A case study predicting environmental impacts of urban transport planning in China

Predicting environmental impacts is essential when performing an environmental assessment on urban transport planning. System dynamics (SD) is usually used to solve complex nonlinear problems. In this study, we utilized system dynamics (SD) to evaluate the environmental impacts associated with urban transport planning in Jilin City, China with respect to the local economy, society, transport, the environment and resources. To accomplish this, we generated simulation models comprising interrelated subsystems designed to utilize changes in the economy, society, road construction, changes in the number of vehicles, the capacity of the road network capacity, nitrogen oxides emission, traffic noise, land used for road construction and fuel consumption associated with traffic to estimate dynamic trends in the environmental impacts associated with Jilin's transport planning. Two simulation scenarios were then analyzed comparatively. The results of this study indicated that implementation of Jilin transport planning would improve the current urban traffic conditions and boost the local economy and development while benefiting the environment in Jilin City. In addition, comparative analysis of the two scenarios provided additional information that can be used to aid in scientific decision-making regarding which aspects of the transport planning to implement in Jilin City. This study demonstrates that our application of the SD method, which is referred to as the Strategic Environmental Assessment (SEA), is feasible for use in urban transport planning.     

Generalized models for prediction of pentachlorophenol dissipation dynamics in soils

Dissipation of pentachlorophenol (PCP) in soil was investigated and the chemical relationships with soil properties were addressed. The results indicate that the dissipation of extractable PCP residues can be described using first-order kinetics equations, with a half-dissipation time (T(1/2)) ranging from 6.5 to 173.3d. The sharply different patterns of PCP dissipation in different soils were closely related to soil properties. Correlations of stepwise regression equations obtained were significant at 0.01 probability level between soil parameters and extractable PCP residues (R(2)=0.974**) as well as T(1/2) values (R(2)=0.882**). Using pH together with organic carbon content (OC) and soil particle size distribution, the dissipation dynamics of PCP in soil could be accurately predicted.     

Filterability of oil sands tailings

In Canadian oil sands mining operations, bitumen is extracted from oil sands using the hot water extraction process, which produces tremendous amounts of tailings. Currently, these tailings are disposed of in large ponds, in which coarse particles settle out relatively quickly and fine particles remain suspended in water and settle very slowly. After years of settling, the fine particles form a stable suspension in water known as mature fine tailings (MFTs). Long-term storage of the MFT is costly and poses a major environmental liability.Oil sands companies are now actively investigating different approaches to replace or reduce the use of oil sands tailings ponds. Filtration of the tailings to produce “dry tailings” for stacking is now being investigated as an alternative by a number of companies. Fast water drainage is a critical step for the filtration process. In this paper, we use simple laboratory-scale filtration tests to evaluate the filterability of the oil sands tailings and to generate a parameter that can be used in filtration scale-up. It was found that the filterability of the original coarse oil sands tailings was relatively low. However, after the fines are flocculated with the coarse particles to form uniform flocs the filterability was improved by several orders of magnitude. The results demonstrate that filtration of the flocculated coarse tailings to produce the “dry” stackable tailings may be viable.     

城市垃圾预处理改善焚烧特性的探讨

针对目前我国城市垃圾的高水分、低热值的特性,提出了2种改善城市垃圾焚烧特性的有效措施:生物质垃圾源分类和生物干燥.在我国建立生物质垃圾源分类体系,将生物质垃圾源头分类后,剩余垃圾的热值可以提高约50%～120%,已适合直接入炉焚烧,同时分离出来的生物质垃圾也更易于好氧堆肥或厌氧消化.另外一项技术措施是在焚烧前利用生物干燥技术,降低城市垃圾的水分含量,提高入炉垃圾的热值,这种方法主要是利用生物反应热来干燥城市生活垃圾,只需要在垃圾投入焚烧炉前增加一个预处理步骤,不必改变目前的垃圾收运体系,而且进行生物干燥后的垃圾更易于分选其中的可回收物质.     

有毒气体危害区域划分之临界浓度标准研究

通过研究毒物伤害准则进而界定有毒气体危害浓度,对有毒气体泄漏扩散后的危害区域进行分级划分,以采取相应的防护措施,最大限度的即时有效的减轻有毒气体对人员的伤害.最后,以氯为例,对比其在不同伤害准则下危害区域划分临界浓度标准值,数值差别较大,因此在使用时应根据实际情况加以判断,选择最适当的标准来划分危害区域.     

基于遥感的新疆北疆积雪盖度及雪深监测

新疆北疆为雪灾多发区,准确地监测积雪分布及雪深具有十分重要的意义,可为雪灾多发区在冬季做好防灾减灾工作提供依据.目前MODIS数据主要运用归一化差分雪盖指数(NDSI)对积雪进行提取.NDSI是基于雪对可见光与近红外波段的反射特性和反射差相对大小的一种测量方法,然而它只能将像元辨别为积雪或非积雪,满足不了高精度的流域制图及雪盖提取要求.以新疆北疆为研究对象,应用MODIS数据和线性光谱混合模型提取像元内积雪所占比例(积雪盖度),并与NDSI建立关系,讨论了NDSI是否可以作为估算积雪盖度的标准,进而提高NDSI在积雪监测中的应用精度.结果表明,二者具有较好的线性关系,经过野外实测点的验证,25个点的平均绝对误差是0.06.此外,以MODIS数据与地面实测雪深资料为基础,通过分析和研究MODIS窗区通道的光谱特点,探讨了适合于积雪雪深监测最佳的MODIS通道,初步建立起应用MODIS资料监测北疆地区积雪深度的反演模型.     

Effect of heavy metals (Cu, Pb, and As) on the ultrastructure of Sargassum pallidum in Daya Bay, China

Concentrations of Cu, Pb, and As were determined in seawater, surface sediment, Sargassum pallidum collected from the Daya Bay, China. The influence of metal contamination on the marine alga was investigated at chemical and ultrastructural level. Mean concentrations of Cu (19.44 mg kg^?1) and Pb (33.99 mg kg^?1) were found to be high in sediment, whereas concentration of As (122.29 mg kg^?1) in S. pallidum was higher than that in water and sediment. The ultrastructure of S. pallidum cells was anomalous and aberrant. Energy-dispersive x-ray spectroscopic analysis revealed that the nanometal particles in the form of comparatively high-electron density substance diffused in the cell structures constituted by Cu, Pb, As, etc. There is a remarkable similarity or correspondence in the anomalous elements between the geochemistry and the botanic cell, and the heavy metals have potential hazardous effect on the ocean ecology system in Daya Bay.     

Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China

The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8 % of total district area) in 1974 to 1,128,380 ha (43.9 %) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1 % and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2 % and 86.5 % of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.