Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model

High-resolution full-coverage ozone (O3) estimates using a data-driven spatial random forest model in Beijing-Tianjin-Hebei region, China

The Beijing-Tianjin-Hebei (BTH) region is severely polluted by ozone (O3). Accurate O3 estimates are essential for identifying high-polluted zones and developing targeted interventions to relieve the burden of diseases. Although many studies have estimated high-resolution O3 concentrations in BTH, the estimation accuracies are still insufficient. In this study, we incorporated data-driven spatial weight matrices (DDWs) into a random forest (RF) model to fully utilize both the spatial homogeneity and heterogeneity of maximum daily 8-h ozone concentration (MDA8O3), and obtained full-coverage MDA8O3 concentrations at 1 km×1 km in BTH from 2014 to 2022. DDW-RF exhibited satisfactory accuracy (10-fold cross-validation R2 =0.937, RMSE=13.919 μg/m3). Overall O3 level presented a spatial pattern of lower in the north and higher in the southeast and showed a distinct temporal trend, i.e., first increasing and then decreasing during 2014-2021 and increasing slightly in 2022. The accurate MDA8O3 estimates indicates that more attention and resources should be poured into the areas adjacent to Bohai Rim, Shandong and Henan. Regulated operation of factories under specific meteorological conditions and upgrading industrial structure and production modes are recommended to mitigate the formation of O3 precursors and reduce O3 generation. Our findings provide evidence and reference for environmental cleaning policies and targeted interventions.

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.

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

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 (PM2.5) 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.