Community Multiscale Air Quality (CMAQ) model can predict air pollutant concentration at
every pre-determined location but usually suffers from considerable systematic bias. By fusing
the CAMQ simulations and ground observations, first part of this study developed an
unsupervised hybrid model, namely K-means-cluster-Observation-examiner-Boundary-denoiser
(KOB) model, to segment region and to systematically reduce the bias of CMAQ
simulations. The KOB model was then applied to improve prediction of CMAQ predictions of
respirable suspended particulates (RSP) concentrations in the Pearl River Delta (PRD) region
of southern China in 2019, using observations from 69 air quality monitoring stations in 2019.
Compared to the CMAQ simulations, the KOB model significantly improves the 1-hour
predictions of RSP at all stations, with index of agreement (IOA) increasing from 0.70 to 0.86
and average root mean square error (RMSE) reducing from 28.4 to 20.0 μg/m
3 . Better
agreement was also achieved between monthly average RSP from predictions using the KOB
model and ground observations (e.g., IOA increase from 0.89 to 0.93, RMSE decrease from
13.8 to 9.5 μg/m
3). In addition, the capability of the KOB model predicting polluted hour
concentration greatly improved, with RMSE reduced from 76.3 to 50.8 μg/m
3. Furthermore,
the improvement was identified not only for next 1-hour predictions, but also for the predictions
in next 6 hours. Through the data fusion, our hybrid clustering model can achieve robust real-time
air quality forecasting.
Second part of this study developed a semi-supervised learning method for region segmentation
with a core of constraint k-means guided by bias segmentation results to correct the systematic
bias of CMAQ. The Semi-supervised K-means-cluster-Observation-examiner-Boundary-denoiser
(Semi-KOB) model is developed with RSP in PRD region of China, using
observations from 59 air quality monitoring stations from 2018 to 2019. Compared to the
CMAQ simulations, the Semi-KOB model significantly improves the 1-hour predictions of
RSP at all stations, with index of agreement (IOA) increasing from 0.71 to 0.86 and average root mean square error (RMSE) reducing from 28.1 to 19.5 μg/m
3. Better agreement was also
achieved between monthly average RSP from predictions using the KOB model and ground
observations (e.g., IOA increase from 0.91 to 0.93, RMSE decrease from 12.8 to 8.4 μg/m
3).
In addition, the capability of the Semi-KOB model to predict polluted hour concentration
greatly improved, with RMSE reduced from 75.3 to 50.0 μg/m
3. Furthermore, the
improvement was identified not only for next 1-hour predictions, but also for the predictions
in next 12 hours. Through the data fusion, our hybrid clustering model can achieve robust real-time
air quality forecasting.
Third part of the study, we generalized the Semi-KOB model into other pollutants (PM
10,
PM
2.5, NO
2 and O
3) in PRD region, as well as RSP in other four domains of CMAQ model
(Guangdong Province, South of China and whole China). The Semi-KOB model shows
improvement in all the four pollutants and in all the four domains. In 12-hour predictions, the
model shows improvement in all 12-hour in terms of averages and outperforming rate by
station of all the evaluation metrics. To summarize, in the PRD region of China, the Semi-KOB
model performs best with NO
2, then particulate matters and worst with ozone. And the Semi-KOB model performs better in larger spatial coverage with CMAQ data, that is, performing
best in China, then the South of China, Guangdong province and the PRD region for the
pollutant of RSP.
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