Inter-comparison of chemical transport models (CTMs) was conducted among four modeling research groups. Model performance of the ensemble approach to $O_3$ and $PM_{2.5}$ simulation was evaluated by using observational data with a time resolution of 1 or 6 hours at four sites i...
Inter-comparison of chemical transport models (CTMs) was conducted among four modeling research groups. Model performance of the ensemble approach to $O_3$ and $PM_{2.5}$ simulation was evaluated by using observational data with a time resolution of 1 or 6 hours at four sites in the Kanto area, Japan, in summer 2007. All groups applied the Community Multiscale Air Quality model. The ensemble average of the four CTMs reproduced well the temporal variation of $O_3$ (r=0.65-0.85) and the daily maximum $O_3$ concentration within a factor of 1.3. By contrast, it underestimated $PM_{2.5}$ concentrations by a factor of 1.4-2, and did not reproduce the $PM_{2.5}$ temporal variation at two suburban sites (r=~0.2). The ensemble average improved the simulation of ${SO_4}^{2-}$, ${NO_3}^-$, and ${NH_4}^+$, whose production pathways are well known. In particular, the ensemble approach effectively simulated ${NO_3}^-$, despite the large variability among CTMs (up to a factor of 10). However, the ensemble average did not improve the simulation of organic aerosols (OAs), underestimating their concentrations by a factor of 5. The contribution of OAs to $PM_{2.5}$ (36-39%) was large, so improvement of the OA simulation model is essential to improve the $PM_{2.5}$ simulation.
Inter-comparison of chemical transport models (CTMs) was conducted among four modeling research groups. Model performance of the ensemble approach to $O_3$ and $PM_{2.5}$ simulation was evaluated by using observational data with a time resolution of 1 or 6 hours at four sites in the Kanto area, Japan, in summer 2007. All groups applied the Community Multiscale Air Quality model. The ensemble average of the four CTMs reproduced well the temporal variation of $O_3$ (r=0.65-0.85) and the daily maximum $O_3$ concentration within a factor of 1.3. By contrast, it underestimated $PM_{2.5}$ concentrations by a factor of 1.4-2, and did not reproduce the $PM_{2.5}$ temporal variation at two suburban sites (r=~0.2). The ensemble average improved the simulation of ${SO_4}^{2-}$, ${NO_3}^-$, and ${NH_4}^+$, whose production pathways are well known. In particular, the ensemble approach effectively simulated ${NO_3}^-$, despite the large variability among CTMs (up to a factor of 10). However, the ensemble average did not improve the simulation of organic aerosols (OAs), underestimating their concentrations by a factor of 5. The contribution of OAs to $PM_{2.5}$ (36-39%) was large, so improvement of the OA simulation model is essential to improve the $PM_{2.5}$ simulation.
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제안 방법
In this paper, we evaluate the model performance of the ensemble approach to O3 and PM2.5 simulation.
In this study, the ensemble approach improved the model performance for SO42- and NO3- simulation. This result suggests that current CTMs capture the factors controlling SO42- and NO3- concentrations relatively well.
To evaluate the PM2.5 simulation, we analyzed the sum of the concentrations of five particulate species (EC, OA, SO42-, NO3-, and NH4+; Σ(PM2.5)).
대상 데이터
3. Average model performance at the four measurement sites (S1, Komae; S2, Kisai; S3, Maebashi; S4, Tsukuba) during the analytical period (from 31 July 2007 to 10 August 2007). Fig.
성능/효과
The ensemble average of the four CTMs better reproduced the temporal variation of O3 (r=0.65-0.85) than the individual CTMs. Also, the ensemble average could effectively remove the effect of outlying data and reproduced the daily maximum O3 concentration within 31% at all four sites.
후속연구
Also, this was a case study during 10 days in summer. Further studies in other areas or in other seasons would help us better understand CTM performance.
참고문헌 (19)
Carmichael, G.R., Sakurai, T., Streets, D., Hozumi, Y.,
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