吉林省秸秆打包减排及综合利用分析研究

为量化秸秆打包政策对防控大气污染的影响,基于吉林省2016年秸秆产量、秸秆打包地亩数、卫星火点数据以及大气污染物浓度的变化,对吉林省秸秆打包产生的大气环境及社会经济效益进行了分析,结果表明:1)吉林省打包秸秆量为220.36×10~4t,约占秸秆年产量的5.5%,假如全部加工成生物质颗粒燃料,2016年至少减少14.54×10~4t污染物排放;2)吉林省秸秆打包政策执行以来,秸秆露天燃烧量及秸秆燃烧主要月份的卫星火点数显著下降;3)随着露天燃烧量的减少,区域大气颗粒物浓度降低,空气质量改善。秸秆燃烧主要月份的PM_(2.5)和PM_(10)浓度从2014年的104.5μg·m~(-3)、164μg·m~(-3)分别下降到2016年的48.5μg·m~(-3)、69·μg·m~(-3)。研究结果可为吉林省有效减少污染物排放、综合利用农作物秸秆、改善大气环境质量等提供科技支撑。     

环保公益广告中污染防治意识接受度分析研究

环境保护和污染防治日益成为全球性重点关注问题,环境污染防治作为公益事业,也逐渐成为公众关心的热点,大量的环保公益广告进入人们的视线,在大众传媒时代,环保公益广告对提高人们的环境保护意识,形成环保和污染防治自觉具有重要促进性,分析环保公益广告中污染防治意识接受度,从公益广告的创作元素、人们的认知度和感官能力方面出发,弘扬绿色文明,倡导绿色观念,提高公众的污染防治意识。     

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.     

有关有限边界岸边排放稳态二维混合模式的几个问题的探讨

针对目前我国环境影响评价领域普遍采用的有限边界岸边排放稳态二维混合模式的局限性和不精确性，本文对由河流二维稳态水质模型的基本方程推求出的有限边界岸边排放的二维稳态混合模式的解析解的各项进行分析比较。同时针对费休的混合长度公式进行了计算比较。     

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.     

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.     

天青石及锶矿中硫酸锶的测定

通过实验，解决了测定天青石中硫酸锶时的不足，结果令人满意。     

A study on the orbit of air temperature movement

In this paper, we describe an air temperature movement by mapping its time series to the polar coordinates: the value of the time series is plotted on the radial coordinate and the time on the angular coordinate. In this way, both the yearly and daily air temperature movements from five different locations of China are used to demonstrate that, in an intuitionistic view, air temperature movement is neither random nor chaotic, but has its own orbit. We then propose an elliptic orbit model for the air temperature movement. Our proposed model provides quite good results when it is applied to the evaluation of yearly air temperature movements in eight locations in various parts of the world, and the evaluation of daily air temperature movements in five locations in Hunan Province of China on December 17, 2005. Results show that our approach provides a concise and helpful model for air temperature analysis.     

乌鲁木齐冬季大气混浊度系数与光气候

本文利用30年的气候资料分析了乌鲁木齐冬季光气候与大气混浊度之间的关系,指出了大气混浊度的增加是造成辐射总照度下降的主要原因.     

兰州城区大气粒子态汞的污染状况

通过调查发现在市工业区，粒子态汞浓度高，冬，夏平均值为０．９１和１．００ｎｇ／ｍ＾３，最高达１．９２ｎｇ／ｍ＾３，冬夏季无显著性差异，秋季浓度低，交通频繁区和交通商业混合区冬季平均值０．４５ｎｇ／ｍ＾３，夏季０．４９ｎｇ／ｍ＾３在清洁区大部分在０．１０ｎｇ／ｍ＾３左右，最低０．０７０ｎｇ／ｍ＾３，显示夏季和城区粒子态汞浓度偏高的特征。工业区粒子态汞主要是由人为污染产生的气态汞吸附在同一污染源的粒尘