Regional and complex air pollution has become one of the main environmental and health issues in China.The variation in air pollution consists of the long-term trend and interannual–decadal and synoptic variation. Owing to strict regulations since 2013, the air quality has been greatly improved. Now, the prevention of air pollution has entered a critical stage in combination with climate change mitigation in China.Accurate seasonal to interannual prediction of air pollution (haze, surface ozone, and sandstorms) could support the government in planning for air pollution control on an annual basis.

In a recent paperpublished inAtmospheric and Oceanic Science Letters, Prof. Zhicong YinfromNanjingUniversityofInformation Sciences and Technologyreviews the progress made in air-pollution climate prediction, and gives some critical insights.

According toProf. Yin, the long-term recordsof the PM2.5(fine particulate matter) concentration increase the possibility of predictingseasonalpollutant concentrations.However, these reanalysis datasets contain high levels of uncertainty before the construction of China’s national monitoring network (i.e., before 2013). Therefore, developing and evaluating the available air pollution reanalysis datasets is a pressing issue.

Furthermore, owing to strict regulations since 2013, the air quality has been greatly improvedin China. “Information on rapid changes in human activities and decadal changes in climatic factors should be considered and contained in air pollution prediction models in later work,” emphasizesProf.Yin.

“It is also necessary to design and construct a coupled numerical model targeted at routine seasonal to interannual predictions of air pollution,” Prof. Yin adds. Most atmospheric chemical models are not designed for climate prediction.

 Nanjing Eye Footbridge under air pollution (Credit:Yuyan Li)

Citation:

Z.Yin, H.Wang, H. Liao, K. Fan, B. Zhou, 2021. Seasonal to interannual prediction of air pollution in China: Review and insight. Atmospheric and Oceanic Science Letters, https://doi.org/10.1016/j.aosl.2021.100131.