A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China
A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China
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DOI：
10.1016/j.atmosenv.2015.10.074
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发表时间：
2015-12
影响因子：
5
通讯作者：
Z. Peng;Dongsheng Wang;Zhan-yong Wang;Ya Gao;Si-Jia Lu
中科院分区：
文献类型：
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作者：
Z. Peng;Dongsheng Wang;Zhan-yong Wang;Ya Gao;Si-Jia Lu
测量大气污染物浓度的垂直分布可以为准确估计当地污染物在边界层和对流层之间的扩散机制提供必要的信息。报道了在2014年8月至2014年12月期间，利用装有移动传感器的无人机在杭州海拔1000米以内的16个航班上采集了三维细颗粒物(PM2.5)质量浓度数据的独特方法。该研究论证了携带移动监测设备的无人机作为一种有效且灵活的手段收集三维空气污染物浓度数据的可行性，特别是在监测空气污染物垂直分布方面。实验结果表明，除出现逆温层外，PM2.5浓度总体上随高度的增加而减小，且上午PM2.5浓度的下降幅度大于下午。这是白天人类活动累积污染物排放和气象条件变化的结果。在同一水平层，PM2.5浓度在一天中的不同时段存在波动。PM2.5浓度的垂直波动在两个下午的航班上趋于一致，这与大气混合的程度直接相关。从多元回归模型看，PM2.5相对浓度在垂直观测值和地面观测值之间的分布特征良好，气温、相对湿度、气压和高度四个测量因子的回归系数有效地解释了它们对垂直分布格局的影响。气温和相对湿度是影响PM2.5浓度垂直分布的最主要因素。
Measurements of the vertical distribution of air pollutant concentrations can provide essential information for accurate estimates of the dispersion mechanism of local pollutants between boundary layer and troposphere. This paper reports unique measurements using an unmanned aerial vehicle (UAV) with mobile sensors to collect three-dimensional fine particulate matter (PM2.5) mass concentration data on sixteen flights within 1000 m altitude from August, 2014 to December, 2014 in Hangzhou, China.The study demonstrates the feasibility of UAV with mobile monitoring devices as an effective and flexible means to collect three-dimensional air pollutant concentration data, particularly for monitoring the vertical profile of air pollutants. The experimental results show that in general, the PM2.5concentrations decrease as height increases, with an exception when the air temperature inversion layer appears, and the decrease rate of PM2.5concentrations is larger in the morning than in the afternoon flights. This is a result of the accumulated pollutant emission of human activities during the day and the varied meteorological conditions. At the same horizontal layer, there are fluctuations in PM2.5concentrations during different time periods of the day. The vertical fluctuations of PM2.5concentrations become nearly uniform in two afternoon flights, which is directly related with the extent of atmospheric mixture. Seen from the multiple regression models, the distribution of relative PM2.5concentrations between vertical and ground observations is well characterized and the regression coefficients of four measured factors (i.e., air temperature, relative humidity, air pressure and height) effectively explain their impacts on the vertical distribution patterns. Air temperature and relative humidity are the most influential factors that affect the vertical distribution of PM2.5concentrations.