Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Perceptions of safety on a shared road: Driving,cycling, or walking near an autonomous vehicle

Introduction: While improved safety is a highly cited potential benefit of autonomous vehicles (AVs), at the same time a frequently cited concern is the new safety challenges that AVs introduce. The literature lacks a rigorous exploration of the safety perceptions of road users who will interact with AVs, including vulnerable road users. Addressing this gap is essential because the successful integration of AVs into transportation systems hinges on an understanding of how all road users will react to their presence. Methods: A stated preference survey of the Phoenix, Arizona, metropolitan statistical area (Phoenix MSA) was conducted in July 2018. A series of ordered probit models was estimated to analyze the survey responses and identify differences between various population groups with respect to the perceived safety of driving, cycling, and walking near AVs. Results: Greater exposure to and awareness of AVs are not uniformly associated with increases in perceived safety. Various attitudinal factors, level of AV automation, and other intrinsic and extrinsic factors are related to safety perceptions of driving, walking, and cycling near AVs. Socioeconomic and demographic characteristics, such as gender, age, income, employment, and automobile usage and ownership, have various relationships with perceived safety. Conclusions: Cycling near an AV was perceived as the least safe activity, followed by walking and then driving near an AV. Both similarities and differences were observed among the factors associated with the perceived safety of different travel alternatives. Practical Applications: Public perception will guide the development and adoption of AVs directly and indirectly. To help maintain control of public perception, transportation planners, decision makers, and other stakeholders should consider more deliberate and targeted messaging to address the concerns of different road users. In addition, more careful pilot testing and more direct attention to vulnerable road users may help avoid a backlash that could delay the rollout of this technology.     

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.     

探究环境监测数据在环境执法中的应用

伴随经济发展的迅速,人们对环境越来越重视,国家也制定了相应的环境执法与保护措施,保障环境质量。在环境执法的过程中,必须严格依照环境监测的数据,从而保障信息的准确性,使环境执法策略的开展能够顺应时代发展,符合目前发展的现状。因此,环境执法中环境监测数据占据重要的位置。基于此,本文分析了环境执法中环境监测数据的应用,笔者根据自身经验提出相应的建议。     

基于大数据技术构建生态环境信息化体系及其重点应用研究

本文首先分析了生态环境信息化体系的主要构成要素,其后基于大数据技术的生态环境信息化体系重点领域应用进行了深入研究。     

An Experimental Methodology for Monitoring Contaminant Transport Through Geotechnical Centrifuge Models

In this paper, an attempt has been made to highlight an experimental methodology for monitoring contaminant transport through locally available silty soil and commercially available clay in geotechnical centrifuge models, for different compaction states. Use of multiple depth sensors to determine depth distribution of sodium chloride in the soil column has been detailed. The obtained results have been compared with argentometric method. To validate the centrifuge modelling, modelling of models has been used. The test setup developed can simulate contaminant transport mechanisms through the soil mass, which is approximately 10 m deep, over a period of 600 days. R _e and P _e are found to be N times higher in the centrifuge models. These numbers are found to be several orders less than unity. This indicates that laminar flow prevails and the dominating Cl⁻ transport mechanism in centrifuge is diffusion. The study also highlights the fact that the geotechnical centrifuge modelling can be used as a viable alternative to field scale experimentation.     

Combining Logistic Models with Multivariate Methods for the Rapid Biological Assessment of Rivers Using Macroinvertebrates

This work represents an attempt to define a simple method to classify the relative degree of disturbance of sites in lotic systems on the basis of comparison of their faunistic composition with reference sites. Two ecotypes were selected in northern Portugal where benthic invertebrates were sampled in reaches with different levels of contamination. As a first stage, previous Geographic Information System information was used to define reference sites in each ecotype. Afterwards, multivariate techniques and non linear estimation models were combined to assess biological quality. This method allowed us to quantify sites according to increasing levels of contamination, after the probabilities of occurrence of taxa along a gradient of contamination taking into account the reference condition. The results suggest that this method is sensitive to organic pollution, easy to interpret, namely the species tolerance, and could be a good framework to establish regional rankings depending on the ecological impact of river sites.     

Comparison of Nitrate Levels in Raw Water and Finished Water from Historical Monitoring Data on Iowa Municipal Drinking Water Supplies

Nitrate contamination of water sources is a concern where large amounts of nitrogen fertilizers are regularly applied to soils. Ingested nitrate from dietary sources and drinking water can be converted to nitrite and ultimately to N-nitroso compounds, many of which are known carcinogens. Epidemiologic studies of drinking water nitrate and cancer report mixed findings; a criticism is the use of nitrate concentrations from retrospective drinking water data to assign exposure levels. Residential point-of-use nitrate data are scarce; gaps in historical data for municipal supply finished water hamper exposure classification efforts. We used generalized linear regression models to estimate and compare historical raw water and finished water nitrate levels (1960s--1990s) in single source Iowa municipal supplies to determine whether raw water monitoring data could supplement finished water data to improve exposure assessment. Comparison of raw water and finished water samples (same sampling date) showed a significant difference in nitrate levels in municipalities using rivers; municipalities using other surface water or alluvial groundwater had no difference in nitrate levels. A regional aggregation of alluvial groundwater municipalities was constructed based on results from a previous study showing regional differences in nitrate contamination of private wells; results from this analysis were mixed, dependent upon region and decade. These analyses demonstrate using historical raw water nitrate monitoring data to supplement finished water data for exposure assessment is appropriate for individual Iowa municipal supplies using alluvial groundwater, lakes or reservoirs. Using alluvial raw water data on a regional basis is dependent on region and decade.     

Measurement and Management of Human-Induced Patterns of Forest Fragmentation: A Case Study

In many tropical developing countries, the twin pressures of population and poverty are resulting in substantial fragmentation of forests, increasing the probability of extinction for many species, Forest fragmentation occurs when large contiguous forests are perforated by small holes or broken up into edges and smaller patches to form a nonforested matrix of open spaces. Thus, forest fragmentation refers not only to the area of forest cleared, but also to the pattern of this clearance, the resulting forest’s spatial properties. Both characteristics are important for species survivability. Apart from opening up forests to many abiotic and biotic influences, fragmentation can affect species dispersal and migration through its effects on forest connectivity. Landscape ecology conceptualizes connectivity as a gradient of critical thresholds, ranging from the large intact forest to the small unconnected forest patch. This article reports results from a multiple-scale analysis of forest fragmentation in Jamaica’s Cockpit Country, an area of once contiguous forest now under threat from human encroachment. Spatial forest data derived from classification of ETM+ satellite imagery are used to measure fragmentation patterns representing various degrees of forest connectivity and density. The results suggest that, overall, 81% of the region is in forest. However, fragmentation patterns also suggest that this forest is riven with extensive perforations indicative of an early stage in the decline of contiguity. The results provided by the spatial fragmentation model are a first step in the design of effective conservation and rehabilitation plans for the area. The article concludes with a discussion of possible multiscale management options for the region.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.