Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO<sub>2</sub> concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO<sub>2</sub> concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO<sub>2</sub> suggested a gradual oxidation of NO into NO<sub>2</sub>, with local emissions contributing to 32-70% of the total NO<sub>2</sub> concentration. These findings provide valuable insights into the NO and NO<sub>2</sub> emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.

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Publication Details

Journal
Toxics
Published
2024-10-19
DOI
https://doi.org/10.3390/toxics12100760
Citations
1
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.21
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article

Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network

Zhi Ning, Qingyan Fu, Nirmal Kumar Gali, Jun Pan et al.
1 citations
Toxics
Air Quality Monitoring and Forecasting
0.21
article

Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network

Zhi Ning, Qingyan Fu, Nirmal Kumar Gali, Jun Pan, Xiaoliang Qin, Ying Wang
article en
1 citations

Abstract

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO<sub>2</sub> concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO<sub>2</sub> concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO<sub>2</sub> suggested a gradual oxidation of NO into NO<sub>2</sub>, with local emissions contributing to 32-70% of the total NO<sub>2</sub> concentration. These findings provide valuable insights into the NO and NO<sub>2</sub> emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.

ToxicsVol. 12(10)
Hong Kong University of Science and Technology (HK), Zhejiang Environmental Monitoring Center (CN), Shanghai Academy of Environmental Sciences (CN)
Openalex Percentile: Top 50%
Air Quality Monitoring and Forecasting
0.21
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