Skip to main content
Log in

Global-scale atmospheric modeling of aerosols to assess metal source-receptor relationships for life cycle assessment

  • LCIA OF IMPACTS ON HUMAN HEALTH AND ECOSYSTEMS
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

Metals have often been identified as the main contributors to (eco)toxicological impacts in life cycle assessment (LCA) studies. Indeed, environmental fate models are generally unsuitable for these substances as they were developed for organics. Recent work has focused on improving these models by accounting for biogeochemical conditions (e.g., pH, redox potential, organic matter, etc.). These conditions often dictate metal bioavailability and (eco)toxicity. However, biogeochemical conditions cannot be integrated into an LCA framework due to a lack of high-resolution spatially differentiated factors describing the metal atmospheric pathway. This paper aims to derive worldwide source-receptor relationships for aerosol particles to ascertain the atmospheric mechanisms (i.e., dispersion, transport, and deposition) of metals (i.e., copper, cadmium, lead, nickel, chromium, and zinc) at a relatively high resolution.

Methods

We compared black carbon, sulfate, and nitrate aerosols according to the framework developed by Roy et al. Atmos Environ 62:74–81, (2012), which requires the results of a year-long (2005) GEOS-Chem (a three-dimensional global-scale tropospheric model) simulation. These aerosols are used as proxies since metals may sorb with them for short- and long-range transport. Source-receptor matrices (SRMs), whose elements are fate factors, were calculated at a global 2° × 2.5° resolution.

Results and discussion

The atmospheric fate of metals, as described by black carbon and sulfate aerosols, is similar while the atmospheric fate of nitrate is significantly different: 70% of the black carbon or sulfate emissions deposits within in a radius of less than 2000 km while this percentage drops to 40% with nitrate. Nitrate aerosol also showed the lowest agreement with EMEP modeling. Nitrate should not be considered as the optimal proxy. A case can be made for either sulfate or black carbon as proxies for metal; the latter is recommended as it showed the best agreement with EMEP modeling at the source location and similar agreement in the mid/long-range transport.

Conclusions

The SRMs outlined in this paper facilitate further modeling developments without having to run the underlying tropospheric model, thus paving the way for the assessment of the regional life cycle inventories of a global economy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Barret K, Berge E (1996) Transboundary air pollution in Europe: estimated dispersion of acidifying agents and of near surface ozone. In: EMEP (Ed.), The Norwegian Meteorological Institute

  • Baron P (2016) Generation and behavior of airborne particles (aerosols). Center for disease control and prevention (CDC). [Online] http://www.cdc.gov/niosh/topics/aerosols/pdfs/Aerosol_101.pdf

  • Bartnnicky J, Modzelewski H, Szewczyk-Bartnicka H, Saltbones J, Berge E, Bott A (1993) An Eulerian model for atmospheric transport of heavy metals over Europe: model development and testing. Det Norske Meteorologiske Institutt Technical report 117. http://emep.int/publ/reports/1993/DMNI_1993_TR_117.pdf

  • Bey I, Jacob D, Yantosca R, Logan J, Field B, Fiore A, Li Q, Liu H, Mickley L, Schultz M (2001) Global modeling of tropospheric chemistry with assimilated meteorology: model description and evaluation. J Geophys Res 106:23073–23905

    Article  CAS  Google Scholar 

  • Benedictow A, Fagerli H, Gauss M, Jonson JE, Nyìri A, Simpson D, Tsyro S, Valdebenito A, Vealiyaveetil S, Wind P, Aas W, Hjelbrekke A-G, Mareckova K, Wankmüller R, Harmens H, Cooper D, Norris D, SChröder W, Pesch R, Holy M (2009) Transboundary acidification, eutrophication and ground level ozone in Europe in 2007. In: EMEP (Ed.), EMEP status report. Norwegian Meteorological Institute

  • Bradl HB (ed) (2005) Heavy metals in the environment: origin, interaction and remediation. University of applied sciences trier, Neubrucke, Germany

  • Clarke AD et al (2004) Size distributions and mixtures of dust and black carbon aerosol in Asian outflow: Physiochemistry and optical properties. J Geophys Res 109:D15S09

    Article  CAS  Google Scholar 

  • Csavina J, Field J, Taylor MP, Gao S, Landázuri A, Betterton EA, Sáez AE (2012) A review on the importance of metals and metalloids in atmospheric dust and aerosol from mining operations. Sci Total Environ 433C:58–73

    Article  CAS  Google Scholar 

  • De Lurdes Dinis M, Fiúza A (2011) Exposure assessment to heavy metals in the environment: measures to eliminate or reduce the exposure to critical receptors. In: Simeonov L, Kochubovski M, Simeonova B (eds) Environmental heavy metal pollution and effects on child mental development. NATO Science for Peace and Security Series C: Environmental Security, vol 1. Springer, Dordrecht

  • Emmons LK, Walters S, Hess PG, Lamarque J-F, Pfister G, Filimore D, Granier C, Guenther A, Kinnison D, Laepple T, Orlando J, Tie X, Wiedinmyer C, Baughcum SL, Kloster S (2010) Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4). Geosci Model Dev 3:43–67

    Article  Google Scholar 

  • Fang GC, Huang CS (2011) Atmospheric particulate and metallic elements (Zn, Ni, Cu, Cd and Pb) size distribution at three characteristic sampling sites. Environ Forensic 12(3):191–199

    Article  CAS  Google Scholar 

  • Finnveden G, Hauschild M, Ekvall T, Guinée J, Heijungs R, Hellweg S, Koehler A, Pennington D, Suh S (2009) Recent development in lifecycle assessment. J Environ Manag 91:1–21

    Article  Google Scholar 

  • Fu F, Wang Q (2011) Removal of heavy metal ions from wastewaters: a review. J Environ Manag 92(3):407–418

    Article  CAS  Google Scholar 

  • Gandhi N, Diamond M, van de Meet D, Huijbregts MAJ, Peijnenburg WJGM, Guinée J (2010) New method for calculating comparative toxicity potential of cationic metals in freshwater: application to copper, nickel, and zinc. Environ Sci Technol 44:5195–5201

    Article  CAS  Google Scholar 

  • Gandhi N, Huijbregts MAJ, van de Meeet D, Peijnenburg WJGM, Guinée J, Diamond ML (2011) Implications of geographic variability on comparative toxicity potentials of Cu, Ni and Zn in freshwaters of Canadian ecoregions. Chemosphere 8:268–277

    Article  CAS  Google Scholar 

  • Haye S, Slaveykova VI, Payet J (2007) Terrestrial ecotoxicity and effect factors of metals in life cycle assessment (LCA). Chemosphere 68:1489–1496

    Article  CAS  Google Scholar 

  • Hieu NT, Lee BK (2010) Characteristics of particulate matter and metals in the ambient air from a residential area in the largest industrial city in Korea. Atmos Res 98(2–4):526–537

    Article  CAS  Google Scholar 

  • Hoins U, Charlet L, Sticher H, (1993) Ligand effect on the adsorption of heavy metals: The sulfate — Cadmium — Goethite case. Water, Air, and Water pollution 68(1-2):241–255

  • Huijnen V, Williams J, van Weele M, van Noije T, Krol M, Dentener F, Segers A, Houweling S, Peters W, de Laat J, Boersma F, Bergamaschi P, van Velthoven P, Le Sager P, Eskes H, Alkemade F, Scheele R, Nédélec P, Patz H-W (2010) The global chemistry transport model TM5: description and evaluation of the tropospheric chemistry version 3.0. Geosci Model Dev 3:445–473

    Article  Google Scholar 

  • Jacob D, Liu H, Mari C, Yantosca R (2000) Harvard wet deposition scheme for GMI. Harvard University Atmospheric Chemistry Modeling Group

  • Kolodynska D, Krukoska J, Thomas P (2017) Comparison of sorption and desorption studies of heavy metal ions from biochar and commercial active carbon. Chem Eng J 307:353–363

    Article  CAS  Google Scholar 

  • Lamarque J-F, Emmons LK, Hess PG, Kinnison DE, Tilmes S, Heald CL, Holland EA, Lauritzen PH, Nen J, Orlando JJ, Rasch PJ, Tyndall GK (2012) CAM-Chem: description and evaluation of interactive atmospheric chemistry in the community earth system model. Geosci Model Dev 5:369–411

    Article  CAS  Google Scholar 

  • Neale RB et al (2012) Description of the NCAR community Atmosphere model (CAM 5.0). NCAR technical note. 289 pp. http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf

  • NOAA National Climatic Data Center (2005) State of the Climate: Global Analysis for Annual 2005. www.ncdc.noaa.gov/sotc/global/2005/13 (accessed 05.06.11)

  • Olsen S, Brasseur GP, Wuebbles DJ, Barret SRH, Dang H, Eastham SD, Jacobson MZ, Khodayari A, Selkirk H, Sokolov A, Unger N (2013) Comparison of model estimates of the effects of aviation emissions on atmospheric ozone and methane. Geophys Res Lett 40:6004–6009

    Article  CAS  Google Scholar 

  • Pennington DW et al (2006) Risk and regulatory hazard-based toxicological effect indicators in life-cycle assessment (LCA). Hum Ecol Risk Assess 12(3):450–475

    Article  CAS  Google Scholar 

  • Potting J, Hauschild MZ (2006) Spatial differentiation in life cycle impact assessment: a decade of method development to increase the environmental realism in LCIA. Int J Life Cycle Assess 11:11–13

    Article  CAS  Google Scholar 

  • Pöschl U (2005) Atmospheric aerosols: composition, transformation, climate and health effects. Angew Chem Int Ed 44(46):7520–7540

    Article  CAS  Google Scholar 

  • Rasmussen PE (1998) Long-rage atmospheric transport of trace metals: the need for geoscience perspectives. Environ Geol 22(2–3):96–108

    Article  Google Scholar 

  • Reddington et al (2013) The mass and number size distributions of black carbon aerosol over Europe. Atmos Chem Phys 13:4917–4939

    Article  CAS  Google Scholar 

  • Reiley MC (2007) Science, policy, and trends of metals risk assessment at EPA: how understanding metals bioavailability has changed metals risk assessment at US EPA. Aquat Toxicol 84:292–298

    Article  CAS  Google Scholar 

  • Roy P-O, Huijbregts MAJ, Deschênes L, Margni M (2012) Spatially differentiated atmospheric source-receptor relationships for nitrogen oxides, sulfur oxides and ammonia emissions at the global scale for life cycle impact assessment. Atmos Environ 62:74–81

    Article  CAS  Google Scholar 

  • Roy P-O, Deschênes L, Margni M (2013) Uncertainty and spatial variability in characterization factors for aquatic acidification at the global scale. Int J Life Cycle Assess 19:882–890

    Article  CAS  Google Scholar 

  • Roy P-O, Deschênes L, Margni M (2014) Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty. Sci Total Environ 500:270–276

    Article  CAS  Google Scholar 

  • Seibert P, Frank A (2003) Source receptor matrix calculation with a Lagrangian particle dispersion model in backward mode. Atmos Chem Phys 3:4515–4548

    Article  Google Scholar 

  • Strandesen M et al (2007) Fate and distribution modelling of metals in life cycle impact assessment. Ecol Model 203:327–338

    Article  Google Scholar 

  • Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ (2012) Heavy metals toxicity and the environment. EXS 101:133–164

    Google Scholar 

  • Travnikov O, Ilyin I (2005) Regional model MSCE-HM of heavy metal Transboundary air pollution in Europe. EMEP/MSC-E Technical Report 6/2005, p 59

  • Tuovinen J-P, Krüger O (1994) Re-evaluation of the local deposition correction in the Lagrangian EMEP model. In: EMEP/MSC-W Note 4/94 (Ed.), Norwegian Meteorological Institute

  • US Environmental Protection Agency (2005) Partition coefficient for metals in surface water, soil, and waste. EPA/600/R-05/074

  • US Environmental Protection Agency (2007) Framework for metal risk assessment. http://www.epa.gov/raf/metalsframework/pdfs/chaper3.pdf, 172 pp

  • Wang H, Rasch PJ, Easter RC, Singh B, Zhang R, Ma P-L, Qian Y, Ghan SJ, Beagley N (2014) Using an explicit emission tagging method in global modeling of source-receptor relationships for black carbon in the Arctic: variations, sources, and transport pathways. J Geophys Res Atmos 119:12888–12909

    Article  CAS  Google Scholar 

  • Yantosca B (2005) GEOS-CHEMv7–03-06 User’sGuide. HarvardUniversity. http://wwwas.harvard.edu/chemistry/trop/geos/doc/man/index.html (accessed 15.05.07)

  • Yasemin B, Zeki T (2007) Removal of heavy metals from aqueous solution by sawdust adsorption. J Environ Sci 19:160–166

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pierre-Olivier Roy.

Additional information

Responsible editor: Masaharu Motoshita

Electronic supplementary material

ESM 1

(DOCX 420 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, PO., Bulle, C. & Deschênes, L. Global-scale atmospheric modeling of aerosols to assess metal source-receptor relationships for life cycle assessment. Int J Life Cycle Assess 24, 93–103 (2019). https://doi.org/10.1007/s11367-018-1508-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11367-018-1508-y

Keywords

Navigation