Ozone Concentration Estimation and Meteorological Impact Quantification in the Beijing‐Tianjin‐Hebei Region Based on Machine Learning Models

Abstract

Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use. In this study, we utilized meteorological parameters obtained from european center for medium—range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8‐hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission‐related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015–2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 μg m−3.

Enhancing Turbidity Modeling in the Mississippi River Using Machine Learning and Sentinel‐2 Remote Sensing Data: A Generalizability Analysis

“…This is because DL paradigms require extensive datasets, as well as long computational times. Previous works show that DL performance can yield similar and even inferior outcomes compared to classical ML approaches, when the dataset size is not sufficiently broad [79][80][81]. Therefore, the implementation of DL models in this study was not considered, as the available data for each monitoring station amounts to 2453 for the period under investigation.…”