Modeling Daily Seasonality of Mexico City Ozone using Nonseparable Covariance Models on Circles Cross Time

TL;DR

This paper develops new nonseparable covariance models for circular and linear time data to accurately predict ozone levels in Mexico City, revealing significant spatial and temporal variability in air quality and health risks.

Contribution

It introduces novel nonseparable covariance models suitable for quasi-periodic spatiotemporal data on circles and linear time, applied to ozone prediction in Mexico City.

Findings

Many regions exceed ozone standards during peak season.

Ozone risk can be 55% higher than annual average.

Nearly all city areas exceed thresholds on some days.

Abstract

Mexico City tracks ground-level ozone levels to assess compliance with national ambient air quality standards and to prevent environmental health emergencies. Ozone levels show distinct daily patterns, within the city, and over the course of the year. To model these data, we use covariance models over space, circular time, and linear time. We review existing models and develop new classes of nonseparable covariance models of this type, models appropriate for quasi-periodic data collected at many locations. With these covariance models, we use nearest-neighbor Gaussian processes to predict hourly ozone levels at unobserved locations in April and May, the peak ozone season, to infer compliance to Mexican air quality standards and to estimate respiratory health risk associated with ozone. Predicted compliance with air quality standards and estimated respiratory health risk vary greatly over space and time. In some regions, we predict exceedance of national standards for more than a third of the hours in April and May. On many days, we predict that nearly all of Mexico City exceeds nationally legislated ozone thresholds at least once. In peak regions, we estimate respiratory risk for ozone to be 55% higher on average than the annual average risk and as much at 170% higher on some days.