Ground-level ozone (O₃) 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 O₃ concentrations difficult to understand. This study aimed to i) identify different classes of temporal dynamics of O₃ at daily and 9 km² 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 km². 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 O₃ concentrations (r = 0.84 and r = 0.41, respectively), while the proportion of urbanized area was negatively correlated with O₃ (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.

Over the course of history, the development of human societies implied the exploitation of mineral resources which generated huge amounts of mining wastes leading to substantial environmental contamination by various metal(loid)s. This is especially the case of coal mine tailings which, subjected to weathering reactions, produce acid mine drainage (AMD), a recurring ecological issue related to current and past mining activities. In this study, we aimed to determine the origin, the fate and the ecotoxicity of metal(loid)s leached from a historical coal tailing heap to the Beuveroux river (Franche-Comté, France) using a combination of mineralogical, chemical and biological approaches. In the constitutive materials of the tailings, we identified galena, tetrahedrite and bournonite as metal-rich minerals and their weathering has led to massive contamination of the water and suspended particles of the river bordering the heap. The ecotoxicity of the AMD has been assessed using Chironomus riparius larvae encaged in the field during a one-month biomonitoring campaign. The larvae showed lethal and sub-lethal (growth and emergence inhibition and delay) impairments at the AMD tributary and near downstream stations. Metal bioaccumulation and subcellular fractionation in the larvae tissues revealed a strong bioavailability of, notably, As, Pb and Tl explaining the observed biological responses. Thus, more than 70 years after the end of mining operations, the coal tailings remain a chronic source of contamination and environmental risks in AMD effluent receiving waters.

Background and Aims: One of today's challenges in terms of understanding the real impact of the environment on pregnancy and its complications is the ability to work with real‐life data that are correctly and extensively connected to data on the contamination of living environments and individualized exposures. We present a solution based on a data infrastructure that is directly usable for research. We describe (i) the methodological characteristics of the infrastructure, (ii) the geographical territory and environmental exposures covered by the infrastructure, and (iii) the initial description of the real‐life health data of the included population. Methods: The infrastructure is a relational database that integrates routine obstetric care data from CHUB with individual environmental exposure data linked via home addresses. The infrastructure includes all deliveries at Besançon University Hospital (CHUB) from January 1, 2010, to December 31, 2021. Results: Our infrastructure included information on 20,376 women, 28,344 pregnancies, and 29,190 births. The infrastructure described includes 20% of deliveries in an administrative region of more than 1.5 million inhabitants. The quality of the data is linked to highly contrasted exposure of living areas within this region and temporal coverage of more than 10 years of obstetrical events. This infrastructure is a research tool with the capacity to incorporate data in the future. Conclusion: This infrastructure can be used to address a wide range of questions aimed at describing the phenomena of environmental exposure during pregnancy, multiple exposures and their cumulative effects, particularly the phenomena of mediation, interaction, or confusion.