GlobalHighNO₂: Global Daily 1 km Ground-Level Nitrogen Dioxide Data (2018–Present)
by Jing Wei / University of Maryland, College Park
Available on 1 platform
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Description
2018 to present daily, monthly, and yearly ground-level nitrogen dioxide (NO₂) concentrations over global land areas at a 1 km resolution. The dataset is generated by Jing Wei at the University of Maryland, College Park, using AI to fuse ground measurements, satellite data, reanalysis, and model simulations. It reports a cross-validation R² of 0.92 and an RMSE of 4.76 µg m⁻³ on a daily basis.
Use Cases
Modeling long-term NO₂ exposure for epidemiological studies based on the daily, gapless time series.
Analyzing spatial patterns of air pollution at urban scales based on the 1 km resolution global land coverage.
Validating atmospheric chemistry models based on the high-quality, multi-source fused data.
Assessing policy impacts on air quality trends based on the continuous data from 2018 onward.
Strengths
High spatial resolution of 1 km over all global land areas.
Demonstrated high predictive quality with a daily CV-R² of 0.92 and RMSE of 4.76 µg m⁻³.
Seamless, gapless spatial coverage (100%) derived from multiple big data sources.
Provides data at daily, monthly, and yearly temporal aggregations.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count and file size are unknown, which may limit suitability assessment.
Data is limited to land areas; coverage over oceans is not included.
Provenance
Source
University of Maryland, College Park
Collection Method
Generated using artificial intelligence to fuse ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations.
Time Range
2018 to present
Freshness
Updated to the present, but the specific last update date is unknown.
Geography
Global land areas
License is listed as Open Access (green); specific terms should be verified at the source link.