Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in more developed cities in China. Existing monitoring network for ground pollutant measurements are too sparse, therefore spatial variation in rural regions or places mixed land use pattern may not be well captured and distinguished. Thus, satellite remote sensing techniques play important roles in observing temporal changes of pollutants within troposphere through satellite scans.

In the first part, we combine detailed high resolution satellite products, meteorological and chemical information derived from the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models, then update the spatial Air Mass Factor (A...[ Read more ]

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in more developed cities in China. Existing monitoring network for ground pollutant measurements are too sparse, therefore spatial variation in rural regions or places mixed land use pattern may not be well captured and distinguished. Thus, satellite remote sensing techniques play important roles in observing temporal changes of pollutants within troposphere through satellite scans.

In the first part, we combine detailed high resolution satellite products, meteorological and chemical information derived from the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models, then update the spatial Air Mass Factor (AMF) and conduct tropospheric nitrogen dioxide (NO₂) vertical column density (VCD) retrieval, with the aid of the newly developed Berkeley High Resolution-Hong Kong (BEHR-HK) product. Validation results show that our newly developed product does a better job in capturing changes of NO₂ within lower troposphere, particularly in places with large spatial variabilities and terrain height differences like China.

In the second part, we combine the use of 8 meteorological quantities from WRF and updated tropospheric NO₂ VCD to estimate seasonal and annual spatial distribution of ground level NO₂ concentration. The process is conducted based on “data assimilation” approaches and the use of kernel-based probabilistic models. Based on validation with available measurement datasets from fixed-site ground monitors, we show that the Gaussian Process Regression (GPR) model reaches the best performance in prediction, followed by Ensemble. Several spatial and temporal case studies are discussed within the chapter.

In the third part, we first retrieve atmospheric column-averaged dry-air mole fraction of carbon dioxide (XCO₂) through data assimilation and inverse modeling approaches, by combining optimized surface CO₂ flux with nested domain of atmospheric transport model within East Asia, then identify hotspots and temporal variation of XCO₂ within recent years. We also evaluate interannual variability of fossil fuel CO₂(FFCO₂) emissions within major CO₂ emitters in East Asia, and predict future FFCO₂ emissions with the aid of compound periodical growth (CPG) rate under policy-on or policy-off assumptions, and background mathematical preliminaries.

These findings open new window into long-term ground NO₂ and CO₂ retrieval and prediction, at the same time dealing with environmental problems that require continuous monitoring and assessments. These also provide a good reference for implementation of region-specific pollution abatement goals especially in challenging geographic regions, as well as re-evaluating new environmental policies to comply with international pledges regarding improvement of air quality.