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.     

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.     

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。     

黑龙江省环境空气污染物和气象要素的典型相关分析

通过对黑龙江省4个自然年(2016年1月1日—2019年12月31日)环境空气污染物和气象要素的分析,揭示了黑龙江省气象条件对空气污染物浓度的影响规律与特征.对PM2.5、PM10、SO2、NO2、CO和O3等6项污染物的描述性统计和简单的相关分析显示:黑龙江省环境空气质量呈现逐年变好的趋势,非采暖期环境空气质量好于采...     

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。     

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。     

试点城市O3浓度特征分析

利用2009年O3试点城市的03监测数据,分析了北京、天津、上海、青岛、沈阳和广东的03浓度变化特征,统计了年超标情况,并结合气象要素数据分析了其对03浓度的影响.结果表明,不同城市各点位间03浓度变化趋势基本一致,但因点位类型不同,浓度存在差异;O3浓度呈单峰型日变化,在13:00-15:00出现最大值,6:00-7:00出现最小值;O3超标主要集中在4-8月份,广州和北京超标现象较多;O3浓度受温度、降水、风速和风向等气象要素影响较大.     

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。     

The Seasonal Variation of the Concentrations of Ozone, Sulfur Dioxide, and Nitrogen Oxides in Two Nigerian Cities

A combination of multivariate statistical methods including factor analysis, principal component analysis, principal component regression, and multiple linear regression (MLR) were employed to evaluate the influence of seasons on the concentrations of ozone, sulfur (IV) oxide, and oxides of nitrogen in ambient air of Nigerian cities of Lagos and Ilorin. The former city is located in the coastal area, and it is highly congested with a high intensity of marine, vehicular, and industrial activities, and the latter city is a medium size town, located in the central guinea savannah zone of Nigeria. Samples were collected using a high-volume sampler from near the ground at various sites of diverse human and industrial activities, during wet and dry seasons from 2003 to 2006. The PCA reveals three distinct groupings during the day for all data, which is a reflection of different factors contributing to the atmospheric chemistry of these cities. The predicted ozone concentration values by MLR agree fairly well with the measured data. The dependence of ozone on meteorological parameters including relative humidity, air temperature, and sun exposure and the precursor pollutants depends on weather and the anthropogenic activities. The results for the two cities indicate that reduction in the level of NO₂ is accompanied by an increase in the level of ozone, suggesting the interconversion between the two via photochemical activity.     

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。     

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)。     

西安市黑碳气溶胶浓度特征及与气象因素和常规污染物相关性

利用西安市环境监测站超级站2013年9月1日—2015年5月31日黑碳气溶胶(BC)的监测数据,研究空气中BC浓度特征及其与气象因素和常规污染物相关性。结果表明:BC小时平均浓度均值在春季、夏季和冬季的变化趋势呈"W"型,秋季呈"V"型,且冬季的第一个最低值和峰值比春季和夏季的分别延迟1 h和2~3 h,且20:00~次日6:00秋季BC小时平均浓度均值高于当年冬季。BC浓度在秋季和冬季较高,夏季较低。冬季BC/PM_(2.5)基本最低,秋季BC/PM_(2.5)相对最高。BC日平均浓度与气温、降水和风速的日平均值为极负显著相关,且风速小于1.0 m/s时,其与风速呈最显著的负相关。除O_3外,BC日平均浓度与其他常规空气污染物浓度呈显著相关,表明其同源性很强,且受机动车尾气排放的影响更大。     

海口市臭氧污染特征

基于2013—2015年海口市4个空气质量自动监测站点数据,结合气象资料,分析了海口市O_3的污染特征。结果表明:海口市O_3总体优良,优良天数比例为99.4%,污染天数均为轻度污染;在良和污染天数中,O_3作为首要污染物的天数占40%,超过其他5项污染物占比。海口市10月O_3浓度最高。O_3月均浓度与温度呈负相关关系,同时与风向有密切关系:5—8月气温较高,以南风为主,O_3浓度较低;1月北风频率较高,易受外来污染传输作用,O_3浓度相对较高。O_3超标日以东北风为主,日变化并未呈现单峰型特征,12:00—22:00时段O_3浓度在10%范围内小幅变化。台风外围型和北方冷高压底部型是造成海口市O_3超标的2类典型天气形势。     

PM2.5 and volatile organic compounds (VOCs) in ambient air: a focus on the effect of meteorology

PM(2.5) and VOCs (benzene, toluene, m-p-o-xylenes) concentrations were measured in an urban and a suburban site in Athens, Greece, during the period between April and November 2004. This period, which is considered to be the warmer period in Greece, is characterized by the development of sea-breeze over the Attica Basin. Additionally strong Northern, North-eastern winds called "The Etesians", predominate during the summer months (July-August), acting positively to the dispersion of pollutants. In this campaign, 24 days with sea-breeze development were observed, 15 days with northern winds, 6 days with southern winds while the rest of the days presented no specific wind profile. Maximum concentrations of PM(2.5), VOCs and nitrogen oxides, were detected during the days with sea-breeze, while minimum concentrations during the days with northern winds. Ozone was the only pollutant that appeared to have higher concentrations in the background site and not in the city centre, where benzene presented strong negative correlation with ozone, indicating the photochemical reaction of hydrocarbons that lead to the ozone formation. The BTX ratios were similar for both sites and wind profiles, indicating common sources for those pollutants. T/B ratio ranged in low levels, between 3-5 for site A and 2-5 for site B, suggesting vehicles emissions as the main sources of volatile compounds. Finally, the strong correlations of PM(2.5) and benzene concentrations, between the two sampling sites, indicate that both the city centre and the background site, are affected by the same sources, under common meteorological conditions (sea-breeze, northern winds).     

西太平洋台风“苏迪罗”对我国珠三角地区臭氧污染影响研究

通过资料分析和数值模拟开展了2015年8月1日—10日台风“苏迪罗”对珠三角地区臭氧（O₃）污染影响的机理研究。结果表明,2015年8月5—8日,在台风接近登陆点的过程中,台风外围天气导致了高温、高辐射和静小风等气象条件,促进了光化学反应的进行和污染物的局地积累。同时,高温、高辐射等气象条件加剧了植被源区生物源挥发性有机物（BVOCs）的排放。采用化学传输模式模拟发现,植被BVOCs对O₃污染的贡献最高可达24×10^-9。结合拉格朗日粒子扩散模式（LPDM）探索了影响珠三角地区的主导气团,发现珠三角城市地区和高BVOCs源区存在交互传输的现象。污染期间,高BVOCs源区的一次排放产物（BVOCs）和二次产物(O₃)经区域输送加剧了珠三角地区O₃的污染。此外,研究发现台风外围条件下珠三角内陆盛行的偏北风与海陆热力差异引起的海风在沿海地区辐合,造成污染物局地积累,加剧并延长了O₃污染。研究有利于加强对O₃污染机理的认识,进而更好地采取针对性措施,有助于减小O₃污染带来的危害。     

Establishment of a structural equation model for ground-level ozone: a case study at an urban roadside site

This study established a cause–effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008–2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303–0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672–0.812 and average variances were 0.404–0.547, except for latent variable “primary” in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.     

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.     

Forecasting of daily total atmospheric ozone in Isfahan

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%.     

Estimation of Ozone Trend in Central Greece, Based on Meteorologically Adjusted Time Series

Data referring to an approximately 8-year period (1999–2007) are analyzed in order to estimate the trend of the daily maximum hourly value of ozone concentration at the east coast of central Greece, where the summer background ozone concentration is high. A Kolmogorov–Zurbenko filter is applied to remove the short-term component from the raw time series of ozone and meteorological variables. Regression models are developed in order to produce meteorologically adjusted ozone time series, involving the noise-free temperature, relative humidity, and wind speed as independent variables. The analysis verifies that the meteorological adjustment provides better results on estimating ozone’s trend, which is found to be increasing (α?=?0.001) with an annual rate of 1.34?±?0.07?μg/m³. This trend could mainly be attributed to policy and changes in the emissions of ozone’s precursors. Additionally, the short-term component of ozone concentration is also meteorologically adjusted and its impact on the trend is examined. The analysis shows that its contribution is of minor importance when the ozone trend is adjusted by temperature, relative humidity, and wind speed. Moreover, the sea breeze circulation system that is frequently developed in the area influences the short-term and seasonal ozone variation, and therefore, it should be taken into account when producing meteorologically adjusted time series. The study’s conclusions could be exploited by environmental and agricultural authorities in order to develop their long-term strategies towards the air quality management.     

不同天气形势下杭州市空气污染特征个例分析

利用杭州市气象局观测资料、NCEP再分析资料和中尺度天气预报模式WRF的数值模拟结果,对杭州市2011—2012年春、夏、秋、冬4个季节各一天的污染天气进行分析;同时选取2012年夏季有利于污染物扩散的天气个例进行对比分析。结果表明,杭州市容易发生轻度污染的天气类型主要有4类:高压前部、高压底部、高压控制和高压后部;500 h Pa高空系统稳定,受西南气流影响,850 h Pa有暖平流,1 000 h Pa风速较小时,容易造成污染物的积累,发生空气污染现象。WRF模拟结果显示,当杭州市为偏北风且风速较小时,容易发生空气污染事件,当为偏南风且风速较大时,空气质量一般较好。温度层结分析发现,当近地层以及高空出现较为深厚的逆温层且低层温度层结呈现中性或者稳定时,不利于污染物的扩散,污染物容易在底层积累,出现近地层空气污染现象。