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Multiple Linear Regression Analysis to Estimate Hydrological Effects in Soil Rn-222 at Ghuttu, Garhwal Himalaya, India: A Prerequisite to Identify Earthquake Precursors


Affiliations
1 Wadia Institute of Himalayan Geology, 33 General Mahdeo Singh Road, Dehradun 248 001, India
 

Various geophysical parameters including soil radon (222Rn) are being conTinuously monitored at Ghuttu, Garhwal Himalaya, India since 2007 as a part of earthquake precursor studies. To analyse the earthquake precursory changes in soil radon, it is essential to clean the soil radon data from other effects. For this, we used data for the period of nine years from 2011 to 2019 and assessed the relationship of soil radon with five other parameters using regression analysis. These parameters are water level, atmospheric pressure, rainfall, air temperature and soil temperature at 10 m depth. We also added one more parameter, i.e. the difference of air temperature (Tout) and soil temperature at 10 m depth (Tin). From the observed six parameters, four showed strong correlation with soil radon. These are (i) water level (correlation coefficient (CC) = –0.9), (ii) atmospheric pressure (CC = 0.6), (iii) air temperature (CC = –0.6) and (iv) temperature difference (Tout – Tin; CC = 0.5). For regression analysis, data during the period 2011–2014 were used for training, while data during 2015–2019 were used for tesTing purpose. Based on different models, the one developed using all the six input parameters suggests lowest errors and highest correlation. The observed values of ischolar_main mean square error, mean absolute error and CC were 0.332, 0.281 and 0.931 respectively. The regression coefficients obtained from this model were used to calculate the theoretical radon and residuals. By this approach, the effects of hydrological and atmospheric parameters were found to be reduced to a great extent.

Keywords

Earthquake Precursors, Hydrological Effects, Linear Regression, Soil Radon.
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  • World Health Organization, Brucellosis (human). Excerpt from WHO recommended standards and strategies for surveillance, prevention and control of communicable diseases, 2020, pp. 1–3; https://www.who.int/zoonoses/diseases/Brucellosissurveillance.pdf?ua=1 (accessed on 4 October 2020).

  • CDC, Brucellosis homepage, 2012; http://www.cdc.gov/brucellosis/veterinarians/host-animals.html (accessed on 4 April 2020).

  • de Bagüés, J., María, P., María, I., Arias, M. A., Julián, P., Axel, C. and Michel, S. Z., The new strains Brucella inopinata BO1 and Brucella species 83–210 behave biologically like classic infectious Brucella species and cause death in murine models of infection, J. Infect. Dis., 2014, 210(3), 467–472.

  • FAO, FAO Statistical Yearbook, 2014; http://www.fao.org/3/ai3590e.pdf (accessed on 4 April 2020).

  • Singh, S. V., Singh, N., Singh, M. P., Shankar, H. and Lalwani, D. D., Occurrence of abortions and seroprevalence of brucellosis in goats and sheep. Small Ruminant Res., 1994, 14(2), 161–165.

  • Gupta, V. K., Verma, D. K., Rout, P. K., Singh, S. V. and Vihan, V. S., Polymerase chain reaction (PCR) for detection of Brucella melitensis in goat milk. Small Ruminant Res., 2006, 65(1–2), 79–84.

  • Gupta, V. K., Kumari, R., Verma, D. K., Singh, K., Singh, S. V. and Vihan, V. S., Detection of Brucella melitensis from goat tissues employing PCR. Indian J. Animal Sci., 2006, 76(10), 793–795.

  • Corbel, M. J., Food and Agriculture Organization of the United Nations, World Health Organization and World Organization for Animal Health, Brucellosis in humans and animals, 2006; https://apps.who.int/iris/handle/10665/43597 (accessed on 26 August 2020).

  • OIE, Bovine brucellosis. In Manual of Standard for Diagnostics Tests and Vaccine for Terrestrial Animals, Office International des Epizooties, Paris, 2009, 5th edn.

  • Alton, G. G., Jones, L. M. and Pietz, D. E., Laboratory Techniques in Brucellosis, World Health Organization, Geneva, Switzerland, 1975, 2nd edn.

  • Muñoz, P. M. et al., Efficacy of several serological tests and antigens for diagnosis of bovine brucellosis in the presence of falsepositive serological results due to Yersinia enterocolitica O:9. Clin. Vaccine Immunol., 2005, 12(1), 141–151.

  • Bricker B. J. and Halling, S. M., Differentiation of Brucella abortus bv. 1, 2, and 4, Brucella melitensis, Brucella ovis, and Brucella suis bv. 1 by PCR. J. Clin. Microbiol., 1994, 32(11), 2660–2666.

  • Baily, G. G., Krahn, J. B., Drasar, B. S. and Stoker, N. G., Detection of Brucella melitensis and Brucella abortus by DNA amplification. J. Trop. Med. Hyg., 1992, 95, 271–275.

  • Hénault, S., Calvez, D., Thiébaud, M., Boulière, M. and GarinBastuji, B., Validation of a nested-PCR based on the IS6501/711 sequence for the detection of Brucella in animal samples. In Proceedings of the Brucellosis 2000 International Research Conference (including the 53rd Brucellosis Research Conference), Nîmes, France, 2000, p. 45.

  • Sonekar, C. P. et al., Brucellosis in migratory sheep flock from Maharashtra, India. Trop. Anim. Health Product., 2018, 50(1), 91– 96.

  • Awandkar, S. P., Gosawi, P. P., Khode, N. V., Jadhao, S. G., Mendhe, M. S. and Kulkarni, M. B., Seroepidemiology of Brucellosis in Deccani Sheep. Indian Veter. J., 2012, 89(6), 30–32.

  • Awandkar, S. P., Sardar, V. M., Jadhao, S. G., Khode, N. V. and Kulkarni, M. B., An evidence of brucella zoonoses. In First Annual Conference of SRL and NAWAR and Symposium on Concepts in Zoonoses and Health in New Millennium at Nagpur, India, 2015, p. 53.

  • Sadhu, D. B., Panchasara, H. H., Chauhan, H. C., Sutariya, D. R., Parmar, V. L. and Prajapati, H. B., Seroprevalence and comparison of different serological tests for brucellosis detection in small ruminants. Veter. World, 2015, 8(5), 561–566.

  • Saxena, N., Singh, B. B. and Saxena, H. M., Brucellosis in sheep and goats and its serodiagnosis and epidemiology. Int. J. Curr. Microbiol. Appl. Sci., 2018, 7(1), 1848–1877.

  • Leahy, E., Shome, R, Deka, P., Sahay, S., Grace, D., Mazeri, S. and Lindah, J. F., Risk factors for Brucella spp. and Coxiella burnetii infection among small ruminants in Eastern India. Inf. Ecol. Epidemiol., 2020, 10(1); https://doi.org/10.1080/20008686.2020.1783091.

  • Lone, I. M., Baba, M. A., Shah, M. M., Iqbal, A. and Sakina, A., Seroprevalence of brucellosis in sheep of organized and unorganized sector of Kashmir valley. Veter. World, 2013, 6(8), 530–533; doi:10.5455/vetworld.2013.530-533.

  • Kanani, A., Dabhi, S., Patel, Y., Chandra, V., Vinodh Kumar, O. R. and Shome, R., Seroprevalence of brucellosis in small ruminants in organized and unorganized sectors of Gujarat state, India. Veter. World, 2018, 11(8), 1030–1036.

  • Chand, P. and Chhabra, R., Herd and individual animal prevalence of bovine brucellosis with associated risk factors on dairy farms in Haryana and Punjab in India. Trop. Anim. Health Product., 2013, 45, 1313–1319.

  • Shome, R. et al., Spatial seroprevalence of bovine brucellosis in India – A large random sampling survey. Comp. Immunol., Microbiol. Infect. Dis., 2019, 65, 124–127.

  • Prasad, S., New Paradigm in Livestock Production from Traditional to Commercial Farming and Beyond, Agrotech Publishing Academy, Udaipur, 2013, pp. 57–80.

  • Rossetti, C. A., Arenas-Gamboa, A. M. and Maurizio, E., Caprine brucellosis: a historically neglected disease with significant impact on public health. PLoS Negl. Trop. Dis., 2017, 11(8), e0005692; https://doi.org/10.1371/journal.pntd.0005692.

  • Singh, A., Gupta, V. K., Kumar, A., Singh, V. K. and Nayakwadi, S., 16S rRNA and Omp31 gene based molecular characterization of field strains of B. melitensis from aborted foetus of goats in India. Sci. World J., 2013, 2013, 160376; doi:10.1155/2013/160376.

  • Goel, S., Goyal, P., Singh, A., Goel, A. K., Gupta, A., Surana, A. and Bhardwaj, A., Incidence and sero epidemiology of brucellosis from a tertiary care centre of rural Maharashtra. Int. Arch. Integ. Med., 2015, 2(8), 71–77.

  • Agasthya, A. S., Isloor, S. and Krishnamsetty, P., Seroprevalence study of human brucellosis by conventional tests and indigenous indirect enzyme-linked immunosorbent assay. Sci. World J., 2012, 2012, 104239.

  • Renukaradhya, G. J., Isloor, S. and Rajasekhar, M., Epidemiology, zoonotic aspects, vaccination and control/eradication of brucellosis in India. Veter. Microbiol., 2002, 90, 183–195.

  • Handa, R., Singh, S., Singh, N. and Wali, J. P., Brucellosis in north India: results of a prospective study. J. Commun. Dis., 1998, 30(2), 85–87.

  • Halliday, J. E. B., Allan, K. J., Ekwem, D., Cleaveland, S. and Kazwala, R. R., Endemic zoonoses in the tropics: a public health problem hiding in plain sight. Veter. Rec., 2015, 176, 220–225.

  • Mangalgi, S. S., Sajjan, A. G., Mohite, S. T. and Kakade, S. V., Serological, clinical, and epidemiological profile of human brucellosis in rural India. Indian J. Commun. Med., 2015, 40(3), 163–167.

  • Singh, B. B., Khatkar, M. S., Aulakh, R. S., Gill, J. P. S. and Dhand, N. K., Estimation of the health and economic burden of human brucellosis in India. Prev. Veter. Med., 2018, 154, 148–155.


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  • Multiple Linear Regression Analysis to Estimate Hydrological Effects in Soil Rn-222 at Ghuttu, Garhwal Himalaya, India: A Prerequisite to Identify Earthquake Precursors

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Authors

Vishal Chauhan
Wadia Institute of Himalayan Geology, 33 General Mahdeo Singh Road, Dehradun 248 001, India
Naresh Kumar
Wadia Institute of Himalayan Geology, 33 General Mahdeo Singh Road, Dehradun 248 001, India
Vaishali Shukla
Wadia Institute of Himalayan Geology, 33 General Mahdeo Singh Road, Dehradun 248 001, India
Sanjay Kumar Verma
Wadia Institute of Himalayan Geology, 33 General Mahdeo Singh Road, Dehradun 248 001, India

Abstract


Various geophysical parameters including soil radon (222Rn) are being conTinuously monitored at Ghuttu, Garhwal Himalaya, India since 2007 as a part of earthquake precursor studies. To analyse the earthquake precursory changes in soil radon, it is essential to clean the soil radon data from other effects. For this, we used data for the period of nine years from 2011 to 2019 and assessed the relationship of soil radon with five other parameters using regression analysis. These parameters are water level, atmospheric pressure, rainfall, air temperature and soil temperature at 10 m depth. We also added one more parameter, i.e. the difference of air temperature (Tout) and soil temperature at 10 m depth (Tin). From the observed six parameters, four showed strong correlation with soil radon. These are (i) water level (correlation coefficient (CC) = –0.9), (ii) atmospheric pressure (CC = 0.6), (iii) air temperature (CC = –0.6) and (iv) temperature difference (Tout – Tin; CC = 0.5). For regression analysis, data during the period 2011–2014 were used for training, while data during 2015–2019 were used for tesTing purpose. Based on different models, the one developed using all the six input parameters suggests lowest errors and highest correlation. The observed values of ischolar_main mean square error, mean absolute error and CC were 0.332, 0.281 and 0.931 respectively. The regression coefficients obtained from this model were used to calculate the theoretical radon and residuals. By this approach, the effects of hydrological and atmospheric parameters were found to be reduced to a great extent.

Keywords


Earthquake Precursors, Hydrological Effects, Linear Regression, Soil Radon.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi12%2F1905-1911