Current - Issue
Original Article
Multivariate Statistical Modelling and Interaction Analysis of Atmospheric Particulate Dynamics: A Predictive IoT-Based Approach
Prince Pawar1
Dr. Mamta Sood2
Dr. Sandeep Garg3
1 B.tech Student, Department of Electronics & Communication Engineering, Oriental College of Technology, Bhopal, India. 2 Associate Professor, Oriental College of Technology, Bhopal, India. 3 Professor, Oriental College of Technology, Bhopal, India.
Published Online: July-August 2026
Pages: 28-33
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260604003References
1. Al-Turjman, F. (2019). Smart cities: An IoT-centric approach. Springer Nature.
2. Box, G. E. P., & Draper, N. R. (2007). Response surfaces, mixtures, and ridge analyses. Wiley.
3. Chatzipanagioti, M., & Kampanos, G. (2023). Calibration challenges and baseline drift compensation in metal oxide semiconductor gas
sensors (MQ series) under volatile humidity conditions. Sensors and Actuators B: Chemical, 375, 132910.
4. Kumar, A., & Goyal, P. (2021). IoT-enabled environmental monitoring for sustainable urban planning: A review. Journal of Urban
Management, 10(3), 201-215.
5. Kozak, M., & Scrimgeour, F. (2013). Applied multivariate statistics in environmental science. Springer.
6. Liu, H., & Zhang, Y. (2021). Machine learning approaches for air quality prediction: A systematic review. Environmental Pollution, 270,
116245.
7. Montgomery, D. C. (2020). Design and Analysis of Experiments (10th ed.). Wiley.
8. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization
Using Designed Experiments. Wiley. 9. Oke, T. R. (2002). Boundary Layer Climates (2nd ed.). Routledge.
9. Pal, P., & Roy, S. (2020). Assessment of atmospheric scrubbing by rainfall using localized IoT gas sensor nodes. Environmental Science
and Pollution Research, 27(14), 16400-16412.
10. Ramamurthy, S. R., & Jain, R. (2019). An Internet of Things based environmental monitoring system using ESP32. International Journal
of Computer Applications, 174(22), 15-22.
11. Saini, J., Raina, A., & Shah, S. (2016). IoT-based air quality monitoring system. International Journal of Advanced Research in Computer
and Communication Engineering, 5(6), 46-52.
12. Sun, Y., & Zhao, Y. (2022). Empirical evaluation of atmospheric wet deposition and particulate scavenging by convective rainfall events.
Atmospheric Environment, 274, 118985.
13. Wang, J., & Li, Y. (2023). Predictive analytics for smart city air quality monitoring using multivariate regression and IoT telemetry.
Journal of Cleaner Production, 382, 135244.
14. Yadav, A., & Kumar, R. (2020). Performance analysis of low-cost gas sensors in highhumidity urban environments. IEEE Sensors
Journal, 20(15), 8752-8760.
15. Zhang, Q., & Wang, X. (2024). Wet deposition and airborne pollutant precipitation dynamics: Analysing natural atmospheric scrubbing
mechanisms using distributed edge nodes. Science of The Total Environment, 906, 167431.
16. Zhu, Y., & Zhao, X. (2022). Urban heat island mitigation through predictive modelling of microclimatic variables. Urban Climate, 43,
101142.
2. Box, G. E. P., & Draper, N. R. (2007). Response surfaces, mixtures, and ridge analyses. Wiley.
3. Chatzipanagioti, M., & Kampanos, G. (2023). Calibration challenges and baseline drift compensation in metal oxide semiconductor gas
sensors (MQ series) under volatile humidity conditions. Sensors and Actuators B: Chemical, 375, 132910.
4. Kumar, A., & Goyal, P. (2021). IoT-enabled environmental monitoring for sustainable urban planning: A review. Journal of Urban
Management, 10(3), 201-215.
5. Kozak, M., & Scrimgeour, F. (2013). Applied multivariate statistics in environmental science. Springer.
6. Liu, H., & Zhang, Y. (2021). Machine learning approaches for air quality prediction: A systematic review. Environmental Pollution, 270,
116245.
7. Montgomery, D. C. (2020). Design and Analysis of Experiments (10th ed.). Wiley.
8. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization
Using Designed Experiments. Wiley. 9. Oke, T. R. (2002). Boundary Layer Climates (2nd ed.). Routledge.
9. Pal, P., & Roy, S. (2020). Assessment of atmospheric scrubbing by rainfall using localized IoT gas sensor nodes. Environmental Science
and Pollution Research, 27(14), 16400-16412.
10. Ramamurthy, S. R., & Jain, R. (2019). An Internet of Things based environmental monitoring system using ESP32. International Journal
of Computer Applications, 174(22), 15-22.
11. Saini, J., Raina, A., & Shah, S. (2016). IoT-based air quality monitoring system. International Journal of Advanced Research in Computer
and Communication Engineering, 5(6), 46-52.
12. Sun, Y., & Zhao, Y. (2022). Empirical evaluation of atmospheric wet deposition and particulate scavenging by convective rainfall events.
Atmospheric Environment, 274, 118985.
13. Wang, J., & Li, Y. (2023). Predictive analytics for smart city air quality monitoring using multivariate regression and IoT telemetry.
Journal of Cleaner Production, 382, 135244.
14. Yadav, A., & Kumar, R. (2020). Performance analysis of low-cost gas sensors in highhumidity urban environments. IEEE Sensors
Journal, 20(15), 8752-8760.
15. Zhang, Q., & Wang, X. (2024). Wet deposition and airborne pollutant precipitation dynamics: Analysing natural atmospheric scrubbing
mechanisms using distributed edge nodes. Science of The Total Environment, 906, 167431.
16. Zhu, Y., & Zhao, X. (2022). Urban heat island mitigation through predictive modelling of microclimatic variables. Urban Climate, 43,
101142.
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