How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States?

Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

doi:10.1016/j.atmosenv.2014.12.010

Cited by 160 publications

(80 citation statements)

References 53 publications

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“…Table 5 shows that some years are associated with favourable conditions for spaceborne retrievals of aerosol mass. While our single AOT-to-PM2.5 relationship gives reasonable results for most of the present cases, it is unclear how much of the uncertainties can be explained by differences in aerosol type (mass-scattering efficiency), aerosol heterogeneity, humidity and presence of clouds Á all of which might vary from case to case (Li et al, 2015). Hence, dedicated campaigns (Crumeyrolle et al, 2014) and measurement networks (Snider et al, 2015) are necessary to further explore the potential of using satellite remote sensing for spaceborne monitoring of aerosol mass and particleloading-related air quality.…”

Section: General Results and Conclusionmentioning

confidence: 95%

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Tesche¹,

Glantz²,

Johansson

2016

Tellus B: Chemical and Physical Meteorology

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A B S T R A C TThis article investigates the feasibility of using spaceborne observations of aerosol optical thickness (AOT) derived with the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring of fine particulate matter (PM2.5) in an environment of low aerosol loading. Previous studies of the AOT-to-PM2.5 relationship benefit from the large range of observed values. The Stockholm region features a comprehensive network of ground-based monitoring stations that generally show PM2.5 values B20 mg m (3 . MODIS AOT at 555 nm is usually B0.20 and in good agreement with ground-based sun photometer observations in this region. We use MODIS Collection 5 AOT data with a horizontal resolution of 10 km )10 km and ground-based in-situ PM2.5 observations to derive an AOT-to-PM2.5 relationship that can be used to estimate fields of PM2.5. This has been carried out with respect to the months from April to September of the period 2000Á2013. Relative average absolute deviations of 33Á55 % (mean of 45 %) are obtained between MODIS-retrieved and ground-based PM2.5. The root mean square error is 0.2159 mg m (3 between retrieved and measured PM2.5. From spaceborne lidar observations, it is found that elevated aerosol layers are generally sparse in the Stockholm region. This favours remote sensing of PM2.5 from space. The deviations found between measured and retrieved PM2.5 are mainly attributed to infrequent situations of inhomogeneous aerosol layering for which column-integrated observations cannot be connected to surface conditions. Using MODIS Collection 6 data with a resolution of 3 km)3 km in a case study actually gives far fewer results than the coarser Collection 5 product. This is explained by the complex geography of the Stockholm region with a coastline and an abundance of lakes, which seems to induce biases in the retrieval of AOT at higher spatial resolution.

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Section: General Results and Conclusionmentioning

confidence: 95%

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Tesche¹,

Glantz²,

Johansson

2016

Tellus B: Chemical and Physical Meteorology

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“…These three quantities can be treated identically, using their local probability distributions to define extreme events; however, the ASI, being Boolean, cannot define extremes in terms of severity. PM 2.5 extremes can occur in both summer and winter in ENA (38), but because the vast majority of temperature and O 3 extremes occur between late spring and early fall, our statistics consider only days during the extended summer season (April 1-September 30). We adopt the ∼95th percentile (see below) as the local extreme threshold, because that is equivalent to our 100-d per decade definition if all extremes occur during this extended summer period.…”

Section: Resultsmentioning

confidence: 99%

Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America

Schnell

Prather

2017

Proc. Natl. Acad. Sci. U.S.A.

164

142

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Heat waves and air pollution episodes pose a serious threat to human health and may worsen under future climate change. In this paper, we use 15 years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of commensurately gridded (1°x 1°) surface observations of extended summer (April-September) surface ozone (O 3 ), fine particulate matter (PM 2.5 ), and maximum temperature (TX) over the eastern United States and Canada to construct a climatology of the coincidence, overlap, and lag in space and time of their extremes. Extremes of each quantity are defined climatologically at each grid cell as the 50 d with the highest values in three 5-y windows (∼95th percentile). Any two extremes occur on the same day in the same grid cell more than 50% of the time in the northeastern United States, but on a domain average, co-occurrence is approximately 30%. Although not exactly co-occurring, many of these extremes show connectedness with consistent offsets in space and in time, which often defy traditional mechanistic explanations. All three extremes occur primarily in large-scale, multiday, spatially connected episodes with scales of >1,000 km and clearly coincide with large-scale meteorological features. The largest, longest-lived episodes have the highest incidence of co-occurrence and contain extreme values well above their local 95th percentile threshold, by +7 ppb for O 3 , +6 μg m −3 for PM 2.5 , and +1.7°C for TX. Our results demonstrate the need to evaluate these extremes as synergistic costressors to accurately quantify their impacts on human health.extremes | ozone | particulate matter | heat waves

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“…This particular economic region suffers frequently from haze episodes, especially in the winter heating period, due to coal burning in heating stations and power plants. variability comparison between satellite-derived AOD and PM2.5, Li et al [36] found diverse, even opposite, seasonal cycles in the PM2.5-AOD relationship owing to the varied height of the atmospheric mixing layer. Such studies highlighted the importance of spatio-temporal variability in PM2.5-AOD relations, but failed to explicitly and simultaneously incorporate variable factors into models to infer ground-level PM2.5 concentrations from satellite AOD estimations.…”

Section: Study Regionmentioning

confidence: 99%

“…The measurement of pollutant concentrations including ground-level PM 2.5 is therefore performed regularly in many industrialized countries that have established air quality monitoring stations a short distance from residential or manufacturing districts [6][7][8][9]. Traditional air quality estimations from ground-based stationary ambient variability comparison between satellite-derived AOD and PM 2.5 , Li et al [36] found diverse, even opposite, seasonal cycles in the PM 2.5 -AOD relationship owing to the varied height of the atmospheric mixing layer. Such studies highlighted the importance of spatio-temporal variability in PM 2.5 -AOD relations, but failed to explicitly and simultaneously incorporate variable factors into models to infer ground-level PM 2.5 concentrations from satellite AOD estimations.…”

Section: Introductionmentioning

confidence: 99%

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

et al. 2016

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Abstract:Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 µm in aerodynamic diameter (PM 2.5 ) is involved in many studies. To overcome the limitations of ground measurements on PM 2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM 2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM 2.5 -AOD are insufficient to investigate PM 2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM 2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM 2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM 2.5 -AOD relationship. The estimated ground-level PM 2.5 concentration has 500 m resolution at the MODIS satellite's overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R 2 ) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).

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How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States?

Cited by 160 publications

References 53 publications

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

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