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.     

Before the crash: A systematic analysis of police narratives to determine the prevalence of pre-crash factors in fatality road transport crashes

Abstract

Objectives: The objectives of this study were to identify the prevalence of pre-crash factors that were present in fatal road transport crashes for the deceased and counterpart road user.

Methods: The study is a retrospective population-based case series study of transport-related deaths reported to coroners in Australia from 2013 to 2016. Data was extracted from the National Coronial Information System.

Results: In total, 6,137 fatality crashes occurred during the study period. Police reports were available for 5,523 crashes (89.9%). The most frequently reported pre-crash factors reported behaviour specifically drivers (e.g., driving without a license or while license was disqualified). Presence of intoxicating substances were also reported in the deceased and counterparts. Analyses of toxicology reports are continuing to determine if rates are comparable to level of use in community.

Conclusions: Coronial report provide detailed information that may be pertinent to understanding and potentially preventing crashes. Also emerging from the data is the extent of pre-crash factors that relate to illegal or deviant behavior and whether these are contextual or contributory factors.     

北京与津冀之间隐性土地资源流动研究

京津冀区域面临着严重的资源紧缺、生态环境恶化和发展失衡的问题,亟需制定科学合理的区域合作新机制。京津冀之间紧密的产品贸易背后隐藏的土地资源流动情况可以为新机制的建立提供科学支撑。这种隐性土地资源的流通情况可以用生态足迹来表征。因此,本研究主要运用京津冀三省市之间的投入产出矩阵来计算北京与津冀之间的生态足迹转入和转出量,并进行分析。研究结果表明,北京的社会经济发展需要周边津冀生态生产性土地资源的支撑,尤其是河北的农用地资源,包括耕地、林地和水产养殖用地。从生态贸易类型来看,2002年和2007年,就北京和河北之间生态足迹流通情况而言,北京是净生态输入型,属于"贸易逆差";河北是净生态输出型,属于"贸易顺差"。2007年北京对津冀生态资源的使用量比2002年有所下降。总体而言,北京对河北生态生产性土地资源的消耗对河北的生态压力不大,其中,水产养殖用地的生态压力最大,2007年水产养殖净流量占河北水产养殖土地的54.27%。2002年北京对天津生态生产性土地资源的依赖加大了天津的生态压力,林业用地和水产养殖用地的净流出量分别是天津相应承载土地面积的4.96倍和3.57倍;2007年,北京对天津生态土地资源利用程度降低,随之,北京对天津生态生产性土地资源的压力变小。尽管2007年与2002年相比,北京对河北生态生产性土地的消耗量降低了,但是,由于河北土地利用结构的变化影响着北京和区域的可利用的土地资源质量和生态环境质量,国家和北京应该帮助河北保护其土地资源,尤其是河北东南部的农田资源在区域中承担着重要的绿色隔离作用,应该受到严格的保护。此外,京津冀区域应该制定整体空间开发战略,加强土地资源保护力度,合理布局产业结构,构建生态安全格局。     

Injury risk functions for frontal oblique collisions

Objective: The objective of this article was the construction of injury risk functions (IRFs) for front row occupants in oblique frontal crashes and a comparison to IRF of nonoblique frontal crashes from the same data set.

Method: Crashes of modern vehicles from GIDAS (German In-Depth Accident Study) were used as the basis for the construction of a logistic injury risk model. Static deformation, measured via displaced voxels on the postcrash vehicles, was used to calculate the energy dissipated in the crash. This measure of accident severity was termed objective equivalent speed (oEES) because it does not depend on the accident reconstruction and thus eliminates reconstruction biases like impact direction and vehicle model year. Imputation from property damage cases was used to describe underrepresented low-severity crashes―a known shortcoming of GIDAS. Binary logistic regression was used to relate the stimuli (oEES) to the binary outcome variable (injured or not injured).

Results: IRFs for the oblique frontal impact and nonoblique frontal impact were computed for the Maximum Abbreviated Injury Scale (MAIS) 2+ and 3+ levels for adults (18–64 years). For a given stimulus, the probability of injury for a belted driver was higher in oblique crashes than in nonoblique frontal crashes. For the 25% injury risk at MAIS 2+ level, the corresponding stimulus for oblique crashes was 40 km/h but it was 64 km/h for nonoblique frontal crashes.

Conclusions: The risk of obtaining MAIS 2+ injuries is significantly higher in oblique crashes than in nonoblique crashes. In the real world, most MAIS 2+ injuries occur in an oEES range from 30 to 60 km/h.     

二维平流扩散模型下的集合卡尔曼滤波模拟同化研究

为提高环境数值预报水平,构建了一个针对污染物扩散的模拟数据同化系统。采用集合卡尔曼滤波方法对二维平流扩散模型的状态变量进行了实时校正,实现污染物浓度的实时模拟预报,完成了敏感性实验中集合数目变化、观测方差变化和同化窗口长度变化研究。比较考察观测点位置与污染源距离不同时的预报效果,探讨了优化条件下的同化策略,提出一种根据距离远近动态调节卡尔曼增益权重的方法。在集合数目较小时,可降低计算代价,得到优化的同化效果。     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

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.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long‐standing “Digital Divide” in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time‐varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long‐time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed “data rods,” are pre‐generated or generated on‐the‐fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on “data curtains.” The on‐the‐fly generation of data rods uses “data cubes,” NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.     

基于智能电网大数据的工业企业大气污染排放特征研究

本文尝试构建基于智能电网大数据的工业企业污染排放预测方法。通过分析上海大中型工业企业用电量与工业总产出、工业总产出与主要污染物直接排放量之间的关联关系,本文建立了工业企业基于用电量的直接污染排放清单估算方法。利用此估算方法,可在实时的智能电网大数据基础上估算工业企业直接污染排放量,服务于大气污染的实时预警和预测。本文研究表明,这种清单估算方法可直接应用于工业企业污染的实时防控,既可服务于政府大气污染监测、应急机制启动时防控对象的选择,也可服务于未来的污染物排放权实时交易市场的供需分析等,是大数据在污染防治领域应用的可行路径。