Ecosystem states: Creating a data-derived, ecosystem-scale ecological response model that is explicit in space and time

Increasing difficulties associated with balancing consumptive demands for water and achieving ecological benefits in aquatic ecosystems provide opportunities for new ecosystem-scale ecological response models to assist managers. Using an Australian estuary as a case study, we developed a novel approach to create a data-derived state-and-transition model. The model identifies suites of co-occurring birds, fish, benthic invertebrates and aquatic macrophytes (as ‘states’) and the changing physico-chemical conditions that are associated with each (‘transitions’). The approach first used cluster analysis to identify sets of co-occurring biota. Differences in the physico-chemical data associated with each state were identified using classification trees, with the biotic distinctness of the resultant statistical model tested using analysis of similarities. The predictive capacity of the model was tested using new cases. Two models were created using different time-steps (annual and quarterly) and then combined to capture both longer-term trends and more-recent declines in ecological condition. We identified eight ecosystem states that were differentiated by a mix of water-quantity and water-quality variables. Each ecosystem state represented a distinct biotic assemblage under well-defined physico-chemical conditions. Two ‘basins of attraction’ were identified, with four tidally-influenced states, and another four independent of tidal influence. Within each basin, states described a continuum of relative health, manifest through declining taxonomic diversity and abundances. The main threshold determining relative health was whether freshwater flows had occurred in the region during the previous 339 days. Canonical analyses of principal coordinates tested the predictive capacity of the model and demonstrated that the variance in the environmental data set was well captured (87%) with 52% of the variance in the biological data set also captured. The latter increased to >80% when long- and short-term biological data were analysed separately, indicating that the model described the available data for the Coorong well. This approach thus created a data-derived, multivariate model, where neither states nor transitions were determined a priori. The approach did not over-fit the data, was robust to patchy or missing data, the choice of initial clustering technique and random errors in the biological data set, and was well-received by local natural resource managers. However, the model did not capture causal relationships and requires additional testing, particularly during future episodes of ecological recovery. The approach shows significant promise for simplifying management definitions of ecological condition and, via scenario analyses, can be used to assist in manager decision-making of large, complex aquatic ecosystems in the future.     

Development of a coupled reservoir operation and water diversion model: Balancing human and environmental flow requirements

Maintaining a natural flow regime helps preserve the health of riverine ecosystems. Conventional studies on reservoir operations have focused mainly on identifying optimal operational schemes for satisfying human water demands. To systematically reflect the ecological effects of both natural and human-induced hydrologic alterations, water diversions downstream of the reservoirs should be considered as well. This research focused on a coupled reservoir operation and water diversion (CROWD) model, created through the integration of a reservoir operation model and a water diversion model. The proposed model considers both human and environmental flow requirements, and represents a compromise that balances ecological protection (preservation of the natural flow regime of a river) and human needs (reduced water shortages). In the reservoir operation model, the reservoir space is divided into three zones and different operating rules are developed for directing reservoir operation when water levels are in different zones; in the water diversion model, different water users are assigned different supply priorities with the instream flows no more than the minimum environmental flows having the highest priority; and the two models are coupled by the water mass balance between the two hydraulic facilities. The non-dominated-sorting genetic algorithm II (NSGA-II) was used to determine the parameters of the developed CROWD model and the model was applied to support the joint operational management of the Tanghe Reservoir and the Liaoyang Diversion in the Tang river basin, China. The resulting reservoir operation and water diversion schemes indicate that the CROWD model is useful for optimizing the operation of reservoirs and water diversion schemes. Moreover, it helps to analyze tradeoffs between human and environmental water needs, resulting in solutions that reduce the risk of water shortages and minimize ecological integrity disturbances.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.     

Regulatory attitudes and environmental innovation in a model combining internal and external R&D

The extent to which environmental regulatory institutions are either ‘green’ or ‘brown’ impacts not just the intensity of regulation at any moment, but also the incentives for the development of new pollution-control technologies. We set up a strategic model of R&D in which a polluter can deploy technologies developed in-house, or license technologies developed by specialist outsiders (an ‘eco-industry’). Polluters exert R&D effort and may even develop redundant technologies to improve the terms on which they procure technology from outside. We find that, while regulatory bias has an ambiguous impact on the best-available technology, strategic delegation to systematically biased regulators can improve social welfare.     

宝钢环境信息化系统建设探索

系统介绍了宝钢环境信息化系统建设架构、功能、特点,以及对污染减排、环境自动监测及信息化技术进行有效融合后的应用实践,并对该信息化系统在提高环境监控能力,完善系统建设上提出扩展监控范围、提升功能等构想.     

2000-2007年省级区域自然灾害灾情分析

自然灾害是严重影响人民正常生产生活、国家经济持续发展的突发公共事件。区域自然灾害评估对于做好防灾减灾工作有着重要的意义。对区域灾情进行了综合评估,归纳总结了区域灾情分布规律。选择受灾人口、死亡人口、紧急转移安置人口、农作物受灾面积、农作物绝收面积、倒塌房屋、损坏房屋和直接经济损失等8个基本指标参与区域灾情评估,并利用灾情综合指数法构建了灾情指数,其中绝对指数反映了区域灾情的绝对强度,相对指数反映了区域灾情对当地社会经济的影响程度。用灾情指数综合评估了我国东、中、西部三大地带以及省级区域的灾情强度和空间分布规律,认为西部区域受灾程度最大,自然灾害对其的影响程度最深,其次是中部地区和东部地区;文章利用综合灾情的绝对指数和相对指数的对应关系,将我国大陆地区的31个省级单元划分为4类地区,即灾情总量较大且对本地影响较大、灾情总量较大但对本地影响较小、灾情总量较小但对本地影响较大以及灾情总量较小且对本地影响较小等4种类型。文章给出的灾情区域分布规律符合灾害系统理论,特别反映了承灾体的暴露度、脆弱性和抗灾能力是区域灾情强度的决定因素。评估结果与区域实际情况相符,说明所给出的灾情指数评估法是评估区域灾情强弱的合理有效方法。     

环境规制影响资源型城市绿色全要素生产率的途径与政策建议

环境规制对资源型城市绿色全要素生产率是否具有推动作用,通过何种路径驱动以及是否存在城市类型异质性有待进一步理清。本文选取2004—2019年中国113座资源型城市的面板数据,利用非径向、非角度的SBM模型与GML指数对资源型城市绿色全要素生产率进行测算,随后通过构建多重中介效应模型实证检验了科技创新、外商直接投资和产业结构优化在环境规制影响绿色全要素生产率的传导机制及其在城市类型间的异质性。研究表明：（1）从全样本回归结果看,环境规制对资源型城市绿色全要素生产率的作用系数显著为正,产业结构优化和外商直接投资均发挥了显著的正向中介效应,而科技创新表现为间接抑制作用;在非资源型城市中,三类中介效应均不显著。（2）类型异质性检验显示,科技创新在成长型、成熟型城市中发挥了正向中介效应,在再生型城市中表现为间接抑制作用;外商直接投资在衰退型城市中的中介效应值为正,而在再生型城市中表现为负向中介效应;产业结构优化在成长型与成熟型城市中发挥了正向中介效应。（3）对比中介效应结果表明,全样本回归时,产业结构优化、外商直接投资和科技创新的中介效应梯次递减,产业结构优化的中介效应占比接近80%,环境规制的...     

市场激励型环境规制工具对中国资源型城市高质量发展的影响

基于中国114个地级资源型城市2011—2018年的面板数据,采用熵权法测算各资源型城市的高质量发展指数,并利用系统GMM模型、静态面板门限模型和动态面板门限模型,探讨市场激励型环境规制工具对中国资源型城市高质量发展的影响及其作用机制。结果表明：（1）2011—2018年中国资源型城市高质量发展水平在总体向好的同时也存在一定程度的极差扩大现象,且在空间上呈现出“东高西低”的分布态势;（2）市场激励型环境规制工具对资源型城市高质量发展指数存在显著的正向影响;（3）市场激励型环境规制工具对资源型城市高质量发展的促进作用受到城市低碳生活基础设施水平的调节和制约,存在门限效应。基于实证研究结果进一步分析了相应的政策含义。     

炼化常减压装置挥发性有机物无组织源谱分析

以典型常减压装置中工艺管线无组织逸散VOCs为切入点,选取不同地域的多套常减压装置,参照美国EPA包袋法采样标准,并按照HJ 644-2013标准将气态样品转移到组合三吸附管内,利用气相色谱-质谱仪进行分析检测.结果表明,常减压装置无组织排放的VOCs共含116种物质,以烷烃类、烯烃类和醛酮类物质占主导,其中烷烃类物质占总排放的65%以上.在此基础上,对检出物质占比量进行量化分析,明确了常减压装置VOCs无组织排放因子以2-甲基丁烷、丁烯和苯等物质为主,并检出了微量的萘.相关结论可为炼化企业构建VOCs排放清单提供有力支撑,对于后续精细化管理炼化行业的VOCs排放具有指导意义.     

Comprehensive and high-resolution emission inventory of atmospheric pollutants for the northernmost cities agglomeration of Harbin-Changchun, China: Implications for local atmospheric environment management

Using a bottom-up estimation method, a comprehensive, high-resolution emission inventory of gaseous and particulate atmospheric pollutants for multiple anthropogenic sectors with typical local sources has been developed for the Harbin-Changchun city agglomeration (HCA). The annual emissions for CO, NOx, SO₂, NH₃, VOC_S, PM_2.5, PM₁₀, BC and OC during 2017 in the HCA were estimated to be 5.82 Tg, 0.70 Tg, 0.34 Tg, 0.75 Tg, 0.81Tg, 0.67 Tg, 1.59 Tg, 0.12 Tg and 0.26 Tg, respectively. For PM₁₀ and SO₂, the emissions from industry processes were the dominant contributors representing 54.7% and 49.5%, respectively, of the total emissions, while 95.3% and 44.5% of the total NH₃ and NOx emissions, respectively, were from or associated with agricultural activities and transportation. Spatiotemporal distributions showed that most emissions (except NH₃) occurred in November to March and were concentrated in the central cities of Changchun and Harbin and the surrounding cities. Open burning of straw made an important contribution to PM_2.5 in the central regions of the northeastern plain during autumn and spring, while domestic coal combustion for heating purposes was significant with respect to SO₂ and PM_2.5 emissions during autumn and winter. Furthermore, based on Principal Component Analysis and Multivariable Linear Regression model, air temperature, relative humidity, electricity and energy consumption, and the urban and rural population were optimized to be representative indicators for rapidly assessing the magnitude of regional atmospheric pollutants in the HCA. Such indicators and equations were demonstrated to be useful for local atmospheric environment management.