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Criteria pollutant impacts of volatile chemical products informed by near-field modelling

Abstract

Consumer, industrial and commercial product use is a source of exposure to potentially hazardous chemicals. In addition, cleaning agents, personal care products, coatings and other volatile chemical products (VCPs) evaporate and react in the atmosphere, producing secondary pollutants. Here, we show that high air emissions from VCP use (≥14 kg per person per yr, at least 1.7× higher than current operational estimates) are supported by multiple estimation methods and constraints imposed by ambient levels of ozone, hydroxyl radical reactivity and the organic component of fine particulate matter (PM2.5) in Pasadena, California. A near-field model, which estimates human chemical exposure during or in the vicinity of product use, indicates that these high air emissions are consistent with organic product use up to ~75 kg per person per yr, and the inhalation of consumer products could be a non-negligible exposure pathway. After the PM2.5 yield is constrained to 5% by mass, VCPs produce ~41% of the photochemical organic PM2.5 (1.1 ± 0.3 μg m−3) and ~17% of the maximum daily 8 hr average ozone (9 ± 2 ppb) in summer in Los Angeles. Therefore, both toxicity and ambient criteria pollutant formation should be considered when organic substituents are developed for VCPs in pursuit of safer and more sustainable products and cleaner air.

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Fig. 1: Comparison of organic product usage, EFs and PM2.5 yields across different methodologies.
Fig. 2: Organic gas emissions and PM2.5 SOA formation potentials for personal care products based on current and new emission composition, respectively, using parameters in CMAQ.
Fig. 3: NMB between CMAQ-predicted and observed SOAprompt in Pasadena, California, for different combinations of VCP emission magnitude and VCP PM2.5 yield adjustments.
Fig. 4: Diurnal variation of simulated and observed SOAprompt at Pasadena, California.
Fig. 5: Simulated MDA8 O3 and OH reactivity versus observations at Pasadena.

Data availability

The VCP emission estimates in the form of population-based EFs are available in the Supplementary Information. The air quality modelling data for Pasadena during the 2010 CalNex campaign are available at https://doi.org/10.23719/1506136. The CalNex observations are publicly accessible at https://www.esrl.noaa.gov/csl/groups/csl7/measurements/2010calnex/. The full CMAQ outputs that support the findings of this study are archived on the EPA’s high-performance computing system and can be obtained from the corresponding author upon request. Source data are provided with this paper.

Code availability

The source code of the operational CMAQ model is available at https://github.com/USEPA/CMAQ, with specific modifications applied in this work accessible at https://doi.org/10.23719/1506136. The code for the simulation data processing is available upon request from the corresponding author. The source code of the SHEDS-HT model is available at https://github.com/HumanExposure/SHEDSHTRPackage. The modified SHEDS model including inputs and outputs from this study is available at https://github.com/HumanExposure/SHEDS_Applications/. The Solvent Tool is available at ftp://newftp.epa.gov/air/nei/2014/doc/2014v2_supportingdata/nonpoint/Solvent_Tool_v1.7.zip.

References

  1. McDonald, B. C. et al. Volatile chemical products emerging as largest petrochemical source of urban organic emissions. Science 359, 760–764 (2018).

    CAS  Google Scholar 

  2. Trantallidi, M. et al. Ephect III: health risk assessment of exposure to household consumer products. Sci. Total Environ. 536, 903–913 (2015).

    CAS  Google Scholar 

  3. Elberling, J. et al. Mucosal symptoms elicited by fragrance products in a population-based sample in relation to atopy and bronchial hyper-reactivity. Clin. Exp. Allergy 35, 75–81 (2005).

    CAS  Google Scholar 

  4. Gabb, H. A. & Blake, C. An informatics approach to evaluating combined chemical exposures from consumer products: a case study of asthma-associated chemicals and potential endocrine disruptors. Environ. Health Perspect. 124, 1155–1165 (2016).

    CAS  Google Scholar 

  5. Dodson, R. E. et al. Endocrine disruptors and asthma-associated chemicals in consumer products. Environ. Health Perspect. 120, 935–943 (2012).

    CAS  Google Scholar 

  6. Brinke, J. T. et al. Development of new volatile organic compound (VOC) exposure metrics and their relationship to “sick building syndrome” symptoms. Indoor Air 8, 140–152 (1998).

    Google Scholar 

  7. Pope, C. A. & Dockery, D. W. Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manage. Assoc. 56, 709–742 (2006).

    CAS  Google Scholar 

  8. Schlesinger, R. B. et al. The health relevance of ambient particulate matter characteristics: coherence of toxicological and epidemiological inferences. Inhal. Toxicol. 18, 95–125 (2006).

    CAS  Google Scholar 

  9. Ebi Kristie, L. & McGregor, G. Climate change, tropospheric ozone and particulate matter, and health impacts. Environ. Health Perspect. 116, 1449–1455 (2008).

    CAS  Google Scholar 

  10. Zhang, Q. et al. Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically‐influenced Northern Hemisphere midlatitudes. Geophys. Res. Lett. 34, L13801 (2007).

    Google Scholar 

  11. Huang, R. J. et al. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222 (2014).

    CAS  Google Scholar 

  12. Jimenez, J. L. et al. Evolution of organic aerosols in the atmosphere. Science 326, 1525–1529 (2009).

    CAS  Google Scholar 

  13. Ensberg, J. J. et al. Emission factor ratios, SOA mass yields, and the impact of vehicular emissions on SOA formation. Atmos. Chem. Phys. 14, 2383–2397 (2014).

    Google Scholar 

  14. Woody, M. C. et al. Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS. Atmos. Chem. Phys. 16, 4081–4100 (2016).

    CAS  Google Scholar 

  15. Hayes, P. et al. Modeling the formation and aging of secondary organic aerosols in Los Angeles during CalNex 2010. Atmos. Chem. Phys. 15, 5773–5801 (2015).

    CAS  Google Scholar 

  16. Baker, K. et al. Gas and aerosol carbon in California: comparison of measurements and model predictions in Pasadena and Bakersfield. Atmos. Chem. Phys. 15, 5243–5258 (2015).

    CAS  Google Scholar 

  17. Hodzic, A. et al. Modeling organic aerosols in a megacity: potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation. Atmos. Chem. Phys. 10, 5491–5514 (2010).

    CAS  Google Scholar 

  18. Shrivastava, M. K. et al. Effects of gas particle partitioning and aging of primary emissions on urban and regional organic aerosol concentrations. J. Geophys. Res. Atmos. 113, D18301 (2008).

    Google Scholar 

  19. Li, W. et al. Potential of select intermediate-volatility organic compounds and consumer products for secondary organic aerosol and ozone formation under relevant urban conditions. Atmos. Environ. 178, 109–117 (2018).

    CAS  Google Scholar 

  20. Coggon, M. M. et al. Diurnal variability and emission pattern of decamethylcyclopentasiloxane (D5) from the application of personal care products in two North American cities. Environ. Sci. Technol. 52, 5610–5618 (2018).

    CAS  Google Scholar 

  21. Janechek, N. J. et al. Physical properties of secondary photochemical aerosol from OH oxidation of a cyclic siloxane. Atmos. Chem. Phys. 19, 1649–1664 (2019).

    CAS  Google Scholar 

  22. Wu, Y. & Johnston, M. V. Aerosol formation from OH oxidation of the volatile cyclic methyl siloxane (CVMS) decamethylcyclopentasiloxane. Environ. Sci. Technol. 51, 4445–4451 (2017).

    CAS  Google Scholar 

  23. Zhao, Y. et al. Intermediate-volatility organic compounds: a large source of secondary organic aerosol. Environ. Sci. Technol. 48, 13743–13750 (2014).

    CAS  Google Scholar 

  24. Karl, T. et al. Urban flux measurements reveal a large pool of oxygenated volatile organic compound emissions. Proc. Natl Acad. Sci. USA 115, 1186–1191 (2018).

    CAS  Google Scholar 

  25. Fantke, P. et al. Coupled near-field and far-field exposure assessment framework for chemicals in consumer products. Environ. Int. 94, 508–518 (2016).

    CAS  Google Scholar 

  26. Ring, C. L. et al. Consensus modeling of median chemical intake for the U.S. population based on predictions of exposure pathways. Environ. Sci. Technol. 53, 719–732 (2019).

    CAS  Google Scholar 

  27. Wambaugh, J. F. et al. High-throughput models for exposure-based chemical prioritization in the ExpoCast Project. Environ. Sci. Technol. 47, 8479–8488 (2013).

    CAS  Google Scholar 

  28. Barber, M. C., Isaacs, K. K. & Tebes-Stevens, C. Developing and applying metamodels of high resolution process-based simulations for high throughput exposure assessment of organic chemicals in riverine ecosystems. Sci. Total Environ. 605, 471–481 (2017).

    Google Scholar 

  29. Nazaroff, W., Weschler, C. J., Little, J. C. & Hubal, E. A. C. Intake to production ratio: a measure of exposure intimacy for manufactured chemicals. Environ. Health Perspect. 120, 1678–1683 (2012).

    CAS  Google Scholar 

  30. Isaacs, K. K. et al. SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources. Environ. Sci. Technol. 48, 12750–12759 (2014).

    CAS  Google Scholar 

  31. ARB solvent evaporation methodologies—aerosol coatings & consumer products (California Air Resources Board (CARB), 2020); https://ww3.arb.ca.gov/ei/areasrc/consumer_products_2020.pdf

  32. Rule 1113: architectural coatings (California Air Resources Board (CARB), 2013); https://ww2.arb.ca.gov/sites/default/files/classic/technology-clearinghouse/rules/RuleID256.pdf

  33. Rule 1168: adhesive and sealant applications (South Coast Air Quality Management District (SCAQMD), 2017); http://www.aqmd.gov/docs/default-source/rule-book/reg-xi/rule-1168.pdf

  34. Carter, W. P. Development of a database for chemical mechanism assignments for volatile organic emissions. J. Air Waste Manage. Assoc. 65, 1171–1184 (2015).

    CAS  Google Scholar 

  35. Final report SPECIATE version 5.0 database development documentation. Report EPA/600/R-19/098 (Abt Associates, 2019); https://www.epa.gov/sites/production/files/2019-07/documents/speciate_5.0.pdf

  36. Wu, Y. & Johnston, M. V. Molecular characterization of secondary aerosol from oxidation of cyclic methylsiloxanes. J. Am. Soc. Mass. Spectrom. 27, 402–409 (2016).

    CAS  Google Scholar 

  37. Jathar, S. H. et al. Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States. Proc. Natl Acad. Sci. USA 111, 10473–10478 (2014).

    CAS  Google Scholar 

  38. Presto, A. A., Miracolo, M. A., Donahue, N. M. & Robinson, A. L. Secondary organic aerosol formation from high-NOx photo-oxidation of low volatility precursors: n-alkanes. Environ. Sci. Technol. 44, 2029–2034 (2010).

    CAS  Google Scholar 

  39. Griffith, S. M. et al. Measurements of hydroxyl and hydroperoxy radicals during CalNex-LA: model comparisons and radical budgets. J. Geophys. Res. Atmos. 121, 4211–4232 (2016).

    CAS  Google Scholar 

  40. Di Carlo, P. et al. Missing OH reactivity in a forest: evidence for unknown reactive biogenic VOCs. Science 304, 722–725 (2004).

    Google Scholar 

  41. Sheehy, P. M., Volkamer, R., Molina, L. T. & Molina, M. J. Oxidative capacity of the Mexico City atmosphere—part 2: a ROx radical cycling perspective. Atmos. Chem. Phys. 10, 6993–7008 (2010).

    CAS  Google Scholar 

  42. Ren, X. R. et al. OH and HO2 chemistry in the urban atmosphere of New York City. Atmos. Environ. 37, 3639–3651 (2003).

    CAS  Google Scholar 

  43. Shirley, T. R. et al. Atmospheric oxidation in the Mexico City Metropolitan Area (MCMA) during April 2003. Atmos. Chem. Phys. 6, 2753–2765 (2006).

    CAS  Google Scholar 

  44. Williams, A. J. et al. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J. Cheminformatics 9, 61 (2017).

    Google Scholar 

  45. Mansouri, K., Grulke, C. M., Judson, R. S. & Williams, A. J. Opera models for predicting physicochemical properties and environmental fate endpoints. J. Cheminformatics 10, 10 (2018).

    Google Scholar 

  46. Toxicity Estimation Software Tool (TEST) (US Environmental Protection Agency, 2016); https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test

  47. 2011 National Emissions Inventory version 2 technical support document (US Environmental Protection Agency, 2015); https://www.epa.gov/sites/production/files/2015-10/documents/nei2011v2_tsd_14aug2015.pdf

  48. US Industry Forecasts for 2012 & 2017: Solvents (Freedonia, 2008).

  49. Air Emissions Inventory Improvement Program (EIIP) technical report series (US Environmental Protection Agency, 1997); https://www.epa.gov/air-emissions-inventories/air-emissions-inventory-improvement-program-eiip.

  50. Solvent Utilization: Documentation for EPA’s Nonpoint Emissions Estimation Tool (Abt Associates, 2017).

  51. Dionisio, K. L. et al. The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Sci. Data 5, 180125 (2018).

    CAS  Google Scholar 

  52. Simon, H. et al. The development and uses of EPA’s SPECIATE database. Atmos. Pollut. Res. 1, 196–206 (2010).

    CAS  Google Scholar 

  53. Carter, W. P. L. Development of an improved chemical speciation database for processing emissions of volatile organic compounds for air quality models (2020); https://intra.engr.ucr.edu/~carter/emitdb/

  54. Ryerson, T. et al. The 2010 California research at the nexus of air quality and climate change (CalNex) field study. J. Geophys. Res. Atmos. 118, 5830–5866 (2013).

    CAS  Google Scholar 

  55. Pye, H. O. T. et al. Modeling the current and future roles of particulate organic nitrates in the southeastern United States. Environ. Sci. Technol. 49, 14195–14203 (2015).

    CAS  Google Scholar 

  56. Xie, Y. et al. Understanding the impact of recent advances in isoprene photooxidation on simulations of regional air quality. Atmos. Chem. Phys. 13, 8439–8455 (2013).

    Google Scholar 

  57. Murphy, B. N. et al. Semivolatile POA and parameterized total combustion SOA in CMAQv5.2: impacts on source strength and partitioning. Atmos. Chem. Phys. 17, 11107–11133 (2017).

    CAS  Google Scholar 

  58. Pye, H. O. T. et al. On the implications of aerosol liquid water and phase separation for organic aerosol mass. Atmos. Chem. Phys. 17, 343–369 (2017).

    CAS  Google Scholar 

  59. Xu, L. et al. Experimental and model estimates of the contributions from biogenic monoterpenes and sesquiterpenes to secondary organic aerosol in the southeastern United States. Atmos. Chem. Phys. 18, 12613–12637 (2018).

    CAS  Google Scholar 

  60. Lu, Q. et al. Simulation of organic aerosol formation during the CalNex study: updated mobile emissions and secondary organic aerosol parameterization for intermediate-volatility organic compounds. Atmos. Chem. Phys. 20, 4313–4332 (2020).

    CAS  Google Scholar 

  61. Bash, J. O., Baker, K. R. & Beaver, M. R. Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California. Geosci. Model Dev. 9, 2191–2207 (2016).

    CAS  Google Scholar 

  62. Goldstein, A. H. & Galbally, I. E. Known and unexplored organic constituents in the Earth’s atmosphere. Environ. Sci. Technol. 41, 1514–1521 (2007).

    CAS  Google Scholar 

  63. Baker, K. R. et al. Evaluation of surface and upper air fine scale WRF meteorological modeling of the May and June 2010 CalNex period in California. Atmos. Environ. 80, 299–309 (2013).

    CAS  Google Scholar 

  64. Kelly, J. T. et al. Fine-scale simulation of ammonium and nitrate over the south coast air basin and San Joaquin Valley of California during CalNex-2010. J. Geophys. Res. Atmos. 119, 3600–3614 (2014).

    CAS  Google Scholar 

  65. Markovic, M. et al. Measurements and modeling of the inorganic chemical composition of fine particulate matter and associated precursor gases in California’s San Joaquin Valley during CalNex 2010. J. Geophys. Res. Atmos. 119, 6853–6866 (2014).

    CAS  Google Scholar 

  66. Hayes, P. et al. Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign. J. Geophys. Res. Atmos. 118, 9233–9257 (2013).

    Google Scholar 

  67. Bahreini, R. et al. Organic aerosol formation in urban and industrial plumes near Houston and Dallas, Texas. J. Geophys. Res. Atmos. 114, D00F16 (2009).

    Google Scholar 

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Acknowledgements

We thank EPA’s SPECIATE workgroup, K. Seltzer, M. Strum, J. Snyder and T. Rao for useful discussions. This project was supported in part by an appointment to the Research Participation Program at the Office of Research and Development, US Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and the EPA. B.C.M. and S.A.M. were supported by NOAA award no. NA17OAR4320101 to the University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences. A.L.R. was supported by the Environmental Protection Agency STAR assistance agreement no. RD83587301. The US Environmental Protection Agency, through its Office of Research and Development, collaborated in the research described here. The research has been subjected to Agency administrative review and approved for publication but may not necessarily reflect official Agency policy. The views expressed in this Article are those of the authors and do not necessarily represent the views or policies of the US Environmental Protection Agency or of the National Oceanic and Atmospheric Administration.

Author information

Authors and Affiliations

Authors

Contributions

H.O.T.P. and M.Q. conceived and designed the experiments. M.Q., B.N.M., K.K.I. and L.K. performed the experiments. M.Q., H.O.T.P., B.N.M., K.K.I., B.C.M. and S.A.M. analysed and interpreted the data. All of the authors contributed materials and/or analysis tools. M.Q. and H.O.T.P. wrote the manuscript with substantial contributions from all of the authors.

Corresponding authors

Correspondence to Momei Qin or Havala O. T. Pye.

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Extended data

Extended Data Fig. 1 Time series of simulated and observed fossil and non-fossil carbon at Pasadena.

Simulated in the current model and the feasible solution case compared to observations in Woody et al.14.

Source data

Extended Data Fig. 2 Simulated SOA formation efficiency (SOAFE) over 8:30 am – 12:30 pm at Pasadena.

SOAFE quantifies SOA mass formation per volume of organic gases reacted over a time window, which brings together the organic PM2.5 yield, precursor abundance, oxidant level, and reaction rate constant. See more details in Supplementary Notes.

Source data

Extended Data Fig. 3 Simulated OH in the current model estimate and the feasible solution case compared to observations at Pasadena.

The simulation without VCP emissions is indicated with the dashed line.

Source data

Extended Data Fig. 4 Schematic of the methodology.

This work integrated near-field (that is, SHEDS-HT) with far-field (that is, CMAQ modeling with 2011 NEI) modeling and top-down constraints based on ambient measurements. The blue boxes indicate emission estimates that were inter-compared. The emissions in EPA 2011 NEI, containing the estimate for VCP-emitted VOCs (solid outlined), went into air quality modeling. The yellow boxes indicate processing of the NEI including chemical speciation of emissions with the SPECIATE database, and mapping to CMAQ regional model surrogates using a certain chemical mechanism (for example, SAPRC07). For VCPs, SOA formation was parametrized with a fixed SOA yield (large dashed arrow), and thus emission processing was not required for VCP-emitted VOCs. See more details in Methods.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Tables 1–13 and notes.

Source data

Source Data Fig. 1

Data shown in the figure.

Source Data Fig. 2

Data shown in the figure.

Source Data Fig. 3

Data shown in the figure.

Source Data Fig. 4

Source data used to generate the diurnal variation plot.

Source Data Fig. 5

Data shown in the figure.

Source Data Extended Data Fig. 1

Data shown in the figure.

Source Data Extended Data Fig. 2

Data shown in the figure.

Source Data Extended Data Fig. 3

Source data used to generate the diurnal variation plot.

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Qin, M., Murphy, B.N., Isaacs, K.K. et al. Criteria pollutant impacts of volatile chemical products informed by near-field modelling. Nat Sustain 4, 129–137 (2021). https://doi.org/10.1038/s41893-020-00614-1

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