Categorization of the Mexican PM2.5 Air Quality Index Based on Satellite-Derived Data and Meteorological Ground-Based Measurements: Case Study for the Monterrey Metropolitan Area, Mexico.

The Metropolitan Air Quality Index (IMECA) in Mexico, MX-AQI in this document, is a numerical index based on measured concentrations of selected ambient air pollutants and its compliance with regulations. The MX-AQI uses data collected from air quality monitoring stations and reports the severity of air pollution to the public. It also specifies its adverse effects on human health and can be used to suggest preventive measures. The MX-AQI is obtained from the calculation of individual AQI values for each of the six criteria pollutants in ambient air: carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), coarse particulate matter (PM₁₀) and fine particulate matter (PM_2.5).

In this study, we developed a Neural Network model (NN) to estimate daily PM_2.5 concentrations using ground-based data (PM_2.5, temperature, relative humidity and wind components) and satellite-derived data (AOD from MODIS) over the Monterrey Metropolitan Area (MMA), the main urban area in Northeastern Mexico and one of the most polluted regions in the country. Then, both the observed and model-estimated daily PM_2.5 concentrations were classified according to the categories of the MX-AQI for PM_2.5 (PM_2.5 MX-AQI) for a period covering from January 2010 to December 2017.

PM_2.5 MX-AQI categories for the observed PM_2.5 were found to be good, moderate and bad. The observed PM_2.5 MX-AQI revealed a distinct seasonal variation: a decreasing trend was observed from spring to summer, but then concentrations increased from fall to winter, indicating that air quality across the region is worse in winter than in summer. The PM_2.5 MX-AQI classification using the NN presented similar seasonal variation to that obtained with observed PM_2.5 MX-AQI. The developed NN showed 90.35 % overall accuracy, 2.45 % overestimation and 7.20 % underestimation.

The results obtained in this study confirmed that AOD satellite-derived data in combination with meteorological fields can be used to estimate PM_2.5 MX-AQI with high accuracy. The methodology developed here can also help to fill data gaps particularly in locations where there is a lack of ground-based information.