Fine-resolution mapping of particulate matter concentration in urban areas and population exposure analysis via dispersion modeling: a study in Daejeon, South Korea
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To deliver accurate particulate matter information to citizens, a detailed particulate matter dispersion model including factors such as land use and meteorological information was developed and used to create particulate matter concentration distribution maps for Daejeon Metropolitan City (South Korea). The results showed differences from existing particulate matter concentration distribution maps created using established methods. For PM2.5, approximately 3600 concentration maps were constructed. Taking a map as an example, according to existing methods, the PM2.5 concentration was “good” in 56% and “moderate” in 44% of areas. However, according to our modeling, the PM2.5 concentration was good in 31%, moderate in 26%, “unhealthy” in 28%, and “very unhealthy” in 15% of areas. Furthermore, the existing methods indicated that no portion of the population was exposed to poor particulate matter concentrations, while the proposed model found that over 170,000 people were exposed to such concentrations. These results will contribute to sustainable urban and environmental planning.
KeywordsAtmospheric pollution Dispersion modeling Particulate matter Urban and environmental planning Air quality monitoring systems Population exposure
This study was funded by the Korea Environmental Industry & Technology Institute (KEITI) under grant number 2016000200009 and conducted by the Korea Environment Institute (KEI).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Eckhardt S, Cassiani M, Evangeliou N, Sollum E, Pisso I, Stohl A (2017) Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10. 2 in backward mode. Geosci Model Dev 10:4605–4618. https://doi.org/10.5194/gmd-10-4605-2017 CrossRefGoogle Scholar
- Hanna SR, Britter RE (2010) Wind flow and vapor cloud dispersion at industrial and urban sites, vol 7. John Wiley & Sons, HobokenGoogle Scholar
- Lin JC, Gerbig C, Wofsy SC, Andrews AE, Daube BC, Davis KJ, Grainger CA (2003) A near-field tool for simulating the upstream influence of atmospheric observations: the stochastic time-inverted Lagrangian transport (STILT) model. J Geophys Res Atmos 108:ACH 2-1–ACH 2-17. https://doi.org/10.1029/2002JD003161 Google Scholar
- Panofsky HA, Dutton JA (1984) Atmospheric turbulence: models and methods for engineering applications. Wiley, New York, p 397Google Scholar
- Son SW, Ahn TM (2013) Sensitivity analysis on the population within and outside of the Urban Park service areas—focused on Daegu Metropolitan City neighborhood parks and resident registration number data. J Korean Inst Landsc Archit 41:9–18. https://doi.org/10.9715/KILA.2013.41.5.009 CrossRefGoogle Scholar
- Weber K, Heweling G, Fischer C, Lange M (2017) The use of an octocopter UAV for the determination of air pollutants—a case study of the traffic induced pollution plume around a river bridge in Duesseldorf, Germany. Int J Environ Sci 2:63–68Google Scholar
- World Health Organization (2016) Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization, GenevaGoogle Scholar
- Zheng Y, Liu F, Hsieh HP (2013) U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1436-1444. ACM, ChicagoGoogle Scholar