Comparison of ozone levels between working days and weekends in Athens,Greece

Athens has a major problem of ozone air pollution due to its climate which is dominated by hot and dry summers with intensesunshine. As a result there is a high violation of the air quality limits of ozone. By comparing ozone air pollution betweenweekdays and weekends it is possible to estimate the effect of the reduction of primary pollutants causing the formation ofozone on the levels of this pollutant. Thus it was estimated thatwhile during the weekends the concentrations of the ozone precursors (nitrogen oxides and volatile organic compounds) werereduced by the order of 10–20%, the effect on ozone levels was lower.     

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。     

An Assessment on Variation of Sulphur Dioxide and Particulate Matter in Erzurum (Turkey)

Sulphur dioxide and PM₁₀ levels are investigated in Erzurum during the periods of 1990–2000 heating season to assess air pollution level. For that reason, emissions of sulphur dioxide and particulate matter were calculated by using consumption of fuels and Turkish emission factors. These emission values were evaluated together with air pollution levels, which were measured at six stations in Erzurum atmosphere during 1990–2000 winter periods. Results reveal that in 1990–1994 heating period, there is an increasing trend in the emissions and air pollution levels over Erzurum, and the air quality limits were not met. The daily 24 h limit (short-term limit) was exceeded 127 days in 1992–1993 winter period. The reason for this increase was found to be the switching to use of low-quality fossil fuels instead of cleaner ones. Results also indicated that there was a considerable decrease in emissions of air pollutants and air pollution levels after 1995. This can be explained by the consumption of more high-quality fossil fuels. The correlation coefficient of SO₂ with PM₁₀ is obtained as r² = 0.85, which is a high value supporting the idea that both pollutants are emitted from the same source.     

臭氧数值预报模型综述

光化学大气质量模型在研究臭氧(O_3)污染以及O_3预报方面具有核心作用,是O_3污染防治决策者的有力工具。文章结合目前中国及国际区域尺度光化学大气质量预报模型的研究与应用,重点论述与O_3有关的大气化学过程在数值预报模型中的数学表达和计算方法,阐述大气物理与大气化学过程在主流大气质量数值预报模型中的实现方法及其优势和缺陷,介绍用于数值预报模型的大气物理过程和湍流参数化方案的最新进展。就当前O_3数值模拟的主要输入资料进行讨论,强调那些易被忽视但又显著影响模型预报能力和效果的诸多因素以及模型效果评估的重要性。结合O_3与复合型大气污染的关系,强调区域大气质量数值预报模型的发展趋势与方向以及在大气环境管理方面的意义和作用。     

中欧环境空气臭氧评价对比与启示

欧盟臭氧(O₃)监测与评价起步相对较早。对比中国和欧盟O₃例行评价,欧盟在O₃标准限值、保护对象、评价指标、评价时间尺度、参评点位类型的设定和选取,以及O₃浓度与前体物排放量的关联性分析等方面,均对中国O₃评价具有一定的借鉴意义。以2017—2020年石家庄市8个国控站点O₃观测数据为分析案例,采用欧盟常用的日最大8 h滑动平均浓度(MDA8)第4高值,以及O₃暴露指标SOMO35和AOT40等3项评价指标,开展了尝试性评价应用和浓度对比。对照欧盟O₃评价指标应用经验,未来可考虑从兼顾两类功能区、丰富评价指标、扩展参评点位类型、纳入暴露影响评估、关联前体物排放变化等方面,进一步完善中国环境空气O₃评价方式,以更好地发挥其对空气质量精细化管理的数据支撑作用。     

京津冀区域臭氧污染趋势及时空分布特征

为研究京津冀区域的臭氧(O_3)污染情况及其时空分布特征,对2013—2015年京津冀区域13个城市80个国家环境空气监测点位的监测数据进行了统计分析。结果表明:2013—2015年,京津冀区域O_3污染状况整体呈加重趋势,其中2014年污染状况最为严重。13个城市中O_3污染最严重的城市为北京和衡水,连续3年均超标,且处于上升态势中。区域内不同城市O_3污染趋势并不相同。京津冀区域O_3浓度变化呈明显的季节变化特征,春末和夏季的O_3污染最严重。O_3-8 h(臭氧日最大8 h均值)年均值的高值区主要分布在北京中北部、承德和衡水等,2013—2015年第90百分位O_3-8 h的高值区均集中分布在北京。O_3的浓度峰值时间要晚于NOx2~5 h。O_3在春、夏季呈单峰分布,白天15:00左右出现最大值,在秋、冬季浓度较低,全天波动不大。     

Measurements of Ozone Concentrations in Zagreb

Ozone concentrations were measured in Zagreb at four sites from May 1999 to April 2001 in order to categorize the air quality with respect to ozone. In the summer of 2000, the ozone measurements were also extended to four sites in the suburbs of Zagreb. Methods of active and passive sampling with nitrite ion as a reagent were used. In the northern part of the town ozone was analyzed by an automatic device. Automatic device measurements in the years 1999 and 2000 showed that hourly averages of ozone concentrations did not exceed the Croatian recommended value of the 98th percentile (180 g m^–3). Over the two-year period, 24-h averages occasionally exceed 110 g m^–3 in city center and in the northern part of the town. Regardless of these isolated examples, ozone was well within acceptable concentrations. Ozone concentrations measured in summer 2000 were higher in the suburbs of Zagreb than in the city. The 98th percentile values higher than 110 g m^–3 were recorded at three sites.     

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)。     

山西省大气污染特征及对公众健康的空间影响

利用山西省11个地级市大气环境监测站的PM_2.5、PM₁₀和O₃浓度数据,分析了2015—2020年山西省PM_2.5、PM₁₀和O₃浓度时空变化特征,采用空间计量模型和岭回归方法,分析了空气污染对公众健康的空间影响。结果表明:PM_2.5和PM₁₀年均质量浓度总体下降,两者在2017年最高,2020年最低;O₃年均浓度总体增加。在季节尺度上,PM_2.5和PM₁₀质量浓度在冬季的12月和1月最高,夏季的8月最低;O₃浓度在6月最高。空间上,相较2015年,2020年山西省各地级市PM_2.5污染程度均有改善,其中长治改善效果最好;2020年山西各地级市PM₁₀污染兼有加重和减轻的情形,所有地级市PM_2.5和PM₁₀污染水平均超过国家二级污染浓度限值;2020年山西多数地级市O₃浓度升高。山西公众健康水平具有明显的空间离散特征,PM_2.5和PM₁₀浓度的局部空间自相关特征高度一致,呈现"南高北低"的格局,O₃浓度分布呈"南部高,中北部低"的格局。大气环境质量和经济发展水平均对医疗机构诊疗人数和健康体检人数的变化有正向影响,每万人卫生技术人员数量和公共财政支出比例对公众健康均有负向影响,其中经济发展水平和大气环境质量的影响最显著。山西省PM_2.5治理取得一定成效,但大部分城市PM_2.5和PM₁₀达标率较低,O₃浓度有持续升高的趋势,PM₁₀和O₃污染改善缓慢,深度减排仍面临挑战。PM_2.5和PM₁₀是危害山西公众健康的主要大气污染物,未来需要加强PM_2.5、PM₁₀和O₃的精细化管理及协同治理。     

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。     

平流层臭氧深度侵入对典型城区的影响过程分析

中纬度平流层臭氧深度侵入是造成对流层至近地面臭氧浓度突增的原因之一。筛选春夏季臭氧浓度升高时段的高分辨率大气再分析数据ERA5,以位涡值的下沉趋势分析了对流层顶折叠位置及变化过程;以AIRS数据反演了臭氧浓度、一氧化碳浓度和相对湿度的垂直廓线,并估计了其分布及相关性;以近地表污染物浓度变化、HYSPLIT模型后向轨迹分析结果证实了臭氧侵入气团的运移轨迹和局地效应;通过激光雷达监测结果观测臭氧垂直浓度分布,确定了臭氧浓度最大值所处高度,判定了受影响近地点的浓度升高时刻;以边界层高度变化、气象条件分析结果及当地与周边城市地面监测数据的逐小时变化情况等综合信息,进行了区域确认和近地面影响判定。通过以上数值综合分析,对城市地区受平流层臭氧深度侵入影响的过程和具体时间进行了详细再现,可为排除非人为排放因素导致的近地表臭氧浓度增加提供回溯分析,为臭氧污染防控决策提供依据。     

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。     

Exposure estimates for the Houston Area Asthma Study

Procedures for determining exposure extimates for the Houston Area Asthma Study are discussed. Two residential clusters of asthmatics in the Houston area have been studied as part of an overall attempt to assess the health effects of air pollution in the Texas Gulf Coast area. Air pollutant exposure data have been produced according to a three-tier monitoring scheme, i.e. (1) continuous data from centrally-located fixed stations, (2) residential indoor/outdoor measurements, and (3) personal monitoring. This air monitoring network should yield estimates of individual exposures for use in health effect correlations.The fixed sites and mobile continuously monitor ambient levels of ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and meteorological parameters (wind speed, wind direction, temperature, dew point, solar irradiation, and precipitation). Continuous sequential integrated sampling is preformed for total suspended particulates (TSP) and, inhalable particulates (IP) (total of < 2.5m and 2.5–15.0 m) by dichotomous samplers for 24 hr samplers), aldehydes (bubblers), and aeroallergens (intermittent rotorod samplers). In addition to gravimetric determinations, particulate filters are routinely analyzed for sulfate, nitrate, and (every third day) trace elements. Personal monitoring includes ozone, respirable particles, nitrogen dioxide and formaldehyde. A quality assurance/control program that meets specifications established in the EPA Quality Assurance Handbook for air pollution measurement systems was implemented. Examples of results consisting of unvalidated preliminary data from the monitoring for ozone for three participants is presented and approaches to determining exposure estimates are discussed for both an integrated exposure estimate and short time period exposure estimate.(These studies have been funded by the U.S. Environmental Protection Agency through Cooperative Agreement CR 807108010 and Grant R 808738010. Statements made in this article should not be taken as Agency policy.)     

衡阳市区和衡山背景站臭氧浓度变化规律的对比分析

选取衡阳市区和衡山背景站臭氧自动监测数据,分析两地的臭氧污染特征。对空气质量的优良率情况、臭氧作为首要污染物的变化情况、臭氧浓度的日变化特征、典型时段的浓度变化特征、臭氧浓度的月际变化特征和臭氧与PM_(2.5)的关联情况等进行了分析。结果表明,多云及阴雨天气时,衡阳市区的臭氧浓度日变化幅度大于衡山背景站。夏季,衡阳市区和衡山背景站的臭氧浓度的日变化特征规律差异较大,臭氧浓度分布比较分散,前者为典型的单峰形,后者则波动平缓。冬季,日变化幅度不大,但衡阳市区的臭氧浓度明显低于衡山背景站。衡山背景站和衡阳市区的臭氧基本同步变化,但日均值高于衡阳市区。     

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、...     

Photochemical pollution indicators—an analysis of 12 European monitoring stations

Indicators were devised to classify air pollution monitoring sites according to the type of expected photochemical pollution. The indicators are based on measured ozone volume fractions, the most frequently monitored component of photochemical pollution, and in particular on two contributions: one due to the ratio of daily maximum-to-minimum ozone volume fractions and the other to observed peak values. The two contributions regarded as independent are logically connected by “and” and therefore mathematically combined by multiplication. The criterion of classification is mainly described by the mentioned ratio and incidences of ozone volume fractions exceeding the limit of 80 ppb. Twelve monitoring stations within the European network (Cooperative programme for monitoring and evaluation of long-range transmission of air pollutants in Europe, EMEP) were classified according to this indicator predicting what ozone levels can be expected at the particular sites during the growth season (April through September) into three groups: clean, medium, and polluted, based on the data for the 7 years (1997 to 2003).     

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables.Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation.The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.     

Air pollution exposure monitoring and estimation. Part III. Development of new types of air quality indicators

The temporal pattern of exposure to a specific compound may affect health in several ways. Exposure to pollution can have short-term effects or long-term effects. For some compounds there is a threshold under which there is no presumed measurable effect, whereas for other compounds, there is no presumed threshold. For short-term effects, the exposure to a high concentration of a compound one day may either increase or decrease the response if values of the same compound become high again the next day. Adaptation to effects of short-term exposure to ozone, for example, is reported. Similarly, health response to sudden high peaks of concentration may also possibly differ in effect from those to peaks attained more gradually. For long-term effects of some compounds, the cumulative exposure may be more decisive in influencing health. This paper proposes and describes in detail several air quality indicators that reflect the time variability and the episodic nature of air pollution exposure, as an attempt to represent the temporal aspects of pollution exposure that may have important effects on health. Mean concentrations, 98th percentile and maximum values are the traditional indicators for estimating exposure. The temporal variability of particulate matter (PM10) and NO2, however, is here described by means of: (1) the rate of change of pollution as the difference between two consecutive hourly or daily values, and of (2) episodes, described in terms of number, duration and inter-episode period, maximum concentration in the episode, and integrated episode exposure.     

Why Should We Calculate Complex Indices of Ozone Exposure? Results from Mediterranean Background Sites

While moving towards a flux-based approach, exposure-based ozone metrics are still a practical measure for summarising ambient air quality. Ozone hourly concentrations for the period 2000–2004 from sites in the Mediterranean Italy (≤600 m a.s.l.) were examined to define the O₃ summary statistic in the area, and to determine how O₃ exposure indices correlate to each other. Thirty-four of the most common O₃ exposure metrics were calculated. The results show that background O₃ pollution in Italy exceeds the European and North American standards. The exceedances of the target value, information and alert thresholds set by the 2002/3/CE Directive should encourage Italy to take the appropriate measures to reduce the risk. All the O₃ exposure indices, except the maximum permissible ozone concentration (MPOC) for forests, point to the potential for negative effects on vegetation and human health across Italy. As indices evaluated significantly correlated with each other, we suggest use of the most biologically meaningful metric when summarizing air quality information.     

天津市臭氧污染特征与影响因素分析

2013—2015年,天津市臭氧(O_3)浓度整体呈下降趋势,污染状况略低于京津冀区域的其他城市。O_3浓度春、夏季高,冬季低,高值主要集中在5—9月,浓度从早上06:00开始升高,至中午14:00达到峰值。污染主要集中在中心城区、西部和北部地区,东部、南部和西南部地区污染相对较轻。O_3浓度在温度303 K以上、相对湿度70%以下或西南风为主导时较高。VOCs/NOx比值低于8,O_3的生成处于VOCs控制区。芳香烃类和烯烃类对天津市O_3生成贡献最大,其中,乙烯和甲苯为O_3生成潜势贡献最大的物种,其次为间/对二甲苯、丙烯、邻二甲苯、异戊二烯、反-2-丁烯、乙苯等,通过控制汽车尾气、化工行业及溶剂使用等对O_3生成潜势贡献大的VOCs排放源可有效控制天津市O_3污染。