Skip to main content
SpringerLink
Log in

BVOC Emissions Along the Eastern and Western Slopes of the Andes Central Range with Strong Altitudinal Gradient over a Wide Range of Andean Ecosystems: Model Estimation/Disaggregation with BIGA

Environmental Modeling & Assessment Aims and scope Submit manuscript

Cite this article

Abstract

Spatial and temporal emissions of biogenic volatile organic compounds (BVOCs) were estimated over a wide range of Andean ecosystems/ecotones, exhibiting high variability and highlighting the importance of BVOC emissions in the Tropical Andes, as precursors of secondary pollutants, of which the main concern is tropospheric ozone. The biogenic altitudinal gradient (BIGA) model was applied to a 7436-km2 area of the Colombian Andes with an altitude ranging from 140 to 5287 m a.s.l. Preliminary results revealed critical points of BVOC emission in lower elevational zones. Isoprene and monoterpene emissions were 41% and 20%, respectively, and were higher on dry days. For both dry and wet, the maximum fluxes occurred at 15:00 hours. Isoprene emissions were also estimated with the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model that incorporates the module of Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGANv2.1). Isoprene comparison between MEGAN-WRF-Chem and BIGA suggests that these models estimate similar emission fluxes (maximum 13,200 μg m−2 h−1) on the same regions. However, the BIGA model was able to estimate higher-resolution flux and indicated the importance to resolve in mountain zones the altitude effect on BVOC emission. The BIGA model requires information from surface temperature and solar radiation (SR), a digital elevation model (DEM), and land cover and use (LCU) maps. This local information was processed at a resolution of 90 m × 90 m. The basic algorithm proposed by Guenther et al. (Journal of Geophysical Research 98:12609–12617, 1993) was implemented in the BIGA model using Matlab; the results were visualized with ArcGIS. In the Tropical Andes, small areas can be characterized by many distinct climactic zones that range from grasslands to mountain forests and paramo impacting BVOC emission rates and spatial distribution. Preliminary results show that the BIGA model adequately incorporated the strong Andean altitudinal gradient and differs from the global model MEGAN-WRF-Chem.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Müller, J.-F. (1992). Geographical distribution and seasonal variation of surface emissions and deposition velocities of atmospheric trace gases. Geophysical Research, 97(D4), 3787–3804. https://doi.org/10.1029/91JD02757.

    Article  Google Scholar 

  2. Bai, J., Guenther, A., Turnipseed, A., & Duhl, T. (2015). Seasonal and interannual variations in whole–ecosystem isoprene and monoterpene emissions from a temperate mixed forest in Northern China. Atmospheric Pollution Research, 6, 696–707. https://doi.org/10.5094/APR.2015.078.

    Article  CAS  Google Scholar 

  3. Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., et al. (2014). Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years. Atmospheric Chemistry and Physics, 14, 9317–9341. https://doi.org/10.5194/acp-14-9317-2014.

    Article  CAS  Google Scholar 

  4. EEA. (2013). B1101 non-managed & managed forests GB2013. EMEP/EEA air pollutant emission inventory guidebook 2013. Luxembourg: Technical guidance to prepare national emission inventories. https://doi.org/10.2800/92722.

    Book  Google Scholar 

  5. Kallenbach, M., Oh, Y., Eilers, E. J., Veit, D., Baldwin, I. T., & Schuman, M. C. (2014). A robust, simple, high-throughput technique for time-resolved plant volatile analysis in field experiments. Plant Journal, 78(6), 1060–1072. https://doi.org/10.1111/tpj.12523.

    Article  CAS  Google Scholar 

  6. Guenther, A., Zimmerman, P. R., Harley, P. C., Monson, R. K., & Fall, R. (1993). Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analyses. Journal of Geophysical Research, 98(D7), 12609–12617. https://doi.org/10.1029/93JD00527.

    Article  Google Scholar 

  7. Zhu, W., Luo, L., Cheng, Z., Yan, N., Lou, S., & Ma, Y. (2018). Characteristics and contributions of biogenic secondary organic aerosol tracers to PM2.5 in Shanghai, China. Atmospheric Pollution Research, 9(2), 179–188. https://doi.org/10.1016/j.apr.2017.09.001.

    Article  CAS  Google Scholar 

  8. Da Silva, C. M., Da Silva, L. L., Corrêa, S. M., & Arbilla, G. (2018). A minimum set of ozone precursor volatile organic compounds in an urban environment. Atmospheric Pollution Research, 9(2), 369–378. https://doi.org/10.1016/j.apr.2017.11.002.

    Article  CAS  Google Scholar 

  9. Li-Ramírez, J. A., Pérez-Zapata, Á. M., Duque-Méndez, N. D., & Aristizábal-Zuluaga, B. H. (2016). Generación y representación de Indicadores de calidad de aire: caso de estudio aplicado a Manizales. Iteckne, 13(2), 174–184.

    Article  Google Scholar 

  10. Koca, H., Yaman, B., Meltem, A. Y., Altiok, H., Kara, M., Dumanoglu, Y., et al. (2013). Preparation of a national inventory of biogenic volatile organic compound (BVOC) emissions in Turkey-China STM focus. International Journal of Chemical, Environmental & Biological Sciences (IJCEBS), 1(4), 600–604.

    Google Scholar 

  11. UNC. (2014). Community modeling and analysis system CMAS. SMOKE v3.6 user’s manual. Chapel Hill: The Institute for the Environment-University of North Carolina.

    Google Scholar 

  12. Vukovich, J. M., & Pierce, T. (1988). The implementation of BEIS3 within the SMOKE modeling framework.

    Google Scholar 

  13. Sakulyanontvittaya, T., Piyachaturawat, P., Yarwood, G., & Guenther, A. (2010). Enhancement of Globeis, (582).

    Google Scholar 

  14. Baek, B. H., & Seppanen, C. (2018). Sparse Matrix Operator Kerner Emissions Modeling System (SMOKE). https://doi.org/10.5281/ZENODO.1421403.

    Book  Google Scholar 

  15. Guenther, A., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., & Wang, X. (2012). The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geoscientific Model Development, 5(6), 1471–1492. https://doi.org/10.5194/gmd-5-1471-2012.

    Article  CAS  Google Scholar 

  16. Cope, K. R., Snowden, M. C., & Bugbee, B. (2014). Photobiological interactions of blue light and photosynthetic photon flux: effects of monochromatic and broad-spectrum light sources. Photochemistry and Photobiology, 90(3), 574–584. https://doi.org/10.1111/php.12233.

    Article  CAS  Google Scholar 

  17. WMO, & UNEP. (2017). Intergovernmental Panel on Climate Change. Task Force on National Greenhouse Gas Inventories.

  18. Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., & Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027.

    Article  CAS  Google Scholar 

  19. Li, L. Y., & Xie, S. D. (2014). Historical variations of biogenic volatile organic compound emission inventories in China, 1981–2003. Atmospheric Environment, 95, 185–196. https://doi.org/10.1016/j.atmosenv.2014.06.033.

    Article  CAS  Google Scholar 

  20. Wong, C.-L., Yusop, Z., Venneker, R., & Uhlenbrook, S. (2018). Effects of topographic heterogeneity on coarse resolution grid-based runoff simulation—assessment for three river basins in Peninsular Malaysia. Environmental Modeling and Assessment, 23(3), 277–288. https://doi.org/10.1007/s10666-017-9576-0.

    Article  Google Scholar 

  21. Gu, D., Guenther, A. B., Shilling, J. E., Yu, H., Huang, M., Zhao, C., Yang, Q., Martin, S. T., Artaxo, P., Kim, S., Seco, R., Stavrakou, T., Longo, K. M., Tóta, J., de Souza, R. A. F., Vega, O., Liu, Y., Shrivastava, M., Alves, E. G., Santos, F. C., Leng, G., & Hu, Z. (2017). Airborne observations reveal elevational gradient in tropical forest isoprene emissions. Nature Communications, 8(May), 15541. https://doi.org/10.1038/ncomms15541.

    Article  CAS  Google Scholar 

  22. Ocampo, O. (2012). Análisis de Vulnerabilidad de la cuenca del río Chinchiná. Manizales: Universidad Nacional de Colombia.

    Google Scholar 

  23. Jaramillo Robledo, Á. (2005). Clima andino y café en Colombia (Hector Fab.). Chinchiná, Caldas: Federación Nacional de Cafeteros de Colombia.

    Google Scholar 

  24. Corpocaldas. (2007). Plan de gestión ambiental regional PGAR 2007-2019. Manizales.

  25. Hincapié Suárez, J. N., Romo Melo, L., Vélez Upegui, J. J., & Chan, P. (2016). Classification of rainfall events for weather forecasting purposes in andean region of Colombia. In EGU General Assembly Conference Abstracts (Vol. 18, pp. 1–569).

    Google Scholar 

  26. NOAA. (2016). Cold & warm episodes by season. In Historical El Nino/La Nina episodes (1950-present). College Park.

  27. IDEA & Corpocaldas. (2015). Centro de Datos e Indicadores Ambientales de Caldas (CDIAC). Generador de indicadores de clima.

    Google Scholar 

  28. Goodale, C., Aber, J., & Ollinger, S. (1998). Mapping monthly precipitation, temperature, and solar radiation for Ireland with polynomial regression and a digital elevation model. Climate Research, 10, 35–49. https://doi.org/10.3354/cr010035.

    Article  Google Scholar 

  29. Fries, A., Rollenbeck, R., Göttlicher, D., Nauss, T., Homeier, J., Peters, T., & Bendix, J. (2012). Thermal structure of a diverse andean mountain ecosystem in Southern Ecuador and its regionalization. Agricultural and Forest Meteorology, 152(1), 17–30. https://doi.org/10.3112/erdkunde.2009.04.03.

    Article  Google Scholar 

  30. IDEAM. (2010). Leyenda Nacional de Coberturas de la Tierra. In N. J. M. Ardila, U. G. Murcia, & Garcí (Eds.), Metodología CORINE Land Cover Adapta para Colombia, Escala 1:100.000. Bogotá. D.C: Instituto de Hidrología, Meteorología y Estudios Ambientales.

    Google Scholar 

  31. Potosnak, M. J., Baker, B. M., LeStourgeon, L., Disher, D. M., Griffin, K. L., Bret-Harte, M. L., & Starr, G. (2013). Isoprene emissions from a tundra ecosystem. Biogeosciences, 10, 871–889. https://doi.org/10.5194/bg-10-871-2013.

    Article  CAS  Google Scholar 

  32. Guenther, A., Karl, T., Wiedinmyer, C., Palmer, P., & Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmospheric Chemistry and Physics, 6, 3181–3210.

    Article  CAS  Google Scholar 

  33. Fries, A., Rollenbeck, R., NauB, T., Peters, T., & Bendix, J. (2012). Near surface air humidity in a megadiverse Andean mountain ecosystem of southern Ecuador and its regionalization. Agricultural and Forest Meteorology, 152(1), 17–30. https://doi.org/10.1016/j.agrformet.2011.08.004.

    Article  Google Scholar 

  34. Kumar, L., Skidmore, A. K., & Knowles, E. (1997). Modelling topographic variation in solar radiation in a GIS environment. International Journal of Geographical Information Science, 11(5), 475–497. https://doi.org/10.1080/136588197242266.

    Article  Google Scholar 

  35. Hardy, D. R., Vuille, M., Braun, G., Keimig, F., & Bradley, R. S. (1998). Annual and daily meteorological cycles at high altitude on a tropical mountain. Bulletin of the American Meteorological Society, 79(9), 1899–1913. https://doi.org/10.1175/1520-0477(1998)079<1899:AADMCA>2.0.CO;2.

    Article  Google Scholar 

  36. Guenther, A., Hewitt, C., & Erickson, D. (1995). A global model of natural volatile organic compound emissions. Journal of Geophysical Research, 100(94), 8873–8892. https://doi.org/10.1029/94JD02950.

    Article  CAS  Google Scholar 

  37. Thimijan, R. W., & Heins, R. D. (1983). Photometric, radiometric, and quantum light units of measure: a review of procedures for interconversion. HortScience, 18(December), 818–822.

    Google Scholar 

  38. Aculinin, A. (2008). Photosynthetically active radiation in Moldova. Moldavian Journal of the Physical Sciences, 7(N1), 115–123.

    Google Scholar 

  39. González, C. M., Ynoue, R. Y., Vara-Vela, A., Rojas, N. Y., & Aristizábal, B. H. (2018). High-resolution air quality modeling in a medium-sized city in the Tropical Andes: assessment of local and global emissions in understanding ozone and PM10 dynamics. Atmospheric Pollution Research, 9(5), 934–948. https://doi.org/10.1016/j.apr.2018.03.003.

    Article  CAS  Google Scholar 

  40. Cárdenas, P. A. (2012). Desarrollo de un inventario geo-referenciado de emisiones biogénicas para el dominio de modelación meso-escala de Bogotá. Universidad Nacional de Colombia.

  41. Toro, G. M. V., Cremades, O. L. V., & Ramirez, B. J. J. (2001). Inventario de emisiones biogenicas en el valle de Aburrá. Revista Ingeniería y Gestión Ambiental, 17(32).

  42. US Environmental Protection Agency. (2014). National Emission Inventory (NEI) report, (April). Retrieved from https://www.epa.gov/sites/production/files/2017-04/documents/2014neiv1_profile_final_april182017.pdf

  43. Greenberg, J. P., Guenther, A. B., Pétron, G., Wiedinmyer, C., Vega, O., Gatti, L. V., et al. (2004). Biogenic VOC emissions from forested Amazonian landscapes. Global Change Biology, 10(5), 651–662. https://doi.org/10.1111/j.1365-2486.2004.00758.x.

    Article  Google Scholar 

  44. Alves, E. G., Harley, P., De, F., Gonçalves, C., Da, C. E., Moura, S., & Jardine, K. (2014). Effects of light and temperature on isoprene emission at different leaf developmental stages of Eschweilera coriacea in Central Amazon. Acta Amaz, 44(1), 9–18. https://doi.org/10.1590/S0044-59672014000100002.

    Article  CAS  Google Scholar 

  45. Finlayson-Pitts, B. J., & Pitts, J. N. (2000). Chemistry of the upper and lower atmosphere theory experiments and applications (2nd ed.). New Jersey: Academic Press. https://doi.org/10.1016/B978-0-12-257060-5.X5000-X.

    Book  Google Scholar 

  46. Sarofim, M. C. (2012). The GTP of methane: modeling analysis of temperature impacts of methane and carbon dioxide reductions. Environmental Modeling and Assessment, 17(3), 231–239. https://doi.org/10.1007/s10666-011-9287-x.

    Article  Google Scholar 

  47. Finlayson-Pitts, B. J., & Pitts, J. N. (2000). The atmospheric system. In Chemistry of the upper and lower atmosphere. Theory, experiments, and applications (pp. 15–42). New Jersey: Academic Press. https://doi.org/10.1016/B978-012257060-5/50004-6.

    Chapter  Google Scholar 

  48. Monson, R. K., Jones, R. T., Rosenstiel, T. N., & Schnitzler, J. P. (2013). Why only some plants emit isoprene. Plant, Cell and Environment, 36(3), 503–516. https://doi.org/10.1111/pce.12015.

    Article  CAS  Google Scholar 

  49. Poupkou, A., Giannaros, T., Markakis, K., Kioutsioukis, I., Curci, G., Melas, D., & Zerefos, C. (2010). A model for European biogenic volatile organic compound emissions: software development and first validation. Environmental Modelling and Software, 25(12), 1845–1856. https://doi.org/10.1016/j.envsoft.2010.05.004.

    Article  Google Scholar 

  50. Zhao, C., Huang, M., Fast, J. D., Berg, L. K., Qian, Y., Guenther, A., et al. (2016). Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California. Geoscientific Model Development, 9(5), 1959–1976. https://doi.org/10.5194/gmd-9-1959-2016.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the regional environmental authority (CORPOCALDAS) for supporting this project through “Convenio Interadministrativo de Asociación 130-2014 (Inter-Administrative Association Agreement 130-2014)” and the Universidad Nacional de Colombia Sede Manizales by the support through “Convocatoria para la Movilidad Internacional de la Universidad Nacional de Colombia 2016-2018 (Call for International Mobility 2016-2018).” Special thanks to Professor Alex Guenther for providing MEGANv2.1 Beta (Excel version) with the EF information and valuable advice. Also thanks to Carlos Mario Gonzalez who helps with the estimation of isoprene using the MEGAN-WRF-Chem model.

BIGA Model Download

The code of the model and a template are available at the official page: http://idea.manizales.unal.edu.co/gta/ingenieria_hidraulica/BIGA/index.php.

Author information

Authors and Affiliations

  1. Hydraulic Engineering and Environmental Research Group, Universidad Nacional de Colombia, Manizales, Cra 27 64-60 Bloque H Palogrande, Manizales, Colombia

    Jade Alexandra Li Ramírez, Jeannette del Carmen Zambrano Nájera & Beatriz Helena Aristizábal Zuluaga

Authors
  1. Jade Alexandra Li Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeannette del Carmen Zambrano Nájera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beatriz Helena Aristizábal Zuluaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jade Alexandra Li Ramírez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 1480 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li Ramírez, J.A., Zambrano Nájera, J.d.C. & Aristizábal Zuluaga, B.H. BVOC Emissions Along the Eastern and Western Slopes of the Andes Central Range with Strong Altitudinal Gradient over a Wide Range of Andean Ecosystems: Model Estimation/Disaggregation with BIGA. Environ Model Assess 25, 761–773 (2020). https://doi.org/10.1007/s10666-020-09698-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-020-09698-7

Keywords

search

Navigation