Descriptive statistics methods were used to study the spatiotemporal variations and sources of ambient particulate matter ($PM_{10}$) in Luzhou, China. The analyzed datasets were collected from four national air quality monitoring stations: Jiushi (S1), Xiaoshi (S2), Zhongshan (S3), Lanti...
Descriptive statistics methods were used to study the spatiotemporal variations and sources of ambient particulate matter ($PM_{10}$) in Luzhou, China. The analyzed datasets were collected from four national air quality monitoring stations: Jiushi (S1), Xiaoshi (S2), Zhongshan (S3), Lantian (S4) over the period of 2003-2012. This city was subjected serious $PM_{10}$ pollution, and the long-term annual average $PM_{10}$ concentrations varied from 76 to $136{\mu}g/m^3$. The maximum concentration was more than 3-fold of the annual average ($40{\mu}g/m^3$) issued by EPA-China for the ambient air quality. General temporal pattern was characterized by high concentrations in winter and low concentrations in summer, and general spatial gradient was in the reduction order of S2 > S4 > S3 > S1, which were both due to different particulate contributors and special meteorological conditions. The source apportionment indicated that vehicular emissions, road dusts, coal burning and chemical dusts were the major contributors of the identified $PM_{10}$ pollution, and the vehicular emissions and the road wear re-suspended particles dominated the heavy $PM_{10}$ pollution in recent years. Two other potential sources, agricultural and celebration activities could decrease the air quality in a short term. Finally, some corresponding suggestions and measures were provided to improve the air quality.
Descriptive statistics methods were used to study the spatiotemporal variations and sources of ambient particulate matter ($PM_{10}$) in Luzhou, China. The analyzed datasets were collected from four national air quality monitoring stations: Jiushi (S1), Xiaoshi (S2), Zhongshan (S3), Lantian (S4) over the period of 2003-2012. This city was subjected serious $PM_{10}$ pollution, and the long-term annual average $PM_{10}$ concentrations varied from 76 to $136{\mu}g/m^3$. The maximum concentration was more than 3-fold of the annual average ($40{\mu}g/m^3$) issued by EPA-China for the ambient air quality. General temporal pattern was characterized by high concentrations in winter and low concentrations in summer, and general spatial gradient was in the reduction order of S2 > S4 > S3 > S1, which were both due to different particulate contributors and special meteorological conditions. The source apportionment indicated that vehicular emissions, road dusts, coal burning and chemical dusts were the major contributors of the identified $PM_{10}$ pollution, and the vehicular emissions and the road wear re-suspended particles dominated the heavy $PM_{10}$ pollution in recent years. Two other potential sources, agricultural and celebration activities could decrease the air quality in a short term. Finally, some corresponding suggestions and measures were provided to improve the air quality.
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제안 방법
Thus, meteorological difference was not the primary contributor of the higher PM10 concentration in autumn. The possible agricultural sources around all sites were analyzed in order to explain the changes. S4 is located at the junction of urban and rural areas, which could be the best example to certify whether the agricultural activities contributed to the relative bad air quality in autumn or not.
대상 데이터
The monitoring network is composed of four monitoring stations: Jiushi (S1, a scenic spot acting as urban background site), Xiaoshi (S2, a dock and commercial center of this city), Zhongshan (S3, an urban traffic site), Lantian (S4, an urban fringe near chemical industries) (Fig.
The study period covers ten years, from 2003 to 2012, and the PM10 dataset was collected from Environmental Protection Bureau of Luzhou (LEPB). A well-organized air quality monitoring network was built and operated normally from 1990s by LEPB for understanding and predicting the present and future status of the air quality.
This work studied the PM10 concentration variation profiles and its sources through analyzing the data collected from four different monitoring stations established in Luzhou city from January 2003 to December 2012. Generally, a number of interesting observations can be derived from the findings:
성능/효과
concentration distribution in Luzhou was obtained by analyzing the data collected in different sites. Using one-way ANOVA procedure on the long term mean PM10 concentrations of different monitoring sites, the concentrations were found to be different from each other significantly (0.95 confidence level). Universal negative correlation between the concentrations and the distance of sites away from the city center, however, were not found in our research through Pearson correlation analysis, which is due to the concentrations affected progressively by the local sources and the adverse meteorological conditions.
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