基于Kolmogorov-Zurbenko(KZ)滤波和Robust Extraction of Baseline Signal(REBS)方法处理了青藏高原纳木错站和珠峰站多年连续的近地面臭氧原始观测数据.通过合理选择KZ滤波器和REBS滤波窗口与带宽,成功去除了臭氧时间序列中的短期变动,提取了代表两站近地面臭氧背景浓度信号.结果表明,KZ滤波法得到的纳木错站和珠峰站臭氧年平均背景浓度分别为89.71 μg/m3和88.98μg/m3;REBS方法得到的分别为89.62μg/m3和88.52μg/m3.两种方法得到的背景臭氧浓度一致性较好,都呈现出相似的季节变化规律.4～5月臭氧浓度较高,而季风期间则较低.KZ滤波更侧重反映长期趋势和季节周期性变化,而REBS在局部污染时段的表现更好.两种非参数统计方法均可有效应用于背景区域臭氧时间序列分析,为监测和防治臭氧污染提供重要依据.
Abstract
The atmospheric background concentration can reflect the degree of pollution and long-term trends on a regional scale.This work was based on the Kolmogorov-Zurbenko(KZ)filtering and Robust Extraction of Baseline Signal(REBS)methods and processed multiple years of continuous near-surface ozone raw observational data from Nam Co Station and Qomolangma Station on the Tibetan Plateau.By optimizing the KZ filter and REBS filtering window and bandwidth,short-term fluctuations in the ozone time series were successfully removed,and signals representing the background concentration of near-surface ozone at both stations were extracted.The results showed that the annual average background ozone concentrations at Nam Co Station and Qomolangma Station were 89.71μg/m3 and 88.98μg/m3 respectively according to the KZ filtering method,and 89.62μg/m3 and 88.52μg/m3 respectively according to the REBS method.The background ozone concentrations obtained by both methods demonstrated good consistency and exhibited similar seasonal variation patterns.The ozone concentration was higher in April-May and lower during the monsoon period.KZ filtering focused more on reflecting long-term trends and seasonal cyclical changes,while REBS performed better during local pollution periods.Both non-parametric statistical methods were effectively applied to the analysis of ozone time series in background regions,providing an important basis for monitoring and controlling ozone pollution.
关键词
臭氧/背景浓度/KZ滤波/REBS/青藏高原
Key words
ozone/background concentration/Kolmogorov-Zurbenko filter/Robust Extraction of Baseline Signal/Tibetan Plateau
维普
万方数据