  According to the Lancet report on the global burden of disease published in
October 2020, air pollution is among the five highest risk factors for global
health, reducing life expectancy on average by 20 months. This paper describes
a data-driven method for establishing causal relationships within and between
two multivariate time series data streams derived from wearable sensors:
personal exposure to airborne particulate matter of aerodynamic sizes less than
2.5um (PM2.5) gathered from the Airspeck monitor worn on the person and
continuous respiratory rate (breaths per minute) measured by the Respeck
monitor worn as a plaster on the chest. Results are presented for a cohort of
113 asthmatic adolescents using the PCMCI+ algorithm to learn the short-term
causal relationships between lags of \pm exposure and respiratory rate. We
consider causal effects up to a maximum delay of 8 hours, using data at both a
1 minute and 15 minute resolution in different experiments. For the first time
a personalised exposure-response relationship between PM2.5 exposure and
respiratory rate has been demonstrated to exist for short-term effects in
asthmatic adolescents during their everyday lives. Our results lead to
recommendations for work on specific open problems in causal discovery, to
increase the feasibility of this approach for similar epidemiology studies in
the future.
