Gauthier-Manuel Honorine, Bernard Nadine, Boilleaut Mathieu, Giraudoux Patrick, Pujol Sophie, Mauny Frédéric
Chrono-environnement UMR 6249, CNRS, Université de Franche-Comté, F-25000 Besançon, France; Unité de méthodologie en recherche clinique, épidémiologie et santé publique (uMETh), Inserm CIC 1431, Centre Hospitalier Universitaire de Besançon, 25030, Besançon Cedex, France.
Chrono-environnement UMR 6249, CNRS, Université de Franche-Comté, F-25000 Besançon, France; Centre National de La Recherche Scientifique, UMR 6049, Laboratoire ThéMA, Université de Bourgogne Franche-Comté, 25000 Besançon, France.
Environ Int. 2023 Mar;173:107859. doi: 10.1016/j.envint.2023.107859. Epub 2023 Mar 2.
Ground-level ozone (O) is one of the most worrisome air pollutants regarding environmental and health impacts. There is a need for a deeper understanding of its spatial and temporal dynamics. Models are needed to provide continuous temporal and spatial coverage in ozone concentration data with a fine resolution. However, the simultaneous influence of each determinant of ozone dynamics, their spatial and temporal variations, and their interaction make the resulting dynamics of O concentrations difficult to understand. This study aimed to i) identify different classes of temporal dynamics of O at daily and 9 km resolution over a long-term period of 12 years, ii) identify the potential determinants of these dynamics and, iii) explore the spatial distribution of the potential classes of temporal dynamics on a spatial continuum and over about 1000 km. Thus, 126 time series of 12-year daily ozone concentrations were classified using dynamic time warping (DTW) and hierarchical clustering (study area centered on Besançon, eastern France). The different temporal dynamics obtained differed on elevation, ozone levels, proportions of urbanized and vegetated surfaces. We identified different daily ozone temporal dynamics, spatially structured, that overlapped areas called urban, suburban and rural. Urbanization, elevation and vegetation acted as determinants simultaneously. Individually, elevation and vegetated surface were positively correlated with O concentrations (r = 0.84 and r = 0.41, respectively), while the proportion of urbanized area was negatively correlated with O (r = -0.39). An increasing ozone concentration gradient was observed from urban to rural areas and was reinforced by the elevation gradient. Rural areas were both subject to higher ozone levels (p < 0.001), least monitoring and lower predictability. We identified main determinants of the temporal dynamics of ozone concentrations. The joint influence of determinants was also synthesized. This study proposed a systematic, and reproducible way to build exposure area mapping.
地面臭氧（O）是对环境和健康影响最令人担忧的空气污染物之一。有必要更深入地了解其时空动态。需要模型以高分辨率提供臭氧浓度数据的连续时空覆盖。然而，臭氧动态的每个决定因素的同时影响、它们的时空变化及其相互作用使得O浓度的最终动态难以理解。本研究旨在：i）在12年的长期期间，以每日和9公里分辨率识别O的不同时间动态类别；ii）识别这些动态的潜在决定因素；iii）在空间连续体上和大约1000公里范围内探索潜在时间动态类别的空间分布。因此，使用动态时间规整（DTW）和层次聚类（以法国东部贝桑松为中心的研究区域）对126个12年每日臭氧浓度的时间序列进行了分类。获得的不同时间动态在海拔、臭氧水平、城市化和植被表面比例方面存在差异。我们识别出了不同的每日臭氧时间动态，它们在空间上具有结构，并在称为城市、郊区和农村的区域重叠。城市化、海拔和植被同时作为决定因素。单独来看，海拔和植被表面与O浓度呈正相关（r分别为0.84和0.41），而城市化面积比例与O呈负相关（r = -0.39）。从城市到农村地区观察到臭氧浓度梯度增加，并因海拔梯度而增强。农村地区臭氧水平较高（p < 0.001）、监测最少且可预测性较低。我们识别出了臭氧浓度时间动态的主要决定因素。还综合了决定因素的联合影响。本研究提出了一种系统且可重复的方法来构建暴露区域地图。
