Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution
Abstract
:1. Introduction
2. Materials and Methods
2.1. Development of Emission Scenarios
2.2. Data
2.3. PM2.5 Modeling and Evaluation Methods
- Step1.
- The meteorological data and PM2.5 observations of the NSF period ( and ) and NC period ( and ) were inputted into the STRF models, respectively.
- Step2.
- The STRF models were trained in R software using the “randomForest” function. Then, the trained model and were generated with the following equations,
- Step3.
- Then, the meteorological data of SD () was inputted into and to generate the predictions of PM2.5 concentration using the following equations.
- Step4.
- By comparing , , and from multiple perspectives, we analyzed how the restriction of factories and vehicles during SD affected the PM2.5 pollution level both spatially and temporally. It is noted that , , and represent the observed or predicted PM2.5 concentrations induced by the meteorological condition of SD under the emission scenarios of SD, NSF, and NC, respectively. The meteorological and emission scenarios of , , and are listed in Table 2.
- Step 1.
- The sample size of the training dataset was assumed to be n. Then, the algorithm first drew n bootstrap samples from the whole training dataset.
- Step 2.
- These samples were used to grow an unpruned regression tree. At each node, the best split factor was chosen from M randomly selected candidate factors to make the uncertainty of the split subsets reach the least.
- Step 3.
- The abovementioned steps were repeated times to grow trees. Predictions were made by averaging the predictions of trees. Considering that PM2.5 concentration must be positive values, the final prediction of PM2.5 concentration was determined as the maximum value between the original prediction and 0.
3. Results
3.1. Reliability of the STRF Model
3.2. Temporal Analysis of the Effect of Fireworks, Factories, and Vehicles
3.3. Spatial Analysis of the Effect of Fireworks, Factories, and Vehicles
4. Discussion
5. Conclusions
- (1)
- The impacts of restricting factories and vehicles on declining PM2.5 concentration shows obvious diurnal variations. Due to the reduction of traffic flow and human activities during the daytime, the cumulative emissions were reduced significantly. Consequently, the PM2.5 concentration at 14:00 was reduced the most (25.00 ), followed by 20:00 and 02:00 during the nighttime (19.07 and 18.57 , respectively), and the air pollution at 08:00 during the morning rush-hour was also alleviated to some extent (16.22 ).
- (2)
- The air quality is not only affected by the emissions of current day, but is also influenced by the cumulative emissions discharged in the previous period. Therefore, there was a delay in the time it took for the restriction of factories and vehicles to have a significant effect on improving air quality and the delay time for Hubei and YRD were 17 and 23 days, respectively.
- (3)
- The effect of restricting factories and vehicles shows obvious regional differences. Due to the discrepancies in the composition of PM2.5, the contribution ratio of industrial and vehicular emissions, and the geographic conditions, BTH, YRD, and Hubei experienced a 8.20, 23.52, and 27.05 decrease of PM2.5 concentration, respectively. On average, the air quality of Hubei was improved the most significantly and the fastest, followed by YRD. The air quality of BTH was improved the slightest because of the emissions from coal combustion and unfavorable meteorological conditions for air pollutants to be spread.
- (4)
- On account of the impact of more intensive human activities, cultivated, urban, and rural land are more sensitive to the emissions from factories, vehicles, and fireworks. The air quality in these areas was improved much more significantly than the forest and grass land after restricting the above emission sources.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Time | Emission Scenarios | Abbreviation | Fireworks | Heating | Factories and Vehicles |
---|---|---|---|---|---|---|
1 | 21 January 2020–20 February 2020 | Shut Down factories and vehicles | SD | Yes | Normal | Shut Down |
2 | 21 December 2019–20 January 2020 | Normal Commute | NC | None | Normal | Normal |
3 | 1 February 2019–3 March 2020 | Normal Spring Festival | NSF | Yes | Normal | Normal |
Data | Type | Meteorological Scenario | Emission Scenario |
---|---|---|---|
Observed | SD | SD | |
Predicted | SD | NSF | |
Predicted | SD | NC |
Hour (BJT) | Region | Normal Spring Festival Period | Normal Commute Period | ||||||
---|---|---|---|---|---|---|---|---|---|
Obs. | RMSE | MAE | R2 | Obs. | RMSE | MAE | R2 | ||
02:00 | BTH | 96.04 | 35.06 | 20.15 | 0.83 | 78.88 | 25.69 | 16.72 | 0.85 |
YRD | 59.69 | 16.15 | 10.54 | 0.87 | 57.73 | 15.54 | 10.42 | 0.88 | |
Hubei | 70.78 | 19.48 | 12.39 | 0.79 | 65.18 | 15.61 | 11.40 | 0.82 | |
All | 71.47 | 23.88 | 13.49 | 0.84 | 64.73 | 19.01 | 12.32 | 0.87 | |
08:00 | BTH | 84.22 | 27.27 | 17.13 | 0.85 | 71.82 | 25.56 | 16.22 | 0.84 |
YRD | 59.02 | 18.69 | 11.22 | 0.82 | 57.51 | 15.47 | 10.58 | 0.87 | |
Hubei | 70.40 | 20.37 | 13.05 | 0.80 | 59.82 | 15.34 | 11.44 | 0.79 | |
All | 67.71 | 21.73 | 13.15 | 0.84 | 61.86 | 18.88 | 12.28 | 0.85 | |
14:00 | BTH | 70.22 | 21.49 | 13.25 | 0.89 | 66.69 | 20.89 | 13.36 | 0.89 |
YRD | 57.12 | 14.25 | 9.44 | 0.88 | 55.59 | 13.25 | 9.24 | 0.89 | |
Hubei | 67.48 | 16.22 | 11.41 | 0.82 | 61.49 | 14.15 | 10.43 | 0.83 | |
All | 62.27 | 16.93 | 10.81 | 0.88 | 59.54 | 15.92 | 10.56 | 0.89 | |
20:00 | BTH | 85.96 | 25.53 | 15.47 | 0.86 | 79.87 | 24.06 | 16.13 | 0.85 |
YRD | 60.96 | 17.30 | 11.57 | 0.84 | 59.92 | 15.16 | 10.45 | 0.87 | |
Hubei | 67.01 | 16.79 | 11.83 | 0.80 | 68.25 | 14.66 | 10.99 | 0.84 | |
All | 68.84 | 19.96 | 12.70 | 0.85 | 66.71 | 18.10 | 12.13 | 0.86 |
Area | − | − | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
02:00 | 08:00 | 14:00 | 20:00 | All | 02:00 | 08:00 | 14:00 | 20:00 | All | |
BTH | −11.11 | −11.54 | −4.67 | −5.47 | −8.20 | 4.01 | 4.09 | 1.61 | 1.25 | 2.74 |
YRD | −20.93 | −16.75 | −32.12 | −24.26 | −23.52 | −17.32 | −13.21 | −19.12 | −20.30 | −17.49 |
Hubei | −23.75 | −23.33 | −36.28 | −24.83 | −27.05 | −11.98 | −9.31 | −18.08 | −23.00 | −15.59 |
All | −18.57 | −16.22 | −25.00 | −19.07 | −10.57 | −7.80 | −13.15 | −14.63 |
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Yang, M.; Fan, H.; Zhao, K. Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution. Int. J. Environ. Res. Public Health 2020, 17, 4828. https://doi.org/10.3390/ijerph17134828
Yang M, Fan H, Zhao K. Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution. International Journal of Environmental Research and Public Health. 2020; 17(13):4828. https://doi.org/10.3390/ijerph17134828
Chicago/Turabian StyleYang, Mei, Hong Fan, and Kang Zhao. 2020. "Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution" International Journal of Environmental Research and Public Health 17, no. 13: 4828. https://doi.org/10.3390/ijerph17134828