Modeling the response of negative air ions to environmental factors using multiple linear regression and random forest

• Published in: Ecological informatics 2021-12, Vol.66, p.101464, Article 101464
• Main Authors: Shi, Guang-Yao, Zhou, Yu, Sang, Yu-Qiang, Huang, Hui, Zhang, Jin-Song, Meng, Ping, Cai, Lu-Lu
• Format: Article
• Language: English
• Subjects: Environment factor; Machine learning; Multiple linear regression; Negative air ion; Random forest model
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2021.101464

Negative air ion (NAI) plays a vital role in promoting the psychological and physiological functions of the human body and is an essential indicator for measuring the air cleanliness of a given area. In this paper, we presented and compared the results of two methods for identifying the main environmental factors affecting changes of NAI in a warm-temperate region of China. NAI concentration was estimated based on measured data during the main growing season in a warm-temperate forest and was used as the dependent variable in the traditional multiple linear regression and random forest models. Air pollutants and certain weather, radiation, and soil factors were selected as predictors based on their potential influence on NAI. Two methods were applied for the analysis, and the latter was a non-parametric alternative based on an ensemble of classification and regression trees. We compared the precision of the two models, and the variables of each method on the basis of their levels of importance; Independent samples was used in model validation, then we discussed the important environmental factors affecting changes of NAI concentration for both linear and nonlinear perspectives, along with the potential implications of environmental factors on NAI. The random forest model showed a higher accuracy comparison with the multiple linear regression model. Furthermore, the analysis also indicated its better performance by using independent test data for 10-fold cross-validation of the random forest model, and showing that this method has potential for broad-scale application in the assessment of environmental-factor influence on NAI. Certain selected variables that were common to both models (particulate matter 2.5, soil moisture, and relative humidity) appeared to influence NAI to a relatively large extent, demonstrating the decidedly influential role of these parameters on NAI concentration. •Two models are tested to optimize application in NAI–environmental factor influence.•Particulate matter, soil moisture, and relative humidity strongly influence NAI.•Random forest has high predictive ability and better model performance.