Forecasts of tropospheric ozone in the Metropolitan Area of Rio de Janeiro based on missing data imputation and multivariate calibration techniques

• Published in: Environmental monitoring and assessment Vol. 193; no. 8; p. 531
• Main Authors: de Oliveira, Rafael C. G., Cunha, Camilla L., Tôrres, Alexandre R., Corrêa, Sergio M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2021; Springer Nature B.V
• Subjects: Air; Air monitoring; Air pollution; Air quality; Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Calibration; Carbon monoxide; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Mean square errors; Methods; Metropolitan areas; Missing data; Modelling; Monitoring systems; Monitoring/Environmental Analysis; Multivariate analysis; Nitrogen oxides; Ozone; Photochemicals; Pollutants; Predictions; Relative humidity; Solar radiation; Stations; Support vector machines; Troposphere; Tropospheric ozone; Wind speed; Brazil; Rio de Janeiro Brazil; Wilcoxon test; MissForest; Missing data; Ozone; Random forest; Support vector machine
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-021-09333-2

Multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018. These techniques provide an easy and feasible way of modeling and analyzing air pollutants and can be used when coupled with other methods. The results showed that random forest and support vector machine chemometric techniques can be used in modeling and predicting tropospheric ozone concentrations, with a coefficient of determination for making predictions up to 0.92, a root-mean square error of calibration between 4.66 and 27.15 µg m −3 , and a root-mean square error of prediction between 4.17 and 22.45 µg m −3 , depending on the air quality monitoring stations and season.