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Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data
- Source :
- Environmental Monitoring and Assessment. 191
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 μm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.
- Subjects :
- 010504 meteorology & atmospheric sciences
Meteorology
General Medicine
010501 environmental sciences
Management, Monitoring, Policy and Law
Wind direction
01 natural sciences
Pollution
Wind speed
Aerosol
Data assimilation
Environmental science
Relative humidity
Satellite
Precipitation
Predictive modelling
0105 earth and related environmental sciences
General Environmental Science
Subjects
Details
- ISSN :
- 15732959 and 01676369
- Volume :
- 191
- Database :
- OpenAIRE
- Journal :
- Environmental Monitoring and Assessment
- Accession number :
- edsair.doi...........94dadc9909902d9efa22d2b100599b78
- Full Text :
- https://doi.org/10.1007/s10661-019-7414-3