Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Titre : Satellite-derived data and ground-based measurements relationships for assessing local and regional distributions of PM2.5 : model development and applications

Auteur : Carmona García, Johana Margarita

Etablissement de soutenance : Instituto de Tecnológico de Estudios Superiores de Monterrey (ITESM)

Grade : Doctor of Philosophy in Engineering Science 2020

Résumé partiel
Estimating concentrations of ground-level PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) from satellite-derived Aerosol Optical Depth (AOD) through statistical models is a promising method to evaluate the spatial and temporal distribution of PM2.5. Although PM concentrations are most accurately measured using ground-based instruments, the spatial coverage is often too sparse to determine local and regional variations in PM2.5. AOD satellite data offers the opportunity to overcome the spatial limitation of ground-based measurements. Combining ground-based measurements with satellite and reanalysis data can be a robust tool to assess air pollution models. However, estimating PM2.5 surface concentrations from AOD satellite data is challenging, since multiple factors can affect the relationship between the total-column of AOD and the surface-concentration of PM2.5. This study aims to establish a relationship between AOD satellite data and ground-based data that will allow designing relational models for the study of local PM2.5 pollution and the regional PM2.5 distributions in northeastern Mexico (NEM). First, an Ensemble Multiple Linear Regression Model (MLR) and a Neural Network Model (NN) were developed to estimate the relationship between the AOD and ground-concentrations of PM2.5 within the MMA. The best performance of the models was obtained using a daily scheme, an AOD at 550 µm from the MYD04_3k product in combination with Temperature, Relative Humidity, Wind Speed and Wind Direction ground-based data. For the MLR developed, a correlation coefficient of R 0.57 and mean percentage error -6% were obtained. The NN showed a better performance than the MLR, with a correlation coefficient of R 0.73 and mean percentage error –3%. The results obtained confirmed that satellite-derived AOD in combination with meteorological fields may allow to estimate PM2.5 local distributions. Then, both the observed and model-estimated daily PM2.5 concentrations were classified according to the categories of the Mexican Air Quality Index for PM2.5 (PM2.5 MX-AQI). The observed PM2.5 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.

Présentation

Version intégrale 13 Mb)