污染物排放相关指标计算方法的研究

针对传统的污染排放数据指标计算方法存在的问题,提出污染物排放指标应根据排污强度和经济指标确定的新思路,同时提出污染物排放指标数据的快速测算模型设计.建议通过环境监测数据或物料衡算数据确定每个排污单位的平均排污强度,作为计算机处理的参数,再由该排污单位申报每月的经济指标(产量或产值),输入计算机计算其产污量、排污量和去除量.实施计算机管理的关键是选取适当的数据计算模型.新思路应该能够规范计算程序,建立污染源动态数据库,对排污数据实行有效的统计分析.     

Use of Microbial Community to Evaluate Performance of a Wetland System in Treating Pb/Zn Mine Drainage

The performance of a wetland system in treating lead (Pb)/zinc (Zn) mine drainage was evaluated by using the polyurethane foam unit (PFU) microbial community (method), which has been adopted by China as a standardized procedure for monitoring water quality. The wetland system consisted of four cells with three dominant plants: Typha latifolia, Phragmites australis and Paspalum distichum. Physicochemical characteristics [pH, EC, content of total suspended solid (TSS) and metals (Pb, Zn, Cd, and Cu)] and PFU microbial community in water samples had been investigated from seven sampling sites. The results indicated that the concentrations of Pb, Zn, Cd, Cu, and TSS in the mine drainage were gradually reduced from the inlet to the outlet of the wetland system and 99%, 98%, 75%, 83%, and 68% of these metals and TSS respectively, had been reduced in concentration after the drainage passed through the wetland system. A total of 105 protozoan species were identified, the number of protozoa species and the diversity index (DI) gradually increased, while the heterotrophic index (HI) gradually decreased from the inlet to the outlet of the wetland system. The results indicated that DI, HI, and total number species of protozoa could be used as biological indicators indicating the improvement of water quality.     

Hot Spots of Perforated Forest in the Eastern United States

National assessments of forest fragmentation satisfy international biodiversity conventions, but they do not identify specific places where ecological impacts are likely. In this article, we identify geographic concentrations (hot spots) of forest located near holes in otherwise intact forest canopies (perforated forest) in the eastern United States, and we describe the proximate causes in terms of the nonforest land-cover types contained in those hot spots. Perforated forest, defined as a 0.09-ha unit of forest that is located at the center of a 7.29-ha neighborhood containing 60–99% forest with relatively low connectivity, was mapped over the eastern United States by using land-cover maps with roads superimposed. Statistically significant (P < 0.001) hot spots of high perforation rate (perforated area per unit area of forest) were then located by using a spatial scan statistic. Hot spots were widely distributed and covered 20.4% of the total area of the 10 ecological provinces examined, but 50.1% of the total hot-spot area was concentrated in only two provinces. In the central part of the study area, more than 90% of the forest edge in hot spots was attributed to anthropogenic land-cover types, whereas in the northern and southern parts it was more often associated with seminatural land cover such as herbaceous wetlands.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Connecting the Dots: Public Engagement with Environmental Data

Air pollution and other environmental hazards are often imperceptible and need to be made publicly visible. The paper argues for the importance of visualizations in drawing public attention to imperceptible hazards and in providing the public with access to empirical data describing the risks. It also argues for critical inquiry into hazards’ selective visibility as it is produced by visualizations. The impact of visualizations and their selective visibility are considered through the example of a public art project called Particle Falls installed in 2014 in Pittsburgh, a city with a long history of both ignoring air pollution and working to ameliorate this problem. I examine the impact and selective visibility of Particle Falls by considering the underlying production of data, as well as context and support systems for this visualization, and by comparing it with other visualizations of local air quality.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.     

辽河三角洲地区土地利用及生态服务价值变化研究

以辽河三角洲地区为例,利用修正的生态服务价值当量因子表结合服务价值评估方法,分析了3个时期土地利用格局时空动态变化特征及其服务价值变化状况。结果表明:研究区土地利用以水田为主,占56.76%,其次为湿地和建设用地,分别占15.29%和14.77%,3种土地利用类型占总面积的86.82%;1986~2000年土地利用格局变化较大,而2000~2010年土地利用格局变化程度较小,两个时段内建设用地面积都稳步增加,湿地面积均逐渐下降;研究区土地利用服务的总价值逐年减少,湿地服务价值对总服务价值的贡献度最大,占总服务价值的60%以上。     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.