PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022

Long-term PM2.5 data are essential for the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated using a machine learning method for the period from 1959 to 2022 in the Nor...

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Veröffentlicht in:Earth system science data 2024-09, Vol.16 (9), p.4051-4076
Hauptverfasser: Hao, Hongfei, Wang, Kaicun, Wu, Guocan, Liu, Jianbao, Li, Jing
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Sprache:eng
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Zusammenfassung:Long-term PM2.5 data are essential for the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated using a machine learning method for the period from 1959 to 2022 in the Northern Hemisphere based on near-surface atmospheric visibility. They are extracted from the Integrated Surface Database (ISD). Daily continuous monitored PM2.5 concentration is set as the target, and near-surface atmospheric visibility and other related variables are used as the inputs. A total of 80 % of the samples of each site are the training set, and 20 % are the testing set. The training result shows that the slope of linear regression with a 95 % confidence interval (CI) between the estimated PM2.5 concentration and the monitored PM2.5 concentration is 0.955 [0.955, 0.955], the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.2 µg m-3, and the mean absolute error (MAE) is 3.2 µg m-3. The test result shows that the slope within a 95 % CI between the predicted PM2.5 concentration and the monitored PM2.5 concentration is 0.864 [0.863, 0.865], the R2 is 0.79, the RMSE is 14.8 µg m-3, and the MAE is 7.6 µg m-3. Compared with a global PM2.5 concentration dataset derived from a satellite aerosol optical depth product with 1 km resolution, the slopes of linear regression on the daily (monthly) scale are 0.817 (0.854) from 2000 to 2021, 0.758 (0.821) from 2000 to 2010, and 0.867 (0.879) from 2011 to 2022, indicating the accuracy of the model and the consistency of the estimated PM2.5 concentration on the temporal scale. The interannual trends and spatial patterns of PM2.5 concentration on the regional scale from 1959 to 2022 are analyzed using a generalized additive mixed model (GAMM), suitable for situations with an uneven spatial distribution of monitoring sites. The trend is the slope of the Theil–Sen estimator. In Canada, the trend is -0.10 µg m-3 per decade, and the PM2.5 concentration exhibits an east–high to west–low pattern. In the United States, the trend is -0.40 µg m-3 per decade, and PM2.5 concentration decreases significantly after 1992, with a trend of -1.39 µg m-3 per decade. The areas of high PM2.5 concentration are in the east and west, and the areas of low PM2.5 concentration are in the central and northern regions. In Europe, the trend is -1.55 µg m-3 per decade. High-concentration areas are distributed in eastern
ISSN:1866-3508
1866-3516
DOI:10.5194/essd-16-4051-2024