School of Economics, Qingdao University, Qingdao 266071, China.
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
Int J Environ Res Public Health. 2022 Jul 12;19(14):8511. doi: 10.3390/ijerph19148511.
Surface ozone (O) 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) and O. However, high-accuracy near-surface O maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O 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 O 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) R value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m. In addition, the estimated O concentration is accurately determined at varying temporal scales with sample-based 10-CV R 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 O concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 μg m at 15:00 local time (1500 LT) in the BTH region. Summertime O 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 O pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.
地面臭氧（O）是一种重要的大气痕量气体，对生态安全和人类健康构成巨大威胁。目前，中国空气污染控制的核心目标是实现细颗粒物（PM）和 O 的联合治理。然而，高精度的近地面 O 地图仍然缺乏。因此，我们建立了一个新的模型，该模型将 WRF-Chem 和随机森林（RF）模型与人为排放数据和气象数据集相结合，以确定全覆盖的每小时 O 浓度。基于此方法，以 2018 年的京津冀（BTH）地区为例，生成了水平分辨率为 9km 的全覆盖每小时 O 地图。性能评估结果表明，新模型具有较高的可靠性，基于样本（站）的 10 倍交叉验证（10-CV）R 值为 0.94（0.90），均方根误差（RMSE）为 14.58（19.18）µg m。此外，该模型能够准确地确定不同时间尺度的 O 浓度，基于样本的 10-CV R 值分别为 0.96、0.98 和 0.98，在日、月和季节尺度上，这明显优于传统推导算法和之前研究中的其他技术。该模型能够捕捉到与温度和太阳辐射变化相关的 O 浓度的日变化，表现为初始增加随后减少。在 BTH 地区，O 浓度的最高值约为 112.73±9.65µg m，出现在当地时间（LT）15:00。夏季整个 BTH 地区的 O 污染风险较高，尤其是南部城市，污染持续时间占夏季的 50%以上。此外，2018 年 BTH 地区有 43 天和两天分别出现了轻度和中度 O 污染。总的来说，该新方法可以在高时空分辨率下用于近地面 O 的估算，这对于相关领域的研究具有重要意义。
