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Spatial and Temporal Variations of PM 2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China

View Article: PubMed Central - PubMed

ABSTRACT

The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5) and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m3 to 336.4 μg/m3. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.

No MeSH data available.


Regional distribution of the average PM2.5 mass concentrations in the past three years in Nanjing.
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ijerph-13-00921-f002: Regional distribution of the average PM2.5 mass concentrations in the past three years in Nanjing.

Mentions: Figure 2 shows the spatial distribution of the average PM2.5 concentrations for each monitoring site of Nanjing in the past three years. The map shows that PM2.5 pollution was most serious at Maigaoqiao, followed by Aotizhongxin and Ruijinlu. The finest air quality was observed at Xuanwuhu and Xianlindaxuecheng. In order to exhaustively explain the spatial distribution of PM2.5, the typical characteristics of the nine monitoring sites in Nanjing were collected in Table 2. According to the information shown in Table 2, Maigaoqiao had the worst environment, and the main sources of pollution found at the Aotizhongxin site came from urban construction activities while Ruuijinlu station is located in a dense residential area; on the contrary, Xuanwuhu and Xianlindaxuecheng sites owe their superior environment to the lack of big emissions of particulate pollutants. Through the above analysis, spatial distribution was closely related to geographical location. Meanwhile, due to the systematic information of particulate matter pollution, the impact of terrain, vegetation cover, and weather conditions cannot be ignored in the process.


Spatial and Temporal Variations of PM 2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China
Regional distribution of the average PM2.5 mass concentrations in the past three years in Nanjing.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036754&req=5

ijerph-13-00921-f002: Regional distribution of the average PM2.5 mass concentrations in the past three years in Nanjing.
Mentions: Figure 2 shows the spatial distribution of the average PM2.5 concentrations for each monitoring site of Nanjing in the past three years. The map shows that PM2.5 pollution was most serious at Maigaoqiao, followed by Aotizhongxin and Ruijinlu. The finest air quality was observed at Xuanwuhu and Xianlindaxuecheng. In order to exhaustively explain the spatial distribution of PM2.5, the typical characteristics of the nine monitoring sites in Nanjing were collected in Table 2. According to the information shown in Table 2, Maigaoqiao had the worst environment, and the main sources of pollution found at the Aotizhongxin site came from urban construction activities while Ruuijinlu station is located in a dense residential area; on the contrary, Xuanwuhu and Xianlindaxuecheng sites owe their superior environment to the lack of big emissions of particulate pollutants. Through the above analysis, spatial distribution was closely related to geographical location. Meanwhile, due to the systematic information of particulate matter pollution, the impact of terrain, vegetation cover, and weather conditions cannot be ignored in the process.

View Article: PubMed Central - PubMed

ABSTRACT

The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5) and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m3 to 336.4 μg/m3. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.

No MeSH data available.