南京市典型地区大气多环芳烃污染特征及健康风险评价

采用气相色谱-质谱法,于2016年9月和12月对南京市2个典型地区大气中16种多环芳烃(PAHs)的质量浓度进行分析,并开展了PAHs组成特征、来源解析及人体健康风险评价研究。结果表明,工业区(六合区)和生活区(江宁区)大气(气态和可吸入颗粒态)中16种PAHs的质量浓度分别为914.82和712.27 ng/m~3,苯并[a]芘毒性等效浓度分别为274.1和309.84 ng/m~3,且呈现冬季高、秋季低的特征。比值法源解析结果表明,燃煤污染是六合区PAHs污染主要来源,而江宁区主要表现为交通污染。人体健康风险评价结果表明,六合区和江宁区人群通过大气吸入PAHs的超额致癌风险分别为5.17×10~(-5)和5.85×10~(-5),均略高于可接受水平10~(-6)。     

基于多时相遥感数据的典型区生态格局变化分析

利用1985、2000、2013年遥感影像提取的土地覆盖数据,通过景观格局指数、动态度计算、转移矩阵等,分析1985—2013年我国典型地区各类型生态系统景观格局及其动态变化特征、生态系统相互转化时空变化特征等,揭示1985—2013年生态环境格局变化的特点和规律:一级分类生态系统综合变化率,赣江、闽江、白龙江和岷江上游流域分别为4.7%、3.9%、3.3%和1.7%,生态系统变化强度1985—2000年较缓,2000—2013年更剧烈。1985—2013年典型区生态系统的主要转化方向具有持续性和双向性特征,岷江、白龙江和赣江上游流域退耕还林还草政策效果明显,出现较高比例的耕地转为森林和草地;面积占67.4%生态系统类型变化与耕地生态系统和人工表面生态系统变化有关;生态系统变化具有明显的区域差异,生态变化主要表现为沿主要河流谷地的线状延伸,主要城镇居民点附近生态系统类型变化较为突出,人类活动是典型地区生态系统类型格局变化的主要驱动力;典型区尤其是敏感区应加大退耕还林还草政策,减少人类经济活动,降低洪水泥石流灾害发生的概率和程度。     

北京市城区挥发性有机物污染特征及其对臭氧影响分析

对2020年4月—2021年3月北京市建成区挥发性有机物（VOCs）的化学特征、污染来源及其对臭氧（O₃）污染的影响进行了研究。结果显示：O₃日最大8 h滑动平均值在臭氧季（4—9月）均值为134 μg/m³，是非臭氧季（10月至次年3月）均值（59.6 μg/m³）的2.2倍。臭氧季VOCs体积浓度均值为14.3×10^-9，明显低于非臭氧季（21.1×10^-9），可能与光化学反应速率和VOCs来源的季节性差异有关。臭氧生成潜势（OFP）贡献率排名前10位的物种在臭氧季和非臭氧季相似，均包括间/对-二甲苯、甲苯、乙烯、邻二甲苯、异戊烷、正丁烷、丙烯、反式-2-丁烯和1，2，4-三甲基苯，但排名有所差异，燃煤源特征明显的乙烯等物种在非臭氧季上升明显，与溶剂使用、油气挥发相关的间/对二甲苯、甲苯、异戊烷和正丁烷等物种在臭氧季上升明显。甲苯/苯的值和异戊烷/正戊烷的值在臭氧季明显高于非臭氧季，反映出机动车排放和油气挥发等在臭氧季影响突出，非臭氧季是燃煤影响显著。基于正交矩阵因子分解模型（PMF），臭氧季解析出机动车尾气排放（40.9%）、溶剂使用（20%）、油气挥发（16.4%）、工业排放（17.6%）和植物排放（5.1%）等5种污染源；非臭氧季解析出机动车尾气（38.9%）、燃烧源（26.3%）、工业排放（17.8%）和溶剂使用（17%）等4种污染源。     

面向管理的海洋修复工程生态绩效考核指标体系构建研究

目前国内关于海洋修复工程的考核研究多为修复工程的生态效果考核,极少包含成本效益考核,以致无法比较和分析不同类型、不同规模的海洋修复工程的生态绩效,不利于海洋修复工程的考核和验收管理。根据当前自然资源部实施“蓝色海湾”等整治修复工程的检查验收管理的新需求,本文构建了基于海洋生态系统健康恢复、生态系统服务价值增长和单位成本生态效益等理论的海洋修复工程生态绩效考核指标体系。此外,本文以温州海域2014—2016年海洋修复的生态绩效的考核为例,结合考核结果以及直接相关性、独立性、可测量性和业务化等指标选取原则,对指标体系进行了改进。结果表明本指标体系可以在海洋修复工程的检查验收中被用于生态绩效考核,管理部门可根据海洋修复工程的类型和管理需求,有选择性地采纳部分或全部指标进行考核。     

Developing shared qualitative models for complex systems

Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change.     

基于地理探测器的土壤重金属影响因子分析及其污染风险评价

地理探测器能快速定量化揭示驱动重金属含量影响因素的强度,这对于重金属空间预测模型构建变量的确定和土壤污染修复措施的精准实施具有重要意义。利用地理探测器模型,对5种土壤重金属元素Cu、Zn、Pb、Cr、Ni的空间分布和11种环境因子的交互作用进行定量评估,通过单因子指数法进行重庆市土壤重金属污染风险评价。结果表明:研究区内土壤Cu、Zn、Cr和Pb的平均含量是重庆市土壤背景值的1.3—1.4倍,Ni含量低于背景值;其中Cu、Pb达到重度污染水平,其余3种重金属为中度或轻度污染水平。5种重金属元素中Cu和Pb为高度变异(变异系数为0.57、0.4),Zn、Cr和Ni为中等变异(变异系数为0.22—0.29),且各重金属元素之间呈显著正相关性,表明研究区重金属富集受人为干扰影响较大,且污染具有复合性或同源性。地理探测器的因子探测发现高程、坡度和土壤类型对5种土壤重金属含量的解释力最显著,说明地势和土壤类型是土壤重金属含量分布差异的最主要影响因素。交互作用探测发现,高程与其他因子交互作用是重金属空间分异的主导因素,气候条件和土壤类型也是重要影响因子。土壤重金属空间分布是多种因素共同作用的结果,而高程、坡度和土壤类型具有较强的解释力,这些因子可作为土壤重金属含量空间预测模型的辅助变量,也可促进重金属污染治理措施的靶向实施。     

典型地区农用地污染调查及风险管控标准探讨

针对《土壤污染风险管控标准——农用地土壤污染风险管控标准》(GB 15618—2018),提出以土壤中全量浓度筛选值和管控值作为衡量农用地土壤污染风险管控的标准,对湖南省部分稻田农用地土壤及点对点稻米样品中镉、铅、砷、汞的总量和有效态浓度及稻米中含量进行监测,根据重金属总量浓度分为低风险、中风险、高风险3组。结果显示:(1)土壤及稻米中镉含量基本为随着风险级别的升高而增加,铅、砷在土壤和稻米中含量无规律性结果,汞监测结果均为未检出。(2)低风险组稻米镉超标率为12. 0%,高风险组稻米镉达标率为33. 3%,表明利用总量浓度对农用地土壤潜在风险进行分组存在一定的局限性。(3)依据4种重金属在土壤中总量及稻米(早稻)中含量情况,对风险级别进行调整并综合判断:有58个样品为低风险组,占样品总数的68. 2%,超标率为零;有15个样品为中风险组,占样品总数的17. 7%,超标率为80. 0%;有12个样品为高风险组,占样品总数的14. 1%,超标率为100. 0%。调整后评价结果与上述标准的划分目标更接近,能够提高上述标准的准确性和实用性。     

A proactive process risk assessment approach based on job hazard analysis and resilient engineering

Complex systems often experience a long period of incubation before accidents occur. Therefore, a proactive risk assessment is essential for process safety. The conventional job hazard analysis (JHA) method has been an effective tool to conduct a process risk assessment in the high-risk industrial field. However, the conventional JHA is inadequate for the proactive risk assessment since it is usually conducted during and before one specific operation process. Operations such as startup and maintenance are performed repeatedly on the lifecycle of a plant. Therefore, the risk reduction measures for the industrial process should include not only preventive actions obtained from the conventional JHA but also recovery ones. Resilience engineering (RE) has proven to be helpful for the recovery analysis of a complex system. The objective of this paper is to propose a proactive and comprehensive process risk assessment approach based on JHA and RE. The mechanism of applying RE to address operation process risk is illustrated. The integrated approach can provide guidelines to establish proactive risk reduction measures as well as maintain a low-risk level. Finally, a gas transmission startup process risk assessment case is presented to demonstrate its applicability.     

Assessment of deep aquifer groundwater quality in a geographically unique climatic region of Southern Western Ghats,India using water quality indices

Water quality index (WQI) models are generally used in hydrochemical studies to simplify complex data into single values to reflect the overall quality. In this study, deep groundwater quality in the Chittur and Palakkad Taluks of the Bharathapuzha river basin of Kerala, India, was assessed by employing the WQI method developed by the Canadian Council of Ministers of the Environment (CCME). The assessment of overall water quality is indispensable due to the specific characteristics of the study area, such as geography, climate, over-drafting, and prevalent agricultural practices. Forty representative samples were collected from the study area for monsoon (MON) and pre-monsoon (PRM) seasons. The results showed a general increase of contents from MON to PRM. The major cations were spread in the order Ca²⁺>Na+>Mg²⁺>K⁺ and the anions HCO₃⁻>Cl⁻>CO₃²⁻ based on their relative abundance. Among various parameters analysed, alkalinity and bicarbonate levels during MON were comparatively high, which is indicative of carbonate weathering, and 90% of the samples failed to meet the World Health Organization (WHO, 2017)/Bureau of Indian Standards (BIS, 2012) drinking water guidelines. The CCME WQI analysis revealed that nearly 50% of the samples during each season represented good and excellent categories. The samples in the poor category comprised 10% in MON and 15% in PRM. The overall WQI exhibited 15% of poor category samples as well. The spatial depiction of CCME WQI classes helped to expose zones of degraded quality in the centre to eastward parts. The spatial and temporal variations of CCME WQI classes and different physicochemical attributes indicated the influence of common factors attributing to the deep groundwater quality. The study also revealed inland salinity at Kolluparamba and Peruvamba stations, where agricultural activities were rampant with poor surface water irrigation.     

砷尾矿污染土壤的细菌群落结构多样性及其相关环境影响因子分析

以湖南石门雄黄尾矿污染土壤为对象,研究纵向不同深度、横向不同距离土样中的重金属污染程度以及细菌群落结构变化规律,查明砷污染土壤的核心微生物组成并将其与土壤理化指标进行共存网络图分析。结果表明:该尾矿区的土壤各项重金属指标严重超标,尤以铅(626.54 mg·kg^?1,Ei=105.48)、砷(1804.75 mg·kg^?1,Ei=565.75)、镉(31.46 mg·kg^?1,Ei=7491.5)的生态危害性最强;土壤采样深度与重金属综合潜在生态风险指数(RI)呈显著正相关(r=0.79,P=0.000),而横向样品中RI与采样距离显著负相关(r=?0.85,P=0.000)。在污染土壤中,变形杆菌门(Proteobacteria,54.35%±17.16%)和放线杆菌门(Actinobacteria,22.39%±10.64%)占主导地位,属层级中假单孢杆菌属(Pseudomonas,16.47%±11.84%)、不动杆菌属(Acinetobacter,8.07%±7.11%)以及硫酸状杆菌属(Acidithiobacillus,7.53%±14.68%)相对丰度较高;而26个共享类群占据了该尾矿污染土壤中微生物群落总平均相对丰度90%以上,尽管不同属的具体相对丰度在不同样品间的分布趋势差异较大。纵剖采集的污染土样中,铁原体属(Ferroplasma)、硫酸状杆菌属(Acidithiobacillus)、硫化杆菌属(Sulfobacillus)、乳杆菌属(Lactobacillus)和硝化螺旋菌属(Nitrospira)占优势,与理化的共存网络图分析(相关系数|r|≥0.6,P<0.05)显示部分类群与亚铁、游离态砷和镉成显著正相关,而与pH成显著负相关;横向采集的土样中,以嗜酸菌属(Acidiphilium)、假单孢杆菌属(Pseudomonas)、棒状杆菌属(Corynebacterium)、硫杆菌属(Thiobacillus)等为主,部分类群与总砷、铅成显著正相关,而与结合态或包蔽型砷成显著负相关。综上,该研究不仅对目标区域尾矿不同重金属污染程度进行了分析,同时探讨了污染土壤中核心响应类群的组成多样性,为筛选潜在重金属抗性菌群或工程菌群提供理论基础。