David Kocman

Globally, foliar uptake of atmospheric mercury (Hg) is a key deposition pathway for terrestrial surfaces. However, diverse sampling and pre-treatment methods across studies can influence the comparability of foliar Hg concentrations and isotopic measurements, leading to biases that impact our understanding of Hg inter-compartmental exchanges at regional and global scale. In this study, we tested the effect of different sample drying and rinsing methods on foliage samples collected from three distinct locations on the tree crown of a European hornbeam (Carpinus Betulus L.). Samples were taken from Ljubljana, an urban area, and Idrija, a historically Hg-contaminated mining site, representing contrasting environments. Vertical crown stratification significantly influenced Hg concentrations, with inner canopy foliage in Ljubljana containing 46–50% higher Hg than outer and upper positions due to reduced solar exposure and enhanced throughfall deposition. Drying methods did not alter Hg concentrations (p > 0.05), validating cross-study comparability for low-Hg environments. Conversely, rinsing removed 24–60% of Hg in Idrija samples (p < 0.01), attributable to particulate-bound Hg (PBM) enriched in heavier isotopes (δ^{20 2}Hg: -1.9 ± 1.5‰ unrinsed vs. -3.2 ± 1.0‰ rinsed). We recommend that rinsing be applied when studying internal plant Hg cycling, but omitted when total foliage-associated Hg, including surface-bound fractions, is of interest. Future studies should investigate inter-species differences, seasonal variability in foliar Hg dynamics, and the effects of drying methods on Hg isotopic fractionation to improve methodological consistency and understanding of Hg cycling.

Background: The European Water Framework Directive foresees the establishment of emission inventories for micropollutants (MP) to facilitate an evidence-based development of mitigation measures. Regionalized pathway analysis constitutes a moderately data-intensive approach to quantify the contribution of different pathways to the total pollution of surface waters. So far, only few European member states have created an inventory that includes diffuse pathways. The fundamental basis to enable it is an accessible, well-structured and harmonized database with data on the concentration of MPs in multiple compartments, such as soils, groundwater, atmospheric deposition and urban systems. Combined with the water and suspended substance balance in river basins, such data enables the estimation of emission loads via specific pathways. In the Danube River Basin, but in general in Europe, a public data management platform with such scope and criteria is still lacking. Results: We collected and harmonized MP measurements across multiple compartments and countries together with key metadata, harmonized and combined them into a new database. The resulting tool, available for download, facilitates the assessment of current data availability, in terms of quantity and quality. For example, while the majority of available data stems from groundwater and surface water, other highly relevant compartments are scarcely represented. By examining differences in MP concentration level across compartments, the database can lead to understand the relevance of specific emission pathways and thus to prioritize data-retrieval and calculation efforts in modelling applications. Selected examples show how to exploit the metadata associated to the measurements to extrapolate the results to regions not covered by specific monitoring programmes. For example, PFAS concentrations in treated wastewater show significant dependence on the design capacity of the treatment plant. Conclusions: This study showcases how such database can support the setup of emission inventories, guide data providers and national authorities in prioritizing the allocation of resources for new surveys and in optimizing their national data collection and management systems. The process tested showed a great need for enhanced data literacy across countries and institutions to increase data availability and quality to secure the exploitation of the full information potential generated via monitoring programmes.

Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML’s potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.

Scientists and practitioners implementing citizen science projects, in which the involvement of citizens is key, often need ideas and tools that help in reaching citizens, engaging them in the project and maximizing their contributions. In this paper, we describe the creation of an open toolkit, a web-based portal citizensciencetoolkit.eu¹ designed in the framework of the CitieS-Health project for the development and promotion of citizen science (CS) projects. The main focus is on projects linking urban environmental exposures and health, but it has applicability to other settings. The toolkit comprises the steps and phases during the implementation of CS activities in environmental epidemiology. A total of 28 tools are currently included, of which most were co-designed and tested within CitiesS-Health, as well as some resulting from external projects. For each tool, in addition to the details of its purpose and the specific challenges it poses, downloadable support content is available. To help facilitate inspiration and the adoption of tools provided, an illustrative step-by-step demonstration and description of its use in a CS project in a real-life setting is included, focusing on recommendations derived from the user experience. The portal is designed as a live inventory of tools, enabling interested CS practitioners not only to replicate, but also to continuously update and enrich it is content. To this end, the long-term objective of the portal is to act as a hub of resources that would enable the active inclusion of citizens in all the phases of the participatory research projects.