Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China

Li, Rong; Mei, Xin; Chen, Liangfu; Wang, Zifeng; Jing, Yingying; Wei, Lifei

doi:10.3390/rs12040736

Cited by 8 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the AOD-based PM 2.5 retrieval models may be affected by the AOD spatial resolution. However, the conclusions of this issue are not consistent in previous studies [17,[21][22][23]; their work mainly focuses on the traditional statistical regression models, and such analysis is lacking for machine learning models. Lee et al [23] used the linear mixed-effect model to estimate PM 2.5 in the Southeastern United States.…”

Section: Introductionmentioning

confidence: 69%

“…They found that the model using 1 km AOD data showed a better performance than that using 10 km AOD data. Li et al [21] conducted similar work in northern China but found that the 1 km model performance was slightly lower than the 10 km model. The YRD region also experienced such a difference in performance between 3 and 10 km AOD data [17].…”

Section: Introductionmentioning

confidence: 97%

“…As outlined above, satellite AOD spatial resolution may affect the performance of the PM 2.5 retrieval model. Previous studies, however, have drawn inconsistent conclusions on this topic [17,[21][22][23], and fewer have investigated the spatial resolution effect using machine learning models. Regarding the sensitivity of the PM 2.5 -related health impacts to spatial resolution, existing studies were mostly based on the air quality model simulations and mainly focused on Western countries or regions [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

et al. 2022

View full text Add to dashboard Cite

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.

show abstract

Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Yang et al [32] studied the relationship between PM 2.5 and AOD using AOD data obtained from MODIS, and found that the meteorological influence on PM and AOD was strong in June. Li et al [33] estimated daily PM 2.5 concentrations in the Beijing-Tianjin-Hebei region of northern China during 2017 using a mixed-effects model. The impact of the spatial resolution and sampling frequency of the AOD on PM 2.5 prediction was then assessed.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

et al. 2021

View full text Add to dashboard Cite

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement.

show abstract

“…Satellite-derived AOD is the overall extinction value of the atmospheric column, and satellite aerosol products have the advantage of global coverage. Studies have shown that AOD has a good correlation with PM 2.5 concentrations near the surface [3], and it has been widely used in the estimation of PM 2.5 concentrations [4]. The PM 2.5 concentrations can be estimated by establishing a linear or nonlinear relationship between AOD and PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

View full text Add to dashboard Cite

Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

show abstract

Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China

Cited by 8 publications

References 31 publications

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

Contact Info

Product

Resources

About