DOI: 10.1016/j.atmosenv.2014.12.010
Scopus记录号: 2-s2.0-84916883351
论文题名: How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States?
作者: Li J ; , Carlson B ; E ; , Lacis A ; A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 102 起始页码: 260
结束页码: 273
语种: 英语
英文关键词: Aerosol optical depth
; PM2
; Principal component analysis
; Satellite remote sensing
; Spatial and temporal variability
Scopus关键词: Atmospheric aerosols
; Band structure
; Climate change
; Environmental Protection Agency
; Image reconstruction
; Imaging techniques
; Importance sampling
; Optical properties
; Optical radar
; Radiometers
; Remote sensing
; Satellite imagery
; Spectrometers
; Spectrum analysis
; Ultraviolet spectrometers
; Aerosol optical depths
; Cloud-aerosol lidar with orthogonal polarizations
; Moderate resolution imaging spectroradiometer
; Multiangle imaging spectroradiometer
; Ozone monitoring instruments
; Satellite remote sensing
; Sea-viewing wide field-of-view sensors
; Spatial and temporal variability
; Principal component analysis
; ozone
; concentration (composition)
; lidar
; MODIS
; observational method
; particulate matter
; principal component analysis
; satellite data
; satellite imagery
; satellite sensor
; spatiotemporal analysis
; aerosol
; air quality
; Article
; cloud
; particulate matter
; seasonal variation
; spectroscopy
; summer
; time series analysis
; United States
; winter
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique - Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations. © 2014 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82067
Appears in Collections: 气候变化事实与影响
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作者单位: NASA Goddard Institute for Space Studies, New York, NY, United States; Department of Applied Physics and Applied Math, Columbia University, New York, NY, United States
Recommended Citation:
Li J,, Carlson B,E,et al. How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States?[J]. Atmospheric Environment,2015-01-01,102