Despite years of attention, air pollution continues to be a major problem in many large cities throughout the world. One type of pollution is particulate matter, tiny particles that are suspended in the air. These particles can be hazardous, causing severe health effects as they get into the lungs and heart.
While much effort has gone into reducing the amount of particulate matter that is released, there has been less attention to reducing the amount after dispersion into the atmosphere. In a recent study, MSI PIs Lian Shen and David Pui and members of their research groups used computer modeling to look at the performance of an air-pollution cleaning system. The system, called the Solar-Assisted Large-Scale Cleaning System (SALSCS), uses solar energy to generate airflow through filters that clean particulates out of the air. It is designed to remove PM2.5, fine particles with a diameter of 2.5mm or less.
The researchers used data from seven air-pollution episodes in Beijing during the winters of 2015-17. They used the Weather Research and Forecasting Model (WRM), a powerful tool for studying weather that has also been used for air-pollution simulations. The authors adapted the model to include eight SALSCS installations at suburban areas around Beijing and tested two flow rates. The model’s results showed that PM2.5 rates were reduced under both flow rate conditions, and that the system needs to be improved for better performance in downtown areas. The computations were performed on MSI’s supercomputers.
The paper can be found on the website of the journal Science of the Total Environment: Q Cao, L Shen, S-C Chen, DYH Pui. 2018. WRF Modeling of PM2.5 Remediation by SALSCS and Its Clean Air Flow Over Beijing Terrain. Science of the Total Environment 626:134-146. DOI: 10.1016/j.scitotenv.2018.01.062.
Professors Pui and Shen are both in the Department of Mechanical Engineering. Professor Pui is the head of the department’s Center for Filtration Research and the Particle Technology Laboratory; researchers at both labs use MSI resources for their work. Professor Shen has been Director of the St. Anthony Falls Laboratory (SAFL) since 2016. Researchers in his group use MSI for projects using computational fluid dynamics calculations. Several other MSI PIs are also members of SAFL, which is an interdisciplinary fluids research and educational facility in the College of Science and Engineering. The lab is celebrating its 80th anniversary in 2018; read about its history on the SAFL website.
Image description: Left: Schematic diagram of the SALSCS with (1) solar collector, (2) tower, (3) filtration elements, and (4) fans (optional). Right: Land use categories over the computational domain with the numbers points displaying locations of the eight SALSCSs installed next to the 6th Ring Road of the Beijing city. Land use categories displayed in the figure: 1. evergreen needleleaf forest; 2. evergreen broadleaf forest; 3. deciduous needleleaf forests; 4. deciduous broadleaf forests; 5. mixed forests; 6. closed shrublands; 7. open shrublands; 8. woody savannas; 9. savannas; 10. grasslands; 11. permanent wetlands; 12. croplands; 13. urban and built-up; 14. cropland/natural vegetation mosaic; 15. snow and ice; 16. barren or sparsely vegetated; 17. water; 18. wooded tundra; 19. mixed tundra; 20. barren tundra. Image and description, Q Cao, et al. Science of the Total Environment 626:134-146 (2018). DOI: 10.1016/j.scitotenv.2018.01.062.
posted on August 15, 2018