Large Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales
McDuffie, E., Martin, R., Spadaro, J., Burnett, R., Smith, S., O'Rourke, P., Hammer, M., van Donkelaar, H., Bindle, L., Shah, V., Jaegle, L., Luo, G., Yu, F., Adeniran, J., Lin, J., Brauer, M.: Nature Communications, 12, 3594, 2021 doi: https://doi.org/10.1038/s41467-021-23853-y [PDF File]
We provide a contemporary and comprehensive evaluation of sector and fuel specific contributions to the PM2.5 disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95 % confidence interval: (0.74-1.34) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion. Coal contributes to over half of these deaths. Regions with large anthropogenic contributions typically had the highest attributable deaths, which suggests large potential health benefits by replacing traditional energy sources.
Large global variation in measured airborne metal concentrations driven by anthropogenic sources
McNeill, J., Snider, G., Weagle, C. L., Walsh, B., Bissonnette, P., Stone, E., Abboud, I., Akoshile, C., Xuan Anh, N., Balasubramanian, R., Brook, J. R., Coburn, C., Cohen, A., Dong, J., Gagnon, G., Marland, R. M., He, K., Holben, B. N., Kahn, R., Sung Kim, J., Lagrosas, N., Lestari, P., Liu, Y., Jeba, F., Shaifullah Joy, K., Martins, J. V., Misra, A., Norford, L. K., Quel, E. J., Salam, A., Schichtel, B., Tripathi, S. N., Wang, C., Zhang, Q., Brauer, M., Gibson, M. D., Rudich, Y., Martin, R. V.: Scientific Reports, 10, 21817, 2020 doi: https://doi.org/10.1038/s41598-020-78789-y [PDF File]
We examine ambient PM2.5 collected on filters through the globally distributed surface particulate matter sampling network (SPARTAN), looking specifically at particulate mass and trace metal content. Metal concentrations varied across the 19 locations sampled, 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, and levels of metals in PM2.5 and PM10 exceeded health guidelines at multiple sites. The high concentrations of several potentially harmful metals in densely populated cites worldwide motivates expanded measurements and analyses.
Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model
Weagle, C. L., Snider, G., Li, C., van Donkelaar, A., Philip, S., Bissonnette, P., Burke, J., Jackson, J., Latimer, R., Stone, E., Abboud, I., Akoshile, C., Nguyen, X. A., Brooke, J. R., Cohen, A., Dong, J., Gibson, M. D., Griffith, D/. He, B., Holben. B. N., Kahn, R., Keller, C. A>, Kim, J. S., Lagrosas, N., Lestari, P., Khian, Y. L., Liu, Y., Marais, E. A., Martins, J. V., Misra, A., Muliane, E., Pratiwi, R., Quel, E. J., Salam, A., Segev, L., Tripathi, S. N., Wang, C., Zhang, Q., Brauer, M., Rudich, Y., Martin, R. V.: Environmental Science & Technology, 52 (20) 11670, 2018 https://doi.org/10.1021/acs.est.8b01658 [PDF File]
A global chemical transport model (GEOS-Chem) constrained by satellite-based estimates of PM2.5 is used to interpret the globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Interpretation of these measurements yields insight into the dominant sources contributing to PM2.5 at each site. Global population-weighted PM2.5 concentrations are driven by the residential energy sector with industrial activity tailing close behind. Insight is gained into the sources and process that influence the global spatial variation in PM2.5.
Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models
Philip, S., Martin, R. V., Snider, G., Weagle, C. L., van Donkelaar, A., Brauer, M., Henze, D. K., Klimont, Z., Venkataraman, C., Guttikunda, S. K. and Zhang, Q.: Environ. Res. Lett. 12, 044018, 2017 doi:https://doi.org/10.1088/1748-9326/aa65a4 [PDF File].
Global measurements of the elemental composition of fine particulate matter (PM2.5) across several urban locations by the Surface PARTiculate mAtter Network (SPARTAN) reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, especially over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We estimate a 2-16 μg/m3 increase in PM2.5 concentrations across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with SPARTAN measurements at 13 globally dispersed locations and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg/m3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models.
Figure: Annual mean concentration of PM2.5 total dust (top panel), natural mineral dust (middle panel), and anthropogenic fugitive dust (bottom panel) simulated with the GEOS-Chem model. Coloured concentric circles in the bottom panel denote SPARTAN-measured campaign-mean PM2.5 dust concentration (inner circle) and the coincident simulated value (outer circle).
Variation in Global Chemical Composition of PM2.5: Emerging Results from SPARTAN
Snider, G., Weagle, C. L., Murdymootoo, K. K., Ring, A., Ritchie, Y., Walsh, A., Akoshile, C., Anh, N. X., Brook, J., Qonitan, F. D., Dong, J., Griffith, D., He, K., Holben, B. N., Kahn, R., Lagrosas, N., Lestari, P., Ma, Z., Misra, A., Quel, E. J., Salam, A., Schichtel, B., Segev, L., Tripathi, S. N., Wang, C., Yu, C., Zhang, Q., Zhang, Y., Brauer, M., Cohen, A., Gibson, M. D., Liu, Y., Martins, J. V., Rudich, Y., and Martin, R. V.: Atmos. Chem. Phys. 16, 9629-9653, 2016 doi:10.5194/acp-16-9629-2016, .[PDF File].
We examine the chemical composition of fine particulate matter (PM2.5) collected on filters at traditionally under-sampled, globally dispersed urban locations. Several PM2.5 chemical components (e.g. ammonium sulfate, ammonium nitrate, and black carbon) vary by more than an order of magnitude between sites while water uptake values vary by a factor of two. We also observe enhanced anthropogenic dust fractions are apparent from high Zn:Al ratios.
SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications
Snider, G., Weagle, C. L., Martin, R. V., van Donkelaar, A., Conrad, K., Cunningham, D., Gordon, C., Zwicker, M., Akoshile, C., Artaxo, P.,Anh, N. X., Brook, J., Dong, J., Garland, R. M., Greenwald, R., Griffith, D., He, K., Holben, B. N., Kahn, R., Koren, I., Lagrosas, N.,Lestari, P., Ma, Z., Vanderlei Martins, J., Quel, E. J., Rudich, Y., Salam, A., Tripathi, S. N., Yu, C., Zhang, Q., Zhang, Y., Brauer, M.,Cohen, A., Gibson, M. D., and Liu, Y.: Atmos. Meas. Tech., 8, 505-521, 2015 , doi:10.5194/amt-8-505-2015 [Full Text (PDF)].
We have initiated a global network of ground-level monitoring stations to measure concentrations of fine aerosols in urban environments. Our findings include major ions species, total mass, and total scatter at three wavelengths. Results will be used to further evaluate and enhance satellite remote sensing estimate.
SPARTAN White Paper
Satellite remote sensing offers a promising approach to provide PM2.5 exposure information at regional-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and columnar aerosol optical depth (AOD), sampled at specific overpass times during cloud-free conditions. We are developing a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health effects research and risk assessment.
The complete original network white paper is available. [PDF].