Spectral, spatial, and temporal sensitivity of correlating MODIS aerosol optical depth with ground-based fine particulate matter (PM2.5) across southern Ontario

2010 ◽  
Vol 36 (2) ◽  
pp. 119-128 ◽  
Author(s):  
Jie Tian ◽  
Dongmei Chen
Keyword(s):  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Southern Ontario ◽  
Fine Particulate

scholarly journals Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals

2014 ◽  
Vol 89 ◽  
pp. 189-198 ◽  
Author(s):  
Alexandra A. Chudnovsky ◽  
Petros Koutrakis ◽  
Itai Kloog ◽  
Steven Melly ◽  
Francesco Nordio ◽  
...  
Keyword(s):  
Particulate Matter ◽  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Fine Particulate

Relationship between the concentration of fine particulate matter and aerosol optical depth in the Middle Urals

2021 ◽  
Author(s):  
Anna P. Luzhetskaya ◽  
Ekaterina S. Nagovitsyna ◽  
Elena V. Omelkova ◽  
Vasiliy A. Poddubny ◽  
Alexey A. Shchelkanov ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Middle Urals ◽  
The Middle Urals ◽  
Fine Particulate

scholarly journals A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado

2019 ◽  
Vol 12 (12) ◽  
pp. 6385-6399 ◽  
Author(s):  
Bonne Ford ◽  
Jeffrey R. Pierce ◽  
Eric Wendt ◽  
Marilee Long ◽  
Shantanu Jathar ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Aerosol Optical Depth ◽  
Citizen Science ◽  
Optical Depth ◽  
Spatial Scales ◽  
Fine Particulate Matter ◽  
Optical Measurements ◽  
Fine Particulate ◽  
Northern Colorado

Abstract. A pilot field campaign was conducted in the fall and winter of 2017 in northern Colorado to test the deployment of the Aerosol Mass and Optical Depth (AMOD) instrument as part of the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) network. Citizen scientists were recruited to set up the device to take filter and optical measurements of aerosols in their backyards. The goal of the network is to provide more surface particulate matter and aerosol optical depth (AOD) measurements to increase the spatial and temporal resolution of ratios of fine particulate matter (PM2.5) to AOD and to improve satellite-based estimates of air quality. Participants collected 65 filters and 160 multi-wavelength AOD measurements, from which 109 successful PM2.5 : AOD ratios were calculated. We show that PM2.5, AOD, and their ratio (PM2.5 : AOD) often vary substantially over relatively short spatial scales; this spatial variation is not typically resolved by satellite- and model-based PM2.5 exposure estimates. The success of the pilot campaign suggests that citizen-science networks are a viable means for providing new insight into surface air quality. We also discuss lessons learned and AMOD design modifications, which will be used in future wider deployments of the CEAMS network.

scholarly journals A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 1: Specifications and testing

2019 ◽  
Vol 12 (10) ◽  
pp. 5431-5441 ◽  
Author(s):  
Eric A. Wendt ◽  
Casey W. Quinn ◽  
Daniel D. Miller-Lionberg ◽  
Jessica Tryner ◽  
Christian L'Orange ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Aerosol Mass ◽  
Fine Particulate ◽  
The Cost ◽  
Equivalent Method

Abstract. Globally, fine particulate matter (PM2.5) air pollution is a leading contributor to death, disease, and environmental degradation. Satellite-based measurements of aerosol optical depth (AOD) are used to estimate PM2.5 concentrations across the world, but the relationship between satellite-estimated AOD and ground-level PM2.5 is uncertain. Sun photometers measure AOD from the Earth's surface and are often used to improve satellite data; however, reference-grade photometers and PM2.5 monitors are expensive and rarely co-located. This work presents the development and validation of the aerosol mass and optical depth (AMOD) sampler, an inexpensive and compact device that simultaneously measures PM2.5 mass and AOD. The AMOD utilizes a low-cost light-scattering sensor in combination with a gravimetric filter measurement to quantify ground-level PM2.5. Aerosol optical depth is measured using optically filtered photodiodes at four discrete wavelengths. Field validation studies revealed agreement within 10 % for AOD values measured between co-located AMOD and AErosol RObotics NETwork (AERONET) monitors and for PM2.5 mass measured between co-located AMOD and EPA Federal Equivalent Method (FEM) monitors. These results demonstrate that the AMOD can quantify AOD and PM2.5 accurately at a fraction of the cost of existing reference monitors.

scholarly journals Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1018
Author(s):  
Chun-Sheng Huang ◽  
Ho-Tang Liao ◽  
Tang-Huang Lin ◽  
Jung-Chi Chang ◽  
Chien-Lin Lee ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Elemental Composition ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Prediction Models ◽  
Fine Particulate Matter ◽  
Sensitivity Analyses ◽  
Land Use Regression ◽  
Fine Particulate

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

scholarly journals Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth

2019 ◽  
Vol 19 (1) ◽  
pp. 295-313 ◽  
Author(s):  
Xiaomeng Jin ◽  
Arlene M. Fiore ◽  
Gabriele Curci ◽  
Alexei Lyapustin ◽  
Kevin Civerolo ◽  
...  
Keyword(s):  
Optical Properties ◽  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Model Representation ◽  
Aerosol Optical Properties ◽  
Total Uncertainty ◽  
Fine Particulate ◽  
Temporal Variance

Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

scholarly journals Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite observed aerosol optical depth

2018 ◽  
Author(s):  
Xiaomeng Jin ◽  
Arlene M. Fiore ◽  
Gabriele Curci ◽  
Alexei Lyapustin ◽  
Kevin Civerolo ◽  
...  
Keyword(s):  
Optical Properties ◽  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Aerosol Optical Properties ◽  
Model Uncertainties ◽  
Total Uncertainty ◽  
Fine Particulate ◽  
Temporal Variance

Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resolution for 2011 over the Northeast USA by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12 × 12 km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly, to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5/AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5/AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5/AOD lead to an error of 11 μg/m3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 μg/m3. Using multi-platform ground, airborne and radiosonde measurements, we show that uncertainties of modeled PM2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model uncertainties of relative humidity and aerosol vertical profile shape contribute some systematic biases. The parameterization of aerosol optical properties, which determines the mass-extinction efficiency, also contributes to random uncertainty, with the size distribution the largest source of uncertainty, and hygroscopicity of inorganic salt the second. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

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