Integration of air quality model with GIS for the monitoring of PM2.5 from local primary emission at a rural site

• Published in: Environmental monitoring and assessment 2021-10, Vol.193 (10), p.682-682, Article 682
• Main Authors: Lee, Kwon-Ho, Bae, Min-Suk
• Format: Article
• Language: English
• Subjects: Air monitoring; Air pollution; Air quality; Air quality models; Airborne particulates; Asphalt; Atmospheric models; Atmospheric Protection/Air Quality Control/Air Pollution; Data analysis; Earth and Environmental Science; Ecology; Ecotoxicology; Emission analysis; Emissions; Emissions control; Environment; Environmental Management; Environmental monitoring; Environmental science; Factor analysis; Geographic information systems; Geographical information systems; Information systems; Manufacturing industry; Modelling; Monitoring/Environmental Analysis; Outdoor air quality; Particulate emissions; Particulate matter; Particulate matter emissions; Particulate matter monitoring; Pollutants; Pollution monitoring; Public health; Receptors; Residential areas; Rural areas; Suspended particulate matter; Villages; Wind; Wind speed
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-021-09461-9

Local primary emissions of air pollutants are responsible for public health, decreasing productivity, and cultural activities in local residential areas. In this study, an integrated air quality observation and modeling system with a geographical information system (GIS) was developed to characterize the air pollution caused by local primary emission sources. This integrated system could provide air quality monitoring, data analysis, and visualization results that reflect air pollutant concentration data in a study area containing a local rural village (LRV) and an asphalt manufacturing facility (AMF). Additionally, the model was used to estimate the contributions of air quality from an emission source at the receptor and determine the control factor for the emission rate or meteorological changes. From the forward and backward modeling results, we found that the concentrations of particulate matter smaller than 2.5 μm (PM 2.5 ) concentrations in the village were affected by the unique meteorological and emission conditions. The PM 2.5 concentration was significantly increased for the cases with a slow wind speed of 1 m/s or high wind speed of 3 m/s, with an emission rate of 10 g/s. The contribution of AMF emissions was explained by contribution factor analysis. During the study period of December 2014–December 2015, the incoming contribution of PM 2.5 at the LRV measurement station was approximately 47.6%. These results suggest that the proposed method can be useful for understanding adverse air quality conditions and estimating the emissions of air pollutants from primary sources for local environmental and public health authorities.