Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77004, USA.
Environ Pollut. 2023 Jun 1;326:121508. doi: 10.1016/j.envpol.2023.121508. Epub 2023 Mar 24.
The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.
臭氧监测站数量有限，这给各种应用带来了不确定性，因此需要采用准确的方法来获取所有地区的臭氧值，特别是那些没有现场测量数据的地区。本研究使用深度学习（DL）来准确估计每日最大 8 小时平均值（MDA8）臭氧，并研究了 2019 年美国大陆（CONUS）地区多个因素对臭氧水平的空间贡献。将现场观测值与 DL 估计的 MDA8 臭氧值进行比较，得到相关系数（R）为 0.95、一致性指数（IOA）为 0.97 和平均绝对偏差（MAB）为 2.79 ppb，这表明深度卷积神经网络（Deep-CNN）在估计地表 MDA8 臭氧方面具有良好的性能。空间交叉验证也证实了该模型具有较高的空间精度，当在单独的站点上进行训练和测试时，该模型的 R 为 0.91、IOA 为 0.96 和 MAB 为 3.46 ppb。为了解释我们的 DL 模型的黑盒性质，我们使用 Shapley 加性解释（SHAP）生成空间特征贡献图（SFCM），结果证实了 Deep-CNN 捕捉大多数预测变量与臭氧之间相互作用的高级能力。例如，该模型表明，太阳辐射（SRad）SFCM 的值越高，臭氧的形成就越强，特别是在美国 CONUS 的南部和西南部。由于 SRad 通过光化学反应引发臭氧前体生成臭氧，因此它会增加臭氧浓度。该模型还表明，湿度较低的地区，臭氧浓度会增加。湿度与臭氧水平之间的负相关关系可能归因于湿度增加导致臭氧分解增加以及 OH 自由基增加等因素。本研究首次引入 SFCM 来研究预测变量对估计的 MDA8 臭氧水平变化的空间作用。
