Login / Signup

Uncovering global-scale risks from commercial chemicals in air.

Qifan LiuLi LiXianming ZhangAmandeep SainiWen-Long LiHayley HungChunyan HaoKun LiPatrick LeeJeremy J B WentzellChunyan HuoShao-Meng LiTom HarnerJohn Liggio
Published in: Nature (2021)
Commercial chemicals are used extensively across urban centres worldwide 1 , posing a potential exposure risk to 4.2 billion people 2 . 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 Convention 3 . 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 challenge 4 . 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 concern 5 , 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.
Keyphrases
  • human health
  • particulate matter
  • healthcare
  • risk assessment
  • transcription factor
  • mass spectrometry
  • multidrug resistant
  • machine learning
  • ionic liquid
  • genetic diversity
  • breast cancer risk