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Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

This paper reveals a study on air pollution process over North China, applying remote sensing technology, using satellite observation and in situ measurements during the twenty-first Asia-Pacific Economic Cooperation (APEC) meeting, which was held in Beijing between 6 and 12 November when the clear weather was called "APEC-Blue". In the meantime, pollutants concentrations including PM2.5 and PM10 in Beijing were lower than 100 μg/m3 owing to the effective government measures and policies, as well as meteorological conditions. High aerosol loading (AOD > 1) was observed over south of Beijing and vertical observations showed that the pollutants were prominent near the land surface. Different from the meeting period, high pollutants concentrations with explosive growth (the values of PM2.5 and PM10 peaking at 291 μg/m3 and 360 μg/m3 respectively) appeared over Beijing after the meeting period, accompanied by strong temperature inversion and high Relative Humidity (RH) values. The pollution particles transferred from Beijing area to south part of North China. Otherwise, fine-mode particles with strong absorption characteristic (UVAI > 1.5, AOD > 1) covered the Beijing sky in the meantime, indicating the existence of black carbon aerosols. [ABSTRACT FROM AUTHOR]

With the rapid urbanization and industrialization of the past few decades, environmental issues have become increasingly prominent, and air pollution in the form of PM2.5 has become a major concern in China. The Chinese government has recognized the severity of these environmental challenges and has placed great emphasis on protecting the environment while promoting economic growth. This study utilizes remote sensing technology to analyze the spatio-temporal evolution characteristics of the decoupling between land-use intensity and PM2.5 in the Yangtze River Economic Belt (YREB) using 2000–2021 series of PM2.5 and land-use-change data, with the Mean Center Change analysis and the Tapio Decoupling Model. This study attempts to analyze the causes of change from the perspectives of economic development stage and policy execution. The study found that the average PM2.5 level decreased by 11.77 μg/m3 during this period, with high levels concentrated in urban areas and low levels found in less developed regions. The central trajectory of the land-use intensity shifted southeast, indicating a consistent trend in urbanization and industrialization in that direction. Meanwhile, the average land-use intensity value increased by 0.19, with the highest values concentrated in urban areas. A total of 71.12% of the regions in the YREB exhibited strong negative decoupling or negative decoupling between land-use intensity and PM2.5 levels, suggesting rapid urbanization and industrialization with a decrease in PM2.5 levels. These findings provide insight into the dynamic relationship between economic development, urbanization, and PM2.5 in China's Yangtze River Economic Belt. [ABSTRACT FROM AUTHOR]