OPEN Large global variations in measured airborne metal concentrations driven...

McNeill, J., G. Snider, C. L. Weagle, B. Walsh, P. Bissonnette, E. Stone, I. Abboud, C. Akoshile, N. X. Anh, R. Balasubramanian, J. R. Brook, C. Coburn, A. Cohen, J. Dong, G. Gagnon, R. M. Garland, K. He, B. Holben, R. Kahn, J. S. Kim, N. Lagrosas, P. Lestari, Y. Liu, F. Jeba, K. S. Joy, J. V. Martins, A. Misra, L. K. Norford, E. J. Quel, A. Salam, B. Schichtel, S. N. Tripathi, C. Wang, Q. Zhang, M. Brauer, M. D. Gibson, Y. Rudich, and R. V. Martin (2022), OPEN Large global variations in measured airborne metal concentrations driven by anthropogenic sources, Nature, doi:10.1038/s41598-020-78789-y.

Globally consistent measurements of airborne metal concentrations in fine particulate matter (PM2.5) are important for understanding potential health impacts, prioritizing air pollution mitigation strategies, and enabling global chemical transport model development. PM2.5 filter samples (N ~ 800 from 19 locations) collected from a globally distributed surface particulate matter sampling network (SPARTAN) between January 2013 and April 2019 were analyzed for particulate mass and trace

metals content. Metal concentrations exhibited pronounced spatial variation, primarily driven by

anthropogenic activities. PM2.5 levels of lead, arsenic, chromium, and zinc were significantly enriched

at some locations by factors of 100–3000 compared to crustal concentrations. Levels of metals in

PM2.5 and PM10 exceeded health guidelines at multiple sites. For example, Dhaka and Kanpur sites 

exceeded the US National Ambient Air 3-month Quality Standard for lead (150 ng m−3). Kanpur,

Hanoi, Beijing and Dhaka sites had annual mean arsenic concentrations that approached or exceeded

the World Health Organization’s risk level for arsenic (6.6 ng m−3). The high concentrations of several

potentially harmful metals in densely populated cites worldwide motivates expanded measurements

and analyses.

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Research Program: 
Applied Sciences Program (ASP)
Atmospheric Composition Modeling and Analysis Program (ACMAP)