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应用生态学报 ›› 2017, Vol. 28 ›› Issue (1): 257-265.doi: 10.13287/j.1001-9332.201701.025

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南昌市空气PM2.5和PM10的时空动态及其影响因素

苏维1,2, 张帅珺1, 赖新云3, 古新仁1, 赖胜男1, 黄国贤1, 张志坚1, 刘苑秋1*   

  1. 1江西农业大学林学院, 南昌 330045
    2河南科技大学林学院, 河南洛阳 471003
    3南昌市环境监测站, 南昌 330038
  • 收稿日期:2016-07-06 修回日期:2016-10-31 发布日期:2017-01-18
  • 通讯作者: *E-mail:liuyq404@163.com
  • 作者简介:苏维,男,1979年生,博士研究生.主要从事城市森林研究.E-mail:suwei8405@126.com
  • 基金资助:
    本文由江西省自然基金项目(20152ACB20006)资助

Spatiotemporal dynamics of atmospheric PM2.5 and PM10 and its influencing factors in Nanchang, China

SU Wei1,2, ZHANG Shuai-jun1, LAI Xin-yun3, GU Xin-ren1, LAI Sheng-nan1, HUANG Guo-xian1, ZHANG Zhi-jian1, LIU Yuan-qiu1*   

  1. 1College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
    2College of Forestry, Henan University of Science & Techno-logy, Luoyang 471003, Henan, China
    3Nanchang Environmental Monitoring Station, Nanchang 330038, China
  • Received:2016-07-06 Revised:2016-10-31 Published:2017-01-18
  • Contact: *E-mail:liuyq404@163.com
  • Supported by:
    This work was supported by the Natural Science Foundation of Jiangxi Province(20152ACB20006)

摘要: PM2.5和PM10已成为我国大部分城市空气的首要污染物.本文通过分析南昌市2013—2015年的空气PM2.5和PM10质量浓度、气象因素、交通流量的监测数据,探讨了空气颗粒物污染的时空动态规律以及气象、交通对颗粒物浓度变化的影响.结果表明: 2013、2014、2015年,南昌市PM2.5浓度(70.92 μg·m-3>53.70 μg·m-3>43.65 μg·m-3)、PM10浓度(119.72 μg·m-3>86.11 μg·m-3>73.32 μg·m-3)逐年降低,并呈现出夏季低(PM2.5和PM10平均浓度分别为36.74、69.20 μg·m-3)、冬季高(PM2.5和PM10平均浓度分别为74.29、111.64 μg·m-3)的季节动态和由城市中心向郊区递减的城乡梯度变化; PM2.5/PM10值(0.595>0.584>0.557)逐年降低,并且表现出城市中心高、城市边缘低的空间分布格局;PM2.5、PM10浓度受到多种气象因素的影响,与气压、温度、相对湿度、风速、降水量、日照时数显著相关,各种气象因子对PM2.5、PM10浓度的影响存在差异;车流量会显著提高周边PM2.5浓度,但对PM10浓度影响不明显.

关键词: 交通流量, 气象因素, PM2.5, PM10

Abstract: PM2.5 and PM10 have become the primary pollutants of most cities in China. Atmospheric PM2.5 and PM10 mass concentrations, meteorological factors, traffic flow from 2013 to 2015 in Nanchang were analyzed. Spatiotemporal dynamic pattern of atmospheric particulate matter pollution and the effect of weather and traffic on particle concentration change were discussed in this paper. The results showed that PM2.5(70.92 μg·m-3 in 2013 > 53.70 μg·m-3 in 2014 > 43.65 μg·m-3 in 2015) and PM10(119.72 μg·m-3 in 2013 > 86.11 μg·m-3 in 2014 > 73.32 μg·m-3 in 2015) concentrations decreased gradually from 2013 to 2015. In addition, low concentrations of PM2.5 and PM10 in summer (average PM2.5 concentration 36.74 μg·m-3, average PM10 concentration 69.20 μg·m-3) but high concentrations in winter (average PM2.5 concentration 74.29 μg·m-3, average PM10 concentration 111.64 μg·m-3) were observed. Moreover, PM2.5 and PM10 concentrations changed with the urban-rural gradient, decreasing from the city center to suburb. The ratio of PM2.5/PM10(0.595 > 0.584 > 0.557) decreased year by year from 2013 to 2015 and was higher in the city center area than in the edge of city. PM2.5 and PM10 concentrations were affected by various meteorological factors and significantly related to air pressure, temperature, relative humidity, wind speed, precipitation and sunshine time. The influence of meteorological factors differed on PM2.5 and PM10 concentrations. Traffic flow significantly increased the surrounding PM2.5 concentration, but not PM10 concentration.

Key words: PM10, PM25, meteorological factors, traffic flow