PM <sub>2.5</sub> concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022
Bibliographic record
Abstract
Abstract. 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 Europe, and the low-concentration areas are in northern and western Europe. In China, the trend is 2.09 µg m−3 per decade. High- concentration areas are distributed in northern China, and the low-concentration areas are distributed in southern China. The trend is 2.65 µg m−3 per decade up to 2011 and −22.23 µg m−3 per decade since 2012. In India, the trend is 0.92 µg m−3 per decade. The concentration exhibits a north–high to south–low pattern, with high-concentration areas distributed in northern India, such as the Ganges Plain and Thar Desert, and the low-concentration area in the Deccan Plateau. The trend is 1.41 µg m−3 per decade up to 2013 and −23.36 µg m−3 per decade from 2014. The variation in regional PM2.5 concentrations is closely related to the implementation of air quality laws and regulations. The daily site-scale PM2.5 concentration dataset from 1959 to 2022 in the Northern Hemisphere is available at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".