厦门市空气质量臭氧预报和评估系统

为了评价和预测厦门市区空气中臭氧的污染水平,运用2006～2009年的监测数据对臭氧的污染成因及其变化规律进行研究。通过风向、风速、气温、湿度等气象因子对臭氧浓度影响的分析,进而运用多元线性回归法建立厦门市臭氧预报及评估系统。     

Monitoring of the Endangered Pinna nobilis Linné, 1758 in the Mar Grande of Taranto (Ionian Sea, Italy)

Two methods were used to calculate the meteorologically adjusted ground level ozone trends in southern Taiwan. The first method utilized is a robust linear regression method. The second approach uses a multilayer perceptron (MLP) artificial neural network (ANN) method. The observations obtained from 16 monitoring stations were analyzed and divided into six groups by hierarchical divisive clustering procedure. The daily maximum 1 and 8 h ozone concentrations for each group are then calculated. The meteorologically adjusted trends obtained by linear regression and MLP methods are smaller than the unadjusted trends for all groups and average time. It indicts that the meteorological conditions in Taiwan tend to increase ambient ozone concentrations in recent years.     

气象条件对沈阳市环境空气臭氧浓度影响研究

利用2013年沈阳市环境空气监测点位臭氧监测数据,分析沈阳臭氧浓度变化特征,结合气象资料分析了其对臭氧浓度的影响。结果表明,沈阳市不同区域臭氧浓度变化特征基本一致。臭氧浓度日变化呈单峰趋势,最大值出现在14:00左右,最小值出现在6:00左右;臭氧浓度变化具有明显的季节特征,夏季臭氧浓度最高,春秋次之,冬季最低;臭氧浓度受温度、风速、湿度、能见度、天气情况影响,臭氧浓度变化是多因素共同作用的结果。     

佛山市近地面臭氧污染特征及相关气象因子分析

利用2014年佛山市8个国控大气自动监测点位的O_3监测数据,分析了佛山市的O_3污染特征,结果表明,2014年O_3日最大8 h平均值的第90百分位数为167μg/m~3,O_3为首要污染物的超标天数为43d,占比46.7%;ρ(O_3)区域变化不大;ρ(O_3)月变化呈现"三峰型",全年高ρ(O_3)集中在6—10月份,其中7月份出现全年最高峰值;ρ(O_3)日变化呈单峰型分布,夜间浓度较低且变化平缓,14:00—16:00左右达到峰值,并存在一定的"周末效应",但并不明显;ρ(O_3)与气温呈显著正相关,与湿度、气压、雨量呈显著负相关,与风向、风速的相关性相对较弱;总体上看,高温、低湿、微风、偏南风、低压、无雨的天气条件下高ρ(O_3)更容易出现。     

臭氧综合指数与臭氧潜势指数的构建与应用

随着社会经济的快速发展,我国臭氧污染日益严重,因此,研发出能定量评估气象条件对臭氧污染影响程度的诊断指数,成为提高和改善气象服务质量的重要任务之一。利用中国大陆地区2018年温度、总云量、风速、风向、相对湿度等气象场数据与臭氧浓度数据,研究臭氧污染敏感气象条件,统计各气象因子分布在不同数值区间时发生臭氧污染事件的相对频率(即分指数),按照分指数最大值和最小值的差值大小进行排序,筛选出10个与臭氧污染密切相关的气象因子,将10个气象因子的分指数进行累加,即得出臭氧综合指数。随后,对各地构建臭氧综合指数时采用的气象要素进行统计,得到出现频率最高的3个气象要素,并参考这些气象要素构建了臭氧潜势指数。分别以臭氧潜势指数和臭氧综合指数对北京市2019年臭氧日最大浓度建立拟合预报模型,结果表明:两类指数的拟合预报值与实测值有着相似的变化趋势;利用臭氧综合指数计算得到的预报值与实测值的相关系数为0.76,优于利用臭氧潜势指数计算得到的预报值与实测值的相关系数(0.64)。     

Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago

Both canonical correlation analysis (CCA) and principal component analysis (PCA) were applied to atmospheric aerosol and trace gas concentrations and meteorological data collected in Chicago during the summer months of 2002, 2003, and 2004. Concentrations of ammonium, calcium, nitrate, sulfate, and oxalate particulate matter, as well as, meteorological parameters temperature, wind speed, wind direction, and humidity were subjected to CCA and PCA. Ozone and nitrogen oxide mixing ratios were also included in the data set. The purpose of statistical analysis was to determine the extent of existing linear relationship(s), or lack thereof, between meteorological parameters and pollutant concentrations in addition to reducing dimensionality of the original data to determine sources of pollutants. In CCA, the first three canonical variate pairs derived were statistically significant at the 0.05 level. Canonical correlation between the first canonical variate pair was 0.821, while correlations of the second and third canonical variate pairs were 0.562 and 0.461, respectively. The first canonical variate pair indicated that increasing temperatures resulted in high ozone mixing ratios, while the second canonical variate pair showed wind speed and humidity’s influence on local ammonium concentrations. No new information was uncovered in the third variate pair. Canonical loadings were also interpreted for information regarding relationships between data sets. Four principal components (PCs), expressing 77.0 % of original data variance, were derived in PCA. Interpretation of PCs suggested significant production and/or transport of secondary aerosols in the region (PC1). Furthermore, photochemical production of ozone and wind speed’s influence on pollutants were expressed (PC2) along with overall measure of local meteorology (PC3). In summary, CCA and PCA results combined were successful in uncovering linear relationships between meteorology and air pollutants in Chicago and aided in determining possible pollutant sources.     

内蒙古近地面臭氧污染时空分布特征及气象条件分析

使用2018—2020年内蒙古臭氧(O₃)、气象要素观测资料和NCEP FNL资料,统计分析内蒙古近地面O₃质量浓度的时空分布特征和变化趋势,并针对全区O₃污染典型个例分析其天气形势和气象要素的影响作用。结果表明:内蒙古2018—2020年O₃质量浓度年评价值呈逐年下降趋势,2020年较2018年下降10.3%,各盟市O₃超标率也显著降低,仅赤峰市和通辽市略微上升。内蒙古O₃质量浓度高值分布在中西部偏南地区,尤其是乌海市和鄂尔多斯市;O₃超标率峰值主要出现在5—7月,周末效应存在东西部差异。O₃浓度变化和天气形势关系密切,南部暖平流和暖高压控制有利于O₃生成,西北部冷平流和冷涡发展使得O₃浓度下降;高温、低湿、微风和较高的能见度均为诱发O₃污染的重要气象条件,而西北大风通过降低温度、能见度和易于扩散的风向使得内蒙古O₃浓度降低,但同时可能会导致PM₁₀污染。     

Analysis and assessment of ground-level ozone measured at two stations in Tehran

In this study, variations of ambient ozone level are thoroughly analysed according to the monitored data in a mixed residential, commercial and industrial city, Tehran, based on considering the meteorological factors. Ozone as a pollutant shows typical annual, weekly and diurnal cycles. This analysis has shown that the ozone level concentrations were below the WHO guidelines in Tehran during 2000–2003. The relation between ozone level at two different stations (Aghdasieh and Fatemi) is found (r?=?0.51). The ozone level response to meteorological parameters is investigated. The results suggest that the ozone level is affected (positively or negatively) by meteorological conditions, e.g. relative humidity, solar radiation, air temperature, wind speed and wind direction.     

广州市近地面臭氧时空变化及其与气象因子的关系

利用2012年1月至2016年2月广州市环境空气自动监测数据和气象观测数据,对广州市近地面臭氧的时空分布特征及其与气象因子的关系进行分析。结果表明:2012—2015年广州市臭氧日最大8 h滑动平均值的第90百分位数波动变化,年变化率依次为-14.3%、5.8%、-12.1%;广州市臭氧浓度呈现夏、秋季高,春、冬季低的显著季节变化特征;臭氧日最大8 h平均值的月均值和第90百分位数最高的月份一般分别出现在10月和7—8月;臭氧浓度的日变化曲线为单峰型,最大值一般出现在14:00或15:00;臭氧浓度随垂直高度的升高而增大,从低层(6 m点位或地面站)到中层(118 m和168 m点位)、中层到高层(488 m点位)臭氧日最大8 h滑动平均值的增长率分别为18.3%和39.1%;广州市中心城区臭氧浓度低于南北部城郊,夏、秋季高值区与夏、秋季主导风向相对应;臭氧浓度受降水、气温、相对湿度和风速等气象因子影响,臭氧浓度的超标是多种因素综合作用的结果。     

安徽省臭氧污染特征及气象影响因素分析

基于2016—2018年安徽省68个国控环境空气质量自动监测站点的臭氧(O₃)监测数据,研究分析了安徽省O₃污染特征及其与气象因子的相关性。结果表明:安徽省O₃污染程度呈现逐年加重趋势,并有显著的季节和月度变化特征。2016—2018年,各年度单月O₃日最大8小时滑动平均质量浓度第90百分位数的最大值分别出现在9月、5月、6月。O₃日变化趋势为典型的单峰形,各年度最低值出现在晨间07:00左右,最高值则是在15:00—16:00。全省O₃浓度总体上呈现出北高南低的空间特征。温度、相对湿度与O₃浓度分别呈现显著正相关、负相关,但在不同季节存在一定差异,其中,春秋季温度与O₃浓度的相关性好于夏冬季,夏季相对湿度与O₃浓度的相关性最为显著。O₃浓度在平均风速为2.1～2.2 m/s时更易出现超标。中部和北部城市在东南风的作用下易出现O₃超标并达到O     

Surface ozone and meteorological condition in a single year at an urban site in central–eastern China

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.     

大连市臭氧污染特征及典型污染日成因

通过对大连市区10个空气监测子站的监测数据进行分析,探讨了大连市臭氧污染的时空分布、气象条件对臭氧污染的影响,对臭氧污染日进行了归类分析。结果表明,大连市臭氧污染主要出现在4—10月。在强紫外辐射、高温、低湿、低压和低风速的气象条件下,监测点位的臭氧浓度较高。臭氧污染日的日变化分为单峰型、双峰型和夜间持续升高型3种类型。通过对2015年的一次高浓度臭氧污染过程的气象条件、污染物浓度和污染气团轨迹进行分析,发现臭氧浓度在夜间持续升高现象与区域输送密切相关。     

Influence of Meteorological Parameters on Particulates and Atmospheric Pollutants at Taichung Harbor Sampling Site

Atmospheric aerosol particles and metallic concentrations, ionic species were monitored at the Experimental harbor of Taichung sampling site in this study. This work attempted to characterize metallic elements and ionic species associated with meteorological conditions variation on atmospheric particulate matter in TSP, PM_2.5, PM_2.5–10. The concentration distribution trend between TSP, PM_2.5, PM_2.5–10 particle concentration at the TH (Taichung harbor) sampling site were also displayed in this study. Besides, the meteorological conditions variation of metallic elements (Fe, Mg, Cr, Cu, Zn, Mn and Pb) and ions species (Cl⁻, NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, Mg²⁺, Ca²⁺ and Na⁺) concentrations attached with those particulate were also analyzed in this study. On non-parametric (Spearman) correlation analysis, the results indicated that the meteorological conditions have high correlation at largest particulate concentrations for TSP at TH sampling site in this study. In addition, the temperature and relative humidity of meteorological conditions that played a key role to affect particulate matter (PM) and have higher correlations then other meteorological conditions such as wind speed and atmospheric pressure. The parameter temperature and relative humidity also have high correlations with atmospheric pollutants compared with those of the other meteorological variables (wind speed, atmospheric pressure and prevalent wind direction). In addition, relative statistical equations between pollutants and meteorological variables were also characterized in this study.     

石河子市大气中氮氧化物与气象条件的关系

用实测数据分析了石河子市大气中ＮＯｘ与气温，湿度和风速等气象因子的关系，得出结论是气温升高，风速增强，使大气中ＮＯｘ浓度值降低，相对湿度在７０％～８０％之间，其ＮＯｘ浓度较高，为此，气象条件诸如水平风速，大气温度，湿度和大气层结构的稳定与否均大对气污染物浓度产生重要影响。     

上海市臭氧污染时空分布及影响因素

分析2006—2016年上海市的监测数据发现,臭氧(O_3)浓度存在逐年上升趋势,污染持续时间有所增加,但除水平风速有下降趋势外,其他相关气象因素的年际变化趋势并不显著。空间分析结果表明,上海市O_3超标主要集中在西南部郊区,但市区O_3超标潜势不容忽视。O_3污染高发季节的污染玫瑰图分析发现,上海市南部地区是影响上海市O_3污染的关键区域;对于NO_2减排的影响分析发现,尽管上海市O_3平均浓度总体处于上升趋势,但在NO_2下降幅度最为明显的内环市区和北部郊区,O_3上升幅度低于NO_2下降幅度较小的内外环区域和西部郊区,表明上海市的O_3污染控制仍需持续推进NOx的减排,并同步推进VOCs的减排。     

Analysis of tropospheric ozone concentration on a Western Mediterranean site: Castellon (Spain)

Ozone dynamics in our study area (Castellon, Spain) is both strongly bound to the mesoscale circulations that develop under the effect of high insolation (especially in summer) and conditioned by the morphological characteristics of the Western Mediterranean Basin. In this work we present a preliminary analysis of ozone time series on five locations in Castellon for the period 1997–2003. We study their temporal and spatial variations at different scales: daily, weekly, seasonally and interannually. Because both the O₃ concentration and its temporal variation depend on the topographic location of the observing station, they can show large differences within tens of kilometer. We also contrast the variation in the ozone concentration with the variations found for meteorological variables such as radiation, temperature, relative humidity and recirculation of the air mass. The link between elevated ozone concentrations and high values of the recirculation factor (r=0.7–0.9) shown the importance of recirculating flows on the local air pollution episodes.     

重庆市北碚区空气质量改善效果评估分析

基于2014—2020年重庆市中心城区北碚区环境监测数据及地面观测气象要素,分析了北碚区大气污染特征,利用KNN算法建立大气污染的评估模型,对空气质量改善效果进行评估。结果表明,重庆市中心城区北碚区的PM_2.5浓度逐年呈明显下降趋势,O₃浓度除夏季有一个弱的下降趋势外,其余3个季节和年平均值整体均呈上升趋势。全年以优良天气为主且呈增加趋势。O₃与气温、日照时间呈正相关,与相对湿度呈负相关性,PM_2.5与气温、降水及风速呈负相关。基于KNN算法对空气质量改善状况评估表明,减排对O₃污染平均贡献率在-4.7%左右,对PM_2.5污染平均贡献率为-52%,气象条件对O₃污染的平均贡献率在17%左右,对PM_2.5污染的平均贡献率在-7%左右。该大气污染评估模型能够有效地评估空气改善效果。     

基于EOF/SVD的2018年湖北省臭氧特征及其高值与气象要素关系研究

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。     

海口市臭氧污染及气象条件特征

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。     

库尔勒市大气颗粒物污染特征与影响因素分析

针对库尔勒市PM 10、PM 2.5年均浓度超标现象,基于市区3个环境监测站2013—2017年的逐时观测数据,分析PM 10、PM 2.5污染特征、成因及其主要影响因素。结果表明:①2013—2017年库尔勒市PM 10年均浓度变化较大且无明显趋势,PM 2.5年均浓度整体呈下降趋势;②季节尺度上,库尔勒市PM 10在每年2—5月呈现高浓度,PM 2.5高浓度期则为10月至翌年5月;③城郊的开发区站PM 10浓度最高,老城区的州政府站PM 2.5浓度最高,在PM 10和PM 2.5的高浓度期空间差异尤其显著;④PM 10与风速显著正相关,来自塔克拉玛干沙漠的风蚀沙尘颗粒物是库尔勒地区颗粒污染物的主要来源;⑤库尔勒市PM 10主要为外源输入,PM 2.5则以城市内源为主,相对湿度、风速、风向、温度等气象条件是影响大气颗粒物浓度及分布的重要因素。