REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM<sub>2.5</sub> USING
SATELLITE REMOTE SENSING

Li, Tongwen; Zhang, Chengyue; Shen, Huanfeng; Yuan, Qiangqiang; Zhang, Liangpei

doi:10.5194/isprs-annals-iv-3-143-2018

Cited by 5 publications

(4 citation statements)

References 15 publications

(15 reference statements)

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“…One of the reasons for the absence of AHI AOD was the interference of clouds and fog. Moreover, the AOD in the area affected by clouds and fog was generally higher than that in non-cloud-affected areas, which is consistent with [25,48].…”

Section: Spatiotemporal Distribution Analysis Of Full-coverage Aodsupporting

confidence: 79%

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

et al. 2023

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Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics.

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Section: Spatiotemporal Distribution Analysis Of Full-coverage Aodsupporting

confidence: 79%

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

et al. 2023

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“…It was launched on 7 October 2014 and carries the Advanced Himawari Imager (AHI) sensor, which is equipped with 16 bands from visible to infrared [36]. Himawari-8 releases AOD products at two levels, namely Level 2 (10 min temporal) and Level 3 (hourly and daily), which have been used for various applications including estimation of PM [96][97][98][99], dust detection [100] and aerosol data assimilation [101]. The L3 product is an improved version of the L2 AOD product that minimized cloud contamination [102] and has a 5 km spatial resolution.…”

Section: Satellite Datamentioning

confidence: 99%

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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“…The aerosol optical depth (AOD) data has been widely used for the estimation and inversion of surface PM 2.5 concentration for its spatial continuity [40,41]. However, some areas frequently face substantial gaps in AOD product coverage during the autumn and winter seasons, and it is difficult to achieve a high level of prediction accuracy with the existing means of filling [42]. In the past, PM 2.5 prediction studies in the Xinjiang Uygur Autonomous Region (hereinafter Xinjiang) often superimposed daily AOD data and averaged it to predict the annual or monthly average concentration of PM 2.5 , resulting in relatively low prediction accuracy [43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China

Wu,

Xu,

et al. 2024

Atmosphere

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Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events.

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REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING

Cited by 5 publications

References 15 publications

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

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

An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China

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REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING

Cited by 5 publications

References 15 publications

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

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

An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China

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REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING