Prediction model for AQI through Indian Vedic science: knowledge management technique to control pollution and for sustainable society

Rohit Rastogi et al.

International Journal of Data Analysis Techniques and Strategies2026https://doi.org/10.1504/ijdats.2026.151637article
AJG 1
Weight
0.50

What the paper says

The paper provides an essence of how Indian Vedic Sciences can be used for preventing and predicting the ill effects of pollution on the human body and nature through adopting simple methods of Yajna and Hawan in daily routine. With respect to any other resource like land and water, air is considered as the most important resource. Evidence shows that Indian Vedic Sciences primarily focus on 'prana vayu' which means 'air that we breathe'. The author's team and the Central Pollution Control Board (CPCB) have gathered the data and reading of the last four months through installed sensors in an isolated as well as non-isolated environment that was continuously under the effects of Yajna and Hawan.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijdats.2026.151637

Or copy a formatted citation

@article{rohit2026,
  title        = {{Prediction model for AQI through Indian Vedic science: knowledge management technique to control pollution and for sustainable society}},
  author       = {Rohit Rastogi et al.},
  journal      = {International Journal of Data Analysis Techniques and Strategies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijdats.2026.151637},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Prediction model for AQI through Indian Vedic science: knowledge management technique to control pollution and for sustainable society

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.