| 摘要: |
| 结合贵阳基准站气溶胶质量浓度观测中出现的数据异常情况，提出相应质控措施，编写小时数据检查程序以提醒值班员及时查看报警原因，尽量减少原始数据的缺失及野值的产生。采用7-5-3hanning平滑滤波法，将处理后的PM2.5数据与原始分钟数据比对，结果显示该方法在剔除异常值的同时保留了原序列应有的变化特征。利用2013-2016年本站PM2.5质控数据及同期气象资料对PM2.5质量浓度的变化特征进行了简要分析，结果表明，PM2.5月均浓度呈现明显的冬高夏低的单谷多峰走势；以2013年1月一次连续9天以PM2.5为首的空气污染时段为例就PM2.5质量浓度与同时段气象要素的相关性进行分析，数据显示PM2.5与风速、降水呈明显负相关性，即风速越大，PM2.5浓度越小，降水对净化空气作用明显，PM2.5浓度明显降低 。 |
| 关键词: PM2.5;异常;质控;分析 |
| DOI： |
| 投稿时间：2018-06-10修订日期：2019-01-14 |
| 基金项目: |
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| Aerosol data quality control measures of GuiYang meteorological Station and Influence of meteorological factors on PM2.5 |
| wuyouheng,minchanghong,wushimei |
| (Guiyang meteorological bureau;Guiyang Meteorological Bureau) |
| Abstract: |
| Combined with the abnormal data in the aerosol mass concentration observation of Guiyang Meteorological Station, the corresponding quality control measures are put forward. The hourly data checking program is compiled to remind the attendants to check the alarm reasons in time, so as to minimize the loss of original data and the generation of outliers. By using 7-5-3 Hanning smoothing filtering method, the processed PM2.5 data are compared with the original minute data.The results show that this method can eliminate the outliers while retaining the variation characteristics of the original sequence
.Based on the quality control data of PM2.5 from 2013 to 2016 and the meteorological data of the same period, the variation characteristics of PM2.5 mass concentration are analyzed briefly.The results show that the PM2.5 monthly mean concentration presents a single valley and multi-peak trend,it was high in winter and low in summer.The correlation between PM2.5 mass concentration and meteorological factors in the same period was analyzed with an air pollution period of 9 consecutive days in January 2013 as an example. The data showed that PM2.5 was negatively correlated with wind speed and precipitation. That is, the higher the wind speed, the smaller the PM2.5 concentration, the more obvious the precipitation effect on purifying air, and the lower the PM2.5 concentration. |
| Key words: PM2.5; Abnormality; Quality Control; Analysis |
