用EXCEL进行精密度-偏性分析质控数据处理

利用EXCEL强大的数据处理功能，可以方便地对精密度—偏性分析质控数据进行处理，具有较高的推广价值。     

阿图什市空气质量变化趋势分析

分析了阿图什市“八五”至“九五”期间空气质量监测数据不同年度、不同季节及不同污染因子的动态变化趋势．结合当地能源结构、气候特征、城市环境综合发展水平，指出影响阿图什市空气质量的主要因素，为防治和减轻阿图什市的空气污染提供了科学依据。     

乌鲁木齐市餐饮业油烟污染的调查及防治初探

通过对乌鲁木齐市20家餐饮业油烟污染的调查分析，结果表明，乌鲁木齐市餐饮业油烟污染严重，并提出了防治措施。     

应急监测案例分析

叙述了某突发性不明污染源的应急监测经过,探讨了对此类应急监测应采取的措施,并阐述了相应的意见和看法。     

兰州市功能区大气细菌污染和城市绿地系统杀菌效应分析

在测定兰州市城市不同功能区和不同类型绿地系统空气细菌数量的基础上,分析了各功能区空气细菌污染状况,以及人流量和车流量与空气细菌数量之间的关系;探讨了不同绿地系统的杀菌效应,指出各绿地系统杀菌能力:乔、灌、草>乔、灌>草、灌>草坪。     

Economic development and environmental protection: an ecological economics perspective

This paper argues on both theoretical and empirical grounds that, beyond a certain point, there is an unavoidable conflictbetween economic development (generally taken to mean 'materialeconomic growth') and environmental protection. Think for a moment of natural forests, grasslands, marine estuaries, salt marshes, and coral reefs; and of arable soils, aquifers, mineraldeposits, petroleum, and coal. These are all forms of 'natural capital' that represent highly-ordered self-producing ecosystemsor rich accumulations of energy/matter with high use potential (low entropy). Now contemplate despoiled landscapes, eroding farmlands, depleted fisheries, anthropogenic greenhouse gases,acid rain, poisonous mine tailings and toxic synthetic compounds.These all represent disordered systems or degraded forms of energy and matter with little use potential (high entropy). The main thing connecting these two states is human economic activity. Ecological economics interprets the environment-economyrelationship in terms of the second law of thermodynamics. The second law sees economic activity as a dissipative process. Fromthis perspective, the production of economic goods andservices invariably requires the consumption of available energy and matter. To grow and develop, the economynecessarily 'feeds' on sources of high-quality energy/matter first produced by nature. This tends to disorder and homogenizethe ecosphere, The ascendance of humankind has consistently been accompanied by an accelerating rate of ecological degradation, particularly biodiversity loss, the simplificationof natural systems and pollution. In short, contemporary political rhetoric to the contrary, the prevailing growth-oriented global development paradigm is fundamentally incompatible with long-term ecological and social sustainability. Unsustainability is not a technical nor economic problem as usually conceived, but rather a state of systemic incompatibilitybetween a economy that is a fully-contained, growing, dependent sub-system of a non-growing ecosphere. Potential solutions fly inthe face of contemporary development trends and cultural values.     

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.     

Distribution of airborne particles from multi-emission source

The purpose of this work was to study the distribution of airborne particles in the surroundings of an iron and steel factory in southern Finland. Several sources of particulate emissions are lying side by side, causing heavy dust loading to the environment. This complicated multi-pollutant situation was studied mainly by SEM/EDX methodology.Particles accumulated on Scots pine bark were identified andquantitatively measured according to their element content,size and shape. As a result, distribution maps of particulateelements were drawn and the amount of different particle typesalong the study lines was plotted. Particulate emissions fromthe industrial or energy production processes were not themain dust source. Most emissions were produced from theclinker crusher. Numerous stockpiles of the industrial wastesand raw materials also gave rise to particulate emissions as aresult of wind erosion. It was concluded that SEM/EDXmethodology is a useful tool for studying the distribution ofparticulate pollutants.     

Trace Metal Concentration in Roadside Surface Soil and Tree Back: A Measurement of Local Atmospheric Pollution in Abuja, Nigeria

Cadmium, copper, lead, nickel and zinc concentrations were analysed by atomic absorption spectrophotometry in surface soil and tree bark from different districts of Abuja, Nigeria, in order to determine the atmospheric trace metal input in the area.Elevated concentrations of some of the studied metals were observed in the soil and tree bark samples from the commercial/high traffic areas of the city compared to backgroundvalues. In soil samples, the average concentration of the metals were 0.6±0.4, 18.0±4.0, 281±39, 16±4 and66±23 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively, whilst the average concentrations in tree bark were 0.3±0.2, 12±4, 133±32, 13±3 and 61±10 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively. The trend in trace metal levels suggested that automobile emissions are a major source of these metals as the highest concentrations of Pb and Zn were recorded in the commercial areas of the city known for their high traffic densities. The levels of metal in the study area were relativelylow compared to levels found in some larger and older cities in various countries worldwide.     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.