Search Results (3)

Owing to a series of air pollution prevention and control policies, China’s PM_2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM_2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM_2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM_2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R² = 0.91 and RMSE = 9.91 μg/m³ for the daily models, along with the site-based cross-validation R²s and RMSEs of 0.86–0.87 and 12–12.33 μg/m³; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM_2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM_2.5 at three different time scales. Overall, national PM_2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m³, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM_2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM_2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making. Full article

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM_2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM_2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM_2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R² of 0.69, RMSE of 5 μg/m³, and MAE of 3.3 μg/m³. The outcome of this study is a daily full coverage PM_2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM_2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies. Full article

Current PM_2.5 retrieval maps have many missing values, which seriously hinders their performance in real applications. This paper presents a framework to map full-coverage daily average PM_2.5 concentrations from MODIS C6 aerosol optical depth (AOD) products and fill missing pixels in both the AOD and PM_2.5 maps. First, a two-stage inversed variance weights (IVW) algorithm was adopted to fuse the MODIS C6 Terra and Aqua AOD products, which fills missing data in MODIS standard AOD data and obtains a high coverage daily average. After that, using the fused MODIS daily average AOD and ground-level PM_2.5 in all grid cells, a two-stage generalized additive model (GAM) was implemented to obtain the full-coverage PM_2.5 concentrations. Experiments on the Yangtze River Delta (YRD) in 2013–2016 were carefully designed to validate the performance of our proposed framework. The results show that the two-stage IVW could not only improve the spatial coverage of MODIS AOD against the original standard product by 230%, but could also keep its data accuracy. When compared with the ground-level measurements, the two-stage GAM can obtain accurate PM_2.5 concentration estimates (R² = 0.78, RMSE = 19.177 μg/m³, and RPE = 28.9%). Moreover, our method performs better than the inverse distance weighted method and kriging methods in mapping full-coverage daily PM_2.5 concentrations. Therefore, the proposed framework provides a good methodology for retrieving full-coverage daily average PM_2.5 concentrations from MODIS standard AOD products. Full article