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

Nature Sustainability volume 4, pages 129–137 (2021)Cite this article

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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.

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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.

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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

  1. Oak Ridge Institute for Science and Education (ORISE) Research Participant at the Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA

    Momei Qin

  2. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China

    Momei Qin

  3. Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA

    Benjamin N. Murphy, Kristin K. Isaacs & Havala O. T. Pye

  4. Chemical Sciences Laboratory, NOAA Earth System Research Laboratories, Boulder, CO, USA

    Brian C. McDonald & Stuart A. McKeen

  5. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Quanyang Lu & Allen L. Robinson

  6. Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA

    Quanyang Lu & Allen L. Robinson

  7. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Stuart A. McKeen

  8. Oak Ridge Associated Universities (ORAU) at the Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA

    Lauren Koval

  9. General Dynamics Information Technology, Research Triangle Park, NC, USA

    Christos Efstathiou & Chris Allen

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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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