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(searched for: doi:10.1038/s41598-021-01824-z)
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Sanjeev Kimothi, Asha Thapliyal, Anita Gehlot, Arwa N. Aledaily, Anish Gupta, Naveen Bilandi, Rajesh Singh, Praveen Kumar Malik, Shaik Vaseem Akram
Published: 1 February 2023
Sustainable Energy Technologies and Assessments, Volume 55; https://doi.org/10.1016/j.seta.2022.102956

Li Wang, Yuan Zhao, Jinsen Shi, Jianmin Ma, Xiaoyue Liu, Dongliang Han, Hong Gao,
Published: 1 February 2023
Environmental Pollution, Volume 318; https://doi.org/10.1016/j.envpol.2022.120798

Published: 17 January 2023
by MDPI
Journal: Urban Science
Urban Science, Volume 7; https://doi.org/10.3390/urbansci7010009

Abstract:
Atmospheric ozone (O3) concentration is impacted by a number of factors, such as the amount of solar radiation, the composition of nitrogen oxides (NOx) and hydrocarbons, the transport of pollutants and the amount of particulate matter in the atmosphere. The oxidative potential of the atmosphere and the formation of secondary organic aerosols (SOAs) as a result of atmospheric oxidation are influenced by the prevalent O3 concentration. The formation of secondary aerosols from O3 depends on several meteorological, environmental and chemical factors. The relationship between PM2.5 and O3 in different urban environmental regimes of India is investigated in this study during the summer and winter seasons. A relationship between PM2.5 and O3 has been established for many meteorological and chemical variables, such as RH, WS, T and NOx, for the selected study locations. During the winter season, the correlation between PM2.5 and O3 was found to be negative for Delhi and Bengaluru, whereas it was positive in Ahmedabad. The city of Bengaluru was seen to have a positive correlation between PM2.5 and O3 during summer, coinciding with the transport of marine air masses with high RH and low wind speed (as evident from FLEXPART simulations), leading to the formation of SOAs. Further, O3 concentrations are predicted using a Recurrent Neural Network (RNN) model based on the relation obtained between PM2.5 and O3 for the summer season using NOx, T, RH, WS and PM2.5 as inputs.
Lakhima Chutia, Narendra Ojha, , Binita Pathak, Lokesh K. Sahu, Chandan Sarangi, Johannes Flemming, Arlindo da Silva,
Published: 1 September 2022
Atmospheric Environment, Volume 284; https://doi.org/10.1016/j.atmosenv.2022.119189

Biao Zhang, Ying Zhang,
Published: 2 June 2022
Scientific Reports, Volume 12, pp 1-10; https://doi.org/10.1038/s41598-022-13498-2

Abstract:
Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term average ozone concentration of the site. A Bayesian optimization-based XGBoost-RFE feature selection model BO-XGBoost-RFE is proposed, and a variety of machine learning algorithms are used to predict ozone concentration based on the optimal feature subset. Since the selection of the underlying model hyperparameters is involved in the recursive feature selection process, different hyperparameter combinations will lead to differences in the feature subsets selected by the model, so that the feature subsets obtained by the model may not be optimal solutions. We combine the Bayesian optimization algorithm to adjust the parameters of recursive feature elimination based on XGBoost to obtain the optimal parameter combination and the optimal feature subset under the parameter combination. Experiments on long-term ozone concentration prediction on a global scale show that the prediction accuracy of the model after Bayesian optimized XGBoost-RFE feature selection is higher than that based on all features and on feature selection with Pearson correlation. Among the four prediction models, random forest obtained the highest prediction accuracy. The XGBoost prediction model achieved the greatest improvement in accuracy.
VigneshKumar Balamurugan, Vinothkumar Balamurugan,
Published: 5 April 2022
Scientific Reports, Volume 12, pp 1-8; https://doi.org/10.1038/s41598-022-09619-6

Abstract:
Surface ozone (O $$_3$$3 ) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R $$^{2}$$2 = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R $$^{2}$$2 = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R $$^{2}$$2 ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O $$_3$$3 precursors information has little effect on the ML model’s performance.
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