Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Amanollahi, Jamil; Tzanis, Chris G.; Abdullah, Ahmad Makmom; Ramli, Mohammad Firuz; Pirasteh, Saied

doi:10.1007/s13762-012-0150-7

Cited by 37 publications

(11 citation statements)

References 32 publications

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“…These studies suggest that the urban air quality control needs efforts from urban, rural, and even international collaborations. Moreover, other significant effects of aerosols (e.g., effects on the visibility, the nutrient balance, and acidity of soil) have also been reported [84,85].…”

Section: Discussionmentioning

confidence: 99%

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

Ali

El-Magd

et al. 2019

Remote Sensing

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This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.

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Section: Discussionmentioning

confidence: 99%

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

Ali

El-Magd

et al. 2019

Remote Sensing

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“…To avoid the negative effects of PM 2.5 , a series of laws, regulations, and standards have been implemented in various countries [6]. The determination of ground-level PM 2.5 concentrations with high accuracy, resolution, and coverage, either from measuring or modeling [7,8], is critical for accurate assessment of both PM 2.5 -related effects and policy measures. Inferring ground-level PM 2.5 concentrations from satellite-retrieved aerosol optical depth (AOD) data is an effective tool that can partially fill the monitoring gap left by ground monitors, which has led to its increase in popularity for predicting PM 2.5 concentrations [9][10][11] and PM 2.5 -related health effects [12,13].…”

Section: Introductionmentioning

confidence: 99%

Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

Yao

et al. 2019

Remote Sensing

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Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.

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“…PM 2.5 can directly reduce atmospheric visibility by scattering and absorbing the sunlight [14,15]. In addition, PM 2.5 can affect the amount of solar ultraviolet radiation reaching the Earth's surface [16][17][18], the nutrient balance and acidity of soil [19,20], and climate change [21]. Chronic exposure to PM 2.5 is associated with a wide range of diseases [4,10], mainly since atmospheric PM 2.5 can carry varieties of pathogenic components, such as heavy metals, viruses, and bacteria.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Sources of Heavy Metals in PM2.5 during a Typical Haze Episode in Rural and Urban Areas in Taiyuan, China

et al. 2017

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PM 2.5 samples were collected in the rural and urban areas of Taiyuan, China during a typical haze episode and the heavy metals (Cr, Mn, Ni, Cu, Zn, As, Cd and Pb) in PM 2.5 were analyzed. The haze was characterized by start-up stage with a daily mean PM 2.5 of 149.34 ± 52.33 and 146.73 ± 18.96 µg m −3 in the rural and urban sites, respectively, a peak stage (288.20 ± 12.43 and 323.44 ± 5.23 µg m −3 ), and a weakening stage (226.59 ± 12.43 and 195.60 ± 2.93 µg m −3 ). The concentrations of PM 2.5 in the rural and urban sites in the peak stage were 5.9 and 5.5 times higher than those in the normal stage, respectively. The order of concentrations of heavy metals in PM 2.5 at the rural and urban sites were the same and are listed as follows: Zn > Pb > Mn > Cr > Cu > Ni > Cd > As. Pb at the rural site, As at the urban site, and Cd at the both sites failed to meet the air quality standard. The concentrations of Pb and Zn were higher at the rural site than those at the urban site. Principal component analysis indicated that the main sources of heavy metals for the rural area were raw coal combustion and soil/road dust, and for the urban area were coal combustion/industrial emissions, road/soil dust, and vehicle emissions/oil combustion.

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Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Cited by 37 publications

References 32 publications

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

Characteristics and Sources of Heavy Metals in PM2.5 during a Typical Haze Episode in Rural and Urban Areas in Taiyuan, China

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