Author(s):
Anjna Kumari, Ruchi Sangal
Email(s):
anjnagch@g mail.com , sangalruchi01@gmail.com
Address:
Anjna Kumari1, Ruchi Sangal2
1Assistant Professor, Dept. of Chemistry, NSCBM Govt. College Hamirpur (H.P)
2Assistant Professor, Dept. of Zoology, Sidharth Govt. College Nadaun (H.P)
Published In:
Conference Proceeding, Proceeding of ICAMAS-2025
Year of Publication:
July, 2025
Online since:
July 11, 2025
DOI:
Not Available
ABSTRACT:
Science and technology speak mathematics. It serves as the foundation for developments in data analysis, computer science, artificial intelligence, and environmental science. Math plays a key role in healthcare by helping to analyse medical data, optimize treatment regimens, and create models that forecast the spread of diseases. The environment, which is a complex mixture of ecosystems, weather, and resources, may seem distant from numbers and equations. On the inside, however, mathematics is a strong language that unites all things. From monitoring climate change to enhancing conservation initiatives, math serves as an unseen architect that aids in our understanding and preservation of the natural world. Math is used by environmental scientists to simulate, model, analyse data, and make well-informed decisions. Understanding climate change, maximizing resource management, conservation biology, predicting natural disasters, remote sensing, environmental impact assessment, epidemiology and public health, and more are all addressed by a variety of mathematical applications. In order to better comprehend the natural world and provide sustainable solutions for environmental management and conservation, mathematics offers crucial tools and methods for researching and tackling environmental issues. Thus, this paper investigates the application of mathematics to environmental issues and the development of a more sustainable future.
Cite this article:
Anjna Kumari, Ruchi Sangal. Mathematical Modelling in Environment Towards Sustainable Future. Proceeding of ICAMAS-2025.174-178.
REFERENCES:
1. Anderson, R. M., and May, R. M. (1991). Infectious diseases of humans: Dynamics and control. Oxford University Press.
2. Betts, A. K., and Miller, M. J. (1986). A new convective adjustment scheme. Journal of Geophysical Research, 91(D5), 9790-9798.
3. Boersma, K., et al. (2001). Ecosystem-based management: A framework for marine conservation. Ecology and Society, 5(2), 12-23.
4. Brauer, F., and Castillo-Chavez, C. (2012). Mathematical models in population biology and epidemiology (2nd ed.). Springer.
5. Carvalho, R., Souza, J., and Moura, J. (2017). Stochastic modeling for renewable energy forecasting and grid management. Renewable and Sustainable Energy Reviews, 74, 210-221.
6. Chinneck, J. W. (2008). Practical optimization: A gentle introduction. Springer Science and Business Media.
7. Clark, W. C. (2007). Environmental science and sustainability: A global perspective. Cambridge University Press.
8. Costanza, R., d'Arge, R., de Groot, R., et al. (1997). The value of the world's ecosystem services and natural capital. Nature, 387, 253-260.
9. Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J. (2000). Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408(6809), 184-187.
10. Dantzig, G. B. (1963). Linear programming and extensions. Princeton UniversityPress.
11. Diekmann, O., Heesterbeek, J. A. P., and Roberts, M. G. (2010). The construction of next-generation matrix models for compartmental epidemic models. Bulletin of Mathematical Biology, 72(4), 255-276.
12. Forrester, J. W. (1961). Industrial dynamics. MIT Press.
13. Friedlingstein, P., et al. (2014). Persistent growth of CO2 emissions and implications for climate change. Nature Geoscience, 7(10), 709-712.
14. Gnanadesikan, A. (1999). A simple predictive model for the structure of the oceanic pycnocline. Science, 283(5401), 2065-2068.
15. Grimm, V., and Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton University Press.
16. Gurney, W. S. C., and Nisbet, R. M. (1998). Ecological dynamics. Oxford University Press.
17. Holling, C. S. (1978). Adaptive environmental assessment and management. Wiley-Interscience.
18. Holton, J. R. (2004). An introduction to dynamic meteorology (4th ed.). ElsevierAcademic Press.
19. Houghton, R. A., Jenkins, G. J., andEphraums, J. J. (2001). Climate change 2001: The scientific basis. Cambridge University Press.
20. Huybrechts, P., and De Wolde, J. (1999). The dynamic response of the Greenland and Antarctic ice sheets to multiple-century climate warming. Journal of Climate, 12(8), 2169-2188.
21. Keitt, T. H., Ruan, S., and Warne, R. (1997). A spatially explicit model of predator-prey dynamics. Ecological Modelling, 103(1), 27-39.
22. Kemeny, J. G., and Snell, J. L. (1960). Finite Markov chains. Van Nostrand.
23. Kwon, O., Lee, J., and Lee, Y. (2016). Stochastic modeling of energy demand and renewable energy supply. Energy Economics, 59, 189-198.
24. Levin, S. A. (1999). Fragile dominion: Complexity and the commons. Perseus Books.
25. Lilly, D. K. (1962). On the motion of the atmosphere. Journal of Atmospheric Sciences, 19(6), 489-505.
26. Lin, Y.-L., and Chang, J.-S. (2000). Microphysics and cloud dynamics in the tropical atmosphere. Atmospheric Research, 53(1-4), 29-52.
27. Lotka, A. J. (1910). Contribution to the theory of periodic reactions. Journal of the American Chemical Society, 32(9), 145-174.
28. Mahlman, J. D. (1997). A critical review of radiative forcing and the observed evidence for anthropogenic effects on the Earth's climate. Journal of Climate, 10(5), 845-863.
29. Manabe, S., and Stouffer, R. J. (1988). Two stable equilibria of a coupled ocean-atmosphere model. Journal of Climate, 1(9), 841-866.
30. Manabe, S., and Wetherald, R. T. (1967). Thermal equilibrium of the atmosphere with a given distribution of relative humidity. Journal of the Atmospheric Sciences, 24(3), 241-259.
31. Murray, J. (2002). Mathematical biology: I. An introduction. Springer-Verlag.
32. Paterson, W. S. B. (1994). The physics of glaciers (3rd ed.). Pergamon Press.
33. Seinfeld, J. H., andPandis, S. N. (2016). Atmospheric chemistry and physics: From air pollution to climate change (3rd ed.). Wiley.
34. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. McGraw-Hill.
35. Sukhorukova, I. V., and Chistyakova, N. A. (2020). Mathematical model for assessing environmental risk. IOP Conference Series: Materials Science and Engineering, 828(1), 012027.
36. Tilman, D. (2000). Causes, consequences, and ethics of biodiversity. Nature, 405(6783), 208-211.
37. Volterra, V. (1926). Variazioni e fluttuazionidei numeri d'individui in specie animaliconviventi. Memorie della R. Academia delleScienze di Torino, 3(6), 31-113.
38. Von Neumann, J., and Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.