DOI: 10.6084/m9.figshare.28946087.v2
title: Ambient Air Pollutant Dynamics (2015–2019) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event

description: <b>TL;DR</b><br>Three files (Excel, PDF, PNG) present a 14-day moving-average time series for ten air-pollutant metrics (Jan 2015–Jun 2019), a summary table of four winter “burdens,” and a PDF of multiple plots. The focus is on the extreme Dec 2016–Mar 2017 episode and its link to a documented spike in urinary U/As (https://doi.org/10.6084/m9.figshare.27435639.v5).between March and May 2017.<br><b>Abstract</b><br>Continuous ambient air quality monitoring in Zurich-Kaserne and all other Swiss Plateau NABEL measuring sites between January 2015 and mid-2019 revealed an exceptionally severe and compositionally distinct pollution episode during the winter of 2016–2017. This dataset provides a comprehensive analysis of key atmospheric pollutants, including PM₁₀, PM₂.₅, PM₁₀₋₂.₅ (coarse fraction), elemental carbon (EC), CO, NO₂, SO₂, and derived diagnostic ratios (EC/PM₂.₅, NO₂/SO₂, PM₂.₅/PM₁₀₋₂.₅). The winter of 2016–17 is of particular public health interest, as it immediately preceded a documented, significant rise in urinary uranium (U) and arsenic (As) concentrations in both a local patient cohort and an overregional cohort referred from Düsseldorf¹. This repository offers smoothed time-series data for detailed trend analysis, a qualitative "winter-burden" table comparing the 2016-17 event with three other Dec–Mar intervals, and visual plots for each metric. The findings support a hypothesis linking the observed U/As exposure to the long-range atmospheric transport of U/As-bearing industrial emissions, likely coal fly ash, during this unique pollution period.<br><b>Background & Rationale</b><br>The unusual spike in patient urinary uranium and arsenic in early 2017¹ prompted an investigation into potential environmental exposure pathways. Ambient air pollution, particularly fine particulate matter capable of long-range transport, was considered a primary candidate. This dataset was compiled and analyzed to:<br>Characterize the air quality profile during the winter (Dec 2016–Mar 2017) immediately preceding and overlapping with the initial phase of the U/As exposure event.Compare this "critical winter" to analogous periods in other years to identify unique atmospheric conditions or pollutant signatures.Evaluate whether the observed air pollutant characteristics were consistent with sources known to emit uranium and arsenic.<b>Methods & Data Sources</b><b>Primary Measurements:</b> Daily mean concentrations of PM₁₀, PM₂.₅, PM₁₀₋₂.₅ (calculated as PM₁₀ - PM₂.₅), Elemental Carbon (EC), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), and Sulfur Dioxide (SO₂) were obtained from the Bundesamt für Umwelt (BAFU) Ambient Air Quality Database for [Specify Monitoring Station, e.g., Zurich-Kaserne].<b>Derived Diagnostic Ratios:</b> To further characterize emission sources and atmospheric processes, the following ratios were calculated from the daily mean data:EC/PM₂.₅: Indicates the fraction of elemental carbon (soot) within the fine particulate mass, often used as a tracer for primary combustion sources.NO₂/SO₂: Can help differentiate between combustion source types (e.g., traffic vs. certain industrial emissions or coal types).PM₂.₅/PM₁₀₋₂.₅: Highlights the relative contribution of fine versus coarse particles, with higher ratios suggesting dominance of combustion aerosols or secondary particle formation over mechanically generated dust.<b>Data Smoothing:</b> To discern underlying trends and reduce the impact of daily variability, 14-day moving averages were calculated for all pollutants and most ratios. Due to its potentially higher short-term fluctuations, a 4-week (28-day) moving average was applied to the PM₂.₅/PM₁₀₋₂.₅ ratio.<b>Comparative Winter Periods:</b> Four distinct winter periods (December 1st to March 31st) were defined for comparative analysis of cumulative pollutant "burden":Winter 1: Dec 2015 – Mar 2016Winter 2: Dec 2016 – Mar 2017 (identified as the critical "U/As precursor" period)Winter 3: Dec 2017 – Mar 2018Winter 4: Dec 2018 – Mar 2019<b>Missing-Data Handling & Imputation:</b> The dataset's completeness, particularly for PM₂.₅, was enhanced using imputation methods detailed in an associated dataset (https://doi.org/10.6084/m9.figshare.28830278.v14), primarily employing gradient-boosted regression. This ensures robust time-series analysis.<b>Data Source:</b> All primary air quality data originates from the Bundesamt für Umwelt (BAFU) Ambient Air Quality Database, Switzerland.<b>Files Included (3 total)</b><b>Compare_AP_2015–2019.xlsx</b> (Microsoft Excel Spreadsheet)<b>Contents:</b> This file is the core data repository. It includes sheets for:Imputed_AP_Data_Zurich: Daily raw (or imputed where necessary) concentrations for primary pollutants and derived ratios.NO2_SO2_Zurich: Specific focus on NO2 and SO2 data.AP_Time_Graphs: The 14-day (or 4-week) smoothed time-series data used for generating the plots and for the qualitative burden assessment, covering January 2015 – June 2019.<b>Utility:</b> Enables users to perform their own statistical analyses, re-plot data, or examine daily values.<b>air_pollutants_timeseries_uploaded.pdf</b> (Multi-page PDF Document)<b>Contents:</b> A document presenting compiled individual time-series plots for each of the ten pollutants and ratios. Each panel displays the 14-day (or 4-week) moving average from January 2015 to June 2019, with vertical lines indicating January 1st of each year for easy temporal orientation.<b>Utility:</b> Provides a quick, comprehensive visual overview of the temporal trends for all analyzed metrics.<b>Table 1.png</b> (Image File)<b>Contents:</b> A high-resolution image of the "Qualitative Comparison of Estimated Winter Air Pollutant Burdens." This table summarizes the relative pollutant load (categorized from Low ↔ Very High) for each of the four defined winter periods across all ten metrics.<b>Utility:</b> Offers an at-a-glance comparison highlighting the unique characteristics of the Winter 2016–2017 period.<b>Key Findings & Discussion</b><br>The analysis reveals that the winter of December 2016–March 2017 was exceptionally polluted, with several distinguishing features:<br><b>Dominance of Fine Particulate Matter:</b> Between January and March 2017, both PM₂.₅ and PM₁₀ concentrations reached their highest levels of the entire 2015–2019 study period. The PM₂.₅/PM₁₀₋₂.₅ ratio was also notably high, confirming that fine, respirable aerosols overwhelmingly dominated the particulate mass during this event. This suggests sources like combustion or secondary aerosol formation rather than local coarse dust resuspension.<b>Peak in Combustion-Related Tracers:</b> Absolute concentrations of elemental carbon (EC), carbon monoxide (CO), and nitrogen dioxide (NO₂) all peaked during early 2017, representing the maxima observed over the 4.5-year study. Concurrently, the NO₂/SO₂ ratio soared to its highest levels, indicating a pollution mixture heavily influenced by NOx-rich combustion sources.<b>Nuance in EC Fraction:</b> Interestingly, while the mass of EC was at its peak, the EC/PM₂.₅ ratio (the fraction of soot in fine particles) during winter 2016-17 was slightly lower than the peak observed in winter 2015–16. This crucial finding suggests that the extreme PM₂.₅ levels in 2017 were composed of a very large mass of non-EC fine particulate matter in addition to significant soot, consistent with aged industrial plumes like fly ash, which contain mineral components alongside some carbon.<b>Moderate SO₂ and Coarse PM Levels:</b> During the Dec 2016–Mar 2017 episode, local SO₂ levels remained only moderate—well below the winter 2015–16 peak—and the coarse‐particle fraction (PM₁₀–₂.₅) showed no exceptional surge. Because SO₂ is oxidized and removed within 1–3 days on regional scales, modest local SO₂ does not rule out a combustion source; it merely implies any SO₂‐emitting facility lay far enough away that its SO₂ decayed before reaching Zürich. In contrast, U/As‐laden PM₂.₅ particles—with residence times of several days—could survive true long‐range transport, whereas mechanically generated coarse dust, which deposits quickly, would not. These patterns therefore point toward a distant fine‐particle (fly‐ash) source rather than local SO₂‐rich or coarse‐dust emissions.<b>Link to Uranium/Arsenic Exposure Event:</b><br>The collective atmospheric profile of the Dec 2016–Mar 2017 period—characterized by extremely high fine particulate matter with a significant non-EC component, elevated NOx, and tracers of widespread combustion—strongly aligns with conditions conducive to the inhalation of aged, long-range transported industrial aerosols. Specifically, these characteristics are consistent with the atmospheric fingerprint of U/As-bearing coal fly ash. The timing of this exceptional pollution episode immediately preceding and overlapping with the documented increase in patient urinary U/As levels¹ provides compelling circumstantial evidence for an atmospheric exposure pathway.<br><b>Potential Uses of this Dataset:</b><br>This dataset can be valuable for:<br>Researchers investigating air pollution trends and extreme events in [Specify City/Region] or Central Europe.Epidemiological studies exploring links between specific air quality profiles and health outcomes.Atmospheric scientists working on source apportionment or validating transport models.Comparative analyses with air quality data from other urban or regional locations.<b>References</b>Carmine, T. C. (2024). The Uranium Episode (March–May 2017) in Temporal Context: Associations with CEMET Uranium, Aluminum, and Local PM₁₀ Exposure (2016–2019). Figshare. Dataset. https://doi.org/10.6084/m9.figshare.27435639.v5Carmine, Thomas Clemens (2025). Reconstructed Zurich Air Pollution Data (2015–2019) with Lag Structure for Urinary Metal Toxicokinetic Analysis. figshare. Dataset. https://doi.org/10.6084/m9.figshare.28830278.v14<b>Citation</b><br>Please cite this repository using its assigned DOI, and acknowledge the specific DOIs for associated datasets (References 1 and 2) when reusing data or figures.<br>