焦作市工业燃煤量与大号SO2污染关系模型研究

研究了焦作市工业燃煤量与大气SO2污染的关系,建立了适合于焦作市城区大气SO2污染的解析模型.在模型中尝试性地将面源污染产生的浓度贡献转化到背景值中计算,简化了解析模型.利用该模式计算了1996～2000年焦作市城区大气中的SO2浓度,并与监测数据进行对比分析,结果表明该模型适合研究区的实际情况,基于该模型实现了焦作市城区大气SO2浓度的动态预测.     

添加剂对煤燃烧特性的影响研究

为研究助燃添加剂对煤燃烧特性的影响 ,选择了 5种金属的氧化物及盐类作为基本成分 ,运用正交试验方法考察了添加剂对炭粒燃烧特性的影响 ,然后根据极差分析和方差分析 ,得到了添加剂的最佳组成。本研究所推荐的添加剂能有效降低煤的着火温度 ,并提高其燃烧发热量。与某商品煤伴侣比较 ,在各种添加量时的助燃效果均优于该商品煤伴侣。     

改性粉煤灰及其处理废水的机理

应用正交设计的基本理论,研究了PDMDAAC(聚二甲基二烯丙基氯化铵)改性粉煤灰的最佳条件,并将改性粉煤灰用于模拟印染废水的处理,探索了改性粉煤灰处理废水的机理。结果表明,在原PDMDAAC的pH下．控制PDMDAAC的浓度为40g／L,恒温30℃．反应1h为最佳改性工艺,改性粉煤灰处理废水的机理以吸附电中和为主。     

一种处理爆炸多能法的新方法

以多能法中距离－超压关系图为例,介绍了一种运用前馈多层感知器网络对现行多能法中查图步骤进行改进的方法。此法从很大程度上解决了多能法在爆炸灾害评估中手工查图误差大且不方便的缺点。模拟结果表明,该处理方法能够很好的逼近多能法中的超压函数P＝f(R0,C),最后提出了人工神经网络方法在爆炸破坏效应评估中进一步应用的设想。     

军用物资集装箱储运防霉研究

针对军用物资集装箱储运过程中存在各种霉菌的实际情况,详细分析了其对箱体和箱内物资的影响,根据霉菌腐蚀机理和生长条件,较全面地总结了预防霉菌的各项措施。     

2005年中国燃煤大气锑排放清单

采用基于燃料消耗的排放因子法,按照经济部门(电力、工业、生活消费及其他)、燃料类型(原煤、洗煤、焦炭、型煤)、燃烧方式(层燃炉、煤粉炉、流化床锅炉等)和除尘脱硫设施(机械式除尘器、湿式除尘器、电除尘器、袋式除尘器等)等将燃煤排放源进行分类,根据各省、市、自治区不同排放源类型的煤炭消耗量及平均锑含量,建立了2005年中国燃煤大气锑排放清单.结果表明,2005年中国燃煤导致的大气锑排放总量约为530.86t,排放量超过30t的省份依次为贵州(49.28t)、湖南(45.96t)、河北(37.36t)、山东(35.12t)、安徽(30.92t),表明中国燃煤大气锑排放主要集中在中东部地区.大部分排放来自工业和电力部门,分别占排放总量的47.2%和39.9%.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Network design for water quality monitoring of surface freshwaters: a review

To date, many water quality monitoring networks for surface freshwaters have been rather haphazardly designed without a consistent or logical design strategy. Moreover, design practices in recent years indicate a need for cost-effective and logistically adaptable network design approaches. There are many variables that need to be included in a comprehensive yet practical monitoring network: a holistic appraisal of the monitoring objectives, representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples. In order to investigate the factors which affect the development of an effective water quality monitoring network design methodology, a review of past and current approaches is presented.     

River-Stream Connectivity Affects Fish Bioassessment Performance

Stream fish bioassessment methods assume that fish assemblages observed in sample sites reflect responses to local stressors, but fish assemblages are influenced by local factors as well as regional dispersal to and from connected streams. We hypothesized that fish movement to and from refugia and source populations in connected rivers (i.e., riverine dispersal) would weaken or decouple relations between fish community metrics and local environmental conditions. We compared fish-environment relations between streams that flow into large rivers (mainstem tributaries) and streams that lack riverine confluences (headwater tributaries) at multiple spatial grains using data from the USEPA's Environmental Monitoring and Assessment Program in the mid-Atlantic highlands, USA (n = 157 sites). Headwater and mainstem tributaries were not different in local environmental conditions, but showed important differences in fish metric responses to environmental quality gradients. Stream sites flowing into mainstem channels within 10 fluvial km showed consistently weaker relations to local environmental conditions than stream sites that lacked such mainstem connections. Moreover, these patterns diminished at longer distances from riverine confluences, consistent with the hypothesis of riverine dispersal. Our results suggest that (1) the precision of fish bioassessment metrics may be improved by calibrating scoring criteria based on the spatial position of sites within stream networks and (2) the spatial grain of fish bioassessment studies may be manipulated to suit objectives by including or excluding fishes exhibiting riverine dispersal.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.