| Abstract | Exposure to pollution in the environment is a major contributor to disease globally and is a topic of great significance. There remains, however, a dearth of knowledge about the levels and distribution of airborne pollutants in the environment, along with how exposure to complex mixtures of airborne chemicals impacts health outcomes. Recent collaborations between artificial intelligence (AI) researchers and environmental health have demonstrated a great potential to help advance the science of air pollution epidemiology, urban planning and public policy. In this chapter, we discuss how AI can be leveraged to improve knowledge and understanding about air pollution and environmental health. We explore this question in general and present a case study on the DoMiNo project, which utilises AI algorithms in combination with pattern visualisation via VizAR and traditional epidemiological analysis to generate hypothesis about which mixtures of airborne chemicals negatively impact birth outcomes. Our results highlight both the great potential for AI in this field along with some interesting challenges for AI researchers to address in future work with environmental health researchers. |
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