the ground and NOx with lower density reduces ozone production rates [23]. Other sources for upper tropospheric NOx include minor sources like emissions from mid and long-range aircraft [22] and the stratospheric injection of NOx formed by photolysis of nitrous oxide and nitric acid (HNO3) [14]. Lightning NOx affects the generation of tropospheric O3. The production of O3 caused by this NOx is six times greater than that of anthropogenic NOx emissions [24]. Volume 1, Issue 1 91 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) In tropical and subtropical regions, more than 70% of NOx is due to lightning [18]. Lightning- produced NOx is primarily made up of NO and NO2, with NO being the major portion (75- 95%) of the total [11]. The NO thus formed in the lightning channel reacts quickly with 03 to form NO2 and during the daytime a photochemical equilibrium between NO and NO2 is established. Estimates of the global lightning-produced NOx ranges from 1 Tg N/yr [15] to 100 Tg N/yr [10] [Tg = 1012 g]. Considerable progress has been made in the study of lightning induced NOx which includes satellite observations of global lightning [4], satellite observations of NO2 column distributions [2], airborne in-situ measurements of NOx abundance near thunderstorms at mid-latitudes [21] and over tropical continents, cloud- resolving model studies [9], and improved global models [8]. The spatial and temporal distributions of vertical column densities of tropospheric nitrogen dioxide (NO2 VCDs) and lightning activity were extensively analyzed using satellite measurements by Guo et al [12] with the results showing the spatial distribution of lightning activity with NO2 VCDs is greater in the east than in the west of China. Effect of lightning activity over surface NOx and O3 have been studied by Pawar et al [19] over Pune region during the pre-monsoon and monsoon seasons. The results suggest that surface concentration of NOx increases significantly at the dissipation stage of thunderstorm and increase in NOx greater than titration threshold level reduces the surface ozone concentration. It is also observed that enhancement in NOx at the surface after thunderstorm activity is much greater in pre-monsoon periods compared to the monsoon period. DeCaria et al. [7] used a 3-D cloud scale chemical transport model to simulate the chemical characteristics of a storm in order to estimate convective transport of a variety of chemical species, lightning NOx production, and the impact on tropospheric ozone. It was reported that lightning NOx production resulted in an average ozone enhancement of 10 ppbv/day at 10.5 km MSL which is more than the upper tropospheric enhancement of 7 ppbv as estimated earlier [6]. However, NOx and ozone due to lightning activity for the West Bengal region has not been studied extensively. Middey et al., 2012 reported that increased surface pollution results in increased lightning flash rate, which results in increased surface NOX and thereby increasing surface ozone concentration over the station Kolkata using a Tropical Rainfall Measuring Mission‘s (TRMM) satellite-based instrument Lightning Imaging Sensor (LIS) data. Total lightning activity increases significantly during the pre-monsoon (March, April,May and early June), summer thunderstorms season, locally known as ―Nor‘wester", over the Gangetic West Bengal (GWB). In this study the data of surface NOx on thunderstorm events have been analyzed along with lightning flash rate during the pre-monsoon season for the first-time using ground based lightning network data considering CG, IC and total lightning events. This paper illustrate the importance of lightning NOx for tropospheric chemistry and its impact on ground ozone which can be a potential threat to human life. This study shows the variation of surface NO2, O3 on thunderstorm days and their relations with the in-cloud (IC), cloud-to-ground (CG) and total lightning for the pre-monsoon period of 2018 over a tropical metropolitan, Kolkata. Volume 1, Issue 1 92 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 2 Data Observation Site Location and surface pollutant data: The data over the station Victoria, Kolkata (22°34′N, 88°21′E) is collected Central Pollution Control Board (CPCB) which includes the concentration of nitrogen dioxide (NO2), and ozone (O3) for the pre-monsoon moths (March, April, May and early June) for the year 2018. The concentration of these pollutants is measured in µg/m3. Lightning data: The lightning data in this paper is obtained from Earth Networks total lightning detector cum weather station which has been operating in Kolkata since June 2016. This detector is a part of Earth Networks Total Lightning Network (ENTLN) operated globally for ground-based monitoring of Total lightning activity. Optical Transient Detection/Lightning Imaging Sensor (OTD/LIS) gridded dataset from the Global Hydrology Resource Centre are the two detectors which observe total lightning flashes but do not distinguish cloud from ground discharges.(Cecil et al., 2014). This instrument is capable of monitoring various weather parameters as well as for locating a place using the electric field data. The integrated lightning sensor of the instrument measures the electromagnetic signals from very low frequency (VLF) to high frequency (HF) of each lightning discharge operating in a frequency range from 1 Hz to 12 MHz (Bui et al, 2015).The ENTLN can detect weaker pulses at longer distances by extending the frequency range of detection from VLF into the MF and HF spectrums (Heckman and Liu, 2010). Time of arrival technique is being used to locate a lightning event. In the ENTLN, new lightning classification algorithm has been added to classify a flash as a CG flash if it contains at least a return stroke, otherwise it is classified as an IC flash. ENTLN can also detect the polarity (positive or negative) for both types of flashes. 3 Methodologies Hourly average concentration of NO2 & O3 over Victoria on non-thunderstorm days is considered to calculate the monthly mean diurnal variation. This monthly mean diurnal variation along with hourly standard deviations is set as background level. Deviation in the diurnal variations of NO2 and O3 for the thunderstorm dates over this background level is considered for analysis. On the other hand, lightning data (IC, CG) collected from the Earth Networks Total Lightning Network (ENTLN) is the Lightning flash rate i.e., Lightning counts per minute (IC and CG) identify the thunderstorms onset, mature and dissipating phases. Lightning data are collected within a square box of 10 km, 20 km, 30 km, 40 km, 50 km around Victoria to check the correlation with NO2. 4 Results and Discussion Figure 1 represents lightning flash counts per minutes on 17th April 2018 & diurnal variation of NO2 and O3 on the day. NO2 concentration has shown a significant increase at the Volume 1, Issue 1 93 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) dissipating stage (20:30hr) of the thunderstorm. Very little change in O3 concentration has been noticed at the dissipation stage. Figure 1 Norwester‘ of 17th April 2018 Figure 2 represents lightning flash counts per minutes on 16th May 2018 & diurnal variation of NO2 and O3 on the day. In this figure NO2 concentration has shown a significant increase at the dissipation stage (11:00hr) of the thunderstorm. In this case also, changes in O3 concentration are low compared to NO2 changes. Normal diurnal variation of O3 is disturbed due to lightning produced NO2. Volume 1, Issue 1 94 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 2 Norwester‘ of 16th May 2018 The Fig 3 represents lightning flash counts per minutes on 31st May 2018 & diurnal variation of NO2 and O3 on the day. In this figure NO2 concentration has shown a significant increase (>3sigma) at the dissipation stage (15:30hr) of the thunderstorm while changes in O3 concentration are more or less low, almost within 1 sigma level. Figure 3 Norwester‘ of 31st May 2018 Volume 1, Issue 1 95 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Surface concentration of NO2 is found to be increased significantly at the dissipation stage of thunderstorms by an amount of 80-100 µg/m3, which is much higher than earlier study over GWB (Middey et al. 2012). Also, in contrary to previous research over Kolkata no significant increase of surface O3 concentration at the dissipation stage of thunderstorm has been found. Lightning flash rates for IC, CG and Total lightning at different radial distances (5 km, 10 km, 15 km, 20km, 25 km) around the stations are analyzed to check correlations of surface NO2 and O3 with IC, CG and total lightning counts in Fig 4a-c. Figure 4a Correlation graph between CG flash count and NO2 at a radial distance of 10km from the sensor Volume 1, Issue 1 96 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 4b Correlation graph between IC flash count and NO2 at a radial distance of 10km from the sensor Figure 4c Correlation graph between Total flash count and NO2 at a radial distance of 10km from the sensor Volume 1, Issue 1 97 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Greater correlation between lightning flash count and NO2 enhancement for CG discharges(R=0.62). Maximum correlation is found for CG lightning within radial distance of 10 km of the pollutant sensor, whereas the correlation with IC and Total lightning are less. 5 Conclusions In the severe thunderstorm days lightning frequency is higher and therefore more number of electric discharges produces a large amount of NO2. This NO2 do not immediately contribute to photochemical ozone production and can lead to ozone decrease by both direct titration and night time chemistry that further reduces ozone. Very little to no change in ozone concentration is observed during the dissipation stage of thunderstorms. This decrease in concentration may be due to reduced solar radiation because of the presence of thunderclouds. A greater correlation is observed with maximum NO2 and CG flash count at a radial distance of 10km. This finding is in line with the fact that, in general, the core of a thunderstorm cell has a dimension of approximately 10 km. Therefore, lightning from outside the radial distance of 10 km zone of the pollutant sensor have less effects on the measurements of surface NO2 and O3. Acknowledgements The authors would like to thank Central Pollution Control Board (CPCB) for providing us the continuous NOx and Ozone data and Earth Networks Total Lighting Network (ENTLN) for providing lightning data over Kolkata for our analysis. This study has been supported by the grant from University Grants Commission-UGC UPE II (non focus) project, under Reference Number: DPO/223/UPEII/Non Focus. References 1. Bui Vy, Chang Lin-Ching, Heckman Stan. 2015, A Performance Study of Earth Networks Total Lighting Network (ENTLN) and Worldwide Lightning Location Network (WWLLN) International Conference on Computational Science and Computational Intelligence 2. Burrows, J. P., Weber, M., Buchwitz, M., 1999, The Global Ozone Monitoring Experiment (GOME): mission concept and first scientific results, J. Atmos. Sci., 56, 151– 175. 3. Cecil, D.J., Buechler, D.E. and Blakeslee, R.J., 2014, Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmospheric Research, 135, 404-414. 4. Christian, H. J., Blakeslee, R. J., Boccippio, D. J., 2003, Global frequency and distribution of lightning as observed from space by the Optical Transient Detector, J. Geophys. Res., 108, 4005. 5. Crutzen, P. J. 1970, The influence of nitrogen oxides on atmospheric ozone content. Quart. J. Roy. Meteor. Soc., 96, 320–325. 6. DeCaria, A. J., Pickering, K. E., Stenchikov, G. L., Scala, J. R., Stith, J. L., Dye, J. E., Ridley, B. A., and Laroche, P.2000, A cloud-scale model study of lightning-generated Volume 1, Issue 1 98 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) NOx in an individual thunderstorm during STERAO-A, J. Geophys. Res., 105, 11601– 11616. 7. DeCaria, A. J., Pickering, K. E., Stenchikov, G. L., and Ott, L. E.2005, Lightning- generated NOx and its impact on tropospheric ozone production: A three-dimensional modeling study of a Stratosphere-Troposphere Experiment: Radiation, Aerosols and Ozone (STERAO-A) thunderstorm, J. Geophys. Res., 110, 1–13. 8. Dentener, F. J.: Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050. Data set, (http://daac.ornl.gov/), Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA, 2006. 9. Fehr, T., H¨oller, H., and Huntrieser, H.2004, Model study on production and transport of lightning-produced NOx in a EULINOX supercell storm, J. Geophys. Res., 109, 1–17. 10. Franzblau, E. and Popp, C. J,1989, Nitrogen oxides produced from lightning, J. Geophys. Res., 94, 11089–11104. 11. Franzblau, E.1991, Electrical discharges involving formation of NO, NO2, HNO3, and O3, J. Geophys. Res., 96, 22337–22345. 12. Guo, F., Ju, X., Bao, M., Lu, G., Liu, Z., Li, Y. and Mu, Y., 2017. Relationship between lightning activity and tropospheric nitrogen dioxide and the estimation of lightning- produced nitrogen oxides over China. Advances in Atmospheric Sciences, 34(2), 235-245. 13. Heckman, S., and Liu, C.2010, The application of total lightning detection and cell tracking for severe weather prediction, in Proc. of GROUND‘2010 & 4th LPE, Salvador, Brazil, 234–240. 14. Huntrieser, H., H. Schlager, C. Feigl, and H. H¨oller, 1998, Transport and production of NOx in electrified thunderstorms: Survey of previous studies and new observations at midlatitudes. J. Geophys. Res., 103(D21), 28247–28264. 15. Levine, J. S.1981, Simultaneous measurements of NOx, NO, and O3 production in a laboratory discharge: atmospheric implications, Geophys. Res. Lett., 8, 357–360. 16. Levy II, H. 1971, Normal atmosphere: Large radical and formaldehyde concentrations predicted, Science, 173, 141–143. 17. Levy II, H., Moxim, W. J., and Kasibhatla, P. S.1996, A global three dimensional time- dependent lightning source of tropospheric NOx, J. Geophys. Res., 101, 22911–22922. 18. Martin, R. V., and Coauthors, 2002, Interpretation of TOMS observations of tropical tropospheric ozone with a global model and in situ observations. J. Geophys. Res., 107(D18), ACH 4-1–ACH 4-27. 19. Pawar, V., S. D. Pawar, G. Beig, and S. K. Sahu ,2012, Effect of lightning activity on surface NOx and O3 over a tropical station during premonsoon and monsoon seasons, J. Geophys. Res., 117, D05310. 20. Pickering, K., Wang, Y., Tao, W.-K., Price, C., and Müller, J.F., 1998, Vertical distributions of lightning NOx for use in regional and global chemical transport models, J. Geophys. Res. 103, 31203–31216. 21. Ridley, B., Ott, L., Pickering, K., 2004, Florida thunderstorms: A faucet of reactive nitrogen to the upper troposphere, J. Geophys. Res., 109, 1–19. Volume 1, Issue 1 99 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 22. Schumann, U., 2000, Pollution from aircraft emissions in the North Atlantic flight corridor: Overview on the POLINAT projects. J. Geophys. Res., 105, 3605–3631. 23. Seinfeld, J. H., and S. N. Pandis, 2016, Atmospheric Chemistry and Physics: From Air Pollution to Climate. 3rd ed., Wiley,1152. 24. Wu, S. L., L. J. Mickley, D. J. Jacob, J. A. Logan, R. M. Yantosca, and D. Rind, 2007, Why are there large differences between models in global budgets of tropospheric ozone. J. Geophys. Res., 112, D05302. 25. Xu, Y. F., Huang Y., and Li Y. C. 2012, Summary of recent climate change studies on the carbon and nitrogen cycles in the terrestrial ecosystem and ocean in China. Adv. Atmos. Sci., 29(5), 1027–1047. Volume 1, Issue 1 100 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Air Pollution Status: A case study in West Bengal Debanjali Roy1, Subhangi Chakraborty1, Koustav Bhowmik1, Sourav Mandal1, Priyadarshini Ghosh2, Amitlal Bhattacharya3, Soumini Chaudhury1 and Rina Bhattacharya1 1Department of Physics, JIS University, Agarpara, West Bengal, 700109 2Department of Physics, S.R.F. College, Beldanga, West Bengal, 742133 3Department of Physics, D.N. College, Aurangabad, West Bengal, 742201 Email: [email protected] Abstract: Air pollution is one of the major concerns to the living world. We are living dangerously in the poor air vicinity which is creating severe health hazards and at the extreme may cause even deaths. Pollutants viz. NO2, SO2, PM2.5 and PM10 are responsible for respiratory, heart and cardiovascular diseases. In this paper, we have analyzed the air quality of Kolkata city as a case study and compared its status in comparison with the most polluted New Delhi city and an industrial belt of Haldia in West Bengal. Air Quality of Kolkata is found marginally well than New Delhi. However Kolkata is more polluted than Haldia with respect to NO2 and PM2.5 pollutants. Therefore further study is highly needed to improve our air quality and at the same time to raise public awareness as well. Key Words: Air pollution; PM10,; PM2.5; Health hazard; Air quality index 1 Introduction In our globe, approximately 7 million people die every year due to air pollution related diseases viz. respiratory illness, heart diseases, cancer and cardiovascular problems [1, 2, 3]. 3 India has the highest PM2.5 concentration of 89.9 µg/m (as on 2017) causing 6.7 lakhs deaths annually [4]. The air pollutants which cause health hazards are mainly due to NO2, SO2, CO, PM2.5, PM10, NH3, O3 and Pb [5]. Sources including industry, transport, agriculture, waste management, households etc. are held responsible for adding these pollutants into the atmosphere. Natural phenomenon like volcanic eruptions and dust storms at micro scale disturbances also contribute to air pollution. Among the air pollutants, particulate matter less than 2.5 micron-m (PM2.5) poses the greatest health risk. These particles can get deep into lungs causing lung cancer and some may even get into the blood stream. Carbon particles from fuel combustion and atmospheric ozone are also the major components responsible for rapid climate change. Air pollution has an impact on atmospheric stability as well. The 3 annual mean concentration of PM2.5 in µg/m of different parts of the world viz Africa, America, Eastern Mediterranean, Europe, South East Asia and Western Pacific as per WHO‘s data are 66, 54, 117, 90, 153 and 80 respectively. The air pollution induced deaths in those regions are 06.79, 02.27, 04.08, 05.82, 22.75 and 28.85 lakhs respectively [6, 7]. The percentage value of leading diseases which are causing human deaths in World and India are shown in Figure 1. Globally majority of the casualties are due to heart diseases but in India it is due to cerebrovascular stroke [6, 7]. In south East Volume 1, Issue 1 101 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 3 Asia, India has the highest PM2.5 concentration of 153 µg/m and has led to 6.7 lakhs deaths in year 2017 compared to 6.2 lakhs in 2005. It was reported that long-term effects of air pollution cause the onset of different types of diseases viz respiratory inflammations, cardiovascular dysfunctions and even cancer and therefore linked with millions of death globally [8,9,10]. Figure 1 Comparison of death rate (%) in India (dotted line) and World (Solid line) Kolkata is an important city in West Bengal where high commercial growth has been occurred since last decade. It is situated at latitude 22.5625°N and longitude 88.3531°E and an elevation of 230 m above msl (mean sea level) and expands over an area of an area of 205 square kilometers and sub-divided into 16 boroughs by the Municipal Corporation. Kolkata is also second most air polluted city in India after New Delhi having particulate levels doubles that of Beijing [11]. So, improvement of our air quality will not only influence our health but also save our life. In this study an attempt has been made to analyze the status of air pollution and associated health risk of the city Kolkata in comparison with Delhi and industrial belt of Haldia. 2 Materials and Methods The pollution level can be assessed by Air Quality Index, AQI [12]. It is an index for reporting air quality of a location. It tells you how clean or polluted the air is, and what associated health effects it might pose. The National Air Quality Index (AQI) was launched on September 17, 2014 in New Delhi under the Swachh Bharat Abhiyan to protect public Volume 1, Issue 1 102 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) health. Table 1 gives the six different levels of AQI, level of health concerns and corresponding impacts [13]. The following equation is used to compute AQI from the pollutant concentration [14] I = (C - ) + …………………………(1) where I = the (Air Quality) index, C = the pollutant concentration, and = the concentration breakpoint that is ≤ C and ≥ C respectively, and = the index breakpoint corresponding to and respectively. Table 1 Levels of AQI and associated health concerns AQI Level Status of health Health Impact 0-50 I Good Air quality is satisfactory. 51-100 II Satisfactory Air quality is acceptable; Though, a very small number of people may face moderate health concern for some pollutants. For example, people who are extremely sensitive to ozone may experience respiratory symptoms. 101-200 III Moderate At this AQI range, although general public is not affected, but people suffering from lung disease, older adults and children are at a greater risk due to particulate matter present in the atmosphere. 201-300 IV Unhealthy Everybody may begin to experience some significant health effects, and sensitive people may experience more serious effects 301-400 V Very unhealthy Everybody may experience more serious adverse health issues. 401-500 VI Hazardous This would imply health warnings of emergency conditions for any healthy person.. 3 Results and Discussions We have compared the pollution level of Kolkata and New Delhi considering AQI data for four consecutive years (2016-2019) during monsoon months i.e. June, July and August (JJA) and winter months i.e. December, January and February (DJF) seasons. The status of polluted air has been analyzed by using AQI data in relation to PM2.5 [www.aqicn.org]. Volume 1, Issue 1 103 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 2 Variation of AQI in monsoon for (a) Kolkata and (b) New Delhi Figure 3 Variation of AQI in winter for (a) Kolkata and (b) New Delhi The variation of the average AQI in the two cities Kolkata and New Delhi during monsoon and winter are depicted in Figure 2 and Figure 3 respectively. The range of AQI for Kolkata and New Delhi are respectively from 7 to 312 and 8 to 562 during JJA months whereas it varies from 47 to 565 and 60 to 562 during DJF months as obtained from Table 2 and 3. Volume 1, Issue 1 104 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Table 2 AQI in monsoon for Kolkata and New Delhi Months KOLKATA NEW DELHI Maximum Minimum Maximum Minimum June’16 173 52 185 51 July’16 158 61 189 44 August’16 174 39 207 38 June’17 164 42 232 25 July’17 156 40 166 12 August’17 166 45 166 25 June’18 162 37 562 52 July’18 165 35 173 37 August’18 312 15 172 8 June’19 153 16 241 52 July’19 156 7 189 49 August’19 152 10 167 13 Table 3 Data of AQI in winter for Kolkata and New Delhi KOLKATA NEW DELHI Maximum Minimum Maximum Minimum Jan’16 516 164 554 157 Feb’16 409 68 474 73 Dec’16 419 161 510 151 Jan’17 597 151 519 60 Feb’17 322 61 335 62 Dec’17 458 64 520 122 Jan’18 565 160 463 150 Feb’18 374 152 361 78 Dec’18 471 55 517 103 Jan’19 495 141 504 62 Feb’19 414 47 328 60 Volume 1, Issue 1 105 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 4 Hourly variation of AQI in (a) Kolkata and (b) Delhi It is evident from the figure that the highest average AQI for Kolkata was during January 2018 (277.03) and the same for New Delhi was during January 2016 (330.76). It was observed that 8.79% and 17.74% days of Kolkata belong to level IV and V whereas for Delhi the levels of exposure are 24.73% and 20.55% days during December and January respectively. 4.66% days of January at Kolkata are in danger and warning should be issued regarding public health. This falls in the ―very unhealthy‖ category of air quality and can trigger serious health effects even to a healthy individual. During monsoon the level of pollution varies between category I and II for both the cities. However in winter the air quality is always poor. It is interesting to note that there are a positive trend (+0.306) and a negative trend (-26.75) are observed in Kolkata and Delhi respectively. For both the cities the pollution level is lower in the monsoon than the winter season. The average air quality index of Kolkata (243.99) is slightly less than Delhi (265.57) for winter. The variation of AQI on high pollution day in Kolkata and Delhi for both winter and monsoon seasons during study period is shown in Figure 4. We have also compared the levels of four pollutants like SO2, NO2, PM10 and PM2.5 of Kolkata with an industrial town Haldia of West Bengal from 2013 to 2017 [11]. The year wise minimum, maximum and average concentration of the pollutants of these two cities is given in Table 4. It is seen from the table that highest value of PM10 is 376 in Haldia in Volume 1, Issue 1 106 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 2017, whereas, PM 2.5 is maximum (197) in Kolkata in 2016. Also it is found that SO2 value is comparatively high in Haldia but NO2 is high in Kolkata. Table 4 Pollutants concentrations in Kolkata and Haldia Year Pollutant Kolkata Haldia Min. Max. Annual Av. Min. Max. Annual Av. 2017 SO2 02 10 04 09 22 14 NO2 21 75 35 32 57 40 PM10 37 293 108 59 376 88 PM2.5 29 169 72 24 62 35 2016 SO2 02 26 04 11 28 18 NO2 22 77 43 34 56 42 PM10 24 317 119 60 208 102 PM2.5 18 197 66 20 107 42 2015 SO2 02 15 03 02 15 03 NO2 19 89 42 11 32 17 PM10 30 273 102 46 151 87 PM2.5 16 181 50 * * * 2014 SO2 02 16 05 06 58 11 NO2 12 87 44 19 52 38 PM10 30 268 120 66 286 136 PM2.5 * * * * * * 2013 SO2 06 15 09 07 16 10 NO2 48 78 62 29 49 39 PM10 95 294 178 72 225 146 PM2.5 11 121 46 * * * Data not available 4 Conclusion The pollutants are changing the climate by reducing visibility, increasing acidity in water bodies, discomfortness to our livelihood, instability in atmosphere and even damaging the forest and soil quality to a considerable level [15]. However, it is not yet possible to estimate all the pollutants in the atmosphere. Road transport is the major cause of pollution as seen in the case of Kolkata where PM2.5 level is recorded higher in compared to industrial area likes Haldia. So people of our earth should be more conscious and should be well aware of the air pollution reasons. A negative trend of AQI in case of the city Delhi whereas in Kolkata a slight increasing trend is observed. A more detailed study and awareness will make our air quality good for the benefit of human existence. Moreover, our study will provide status of air pollution exposure and its potential impact on human beings in the most populated and productive region of West Bengal. Volume 1, Issue 1 107 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Acknowledgements The authors pay sincere thanks to the JIS University for providing all facilities to carry out this work. The authors are thankful to WHO, National Pollution Control Board and American embassy in India for the relevant data. References 1. WHO. 2014, Public health, Environmental and Social Determinants of Health (PHE), Issue 63 (March): 1-2 2. WHO. 2013, Health Effects of Particulate Matter: Policy implications for countries in Eastern Europe, Caucasus and Central Asia. Copenhagen: World Health Organisation Regional Office for Europe: 6 3. Kan, H., Chen, B., Zhao, N., London, S.J., Song, G., Chen, G., et al. 2010, A time-series study of ambient air pollution and daily mortality in Shanghai, China. Res Rep Health Eff Inst. 154, 17–78. 4. Balakrishnan, K., et al, 2019, The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the Global Burden of Disease Study 2017, 2018, Lancet Planet Health, 3(1):29-31 5. CPCB. 2014, National Ambient Air Quality Status & Trends. Central Pollution Control Board. Ministry of Environment & Forests. Government of India : New Delhi: 2 6. Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., et al. 2010, A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380(9859): 2224-60 7. TERI. 2015. Air Pollution and Health. Discussion Paper by The Energy and Resources Institute: New Delhi. 8. Greenstone, M., Nilekani, J., Pande, R., Ryan, N., Sudarshan, A. and Sugathan A. 2015, Lower Pollution, Longer lives: Life Expectancy Gains if India Reduced Particulate Matter Pollution. Economic and Political weekly, L(8):40-46 9. Biggeri, A., Bellini, P., Terracini, B. 2004, Meta-analysis of the Italian studies on short- term effects of air pollution – MISA 1996-2002. Epidemiol Prev. 28, 4–100. 10. Rumana, H.S., Sharma, R.C., Beniwal, V., Sharma, A.K. 2014, A retrospective approach to assess human health risks associated with growing air pollution in urbanized area of Thar Desert, Western Rajasthan, India. J Environ Health Sci Eng. 12:23. 11. National Ambient Air Quality Monitoring Programme (http://www.cpcbenvis.nic.in/air_quality_data.html) 12. Air Quality Index (AQI) 2012, Air Quality Communication Workshop San Salvador, El Salvador April 16-17, 2012 Volume 1, Issue 1 108 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 13. CPCB. 2014, National Ambient Air Quality Status & Trends. Central Pollution Control Board. Ministry of Environment & Forests. Government of India : New Delhi, 6 14. EPA (Environmental protection Agency), Measuring Air quality: The pollutants standards index.EPA451/K.94-001, (1994). 15. Zhang, W., Qian, C.N., Zeng, Y.X., 2014, Air pollution: A smoking gun for cancer. Chin J Cancer, 33:173–175. Volume 1, Issue 1 109 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Exploration of dietary Habits of IVF Children – A Comparative Evaluation Sudipta Kar, T. K. Pal Department of Oral and Dental Sciences, JIS University, Kolkata [email protected] S. L. Seal Department of Gynaecology and Obstetrics, R. G. Kar Medical College, Hospital, Kolkata Gautam Kumar Kundu Department of Paedodontics & Preventive Dentistry, Guru Nanak Institute of Dental Sciences & Research, Kolkata Abstract: According to WHO, approximately among 250 million couples attempting for parenthood 13 to 19 million couples are likely to be infertile in India at any given point of time. Nearly 8% of infertile couples need use of advanced ART (Assisted Reproductive Technologies) procedures such as IVF (In-virto Fertilization). But this kind of advanced treatment is too costly and the success rate of any ART procedure is below 30% even under the best of suitable conditions. In UK, on July 25, 1978 Dr. Robert G Edwards and Dr. Patrick Steptoe had successfully documented world‘s first IVF child, Louise Brown. In the same year nearly three months later world‘s second IVF child, Durga was born through the untiring efforts of Dr. Subhas Mukherjee and his two colleagues in Kolkata, India. IVF procedure may induce some risk factors like multiple births, premature delivery and low birth weight, birth defects etc. Moreover recurrent IVF procedures may cause financial, physical and emotional stress. Keywords: Infertile Couples, Reproductive Technologies, In-Virto Fertilization, Multiple Births, Premature Delivery 1 Introduction The birth of first ever IVF human baby in the world occurred in Oldham, England on July 25, 1978. This birth was the result of the extensive collaborative work of two innovative personality - Dr. Patrick Steptoe and Dr. Robert Edwards [1]. In the same year, on 3rd October the birth of world‘s 2nd test tube baby ‗Durga‘ was born as a result of untiring effort advocated by Dr. Saroj Bhattacharya and Dr. Subhas Mukherjee [2] in Kolkata, West Bengal. IVF may produce potential short- and long-term adverse consequences like perinatal morbidity, prematurity, multiple births, low birth weight and congenital malformations. Regarding long-term consequences, IVF may precipitate cerebral palsy, malignancies like retinoblastoma or hepatoblastoma, or acute leukemia subtypes. Adverse dietary habit, particularly the intake of free sugars, is considered as a common risk factor for the occurrence of non-communicable diseases [3]. Consumption of free sugars, especially in the form of sugar -sweetened beverages—increases overall energy intake and may reduce the consumption of foods containing more nutritionally adequate calories, Volume 1, Issue 1 110 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) leading to an weight gain and increased risk of various diseases and conditions due to unhealthy diet. Cardiovascular disease, diabetes, obesity and dental caries [3-4] are considered as modern day diseases due to this kind of dietary habit. The World Health Organization considers the promotion of healthy food practices to be one of the most important challenges required to ensure the health of children throughout the world. 2 Materials and Methods The present study was a cross-sectional study, approved by the Ethical Committee of JIS University, Kolkata. A random sample of 2 – 5 years old IVF children and spontaneously conceived children were included in the study. Frequency of sugar consumption and dietary habits based on quality of food of both group of children were evaluated thoroughly. The children in both case (IVF) and control (spontaneously conceived) groups based on the route of pregnancy were enrolled for the study. Specific inclusion criteria of sample selection for the present study were ‑ (a) baby must be of 37–42 weeks gestational age, (b) singleton babies were preferred, (c) only first children were selected for the present study, (d) family should have medium and high socioeconomic condition. Exclusion criteria were – (a) congenitally malformed children, (b) parent having a history of multiple pregnancy, (c) children having severe asphyxia, (d) children having genetic syndromes and chromosomal abnormalities, were excluded from the present study. Confounding variables of this study were social upbringing of the children, different food habits, different behavioral pattern of individual and variation in parental care. The case group consisted of term (gestational age of the babies were 37–42 weeks), singleton babies whom were end result of IVF of the studied area and were chosen by a computer generated random number list. The control group constitutes of term, first child, singleton, and spontaneously conceived 24-71 months old children whom were referred to the Department of Pedodontics and Preventive Dentistry for the purpose of initial dental health check‑up. Case and control studied individual were matched for the, maternal weight, maternal age, socioeconomic status area of residence gestational age and year of birth. The study was conducted after informed consent was procured from the concerned authorities and respective guardians of children. A total of 321 parents of studied samples were approached to participate in our present study. Out of the above total sample, the parents of 107 IVF and 169 spontaneously agreed to participate in this study. To exclude inter‑observer error all examination and documentation were made by a trained single examiner who was not informed about the birth status of our studied samples. ter Informed consents were collected from the parents, Chi‑square test or Z‑tests were used for statistical analysis. 3 Results During comparison of frequency of sugar consumption with meal in between two groups i.e. IVF children group and Spontaneously Conceived children group we found the value of z is Volume 1, Issue 1 111 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 3.0405. The value of p is .00236. The result is significant at p <.05. During comparison of in between meal sugar consumption sections the value of z is -1.4063. The value of p is .15854. The result is not significant at p <.05. When children consume sugar in both meal time and in between meal the value of z is -2.5251. The value of p is .0114. The result is significant at p <.05. (Table 1 and 2) Table 1 Frequency of sugar consumption in IVF Children Sugar consumption IVF Children Male Female Total No. % No. % No. % With meal 53 49.53 31 28.97 84 78.50 In between meal 9 8.41 6 5.60 15 14.01 Both 2 1.86 6 5.60 8 7.47 Never 0 0 0 0 0 0 Total 64 59.81 43 40.18 107 100 Table 2 Frequency of sugar consumption in Spontaneously Conceived Children Sugar consumption Spontaneously Conceived Children Male Female Total No. % No. % No. % With meal 69 40.82 34 20.11 103 60.94 In between meal 23 13.60 12 7.10 35 20.71 Both 15 8.87 16 9.46 31 18.34 Never 0 0 0 0 0 0 Total 107 63.31 62 36.68 169 100 Table 3 Dietary habits based on quality of food in IVF Children Dietary Habits IVF Children Male Female Total No. % No. % No. % Intake of more hard fibrous 42 39.25 33 30.84 75 70.09 food Intake of more soft refined 22 20.56 10 9.34 32 29.90 food Total 64 59.81 43 40.18 107 100 Volume 1, Issue 1 112 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Table 4 Dietary habits based on quality of food in Spontaneously Conceived Children Dietary Habits Spontaneously Conceived Children Male Female Total No. % No. % No. % Intake of more hard fibrous 56 33.13 35 20.71 91 53.84 food Intake of more soft refined 51 30.17 27 15.97 78 46.15 food Total 107 63.31 62 36.68 169 100 During comparison of dietary habits of children of IVF and Spontaneously Conceived Children, considering intake of more hard fibrous food section, the value of z is 2.6861. The value of p is .00714. The result is significant at p <.05. During comparing of intake of soft refined food category the value of z is -2.6861. The value of p is .00714. The result is significant at p <.05. (Table 3 and 4) 4 Discussions WHO recommends the promotion of healthy food practices to be one of the most important challenges required to ensure the worldwide improvement of health of the children. The consumption of sugar in the form of sucrose enables cariogenic microorganisms to use sugar as a primary energy source for their survival and encourages biochemical events through extracellular and intracellular mechanisms[5] Snacking between meals may occur in solid form, like cookies and sweets, or in the form of liquid such as juice , milk, tea, or soft drinks etc. Cross-sectional and longitudinal studies report that the high intake frequency of carbohydrates, especially sucrose, is considered an important factor for the development of caries in children.[6–8] Sugar exposure in infancy is positively associated with the initial acquisition of Streptococcus mutant, one of the causative organisms for dental caries.[9] As no previous studies was found in the field of sugar consumption and dietary habits of IVF children ,no comparison was possible with that of previous study, so more studies are required in this untouched arena . 5 Conclusions This study is the first of its kind in India to find out sugar consumption and dietary habits of IVF children and compare the data with spontaneously conceived children. IVF children are considered better than spontaneously conceived children when studied in relation sugar consumption and dietary habits. This study invites further scope for more study with larger sample size. Volume 1, Issue 1 113 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) References 1. Steptoe PC, Edwards RG. Birth after the reimplantation of a human embryo. Lancet 1978; 2:366. 2. Chakraborty BN. Test Tube Baby Procedures Miracles, Mysteries and Miseries. 1st ed. Kolkata: The Standard Literature Company Pvt. Ltd.; 2005. p. 1‑3. 3. WHO. Guideline: sugars intake for adults and children. Geneva: World Health Organization;2015. 4. Carmo CDS, Ribeiro MRC, Teixeira JXP, Alves CMC, Franco MM, França AKTC, Benatti BB, Cunha-Cruz J, Ribeiro CCC. Added sugar consumption and chronic oral disease burden among adolescents in Brazil. J Dent Res. 2018;97(5):508–14. 5. Loesche WJ. Role of Streptococcus mutans in human dental decay. Microbiol Rev. 1986;50(4):353–80. 6. Grindefjord M, Dahllöf G, Nilsson B, Modéer T. Stepwise prediction of dental caries in children nup to 3.5 years of age. Caries Res. 1996; 30(4):256–66. 7. Milgrom P, Riedy CA, Weinstein P, Tanner AC, Manibusan L, Bruss J. Dental caries and its relationship to bacterial infection, hypoplasia, diet, and oral hygiene in 6- to 36-month- old children. Community Dent Oral Epidemiol. 2000; 28(4):295–306. 8. Tsai AI, Chen CY, Li LA, Hsiang CL, Hsu KH. Risk indicators for early childhood caries in Taiwan. Community Dent Oral Epidemiol. 2006; 34(6):437–45. 9. Wan AK, Seow WK, Purdie DM, Bird PS, Walsh LJ, Tudehope DI. A longitudinal study of Streptococcus mutans colonization in infants after tooth eruption. J Dent Res. 2003;82(7):504–8. Volume 1, Issue 1 114 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Frequency and Amplitude Spectra Analysis of the Sound of Indian Folk Musical Instruments Sudipta Pal , Rinku Sarkar and Sushmita Saha Department of Physics, University of Kalyani, Kalyani-741235, West Bengal Email: [email protected] Abstract: The frequency and amplitude spectrum of the sound of traditional Indian folk musical instruments like Khamak and Ektara has been analyzed by using very common instruments like smartphone (for recording the sound) and AUDACITY software(for analyzing the sound frequency). By using Fourier transformation of recorded sound of the folk instrument the frequency distribution have been analyzed. The fundamental frequency and overtones for Ektara has been estimated. It is interesting to note that quite similar type of frequency and amplitude pattern has been observed for both of the instruments. But the fundamental frequencies and harmonics are very prominent and last longer in case of Ektara. The sound spectra of the musical instruments of different size have also been studied. The detail information about frequency, amplitude of different boles of these folk instruments may be helpful in digital signal processing. This work has been done under MSc project. Keywords: Khamak, Ektara, Indian Folk Musical Instruments, Fourier analysis of Sound Wave 1 Introduction A musical sound is that where all the vibrations of surrounding body are strongly periodic. Wave form of the musical sound is smooth .The root of music in ancient India are found in the Vedic literature of Hinduism. The earliest Indian thought combined three arts, syllabic recital (vadya), melos (gita) and dance (nrtta). As these fields developed, ―sangeeta‖ became a distinct genre of art, in a form equivalent to contemporary music. Music can be a social activity, but it can also be a very spiritual experience. Ancient Indians were deeply impressed by the spiritual power of music, and it is out of this that Indian classical music was born. So, for those who take it seriously, classical music involves single-minded devotion and lifelong commitment. Audio transcription and digitization of musical data is a very interesting and Figure 1 Khamak and Ektara Volume 1, Issue 1 115 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) challenging research topic. Scientific studies on Indian musical instruments were first done by Sir C.V Raman [1]. The relaxing effects of these instrumental music on people‘s psychology can be observed through an acoustical approach. Watkin G R proposed Music therapy in physiological mechanisms and clinical implications [2]. Also there is influence of music upon electrocardiograms and blood pressure [3]. Cardiovascular, cerebro vascular and respiratory changes can be induced by different type of music [4]. There are positive and negative effects of specific sound frequencies on health. The pitch of a spectrally rich sound is known to be more easily perceived than that of a sinusoidal tone [5]. Many techniques have been implemented for this in the context of Western music. However, Indian classical music differs from its western counterparts in the type of instruments being played and their harmonic nature. Fourier analysis is a very effective way to analysis the sound wave nature of Indian classical musical instruments [6]. The musical instruments KHAMAK and EKTARA are used in India to create mainly folk music of Bengal, specially ‗baulgaan‘ (see Figure 1). Khamak is a type of stringed percussion instrument. The instrument consists of two single- headed drums, one very small and one large. Between the two heads two nylon strings have been stretched. The large drum is held in pace under the left arm and the small drum is held in the left hand. As tension is maintained by hand, tension doesn‘t remain constant for Khamak when it is played. Ektara is a one-string instrument most often used in traditional music from Bangladesh, Egypt, India, and Pakistan Ektara is more tuned stringed instrument than Khamak. It consists of one metal string. Its tension can be controlled with a controller at the top. Tension can be maintained constant when it is played. The various sizes of ektara are soprano, tenor, and bass. The ektara is a common instrument in Baul music from Bengal. These musical instruments have tremendous effects on human mind. Sound analysis in these instruments can give an idea about the relaxing effect on human brain. So our main object in the present work is to do a basic study about the folk instruments like Khamak and Ektara and to analyse the frequency distribution of various boles and notes. 2 Experimental Procedures Recording of sound was done by a mobile phone Moto G5S Plus in a silent place, using stock recording app. Next the recorded sound was converted to ―WAV‖ format. After this, using ―Audacity‖ software the recorded sound was transformed to ‗frequency vs amplitude graph‘ and ‗time vs frequency graph‘ [7]. In Figure 2 a screen shot from the AUDACITY software window of frequency vs Amplitude plot has been shown for a tuning fork, which is a U-shaped metallic two-pronged fork produces single frequency (known) due to vibration. The frequency of the tuning fork is 341 Hz (written on the tuning fork). From the fig it is clear that there is a distinct peak around 370Hz which is considered to be fundamental tone. There are also peaks in higher frequencies which may be considered as harmonics since tuning fork is not always produce single frequency sound. In the time vs frequency graph (Figure 3) the higher frequency sounds or harmonics die very soon. So by using the Audacity Volume 1, Issue 1 116 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Figure 2 Frequency vs Time spectrogram plot in Audacity software. Here one can see 370 Hz frequency sound is clearly dominating and is represented by white bar in the plot software, we get the frequency of that tuning fork 370 Hz. Thus, the given and measured frequency of the tuning fork is nearly same. So it has been concluded that this software is good enough for study the frequency analysis of musical sound. Two different sizes of Khamak and Ektara were taken as sample instruments. A very good player freely played the instruments and the sound was recorded for several times. The string length was noted for each tone by using a normal meter scale (small division 0.1cm). The length of the string and the dimension of the instruments using meter scale were also measured. Figure 3 Amplitude vs Frequency Fourier transformed plot in Audacity software. The 370 Hz frequency is the most intense sound and is represented by a large peak in the plot. Volume 1, Issue 1 117 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) 3 Results and Discussions a) Medium size Khamak : In Figure 4, the waveforms of the bole ―Dha gi ti ta na ki dhin‖, obtained by Audacity software, is shown in Amplitude vs time plot. We have also identified different segments of the sound in the plot by listening the part. The bole ―Dha gi ti ta na ki dhin‖ as usually named marely played for 1.8 sec. Figure 4 Waveforms of the bole ―Dha gi ti ta na ki dhin‖, given by Audacity software, is shown. We have also identified different notes of the bole. Next all the individual notes of the total bole have been analysed. Figure 5 Frequency vs Time waveform plot for ―Dha‖ selected from the total spectrum Volume 1, Issue 1 118 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) For example one note (say ―Dha‖) of each bole has been taken for frequency and amplitude analysis. Here the region of ―Dha‖ from ―Dha gi ti ta na ki dhin‖ has been zoomed in more expanded region of time (Figure 5). Selecting this region the spectrum has been replotted in frequency domain through fast Fourier transformation by the AUDACITY software shown in Figure 6 [8]. One can see from the figure that it is very difficult to identify fundamental and harmonics from the spectrum. A rough estimate of the total number of frequencies and their relative intensity can be done. Since the musical sounds are produced due to two simultaneously vibrating strings, it is very difficult to identify the fundamental and harmonics [9,10]. Figure 6 Intensity vs Frequency Fourier transformed sound spectrum plot for ―Dha‖ In Table 1 the frequency and amplitude data for different Note has been presented identified from intensity vs amplitude plot. Volume 1, Issue 1 119 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Table 1 Frequency and amplitude data for different Note Sl. No. Note Name frequencies in Hz Amplitudes in dB 1 Dha 254 -29.2 441 -11.5 877 -26.6 1313 -30.1 2 Gi 270 -30.3 424 -15.8 808 -44.6 1101 -49.5 3 Ti 278 -28.1 410 -16.9 815 -34.7 889 -33.8 1022 -41.6 4 Ta 262 -25.4 442 -14 655 -33.2 886 -34.5 5 Na 269 -36.1 422 -21.7 610 -47.9 844 -45.7 1065 -48.4 6 Ki 292 -20.6 409 -16.7 780 -29.1 1004 -47.1 7 Dhin 247 -25.8 329 -23.7 423 -19.8 763 -36.7 Volume 1, Issue 1 120 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) The data in table 1 has been plotted in Figure 7. From the figure it is observed that for all the notes the first two frequencies fall almost same region. However in the higher frequency region the data are random. This may be due to noise and other technical difficulties in analysing the sound. Although the Khamak looks very simple, it‘s working mechanism is not -5 Dha -10 Gi -15 Ti Ta -20 Na Ki -25 Dhin -30 Dha Gi -35 Ti Ta Intensity indB Intensity -40 Na -45 Ki Dhin -50 -55 1000 Figure 7 Frequency vs AmplitudeFrequency graph in Hz for (in logall the scale) frequencies present of the boles 10 ―Dha gi ti ta na ki dhin‖ in medium size Khamak so simple as it looks. Many difficulties arise while analyzing its sounds.The Tension of wire is controlled by hands. So, while playing, its tension varies abruptly, resulting in slight change of frequency. Also the base surface and upper surface where the wires are connected are made of membrane so tension on the wires varies. Finally there are two parallel wires. There is a fractional time difference between the plucking of two wires. When 1st wire is struck, the tension varies. So, the frequencies of the sounds emitted from the 2nd wire may change slightly compared to 1st note. Big size Khamak The same procedure has been followed for analyzing the sound spectrum of big size Khamak. A little difference has been observed in the frequency vs amplitude data (Figure 8). The frequency values shifts towards higher region. Volume 1, Issue 1 121 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Dha -10 Gi Na -15 Gi Ti -20 Ta Na -25 Ki Dhi -30 Na -35 AmplitudeindB -40 -45 -50 1000 Log (frequency in Hz) 10 Figure 8 Frequency vs. Amplitude graph for all the frequencies present of the boles―Dha gi ti ta na ki dhin‖ in medium size Khamak Figure 9 Waveform of the notes of ektara b) Ektara: In Figure 9 all the notes in wave form of ektara has been shown in the time vs amplitude plot. The frequencies have been estimated from the Fourier transformed plot similar to earlier procedure. It is observed that the frequencies present in all the notes are same and their Volume 1, Issue 1 122 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) amplitude also show similar pattern (Figure 10). The first peak may be considered as the fundamental (around 700Hz) and other peaks with decreasing magnitude are overtones[10]. . Tone 1 Tone 2 -15 Tone 3 -20 Tone 4 Tone 5 -25 Tone 6 Tone 7 -30 Tone 8 -35 Tone 9 Tone 10 -40 Tone 11 Tone 12 -45 Tone 13 Intensity indB Intensity -50 -55 -60 -65 1000 Frequency in Hz (in Log scale) 10 Figure 10 Frequency vs Amplitude plot for all the frequencies for different notes (indicated as 1-15 in Figure 8) 4 Conclusions Using very common instruments like smartphone (for recording the sound) and AUDACITY software (for analyzing the sound frequency) the frequency and amplitude spectrum of the sound of traditional Indian folk musical instruments like Khamak and Ektara has been analyzed. Fourier transformations of recorded sound of the folk instrument give an idea about the frequency distribution in the sound. It is difficult to determine the fundamental frequency and overtones of Khamak from the plots however in case of Ektara those have been estimated. It is interesting to note that quite similar type of frequency and amplitude pattern has been observed for both of the instruments. The wire of Ektara is made up of metal whereas the wires of Khamak are made up of nylon wire. That is why there is a big difference in the sound the produced which can be differentiated easily by listening to them. The sound produced by Ektara is richer in higher harmonics due to the characteristics of metal wire. The sound spectrum of the musical instruments of different size shows that in case bigger Khamak the frequency spectra shifts towards higher region. Volume 1, Issue 1 123 December, 2019 JISU Journal of Multidisciplinary Research (JISUJMR) Acknowledgement: The authors acknowledge DST-PURSE (phase II) for partial support. References 1. Scientific Papers of C.V. Raman: Volume II: Acoustics by C. V. Raman , Oxford University Press (July 6, 1989) 2. Watkins, 1997 Clin Nurse Spec; 11: 43-50 3. Hyde IH, Scalapino W, 1918, Am J Physiol, 46: 35-38 4. Larsen PD, Galletly DC, 2006, Heart Journal, 92: 445-452 5. Tervaniemi M, Schröger E 2000, Neuroscience Letters, 290(1): 66-70 6. ―Vibrations, Waves and acoustics‖(English Edition)-D. Chattopadhyay and P.C. Rakshit. 7. ―The Physics of Musical Instruments‖ (second edition)-Neville H. Fletcher and Thomas D. Rossing Springer, ISBN-13:978-0-387-94151-6 8. Mandal A.K.,. Wahi P, 2015, Journal of Sound and Vibration, 338:, 224. 42–59 9. C. Valette. Mechanics of Musical Instruments, pages 115–183. Springer, Wien, New York, 1995 10. Damodaran A, 2015, Applied Acoustics, 88: 1-5, Volume 1, Issue 1 124 December, 2019