江苏省级区域空气质量数值预报模式效果评估

采用中国科学院大气物理研究所开发的嵌套网格空气质量模式系统(NAQPMS),搭建江苏省级区域空气质量数值预报模式系统,并测试了该系统对2013年夏季江苏省PM2.5质量浓度未来24 h预报以及7 d潜势预测的效果。结果表明,该系统成功应用于江苏省的空气质量预报;所有地市的24 h预报效果均在合理范围内(平均分数偏差小于±60%且平均分数误差小于75%);7 d潜势预测效果比24 h预报效果略差,整体能准确把握PM2.5质量浓度的变化趋势。     

洛阳市大气污染特征与相关气象要素24小时变化分析

通过对洛阳市环境空气监测资料的统计,归纳出洛阳市区日内24小时环境空气污染变化特征,并结合同步气象观测资料综合分析,找出了二者之间的联系特征。引入气温日较差这一便于观测、预报的气象要素表征空气污染物垂直扩散特征,为今后进行污染预报提供了新的思路。     

乌鲁木齐市一次冷空气过境天气对空气污染的影响分析

根据对2006年11月20～28日乌鲁木齐市出现冷空气过境天气过程的天气形式和主要气象要素及空气污染变化情况进行的分析,结果表明:冷空气过境天气前后,诸多气象要素发生了变化,冷空气导致逆温层的破坏和降水过程使各项污染物均得到有效的清除.通过此分析可为类似天气的空气质量预报提供思路.     

北京市2013年1月一次空气重污染过程分析

采用数值模式与观测资料相结合的方式,对北京市2013年1月9～15日一次空气重污染过程的大气环境背景、气象条件和形成原因进行了初步分析。结果表明,此次重污染过程北京市空气质量从9日的二级跳至10日五级重度污染,11日一13日空气质量维持连续3d严重污染,14日降为重度污染,15日转为轻度污染;重污染过程期间10—14日P（PM2.5）平均值为323μg／m。,平均风速为1．47m／s,平均相对湿度为73．6％,24h变温基本为一2．72～2．68℃,24h平均变压为一3．65～2．63hPa。指出,此次重污染过程与当地气象条件密切相关,稳定的大气环流形势为污染的持续提供了大气环流背景,风速较小、湿度较大、边界层较低、持续逆温是造成重污染的主要原因,地面风场辐合及边界层下沉运动是造成重污染的重要原因。     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.     

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.     

Identification of source regions of PM10 with backward trajectory-based statistical models during PM10 episodes

This study applies backward trajectory-based statistical techniques, residence time, conditional probability and emission attraction to evaluate potential source regions of PM₁₀ over a coastal region. PM₁₀ episodes were selected by principal component analysis for 1998–2005 over the Kaoping air quality basin. Residence time was applied to identify potential regions in which air parcels would remain over their 6- and 12-h trajectories. Emission attraction and conditional probability were used to analyze contribution ratios of distinct emission sources to air quality stations. The PM₁₀ episodes screen 175 days (6 % of total days) and 35.9 % of total station numbers. Residence time and emission attraction clearly identified potential areas in which backward trajectories remained during PM₁₀ episodes and high PM₁₀ events. Emission attraction evaluated relative contributions of various sources (stationary, line, and area) from specific jurisdictions, and provided information on specific sources for high-priority PM₁₀ emissions reduction. The conditional probabilities of emission attraction during high PM₁₀ events show that high values concentrated near stationary and area sources in the city of Kaohsiung.     

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。     

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)。     

精细化预报在首届中国国际进口博览会空气质量保障工作中的应用

重大活动保障预报不同于业务预报,其核心是服务管控,并注重提前预报的时效性和精细化。该文总结了首届中国国际进口博览会空气质量保障预报工作的实践经验:在历次保障预报联合会商组织架构的基础上,完善联合会商机制,强调预报保障主体单位与支持单位的预报工作内容和职责的区分,通过数值模式的优化改进、针对性开展关键气象因素专题等多种技术手段不断提升预报精准度,并创新应用历史相似污染案例库挖掘和匹配分析,注重预报效果评估以及时总结预报经验,不断提升预报的准确性,为管理决策提供强有力的技术支撑。     

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%.     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

The Relationship between Extreme Nitrogen Oxide (NOx) Concentrations in Dublin's Atmosphere and Meteorological Conditions

A study was carried out to investigate the effects of meteorological conditions on atmospheric Nitrogen Oxide (NOx (Nitrogen Oxide and Nitrogen Dioxide)) concentrations at a site in Dublin. Data used in the study (meteorological conditions and hourly NOx concentrations) were compiled from hourly records for the years 1988-1992. The research identified wind speed, air pressure and wind direction as the most important meteorological parameters for understanding the behaviour of extreme NOx concentrations in Dublin's air. Daily, weekly and seasonal variation in NOx concentrations were observed. This work also highlighted the importance of the role played by general synoptic weather conditions over local climatic effects in extreme events.     

Mobilization of aluminium and deposition on fish gills during sea salt episodes--catchment liming as countermeasure

Episodic events may be critical with respect to aluminium (Al) toxicity in moderately acidified salmon rivers. The present work demonstrates that sea salt episodes enhance the toxicity of Al in acidic rivers. The documented sea salt episode (300 [micro sign]M Cl) mobilized positively charged Al species (0.4 to 1.1 [micro sign]M Al(i)), enhanced the Al accumulation on fish gills (0.9 to 10 [micro sign]mol g(-1) dw) and caused increased stress responses (6 to15 mM blood glucose) in fish. Accumulated Al on gills remained high several days after the episode. The presented results are based on a six-week field study in two tributary rivers on the west coast of Norway. Changes in the river water qualities and Al speciation were followed using in situ fractionation techniques. Al accumulation on gills and stress responses were followed for Atlantic salmon (Salmo salar) kept in tanks continually exposed to the changing water quality. The potential mobilization of Al from the two catchments was studied by extracting soils with diluted seawater (salinity of 3). To counteract Al toxicity, one of the tributary catchments has been limed. The potential mobility of Al by sea salt was lower in limed soils compared to acid soils, and the Al deposition on fish gills (<3.5 [micro sign]mol g(-1) dw) and associated stress responses stayed low during the sea salt episode in the river draining the limed catchment. Thus, for acid river systems in coastal areas, catchment liming should be considered as a useful countermeasure for Al toxicity.     

Analysis of a high PM10 episode observed at a coastal site nearby Shanghai, China

A high PM₁₀ episode observed at a coastal site nearby Shanghai during 18–19 January 2007 was analyzed in this study. The maximum hourly averaged PM₁₀ concentrations for the 2 days were 0.58 and 0.62 mg/m³, respectively. The meteorological condition during the episode was favorable for air pollution with large-scale stagnation. There was no dispersing effect by high wind, no scavenging function by precipitation, and no diluting process by clean marine air during the episode. The trajectories for 16–19 January all came from the northern region and kept in low levels, and during the episode peak time, from the morning of 18 to the morning of 19 January, trajectories all came from the northern inland areas and had passed over the coastal region of Jiangsu province before arriving at the site. The variation of the air pollution indexes (APIs) in the cities located in the upwind direction of the site during the episode days clearly shows a process of large-scale air pollution from north to south. The liner correlation coefficient for PM₁₀ and SO₂ concentrations is 0.774 during the episode, while for PM₁₀ and CO, it even reaches 0.995, which indicated that the high PM₁₀ was mainly emitted from the coal burning for domestic heating in winter. Therefore, the observed episode was caused by the transport of domestic heating pollutants accumulated in the boundary layer from northern continental areas.     

上海市郊春季PM10 污染的观测研究

利用上海市郊金山环境监测站2007年春季的逐时PM10和气象参数的观测数据,分析了PM10日平均质量浓度和最大质量浓度的时间变化规律,小时平均质量浓度的分布规律,气象条件对PM10质量浓度的影响,并利用HYSPLIT轨迹模型结合气象观测数据对一次最严重的PM10污染过程进行了分析.结果表明,PM10在春季有11日出现超标,污染比较严重;风和降雨对PM10质量浓度均有较为明显的影响;4月2日监测点PM10日平均和最高质量浓度分别达到0.78 mg/m3和1.0 mg/m3,均为全年最高值,这与北方冷空气携带沙尘南下的影响有关.     

湖南省大气污染特征及气象影响分析

为深入探究湖南省大气污染过程变化特征及气象条件对不同首要污染物的影响,利用2015—2021年湖南省14个市州的大气环境监测数据及气象观测数据,分析研究了68次污染过程的持续时长、分布特征等,并根据污染来源分析结果、传输路径分析结果,结合地形特征,进行了污染影响区域划分,对不同首要污染物的气象因子反馈机制进行了细致剖析。结果表明:湖南省大气污染过程以颗粒物污染为主,年变化特征呈现为单峰形,单次污染过程持续时长一般不超过3 d;3条主要的污染物传播路径均是由湘北地区进入长株潭城市群;污染来源以本地积累为主,外来输送与本地污染叠加次之;颗粒物污染主要受边界层稳定程度、逆温情况和近地面空气湿度的影响,臭氧污染则主要受紫外辐射、前体物浓度和扩散条件等的影响。     

基于激光雷达分析一次重霾过程混合层高度

为深化对冬季重霾天气大气混合层高度的认识,利用Mie散射激光雷达观测了成都市2014年1月23日至2月4日一次典型重霾天气过程。基于Mie散射激光雷达探测获取的后向散射系数,使用SBH99算法计算了该过程的混合层高度,并系统分析其演变特征及其与气象因子的关系,研究结果表明:将激光雷达探测的混合层高度与探空曲线表征出的混合层高度进行对比分析,结果显示两者具有较好的一致性,相关系数为0.893 4;此次重霾过程中,混合层高度平均值较低,约378 m;霾天气发生后,混合层高度显著下降,并且混合层高度的最大值与最小值之间差距缩小,日变化波动不明显;混合层高度的发展与空气温度的变化趋势呈正相关关系,与相对湿度呈负相关关系。     

2018年11—12月北京市重污染天气过程数值预报能力评估

2018年11—12月北京市发生了4次以PM_2.5为首要污染物的重污染天气过程,为了分析数值模型对4次重污染过程的预报能力,将CMAQ模式提前1~7 d对北京市PM_2.5的小时预报结果与观测结果对比,分别从离散统计和分类统计2个方面评估CMAQ模式对4次重污染天气过程的预报效果,并简要分析了偏差产生的气象方面原因。结果表明：CMAQ模式提前1~6 d对重污染天气过程的预报显示出良好的性能,为日常业务预报提供了可借鉴的参考信息,可较好地预报出PM_2.5小时浓度变化趋势和浓度水平,离散统计结果显示提前1~4 d的预报结果好于提前5~7 d,相关系数r基本大于0.8,但有一定程度的低估趋势;分类统计结果显示不同预报时效预报准确率大于70%,探测准确率高于55%,部分时段可以达到80%~90%,对人工预报起到了良好的参考作用;输入的气象场的变化及其偏差对于重污染的起始时间、持续时间及清除时间有一定的影响,对相对湿度预报偏小和风速预报偏大是造成CMAQ模式低估的一个重要原因。     

江西省极端干旱下臭氧污染特征及成因分析

基于气象和环境空气质量监测数据,分析了江西省干旱对臭氧污染的影响,并结合VOCs在线监测数据,对2022年9月极端干旱下江西省臭氧污染过程特征及污染成因进行分析。结果表明:江西省臭氧污染与气象干旱间存在一定联系,干旱情况下缺少降水对臭氧及其前体物VOCs的湿清除作用,易促使臭氧超标概率随着无雨日数的增加逐步上升。江西省2022年9月出现历史性极端干旱情况,干旱期间江西省11个地市共出现151 d臭氧超标天,NO₂常于午夜和早晨出现浓度峰值,从而促进上午臭氧浓度的迅速上升。此外,南昌市林科所站点VOCs在线监测数据也显示:极端干旱期间逐日VOCs体积分数为11.9×10^-9~35.5×10^-9,较8月明显升高。对OFP贡献前十的物种主要为OVOCs和芳香烃,与8月相比,芳香烃、烯烃和烷烃的OFP略有下降,OVOCs的OFP升高明显,其中乙醛对臭氧的贡献甚至上升143%,前期长时间的无降水可能是乙醛等OVOCs浓度上升的重要原因之一。