A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

Hua, Zhenqun; Sun, Weiwei; Yang, Gang; Du, Qian

doi:10.3390/rs11131558

Cited by 33 publications

(10 citation statements)

References 60 publications

(82 reference statements)

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“…Another major challenge in estimating spatially continuous PM 2.5 arises due to spatially non-continuous AOD values owing to cloud coverage, rainfall, and satellite calibrations. Previous studies in the literature have tried to fill MODIS AOD data by using random forest algorithm 16 , 17 , spatiotemporal regression kriging 18 and by using two-staged generalized additive model 19 . However, these studies have their own limitations and specific pre-requisites, limiting model application to real-life situations.…”

Section: Introductionmentioning

confidence: 99%

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Maheshwarkar

Raman

2021

Sci Rep

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Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled and observed PM2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m−3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM2.5 concentration (mean value 82.54 µg m−3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m−3). Our results show that MP had a mean PM2.5 concentration of 58.19 µg m−3 and 56.32 µg m−3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period.

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Section: Introductionmentioning

confidence: 99%

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Maheshwarkar

Raman

2021

Sci Rep

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“…Interpolation is divided into two steps: first, quantitative analysis of the spatial structure of sample points, and then, prediction of unknown points. The general formula of spatiotemporal interpolation is as follows: (10) For any space-time point Z (s, t) that needs dynamic interpolation simulation, the weight λ is calculated by constructing the following weight matrix (formula 11) with the origin of Z (s, t) coordinate (11) In formula 11, , ,…”

Section: Establishment Of the Modelmentioning

confidence: 99%

“…The prediction method was carried out on 3960 grid cells in the ratio of 1 km × 1 km. In the prediction of PM2.5, the data used are more and more diverse, such as satellite, meteorological data, groundbased PM2.5, and geographic data [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamic interpolation simulation and prediction method of fine particulate matter based on multi-source pollution model

2023

E3S Web of Conf.

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Taking the air pollution monitoring data of 34 air monitoring stations in Beijing from February 8 to February 9, 2020, as an example. A spatiotemporal dynamic interpolation model of PM2.5 based on a multi-source pollution model was established. Based on the hourly spatiotemporal data of the day, the dispersion and attenuation of non-point source pollution in Beijing were interpolated. An improved hybrid genetic algorithm was used to solve the parameters of the air pollution model. The spatiotemporal Kriging model was used to predict the PM2.5 concentration diffusion on an hourly scale. The data of this area were analyzed quantitatively and qualitatively. The prediction data based on the spatiotemporal data before the current time was verified by the actual monitoring data. The results show that the model and method constructed in this paper could simulate and predict PM2.5 concentration on an hourly scale well, which could provide a good reference for the analysis, simulation, and prediction of air pollution.

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“…In addition to this, there are several studies based on other methods such as chemical transport simulation and physical correction for PM estimation [22][23][24][25][26]. Among these models, statistical models are the most popular in academia, which include mainly empirical statistical models, such as multiple linear regression [21,27], the mixed effects model [28,29], geographically weighted regression models (GWR) and their derivative models [30][31][32][33], the generalized additivity model (GAM) [34][35][36][37], and machine learning, such as random forest [38][39][40], support vector regression [41,42], and neural network models [43][44][45]. Specifically, the GWR model is a spatial statistical technique used to explore and model spatially varying relationships, which recognizes the variability in connections between variables across geographical locations, enabling focused modeling and analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

Wu,

Sun,

Bai

et al. 2024

Remote Sensing

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Due to the advantage of high spatial coverage, using satellite-retrieved aerosol optical depth (AOD) data to estimate PM2.5 and PM10 mass concentrations is a current research priority. Statistical models are the common method of PM estimation currently, which do not require the knowledge of complex chemical and physical interactions. However, the statistical models rely on station data, which results in less accurate PM estimation concentrations in areas where station data are missing. Hence, a new hybrid model, with low dependency on on-site data, was proposed for PM2.5 and PM10 mass concentration estimation. The Gaofen-1 satellite and MODIS data were employed to estimate PM2.5 and PM10 concentrations with 100 m spatial resolution in Beijing, China. Then, the estimated PM2.5/10 mass concentration data in 2020 were employed to conduct a spatio-temporal analysis for the investigation of the particulate matter characteristic in Beijing. The estimation result of PM2.5 was validated by the ground stations with R2 ranging from 0.91 to 0.98 and the root mean square error (RMSE) ranging from 4.51 μg/m3 to 17.04 μg/m3, and that for PM10 was validated by the ground stations with R2 ranging from 0.85 to 0.98 and the RMSE ranging from 6.98 µg/m3 to 29.00 µg/m3. The results showed that the hybrid model has a good performance in PM2.5/10 estimation and can improve the coverage of the results without sacrificing the effectiveness of the model, providing more detailed spatial information for urban-scale studies.

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A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

Cited by 33 publications

References 60 publications

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model

Spatiotemporal dynamic interpolation simulation and prediction method of fine particulate matter based on multi-source pollution model

Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution

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