Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in ...satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R 2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R 2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.
Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been ...hindered by limited historical PM2.5 concentration data.
We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.
We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.
The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m(3) between 2004 and 2007 and a decrease of 0.46 μg/m(3) between 2008 and 2013.
Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.
Proven by multiple theoretical and practical studies,
multi-angular spectral polarimetry is ideal for comprehensive retrieval of
properties of aerosols. Furthermore, a large number of advanced space
...polarimeters have been launched recently or planned to be deployed in the
coming few years (Dubovik et al.,
2019). Nevertheless, at present, practical utilization of aerosol products
from polarimetry is rather limited, due to the relatively small number of
polarimetric compared to photometric observations, as well as challenges in
making full use of the extensive information content available in these
complex observations. Indeed, while in recent years several new algorithms
have been developed to provide enhanced aerosol retrievals from satellite
polarimetry, the practical value of available aerosol products from
polarimeters yet remains to be proven. In this regard, this paper presents
the analysis of aerosol products obtained by the Generalized Retrieval of
Atmosphere and Surface Properties (GRASP) algorithm from POLDER/PARASOL
observations. After about a decade of development, GRASP has been adapted
for operational processing of polarimetric satellite observations and
several aerosol products from POLDER/PARASOL observations have been
released. These updated PARASOL/GRASP products are publicly available (e.g.,
http://www.icare.univ-lille.fr, last access: 16 October 2018, http://www.grasp-open.com/products/, last access: 28 March 2020); the dataset used in the current study is
registered under https://doi.org/10.5281/zenodo.3887265 (Chen
et al., 2020). The objective of this study is to comprehensively evaluate the GRASP aerosol
products obtained from POLDER/PARASOL observations. First, the validation of
the entire 2005–2013 archive was conducted by comparing to ground-based
Aerosol Robotic Network (AERONET) data. The subjects of the validation are
spectral aerosol optical depth (AOD), aerosol absorption optical depth
(AAOD) and single-scattering albedo (SSA) at six wavelengths, as well as
Ångström exponent (AE), fine-mode AOD (AODF) and coarse-mode AOD
(AODC) interpolated to the reference wavelength 550 nm. Second, an
inter-comparison of PARASOL/GRASP products with the PARASOL/Operational,
MODIS Dark Target (DT), Deep Blue (DB) and Multi-Angle Implementation of
Atmospheric Correction (MAIAC) aerosol products for the year 2008 was
performed. Over land both satellite data validations and inter-comparisons
were conducted separately for different surface types, discriminated by bins
of normalized difference vegetation index (NDVI): < 0.2, 0.2 ≤
and < 0.4, 0.4 ≤ and < 0.6, and ≥ 0.6. Three
PARASOL/GRASP products were analyzed: GRASP/HP (“High Precision”),
Optimized and Models. These different products are consistent but were
obtained using different assumptions in aerosol modeling with different
accuracies of atmospheric radiative transfer (RT) calculations.
Specifically, when using GRASP/HP or Optimized there is direct retrieval of
the aerosol size distribution and spectral complex index of refraction. When
using GRASP/Models, the aerosol is approximated by a mixture of several
prescribed aerosol components, each with their own fixed size distribution
and optical properties, and only the concentrations of those components are
retrieved. GRASP/HP employs the most accurate RT calculations, while
GRASP/Optimized and GRASP/Models are optimized to achieve the best trade-off
between accuracy and speed. In all these three options, the underlying
surface reflectance is retrieved simultaneously with the aerosol properties,
and the radiative transfer calculations are performed “online” during the
retrieval. All validation results obtained for the full archive of PARASOL/GRASP
products show solid quality of retrieved aerosol characteristics. The
GRASP/Models retrievals, however, provided the most solid AOD products, e.g.,
AOD (550 nm) is unbiased and has the highest correlation (R ∼ 0.92) and the highest fraction of retrievals (∼ 55.3 %)
satisfying the accuracy requirements of the Global Climate Observing System
(GCOS) when compared to AERONET observations. GRASP/HP and GRASP/Optimized
AOD products show a non-negligible positive bias (∼ 0.07) when
AOD is low (< 0.2). On the other hand, the detailed aerosol
microphysical characteristics (AE, AODF, AODC, SSA, etc.) provided by
GRASP/HP and GRASP/Optimized correlate generally better with AERONET than do
the results of GRASP/Models. Overall, GRASP/HP processing demonstrates the
high quality of microphysical characteristics retrieval versus AERONET.
Evidently, the GRASP/Models approach is more adapted for retrieval of total AOD,
while the detailed aerosol microphysical properties are limited when a
mixture of aerosol models with fixed optical properties are used. The results of a comparative analysis of PARASOL/GRASP and MODIS products
showed that, based on validation against AERONET, the PARASOL/GRASP AOD (550 nm) product is of similar and sometimes of higher quality compared to the
MODIS products. All AOD retrievals are more accurate and in good agreement
over ocean. Over land, especially over bright surfaces, the retrieval
quality degrades and the differences in total AOD products increase. The
detailed aerosol characteristics, such as AE, AODF and AODC from
PARASOL/GRASP, are generally more reliable, especially over land. The global
inter-comparisons of PARASOL/GRASP versus MODIS showed rather robust
agreement, though some patterns and tendencies were observed. Over ocean,
PARASOL/Models and MODIS/DT AOD agree well with the correlation coefficient
of 0.92. Over land, the correlation between PARASOL/Models and the different
MODIS products is lower, ranging from 0.76 to 0.85. There is no significant
global offset; though over bright surfaces MODIS products tend to show
higher values compared to PARASOL/Models when AOD is low and smaller values
for moderate and high AODs. Seasonal AOD means suggest that PARASOL/GRASP
products show more biomass burning aerosol loading in central Africa and
dust over the Taklamakan Desert, but less AOD in the northern Sahara. It is
noticeable also that the correlation for the data over AERONET sites are
somewhat higher, suggesting that the retrieval assumptions generally work
better over AERONET sites than over the rest of the globe. One of the
potential reasons may be that MODIS retrievals, in general, rely more on
AERONET climatology than GRASP retrievals. Overall, the analysis shows that the quality of AOD retrieval from
multi-angular polarimetric observations like POLDER is at least comparable
to that of single-viewing MODIS-like imagers. At the same time, the
multi-angular polarimetric observations provide more information on other
aerosol properties (e.g., spectral AODF, AODC, AE), as well as additional
parameters such as AAOD and SSA.
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/m3 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.
Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and ...uncertainties during the period 1998–2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5–3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010–2019 by 1.6–2.6 μg/m3/year, with decreases beginning 2–3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.
The interactions between aerosols, clouds, and precipitation remain among the largest sources of uncertainty in the Earth's energy budget. Biomass-burning aerosols are a key feature of the global ...aerosol system, with significant annually-repeating fires in several parts of the world, including Southeast Asia (SEA). SEA in particular provides a "natural laboratory" for these studies, as smoke travels from source regions downwind in which it is coupled to persistent stratocumulus decks. However, SEA has been under-exploited for these studies. This review summarizes previous related field campaigns in SEA, with a focus on the ongoing Seven South East Asian Studies (7-SEAS) and results from the most recent BASELInE deployment. Progress from remote sensing and modeling studies, along with the challenges faced for these studies, are also discussed. We suggest that improvements to our knowledge of these aerosol/cloud effects require the synergistic use of field measurements with remote sensing and modeling tools.
Geophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite ...aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers.
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