Reconstructed Zurich Air Pollution Data (2015–2019) with Lag Structure for Urinary Metal Toxicokinetic Analysis

This dataset contains reconstructed daily air pollution data from the Zurich NABEL monitoring station (“Kaserne”) between 2015 and 2019, including NO₂, SO₂, CO, PM₁₀, PM₂.₅, and derived PM₁₀–₂.₅ values. A few missing values for all pollutants except PM₂.₅ were imputed using linear interpolation, while missing PM₂.₅ values were reconstructed using an XGBoost-based machine learning model trained on the remaining pollutants. All imputed values are flagged accordingly.To enable toxicokinetic modeling of airborne metal exposure, the dataset includes:14-day average concentrations for each pollutant preceding the urine test27 lagged weekly averages (Lag –1 to –26), aligned with the dates of Chelate-Evoked Metal Excretion Testing (CEMET) conducted at a clinical center in Pfäffikon SZ, SwitzerlandThe lag structure was designed to explore the delay between environmental exposure and urinary excretion of heavy metals. These lag times are still largely unknown, which may explain why many environmental impact studies fail to detect associations between pollutant exposure and metal biomarkers. This dataset provides a novel foundation for investigating these temporal dynamics.These lags may reflect a combination of biological and environmental delay mechanisms. For example, the respiratory uptake and translocation of fine dust particles (e.g., PM₂.₅) can involve delayed pulmonary clearance and subsequent redistribution, leading to gradual urinary excretion of associated metals. In parallel, pollutants may also leach into the groundwater or accumulate in the food chain, resulting in delayed indirect exposure and biomarker response. The inclusion of a time-resolved lag structure in this dataset enables the exploration of such toxicokinetic patterns across multiple temporal scales.In addition to the structured numerical data, the dataset includes three supplementary Excel files containing image-based Spearman correlation heatmaps. These visualize associations between Ca-EDTA–DMPS–provoked urinary metal concentrations and lagged air pollutant levels:Organized into <b>full-year</b>, <b>winter (November–April)</b>, and <b>summer (May–October)</b> subsetsEach sheet within these files corresponds to one air pollutant (e.g., PM₁₀, NO₂), with correlations shown across 27 weekly lag intervalsThe heatmaps highlight temporal correlation patterns and are intended as visual tools to support exploratory toxicokinetic interpretation and hypothesis generation