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

Abstract

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.

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

“…However, some studies are inconsistent with this finding. The effect of spatial resolution on satellite-based estimation of PM2.5 was analyzed by Bai et al [55]. Their results showed that the summer AOD-PM2.5 correlation decreased from 0.42 to 0.49 when the spatial resolution decreased from 3 km to 10 km.…”

“…It was found that the performance started to improve with increasing spatial resolution until a maximum response was realized at 200 m, after which the performance decreased. The results in Bai et al [55] and Zheng et al [56] indicated that the performance of the model does not necessarily improve but may decrease as the spatial resolution increases.…”

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

“…PM having ≤2.5 µm of particle size in its diameter, known as PM 2.5 , has been reported as hazardous to human health by causing tuberculosis, lung cancer, and damage to the respiratory tract [1,2]. An increase in the short-term PM 2.5 is exemplary for human health, resulting in affected mortality rates negatively [3][4][5][6]. Different deleterious metals present in PM 2.5 affected the atmosphere, particularly in Asian countries [7].…”

Overview: Random Forest Algorithm for PM2.5 Estimation Based on Remote Sensing

“…RF telah menjadi bidang penelitian yang populer karena mudah dipahami dan kuat [20]. Selain itu studi [21] menyatakan bahwa hasil validasi silang PM2.5 menunjukkan bahwa kinerja model tidak sensitif terhadap perubahan resolusi spasial untuk model LME dan LUR, sebaliknya model RF dapat membuat estimasi PM2.5 yang lebih akurat dengan resolusi spasial AOD yang lebih baik.…”