Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Park, Seohui; Lee, Jung‐Hee; Im, Jungho; Song, Chang Keun; Choi, Myungje; Kim, Jhoon; Lee, Seungun; Park, Rokjin J.; Kim, Sang Min; Yoon, Jongmin; Lee, Jun Young; Quackenbush, Lindi J.

doi:10.1016/j.scitotenv.2020.136516

Cited by 48 publications

(21 citation statements)

References 65 publications

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“…The DNN model showed the best performance for most statistical metrics (R = 0.833, RMSE = 0.118, MBE = 0.002, and IOA = 0.904) compared with the traditional ML models (Table II). In a previous study producing GOCI-based AOD using RF, the correlation with the AERONET AOD yielded R values of 0.61-0.73, with RMSE values of 0.16-0.27 for the "leave one station out" cross-validation results [38], which are less accurate than the results of this study.…”

Section: B Temporal and Spatial Cross-validation Of The Aodcontrasting

confidence: 90%

Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

Yeom

Jeong

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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In this study, a new deep learning method was developed to estimate the spatiotemporal properties of the hourly aerosol optical depth (AOD) because existing physical models are limited in their abilities to separate the reflectance between aerosols and the underlying surface over land, accurately and effectively. By incorporating geostationary ocean color imagery (GOCI), multispectral bands were applied to train data-driven models to estimate the high-spatiotemporal-resolution AOD over Northeast Asia. Physical model and traditional machine learning (ML) models (the random forest (RF) and support vector regression (SVR) models) were compared with the deep neural network (DNN) model to evaluate its accuracy, implementing hold-out validation and k-fold cross-validation approaches. In the statistical results of the hold-out validation, the DNN model showed the higher accuracy (root mean square error (RMSE) = 0.112, mean bias error (MBE) = 0.007, and correlation coefficient (R) = 0.863) relative to the traditional SVR (RMSE = 0.123, MBE = −0.010, and R = 0.833) and RF (RMSE = 0.125, MBE = 0.004, and R = 0.825) models. The DNN model also exhibited the best performance for most statistical metrics among the traditional SVR, RF, and selected physical models (except for the correlation coefficients and index of agreement) in the spatial and temporal cross-validation analyses. Although the DNN model was trained using the match-up dataset between the top of atmosphere (TOA) reflectance from GOCI multispectral bands and AErosol RObotic NETwork measurements, it showed high spatial and temporal generalization performance owing to its deeper and more complicated network structure. Hourly GOCI AOD data obtained using a deep learning approach with high accuracy are expected to be useful for the quantification of aerosol contents and monitoring of diurnal variations in the AOD.

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Section: B Temporal and Spatial Cross-validation Of The Aodcontrasting

confidence: 90%

Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

Yeom

Jeong

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…The statistical indicators from the models' calibration and validation in this study (Supplementary Materials Table S2; Figures 4 and 5) are mostly comparable to those presented in other studies using various methods and ML approaches for estimates of PM concentrations around the world (Table 2). It should be noted that this study obtained reasonable results at national scale without including land use information compared to previous works [128,[143][144][145]. Besides that, the validation techniques may be also different, as for instance [143] used sample and site based 10 CV in order to assess the spatial performance, whilst our study only used sample based 10 CV since it can be used to reflect the overall predictive ability [130].…”

Section: Models For Pm 25 Estimationmentioning

confidence: 61%

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

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“…Recently, machine learning techniques have been applied to satellite remote sensing of aerosol information that is difficult to fully estimate via traditional regression models and physical retrieval approaches. In particular, various machine learning models have been introduced to estimate surface concentrations of particulate matter (PM) using satellite measurements and meteorological data [22][23][24][25][26][27]. In addition to estimates of ground-level PM concentrations, machine-learning techniques have been used to estimate AOD and aerosol height (the altitude of peak aerosol concentration in a vertical profile).…”

Section: Introductionmentioning

confidence: 99%

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

2021

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A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

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Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Cited by 48 publications

References 65 publications

Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

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