Due to poor weather conditions including common heavy cloud cover at polar latitudes, daily satellite imaging is not always accessible. Nevertheless, fast events including heavy rainfall inducing floods appear as significant in the ice and snow budget while being ignored by satellite based studies since the slower sampling rate is unable to observe such short phenomena. We complement satellite imagery with a set of ground based autonomous automated high resolution digital cameras. The recorded oblique views, acquired at a rate of 3 images per day, are processed for comparison with the spaceborne imagery. Delaunay triangulation based mapping using a dense set of reference points provides the means for an accurate projection by applying a rubber sheeting algorithm. The measurement strategy of identifying binary information of ice and snow cover is illustrated through the example of a particular flood event. We observe a snow cover evolution from 100% to 44.5% and back to 100% over a period of 2 weeks.

Les processus du mercure (Hg) et ses conséquences sur la santé sont connus, notamment en milieu amazonien. Le MeHg (Hg organique, forme la plus toxique du Hg) est un neurotoxique puissant et joue un rôle dans l’existence de troubles neurologiques (carmouze et al, 2001). La transformation du Hg en MeHg s’effectue grâce à l’action de bactéries sulfatoréductrices existant notamment sur la végétation aquatique (Jensen, Jornelov, 1969). La contamination des populations ne se fait pas par l’ingestion d’eau transportant du MeHg mais par consommation de poisson lui-même contaminé. En effet, le MeHg est biomagnifiable et bio-accumulable, apparaissant ainsi en concentrations croissantes tout au long de la chaîne trophique (Carmouze et al., 2001). Les poissons carnivores et piscivores situés en bout de chaîne concentrent donc les teneurs maximales de MeHg. Une consommation régulière, même modérée, de ces poissons présente ainsi un risque pour la santé humaine. En effet, l’impact en termes de m...

Health geography in France has -for nearly a century now -contributed to the understanding of health inequalities in countries of the northern and southern hemispheres. Even though its conceptual structure and methodology were for the most part initially developed through the study of vector-borne diseases, health geography has subsequently extended its scope through focussing on emerging health issues. This presentation will demonstrate how a highly fruitful method, widely applied to the understanding of health inequalities of communicable diseases, has allowed us to engage in a novel health issue, barely explored by geographers so far : the study of the exposure in the long term to low level contamination by pollutants. With the example of mercury contamination disparities in the Bolivian Amazon region, this presentation confirms the efficiency of this method, which constitutes a starting point for the development of future research on the subject of environmental pollution.

Artificial Intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, are changing the way researchers can process and analyse qualitative data. In this paper the use of ChatGPT is tested for Quantitative Content Analysis (QCA) by applying it to interview material about digital construction technology and sustainability. Two versions of the same data are compared: (1) complete transcripts of five interviews with professors, and (2) a shorter summarized version of the same interviews (the summaries were prepared by researcher). With the same workflow, ChatGPT did several steps: preprocessing (splitting the text into words, removing very common small words, and reducing words to their basic form), keyword extraction, thematic coding with five categories, and also a simple sentiment analysis. The aims were: (a) to see if ChatGPT can find the main themes in a reliable way, (b) to compare results from full transcripts versus summaries, and (c) to understand what practical advantages and problems appear when undertaking ChatGPT in a real research situation. The results were similar at the general level: Digital Technology and Sustainability were the strongest themes in both datasets, followed by Education/Training, Benefits, and Barriers. The sentiment analysis gave slightly positive values in both (+0.18 for transcripts, +0.16 for summaries). At a more detailed level, the transcripts included more technical words (for example “embodied carbon”, “Life cycle Analysis (LCA)” and standards), while the summaries included more general terms, which made the counts higher. Some practical issues also influenced the work: undertaking a free ChatGPT account caused interruptions, sometimes the tool changed its output style, and it was difficult to export charts or tables, these problems reduced reproducibility. In conclusion, ChatGPT can be useful for first steps in QCA and for saving time in early coding, but it is not enough for final or very detailed analysis. For better use, the following suggestions are provided: a combination of AI with human checking, making domain-specific dictionaries, undertaking clear and repeated prompts, and working with more stable or professional access. This study shows both the opportunities and the real problems when ChatGPT is used for content analysis in construction and sustainability research.