Time-Stamped Air Quality & Weather Data (Paris, 2023)

Dataset: Time-Stamped Air Quality & Weather Data (Paris, 2023) — Reproducible Processing Logs Summary This record provides the full, time-stamped dataset and documentation for an urban air-quality forecasting task in Paris, France (calendar year 2023). The archive includes both the raw measurements and the processed version used for modeling, plus a Data Dictionary and a Processing Log to enable complete transparency and reproducibility. Study area & coverage · Location: Paris, Île-de-France, France · Temporal coverage: 2023-01-01 00:00:00 – 2023-12-31 23:00:00 (local time) · Time zone: CET/CEST (UTC+1 in winter, UTC+2 in summer) · Frequency: Hourly observations (where available) · Primary variables (units): - Pollutants: NO₂ (µg/m³), PM₂.₅ (µg/m³), PM₁₀ (µg/m³), CO (mg/m³ or µg/m³ — see dictionary) - Meteorology: Temperature (°C), Relative Humidity (%), Wind Speed (m/s), [others if present] - Key field: timestamp (ISO 8601: YYYY-MM-DD HH:mm:ss) What’s included · data/Raw.csv — Raw time-series with a unified timestamp column and all measured variables. · data/Processed.csv — Cleaned/chronologically sorted dataset used for modeling (original units retained unless noted). · docs/Data_Dictionary.docx — Variable names, definitions, units, and sources. · docs/Processing_ Tracability . xlsx— Step-by-step preprocessing record (missing-data strategy, outlier policy, scaling, and temporal train/test split). Methodological notes The dataset is organized for time-series modeling. All preprocessing decisions are documented in docs/Processing_Log.docx. To prevent information leakage, feature selection and normalization are to be performed on the training partition only when reproducing the models. A one-click MATLAB pipeline (code/00_run_all.m) is available in the companion repository (see Related resources) to reproduce the splits and exports. Intended use This dataset supports research and teaching in environmental data science, air-quality forecasting, time-series modeling, and reproducible ML. Users can: · Recreate the chronological train/test setup for 2023. · Benchmark alternative models and feature-engineering strategies. · Explore pollutant–meteorology relationships in Paris during 2023. Provenance & quality control Data were compiled for the study from Paris monitoring sources for pollutants and standard meteorological observations. Basic QA steps (timestamp harmonization, duplicate checks, unit checks) are documented in the Processing Log. Please consult docs/Data_Dictionary.docx for variable-level details and known caveats. Licensing & reuse The dataset is released under CC BY 4.0. Please cite this record and the associated article (if applicable) when reusing the data. Related resources NOAA, And OpenAQ How to cite Asklany SA, et al. (2025) Forecasting urban air quality in Paris using ensemble machine learning: A scalable framework for environmental management. PLOS ONE 20(11): e0336897. https://doi.org/10.1371/journal.pone.0336897 Contact [ Somia Asklany ], [ Northern Boarder University ( [e-mail]], [— ORCID: [https://orcid.org/0000-0002-1590-9845