岩溶环境对多环芳烃迁移转化的影响

从水文地质条件、地貌环境和地球化学环境等3个方面,综述了岩溶环境对多环芳烃(PAHs)迁移转化的影响。针对研究现状及存在的不足,提出了未来可能的研究趋势,包括不同气候条件下发育的岩溶系统的对比研究、不同气候条件和不同岩溶系统快速流和慢速流对PAHs迁移的贡献、碳酸盐岩与PAHs相互作用的机理,以及有机质对PAHs迁移行为的影响。     

自然资源资产审计制度的供给侧改革

自然资源是可持续发展的基础。完善自然资源资产审计制度,是保护自然资源、践行生态文明的要求。自然资源资产审计的前提是自然资源的确权和量化,是自然资源与自然资源资产在审计上的“融合”。作为自然资源资产审计的依据,相关制度应该对自然资源的确权、计量和有效利用作出合理安排,才能有效抑制资源滥用冲动。但立足于自然资源资产审计实践,综合运用文献研究、个案调查、跨学科研究等方法,分析现行的自然资源制度,没有发现可操作性的自然资源产权制度、计量制度和评价制度,已有制度存在权责不匹配、责任界定不清晰、公众参与有困难、审计指标不健全;制度系统呈现存量不足、增量困难、实践不力,不能适应新时代要求和自然资源合理利用需要。因此,可以从制度存量、增量和协同三条路径实施制度供给侧改革;在完善制度存量方面,可以在经济责任审计规定中嵌入自然资源资产审计内容,完善自然资源资产评价标准、责任制度和审计结果运用办法等;在提高制度增量方面,完善自然资源资产的权利制度、计量制度、信息公开制度和公众参与制度等;同时,通过适时修订审计法、制定自然资源资产法,强化制度协调,以突破自然资源资产的制度瓶颈,方便审计算好“生态账”、把好“生态关”。在自然资源资产制度改革过程中,需要借助决策层和专家力量,摆脱部门立法局限,强化制度协调;同时,制度改革也是一个不断优化的过程,要兼顾公众、个人、政府及其职能部门等多方利益,才能有效满足资源节约型和环境友好型社会的需要。     

基于视频分析的人群密集场所客流监控预警研究

为实现人群密集场所客流安全隐患早发现，辅助管理人员早决策，人群聚集风险区早疏散，提升对灾难的预见性和主动性。在国内外人群异常聚集监测预警现状分析基础上，对比分析得出监控视频分析技术是解决人群密集场所精准预警难题较为理想的解决方案；构建以视频智能分析的人群计数、密度估计、行人追踪、活动烈度识别为核心技术的人群密集场所风险预警技术框架；将该技术框架应用到某大型商圈的商业街区，获得监控区域内的人群总数、密度分布、行人轨迹和异常活动等特征。结果表明:提出的基于视频分析的人群密集场所风险预警技术框架可为城市大型商圈、交通枢纽、大型活动场所等城市公共场所的安全管理提供参考和借鉴。     

关于生态环境地质的基本特点与技术分析

地球是一个生态系统,其包含了大气圈、水圈以及生物圈等。各个圈层之间存有的关联性十分密切,促进这些圈层的协调发展,才可以保障生态环境的和谐性。     

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。     

采用电芬顿-磁固定化菌株耦合体系治理苯酚废水的研究

为实现对苯酚废水治理工艺的优化,针对传统包埋法制备固定化细胞的工艺方法进行改进,选用磁性纳米颗粒制备磁固定化细胞,将其与电芬顿法结合构建耦合体系,利用耦合协同作用实现催化剂的重复利用,并依托微生物的良好降解性能与稳定性提高苯酚降解率,进一步实现对苯酚废水治理工艺的系统优化。     

区块链技术在环保领域的应用

近年来,区块链技术快速发展,被广泛应用于多个领域,发挥了不错的成效,比如推动公共服务质量的提升。环保领域中,区块链技术的应用处于探索阶段,还面临着诸多挑战和困难需要加以克服。环保领域对区块链技术的需求很大,若能够切实发挥技术的优势,可有效推动环保事业的发展。现结合具体研究,作如下论述。     

分子识别作用下光催化降解偶氮染料酸性红GR

合成了β-环糊精(β-CD)与偶氮染料酸性红GR(ARGR)的包结物,并采用红外光谱仪对ARGR、β-CD及β-CD与ARGR的包结物进行了表征,表征结果显示,β-CD与ARGR的包结物的特征峰的峰形和强度与β-CD相似,但峰位有明显的偏移,说明ARGR进入了β-CD空腔与β-CD发生了分子识别作用。采用TiO2作为催化剂,研究了β-CD分子识别后ARGR的光催化降解行为,实验结果表明,在ARGR质量浓度为20.0 mg/L、β-CD浓度为1.8×10-5 mol/L、体系pH为4.0、TiO2加入量为1.0 g/L、光催化反应时间为60 min的条件下,经β-CD分子识别后ARGR的光催化降解率可达100%。     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

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.