Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Temporal and Spatial Variability of Water Supply Stress in the Haihe River Basin,Northern China1

Ji, Yuhe, Liding Chen, and Ranhao Sun, 2012. Temporal and Spatial Variability of Water Supply Stress in the Haihe River Basin, Northern China. Journal of the American Water Resources Association (JAWRA) 48(5): 999‐1007. DOI: 10.1111/j.1752‐1688.2012.00671.x Abstract: Water resources are becoming increasingly stressed under the influence of climate change and population growth in the Haihe River Basin, Northern China. Assessing the temporal and spatial variability of water supply stress is urgently needed to mitigate water crisis caused by water resource reallocation. Water supply and use data were compiled for the time period of 1998‐2003 in this synthesis study. The Water Supply Stress Index (WSSI) as defined as Water Demand/Water Supply was used to quantitate whether water supply could meet the demand of human activities across the study region. We found a large spatial gradient of water supply stress in the study region, being much higher in the eastern subbasins (ranging from 2.56 to 4.31) than the west subbasins (ranging from 0.56 to 1.92). The eastern plain region not only suffered more serious water supply stress but also had a much higher interannual variability than the western hilly region. The uneven spatial distribution of water supply stress might result from the distribution of land use, population, and climate. Future climate change and rapid economic development are likely to aggravate the existing water crisis in the study region.     

PARAMETRIC ESTIMATION OF COVARIANCES OF REGIONALIZED VARIABLES1

ABSTRACT: The usefulness of stochastic models in describing the spatial variability of hydrogeologic quantities, such as permeability, storativity, piezometric head, seepage velocity, and solute concentrations is now widely recognized. In practice, these quantities are represented as the sum of a well-structured component, or drift, and a more erratic fluctuation component which is described statistically through its covariance function. This paper reviews some of the most recent and most promising methods for the estimation of parameters of these covariances from existing data. They are maximum likelihood, restricted maximum likelihood, minimum-variance unbiased quadratic estimation, and minimum-norm (weighted least squares) estimation. The applicability of such methods to conditional and unconditional probability problems is discussed.     

虎林湿地变化造成农业损失的估算

自50年代初至2001年,50年来虎林市境内以平原沼泽型为主的湿地面积损失了三分之二,引起灾害性气候增加,造成了一定程度的农业损失,文章通过公式计算得出了农业损失值。     

陕西省水资源供求指数和综合干旱指数及其时空分布

识别区域的干旱时空特征对于防旱抗旱具有重要指导意义。论文采用两种方法研究了陕西省干旱时空分布特征并比较了两种方法在应用中的优缺点：其一,考虑社会经济因素对干旱的影响,采用基于水资源供水和需水平衡的陕西省社会经济干旱指标（水资源供求指数）;其二,结合SPI、PDSI和SPEI的特点,运用模糊综合法建立了综合干旱指数。结果表明：陕西省干旱发生频率很高,南北多发生轻微干旱,中部多发生中重度干旱。陕西省干旱在空间上分布差异大,两种方法均表明咸阳、西安、渭南和铜川地区旱情最为严重。在水资源量存在巨大差异的地区,如商洛与榆林,仅用一种类型的干旱指标无法全面体现实际情况,两种方法的结论存在差异：综合干旱指数表明榆林干旱较重,但供求指数为轻旱;商洛供水可满足需水,但综合干旱指数为轻旱。气象干旱指数,在水资源短缺的地区或自然生态系统中,结果更贴近于实际;社会经济干旱指数——水资源供求指数,更适宜用于水资源极其丰富且供水能力强的地区。     

Effects of Urban Spatial Structure,Sociodemographics, and Climate on Residential Water Consumption in Hillsboro,Oregon1

House-Peters, Lily, Bethany Pratt, and Heejun Chang, 2010. Effects of Urban Spatial Structure, Sociodemographics, and Climate on Residential Water Consumption in Hillsboro, Oregon. Journal of the American Water Resources Association (JAWRA) 46(3):461-472. DOI: 10.1111/j.1752-1688.2009.00415.x Abstract: In the Portland metropolitan area, suburban growth in cities such as Hillsboro is projected to increase as people seek affordable housing near a burgeoning metropolis. The most significant determinants for increases in water demand are population growth, climate change, and the type of urban development that occurs. This study analyzes the spatial patterns of single family residential (SFR) water consumption in Hillsboro, Oregon, at the census block scale. The following research questions are addressed: (1) What are the significant determinants of SFR water consumption in Hillsboro, Oregon? (2) Is SFR water demand sensitive to drought conditions and interannual climate variation? (3) To what magnitude do particular census blocks react to drought conditions and interannual climate variation? Using ordinary least squares multiple regression and spatial regression methods, we found that base use, representing indoor water use, is dependent on household size and that seasonal use, representing external water use is dependent on both education level and the size of the property’s outdoor space. Spatial analysis techniques determined that although the water demand of the study area as a whole is not sensitive to drought conditions, certain individual census blocks do respond with a higher magnitude of water use. The most climate-sensitive census blocks tend to contain newer and larger homes, and have higher property values and more affluent and well-educated residents.     

动态膜压法测定江、污混合水体中有机物含量的探讨

用双驱动动态膜压仪测定江、污混合水中有机物含量。通过测定白龙港污水和长江水混合后的膜压和滞回环面积，用它们与相应的经典法CODCr作图，获得了滞回环面积和CODCr之间良好地线性关系，表明用动态膜压法可快速测定混合水中的有机物含量，不须加任何试剂，不会产生二次污染。同时，对实验结果进行了初步的机理探讨。     

基于水污染治理目标需求的AHP-TOPSIS技术适用性评估方法研究

针对水污染治理目标的差异化需求,构建了一套水污染治理技术适用性评估方法。评估指标体系既包括反映水污染治理目标的评估指标,又包含技术本身的性能指标;在具体评估中,先采用层次分析法（AHP）对指标体系中每项指标赋权,再采用优劣解距离法（TOPSIS）对备选技术进行综合性能评估,进而筛选出满足水污染治理目标需求的适配性推荐技术。采用该方法对某海绵城市建设和某黑臭水体治理项目进行案例应用研究,考察所构建的技术适用性评估方法是否能够达到预期效果。这2个案例由于治理目标需求不同,构建的技术评估指标体系和评估指标权重不同,导致从同一技术储备库中筛选出的适配性推荐技术并不一致,表明AHP-TOPSIS技术适用性评估方法可以依据水污染治理目标需求的差异性筛选出适配性推荐技术。     

Electrochemical degradation of Acid Red I dye and its treatment by UV light

Degradation of Acid Red I (ARI) dye by electrochemical treatment and UV light irradiation was studied in this work. The effect of the current density and the concentration of NaCl and dye on the decoloration and degradation efficiency were studied and optimized. The UV irradiation was monitored by UV-Vis spectrophotometry and chemical oxygen demand. The kinetic constants for decoloration and degradation were calculated. Degradation of 86% was achieved in 160?min and the first-order kinetic constant was 0.013?min^?1. Degradation products were identified by gas chromatography/mass spectrometry. The mechanism for the degradation of ARI was proposed.