Kalbande, Ritesh and Kumar, Bipin and Maji, Sujit and Yadav, Ravi and Atey, Kaustubh and Rathore, Devendra Singh and Beig, Gufran
(2023)
Machine learning based quantification of VOC contribution in surface ozone prediction.
Chemosphere, 326.
Full text not available from this repository.
| Abstract: |
The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014–2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results. |
| Item Type: |
Journal Paper
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| Subjects: |
School of Natural and Engineering Sciences > Energy and Environment |
| Divisions: |
Schools > Natural Sciences and Engineering |
| Date Deposited: |
30 Jun 2023 07:10 |
| Last Modified: |
30 Jun 2023 07:10 |
| Official URL: |
https://www.sciencedirect.com/science/article/abs/... |
| Related URLs: |
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| Funders: |
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| Projects: |
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| DOI: |
https://doi.org/10.1016/j.chemosphere.2023.138474 |
| URI: |
http://eprints.nias.res.in/id/eprint/2520 |
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