Simulation of airborne trace metals in fine particulate matter over North America

Abstract

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM 2.5 ), PM 2.5 -related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM 2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Association of Sulfur, Transition Metals, and the Oxidative Potential of Outdoor PM2.5 with Acute Cardiovascular Events: A Case-Crossover Study of Canadian Adults

“…Although models are currently available to predict large-scale variations in components using remote sensing/chemical transport models ( van Donkelaar et al. 2019 ; Xu et al. 2019 ), large-scale models for oxidative potential are only beginning to emerge ( Daellenbach et al.…”

“…More broadly, widespread integration of PM 2:5 composition/OP data into the risk assessment/risk management process will require new exposure models capable of estimating these parameters over broad spatiotemporal scales. Although models are currently available to predict large-scale variations in PM 2:5 components using remote sensing/chemical transport models (van Donkelaar et al 2019;Xu et al 2019), large-scale models for oxidative potential are only beginning to emerge (Daellenbach et al 2020). Nevertheless, an increasing number of large-scale exposure models for OP are likely to be developed in the coming years as ground-level data support continues to increase over broad geographic areas.…”

Modeling Transition Metals in East Asia and Japan and Its Emission Sources

“…There have been several studies for the development and evaluation of emission inventories of metals (Table 1). Reff et al (2009) developed an anthropogenic emission inventory of dozens of hazardous trace elements in PM 2.5 in the United States, which was evaluated by transport models and observation data across the United States and Canada (Appel et al, 2013; Xu et al, 2019). Dore et al (2014) evaluated the toxic heavy metals in total suspended particle (TSP) emission in the United Kingdom with a transport model.…”

Global impact of atmospheric arsenic on health risk: 2005 to 2015

“…Model Configuration. A three-dimensional atmospheric chemical transport model GEOS-Chem (v11-02; http://acmg.seas.harvard.edu/geos/) was used to simulate the distribution and evolution of global atmospheric arsenic, which has been used in previous arsenic-related studies (29,45). The model is driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 meteorological data with a horizontal resolution of 4°latitude by 5°longitude and 72 vertical layers.…”