How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.     

Key considerations and challenges in the application of social-network research for environmental decision making

Attempts to better understand the social context in which conservation and environmental decisions are made has led to increased interest in human social networks. To improve the use of social-network analysis in conservation, we reviewed recent studies in the literature in which such methods were applied. In our review, we looked for problems in research design and analysis that limit the utility of network analysis. Nineteen of 55 articles published from January 2016 to June 2019 exhibited at least 1 of the following problems: application of analytical methods inadequate or sensitive to incomplete network data; application of statistical approaches that ignore dependency in the network; or lack of connection between the theoretical base, research question, and choice of analytical techniques. By drawing attention to these specific areas of concern and highlighting research frontiers and challenges, including causality, network dynamics, and new approaches, we responded to calls for increasing the rigorous application of social science in conservation.     

中国工业CO_2排放的行业间传导网络及协同减排

工业是碳排放的主要部门,科学识别工业CO_2排放的行业间传导并揭示其联动结构对于跨行业协同减排具有重要的理论和现实意义。本文基于1991-2012年中国工业36个两位数行业数据,在VAR模型框架下,利用Granger因果检验方法对CO_2排放的行业间传导关系进行了识别,并从关系视角出发,借助社会网络分析方法(SNA)揭示工业CO_2排放行业间传导的整体和个体网络结构特征,研究发现:工业CO_2排放的行业间传导呈网络结构形态,网络密度在1-6期的滞后阶数下呈先升高后下降的趋势,并且在滞后2期达到最高;燃气生产和供应业、电力热力生产和供应业、服装业、造纸及纸制品业、木材加工业具有较高的度数中心度、中介中心度、接近中心度,在网络中处于核心位置,并发挥中介和桥梁作用;在CO_2排放的行业传导网络中,煤炭采选业、黑色金属矿采选业、有色金属矿采选业等10个行业属于经纪人板块,医药制造业、饮料制造业、化学纤维制造业等9个行业属于净受益板块,金属制品业、塑料制品业、农副食品加工业等11个行业属于双向溢出板块,烟草加工业、石油加工及炼焦业、燃气生产和供应业等6个行业属于净溢出板块。基于上述结论,本文提出了工业CO_2排放的跨行业协同减排思路。     

Bibliometric analysis of simulated driving research from 1997 to 2016

Objective: The objective of this study was to explore the evolution footprints of simulated driving research in the past 20 years through rigorous and systematic bibliometric analysis, to provide insights regarding when and where the research was performed and by whom and how the mainstream content evolved over the years.

Methods: The analysis began with data retrieval in Web of Science with defined search terms related to simulated driving. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis; that is, probe of the performance of institutes, journals, authors, and research hotspots.

Results: A total of 3,766 documents were filtered out and presented an exponential growth from 1997 to 2016. The United States contributed the most publications as well as international collaborations followed by Germany and China. In addition, several universities in The Netherlands and the United States dominated the list of contributing institutes. The leading journals were in transportation and ergonomics. The leading researchers were also recognized among the 8,721 contributing authors, such as J. D. Lee, D. L. Fisher, J. H. Kim, and K. A. Brookhuis. Finally, the co-citation analysis illuminated the evolution of simulated driving research that covered the following topics roughly in chronological order: task-induced stress, drivers with neurological disorders, alertness and sleepiness while driving, trust toward driving assistance systems, driver distraction, the effect of drug use, the validity of simulators, and automated driving.

Conclusions: This article employed bibliometric tools to probe the contributing countries, institutes, journals, authors, and mainstream hotspots of simulated driving research in the past 20 years. A systematic bibliometric analysis of this field will help researchers realize the panorama of global simulated driving and establish future research directions.     

大气颗粒物手工比对监测体系滤膜称量质控技术探讨

健全大气颗粒物手工比对监测体系是"十三五"环境空气自动监测质量管理的一项重点任务,其中颗粒物滤膜的称量直接影响手工监测数据质量。研究调研了国内各级环保部门所属环境监测机构颗粒物滤膜称量工作情况,结合国内外方法标准、技术规范等,重点探讨了称量实验室环境、称量设备、称量影响因素等滤膜称量质控要点,并针对环境管理与监测需求对大气颗粒物手工比对监测滤膜称量质控提出建议。     

城镇化下平原水系变化及河网连通性影响研究

针对平原区城镇化背景下水系减少及连通受阻等下垫面变化引起的洪涝加剧问题,综合考虑水系结构和水力特性等因素,建立满足行洪排涝需求的平原水系连通度量方法,并以浙东沿海平原河网区为例,分析了城镇化下平原水系特征变化及其对河网连通度的影响。结果表明:1近20a来研究区水系数量和结构复杂性均存在不同程度的衰减,河网密度和水面率分别减少20%和30%左右,水系尤其是低等级河道的减少导致河网结构趋于骨干化;2河网连通度受水面率等数量特征和水系空间连接结构的共同影响,在水系数量持续减少情况下,整个河网连通度呈现先减少后增加的趋势,说明了骨干河道工程对水系空间结构特征的优化,可缓解水系减弱对河网连通的负面效应;3建立的河网连通度方法合理反映了水系数量和结构变化下的河网连通度演变规律,以及水系变化下区域排涝能力的空间差异特征,具有操作性强特点。     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.     

广州白云国际机场飞机大气污染物排放分析

根据收集到的2008-2012年广州白云国际机场航班起降次数，参考《珠江三角洲非道路移动源排放清单开发》飞机污染物估算方法及排放因子，计算出此期间机场飞机大气污染物排放量，并与2010-2012年广州市机动车污染物排放情况对比。结果表明：飞机大气污染物排放量随客运量的增长呈逐年上升趋势，而与机动车排放相比，飞机大气污染物排放量较小，故现阶段仍应以机动车作为移动源污染控制的重点。     

基于创新合作联系的城市网络格局分析——以长江经济带为例

现有城市地理研究主要基于重力模型、客货流网络和连锁模型等方法刻画城市网络。这些方法或是仅仅基于假设而可能与事实不符,或是数据难以获取,或是过于强调城市间的等级关系。通过引入合作专利数据,探索更加真实、平等的城市网络衡量方法。基于对长江经济带城市间创新联系的实证分析,发现目前城市间的创新联系以公司间(尤其是总部-分支机构间)合作为主;城市间创新联系存在非对称性和空间差异;实际创新联系与重力模型估算的创新联系存在正相关关系,但是相关系数不高;城市间创新联系的规模-位序符合Zipf定律,且存在两个无标度区;城市间创新联系受空间距离、行政级别、产业结构相似程度等影响,反映了距离衰减定律、行政区经济和产业链分工等规律对城市创新联系的解释力。