Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine learning-based prediction and assessment
- 1School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 2Department of Geography, Hong Kong Baptist University, Hong Kong, China
- 1School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, 210044, China
- 2Department of Geography, Hong Kong Baptist University, Hong Kong, China
Abstract. The summertime air pollution events endangering public health in the Guangdong–Hong Kong–Macao Greater Bay Area are connected with typhoons. The wind of the typhoon periphery results in poor diffusion conditions and favorable conditions for transboundary air pollution. Random Forest models are established to predict typhoon-associated air quality in the area. The correlation coefficients and the root-mean-square errors of the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O3 concentrations are 0.84 (14.88), 0.86 (10.31 µg/m3), 0.84 (17.03 µg/m3), 0.51 (8.13 µg/m3), 0.80 (13.64 µg/m3) and 0.89 (22.43 µg/m3), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature importance output of the models, the differences in the meteorological drivers of typhoon days and non-typhoon days are revealed. On typhoon days, the air quality is dominated by local source emission and accumulation as the sink of pollutants reduces significantly under stagnant weather, while by the transportation and scavenging effect of sea breeze on non-typhoon days. Therefore, our findings suggest that different air pollution control strategies for typhoon days and non-typhoon days should be proposed.
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Yilin Chen et al.
Status: final response (author comments only)
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RC1: 'Comment on amt-2022-225', Anonymous Referee #1, 03 Oct 2022
This paper used the random forest models to predict typhoon-associated air quality in the Guangdong-HongKong-Macao Center Bay Area. This work is interesting and meaningful to the readers of AMT. Generally this paper is well organized. The methods and conclusions are reliable. But I still have some suggestions below.
Detailed comments:
1. This paper used data from 36 air quality monitoring stations in 10 cities of the GBA that is heavily polluted. Why did you not consider the observation data from the rural regions of Guangdong Province?
Are the 36 air quality monitoring data are used for both model training and RF model evaluation? I think it may be more reasonable if you could choose parts of the 36 sites for model training and the others for RF model evaluation.
2. In section 3.1, did you compare the model performance of the RF model with the other traditional air quality models, e.g., CMAQ, WRF/Chem.
Figure 5: Why the data are only available over the seas?
Figure 6: The same data at the air quality monitoring sites are first used for model training and then for model evaluation?
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AC1: 'Reply on RC1', Yilin Chen, 30 Dec 2022
Dear Reviewer,
Thank you for your efforts in reviewing our manuscript. We appreciate receiving your valuable comments. These comments are very constructive and we have revised our manuscript carefully by following all referees’ comments and suggestions. Please find our point-by-point responses.
Please see the supplement.
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AC1: 'Reply on RC1', Yilin Chen, 30 Dec 2022
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RC2: 'Comment on amt-2022-225', Anonymous Referee #3, 09 Dec 2022
This study employs the random forest models to predict typhoon-associated air quality quantitatively in the Guangdong-HongKong-Macao Center Bay Area. The prediction models are estabilished for typhoon and non-typhoon days. Thus, the results suggest that different air pollution control strategies for typhoon days and non-typhoon days should be adopted. The work is innovative well written and interesting to the readers of AMT.
I have two questions below.
1) The present study takes 36 air quality monitoring stations in 10 cities in the GBA (Guangzhou, Shenzhen, Zhuhai, Foshan,
Zhaoqin, Jiangmen, Huizhou, Dongguan, Zhongshan, Hong Kong) as research objects. Why did you not consider data from the rural regions?2) The study used ERA5 reanalysis from meteorological data. Couldn't these data be integrated with those coming from other high-resolution instruments? For example lidar or meteorological radosondes?
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AC2: 'Reply on RC2', Yilin Chen, 30 Dec 2022
Dear Reviewer,
Thank you for your efforts in reviewing our manuscript. We appreciate receiving your valuable comments. These comments are very constructive and we have revised our manuscript carefully by following all referees’ comments and suggestions. Please find our point-by-point responses.
Please see the supplement.
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AC2: 'Reply on RC2', Yilin Chen, 30 Dec 2022
Yilin Chen et al.
Yilin Chen et al.
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