Interdisciplinary methods for researching climate-mental health links through the Methane Early Warning Network (ME-NET): improving ‘visibility’ and integrating complex multi-datasets

Abstract

Climatic and atmospheric conditions impact mental health, including increased incidents of depression associated with air pollution. A growing body of research considers time-bound ‘snap-shots’ of climatic drivers and mental health outcomes. Less is known about the likely effects of future climate change on mental health. Research is often inhibited by data scarcity, the challenge of synthesising data across multiple geospatial and temporal scales, and the under-representation of hard-to-reach groups. Thus, research methods are needed to integrate and analyse complex environmental and mental health multi-datasets while improving the visibility of under-represented groups. In this methods paper we present a novel approach for investigating the impact of climate change on mental health and addressing some challenges with, a) invisibility of vulnerable groups, and b) integrating complex environmental and mental health multi-datasets. The research aim is to pilot a web-based and smartphone application (Methane Early Warning Network (ME-NET)) for investigating the role of methane as a precursor of on-ground ozone, and the impact of ozone on mental health outcomes to improve civic knowledge and health-protection behaviour in the United Kingdom and Ghana. The methods include exploring the feasibility of using machine learning to develop an ozone early warning system and application co-design.