Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai
Abstract With the effective control of air pollutants from industrial sources and motor vehicles, non-road machinery has become the major pollutant source with a total emission accounting for more than 65% of non-road mobile sources in China. However, few efforts were established in the emission inv...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-06-01
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| Series: | Aerosol and Air Quality Research |
| Subjects: | |
| Online Access: | https://doi.org/10.4209/aaqr.230012 |
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| Summary: | Abstract With the effective control of air pollutants from industrial sources and motor vehicles, non-road machinery has become the major pollutant source with a total emission accounting for more than 65% of non-road mobile sources in China. However, few efforts were established in the emission inventory of the non-road machinery, and the current classifications existed inadequacies. Here, the practical classification approaches for estimating and predicting pollutant emissions from non-road machinery are established by using a database in the Baoshan district in Shanghai province (China). The proposed spatial characteristic analysis indicates that high emissions are particularly found in the northwestern part of Luojing Town near the Huangpu River. The total pollutant quantity emitted from in-plant machinery and harbor machinery is higher than other types and accounted for 46.5% and 46.9% of the total emissions of all non-road machinery, respectively. 73.3% of SO2 emission is from in-plant machinery and forklifts can be responsible for this situation (Guo et al., 2020). The prediction suggests that the total emissions of in-plant machinery and agricultural machinery in the medium scenario could decrease by 12.7% and 4.9% in 2025, respectively. For construction machinery, harbor machinery, and other machineries, the total emissions can be predicted to rise by 6.01%, 4.25%, and 7.85%, respectively. The proposed spatial characteristic analysis method and the established classification approaches based on the actual pollution source data may provide guidance for the non-road machinery emissions pollution research investigations in other regions. |
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| ISSN: | 1680-8584 2071-1409 |