Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

Abstract

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

“…The investigation of spatial differences and temporal variation patterns is an important part of the research on air pollution [8]. A study found higher levels of PM 2.5 concentrations in Mexico in winter and spring using the MERRA-2 reanalysis datasets, with the highest concentrations occurring in spring [9]. Fine particulate matter in the United States has shown a decreasing trend over time in recent decades [10].…”

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

“…In northeastern Mexico, only Carmona et al (2020) [17] has evaluated the ability of MERRA-2 aerosol components to represent PM 2.5 ground concentrations and to develop and validate an ENN model to estimate monthly average PM 2.5 ground concentrations. The results of their comparison between the ENN and ground measurements were as follows: R =~0.90, RMSE = 1.81 µg m −3 , and MAE = 1.31 µg m −3 .…”

“…The AOD-PM 2.5 relationship, however, varies in space and time and needs to be specifically assessed for different regions and seasons [15]. Despite Latin America being one of the most urbanized, populated, and polluted regions in the world, where most cities lack robust ground-based PM 2.5 monitoring networks, few studies have examined the relationship between AOD and PM 2.5 in this region (e.g., [16][17][18][19][20]).…”

“…To the best of our knowledge, this is the first such study within this region; only Carmona et al (2020) [17] have recently evaluated the ability of the Modern-Era Retrospective Analysis for Research and Application model, version 2 (MERRA-2), aerosol components to represent PM 2.5 ground concentrations. Therefore, this study contributes to the knowledge and understanding of the performance of MODIS AOD-PM 2.5 retrieval algorithms over urban, mixed-use land coverage areas with limited ground-based observations.…”

COVID-19 lockdown improves air quality in Morocco

“…MERRA-2 data has been found to better simulate surface fine particulate mass PM 2.5 [47]. Some studies have shown the compatibility of MERRA's data trends with ground trends, such as the study conducted in the Monterrey Metropolitan Area in Mexico [48].…”