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.     

同时分解法测定水中总氮和总磷

通过实验找到了同时测定水中总氮和总磷的条件,实现了总氮和总磷的同时测定。     

深圳市几种空气污染物浓度日变化特征分析

通过对深圳市2002年8月至2003年8月五种空气污染物浓度日变化特征、相互关系及不同天气条件下的变化,与人们活动的关系等进行分析,试图找出深圳空气污染的主要成因及变化规律,为环境规划决策提供科学依据。     

低浓度石油类监测与评价的问题探讨

分析表明,当地表水中石油类浓度很低时,受多方面因素影响,其监测结果的准确性和评价结论的可靠性会下降,应选用合适的监测方法,同时要注意引入污染问题。     

活性炭法测量室内空气中氡浓度的影响因素研究

探讨了各种因素对活性炭法测量室内空气中氡浓度的影响,并提出既能保证检测质量,又能符合工程检测需要、提高检测效率的检测条件范围。     

Nitrogen isotope ratios in pine bark as an indicator of N emissions from anthropogenic sources

The article describes the use of Scots pine bark to identifynitrogen sources in eastern Germany, as well as background areas in Russia and Bulgaria, by using natural isotope ratios of total nitrogen (N_t) and individual N compoundssuch as ammonium (NH₄ ⁺), nitrate (NO₃ ^-)and amid nitrogen (amide-N). The samples collected were analysed using an elemental analyser in connection with a gas isotope mass spectrometer (EA-IRMS). Natural ¹⁵N abundances in pine bark from impact areas suggest that the ammonium accumulated on the surface of the bark is releasedfrom livestock management. Bark of Scots pines growing near agricultural land had highly depleted ¹⁵N_t values (between –8 and –12), while bark from background areas (unpolluted areas) displayed slightly negative ¹⁵N_t values (mean ¹⁵N_t = –3.8). It is assumed that part of the N adsorbed on the bark surface is mainly derived from ammonia(mean ¹⁵N_t = –40.3) escaping from livestock housing and during the application of manure. This assumption is confirmed by experiments under controlled conditions in which manure samples were spread on soil. In addition, temporal and spatial variations of ¹⁵N_t abundances in pine bark from various locations in eastern Germany as wellas pine stands in Nature Park Dübener Heath are discussed.     

测定总氮的影响因素探讨

针对水质中总氮测定时蒸馏水、消解剂和消化过程等影响因素进行了讨论,并提出了减小空白值的对策。     

消泡剂硅油I在工业废水氨氮测定中的应用

对色泽较深，含杂质量较高的废水加硅油I以抑制测定中蒸馏产生的泡沫．结果表明，在250ml水样中加硅油IO．10～1．25ml，即可收到理想效果．硅油I性质稳定，不随水蒸汽蒸出，对测定无干扰．     

高压密封消解-流动注射同时测定海水中总氮和总磷

建立了高压密封消解-流动注射同时测定海水中总氮和总磷的方法,方法采用特制聚四氟乙烯密封消解罐,经高压消解锅消解后,用流动注射分析仪同时测定海水中的总氮和总磷。结果显示,总氮和总磷在0~3.20 mg/L范围内线性良好,总氮相关系数(r)=0.9997,检出限为0.012 mg/L,相对标准偏差为0.65%~2.59%,加标回收率为97.8%~102%;总磷相关系数(r)=0.9999,检出限为0.006 mg/L,相对标准偏差为0.50%~6.67%,加标回收率为98.8%~101%。该方法可同时测定海水中总氮和总磷,方法准确度和精密度良好,满足分析要求,适用于大批量海水样品中总氮和总磷的快速准确定量。     

Test of a Modelling System for Estimating Nitrogen Leaching – A Pilot Study in a Small Agricultural Catchment

Nitrogen leaching is a world-wide concern for the quality of ground- and surfacewater. Measures to abate this is still limited by a lack of understanding mechanisms behind all factors describing the leaching process, time lags between countermeasures and improvement in waterquality and spatial distribution of nitrogen leaching. The purpose of this study was to do an initial test of the SOILNDB model system which is built on the existing mechanistic SOIL and SOILN models and an automatic parameterization procedure for use of simplified input data. The test was done by simulating nitrogen leaching from each of the 150 fields in a small catchment during a three-year study period. The simulated mean root-zone nitrogen leaching rate from all the fields in the catchment was compared to measurements of nitrogen flow in the stream draining the catchment and showed good agreement in between-year dynamics.