Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

Survivor needs or logistical convenience? Factors shaping decisions to deliver relief to earthquake‐affected communities,Pakistan 2005–06

In Bureaucratizing the Good Samaritan , Waters (2001) argues that bureaucratic rationality distracts humanitarian agencies from the needs of the people they are supposed to assist, in favour of other values that their institutional frameworks dictate. We test his claim by investigating the response to the Pakistan 2005 earthquake. One of us (Dittemore) worked with the United Nations Joint Logistics Centre in the theatre, managing a relief cargo shipment database. The response, known as 'Operation Winter Race', was hampered by extreme logistical challenges, but ultimately succeeded in averting a second disaster resulting from cold and starvation. We use statistical models to probe whether survivor needs significantly guided decisions to deliver relief to affected communities. Needs assessments remained incomplete and incoherent. We measure needs through proxy indicators and integrate them, on a Geographic Information System (GIS) platform, with logistics and relief delivery data. We find that, despite strong logistics effects, needs orientations were significant. However, the strength of decision factors varies between commodity types (food versus clothing and shelter versus reconstruction materials) as well as over the different phases of the response. This study confirms Thomas's observation that logistics databases are rich 'repositories of data that can be analyzed to provide post-event learning' (Thomas, 2003, p. 4). This article is an invitation for others to engage in creative humanitarian data management.     

Modeling perceived collision risk in port water navigation

An increase in the likelihood of navigational collisions in port waters has put focus on the collision avoidance process in port traffic safety. The most widely used on-board collision-avoidance system is the automatic radar plotting aid which is a passive warning system that triggers an alert based on the pilot’s pre-defined indicators of distance and time proximities at the closest point of approaches in encounters with nearby vessels. To better help pilot in decision making in close quarter situations, collision risk should be considered as a continuous monotonic function of the proximities and risk perception should be considered probabilistically. This paper derives an ordered probit regression model to study perceived collision risks. To illustrate the procedure, the risks perceived by Singapore port pilots were obtained to calibrate the regression model. The results demonstrate that a framework based on the probabilistic risk assessment model can be used to give a better understanding of collision risk and to define a more appropriate level of evasive actions.     

Assessment Tools for Urban Catchments: Defining Observable Biological Potential1

Abstract: Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts.     

Theoretical and Empirical Analysis on Influence Factors of Modern Agricultural Equipment Level in the Processof Agricultural Sustainable Development

To promote modern agricultural equipment level is one characteristic of constructing and developing modern agriculture in China.This paper makes up stepwise linear regression analysis model of influence factors of modern agricultural equipment level,and chooses rural labor,per capita income of rural residents,rural investment,proportion of people at secondary education level and at higher level in per hundred rural labor force and arable land area as independent variables,and total power of machine as induced variable.The major results show that the relativity of modern agricultural equipment level,rural investment and education level of peasants is remarkable,and they are the major influence factors of modern agricultural equipment level.Raising investment level of rural infrastructure construction as well as and research and development and promotion of advanced and applicable modern agricultural equipment,improving quality and education level of peasants can accelerate the development of China’s modern agricultural equipment effectively in the process of agricultural sustainable development.     

遗传投影寻踪回归在城市环境质量评价中的应用

引入投影降维的思想,将遗传投影寻踪与回归分析技术运用到城市环境质量评价中。将此技术与神经网络方法进行实例比较,投影寻踪回归方法不但可以合理地作出环境质量的综合评价,而且消除了神经网络方法中类别判断不够精确的影响。     

基于回归分析的火灾发生气象因子的研究

利用二元回归分析法,对火灾发生次数、空气湿度、风速进行回归分析并检验,得出了火灾发生次数、空气湿度、风速三者之间的显著线性关系。     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

人力资本、社会资本与市民化抑制

新生代农民工问题的终结是市民化,但是目前许多新生代农民工存在市民化阻碍,这不利于我国城市化进程的推进和经济的持续健康发展。对沈阳和余姚两地区进行了案例调查,得到了两地区新生代农民工的调查数据。运用调查数据,首先采用需求可识别的biprobit模型对新生代的农民工市民化抑制程度进行了测度,并预测出新生代农民工个体的市民化抑制程度。在对新生代农民工个体的市民化抑制程度指标进行分位数回归分解的基础上,分析了人力资本和社会资本对新生代农民工市民化抑制程度的影响,以期促进新生代农民工的城市融入。研究发现:新生代农民工的社会资本水平是影响其市民化的关键因素,人力资本水平的提高只有在市民化抑制程度高于80%时才会使市民化抑制程度下降。因此,相应的政策建议为,政府应加强新生代农民工城市公共服务的享用水平;发展和健全农民工工会组织;在市民化抑制程度较高的地区应特别加强新生代农民工职业技能的培训。     

人口与消费对碳排放影响的分析模型与实证

从消费压力人口视角探讨碳排放问题,有利于正确判断和把握气候变化压力的人文因素,以及人口与消费可持续发展的动力学机制.本文通过对STIRPAT模型的扩展,应用岭回归方法计量分析人口、消费及技术因素对碳排放的影响.对我国1980至2007年碳排放情况的统计实证结果表明,扩展的STIRPAT模型对中国国情有较高的解释力.居民消费水平、人口城市化率、人口规模三个因素对我国碳排放总量的变化影响明显;现阶段我国居民消费水平与人口结构变化对碳排放的影响力已高于人口规模变化的影响力,居民消费水平与消费模式等人文因素的变化有可能成为我国碳排放的新的增长点;技术进步因素在此模型中对我国该阶段碳排放的解释力有限,表明我国未来通过技术进步减缓碳排放的潜力巨大.