2013年苏州春季一次重污染天气的过程分析

研究了2013年3月在江苏范围内的一次重污染天气过程，重点分析苏州在此次污染过程中大气污染的变化特征。污染过程中，苏州市颗粒物浓度上升较为明显， PM10的小时质量浓度最高达548μg／m3， PM2．5质量浓度也达到197μg／m3，污染持续时间为2 d，3月8—9日当地空气质量均达到中度污染水平。根据后向轨迹模型、颗粒物离子浓度的分析，此次污染是由外来浮尘及苏州本地污染物排放所造成的区域霾污染影响所致。根据监测结果与实际污染特征，针对性地提出了对策和措施。     

The Metallic Composition of Aerosols at Three Monitoring Sites in Korea During Winter 2002

In the present study, the distribution patterns of various metals were analyzed and compared using PM samples collected concurrently from three monitoring sites located in Korea (Seoul, Busan, and Jeju island) in December 2002. As these sites can represent metal pollution with different degrees of anthropogenic activities, their concentration levels were distinguished in a systematic manner in the order of Jeju, Busan, and Seoul. By comparing the present data sets with those measured previously from other locations in Korea and around the world, we attempted to diagnose the general status of elemental pollution on the Korean peninsula. Through an application of different statistical approaches, the major processes controlling elemental levels were assessed for each of the three study sites. The results indicated the importance of both crustal and anthropogenic sources in all sites with their relative roles varying significantly from each other. The results of the metal analysis data, when examined in relation to back trajectory analysis, confirmed that their concentration changes are affected quite sensitively with air mass movement patterns. The overall results of this study consistently indicated the contribution of a strong anthropogenic source area (e.g., China) to the observed metal concentration levels in the study area, but the strengths of such signals vary considerably across the Korean peninsula.     

城市给水管网优化设计研究

研究了几种典型的城市给水管网优化设计数学模型,并给出了求解方法,指出在给水管网系统的优化设计中进一步完善广义简约(GRG)算法的必要性.     

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.     

Extension of the EUROS Integrated Air Quality Model to Fine Particulate Matter by Coupling to CACM/MADRID 2

The European Operational Smog (EUROS) integrated air quality modelling system has been extended to model fine particulate matter (PM). From an extended literature study, the Caltech Atmospheric Chemistry Mechanism and the Model of Aerosol Dynamics, Reaction, Ionisation and Dissolution were selected and recently coupled to EUROS. Currently, modelling of mass and chemical composition of aerosols in two size fractions (PM_2.5 and PM_10–2.5) is possible. The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, elementary carbon, primary inorganic compounds, primary organic compounds and secondary organic compounds. Calculated PM₁₀ concentrations and chemical composition are presented for two summer months of the year 2003 (1 July to 31 August).     

Ti/MnO2/PbO2电极的制备及电催化降解罗丹明B

采用电沉积法制备了Ti／MnO2／PbO2电极，并用其对质量浓度为20mg／L的罗丹明B模拟废水进行了电催化降解实验。实验结果表明：原水pH为4．95，无须调节废水的pH；最佳电解实验条件为：支持电解质NazSO4浓度0．09mol／L，电解电流密度40mA／cm^2，处理时间40min，在此条件下罗丹明B的去除率接近100％。     

黄孢原毛平革菌处理染料废水过程中细胞结构损伤效应研究

利用透射电子显微镜观察分析白腐真菌(黄孢原毛平革菌)在处理染料废水活性艳红X-3B过程中,染料和盐度对黄孢原毛平革菌的细胞结构产生的毒性作用.结果表明:活性艳红X-3B染料对黄孢原毛平革菌产生生物毒性作用,且随着染料浓度的增加,细胞受损伤程度不断加深.加入100 mg/L的染料活性艳红X-3B后,黄孢原毛平革菌菌丝细胞形态发生变化,出现质壁分离现象;染料浓度进一步加大,菌丝细胞超微结构受到损伤逐渐严重.染料废水中的盐度对黄孢原毛平革菌细胞也会造成损伤,且损伤程度随盐度增大而增大.NaCl浓度为3 g/L时,菌丝细胞发生质壁分离;而当NaCl加入量高于8 g/L时,细胞膜受损,线粒体、细胞核呈现空泡化,表现为受到不可逆的损伤.染料和盐双因子对黄孢原毛平革菌细胞的损伤效应表现为其损伤程度与单因子作用一致,且染料的影响作用占主导.     

β修正罗丹明B光度法测定工业废水中银

对β修正光度法测定废水中痕量银进行了研究，该方法能消除反应液中过量显色剂干扰，提高分析灵敏度和准确度。测定结果表明，银加标回收率９３．０－１０６．０％，精密度ＲＳＤ２．３％，银量低测定浓度０．０２ｍｇ／Ｌ。该方法适合于印相，电镀等行业废水银分析。     

Concentrations and chemical compositions of PM10 during hazy and non-hazy days in Beijing

In order to study the concentrations of major components,characteristics and comparison in hazy and non-hazy days of PM_(10) in Beijing,aerosol samples were collected at urban site in Beijing from December 29,2014 to January 22,2015.Heavy metals like Zn,Pb,Mn,Cu,As,V,Cr and Cd were deeply studied considering their toxic effects on human being;nine water-soluble inorganic ions(SO_4~(2-),NO_3~-,NH_4~+,Na~+,K~+,Cl~-,Ca~(2+) and Mg~(2+)) and carbon fractions(OC and EC) were also analyzed.The concentrations of heavy metals were 1.03–1.98 times higher in hazy days than those in non-hazy days,mainly due to biomass burning and coal burning.The trends in total heavy metals concentrations were basically consistent with the trends in PM concentrations except for two obvious periods(12.29–12.30;1.14–1.15);but when air masses accumulated locally or around Beijing,trends in PM concentrations and heavy metals were opposite.The proportion for NO_3~-/SO_4~(2-) indicated that mobile sources such as automobiles were important reasons for haze in Beijing.Correlation between OC and EC during non-hazy days was strong(R~2= 0.95) but it was low(R~2= 0.67) during hazy days,and large variations for OC/EC values occurred in hazy days.The calculated mass concentration of SOC is 2.58 μg/m~3,which only accounted for 10.1% of the OC concentration.When air masses from the far north-west,they decreased PM concentration in Beijing and they were relatively clean;however,those from the near east,south-east and south of the mainland increased PM concentration and they were dirty.     

降水和风对大气PM2.5、PM10的清除作用分析

对合肥2015—2017年的降水、风和PM_(2.5)、PM_(10)浓度观测数据统计研究发现,降水对PM_(2.5)、PM_(10)有一定的清除作用,尤其在秋冬季节.秋冬季节小雨、中雨分别导致PM_(2.5)和PM_(10)浓度降低23.1%、40.4%和32.0%、63.7%.雨日PM_(2.5)/PM_(10)比例上升8.4%,表明降水对PM_(10)清除作用更显著.降水前后PM_(2.5)浓度变化与降水前PM_(2.5)浓度、降水强度、降水时长密切相关.当降水强度大于4 mm·h~(-1)或PM_(2.5)初始浓度高于115μg·m~(-3)时,降水对PM_(2.5)产生明显清除作用;而降水强度小于1 mm·h~(-1)或PM_(2.5)初始浓度低于115μg·m~(-3)时由于吸湿增长作用极易造成PM_(2.5)浓度反弹升高;且持续3 h以上雨强介于1～4 mm·h~(-1)的降水也对PM_(2.5)产生清除作用.降水前后PM_(10)浓度变化与初始浓度密切相关,而与雨强相关性较弱.当PM_(10)初始浓度大于50μg·m~(-3),降水就对PM_(10)产生明显清除作用,且PM_(10)初始浓度越高,降水后PM_(10)浓度下降越多.风速大于2 m·s~(-1)可显著降低PM_(2.5)浓度,因此,当风速大于4 m·s~(-1)时合肥较少出现中度及以上污染,但易造成地面起尘,使PM_(10)浓度不降反升.合肥冬季严重污染主要出现在西北风向,夏季中度以上污染天气较少,主要出现在风速低于3 m·s~(-1)的东南风向.