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Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China 被引量:20
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作者 linjun cheng Shuai Wang +3 位作者 Zhengyu Gong Hong Li Qi Yang Yeyao Wang 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2018年第5期179-190,共12页
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 ... 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(O3-8 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/m3,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. 展开更多
关键词 OZONE Spatiotemporal variability REOF Regionalizstion
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基于伴随方法的大气污染溯源 被引量:11
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作者 黄顺祥 刘峰 +3 位作者 盛黎 程麟钧 吴琳 李军 《科学通报》 EI CAS CSCD 北大核心 2018年第16期1594-1605,共12页
大气污染防治的核心是找准污染源头,厘清污染成因,实现靶向治理,提高控制效率.本文建立了全国空气质量高分辨率预报与污染控制决策支持系统(NARS,呐思系统),实现了气象与大气化学的监测、同化、预报、溯源、排放源反演和动态优化控制等... 大气污染防治的核心是找准污染源头,厘清污染成因,实现靶向治理,提高控制效率.本文建立了全国空气质量高分辨率预报与污染控制决策支持系统(NARS,呐思系统),实现了气象与大气化学的监测、同化、预报、溯源、排放源反演和动态优化控制等大气污染闭环防控,可为大气污染防控提供一整套解决方案,其中所建立的CAMx伴随溯源模式,实现了排放源动态反演和网格化定量溯源,可快速定量追溯导致目标区域未来7天大气污染的排放源及其贡献率时空分布.针对2016年9月~2017年3月北京主城区PM_(2.5)集中污染时间段进行了排放源反演、气象场预报、空气质量预报和网格化溯源.与京津冀地区国控点监测结果进行了对比分析,结果表明污染过程、污染等级和污染物浓度预报的准确率分别为100%、88.8%和84.7%,预报值和监测值之间的相关系数为0.81.网格化溯源结果表明导致北京主城区PM_(2.5)污染的排放源基本来自于北京西南方向这一条大气污染物传输通道,北京本地、河北、天津及周边地区排放源对北京主城区PM_(2.5)浓度分别贡献了66%、29%、5%.就重污染过程而言,京津冀排放总量的19%导致了北京主城区80%的PM_(2.5)重度及以上污染,其中北京本地占京津冀排放总量的9%贡献63%、河北占京津冀排放总量的10%贡献17%.就整体污染天气而言,京津冀排放的26%导致了北京主城区80%的PM_(2.5)轻度以上污染,其中北京本地占京津冀排放总量的9%贡献61%、河北占排放总量的15%贡献18%,天津占排放总量的2%贡献1%.导致北京主城区PM_(2.5)污染的排放源主要分布在北京城区和南部区域、保定和石家庄所辖的部分区县,贡献排名前6位的区县均为北京辖区,贡献率合计为48%,前20个区县的总体贡献为73%.将动态反演排放源方法与调查排放清单相结合,应用伴随溯源模式对预报结果进行同步大气污染溯源,可为大气重污染应急控制找准控制对象,并进行损益评估,运用自然控制论,实现大气污染应急优化控制. 展开更多
关键词 大气污染 污染溯源 伴随方法 PM2.5
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