Inter-Site Correlation of Ambient Air Pollutants Across Swiss Plateau NABEL Stations (2015–2019)

This dataset presents inter-site Spearman correlation matrices for six ambient air pollutants—PM10, PM2.5, elemental carbon (EC), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO)—recorded by the Swiss NABEL monitoring network between 2015 and 2019.1The analysis focuses on NABEL stations located in the Swiss Plateau, excluding high-altitude and southern Alpine sites due to differing meteorological profiles and lack of relevance to the study population. Strong inter-site correlations across the Plateau support the use of regional data for exposure modeling.2The Zurich–Kaserne monitoring station, located approximately 27 km from the study clinic in Pfäffikon SZ, was used as the primary reference site due to its continuous data coverage and proximity to the majority of patients. Details on PM2.5 data gaps, gradient-boosted regression-based imputation, and the handling of missing data in other pollutants are provided in a related dataset (https://doi.org/10.6084/m9.figshare.28830278.v14)3.Supplementary Table B ("Pollutant Stats" sheet) summarizes station counts and mean inter-site Spearman ρ per pollutant. Particulate pollutants (PM10 and PM2.5) showed particularly high inter-station coherence, with mean ρ values of 0.89 and 0.92, indicating strong regional consistency. Compared to particulate pollutants, combustion-associated pollutants showed slightly lower yet still considerable inter-site correlations: EC (ρ = 0.80) > CO (ρ = 0.77) > NO2 (ρ = 0.67) > SO2 (ρ = 0.65).The subsequent sheets provide detailed station-by-station correlation matrices, including the number of paired observations underlying each correlation, the Spearman ρ values, and the corresponding significance p-values. Separate sheets contain the complete raw time series data for each pollutant as provided by the NABEL network.Two supplementary figures accompany this dataset: Supplementary Figure 1 maps the included NABEL stations across the Swiss Plateau, and Supplementary Figure 2 shows the geographic distribution of patient residences by postcode (2015–2017), illustrating the cohort’s concentration in the Zurich Lake region and proximity to both the clinic and Zurich–Kaserne. Although the time window slightly precedes the air pollution data, the residential pattern remained stable throughout the study period, as corroborated by additional maps depicting patient domicile distributions across different timeframes (available at https://doi.org/10.6084/m9.figshare.27573024.v1)4.The present dataset supports environmental exposure modeling in studies linking regional air pollution to urinary metal excretion in outpatient populations.References:1) Federal Office for the Environment (FOEN). Data query NABEL [Internet]. Bern: Federal Office for the Environment; [date unknown]. [cited 2025 May 8]. Available from: https://www.bafu.admin.ch/bafu/de/home/themen/luft/zustand/daten/datenabfrage-nabel.html2) Gehrig R, Buchmann B. Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long‐term Swiss monitoring data. Atmos Environ. 2003;37(19):2571–2580. doi:10.1016/S1352-2310(03)00221-8.3) Carmine TC. Inter-Site Correlation of Ambient Air Pollutants Across Swiss Plateau NABEL Stations (2015–2019) [Dataset]. figshare; 2025. doi:10.6084/m9.figshare.28843931.v9.4) Carmine TC. Regional uranium content of Swiss drinking water and domiciles of Swiss CEMET patients pre-, during, and post-uranium episode [Figure]. figshare; 2024. doi:10.6084/m9.figshare.27573024.v1.