Pollution State Modeling for Mexico City

TL;DR

This study develops a bivariate spatiotemporal model to predict hourly ozone and PM10 pollution levels in Mexico City, assessing compliance with air quality standards and identifying pollution emergency risks.

Contribution

The paper introduces a novel bivariate spatiotemporal modeling approach for predicting pollution levels and emergencies in Mexico City using hourly data from multiple stations.

Findings

Predicted pollution emergencies are limited to specific times.

Exceedance of air quality standards occurs nearly daily.

Model effectively predicts regional pollution maxima.

Abstract

Ground-level ozone and particulate matter pollutants are associated with a variety of health issues and increased mortality. For this reason, Mexican environmental agencies regulate pollutant levels. In addition, Mexico City defines pollution emergencies using thresholds that rely on regional maxima for ozone and particulate matter with diameter less than 10 micrometers ($\text{PM}_{10}$). To predict local pollution emergencies and to assess compliance to Mexican ambient air quality standards, we analyze hourly ozone and $\text{PM}_{10}$ measurements from 24 stations across Mexico City from 2017 using a bivariate spatiotemporal model. Using this model, we predict future pollutant levels using current weather conditions and recent pollutant concentrations. Using hourly pollutant projections, we predict regional maxima needed to estimate the probability of future pollution emergencies. We discuss how predicted compliance to legislated pollution limits varies across regions within Mexico City in 2017. We find that predicted probability of pollution emergencies is limited to a few time periods. In contrast, we show that predicted exceedance of Mexican ambient air quality standards is a common, nearly daily occurrence.