The purpose of this study was to analyze the spatial and temporal distribution characteristics of PM2.5 in Changwon-si, and to identify the causes of occurrence through field survey and spatial analysis. Gyeongnam Office of Education measurement data was used as basic data, and the every hour averag...
The purpose of this study was to analyze the spatial and temporal distribution characteristics of PM2.5 in Changwon-si, and to identify the causes of occurrence through field survey and spatial analysis. Gyeongnam Office of Education measurement data was used as basic data, and the every hour average from September 2017 to August 2019 was used. By using the IDW technique among the GIS spatial interpolation methods, distribution maps were constructed by month, time slot, and day, and based on this, the temporal and spatial characteristics of PM2.5 distribution and time series concentration changes were confirmed.
First, in order to verify the accuracy of the measurement data, the correlation with AirKorea data managed by the Ministry of Environment was checked. As a result of the analysis, R2 was 0.53~0.83, which showed a very high correlation, so it was determined that it was suitable for the study, and the analysis was conducted. In the monthly analysis, January was high and August was low. By season, winter and spring were high, and summer and autumn were low, confirming the need for a management plan for winter. As a result of the analysis by time, the clock-in time at 06-09 was the highest, and the active time at 09-18 was the lowest. By administrative district, Daesan-myeon, Dong-eup, Buk-myeon, and Sangnam-dong were identified as severe PM2.5 areas, while Taebaek-dong, Ungdong 1-dong, Yeojwa-dong, and Chungmu-dong were low. The characteristics of time series concentration change over two years were analyzed for the entire Changwon-si. As a result, it was confirmed that PM2.5 tended to decrease in most regions for 2 years.
Correlation analysis and box plot analysis were performed to investigate the characteristics of PM2.5 occurrence according to land use types, and field surveys were conducted on 30 high concentration points. Green areas, such as forest areas, can be derived as a reduction factor that has the greatest effect on the generation of PM2.5, and it was confirmed that there are many PM2.5 generations in agricultural areas. As a result of the field survey, six types were derived, and results consistent with the previous results were derived. Since the difference between the suburbs and the city center according to the shape of the watershed was confirmed, it was confirmed that the influence of PM2.5 introduced from the outside should be considered. In addition, outdoor incineration is a cause of local high concentration PM2.5, so it must be managed in a place with a preventive facility, and more detailed measures against illegal incineration are required.
In conclusion, the results of this study will be used as basic data to understand the distribution characteristics of PM2.5 in Changwon-si. In addition, it is thought that the serious areas and the directions for establishing reduction plans derived from the results of this study can be used to prepare more effective policies.
The purpose of this study was to analyze the spatial and temporal distribution characteristics of PM2.5 in Changwon-si, and to identify the causes of occurrence through field survey and spatial analysis. Gyeongnam Office of Education measurement data was used as basic data, and the every hour average from September 2017 to August 2019 was used. By using the IDW technique among the GIS spatial interpolation methods, distribution maps were constructed by month, time slot, and day, and based on this, the temporal and spatial characteristics of PM2.5 distribution and time series concentration changes were confirmed.
First, in order to verify the accuracy of the measurement data, the correlation with AirKorea data managed by the Ministry of Environment was checked. As a result of the analysis, R2 was 0.53~0.83, which showed a very high correlation, so it was determined that it was suitable for the study, and the analysis was conducted. In the monthly analysis, January was high and August was low. By season, winter and spring were high, and summer and autumn were low, confirming the need for a management plan for winter. As a result of the analysis by time, the clock-in time at 06-09 was the highest, and the active time at 09-18 was the lowest. By administrative district, Daesan-myeon, Dong-eup, Buk-myeon, and Sangnam-dong were identified as severe PM2.5 areas, while Taebaek-dong, Ungdong 1-dong, Yeojwa-dong, and Chungmu-dong were low. The characteristics of time series concentration change over two years were analyzed for the entire Changwon-si. As a result, it was confirmed that PM2.5 tended to decrease in most regions for 2 years.
Correlation analysis and box plot analysis were performed to investigate the characteristics of PM2.5 occurrence according to land use types, and field surveys were conducted on 30 high concentration points. Green areas, such as forest areas, can be derived as a reduction factor that has the greatest effect on the generation of PM2.5, and it was confirmed that there are many PM2.5 generations in agricultural areas. As a result of the field survey, six types were derived, and results consistent with the previous results were derived. Since the difference between the suburbs and the city center according to the shape of the watershed was confirmed, it was confirmed that the influence of PM2.5 introduced from the outside should be considered. In addition, outdoor incineration is a cause of local high concentration PM2.5, so it must be managed in a place with a preventive facility, and more detailed measures against illegal incineration are required.
In conclusion, the results of this study will be used as basic data to understand the distribution characteristics of PM2.5 in Changwon-si. In addition, it is thought that the serious areas and the directions for establishing reduction plans derived from the results of this study can be used to prepare more effective policies.
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#Ultra Fine Dust PM2.5 Spatial Bigdata Spatial Interpolation GIS
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