Browsing by Author "David M. Broday"

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Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations
(Elsevier Ltd, 2023) Somaya Falah; Fadi Kizel; Tirthankar Banerjee; David M. Broday
Fine airborne particles (diameter <2.5 μm; PM2.5) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM2.5. The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R2 (RF: 0.753–0.909; XGBoost: 0.741–0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources. © 2023 Elsevier Ltd
Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia
(Elsevier Inc., 2019) Alaa Mhawish; Tirthankar Banerjee; Meytar Sorek-Hamer; Alexei Lyapustin; David M. Broday; Robert Chatfield
The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. Capsule: In comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage. © 2019 Elsevier Inc.
Estimation of High-Resolution PM2.5over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables
(American Chemical Society, 2020) Alaa Mhawish; Tirthankar Banerjee; Meytar Sorek-Hamer; Muhammad Bilal; Alexei I. Lyapustin; Robert Chatfield; David M. Broday
Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 μg/m3. Copyright © 2020 American Chemical Society.
Evaluation of MODIS Collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: Implications of aerosols types and mass loading
(Elsevier Inc., 2017) Alaa Mhawish; Tirthankar Banerjee; David M. Broday; Amit Misra; Sachchida N. Tripathi
This study evaluates the performance of MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) AOD retrieval algorithms, including Dark Target (DT) aerosol optical depth (AOD) at 3 and 10 km spatial resolutions, Deep Blue (DB) AOD at 10 km, and the merged DT-DB AOD at 10 km across the Indo-Gangetic Plain (IGP), South Asia. A total of 14,736 collocated Aqua MODIS C6 AOD at 550 nm were evaluated against AOD from six AERONET stations over IGP, measured during the satellite overpass (± 1 h) from 2006 to 2015. The effects of aerosol heterogeneity, in terms of both aerosol loading and the aerosol type, on the uncertainty of the satellite-borne AOD retrieval were examined. The DT algorithm at both resolutions (3 km and 10 km) overestimated the AOD by 14–25%, with only 51.37–61.29% of the retrievals falling within the expected error (EE). The DT 3 km algorithm underestimates the surface reflectance in comparison to the DT 10 km, with the latter outperforming the former both in terms of number of collocations and retrieval accuracy, especially over urban areas. The DB 10 km was able to retrieve AOD over both arid/desert regions and vegetated surfaces even under low aerosol loading conditions. Yet, its performance was still poor, with retrieval accuracy of 53.76%, low RMSE (0.214), and generally underestimated AOD across the IGP. The merged DT-DB AOD product was mostly dominated by DT retrievals (73%–100%), except over bright land surfaces and 56.03% of the merged DT-DB retrievals fell within the EE. The retrieval accuracy of MODIS C6 products was found to be strongly dependent on the estimated surface reflectance and the aerosol type. Across IGP, DB predicted the surface reflectance better while DT at both resolutions overestimated the surface reflectance at varying extent. For high aerosol loading conditions with varying aerosol size, retrieval accuracy of DT 10 km poses lower sensitivity while DT at 3 km exhibits larger uncertainty in estimating surface reflectance. In contrast, DB 10 km shows greater bias that depends on the aerosol size. For very high aerosol loading conditions, dominated by fine or mixed aerosols, all the algorithms have errors in the aerosol model. The DT 10 km, DB 10 km and the merged AOD performed almost equally within the threshold level while the DT 3 km showed the poorest performance in terms of retrieval accuracy and RMSE. We conclude that across IGP, DB 10 km AOD has the highest accuracy in retrieving fine mode aerosols while DT 10 km AOD has almost identical accuracy in retrieving varying aerosol types. For coarse dominated aerosols, when the dissimilarity between DT and DB remains highest, the merged AOD is found to have higher accuracy in retrieving AOD across IGP. © 2017 Elsevier Inc.
Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis
(Elsevier Ltd, 2021) Somaya Falah; Alaa Mhawish; Meytar Sorek-Hamer; Alexei I. Lyapustin; Itai Kloog; Tirthankar Banerjee; Fadi Kizel; David M. Broday
This work examines the impact of different environmental attributes on the uncertainty in satellite-based Aerosol Optical Depth (AOD) retrieval against the benchmark Aerosol Robotic Network (AERONET) AOD measurements at 21 sites across North Africa, California and Germany, in the years 2007–2017. As a first step, we studied the effects of spatial averaging the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals, and of temporal averaging the AERONET AOD around the satellite (Aqua) overpass, on the agreement between the two products. AERONET AOD averaging over a time-window of ±15 min around the satellite overpass and the 1 × 1 km2 spatial grid of MAIAC were found to provide the best AOD retrieval performance. Next, MAIAC AOD were stratified according to different co-measured environmental attributes (aerosol loading, dominant particle size, vegetation cover, and prevailing particle type) and analyzed against the AERONET AOD. The envelope of the expected retrieval error varied considerably among different environmental attributes categories, with more accurate AOD retrievals obtained over highly vegetated areas (i.e. less surface reflectance) than over arid areas. Moreover, the retrieval accuracy was found to be sensitive to the aerosol loading and particle size, with a large bias between the MAIAC and AERONET AOD during high aerosol loading of coarse particles. In addition, the retrieval accuracy of MAIAC AOD was found to depend on the aerosol type due to the aerosol model assumptions regarding their optical properties. © 2021 Elsevier Ltd
Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions
(MDPI, 2022) Somaya Falah; Alaa Mhawish; Ali H. Omar; Meytar Sorek-Hamer; Alexei I. Lyapustin; Tirthankar Banerjee; Fadi Kizel; David M. Broday
This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health. © 2022 by the authors.
Towards Understanding the dependency of AOD-PM association on the aerosol vertical distribution
(Air and Waste Management Association, 2017) Meytar Sorek-Hamer; Robert B. Chatfield; Anthony W. Strawa; David M. Broday; Tirthankar Banerjee; Furman-Krasnov Helena; Itzik Katra; Kloog Itai
Since the launch of Aqua and Terra satellites, the use of satellite observations to estimate ground particulate matter (PM) concentrations has been a growing trend. Numerous studies used the satellite-based Aerosol Optical Depth (AOD) as an explanatory variable in different statistical models associating the AOD with PM concentration observed at ground level. Although AOD has a better spatial coverage than PM measurements, it sets many constraints on the construction of PM predictive models: a coarse temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AOD, a large number of missing data due to cloud presence, and an unknown vertical distribution. In conjunction with a long record of vertical aerosol distributions from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation), this work analyzed the AOD relationship to surface respirable PM concentrations while searching for the optimum conditions where AOD best represents PM in relation to the location of the detected aerosols in the vertical column. Data from CALIPSO, high resolution satellite-based AOD, and the planetary boundary layer (PBL) thickness were investigated in Northeast USA, Indo Gangetic Plain-India, France, and Israel (2006-2013). We found considerable variability among the different geographic regions and along the years with respect to where, on average, (i.e. below or above the PBL) the aerosols reside. A relatively low number of cases with aerosols detected only within the PBL was found in all locations. Accounting for the vertical location of the aerosols did not result in improved relationships between AOD and PM observations using either Linear regression or Mixed effects models. Although when the aerosols were detected within the PBL the RMSE (estimation error) was relatively low, the explained variability was inconsistent. Understanding the conditions under which the satellite-based AOD represents well the ground PM is still a challenge, this work shades light and should contribute towards this goal.
Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain
(Elsevier Ltd, 2020) K.S. Vinjamuri; Alaa Mhawish; Tirthankar Banerjee; Meytar Sorek-Hamer; David M. Broday; Rajesh K. Mall; Mohd Talib Latif
Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2–50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ± 0.65 km) and wheat (AM: 2.34 ± 1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50–80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling. Smoke aerosols were most abundant over upper Indo-Gangetic Plain and 50–80% of smoke AOD remain close (<3 km) to the surface. © 2019 Elsevier Ltd