危险废物分级管理方法研究

危险废物因具有毒性、易燃性、腐蚀性和反应性等多种危害特性,如果暴露于环境中将会对人类健康和生态环境造成很大威胁.深入分析了欧美等一些国家对危险废物进行分级管理的方法,针对中国目前危险废物的产生现状和管理现状,提出了综合考虑危险废物的产生量和活性大小等各个方面,借助风险评价对危险废物进行分级管理的方法和原则,以及相应的分级管理程序的建议,为完善中国的危险废物管理体系提供参考.     

Sensitivity of schemes for ecotoxicological hazard ranking of chemicals

Data from an environmental hazard profile, including different tests on accumulation and degradation as well as Daphnia acute toxicity were used for a classification of chemicals into three categories: High hazard potential, uncertain hazard potential, and low likelyhood of environmental hazard. Giving the used evaluation factors different weights, leads to a modification in classification.     

常温厌氧消化技术处理城市生活有机垃圾的中试研究

对分类收集的城市生活有机垃圾采用专门设计的兼氧沤解预液化—常温厌氧消化的工艺技术进行处理。试验结果显示 ,兼氧沤解对生活有机垃圾的预液具有较好的效果 ,液化率达 80 %以上 ,可以满足反应器对生活有机垃圾预处理的要求     

洪泽湖水域的环境演变遥感分析

针对传统水质监测的定点、定剖面采样分析时间及空间的局限性,为对湖泊水质进行全面系统的分析,在前人研究工作的基础上,建立了一种水域水质状况图像识别的主成分监督分类方法,通过对洪泽湖湖区的1991年12月15日和2000年1月7日,两个时相TM遥感图像数据,分别进行主成分变换,并依据不同类型水体的光学特性,分析各主成分图像的构成及其环境意义。研究表明：洪泽湖湖区的水质分布,具有从西向东,从北向南由清洁水（优）向一般水依次递减、逐年下降的特征。其清洁水体主要分布于洪泽湖西部的溧河洼、临淮头、西北部的安河洼和成子湖的北部。从1991--2000年,洪泽湖入湖的工业废水及生活污水量有所减少,湖水受污染程度明显减轻,相对“八五”期间水质污染日益加重的现象,“九五”以后水质逐渐改善。将其分类结果与所收集的洪泽湖历年水质资料对比分析表明：主成分监督分类结果与环境要素吻合较好,且基本反映了其水质环境的变化趋势。     

Classification of nutrient emission sources in the Vistula River system

Eutrophication of the Baltic sea still remains one of the biggest problems in the north-eastern area of Europe. Recognizing the sources of nutrient emission, classification of their importance and finding the way towards reduction of pollution are the most important tasks for scientists researching this area. This article presents the chemometric approach to the classification of nutrient emission with respect to the regionalisation of emission sources within the Vistula River basin (Poland). Modelled data for mean yearly emission of nitrogen and phosphorus in 1991-2000 has been used for the classification. Seventeen subcatchements in the Vistula basin have been classified according to cluster and factor analyses. The results of this analysis allowed determination of groups of areas with similar pollution characteristics and indicate the need for spatial differentiation of policies and strategies. Three major factors indicating urban, erosion and agricultural sources have been identified as major discriminants of the groups.     

A classification of acidified catchments in Britain

To compare catchments of possible acidified waters in Britain an extensive database was compiled for nearly 600 catchments of lakes and streams for which chemical data were available. Information on the database included map-derived data, pollutant deposition estimates and hydrological characteristics. To stratify the catchment database a randomly selected subset of 328 catchments was used to derive eight classes of catchments using the classification program TWINSPAN. Several classifications were generated by this method using an increasing number of catchment parameters. TWINSPAN classes were defined on the basis of some catchment parameters (indicators) but were shown to be associated with other variables. A general linear model (GLM) analysis of the available chemical data was used to test the different classifications. A TWINSPAN classification which excluded soil and geology characteristics gave the best GLM model because soil and geology types were poorly correlated with other catchment parameters. However, because of the recognized importance of soils and geology in acidification processes, these parameters were introduced into the GLM model as separate GLM classes. The resulting three-class model (TWINSPAN class, soil type, geology type) proved superior to both the one-class model and to two-class models which included either soil or geology. It was demonstrated that the TWINSPAN classes were associated with particular geographical areas and particular monitoring programmes. The TWINSPAN classification was used to evaluate a set of catchments selected as long-term monitoring sites for Britain. In general, the set covered a wide range of catchment types as defined by the classification. It was possible to identify minor shortfalls in classes of catchment selected and suggest possible additions to the sampling programme. Whilst the classification procedure has been applied to possible acidified waters, the method is of general relevance to all catchments and waters of more diverse nature.     

Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods

The evaluation of toxicological effects at the cellular and molecular levels in organisms are often used to determine sites subjected to contamination problems that pose a threat to the long-term survival of organisms. However, the integration of multiple measurements on the health status of organisms into a model for site discrimination is challenging. This study compares two discrimination methods which are based on rule inference: rough sets (RS) analysis and classification trees (CT) with classical multivariate discriminant analysis (DA). Site classification was attempted with six biomarkers of effects: metallothionein levels, lipid peroxidation, DNA damage, levels of lipophosphoproteins (i.e., vitellins), phagocytosis activity and haemocyte cell viability on clam (Mya arenaria) populations from the Saguenay River fjord (Quebec, Canada). Rule based methods have the advantage of complete independence from data distribution constraints in contrast to the classification methods from multivariate analysis that are more commonly used in ecotoxicology. Results show that RS and CT gave better classifications than DA because they do not require strong distributional assumptions. Moreover, RS provided classification rules that could identify the most important biomarker(s) for site discrimination. RS and CT were shown to be simple and efficient methods for classifying multivariable ecotoxicological data. This methodology would be especially useful when freedom from distributional assumptions is required.     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Classification technology of domestic waste from 2000 to 2019: a bibliometrics-based review

Environmental Science and Pollution Research - Waste classification is to reduce solid waste and its associated environmental pollution. This paper applied bibliometrics to assess publications...     

Application of the criteria for classification of existing chemicals as dangerous for the environment

The criteria for classification and labelling of substances as “dangerous for the environment” agreed upon within the European Union (EU) were applied to two sets of existing chemicals. One set (sample A) consisted of 41 randomly selected compounds listed in the European Inventory of Existing Chemical Substances (EINECS). The other set (sample B) comprised 115 substances listed in Annex I of Directive 67/548/EEC which were classified by the EU Working Group on Classification and Labelling of Existing Chemicals. The aquatic toxicity (fish mortality,Daphnia immobilisation, algal growth inhibition), ready biodegradability and n-octanol/water partition coefficient were measured for sample A by one and the same laboratory. For sample B, the available ecotoxicological data originated from many different sources and therefore was rather heterogeneous. In both samples, algal toxicity was the most sensitive effect parameter for most substances. Furthermore, it was found that, classification based on a single aquatic test result differs in many cases from classification based on a complete data set, although a correlation exists between the biological end-points of the aquatic toxicity test systems.     

两性高分子水处理剂的研究进展

本文综述了近年来国内外两性高分子水处理剂的研究进展,并对今后的研究工作提出了一些建议.     

Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria

Environmental Science and Pollution Research - Machine learning algorithms have proven useful in the estimation, classification, and prediction of water quality parameters. Similarly, indexical...     

循环经济与校园垃圾分类回收

阐述了循环经济的定义及其实施的重要意义，并以校园垃圾分类回收实例，说明了循环经济在实践中是可以实现的。     

天然有机高分子絮凝剂研究与应用

本文根据物质的组分及来源对天然有机絮凝剂进行了分类,并对这三大类天然高分子絮凝剂的性能、应用、结构及研究发展概况等方面进行了论述     

Unmanned aerial vehicle images in the machine learning for agave detection

Environmental Science and Pollution Research - In this study, six supervised classification algorithms were compared. The algorithms were based on cluster analysis, distance, deep learning, and...     

Classifying environmental pollutants: Part 3. External validation of the classification system

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.     

Applications of satellite remote sensing data for estimating biogenic emissions in southeastern Texas

The sensitivity of biogenic emission estimates and air quality model predictions to the characterization of land use/land cover (LULC) in southeastern Texas was examined using the Global Biosphere Emissions and Interactions System (GloBEIS) and the Comprehensive Air Quality Model with extensions (CAMx). A LULC database was recently developed for the region based on source imagery collected by the Landsat 7 Enhanced Thematic Mapper-Plus sensor between 1999 and 2003, and field data for land cover classification, species identification and quantification of biomass densities.  Biogenic emissions estimated from the new LULC data set showed good general agreement in their spatial distribution, but were approximately 40% lower than emissions from the LULC data set currently used by the State of Texas, primarily because of differences in the biomass estimates of key species such as Quercus. Predicted ozone mixing ratios using the biogenic emissions produced from the new LULC data set were as much as 26 ppb lower in some areas on some days, depending on meteorological conditions. Satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system used for accurate biogenic emissions estimates, especially in areas such as riparian corridors that contain dense spatial coverage of key species.     

Chlorinated solvents in a petrochemical wastewater treatment plant: an assessment of their removal using self-organising maps

The self-organising map approach was used to assess the efficiency of chlorinated solvent removal from petrochemical wastewater in a refinery wastewater treatment plant. Chlorinated solvents and inorganic anions (11 variables) were determined in 72 wastewater samples, collected from three different purification streams. The classification of variables identified technical solvents, brine from oil desalting and runoff sulphates as pollution sources in the refinery, affecting the quality of wastewater treatment plant influent. The classification of samples revealed the formation of five clusters: the first three clusters contained samples collected from the drainage water, process water and oiled rainwater treatment streams. The fourth cluster consisted mainly of samples collected after biological treatment, and the fifth one of samples collected after an unusual event. SOM analysis showed that the biological treatment step significantly reduced concentrations of chlorinated solvents in wastewater.     

Sediments as monitors of heavy metal contamination in the Ave river basin (Portugal): multivariate analysis of data

The concentrations of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) were determined in river sediments collected at the Ave river basin (Portugal) to obtain a general classification scenery of the pollution in this highly polluted region. Multivariate data analysis techniques of clustering, principal components and eigenvector projections were used in this classification. Five general areas with different polluting characteristics were detected and several individual heavy metal concentration abnormalities were detected in restricted areas. A good correlation between the overall metal contamination determined by multivariate analysis and metal pollution indexes for all sampling stations was obtained. Some preliminary experiments showed that the metal concentrations normalised to the volatile matter content in the sediment fraction with grain size <63 microm seems to be an adequate method for assessing metal pollution.