Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.

By A. van Donkelaar, Randall V. Martin, M. Brauer, N. C. Hsu, Ralph A. Kahn, Robert C. Levy, Alexei I. Lyapustin, A. Sayer, D. Winker. Environmental Science and Technology, 2016

1,065 citations recorded by the source corpus.

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.

Subfields: Environmental Engineering, Global and Planetary Change, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/9ced0e5b280553b5e3abd806f57901a690d5159e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Ming Zhang
2. Gavin Shaddick
3. James V. Zidek
4. Huanfeng Shen
5. Wenwen Wang
6. Liangpei Zhang
7. Francky Fouedjio

In Global and Planetary Change

1. A. Donkelaar
2. Randall V. Martin
3. Liangpei Zhang

In Health, Toxicology and Mutagenesis

1. M. Brauer
2. Wenwen Wang
3. Ming Zhang

Citations in the corpus

• Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China (2019, methodology): “The annual estimates were generated following a geographically weighted regression technique (van Donkelaar et al., 2016).”
• Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution (2016, methodology): “Recently, Van Donkelaar et al. (2016) extended this approach by using geographically weighted regression to allow calibration (between the measurements and estimates) equations to vary spatially and to utilize additional information related to land use and the chemical composition of particulate…”
• Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach (2017, result): “We also compared our results with previous satellite-derived PM 2.5 data (van Donkelaar et al., 2016).”