Abstract：
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.
摘要：
基于偏最小二乘法的多元校正,随机森林,和支持向量机方法，结合MissForest插补算法，用于了解臭氧和氮氧化物之间的相互作用，一氧化碳,风速,太阳辐射,温度,相对湿度,和其他人,这些数据是由里约热内卢市区的空气质量监测站在四个不同的地点收集的，2014年和,2018.这些技术提供了一种简单可行的方法来建模和分析空气污染物，并且可以与其他方法结合使用。结果表明,随机森林和支持向量机化学计量学技术可用于对流层臭氧浓度的建模和预测,根据空气质量监测站和季节的不同，预测的确定系数高达0.92，校准的均方根误差在4.66至27.15µgm-3之间，预测的均方根误差在4.17至22.45µgm-3之间。
