Abstract Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is a multi-million human silent killer worldwide and contains numerous trace elements (TEs). Understanding TEs relative toxicity is largely limited by lack of data. Here, we used ensembles of machine learning models to generate ~163 billion predictions estimating annual means of ten TEs, namely bromine, calcium, copper, iron, potassium, nickel, lead, silicon, vanadium, and zinc across 3,535 contiguous US urban areas at 50-m spatial resolution and at 1-km in non-urban areas from 2000-2019. Our results highlight substantial intra-urban and inter-urban variations, shrinkages, stagnations, or expansions of hotspots, and trends across 20 years. These data open avenues for future research from epidemiological studies to environmental justice analyses and more.
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