Global-scale atmospheric modeling of aerosols to assess metal source-receptor relationships for life cycle assessment
- 40 Downloads
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.
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.
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.
KeywordsAtmospheric Fate factor Life cycle assessment Metals
- 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 InstituteGoogle Scholar
- 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
- 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 InstituteGoogle Scholar
- Bradl HB (ed) (2005) Heavy metals in the environment: origin, interaction and remediation. University of applied sciences trier, Neubrucke, GermanyGoogle 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, DordrechtGoogle Scholar
- 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 3:43–67CrossRefGoogle 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):241–255Google Scholar
- 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 3:445–473CrossRefGoogle Scholar
- Jacob D, Liu H, Mari C, Yantosca R (2000) Harvard wet deposition scheme for GMI. Harvard University Atmospheric Chemistry Modeling GroupGoogle 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)
- Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ (2012) Heavy metals toxicity and the environment. EXS 101:133–164Google 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 InstituteGoogle Scholar
- US Environmental Protection Agency (2005) Partition coefficient for metals in surface water, soil, and waste. EPA/600/R-05/074Google Scholar
- 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 119:12888–12909CrossRefGoogle 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)