Screening for high-risk emerging contaminants in the atmosphere: Recent advances and new challenges

Air pollution is a major environmental issue of global concern. According to the latest statistics from the 2019 Global Burden of Disease Study, air pollution is the fourth leading cause of premature death and the largest environmental risk factor worldwide. After the implementation of air quality regulations and technological advances, air pollution levels have declined in recent decades. However, the dose-response relationship between the mass concentration of atmospheric particles and adverse effects is nonlinear. Refined pollution control measures that target key high-risk components in the atmosphere, rather than just particulate matter mass concentrations, may become more important for public health. It is very challenging to identify high-risk organic pollutants in the atmosphere because of the extreme complexity of atmospheric particulate matter components. Nontargeted analysis and suspect screening analysis offer analytical approaches for identifying unknown and unregulated organic compounds. Additional tools and strategies are needed to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose high risks to humans and the environment. In this paper, we systematically review the methods and recent findings for screening of high-risk air pollutants: (1) Recognize novel air pollutants using advanced analytical techniques and conduct risk-based prioritization; (2) combine biological and chemical analysis tools to identify key risk drivers in complex atmospheric mixtures. This revealed some previously unknown hazardous pollutants in outdoor air, indoor air, and individual exposure samples. To tackle the persistent challenges of air pollution and enhance the understanding of the link between air pollution and human health, screening methods should be improved. This could be achieved through the development of a global platform for integrating and sharing atmospheric pollutant data, strengthening the integration and analysis of toxic compound data, using machine learning for multi-level mining of big data, and creating toxicological models with high-throughput assessment capabilities that closely resemble human tissues. To enhance our understanding of what we breathe affects our health, it is necessary to consider multiple factors comprehensively, such as mixed exposure effects, unknown toxicological targets, interactions between chemical and biological pollutants, and coupling with natural or social environmental exposure. It is also important to investigate human exposure to atmospheric particulate matters with a focus on susceptible populations and critical windows. The use of multiomics technologies, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics, to analyze the complex multilevel interactions of pollutants with humans has created the exciting new field of exposomics in which exposures are directly linked to disease causal pathways. It is necessary to build statistical analysis models, carry out toxicological simulations, and conduct intervention validation and policy evaluation around the key risk factors, so as to form a risk assessment system with strong causal relationship. Such studies are important for identifying air pollutants for priority control and concentrating efforts to carry out follow-up environmental behavior investigations and toxicity testing.