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.     

Climate impact on social systems: the risk assessment approach

A novel approach to the problem of estimating climate impact on social systems is suggested. This approach is based on a risk concept, where the notion of critical events is introduced and the probability of such events is estimated. The estimation considers both the inherent stochasticity of climatic processes and the artificial stochasticity of climate predictions due to scientific uncertainties. The method is worked out in some detail for the regional problem of crop production and the risks associated with global climate change, and illustrated by a case study (Kursk region of the FSU). In order to get local climatic characteristics (weather), a so-called statistical weather generator is used. One interesting finding is that the 3% risk level remains constant up to 1.0–1.1°C rise of mean seasonal temperature, if the variance does not change. On the other hand, the risk grows rapidly with increasing variance (even if the mean temperature rises very slowly). The risk approach is able to separate two problems: (i) assessment of global change impact, and (ii) decision making. The main task for the scientific community is to provide the politicians with different options; the choice of admissible (from the social point of view) critical events and the corresponding risk levels is the business of decision makers.     

工作场所空气中硫酸雾（SO3）的分析

采用碘量法测定工作场所空气中的三氧化硫及硫酸雾，三氧化硫及硫酸雾经中性水吸收后与碘化钾和碘酸钾反应，析出的碘与淀粉指示剂作用产生蓝色，进行比色测定。     

地面水环境质量评价的有时序多目标决策方法

将有时序多目标决策方法应用于地面水环境质量优势的比较，介绍了决策方法的原理和评价步骤。实例分析表明该方法计算过程简便，分辨率高，排序结果与实际环境质量状况相符合。     

发光细菌法评价工业废水的毒性

用发光细菌测定法估算了两城市污水对河道水体污染的分担率，评价了工厂排放废水的毒性，并据此确定了污染源监督管理中应优先治理的工厂（车间）废水。     

地下水环境质量变化动态研究

以邢台市环境保护监测站１０年来的监测资料为依据，运用单因子比值法，环比发展速率外推法和直线回归法，对地下水环境质量现状及１０年的动态变化进行了分析，研究和评述，并对发展趋势做了预测性评价。     

Methane emissions from natural wetlands

Methane is considered one of the most important greenhouse gases in the atmosphere. Because of the strict anaerobic conditions required by CH₄-generating microorganisms, natural wetland ecosystems are one of the main sources of biogenic CH₄. The total natural wetland area is estimated to be 5.3 to 5.7 × 10¹² m², making up less than 5% of the Earth's land surface. However, natural wetland plays a disproportionately large role in CH₄ emissions. Wetlands are likely the largest natural sources of CH₄ to the atmosphere, accounting for about 20% of the current global annual emission. Out of the total amount of CH₄ emitted, northern wetlands contribute 34%, temperate wetlands 5%, and tropical systems about 60%.Because of the unique characteristics and high productivity, wetland ecosystems are important in the global carbon cycle. Natural wetlands are permanently or temporarily saturated. Strict anaerobic conditions consequently develop, which allows methanogenesis to occur. But the thin oxic layer and the oxic plant rhizophere promote activity of CH₄-oxidizing bacteria or methanotrophs. Thus, both CH₄ formation and consumption in wetland systems are microbiological processes and are controlled by many factors. Eight of the controlling factors, including carbon supply, soil oxidation-reduction status, pH, temperature, vegetation, salinity and sulfate content, soil hydrological conditions and CH₄ oxidation are discussed in this paper.     

用密切值法评价沙湾荒漠区生态环境质量

利用多目标决策中的密切值法进行沙湾荒漠区生态环境综合评价。该方法简便、适用、且分辨率较高，对各监测点群的定量描述符合实际状况。     

基于耦合模型的安徽经济与环境协调发展分析

构建了经济发展与生态环境评价指标体系，选用耦合协调度模型计算2010—2019年安徽省不同区域经济发展与生态环境的耦合协调度，并采用灰色关联度法计算评价指标与耦合协调度的关联系数。研究结果表明：全省经济发展与生态环境的耦合协调度整体呈改善趋势，耦合协调度集中分布在0.393~0.765，基本处于良好协调或低水平协调阶段。全省不同区域的生态环境指数均大于经济发展指数。省会城市、沿江江南地区耦合协调度高于江淮之间和淮河以北地区。工业"三废"排放量、环境质量、环境污染治理力度是影响经济发展与生态环境耦合协调度的重要因素，建议通过改善这些方面进一步提升经济与环境的耦合协调度水平。     

乌鲁木齐市主要街道空气中一氧化碳污染规律的研究

本文通过对影响街道空气中一氧化碳污染的因素进行了大量的监测及综合分析,找出乌鲁木齐市主要街道空气中一氧化碳的污染规律,为控制我市汽车污染提供科学依据.