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.
Highlights
Ambient particulate matter (PM) with aerodynamic diameter less than 2.5 μm (PM2.5) is known to have adverse effects on visibility, ecosystems, and climate change [1]
aerosol optical depth (AOD) was found to have a strong positive relationship with PM2.5 concentrations observed on the surface [60,61], but there is evidence that the use of AOD as the only predictor of PM2.5 is subject to large uncertainties [62]
MERRA-2 AOD data were compared to ground-based PM2.5 measurements to determine its suitability as a proxy for PM2.5 in the Monterrey Metropolitan Area (MMA)
Summary
Ambient particulate matter (PM) with aerodynamic diameter less than 2.5 μm (PM2.5) is known to have adverse effects on visibility, ecosystems, and climate change [1]. A 10 μg m−3 increase in ambient air PM2.5 concentration has shown to have a significant increase in the risk of neurological disorders (i.e., stroke, dementia, Alzheimer’s disease, and Parkinson’s disease) [2], as well as respiratory, cardiovascular, and cerebrovascular [3] morbidity and mortality in all ages [4,5] of the exposed population. The full understanding of the spatiotemporal variations in air pollution exposures that occur due to local emissions sources, such as urban transportation and finer-scale meteorology, could help in identifying at-risk and vulnerable populations, control relevant emissions contributing to exposure, and protect public health [7]. The effective management of air pollution is limited by sparse monitoring networks. Alternative approaches are required to describe large-scale spatial distributions of PM2.5
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