Development of an automated photolysis rates prediction system based on machine learning.

Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of OD, NO, HONO, HO, HCHO, and NO, which are the crucial values for the prediction of the atmospheric oxidation capacity (AOC) and secondary pollutant concentrations such as ozone (O), secondary organic aerosols (SOA). The J-ML can self-select the optimal "Model + Hyperparameters" without human interference. The evaluated results showed that the J-ML had a good performance to reproduce the J-values where most of the correlation (R) coefficients exceed 0.

Multi-scale analysis of the impacts of meteorology and emissions on PM and O trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O trends by a model.

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM and O in China.

Development and application of an automated air quality forecasting system based on machine learning.

As one of the most concerned issues in modern society, air quality has received extensive attentions from the public and the government, which promotes the continuous development and progress of air quality forecasting technology. In this study, an automated air quality forecasting system based on machine learning has been developed and applied for daily forecasts of six common pollutants (PM, PM SO, NO, O, and CO) and pollution levels, which can automatically find the best "Model + Hyperparameters" without human intervention. Five machine learning models and an ensemble model (Stacked Generalization) were integrated into the system, supported by a knowledge base containing the meteorological observed data, pollutant concentrations, pollutant emissions, and model reanalysis data.

A new parameterization of uptake coefficients for heterogeneous reactions on multi-component atmospheric aerosols.

Based on laboratory studies and field observations, a new parameterization of uptake coefficients for heterogeneous reactions on multi-component aerosols is developed in this work. The equivalent ratio (ER) of inorganic aerosol is used to establish the quantitative relationship between the heterogeneous uptake coefficients and the composition of aerosols. Incorporating the new ER-dependent scheme, the WRF-CUACE model has been applied to simulate sulfate mass concentrations during December 2017 in the Beijing-Tianjin-Hebei region and evaluate the role of aerosol chemical components played in the sulfate formation.