PLAM &ndash; a meteorological pollution index for air quality  and its applications in fog-haze forecasts in North China

Yang, Y. Q.; Wang, J. Z.; Gong, S. L.; Zhang, X. Y.; Wang, H.; Wang, Y. Q.; Wang, J.; Li, D.; Guo, J. P.

doi:https://doi.org/10.5194/acp-16-1353-2016

Articles | Volume 16, issue 3

https://doi.org/10.5194/acp-16-1353-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Haze-fog forecasts and near real time (NRT) data application...

https://doi.org/10.5194/acp-16-1353-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 3

Research article

|

08 Feb 2016

Research article |

| 08 Feb 2016

PLAM – a meteorological pollution index for air quality and its applications in fog-haze forecasts in North China

Y. Q. Yang, J. Z. Wang, S. L. Gong, X. Y. Zhang, H. Wang, Y. Q. Wang, J. Wang, D. Li, and J. P. Guo

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Final revised paper (published on 08 Feb 2016)
Preprint (discussion started on 25 Mar 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C1460: 'Comments', Anonymous Referee #1, 09 Apr 2015
AC C2377: 'Response to Review Questions', Yuanqin Yang, 08 May 2015
EC C10329: 'Public Editor Report', Jørgen Brandt, 10 Dec 2015

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Yuanqin Yang on behalf of the Authors (05 Jan 2016)

ED: Publish as is (06 Jan 2016) by Jørgen Brandt

AR by Yuanqin Yang on behalf of the Authors (11 Jan 2016)

Short summary

A new model, PLAM/h, has been developed and used in near-real-time air quality forecasts by considering both meteorology and pollutant emissions, based on the two-dimensional probability density function diagnosis model for emissions. The results show that combining the influence of regular meteorological conditions and emission factors together in the PLAM/h parameterization scheme is very effective in improving the forecasting ability for fog-haze weather in North China.