Empirical relationship between particulate matter and aerosol optical depth over Northern Tien-Shan, Central Asia

Chen, Boris B.; Sverdlik, Leonid; Imashev, Sanjar; Solomon, Paul A.; Lantz, Jeffrey; Schauer, James J.; Shafer, Martin M.; Artamonova, Maria; Carmichael, G. R.

doi:10.1007/s11869-012-0192-5

Cited by 30 publications

(20 citation statements)

References 35 publications

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“…Such observation implies that the aerosol types are possibly indirectly correlated with the AOD prediction model. This result was also noticed by Chen et al (2013). However, the relationship between the predicted AOD and aerosol type as observed in our model is qualitative and preliminary.…”

Section: Examination Of Predicted Aod Valuessupporting

confidence: 86%

“…Two predictors (API and Vis) were introduced to increase statistical reliability. To verify the robustness of multiple regression analysis, in contrast to the simple regression approach, AOD data based on previous simple models were retrieved (Mahowald et al, 2007;Gao and Zha, 2010;Retalis et al, 2010;Chen et al, 2013). R 2 , RMSE and wMAPE values in our calibration model are ≤ 0.72, 0.13, 0.05 % respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Mahowald et al (2007) suggest a similar linear regression model for the AOD prediction model, in which the Vis data were converted to surface extinction coefficients b ext using the Koschmieder equation Vis = K/b ext , where K(= 3.912) is the Koschmieder constant (Koschmieder, 1924). Two other AOD-predicting models were also compared (Gao and Zha, 2010;Chen et al, 2013).…”

Section: Comparison With Other Linear Regression Modelsmentioning

confidence: 99%

“…However, there are studies stating that AOD is not always highly correlated to surface or horizontal measurements especially with the occurrence of an elevated layer of AOD from transported dust or biomass burning (Mahowald et al, 2007;Barladeanu et al, 2012;Chen et al, 2013;Toth et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

et al. 2015

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Abstract. Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloudcover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R 2 , of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R 2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40 % compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured shortand long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.

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Section: Examination Of Predicted Aod Valuessupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Comparison With Other Linear Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

et al. 2015

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“…Crouse [141] not only obtained a high R 2 value (0.792) in 11 Canadian cities in 1987-2001, but also successfully applied their results to the study of long-term health effects of PM 2.5 exposure. Following van Donkelaar's study [57], others studies conducted by Villeneuve, Chen, To and Brauer [142][143][144][145][146] focused on acute and chronic health effects and on the global burden of disease.…”

Section: Theory Background and Applicationmentioning

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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Aerosol and Air Quality

Fuzzi

Gilardoni

Kokhanovsky

et al. 2016

Atmospheric Aerosols

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Empirical relationship between particulate matter and aerosol optical depth over Northern Tien-Shan, Central Asia

Cited by 30 publications

References 35 publications

Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

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

Aerosol and Air Quality

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