Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

Abstract

Surface ozone (O₃) is an important atmospheric trace gas, posing an enormous threat to ecological security and human health. Currently, the core objective of air pollution control in China is to realize the joint treatment of fine particulate matter (PM_2.5) and O₃. However, high-accuracy near-surface O₃ maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O₃ concentration with the WRF-Chem and random forest (RF) models combined with anthropogenic emission data and meteorological datasets. Based on this method, choosing the Beijing-Tianjin-Hebei (BTH) region in 2018 as an example, full-coverage hourly O₃ maps were generated at a horizontal resolution of 9 km. The performance evaluation results indicated that the new model is reliable with a sample (station)-based 10-fold cross-validation (10-CV) R² value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m^-3. In addition, the estimated O₃ concentration is accurately determined at varying temporal scales with sample-based 10-CV R² values of 0.96, 0.98 and 0.98 at the daily, monthly, and seasonal scales, respectively, which is highly superior to traditional derivation algorithms and other techniques in previous studies. An initial increase and subsequent decrease, which constitute the diurnal variation in the O₃ concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 μg m^-3 at 15:00 local time (1500 LT) in the BTH region. Summertime O₃ posed a high pollution risk across the whole BTH region, especially in southern cities, and the pollution duration accounted for more than 50% of the summer season. Additionally, 43 and two days exhibited light and moderate O₃ pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O₃ estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.

Keywords: Beijing-Tianjin-Hebei; WRF-Chem; air pollution; hourly ozone; random forest.

Figure 1

Flowchart of the two-stage model.

Figure 2

Density scatter plots of the sample-based 10-CV results from 08:00 local time (0800 LT) to 18:00 local time (1800 LT) across the BTH region in 2018: (a) all hourly records from 0800 to 1800 LT; and (b–l) sample-based 10-CV values for each hour from 0800 to 1800 LT. The black lines denote 1:1 lines and the red lines denote linear regression fitting lines.

Figure 3

Site-scale evaluation of the estimated hourly O₃ concentration in 2018 across the BTH region. The upper and lower rows indicate the sample- and station-based 10-CV results, respectively. The columns from left to right indicate R², RMSE, MAE and hour of occurrence of the highest 10-CV R² value.

Figure 4

The temporal time series of the consistency between the two-stage model-derived concentrations and surface measurements in 2018 across China. (a–c) was R², RMSE and MAE, respectively.

Figure 5

Spatial distributions of the seasonal MDA8 O₃ concentration (a–d) and proportion of O₃ pollution time in 13 cities across the BTH region in 2018: (a,e) Spring; (b,f) summer; (c,g) autumn; and (d,h) winter.

Figure 6

Daily MDA8 O₃ concentration (a) and IAQI of the daily average O₃ concentration (b) in 13 cities and the whole BTH region.

Figure 7

O₃ pollution level in each city of the BTH region in 2018.