Commercial chemicals are used extensively across urban centres worldwide1, posing a potential exposure risk to 4.2 billion people2. Harmful chemicals are often assessed on the basis of their environmental persistence, accumulation in biological organisms and toxic properties, under international and national initiatives such as the Stockholm Convention3. However, existing regulatory frameworks rely largely upon knowledge of the properties of the parent chemicals, with minimal consideration given to the products of their transformation in the atmosphere. This is mainly due to a dearth of experimental data, as identifying transformation products in complex mixtures of airborne chemicals is an immense analytical challenge4. Here we develop a new framework—combining laboratory and field experiments, advanced techniques for screening suspect chemicals, and in silico modelling—to assess the risks of airborne chemicals, while accounting for atmospheric chemical reactions. By applying this framework to organophosphate flame retardants, as representative chemicals of emerging concern5, we find that their transformation products are globally distributed across 18 megacities, representing a previously unrecognized exposure risk for the world’s urban populations. More importantly, individual transformation products can be more toxic and up to an order-of-magnitude more persistent than the parent chemicals, such that the overall risks associated with the mixture of transformation products are also higher than those of the parent flame retardants. Together our results highlight the need to consider atmospheric transformations when assessing the risks of commercial chemicals.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The RAIDAR model is implemented in a user-friendly online platform named the Exposure and Safety Estimation (EAS-E) Suite, which is free for online use at www.eas-e-suite.com.
Johnson, A. C., Jin, X., Nakada, N. & Sumpter, J. P. Learning from the past and considering the future of chemicals in the environment. Science 367, 384–387 (2020).
United Nations. World Urbanization Prospects. https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf (UN, 2018).
United Nations. Stockholm Convention. http://www.pops.int/ (UN, 2004).
Escher, B. I., Stapleton, H. M. & Schymanski, E. L. Tracking complex mixtures of chemicals in our changing environment. Science 367, 388–392 (2020).
De Boer, J. & Stapleton, H. M. Toward fire safety without chemical risk. Science 364, 231–232 (2019).
Wang, Z. et al. We need a global science-policy body on chemicals and waste. Science 371, 774–776 (2021).
United States. Toxic Substances Control Act. https://www.epa.gov/tsca-inventory (US, 1976).
European Union. Registration Evaluation, Authorization and Restriction of Chemicals. https://echa.europa.eu/regulations/reach/understanding-reach (EU, 2007).
Environment and Climate Change Canada. Science Approach Document: Ecological Risk Classification of Organic Substances. https://www.ec.gc.ca/ese-ees/A96E2E98-2A04-40C8-9EDC-08A6DFF235F7/CMP3%20ERC_EN.pdf (ECCC, 2016).
Jones, K. C. Persistent organic pollutants (POPs) and related chemicals in the global environment: some personal reflections. Environ. Sci. Technol. 55, 9400–9412 (2021).
Saini, A. et al. GAPS-megacities: a new global platform for investigating persistent organic pollutants and chemicals of emerging concern in urban air. Environ. Pollut. 267, 115416 (2020).
Salamova, A., Ma, Y., Venier, M. & Hites, R. A. High levels of organophosphate flame retardants in the Great Lakes atmosphere. Environ. Sci. Technol. Lett. 1, 8–14 (2013).
Molina, M. J. & Rowland, F. S. Stratospheric sink for chlorofluoromethanes: chlorine atom-catalysed destruction of ozone. Nature 249, 810–812 (1974).
Washington, J. W. et al. Nontargeted mass-spectral detection of chloroperfluoropolyether carboxylates in New Jersey soils. Science 368, 1103–1107 (2020).
Hollender, J., Schymanski, E. L., Singer, H. P. & Ferguson, P. L. Nontarget screening with high resolution mass spectrometry in the environment: ready to go? Environ. Sci. Technol. 51, 11505–11512 (2017).
Hites, R. A. & Jobst, K. J. Is nontargeted screening reproducible? Environ. Sci. Technol. 52, 11975–11976 (2018).
Blum, A. et al. Organophosphate ester flame retardants: are they a regrettable substitution for polybrominated diphenyl ethers? Environ. Sci. Technol. Lett. 6, 638–649 (2019).
Fenner, K., Kooijman, C., Scheringer, M. & Hungerbühler, K. Including transformation products into the risk assessment for chemicals: the case of nonylphenol ethoxylate usage in Switzerland. Environ. Sci. Technol. 36, 1147–1154 (2002).
Lienert, J., Güdel, K. & Escher, B. I. Screening method for ecotoxicological hazard assessment of 42 pharmaceuticals considering human metabolism and excretory routes. Environ. Sci. Technol. 41, 4471–4478 (2007).
Peng, Z. & Jimenez, J. L. Radical chemistry in oxidation flow reactors for atmospheric chemistry research. Chem. Soc. Rev. 49, 2570–2616 (2020).
Lopez-Hilfiker, F. D. et al. An extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) for online measurement of atmospheric aerosol particles. Atmos. Meas. Tech. 12, 4867–4886 (2019).
Zheng, Z., Peters, G. M., Arp, H. P. H. & Andersson, P. L. Combining in silico tools with multicriteria analysis for alternatives assessment of hazardous chemicals: a case study of decabromodiphenyl ether alternatives. Environ. Sci. Technol. 53, 6341–6351 (2019).
Harrison, R. M. Urban atmospheric chemistry: a very special case for study. NPJ Clim. Atmos. Sci. 1, 1–5 (2018).
Jimenez, J. L. et al. Evolution of organic aerosols in the atmosphere. Science 326, 1525–1529 (2009).
Saini, A. et al. Flame retardants in urban air: a case study in Toronto targeting distinct source sectors. Environ. Pollut. 247, 89–97 (2019).
Kelly, B. C., Ikonomou, M. G., Blair, J. D., Morin, A. E. & Gobas, F. A. Food web-specific biomagnification of persistent organic pollutants. Science 317, 236–239 (2007).
Neumann, M. & Schliebner, I. Protecting the Sources of our Drinking Water: the Criteria for Identifying Persistent, Mobile and Toxic (PMT) Substances and Very Persistent and Very Mobile (vPvM) Substances under EU Regulation REACH (EC) No 1907/2006 (German Environment Agency, 2019).
Kalgutkar, A. S. et al. A comprehensive listing of bioactivation pathways of organic functional groups. Curr. Drug Metab. 6, 161–225 (2005).
Zhang, Q., Yu, C., Fu, L., Gu, S. & Wang, C. New insights in the endocrine disrupting effects of three primary metabolites of organophosphate flame retardants. Environ. Sci. Technol. 54, 4465–4474 (2020).
Tian, Z. et al. A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon. Science 371, 185–189 (2021).
Vasiljevic, T. & Harner, T. Bisphenol A and its analogues in outdoor and indoor air: properties, sources and global levels. Sci. Total Environ. 789, 148013 (2021).
Vethaak, A. D. & Legler, J. Microplastics and human health. Science 371, 672–674 (2021).
Su, H. et al. Persistent, bioaccumulative, and toxic properties of liquid crystal monomers and their detection in indoor residential dust. Proc. Natl Acad. Sci. USA 116, 26450–26458 (2019).
Liu, Q. et al. Experimental study of OH-initiated heterogeneous oxidation of organophosphate flame retardants: kinetics, mechanism, and toxicity. Environ. Sci. Technol. 53, 14398–14408 (2019).
Liu, Q., Liggio, J., Li, K., Lee, P. & Li, S. M. Understanding the impact of relative humidity and coexisting soluble iron on the OH-initiated heterogeneous oxidation of organophosphate flame retardants. Environ. Sci. Technol. 53, 6794–6803 (2019).
Liu, Q. et al. Atmospheric OH oxidation chemistry of particulate liquid crystal monomers: an emerging persistent organic pollutant in air. Environ. Sci. Technol. Lett. 7, 646–652 (2020).
Liu, Y. & Sander, S. P. Rate constant for the OH + CO reaction at low temperatures. J. Phys. Chem. A 119, 10060–10066 (2015).
Qi, L. et al. Organic aerosol source apportionment in Zurich using an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS)–part 2: biomass burning influences in winter. Atmos. Chem. Phys. 19, 8037–8062 (2019).
Zhang, X., Saini, A., Hao, C. & Harner, T. Passive air sampling and nontargeted analysis for screening POP-like chemicals in the atmosphere: opportunities and challenges. TrAC. Trends Analyt. Chem. 132, 116052 (2020).
Krapf, M. et al. Labile peroxides in secondary organic aerosol. Chem 1, 603–616 (2016).
Buchholz, A. et al. Deconvolution of FIGAERO–CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation. Atmos. Chem. Phys. 20, 7693–7716 (2020).
Arnot, J. A., Mackay, D., Webster, E. & Southwood, J. M. Screening level risk assessment model for chemical fate and effects in the environment. Environ. Sci. Technol. 40, 2316–2323 (2006).
Arnot, J. A. & Mackay, D. Policies for chemical hazard and risk priority setting: can persistence, bioaccumulation, toxicity, and quantity information be combined? Environ. Sci. Technol. 42, 4648–4654 (2008).
Arnot, J. A., Brown, T. N., Wania, F., Breivik, K. & McLachlan, M. S. Prioritizing chemicals and data requirements for screening-level exposure and risk assessment. Environ. Health Perspect. 120, 1565–1570 (2012).
Ring, C. L. et al. Consensus modeling of median chemical intake for the US population based on predictions of exposure pathways. Environ. Sci. Technol. 53, 719–732 (2018).
Arnot, J. A., Toose, L. & Armitage, J. Generation of Physical-Chemical Property Data and the Application of Models for Estimating Fate and Transport and Exposure and Risk Potential for Organic Substances on the Canadian DSL (Technical Report for Environment and Climate Change Canada, 2018).
Arnot, J. A. & Armitage, J. M. Parameterization and Application of the RAIDAR Model to Aid in the Prioritization and Assessment of Chemical Substances (Technical Report for Health Canada, 2013).
Arnot, J. A. & Mackay, D. Risk Prioritization for a Subset of Domestic Substances List Chemicals using the RAIDAR model (CEMC Report No. 200703) (Technical Report for Environment and Climate Change Canada, 2007).
Klasmeier, et al. Application of multimedia models for screening assessment of long-range transport potential and overall persistence. Environ. Sci. Technol. 40, 53–60 (2006).
Webster, E., Mackay, D. & Wania, F. Evaluating environmental persistence. Environ. Toxicol. Chem. 17, 2148–2158 (1998).
Tang, B. et al. Bioconcentration and biotransformation of organophosphorus flame retardants (PFRs) in common carp (Cyprinus carpio). Environ. Int. 126, 512–522 (2019).
Bekele, T. G., Zhao, H., Wang, Y., Jiang, J. & Tan, F. Measurement and prediction of bioconcentration factors of organophosphate flame retardants in common carp (Cyprinus carpio). Ecotoxicol. Environ. Saf. 166, 270–276 (2018).
Wang, G. et al. Bioaccumulation mechanism of organophosphate esters in adult zebrafish (Danio rerio). Environ. Pollut. 229, 177–187 (2017).
Muir, D., Yarechewski, A. & Grift, N. Environmental dynamics of phosphate esters. III. comparison of the bioconcentration of four triaryl phosphates by fish. Chemosphere 12, 155–166 (1983).
Mansouri, K., Grulke, C. M., Judson, R. S. & Williams, A. J. OPERA models for predicting physicochemical properties and environmental fate endpoints. J. Cheminform. 10, 1–19 (2018).
Trapp, S. & Horobin, R. W. A predictive model for the selective accumulation of chemicals in tumor cells. Eur. Biophys. J. 34, 959–966 (2005).
Mansouri, K. et al. Open-source QSAR models for pKa prediction using multiple machine learning approaches. J. Cheminform. 11, 1–20 (2019).
Brown, T. N., Arnot, J. A. & Wania, F. Iterative fragment selection: a group contribution approach to predicting fish biotransformation half-lives. Environ. Sci. Technol. 46, 8253–8260 (2012).
Arnot JA, Gouin T, Mackay D. Practical Methods for Estimating Environmental Biodegradation Rates (Report for Environment Canada, Canadian Environmental Modelling Network, 2005).
Martin, T. User’s Guide for T.E.S.T. (version 5.1) Toxicity Estimation Software Tool: A Program to Estimate Toxicity from Molecular Structure (Sustainable Technology Division, National Risk Management Research Laboratory, US Environmental Protection Agency, Cincinnati, OH, 2020).
van Leeuwen, C. J. & Vermeire, T. G. Risk Assessment of Chemicals: An Introduction (Springer, 2007).
Escher, B. I. et al. Environmental toxicology and risk assessment of pharmaceuticals from hospital wastewater. Water Res. 45, 75–92 (2011).
Escher, B. I. & Fenner, K. Recent advances in environmental risk assessment of transformation products. Environ. Sci. Technol. 45, 3835–3847 (2011).
We thank J. A. Arnot for providing the codes for the latest version of the RAIDAR model. We acknowledge funding support from the Air Pollution programme of Environment and Climate Change Canada (ECCC). This work was also partially funded by the Chemicals Management Plan (CMP). It does not reflect any regulatory conclusions for any substances mentioned.
The authors declare no competing interests.
Peer review information Nature thanks Kevin Jones and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Chemical structures of OPFR parent compounds and transformation products studied here.
a, Nine OPFR parent compounds. b, Ten OPFR transformation products identified in GAPS–MC field samples. TCEP-1 and TCPP-25 (see Supplementary Table 1) are difficult to distinguish from each other as they have identical chemical formulae. However, given that TCEP-1 and TCPP-25 are formed through one-step and two-step photooxidation reactions, respectively, and given that the oxidation timescales associated with urban regions are short23, TCEP-1 is likely to be the dominant product.
Photooxidation reactions proceed predominantly through three main channels. a, OH addition to the substituents attached to the phosphate centre (channel 1). b, OH addition to the phosphate centre (channel 2). c, Photodecomposition of early-generation products (channel 3). Generalized examples for chlorinated and non-chlorinated OPFRs are shown here. Detailed mechanisms for TCPP (a chlorinated OPFR) and EHDP (a non-chlorinated OPFR) are shown in Supplementary Figs. 1, 2.
Extended Data Fig. 3 Mass chromatograms and spectra for six OPFR transformation products in OFR laboratory samples and GAPS–MC field samples.
This information is used in the identification of: a, TCEP-1; b, TCEP-10; c, TCEP-21; d, TCPP-9; e, TCPP-21; and f, TCPP-38. Detailed product information—including chemical formulae, retention times, measured m/z values, isotopic ratios, detection frequencies and concentrations—is summarized in Supplementary Tables 2, 4.
Extended Data Fig. 4 Mass chromatograms and spectra of four OPFR transformation products in OFR laboratory samples and GAPS–MC field samples.
This information is used in the identification of: a, TDCPP-14; b, TBEP-36; c, TPhP-6; and d, TPhP-8. Detailed product information—including chemical formulae, retention times, measured m/z values, isotopic ratios, detection frequencies and concentrations—is summarized in Supplementary Tables 2, 4.
Extended Data Fig. 5 Seasonal variation in Rsignal in Toronto, Canada, as a measure of photochemical production of OPFR products.
Rsignal is the ratio of the signal intensity of an OPFR product to the signal intensity of the corresponding parent OPFR. A higher Rsignal is indicative of increased photooxidation in the summer months. Samples were collected during summer (August to September 2016) and winter (December 2016 to January 2017).
a, Modelled overall persistence for three chlorinated OPFRs (TCEP, TCPP and TDCPP) and their products. b, Modelled overall persistence for six non-chlorinated OPFRs (TBEP, TPhP, EHDP, TCP, TEHP and DPhP) and their products. c, Relative persistence for three chlorinated OPFRs and their transformation products (parent compound = 1). d, Relative persistence for six non-chlorinated OPFRs and their transformation products. Dark red, light red, dark blue and light blue represent chlorinated parent OPFRs, chlorinated OPFR products, non-chlorinated parent OPFRs, and non-chlorinated OPFR products, respectively. Note that a higher overall persistence indicates a higher persistence in the multimedia environment. Compounds marked with asterisks are those identified in megacity field samples.
Extended Data Fig. 7 Octanol–air (log KOA) and octanol–water (log KOW) partition coefficients for OPFRs and their transformation products at a pH of 7.
Dark red, light red, dark blue and light blue represent chlorinated parent OPFRs, chlorinated OPFR products, non-chlorinated parent OPFRs and non-chlorinated OPFR products, respectively.
Extended Data Fig. 8 Bioaccumulation of OPFRs and their transformation products in aquatic organisms.
a, Modelled bioconcentration factor (BCF) for three chlorinated OPFRs (TCEP, TCPP and TDCPP) and their products. b, Modelled bioconcentration factor for six non-chlorinated OPFRs (TBEP, TPhP, EHDP, TCP, TEHP and DPhP) and their products. c, Relative bioaccumulation for three chlorinated OPFRs and their transformation products (parent compound = 1). d, Relative bioaccumulation for six non-chlorinated OPFRs and their transformation products. Dark red, light red, dark blue and light blue represent chlorinated parent OPFRs, chlorinated OPFR products, non-chlorinated parent OPFRs and non-chlorinated OPFR products, respectively. Note that a higher bioconcentration factor indicates a higher potential for bioaccumulation in aquatic organisms. Compounds marked with asterisks are those identified in megacity field samples.
a, Modelled fathead minnow LC50 values for three chlorinated OPFRs (TCEP, TCPP and TDCPP) and their products. b, Modelled fathead minnow LC50 values for six non-chlorinated OPFRs (TBEP, TPhP, EHDP, TCP, TEHP and DPhP) and their products. c, Relative toxicity for three chlorinated OPFRs and their transformation products (parent compound = 1). d, Relative toxicity for six non-chlorinated OPFRs and their transformation products. Dark red, light red, dark blue and light blue represent chlorinated parent OPFRs, chlorinated OPFR products, non-chlorinated parent OPFRs and non-chlorinated OPFR products, respectively. Note that a lower fathead minnow LC50 indicates a higher toxicity. Compounds marked with asterisks are those identified in megacity field samples.
About this article
Cite this article
Liu, Q., Li, L., Zhang, X. et al. Uncovering global-scale risks from commercial chemicals in air. Nature 600, 456–461 (2021). https://doi.org/10.1038/s41586-021-04134-6
Anthropocene Science (2022)
Analytical and Bioanalytical Chemistry (2022)