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1.
利用青藏高原东北部青海瓦里关站1997年3月—2009年11月十多年的臭氧总量地基观测资料,对臭氧总量的年际变化、季节变化、频数分布、低值频率等特征进行分析。结果表明,近十多年来青藏高原东北部大气臭氧总量略有下降,臭氧损耗减缓;各年的频数分布呈左偏态分布,且夏秋季节(6—10月)的臭氧低值频率与同期臭氧总量平均值呈现极好的负相关,这可能是引起其年均值较低的原因之一;该地区臭氧总量具有明显的季节变化,夏秋季的臭氧低值频率远远高于冬春季,冬春季节臭氧总量平均约为300 DU,夏秋季节平均约为270 DU,最大值出现在3月份,最小值出现在9月份。臭氧总量的连续观测与分析对青藏高原的生态环境与气候能够起到预警作用。  相似文献   

2.
利用2016-2020年陕西省环境空气质量自动站的臭氧监测数据,分析西安市大气环境中臭氧污染的时间变化趋势及空间分布特征。从时间分布来看,西安市臭氧年均质量浓度呈先上升后下降的波动变化趋势,且浓度值略高于全国平均水平;臭氧月均浓度具有明显的季节变化特征,月超标天数和月均质量浓度均在6月达到峰值;臭氧质量浓度日变化规律在全年和四季完全一致,均呈单峰型,日内小时平均质量浓度超标最多时段集中在15:00-16:00;臭氧与NO2、CO均呈"此消彼长"的负相关关系。从空间分布来看,西安市12个国控评价点位的O3-8 h浓度分布变化大致分为单峰型和持续递减型,浓度主要集中在40~80 μg/m3;国控点和省控点的臭氧浓度时间分布趋势一致,空间分布存在区域性差异;全市20个区县(开发区)的臭氧污染呈现南北中心城区高、东西远郊区低的空间分布特征。总之,西安市臭氧污染的时空分布主要受到气象条件、污染物排放和城市布局差异的综合性影响。  相似文献   

3.
基于2017年1月至2020年6月的江西省国控点臭氧监测数据和同期气象观测数据,研究江西省臭氧污染特征及其与气象条件的关系。结果表明:2017—2019年,江西省臭氧超标时间和质量浓度呈现出逐年增加的趋势; 4—6月和8—10月是江西臭氧污染高发期,其中8—10月臭氧污染最严重;臭氧1 h浓度日变化呈现"单峰"分布特征,早晚浓度低,上午09:00浓度快速上升,15:00达到峰值。除景德镇外,2017—2019年江西省臭氧污染在空间上总体呈现出南高北低的特征,2019年臭氧污染在空间上呈现出总体平均分布的特征。大体上,江西省11个设区城市臭氧超标天数比例的峰值在(30,35]℃日最高气温区间。晴朗天气时的地面低压系统与臭氧污染关联性强,江西省11个设区城市在日均地面气压(990,1 013.25]hPa、日均地面湿度(50%,70%]和日均地面风速(1,2]m/s条件下臭氧容易超标,臭氧超标时地面主导风向主要为北风和东北风。  相似文献   

4.
青岛市环境空气臭氧污染特征分析   总被引:1,自引:0,他引:1  
冯静  董君  陶红蕾 《干旱环境监测》2013,(4):150-153,173
青岛市是国家环保部确定的臭氧试点监测城市之一。文章结合青岛市市南区东部和四方区空气子站2008—2011年的试点监测数据,从区域差异、时间变化等方面分析了青岛市的臭氧污染特征,结果表明:①二区域臭氧浓度的日分布均呈现“单峰型”,12:00~15:00是一天中臭氧污染最严重的时段;②每月监测累积值市南区东部呈现“双峰型”,四方区呈现“单峰型”;③二区域臭氧污染最突出的月份均为5月;④二区域臭氧平均浓度从高到低季节排序略有差异;⑤2009年二区域臭氧污染最严重,该年四季中春季臭氧污染最为突出。  相似文献   

5.
采用地面站点观测、卫星观测以及UWCM 0-D箱子模型模拟的方法研究湖北2013—2015年臭氧时空分布特征,并探讨其管控措施。从地面站点观测看出,时间分布上,这3年臭氧年平均浓度经历先下降后上升的过程,总体呈上升趋势,而二氧化氮年平均浓度则呈现持续下降的趋势;空间分布上,湖北各区域臭氧浓度分布不均匀,呈现东高西低的递减分布趋势。从卫星观测数据看出,2015年湖北的臭氧柱浓度高于2013、2014年同期。从空间分布来看,臭氧的柱浓度是从东北到西南、从省外到省内逐渐递减,因此推测,除了本地生成,湖北的臭氧有一部分是来源于省外传输。最大臭氧生成量法显示,烯烃(乙烯和丙烯)对湖北夏天臭氧生成量的贡献远大于其他挥发性有机化合物。箱子模型模拟的结果显示,湖北应该通过控制挥发性有机化合物的排放来降低臭氧生成速率,控制氮氧化物反而使臭氧生成速率提高。  相似文献   

6.
本文利用洛阳市老城区豫西宾馆空气质量自动监测点的监测数据,对2012-01~12该区域大气中臭氧污染浓度的连续监测结果及同步气象资料进行了分析。结果表明,洛阳市老城区环境空气中臭氧污染主要表现为臭氧日最大8小时平均浓度污染,全年超过GB3095-2012《环境空气质量标准》中二级标准(0.160mg/m3)的频率为21%。臭氧浓度具有明显的日变化及季节变化特征;由于臭氧污染的季节特点,导致全年污染天数显著增加。通过分析发现气温、风速、降水、太阳紫外线辐射等气象因素对臭氧浓度变化均具有一定影响,臭氧污染气象特征表现为晴朗、高温、低风速的午后时段会出现臭氧的高浓度污染。  相似文献   

7.
选取新疆玛纳斯县2018—2019年空气自动站的监测数据,统计分析了臭氧浓度时空变化规律。主要结果表明,2019年臭氧为首要污染物天数同比2018年增加48.9%,2019年因臭氧浓度超标造成轻度污染天数较上年增加250%,臭氧已成为影响空气质量的重要因子之一。臭氧浓度具有明显的季节变化特征,2018—2019年玛纳斯县臭氧平均浓度均在5—8月达到全年的最高值,11月至翌年1月浓度为全年最低值,臭氧浓度整体呈现夏季高,冬季低的特点,城市形成局部臭氧超标的主要原因及其影响力的大小受温度和风速的影响。在此基础上对臭氧污染进行了溯源及影响因子分析,提出防控臭氧污染的建议。  相似文献   

8.
使用天津市2013—2019年连续污染物监测数据和气象观测数据探讨臭氧污染现状,分析气象条件对臭氧浓度的影响,对不同臭氧污染过程案例进行天气分型,统计出现臭氧污染时的污染气象特征。结果表明:天津市臭氧浓度不降反升,2017—2019年连续3年超过国家二级浓度限值,2019年以臭氧为首要污染物的重污染天约占全年的1/2。春季和秋季臭氧污染日益突出,4月臭氧浓度已明显升高。天津市臭氧日最大8 h滑动平均质量浓度(O3-8 h)在日最高气温超过30℃、相对湿度20%~70%、西南风或东南风风速1~2.5 m/s、白天边界层高度1 400 m以下时较高。将臭氧污染天气形势分为春夏之交、盛夏高温和夏秋静稳3种类型。其中春夏之交天气型易出现臭氧与PM2.5协同污染;盛夏高温天气型平均风速较大,日最高气温大于35℃;夏秋静稳天气型平均风速小、边界层低。  相似文献   

9.
选取2015年珠海市国控监测站ρ(PM_(2.5))数据,分析PM_(2.5)中有机碳(OC)、元素碳(EC)、水溶性离子组分等化学组成,ρ(PM_(2.5))时空分布特征,以及与气象因素的相互关系。结果表明,2015年珠海市PM_(2.5)年均值为31.0μg/m3,表现出显著的时间分布规律,月均值呈现"V"型趋势,PM_(2.5)中主要化学组分是有机物(OM),占总质量的34.0%,其次是硫酸根(SO2-4),占总质量的26.9%,具有明显的季节分布特征,呈现冬高夏低分布;ρ(PM_(2.5))日变化呈现双峰型分布,其值工作日显著高于非工作日;ρ(PM_(2.5))与平均温度、相对湿度、风速呈现负相关关系,与气压呈现显著正相关关系;珠海市ρ(PM_(2.5))空间分布总体呈现"东高西低,北重南轻"变化趋势,有机物、SO2-4和NH+4空间分布呈现东部高于西部趋势,颗粒物浓度受地形、气候因素和海域环境等影响呈现多样化分布趋势。  相似文献   

10.
基于湖北省2018年4-10月臭氧、温度和相对湿度逐小时监测数据以及50 m风场逐小时再分析数据,采用经验正交函数(EOF)和奇异值分解(SVD)方法,分析了2018年湖北省臭氧特征及其高值与气象要素关系。结果表明:湖北省臭氧日最大8 h浓度距平呈现以武汉为正值中心、自鄂东向鄂西递减的主要空间分布型;15:00臭氧与温度呈现较好的正相关关系,以随州、襄阳及其周边最为明显;与14:00相对湿度呈现很好的负相关关系,以孝感、随州、荆门及其周边最为明显;襄阳西部和十堰北部地区15:00 50 m风场的纬向分量对本地臭氧高值有一定影响,武汉北部、黄冈北部以及孝感东部等地15:00 50 m风场的经向分量对本地臭氧高值影响较大。  相似文献   

11.
以沈阳2013—2015年臭氧(O_3)监测数据为基础,从地域差异及时间变化上分析了沈阳O_3浓度变化特征。结果表明:沈阳城市外围O_3浓度高于城市中心;O_3浓度变化具有明显季节特征,夏季O_3浓度最高,冬季最低;O_3浓度日变化呈单峰分布,谷值出现在06:00,峰值出现在14:00;O_3浓度出现明显"周末效应",周末白天O_3浓度高于工作日O_3浓度,夜间差异不大。  相似文献   

12.
A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.  相似文献   

13.
Surface ozone and some meteorological parameters were continuously measured from June 2003 to May 2004 at urban Jinan, China. The levels and variations of surface ozone were studied and the influences of meteorological parameters on ozone were analyzed. Annual and diurnal ozone variation patterns in Jinan both show a typical pattern for polluted urban areas. Daytime ozone concentrations in summer were the highest in the four seasons. However, during nighttime from 2100 to 0600 hours ozone concentrations in spring was higher than that in summer. Daily averaged ozone showed negative correlation with pressure and relative humidity and positive correlation with temperature, total solar radiation, sunshine duration and wind speed during the study period. Further studies show that, solar radiation is a primary influence factor for the daytime variations of ozone concentrations at this site; transport of pollutants by wind could enhance the pollution at this site; precipitation has a significant influence on decreasing surface ozone. A multi-day ozone episode from 16 to 21 June 2003 was observed at this site. Surface meteorological data analysis and backward trajectory computation show that the episode is associated with the influence of typhoon Soudelor, attributing to both local photochemical processes and transport of air pollutants from southeastern coastal region, especially Yangtze River Delta region.  相似文献   

14.
近年来,臭氧已成为许多城市环境空气的主要污染物之一。笔者分析了2020年海口市5个不同方位代表性监测站点逐小时空气质量监测数据及对应站点的气象要素监测数据。研究结果表明:海口市2020年环境空气污染程度为三级以上的天数有11d,其首要污染物均为臭氧。臭氧浓度高值时段主要出现在10-12月。浓度最大值主要出现在每日14:00-17:00,最小值出现在每日05:00-08:00。气象要素日均值与臭氧浓度相关性大小依次为最高温度>平均温度>相对湿度>降水量>日照时数>风速。台风外围下沉气流和东北气流的共同影响是导致海口市臭氧浓度超标的主要因素,下沉气流更有利于低层大气中臭氧的堆积,同时在东北气流影响下,上游区域污染物的传输也会导致海口市臭氧浓度增加。  相似文献   

15.
The compositions, spatial distributions, seasonal variations and ozone formation potential (OFP) of volatile organic compounds (VOCs) were investigated in the atmosphere of Haicang District, Xiamen City, Southeast China. Twenty-four types of VOCs were measured in this study, and ethanol, methylene chloride, toluene, ethyl acetate and isopropyl alcohol were the abundant species based on concentration rank. The concentrations of total VOCs (TVOCs) in industrial areas were higher than those in residential and administrative areas and background site. For industrial areas, the TVOCs concentrations in summer were higher than those in winter, which might result from higher emissions from industrial activities because of stronger evaporation in summer. In contrast, non-industrial areas showed higher concentrations in winter due to the unfavorable meteorological conditions. The spatial distribution of BTEX (benzene, toluene, ethylbenzene and xylene) followed the order of industrial areas > residential and administrative areas > background site, and the concentrations in summer were lower than those in winter for most sites. The high ratios (8.9-14.0) of T/B in this study indicated that industrial emissions were the main sources in this district. X/B ratios were used to assess the ages of air parcels and provided evidence of the transport of air parcels among these sites. Total OFP (TOFP) showed the trend of increase with the increase of TVOCs, and toluene was found as the major contributor to TOFP.  相似文献   

16.
上海臭氧及前体物变化特征与相关性研究   总被引:19,自引:15,他引:4  
于2010年1~12月期间,在上海城区内采用在线连续观测,分析该地区近地臭氧与其前体物的季节变化规律及相关性,探讨了臭氧浓度与OX和NO2光解速率之间的关系。结果表明,观测期间,上海地区O3总超标天数为13天,超标率为3.56%。O3浓度变化呈现明显的秋冬低、春夏高的季节变化。O3浓度日变化规律呈典型单峰变化,O3各前体物呈双峰形分布,冬季O3与NOX的相关性最强。对OX的贡献中,秋冬以NO2为主,春夏以O3为主;夜间以NO2为主,白天以O3为主。臭氧浓度与OX和NO2光解速率变化规律基本一致。  相似文献   

17.
胡晏玲 《干旱环境监测》2009,23(4):220-222,245
利用2009年夏季乌鲁木齐市近地面大气O3及其前体物的自动监测数据,分析了O3浓度的分布特征和时间变化规律。探讨O3与其主要前体物NO2和CO的相关关系。结果表明,乌鲁木齐市夏季的O3污染较轻;O3浓度呈单峰型分布,O3浓度昼间高,夜间低;昼间O3与其主要前体物都呈负相关关系。  相似文献   

18.
This paper presents the first analysis of vertical ozone sounding measurements over Pohang, Korea. The main focus is to analyze the seasonal variation of vertical ozone profiles and determine the mechanisms controlling ozone seasonality. The maxima ozone at the surface and in the free troposphere are observed in May and June, respectively. In comparison with the ozone seasonality at Oki (near sea level) and Happo (altitude of 1840 m) in Japan, which are located at the same latitude as of Pohang, we have found that the time of the ozone maximum at the Japanese sites is always a month earlier than at Pohang. Analysis of the wind flow at the surface shows that the wind shifts from westerly to southerly in May over Japan, but in June over Pohang. However, this wind shift above boundary layer occurs a month later. This wind shift results in significantly smaller amounts of ozone because the southerly wind brings clean wet tropical air. It has been suggested that the spring ozone maximum in the lower troposphere is due to polluted air transported from China. However, an enhanced ozone amount over the free troposphere in June appears to have a different origin. A tongue-like structure in the time-height cross-section of ozone concentrations, which starts from the stratosphere and extends to the middle troposphere, suggests that the ozone enhancement occurs due to a gradual migration of ozone from the stratosphere. The high frequency of dry air with elevated ozone concentrations in the upper troposphere in June suggests that the air is transported from the stratosphere. HYSPLIT trajectory analysis supports the hypothesis that enhanced ozone in the free troposphere is not likely due to transport from sources of anthropogenic activity.  相似文献   

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