A critical assessment of high resolution aerosol optical depth (AOD) retrievals for fine particulate matter (PM) predictions

Chudnovsky, Alexandra; Tang, C.; Lyapustin, A.; Wang, Y.; Schwartz, Joel; Koutrakis, Petros

doi:10.5194/acpd-13-14581-2013

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…One feature of the AOD data is that some of the grid-specific AOD values are missing on some days due to cloud cover or snow cover. 12,17 Thus, the spatial coverage of the AOD data varies considerably by day.…”

Section: Methodsmentioning

confidence: 99%

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

Alexeeff

Schwartz

Kloog

et al. 2014

J Expo Sci Environ Epidemiol

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Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

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Section: Methodsmentioning

confidence: 99%

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

Alexeeff

Schwartz

Kloog

et al. 2014

J Expo Sci Environ Epidemiol

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“…It is an understatement to say that the twin MODIS sensors on Terra and Aqua have served as the workhorse for aerosol research ( [11] and references therein). Although primarily designed to monitor the global distribution of aerosols and to calculate Aerosol Optical Depth (AOD) for climate-related purposes, this data has been used in various applications including PM 2.5 studies [12][13][14]. However, it is important to note that satellite data and their associated products have limitations when estimating PM 2.5 , which are discussed in detail elsewhere [2].…”

Section: Introductionmentioning

confidence: 99%

Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships

Christopher

Gupta

2020

Remote Sensing

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Using a combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) mid-visible aerosol optical depth (AOD) product at 0.1 × 0.1-degree spatial resolution and collocated surface PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) monitors, we provide a global five-year (2015–2019) assessment of the spatial and seasonal AOD–PM2.5 relationships of slope, intercepts, and correlation coefficients. Only data from ground monitors accessible through an open air-quality portal that are available to the worldwide community for air quality research and decision making are used in this study. These statistics that are reported 1 × 1-degree resolution are important since satellite AOD is often used in conjunction with spatially limited surface PM2.5 monitors to estimate global distributions of surface particulate matter concentrations. Results indicate that more than 3000 ground monitors are now available for PM2.5 studies. While there is a large spread in correlation coefficients between AOD and PM2.5, globally, averaged over all seasons, the correlation coefficient is 0.55 with a unit AOD producing 54 μgm−3 of PM2.5 (Slope) with an intercept of 8 μgm−3. While the number of surface PM2.5 measurements has increased by a factor of 10 over the last decade, a concerted effort is still needed to continue to increase these monitors in areas that have no surface monitors, especially in large population centers that will further leverage the strengths of satellite data.

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Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model

Waller

Lyapustin

et al. 2014

Remote Sensing of Environment

288

194

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A critical assessment of high resolution aerosol optical depth (AOD) retrievals for fine particulate matter (PM) predictions

Cited by 5 publications

References 35 publications

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships

Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model

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