深圳市龙岗区大气颗粒物中多溴联苯醚的污染特征研究

为了解深圳市龙岗区大气颗粒物中多溴联苯醚的污染状况,于2017年1月、4月、8月和11月分别采集龙岗区大气颗粒物样品.利用气相色谱-质谱联用仪（GC/MS）对深圳市龙岗区4个功能区（工业区、商业区、居民区和生态区）的80个大气颗粒物样品进行检测,得到PBDEs（多溴联苯醚）8种单体（BDE-28、BDE-47、BDE-99、BDE-100、BDE-153、BDE-154、BDE-183、BDE-209）的质量浓度,同时研究了大气颗粒物中PBDEs质量浓度的时空分布特征.结果表明:①深圳市龙岗区大气颗粒物中ρ（∑₈PBDEs）（∑₈PBDEs为PBDEs 8种单体的加和）范围为0.07~77.80 pg/m3,ρ（∑₈PBDEs）年均值为（6.86±14.17）pg/m3.ρ（BDE-209）范围为0.01~76.23 pg/m3,年均值为（5.10±13.58）pg/m3,BDE-209为主要污染单体.② 4个功能区的ρ（∑₈PBDEs）平均值大小顺序依次为工业区（13.65 pg/m3）>商业区（7.75 pg/m3）>生态区（3.95 pg/m3）>居民区（2.19 pg/m3）,工业区是环境空气颗粒物中PBDEs的主要污染区.③ ρ（∑₈PBDEs）平均值的季节性变化规律为春季（13.32 pg/m3）>冬季（9.18 pg/m3）>秋季（2.17 pg/m3）>夏季（1.51 pg/m3）,符合深圳市亚热带海洋季风气温和气候对环境空气中颗粒物质量浓度的影响规律.研究显示,深圳市龙岗区大气颗粒物中PBDEs质量浓度处于较高水平,污染严重,建议减少含PBDEs产品的生产、使用和运输等活动,或找到更利于环境安全和人体健康的替代品,降低PBDEs对该地区的污染.      

长江流域2000—2015年生态系统质量及服务变化特征

分析生态系统质量与服务现状及变化特征,有助于准确了解流域生态系统状况与趋势,为流域生态保护与修复的高效管理提供参考依据.采用相对生物量密度与相对植被覆盖度指数以及生态系统服务（土壤保持、防风固沙、洪水调蓄、粮食生产）评估模型,评估2000—2015年长江流域生态系统质量及服务变化特征.结果表明:①2000—2015年,长江流域森林、灌丛以及草地生态系统质量提升显著,56.56%的森林、45.65%的灌丛以及19.26%的草地生态系统质量得到不同程度的提高.②研究期间,长江流域粮食生产、洪水调蓄、土壤保持、防风固沙均呈显著上升趋势,其中粮食生产能力增加13.7%,洪水调蓄功能提升10.1%,土壤保持服务提高19.5%,防风固沙总量增加30.0%.③长江流域生态系统质量不高,处于低与差质量等级的生态系统面积比例在25%~35%之间;受不同类型的自然和人类活动影响,长江流域生态系统质量与服务均存在局部退化现象.研究显示,在生态质量与服务评估的基础上,分区域、分类型实施不同的生态系统保护与恢复途径,是今后长江流域加强生态系统管理的重要途径.      

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

膜、柱串联固相萃取-高效液相色谱法测定水中多环芳烃(PAHs)的研究

建立了用膜-柱串联固相萃取(SPE)技术,甲醇和水作为流动相进行梯度洗脱,紫外和荧光检测器串联的高效液相色谱法(HPLC)分析水中EPA优先监控的16种PAHs的方法.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

上海汽车制造业涂装废气VOCs排放特征

于2015年10月对上海市嘉定区4家(A、B、C、D)汽车制造企业涂装废气ρ(VOCs)和组成特征进行了调查分析。结果表明,A、B、C、D厂涂装废气排放口ρ(VOCs)总为0. 743～6. 11 mg/m~3,主要检出物和最高检出值分别为:A厂二甲苯2. 06 mg/m~3、B厂二甲苯0. 578 mg/m~3、C厂甲苯2. 59 mg/m~3、D厂庚烷0. 274 mg/m~3;芳香烃类是A、B、C厂排放比例最高的VOCs组分,烷烃类是D厂排放比例最高的VOCs组分。指出,原、辅料种类影响排放物的主要成分,废气处理工艺类型影响排放物主要成分和浓度。     

Chemical monitoring and evaluation of the water quality of the Douro River at the Crestuma-Lever dam

Water taken from the Crestuma-Lever dam on the Douro River was evaluated considering the Portuguese Decree-law no. 236/98 of 1st August, 1998. This paper presents the metrological characteristics, validation of analytical methodologies and the data obtained from seasonal samplings. The test parameters selected are: temperature, pH, chloride, conductivity, carbon dioxide, alkalinity, hardness, oxidability, dissolved oxygen, BOD5, total suspended solids, sulfide, phosphate, silica, ammonium, nitrite, nitrate, sulfate, sodium, potassium, calcium, magnesium, iron, manganese, copper and zinc. From data analysis we can conclude that the water presented the following characteristics: a pH situated in the neutral zone; a median mineralization; a medium hardness; well oxygenated water; and a carbonate hardness exclusively due to bicarbonates. The temperature, pH, dissolved oxygen, ammonia, chlorides, sulfates, copper, zinc and BOD5 were compared with the legal limits presented in annexes XXI and I of Decree-law 236/98. The results relating to total hardness, nitrites, calcium, magnesium, sodium, potassium, aluminium, sulfides and oxidability were compared with the legal values in annex VI as there are no limits relating to these parameters in annexes XXI and I.     

Soil properties and crop productivity as influenced by flyash incorporation in soil

Field experiments were carried out during 1996–97at Gulawathi, Muthiani and Salarpur Villages, IARI Farm, NewDelhi and NCPP Campus, Dadri to evaluate changes in soilcharacteristics and growth of wheat (Triticum aestivum L.),mustard (Brassica juncea L.), lentil (Lence esculentaMoench.), rice (Oryza sativa L.) and maize (Zea mays L.) byvarying amounts of flyash addition (up to 50t ha^-1) in soils atsowing/transplanting time of crops. Flyash addition in areasadjoining NCPP Thermal Power Plant, Dadri, Ghaziabad, U.P.ranged from 5–12 t ha^-1 yr^-1 in 1995–96. Shoot and root growthand yield of test crops at different locations after flyashincorporation resulted in beneficial effects of flyashaddition in most cases. The silt dominant texture of flyashimproved loamy sand to sandy loam textures of the surfacesoils at the farmers' fields. The increased growth in yield ofcrops with flyash incorporation was possibly due tomodifications in soil moisture retention and transmissioncharacteristics, bulk density, physico-chemical characterssuch as pH and EC and organic carbon content. The response offlyash addition in the soil on soil health and cropproductivity needs to be evaluated on long-term sustainableaspects.