Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China

Owing to the vast territory of China and strong regional characteristic of ozone pollution,it's desirable for policy makers to have a targeted and prioritized regulation and ozone pollution control strategy in China based on scientific evidences. It's important to assess its current pollution status as well as spatial and temporal variation patterns across China.Recent advances of national monitoring networks provide an opportunity to insight the actions of ozone pollution. Here, we present rotated empirical orthogonal function(REOF)analysis that was used on studying the spatiotemporal characteristics of daily ozone concentrations. Based on results of REOF analysis in pollution seasons for 3 years' observations, twelve regions with clear patterns were identified in China. The patterns of temporal variation of ozone in each region were separated well and different from each other, reflecting local meteorological, photochemical or pollution features. A rising trend in annual averaged Eight-hour Average Ozone Concentrations(O_3-8 hr) from 2014 to 2016 was observed for all regions, except for the Tibetan Plateau. The mean values of annual and 90 percentile concentrations for all 338 cities were 82.6 ± 14.6 and 133.9 ± 25.8 μg/m~3,respectively, in 2015. The regionalization results of ozone were found to be influenced greatly by terrain features, indicating significant terrain and landform effects on ozone spatial correlations. Among 12 regions, North China Plain, Huanghuai Plain, Central Yangtze River Plain, Pearl River Delta and Sichuan Basin were realized as priority regions for mitigation strategies, due to their higher ozone concentrations and dense population.     

基于短时傅里叶变换的干扰信号识别方法

目的提高无线电引信识别欺骗式干扰的能力。方法根据炮弹弹体自旋的特点,使得采用连续波线极化天线的无线电引信所收到的干扰信号呈现出有别于地面回波的周期性幅度调制特征,通过对此特征进行短时傅里叶变换后进行识别,可以作为无线电引信是否受到欺骗式干扰的判断依据。结果通过仿真和试验对方法的可行性进行了验证,表明该识别方法可以很好地提高连续波无线电引信的识别欺骗式干扰的能力。结论基于短时傅里叶变换检测旋转调制的对地旋转弹无线电引信欺骗干扰识别的方法可以对干扰信号特征进行识别,算法处理时间短,能够作为引信是否受到干扰的一种判断依据。     

基于环境保护大数据的监测与智能诊断研究

为了提高环境保护的智能监测能力,采用大数据分析方法进行环境监测和诊断分析,提出一种基于环保大数据关联特征挖掘的环境保护智能监测与诊断技术。构建环境监测的约束参量模型,以土壤监测数据、空间污染排放量以及水资源监测数据为原始统计数据,采用关联规则特征提取方法进行环保大数据的有用信息挖掘,结合模糊C均值聚类算法实现对环保大数据的分类处理,根据分类结果进行环保监测和智能诊断分析。仿真结果表明,采用该方法进行环保大数据挖掘的准确性较好,对各种污染信息的分类精度较高,提高了环保监测和诊断的有效性与实时性。