Estimation of particulate matter from satellite-and ground-based observations over Hyderabad, India
TL;DR: In this paper, the authors used a regression analysis between MODIS-AOTs, Microtops-II Sunphotometer MTS AOTs and measured PM2 to estimate surface-level PM2 concentrations over Greater Hyderabad Region GHR, India.
Abstract: Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm PM2 in regard to air quality observations over Greater Hyderabad Region GHR, India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 satellite and July 2009–Dec 2013 ground-based. Terra Moderate Resolution Imaging Spectroradiometer MODIS-derived aerosol optical thickness AOT MODIS-AOTs was validated against that measured from Microtops-II Sunphotometer MTS AOTs MTS-AOTs and then utilized to estimate surface-level PM2 concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM2. In general, the MODIS-estimated PM2 concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM2 from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM2 estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM2 estimates from MODIS. The estimates of PM2 using MTS or MODIS-AOT exhibit a root mean square deference RMSD of about 8–16% against measured PM2 on a seasonal basis. Furthermore, a tendency of increasing PM2 concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM2 estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM2 from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.
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14 Oct 2016
TL;DR: In this paper, the authors reviewed the performance of four predicting models: Multiple Linear Regression (MLR), Mixed-Effect Model (MEM), Chemical Transport Model (CTM), Geographically Weighted Regression and Geographically weighted Regression(GWR).
Abstract: This study reviewed the prediction of fine particulate matter (PM2.5) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM2.5 and other air pollutants.
147 citations
TL;DR: In this paper, the authors developed empirical models for PM10 estimation from space over Malaysia using aerosol optical depth (AOD550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011.
64 citations
TL;DR: In this article, the authors used satellite remote sensing Aerosol optical depth (AODMODIS or MODIS AOD) and ground-based meteorological measurements from April-2010 to March-2014 over Jaipur, semi-arid region in North-western India.
44 citations
TL;DR: Temporal evolution, source apportionment and transport pathways of particulate matter (PM2.5 and PM10) are analyzed over Guwahati, located in the Brahmaputra River Valley (BRV), as a function of meteorological dynamics as discussed by the authors.
40 citations
TL;DR: In this paper, the authors present a 3-year's worth of measurements (January 2012 to December 2014) of PM2.5 and PM10 along with meteorological parameters and seasonal variations at Bhubaneswar an urban-coastal site, in eastern India.
36 citations
References
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TL;DR: Satellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations, with significant spatial agreement with ground-based in situ measurements.
Abstract: BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite...
1,401 citations
"Estimation of particulate matter fr..." refers methods in this paper
...5 mass concentration using aerosol optical thickness (AOT) retrieval from polar (Engel-Cox et al. 2004; Liu, Franklin, and Koutrakis 2007; Gupta and Christopher 2009a, 2009b; Van Donkelaar et al. 2010) and geostationary (Paciorek et al. 2008) orbits....
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TL;DR: In this article, the authors validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites, and find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERO-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9).
Abstract: . NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONET-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported Angstrom exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's–retrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ~0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of
1,069 citations
"Estimation of particulate matter fr..." refers background or result in this paper
...The expected uncertainty in MODIS-AOTs over land is 0.05 ± 0.15 × AOT, while several global validation studies over Aerosol Robotic Network (AERONET) locations revealed that about 72% of MODIS-AOT retrievals were within the expected uncertainty (Levy et al. 2010)....
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...15 × AOT, while several global validation studies over Aerosol Robotic Network (AERONET) locations revealed that about 72% of MODIS-AOT retrievals were within the expected uncertainty (Levy et al. 2010)....
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...The correlation coefficient between MTS-AOT550 and MODIS-AOT from the linear regression was found to be 0.81, which lies within the uncertainties ±(0.05 ± 15% AOT), found in the correlation between MODIS and AERONET AOTs (Levy et al. 2010)....
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...5 estimation from satellites depends on the accuracy of AOT retrieval, which is a function of aerosol type and surface reflectance (Kahn et al. 2010; Levy et al. 2010), vertical aerosol homogeneity, and the atmospheric dynamics within the boundary layer....
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...1 or 24% of the MTS-AOT550, which is comparable to those obtained by various other validation studies over India and globally (e.g. Jethva, Satheesh, and Srinivasan 2007; Prasad and Singh 2007; Levy et al. 2010)....
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TL;DR: Wang et al. as mentioned in this paper explored the relationship between column aerosol optical thickness (AOT) derived from the Moderate Resolution Imaging SpectroRadiometer (MODIS) on the Terra/Aqua satellites and hourly fine particulate mass (PM2.5) measured at the surface at seven locations in Jefferson county, Alabama for 2002.
Abstract: [1] We explore the relationship between column aerosol optical thickness (AOT) derived from the Moderate Resolution Imaging SpectroRadiometer (MODIS) on the Terra/Aqua satellites and hourly fine particulate mass (PM2.5) measured at the surface at seven locations in Jefferson county, Alabama for 2002. Results indicate that there is a good correlation between the satellite-derived AOT and PM2.5 (linear correlation coefficient, R = 0.7) indicating that most of the aerosols are in the well-mixed lower boundary layer during the satellite overpass times. There is excellent agreement between the monthly mean PM2.5 and MODIS AOT (R > 0.9), with maximum values during the summer months due to enhanced photolysis. The PM2.5 has a distinct diurnal signature with maxima in the early morning (6:00 8:00AM) due to increased traffic flow and restricted mixing depths during these hours. Using simple empirical linear relationships derived between the MODIS AOT and 24hr mean PM2.5 we show that the MODIS AOT can be used quantitatively to estimate air quality categories as defined by the U.S. Environmental Protection Agency (EPA) with an accuracy of more than 90% in cloud-free conditions. We discuss the factors that affect the correlation between satellite-derived AOT and PM2.5 mass, and emphasize that more research is needed before applying these methods and results over other areas. INDEX TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0345 Atmospheric Composition and Structure: Pollution—urban and regional (0305); 3360 Meteorology and Atmospheric Dynamics: Remote sensing; 3300 Meteorology and Atmospheric Dynamics. Citation: Wang, J., and S. A. Christopher, Intercomparison between satellitederived aerosol optical thickness and PM2.5 mass: Implications for air quality studies, Geophys. Res. Lett., 30(21), 2095, doi:10.1029/ 2003GL018174, 2003.
796 citations
Additional excerpts
...…between PM2 and AOT analysed above are based on ground measurements over a point location at Hyderabad, while several previous studies (Wang and Christopher 2003; Gupta and Christopher 2009a; Gupta et al. 2013) have shown variability in AOT–PM relationship as a function of time and…...
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TL;DR: In this article, the authors compared true color images and quantitative aerosol optical depth data from the MODIS sensor on the Terra satellite with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 April to 30 September, 2002.
571 citations
"Estimation of particulate matter fr..." refers methods in this paper
...5 mass concentration using aerosol optical thickness (AOT) retrieval from polar (Engel-Cox et al. 2004; Liu, Franklin, and Koutrakis 2007; Gupta and Christopher 2009a, 2009b; Van Donkelaar et al. 2010) and geostationary (Paciorek et al. 2008) orbits....
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TL;DR: In this paper, the authors used the climatology of almucantur retrievals from global Aerosol Robotic Network (AERONET) Sun photometer sites to determine aerosol type as a function of location and season.
Abstract: [1] As more information about global aerosol properties has become available from remotely sensed retrievals and in situ measurements, it is prudent to evaluate this new information, both on its own and in the context of satellite retrieval algorithms. Using the climatology of almucantur retrievals from global Aerosol Robotic Network (AERONET) Sun photometer sites, we perform cluster analysis to determine aerosol type as a function of location and season. We find that three spherical-derived types (describing fine-sized dominated aerosol) and one spheroid-derived types (describing coarse-sized dominated aerosol, presumably dust) generally describe the range of AERONET observed global aerosol properties. The fine-dominated types are separated mainly by their single scattering albedo (ω0), ranging from nonabsorbing aerosol (ω0 ∼ 0.95) in developed urban/industrial regions, to moderately absorbing aerosol (ω0 ∼ 0.90) in forest fire burning and developing industrial regions, to absorbing aerosol (ω0 ∼ 0.85) in regions of savanna/grassland burning. We identify the dominant aerosol type at each site, and extrapolate to create seasonal 1° × 1° maps of expected aerosol types. Each aerosol type is bilognormal, with dynamic (function of optical depth) size parameters (radius, standard deviation, volume distribution) and complex refractive index. Not only are these parameters interesting in their own right, they can also be applied to aerosol retrieval algorithms, such as to aerosol retrieval over land from Moderate Resolution Imaging Spectroradiometer. Independent direct-Sun AERONET observations of spectral aerosol optical depth (τ) are consistent the spectral dependence of the models, indicating that our derived aerosol models are relevant.
556 citations
"Estimation of particulate matter fr..." refers methods in this paper
...…the spectral range 0.41–15.00 µm, with a spatial resolution ranging between 250 and 1000 m. MODIS-AOT retrievals were performed by means of the dark target algorithm, and therefore, retrievals over bright surfaces (such as desert and bright urban areas) are biased (Levy, Remer, and Dubovik 2007)....
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