Long Term Trends in Concentration of Major Pollutants (SO<Subscript>2</Subscript>, CO,NO, NO<Subscript>2</Subscript>, O<Subscript>3</Subscript> and PM<Subscript>10</Subscript>) in Prague – Czech Republic (Analysis of Data Between 1992 and 2005)

To assess the effect of changes in traffic density and fuels used for heating at the beginning of the 1990s, 1992–2005 monthly averages of PM₁₀, SO₂, NO₂, NO, CO and O₃ from Prague, the Czech capital, were analyzed together with long term trends in emissions of major pollutants, fuel consumption and number of vehicles registered in Prague. The data from all monitoring stations were retrieved from the database of the state automated monitoring system. Correlation coefficients between ambient monthly averaged temperature and all pollutants of concern showed distinct seasonal trends. The results showed that while SO₂ and to some extent also CO concentrations dropped namely in the first half of the analyzed period (1992–1997) as a result decreased fossil fuel consumption for local heating, the behaviour of other pollutant concentrations followed a different pattern. PM₁₀ concentrations decreased during the beginning of the 1990s but showed a sign of increase after 2000. Concentrations of ozone and NO₂ did not reveal any significant change throughout the whole studied period. It can be concluded that during the studied period traditional urban sources of pollution, such as coal and oil combustion, lost their importance but were simultaneously substituted by pollutants from automotive transport (namely PM and NO₂) making the problem of air quality even worse.     

Calculating losses of crops in Denmark caused by high ozone levels

In order to help guide air pollution legislation at the European level, harmful air pollution effects on agriculture crops and the consequent economic implications for policy have been studied for more than a decade. Ozone has been labeled as the most serious of the damaging air pollutants to agriculture, where growth rates and consequently yields are dramatically reduced. Quantifying the effects has formed a key factor in policymaking. Based on the widely held view that AOT40 (Accumulated exposure Over Threshold of 40 ppb) is a good indicator of ozone-induced damage, the Danish Eulerian Model (DEM) was used to compute reduced agriculture yields on a 50 km×50 km grid over Europe. In one set of scenarios, a ten year meteorological time series was combined with realistic emission inventories. In another, various idealized emission reduction scenarios are applied to the same meteorological time series. The results show substantial inter-annual variability in economic losses, due in most part to meteorological conditions which varied much more substantially than the emissions during the same period. It is further shown that, taking all uncertainties into account, estimates of ozone-induced economic losses require that a long meteorological record is included in the analysis, for statistical significance to be improved to acceptable levels for use in policy analysis. In this study, calculations were made for Europe as a whole, though this paper presents results relevant for Denmark.     

常州市臭氧污染传输路径和潜在源区

利用NCEP全球再分析资料和HYSPLIT4模式,计算了2013—2015年常州市臭氧(O_3)超标日的气流后向轨迹。结合聚类分析方法和常州市PM2.5、PM10、SO2、NO2、O_3数据,分析了O_3超标日不同类型气团来源对各污染物浓度的影响,并利用引入权重因子后的潜在污染源贡献函数分析了影响常州市O_3超标的潜在污染源区分布特征。结果表明:常州市O_3超标期间易受到东南和西南方向气流影响,其中从东海和黄海途经浙江东北部、上海、江苏南部等地的东南气流占比达50%以上。自内陆途经黄山-湖州-宜兴到常州的气流对应的O_3平均质量浓度最高,为116μg/m3。自山东经枣庄-宿迁-淮安-泰州-苏州-无锡到常州的气流对应的O_3平均质量浓度最低,为78μg/m3,但该气流对应的SO2和NO2平均值为各聚类中的最高。影响常州市O_3的潜在污染源区主要在常州周边200 km以内的区域,且集中在从南京至上海的长江下游沿线区域和杭州湾区域;其中太湖湖区为重点污染源源区之一。O_3超标日影响常州NO2的潜在污染源区主要集中在江苏南部、浙江东北部和上海3个区域,太湖周边的常州、无锡、苏州和湖州等几个临近城市为潜在的重点污染源区。与影响常州O_3的WPSCF高值区相比,影响NO2的高值区分布范围更大、距离更远。影响常州O_3的潜在污染源区分布,与长江三角洲地区人为源大气污染物的高排放区域较为一致,说明长江三角洲地区的O_3污染与本区域的人为源大气污染物排放有着极为密切的关联。     

夏季太湖蓝藻水华暴发与臭氧污染的关联性

夏季受东南季风、湖流等因素影响,太湖蓝藻向西北部水域集聚,该区域平均藻密度可高达1×109个/L以上,其中蓝藻集中堆积的近湖岸区域藻类密度更高,蓝藻在不同生命阶段释放的藻源性VOCs的成分谱和产生量有较大差异,其中烯烃和有机胺反应活性较强。蓝藻水华高发期太湖西岸非甲烷总烃的浓度约为常州市区的3.3倍,日变化趋势符合蓝藻代谢规律。太湖西部蓝藻水华、湖西区的非甲烷总烃浓度和臭氧污染程度时空变化规律表明:太湖西部(宜兴)是整个流域臭氧污染最严重的区域,其臭氧污染的形成与太湖蓝藻水华暴发有关联性。     

沈阳臭氧污染时空分布特征及变化趋势

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。     

青藏高原典型城市拉萨市近地面臭氧污染特征

拉萨市作为青藏高原典型城市,环境空气质量相对较好,但臭氧污染近年来有所凸显。对拉萨市臭氧的现状与污染特征进行分析基础上,探讨臭氧污染的影响因素。结果表明:拉萨市臭氧污染表现出"来得早,去得快"的特征,与内地城市相比,拉萨市臭氧质量浓度在3月即可达到全年平均值(2015年为105μg/m~3),而9月以后将低于全年平均值,并在春末夏初达到峰值;由于青藏高原海拔高,紫外线强,相对内陆地区臭氧均值偏高,2015年拉萨市臭氧年均值比北京市和成都市分别高出7.7%、29.0%,其小时浓度变化呈中午高、早晚低的特征;拉萨市臭氧的浓度变化受空气湿度、日照时间和日均气温的影响;生物质燃料的跨界传输可能也对青藏高原地区臭氧的来源产生一定影响。     

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。     

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。     

我国典型南方城市臭氧污染特征

分析了我国典型南方城市的臭氧污染特征,选取我国4个有代表性的南方重点城市武汉、宁波、中山和南宁的2013—2015年监测数据,使用EXCEL、ORIGIN和MATLAB等统计软件开展研究,结果表明:我国南方典型城市的臭氧质量浓度分布有明显时间变化特征,超标时间跨度大,部分南方城市与氮氧化物存在较明显负相关性,相关系数高于-0.6;受城市所在不同地理位置、气象因素、大气扩散条件及可能的不同本地排放污染源构成等因素影响,4个城市的近3年臭氧浓度月均值、超标情况和年内峰值均存在一定差异和分组相似性;与部分气象因素也表现出显著相关性。     

气象因子对臭氧的影响及其在空气质量预报中的应用

为提高重庆市臭氧(O_3)预报准确率,利用2013—2015年5—10月O_3监测数据和气象数据,通过主成分分析、逐步回归分析等方法,确定了影响重庆O_3浓度的主要气象因素为最高温度、温差、太阳辐射、降水量、相对湿度、水气压和压差;通过基于O_3污染水平相似的主要气象控制因子筛选和最优组合的预报结果优化方法,提高了O_3预报准确率,使2016年5—8月O_3的AQI类别预报准确率由57.7%增至72.4%,O_3超标的预报准确率由38%增至46%。