Forecasting short-term peak concentrations from a network of air quality instruments measuring PM_2.5 using boosted gradient machine models

Machine learning algorithms are used successfully in this paper to forecast reliably upcoming short-term high concentration episodes, or peaks (<60-min) of fine particulate air pollution (PM_2.5) 1 h in advance. Results are from a network around Christchurch, New Zealand, with an objective to forecast the occurrence of short-term peaks using a gradient boosted machine with a binary classifier as the response (1 = peak, 0 = no peak). Results are successful, with 80-90% accurate forecasting of whether a peak in PM_2.5 would occur within the next 60-min period. Elevated and variable nitrogen monoxide, nitrogen dioxide, and lower temperatures and wind gusts are found to be important precursors to the occurrence of PM_2.5 peaks. The use of meteorological data from a network of personal weather stations across the monitored area and from the measurement instruments was able to identify local-scale peak differences in the network. Boosted models using hourly-averaged and daily-averaged peaks as the response are developed separately to showcase differences in precursors between short-term and long-term peaks, with recent wind gusts and nitrogen oxides linked to hourly-averaged peaks and aloft air temperatures and atmospheric pressure linked to daily-averaged peaks. Results could prove useful in exposure mitigation strategies (e.g. as a short-term warning system).