- 著者
- Yiming Sun Qizhong Wu Lanning Wang Baogang Zhang Pingzhong Yan Lingling Wang Huaqiong Cheng Mengfei Lv Nan Wang Shuangliang Ma
- 出版者
- 公益社団法人 日本気象学会
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.2022-022, (Released:2022-05-31)
- 被引用文献数
- 1
The numbers of heavy air pollution events per year in Beijing have decreased significantly since 2017. To find out the reasons and how meteorology and emissions control have played a role in this change, we used the WRF-SMOKE-CMAQ modeling system to reconstruct the characteristics of the fine particulate matter (PM2.5) concentrations from 2013 to 2019. The model system performed well, and the correlation coefficients (R) between the simulated and observed daily PM2.5 concentrations were all above 0.64. The model results also show that the meteorology contributed approximately ±5 μg/m3 to the annual average PM2.5 concentrations. More interestingly, the coincidence degrees of the simulated PM2.5 concentrations to the heavy pollution (daily PM2.5 concentration > 150 μg/m3) dates decreased significantly after 2016. Meteorology plays an important role in reducing the number of heavy pollution days. According to the model results under the same emission scenarios, the average numbers of heavy pollution days from 2017 to 2019 decreased by 33% compared to the period from 2013 to 2016, while the numbers of good days changed by less than 1%. These results also indicate that meteorology made a significant contribution to decreasing the number of heavily polluted days after 2016.
- 著者
- Yiming Sun Qizhong Wu Lanning Wang Baogang Zhang Pingzhong Yan Lingling Wang Huaqiong Cheng Mengfei Lv Nan Wang Shuangliang Ma
- 出版者
- 公益社団法人 日本気象学会
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.18, pp.135-139, 2022 (Released:2022-07-06)
- 参考文献数
- 17
- 被引用文献数
- 1
The numbers of heavy air pollution events per year in Beijing have decreased significantly since 2017. To find out the reasons and how meteorology and emissions control have played a role in this change, we used the WRF-SMOKE-CMAQ modeling system to reconstruct the characteristics of the fine particulate matter (PM2.5) concentrations from 2013 to 2019. The model system performed well, and the correlation coefficients (R) between the simulated and observed daily PM2.5 concentrations were all above 0.64. The model results also show that the meteorology contributed approximately ±5 g/m3 to the annual average PM2.5 concentrations. More interestingly, the coincidence degrees of the simulated PM2.5 concentrations to the heavy pollution (daily PM2.5 concentration > 150 g/m3) dates decreased significantly after 2016. Meteorology plays an important role in reducing the number of heavy pollution days. According to the model results under the same emission scenarios, the average numbers of heavy pollution days from 2017 to 2019 decreased by 33% compared to the period from 2013 to 2016, while the numbers of good days changed by less than 1%. These results also indicate that meteorology made a significant contribution to decreasing the number of heavily polluted days after 2016.