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

Hu, Xuefei; Waller, Lance A.; Lyapustin, A.; Wang, Yujie; Al‐Hamdan, Mohammad Z.; Crosson, William L.; Estes, Maurice G.; Estes, Sue; Quattrochi, Dale A.; Puttaswamy, Sweta Jinnagara; Liu, Yang

doi:10.1016/j.rse.2013.08.032

Cited by 290 publications

(206 citation statements)

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“…Satellite remote sensing provides the column densities of trace gases (e.g., SO 2 and NO 2 ) and parameters that are related to aerosol concentrations, such as the aerosol optical depth (AOD), which has been widely used to estimate surface PM 2.5 concentrations (Chu et al, 2003;Wang and Christopher, 2003;van Donkelaar et al, 2016). Different types of methods can be used to retrieve ground-level PM 2.5 concentrations from satellite AOD data, including the use of CTMs to obtain conversion factors between PM 2.5 and AOD (Liu et al, 2004;van Donkelaar et al, 2010) and the use of statistical models (Hu et al, 2014;Zheng et al, 2016) or semiempirical models Zhang et al, 2015) to investigate the relationship between PM 2.5 , AOD, and other factors. Compared to statistical models and semiempirical models, CTMs do not require ground measurements as input data and can also derive PM 2.5 composition concentrations , making them suitable for studies seeking to explore the historical PM 2.5 chemical composition over China prior to 2013.…”

Section: G Geng Et Al: Chemical Composition Of Ambient Pm 25 Over mentioning

confidence: 99%

Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012

et al. 2017

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Abstract. In this work, we presented the characteristics of PM 2.5 chemical composition over China for the period of 2005-2012 by synthesis of in situ measurement data collected from literatures and satellite-based estimates using aerosol optical depth (AOD) data and the GEOS-Chem chemical transport model. We revealed the spatiotemporal variations in PM 2.5 composition during 2005-2012 and investigated the driving forces behind the variations by examining the changes in precursor emissions using a bottom-up emission inventory. Both in situ observations and satellitebased estimates identified that secondary inorganic aerosols (i.e., sulfate, nitrate, and ammonium; SNA) ranked as the highest fraction of dust-free PM 2.5 concentrations, followed by organic matter (OM) and black carbon (BC). For instance, satellite-based estimates found that SNA, OM, and BC contributed to 59, 33, and 8 %, respectively, of national population-weighted mean dust-free PM 2. In contrast, growth of energy consumption and lack of control measures for NO x resulted in a persistent increase in NO x emissions until the installation of denitrification devices on power plants late in 2011, which began to take effect in 2012. The results of this work indicate that the synchronized abatement of emissions for multipollutants is necessary for reducing ambient PM 2.5 concentrations over China.

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Section: G Geng Et Al: Chemical Composition Of Ambient Pm 25 Over mentioning

confidence: 99%

Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012

et al. 2017

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“…Their R 2 values were generally lower, and varied between different areas. However, these listed models have been gradually optimized or integrated into other models, as with artificial neural networks (ANN, which incorporate LUR in the CTM) [52,61,68,110,111] and the two stage model (TSM, which combine the GWR with MEM) [80,81,119,121]. In recent years, with the development of the AOD-based mathematical model, many new methods have been developed, such as geographically and temporally weighted regression (GTWR) [107], support vector regression methods (SVR) [99] and machine learning regression (which is a combination of SVR, Gauss neural network processes, Decision trees, and Random forests) [28].…”

Section: Other Modelsmentioning

confidence: 99%

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

et al. 2016

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This study reviewed the prediction of fine particulate matter (PM 2.5 ) 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 PM 2.5 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 PM 2.5 and other air pollutants.

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“…AOD is a dimensionless measure of particle optical abundance defined as the integral of aerosol extinction coefficients along the vertical atmospheric column from the ground to the top of the atmosphere. When most particles are generated near the surface and well mixed in the boundary layer, AOD has been shown to be a strong predictor of PM 2.5 concentrations after accounting for the changes of particle composition and size distribution (Liu et al, 2005;Liu et al, 2009b;Lee et al, 2011;Kloog et al, 2012;Hu et al, 2013;Hu et al, 2014). Ma et al (2014) developed a geographically weighted regression (GWR) model with satellite AOD, meteorological and land use data to predict the PM 2.5 concentrations in China, which is the first study that utilized the advanced statistical model to estimate ground PM levels with AOD in China The cross-validation R 2 of daily predicted PM 2.5 concentration on a 50 km grid reached 0.64.…”

Section: Introductionmentioning

confidence: 99%

Satellite and Ground Observations of Severe Air Pollution Episodes in the Winter of 2013 in Beijing, China

Xiong

et al. 2016

Aerosol Air Qual. Res.

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Beginning in early January 2013, Beijing experienced multiple prolonged and severe smog events that were characterized by very high levels of PM 2.5 , with peak daily PM 2.5 over 400 µg m -3 . With PM 2.5 concentration contours created from ground observations and satellite remote sensing data, we describe the spatial and temporal characteristics of these episodes and further investigated the factors that contributed to these episodes. Our results indicated that these smog episodes affected a much larger geographic region, far beyond Beijing metropolitan area, corresponding to a total area of ~550,000 km 2 and ~180 million people. The extremely cold weather in December 2012 and regional pollution transport were likely the main causes of these severe PM pollutions. In addition to aggressive emission control measures for Beijing, coordinated regional policy must be put in place to achieve more blue-sky days. Although the configuration of the current ground monitoring network may be sufficient to record PM 2.5 levels in urban centers, these monitors alone cannot fully characterize the spatial pattern and track the transport of air pollution on a regional scale. Satellite remote sensing data can provide valuable information to fill the gaps left by ground monitors to create a more comprehensive picture of PM 2.5 .

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

Cited by 290 publications

References 37 publications

Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012

Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Satellite and Ground Observations of Severe Air Pollution Episodes in the Winter of 2013 in Beijing, China

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

Cited by 290 publications

References 37 publications

Chemical composition of ambient PM&lt;sub&gt;2. 5&lt;/sub&gt; over China and relationship to precursor emissions during 2005–2012

Chemical composition of ambient PM&lt;sub&gt;2. 5&lt;/sub&gt; over China and relationship to precursor emissions during 2005–2012

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Satellite and Ground Observations of Severe Air Pollution Episodes in the Winter of 2013 in Beijing, China

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Product

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Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012

Chemical composition of ambient PM<sub>2. 5</sub> over China and relationship to precursor emissions during 2005–2012