开发基于机器学习的自动光解率预测系统

IF 5.9 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Sciences-china Pub Date : 2024-04-05 DOI:10.1016/j.jes.2024.03.051
Weijun Pan , Sunling Gong , Huabing Ke , Xin Li , Duohong Chen , Cheng Huang , Danlin Song
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引用次数: 0

摘要

根据观测到的气象要素、光解率(J 值)和污染物浓度,开发了一种机器学习 J 值自动预测系统(J-ML),用于重现和预测 O1D、NO2、HONO、H2O2、HCHO 和 NO3 的 J 值,这些值是预测大气氧化能力(AOC)和臭氧(O3)、二次有机气溶胶(SOA)等二次污染物浓度的关键值。J-ML 可自行选择最佳 "模型+超参数",无需人工干预。评估结果表明,与北京、成都、广州和上海的观测数据以及对流层紫外线和可见光(TUV)辐射模型得出的 J 值相比,J-ML 在再现 J 值方面表现良好,大部分相关系数(R)超过 0.93,准确度(P)在 0.68-0.83 之间。未来 3 天的 R 值为 0.78 至 0.81,未来 7 天的 R 值为 0.69 至 0.71。与使用 TUV 模式的 J 值预测 O3 浓度相比,使用 J-ML 的 J 值建立的基于观测的排放驱动模式(e-OBM)的 R 值提高了 4%-12%,ME 值降低了 4%-30%,表明 J-ML 可以作为传统数值模式的良好补充。特征重要性分析结果表明,对所有 J 值的关键影响参数是地表太阳向下辐射,对所有 J 值的其他主导因素是 2 米平均温度、O3、总云量、边界层高度、相对湿度和地面气压。
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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 O1D, NO2, HONO, H2O2, HCHO, and NO3, which are the crucial values for the prediction of the atmospheric oxidation capacity (AOC) and secondary pollutant concentrations such as ozone (O3), 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 0.93 and the accuracy (P) values are in the range of 0.68-0.83, comparing with the J-values from observations and from the tropospheric ultraviolet and visible (TUV) radiation model in Beijing, Chengdu, Guangzhou and Shanghai. The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days, respectively. Compared with O3 concentrations by using J-values from the TUV model, an emission-driven observation-based model (e-OBM) by using the J-values from the J-ML showed a 4%-12% increase in R and 4%-30% decrease in ME, indicating that the J-ML could be used as an excellent supplement to traditional numerical models. The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values, and the other dominant factors for all J-values were 2-m mean temperature, O3, total cloud cover, boundary layer height, relative humidity and surface pressure.

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来源期刊
Journal of Environmental Sciences-china
Journal of Environmental Sciences-china 环境科学-环境科学
CiteScore
13.70
自引率
0.00%
发文量
6354
审稿时长
2.6 months
期刊介绍: The Journal of Environmental Sciences is an international journal started in 1989. The journal is devoted to publish original, peer-reviewed research papers on main aspects of environmental sciences, such as environmental chemistry, environmental biology, ecology, geosciences and environmental physics. Appropriate subjects include basic and applied research on atmospheric, terrestrial and aquatic environments, pollution control and abatement technology, conservation of natural resources, environmental health and toxicology. Announcements of international environmental science meetings and other recent information are also included.
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