南昌市空气PM_(2.5)和PM_(10)的时空动态及其影响因素

Spatiotemporal dynamics of atmospheric PM_(2.5) and PM_(10) and its influencing factors in Nanchang,China

PM_(2.5)和PM_(10)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PM_(2.5)和PM_(10)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PM_(2.5)浓度(70.92μg·m^(-3)>53.70μg·m^(-3)>43.65μg·m^(-3))、PM_(10)浓度(119.72μg·m^(-3)>86.11μg·m^(-3)>73.32μg·m^(-3))逐年降低,并呈现出夏季低(PM_(2.5)和PM_(10)平均浓度分别为36.74、69.20μg·m^(-3))、冬季高(PM_(2.5)和PM_(10)平均浓度分别为74.29、111.64μg·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;PM_(2.5)/PM_(10)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;PM_(2.5)、PM_(10)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PM_(2.5)、PM_(10)浓度的影响存在差异;车流量会显著提高周边PM_(2.5)浓度,但对PM_(10)浓度影响不明显.

PM2.5 and PMIo 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.