Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are trained using the historical measurement datasets independently collected at the environmental monitoring stations, and their operational forecasts onward by the inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions. In contrast, deterministic chemical transport models (CTM), which simulate the full life cycles of air pollutants, provide forecasts that are continuous in 3D field. However, owing to the complex error sources due to the emission, transport, and removal of pollutants, CTM forecasts are typically biased particularly in fine scale. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our recent regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast. The prediction fusion was conducted using the Bayesian theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented significant improvements than the pure CTM. Moreover, covariance inflation further enhanced the fused prediction particularly in the cases of severe underestimation. 

EnKF-based fusion of site-available machine learning air quality predictions from RFSML v1.0 and gridded chemical transport model forecasts from GEOS-Chem v13.1.0

Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are trained using the historical measurement datasets independently collected at the environmental monitoring stations, and their operational forecasts onward by the inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions. In contrast, deterministic chemical transport models (CTM), which simulate the full life cycles of air pollutants, provide forecasts that are continuous in 3D field. However, owing to the complex error sources due to the emission, transport, and removal of pollutants, CTM forecasts are typically biased particularly in fine scale. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our recent regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast. The prediction fusion was conducted using the Bayesian theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented significant improvements than the pure CTM. Moreover, covariance inflation further enhanced the fused prediction particularly in the cases of severe underestimation.

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Comment on gmd-2022-301

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. Statistical methods, particularly machine learning models, have gained signiﬁcant popularity in air quality predictions. These prediction models are trained using the historical measurement datasets independently collected at the environmental monitoring stations, and their operational forecasts onward by the inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions. In contrast, deterministic chemical transport models (CTM), which simulate the full life cycles of air pollutants, provide forecasts that are continuous in 3D 5 ﬁeld. However, owing to the complex error sources due to the emission, transport, and removal of pollutants, CTM forecasts are typically biased particularly in ﬁne scale. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our recent regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast. The prediction fusion was conducted using the Bayesian theory-based ensemble Kalman ﬁlter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories 10 were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inﬂation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented signiﬁcant improvements than the pure CTM. Moreover, covariance inﬂation further enhanced the fused prediction particularly in the cases of severe underestimation

Abstract. With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).


/pdf/development-of-a-regional-feature-selection-based-machine-2xmdfeod.pdf

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

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Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China