石墨炉原子吸收法测定自来水中总铬升温参数的探讨

对石墨炉原子吸收测定自来水中总铬的方法进行优化。通过对石墨炉升温程序条件进行优化,找出其最佳参数值,其方法的线性关系良好,相关系数为0.999以上,回收率在96.7%~99.5%之间。该法操作简便,灵敏度和准确度高,线性好,便于推广,适合自来水中总铬的测定。     

海南岛近岸海域环境容量与纳污总量分析

通过对海南岛"十二五"期间近岸海域纳污总量、环境质量状况及环境容量比较分析,得出各主要排污区环境容量的剩余环境容量。结果表明,全省主要污染物化学需氧量入海量仅占环境容量的2.6%,氨氮占环境容量的26.6%,虽然全省近岸海域环境基本未受污染物入海量影响,仍有较大的纳污空间,可利用的环境容量非常充裕。为避免出现排污达标但环境质量下降的状况,提出建立近岸海域主要污染物总量控制制度,由浓度控制到总量和浓度控制并存。     

四丰山水库富营养化状况

通过对四丰山水库2006至2010年监测数据的统计,分析水库水质富营养化状况,指出水质富营养化的危害,并提出避免水质恶化的建议.     

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。     

还原-ZPF催化氧化降解2,4-DNT效果及其机制

以实验室制备的α-FeOOH柱撑人造沸石（zeotileartificial pillared byα-FeOOH,ZPF）为催化剂,并利用FTIR与XRD技术进行表征,采用批式实验研究其催化H2O2氧化去除地下水中难降解有机污染物2,4-二硝基甲苯（2,4-DNT）的效能,对比分析不同pH条件下Fe0还原、ZPF催化...     

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。      

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.