Forecasting short-term peak concentrations from a network of air quality instruments measuring PM2.5 using boosted gradient machine models.

Abstract

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 (PM2.5) 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 PM2.5 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 PM2.5 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).