42 Can Assimilation of Ground Particulate Matter Observations …
271
PM10 concentrations with MB equal to −6.15 µg m
−3 , while the GOCART_GSI
slightly overestimated with MB equal to 1.38 µg m
−3 .
Analysis of temporal changes of MB for GOCART_GSI for all stations indicates
that the model underestimated peaks of very high concentrations of PM and slightly
overestimated concentrations in the second (warmer) part of the episode (the figures
not shown here).
42.4 Summary and Conclusions
The study with the WRF-Chem model and the GSI assimilation tool was carried
out for the episode of very high concentrations of particulate matter followed by the
period with lower PM concentrations in Poland on 11–25 February 2017. The results
show that the assimilation of ground based measurements of PM2.5 concentrations
has a positive impact on modelled concentration of PM2.5 and PM10. The greatest
positive impact is noticed for the period with the high measured air pollution concentrations. The results also show that for some stations the GOCART_GSI simulation
overestimates concentrations at the warmer period characterised by lower observed
PM concentrations. Previous analysis done by [2] showed that overestimation of PM
concentrations at warm winter periods can be related to too high emission factors
for the residential emissions. Thus, further study with an application of degree-day
factors for residential emissions and GSI assimilation is planned as the next step.
Acknowledgements The study was supported by the National Science Centre, Poland project no.
UMO-2016/23/B/ST10/01797. We are grateful to the CIEP for providing the emissions data from
the project “Supporting the air quality assessment system with application of modelling of PM10,
PM2.5, SO 2 , NO 2 , B(a)P”.
References
1. M. Chin, P. Ginoux, S. Kinne, O. Torres, B.N. Holben, B.N. Duncan, R.V. Martin, J.A. Logan,
A. Higurashi, T. Nakajima, Tropospheric aerosol optical thickness from the GOCART model
and comparisons with satellite and sun photometer measurements. J. Atmos. Sci. 59, 461–483
(2002)
2. M. Kryza, M. Werner, M. Dore, Application of degree-day factors for residential emission
estimate and air quality forecasting. Int. J. Environ. Pollut. In Review (2018)
3. M. Pagowski, G.A. Grell, S.A. McKeen, S.E. Peckham, D. Devenyi, Three-dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the
weather research and forecasting-chemistry model and grid-point statistical interpolation. Q. J.
R. Meteorol. Soc. 136 (2010)
4. W.R. Stockwell, P. Middleton, J.S. Chang, X. Tang, The second generation regional acid deposition model chemical mechanism for regional air quality modeling. J. Geophys. Res. 95 (1990)
5. J.B. Wu, J. Xu, M. Pagowski, F. Geng, S. Gu, G. Zhou, Y. Xie, Z. Yu, Modeling study of a severe
aerosol pollution event in December 2013 over Shanghai China: an application of chemical data
assimilation. Particuology 20, 41–51 (2015)
271
PM10 concentrations with MB equal to −6.15 µg m
−3 , while the GOCART_GSI
slightly overestimated with MB equal to 1.38 µg m
−3 .
Analysis of temporal changes of MB for GOCART_GSI for all stations indicates
that the model underestimated peaks of very high concentrations of PM and slightly
overestimated concentrations in the second (warmer) part of the episode (the figures
not shown here).
42.4 Summary and Conclusions
The study with the WRF-Chem model and the GSI assimilation tool was carried
out for the episode of very high concentrations of particulate matter followed by the
period with lower PM concentrations in Poland on 11–25 February 2017. The results
show that the assimilation of ground based measurements of PM2.5 concentrations
has a positive impact on modelled concentration of PM2.5 and PM10. The greatest
positive impact is noticed for the period with the high measured air pollution concentrations. The results also show that for some stations the GOCART_GSI simulation
overestimates concentrations at the warmer period characterised by lower observed
PM concentrations. Previous analysis done by [2] showed that overestimation of PM
concentrations at warm winter periods can be related to too high emission factors
for the residential emissions. Thus, further study with an application of degree-day
factors for residential emissions and GSI assimilation is planned as the next step.
Acknowledgements The study was supported by the National Science Centre, Poland project no.
UMO-2016/23/B/ST10/01797. We are grateful to the CIEP for providing the emissions data from
the project “Supporting the air quality assessment system with application of modelling of PM10,
PM2.5, SO 2 , NO 2 , B(a)P”.
References
1. M. Chin, P. Ginoux, S. Kinne, O. Torres, B.N. Holben, B.N. Duncan, R.V. Martin, J.A. Logan,
A. Higurashi, T. Nakajima, Tropospheric aerosol optical thickness from the GOCART model
and comparisons with satellite and sun photometer measurements. J. Atmos. Sci. 59, 461–483
(2002)
2. M. Kryza, M. Werner, M. Dore, Application of degree-day factors for residential emission
estimate and air quality forecasting. Int. J. Environ. Pollut. In Review (2018)
3. M. Pagowski, G.A. Grell, S.A. McKeen, S.E. Peckham, D. Devenyi, Three-dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the
weather research and forecasting-chemistry model and grid-point statistical interpolation. Q. J.
R. Meteorol. Soc. 136 (2010)
4. W.R. Stockwell, P. Middleton, J.S. Chang, X. Tang, The second generation regional acid deposition model chemical mechanism for regional air quality modeling. J. Geophys. Res. 95 (1990)
5. J.B. Wu, J. Xu, M. Pagowski, F. Geng, S. Gu, G. Zhou, Y. Xie, Z. Yu, Modeling study of a severe
aerosol pollution event in December 2013 over Shanghai China: an application of chemical data
assimilation. Particuology 20, 41–51 (2015)