影响总磷的显色实验细节探讨

针对环境样品采用钼酸铵分光光度法测定的总磷中,影响显色的二个因素进行了分析,对加入显色刺后的混合过程对显色结果的影响,以及环境条件的温度对显色的影响进行了研究,对总磷测定时准确度及精密度的提高有着重要的参考价值,是对钼酸铵法测定总磷方法的完善。     

包头市大气氟化物排放总量控制管理研究

结合实践,阐述了总量控制管理的必要性和可能性;介绍和分析了总量管理的内容和程序;对主要问题提出对策和措施;形成运行管理机制,提出排氟量计算、污染物输送扩散模型等构成的技术支持系统。      

大气污染物排放总量控制技术支持体系的建立

为了实施总量控制,在大气环境质量未达标区制定大气污染物排放削减量优化分配方案。得出污染源年度削减限量;在空气质量达标区.依照允许的地面浓度增量,将大气污染物允许排放指标合理地分配到源,以保证经济的持续发展;在大气环境管理中逐步建立起计算机辅助决策支持系统和完善的监测系统。      

包头市大气氟化物总量控制排放许可证研究

总结了包头市实施大气排污许可证制度试点中,关于氟化物总量控制许可值的确定方法,主要包括源强的确定、大气扩散模式筛选与验证、总量控制区划定与执行标准、控制点选择、污染源贡献率和削减量计算、许可值的优化分配。      

实施排污许可证制度的经济手段──环境补偿在太原市的实行

具备了实行环境补偿的法律依据和技术支持体系之后,在搞了三个大气环境补偿试点实践的基础上,结合太原市实施排放大气污染物许可证制度试点工作的需要,又进行了一段时间和一定范围的大气环境补偿试点工作,证明了大气环境补偿是实施大气排污许可证制度的一项有效的经济手段。      

不同日摄氟量居民罹患氟斑釉的研究

人和动物体内的硬组织（牙齿、骨骼等）是氟化物作用的主要部位,严重者可引起氟骨症和氟斑釉,其中氟斑釉是慢性氟中毒的一项特异指标。通常日摄氟量高的居民其氟斑釉率和斑釉指标均增高。本文用日摄氟量作指标评价环境氟对居民牙齿的影响。研究结果揭示,当地居民的斑釉率和斑釉指数均与日摄氟量呈非常显著的正相关,r＝0.9084,P<0.01。     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Design and use of a time-sorting pitfall trap for predatory arthropods

An electronically-controlled time-sorting pitfall trap was designed which improves upon earlier models in having a 24-h rotation time, and in being capable of running continuously for at least one week without attention. Four prototypes were built, using readily available, inexpensive components, and used from June to October, 1995, to collect carabid beetles and linyphiid spiders in clover-undersown and weeded cabbages. The traps operated without malfunction during the hottest weather ever recorded in Aberdeen, when soil surface temperatures exceeded 40°C, and also during periods of torrential rain. Three spider and two carabid species dominated the catch, their nocturnal and diurnal patterns of activity being in general agreement with other published data. Some predators were apparently able to adapt to the contrasting vegetation in the two treatments, two species of Erigone spiders being nocturnal in weeded cabbages but tending to be diurnal in undersown crops.     

A mathematical approach for retrofit and optimization of total site steam distribution networks

This paper presents a generic mathematical model for retrofitting the steam power plants in an industrial site. The industrial sector under review consists of one steel mill, one oil refinery, and several petrochemical plants, where only small-scale steam integration has been implemented before this study. The relevant unit models in a typical steam power plant are established, and the steam plant retrofit problem is formulated as a mixed-integer nonlinear program (MINLP). Feasible retrofit alternatives suggested by experienced field engineers are investigated in sequence to examine the revenue of those possible modifications. The first scenario examines operational optimization of existent plants; the second option allows installation of one new turbine and replacement of several boilers and turbines with lower efficiency; the third scenario considers using a steam ejector to upgrade the disqualified import steam in the oil refinery. The significant merits from these three retrofit alternatives show that the proposed MINLP formulation has been a great help to enhance the inter-plant steam integration in an industrial sector.     

Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images

Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration.