Seasonal to interannual prediction of air pollution in China: Review and insight
Atmospheric and Oceanic Science Letters, ISSN: 1674-2834, Vol: 15, Issue: 1, Page: 100131
2022
- 11Citations
- 16Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Complex air pollution problems have resulted in considerable adverse impacts on the environment, human health, and economy in China. However, 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 O 3, and sandstorms) could support the government in planning for air pollution control on an annual basis. Scientists from all over the world have made great progress in understanding climate change and the variability of air pollution and associated physical mechanisms in China, which has provided a scientific basis for the development of climate prediction of air pollution. This paper reviews the progress made in air-pollution climate prediction, and gives some critical insights including update of predictand, change of predictability, and development of coupled model. 摘要 复合型大气污染对中国环境, 健康和经济存在巨大的不利影响. 2013年以来的减排措施有效改善了空气质量. 目前, 我国已进入大气污染与气候变化协同治理的关键阶段. 在季节 - 年际尺度上, 对大气污染 (霾, 臭氧和沙尘暴) 的准确预测可以为有关部门的减排措施提供有效的科技技撑. 近年来, 全球科学家在理解中国气候变化, 大气污染变率及相关物理机制方面取得了很大进展, 为开展大气污染气候预测提供了科学基础. 本文回顾了大气污染气候预测的相关进展, 并对大气污染气候预测的---些发展方向提出了观点和判断. Image, graphical abstract
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1674283421001100; http://dx.doi.org/10.1016/j.aosl.2021.100131; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85121121601&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1674283421001100; https://dx.doi.org/10.1016/j.aosl.2021.100131
Elsevier BV
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