西安地区供暖初期颗粒物及气态污染物的污染状况及其相关性分析

对2015年供暖初期西安地区细颗粒物(PM_(2.5))、可吸入颗粒物(PM_(10))、二氧化硫和二氧化氮的污染进行研究。结果发现:西安供暖初期可吸入颗粒物污染已经相当严重,其中处于二级污染水平的占22%,处于三级污染水平的占39%,并有2天属大于300μg/m~3的最高6级严重污染状况。利用统计学软件SPSS对各区污染进行聚类分析,得出西安污染最严重的3个区分别为灞桥区、雁塔区和莲湖区。对4种污染物以及温度、相对湿度之间进行相关性分析发现,二氧化氮与二氧化硫、细颗粒物、可吸入颗粒物和相对湿度之间都存在很强的相关性。另外从细颗粒物和可吸入颗粒物的区域分析可知,雁塔区和莲湖区污染严重。     

水质自动监测中的质量保证与质量控制

质量保证与质量控制是水质自动监测中十分重要的技术工作和管理工作,包括人员素质、运行与管理机制、试剂与标准溶液的配制及文件记录等.在实际工作中,监测人员可根据仪器的运行状况,定期时仪器进行校准、标准溶液的核查、比对实验验证及试剂有效性检查,用以检查仪器的基线飘移情况.如果分析中出现一个异常值,它的前后均为正常值,这种数值大多是由仪器的进样、仪器内部试剂传输等原因所致,如果发生原因不明的数据异常,就要及时检查系统的各个环节并采集实际水样进行人工分析,直至查清原因,剔除异常值,并在周报中加以说明,以确保上报数据的准确性、精密性和可比性.     

西安市二氧化硫减排与环境空气质量相关性分析

历年环境统计数据和环境监测数据表明,2006年以来西安市二氧化硫排放量大幅下降,空气中二氧化硫浓度也呈下降趋势,空气质量有所改善,但由于减排措施实施的区域和力度的差异,不同典型监测点位二氧化硫浓度变化情况不尽相同,与我国北方其他省会城市相比,二氧化硫排放量虽然降幅较大,但城市空气中二氧化硫浓度降幅偏小,且工业减排量占总减排量的比例相对较小,因此,今后应高度关注二氧化硫减排工作中存在的问题,不断完善环境管理,多渠道多途径全面提升城市环境空气质量。     

基于景观指数和空间自相关的吉林大安市景观格局动态研究

以松嫩平原西部盐碱化严重的典型区域大安市为研究对象,基于2000和2010年两个时期的土地利用和增强植被指数（EVI）数据,利用移动窗口法和局部空间自相关分析,研究大安市景观格局的变化。结果表明,大安市优势景观类型是农田、草地和盐碱地,2000年农田、草地和盐碱地分别占景观类型总面积的40.20%、19.09%和19.26%,2010年分别占景观类型总面积的41.69%、18.16%和19.94%。2000-2010年,农田和盐碱地面积增加,最大斑块指数增加,景观形状变得复杂,并且盐碱地的景观连通性增强,而草地的面积减少,最大斑块指数降低,景观形状变得复杂。大安市景观格局空间分布特征明显,优势度较高的景观类型分布的区域包括草地、农田和盐碱地,景观连通性强,景观破碎化程度和异质化程度低。而各种景观类型交错分布的区域景观破碎化程度较高,景观异质性较强。大安市2000和2010年EVI的局部Moran’s I分别为0.73和0.75,在空间分布上呈现出明显的空间自相关性。2000和2010年EVI呈高-高自相关的地区大多为农田,这些区域的植被覆盖较好,EVI呈低-低自相关的地区大多为盐碱地,植被覆盖较差。大安市不同景观类型的Moran’s I和斑块密度（PD）以及斑块形状指数（LSI）呈负相关关系,和最大斑块指数（LPI）以及蔓延度指数（CONTAG）呈正相关关系。移动窗口法和空间自相关法的结合分析,有助于了解大安市景观格局的空间变化特征及植被覆盖的空间聚集规律,从而为该地区生态环境保护提供依据。     

基于模糊TOPSIS的道路危险货物运输企业安全评价方法

为了评估道路危险货物运输企业安全性水平,降低危险货物运输风险概率,围绕道路危险货物运输企业安全性评价的重要性,提出一种适用于模糊不确定环境的道路危险货物运输企业安全性评价方法。首先,从人-机-组织综合角度建立道路危险货物运输企业安全性评价指标体系;其次,组合运用模糊决策理论、群体决策和TOPSIS方法提出道路危险货物运输企业安全性评价方法框架;最后以南京市5家道路危险货物运输企业的安全性评价为例进行方法验证。     

Restoration planning to guide Aichi targets in a megadiverse country

Ecological restoration has become an important strategy to conserve biodiversity and ecosystems services. To restore 15% of degraded ecosystems as stipulated by the Convention on Biological Diversity Aichi target 15, we developed a prioritization framework to identify potential priority sites for restoration in Mexico, a megadiverse country. We used the most current biological and environmental data on Mexico to assess areas of biological importance and restoration feasibility at national scale and engaged stakeholders and experts throughout the process. We integrated 8 criteria into 2 components (i.e., biological importance and restoration feasibility) in a spatial multicriteria analysis and generated 11 scenarios to test the effect of assigning different component weights. The priority restoration sites were distributed across all terrestrial ecosystems of Mexico; 64.1% were in degraded natural vegetation and 6% were in protected areas. Our results provide a spatial guide to where restoration could enhance the persistence of species of conservation concern and vulnerable ecosystems while maximizing the likelihood of restoration success. Such spatial prioritization is a first step in informing policy makers and restoration planners where to focus local and large‐scale restoration efforts, which should additionally incorporate social and monetary cost–benefit considerations.     

Integrated models to support multiobjective ecological restoration decisions

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state‐and‐transition, species‐distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade‐offs in management outcomes through integrated modeling and structured decision‐support approaches has wide application for situations in which trade‐offs exist between competing conservation objectives.     

京津冀与长三角地区氢氯氟碳化物本底特征

基于北京上甸子与浙江临安区域大气本底站2011—2019年氢氯氟碳化物(HCFCs)采样观测数据,开展了京津冀与长三角地区6种HCFCs(HCFC-22、HCFC-141b、HCFC-142b、HCFC-124、HCFC-132b和HCFC-133a)本底特征研究。研究结果表明:临安站HCFCs浓度水平和浓度变率比上甸子站明显更高,尤其是HCFC-133a,其浓度及浓度变率均比上甸子站高1个量级,表明长三角地区HCFCs排放量可能较京津冀地区更大。2个站点HCFCs本底浓度基本一致,差异范围为-6.1%~7.1%。上甸子站本底数据占比为26.4%~69.0%,而临安站本底数据占比不足23%。在《关于消耗臭氧层物质的蒙特利尔议定书》的约束下,2个站点多数HCFCs年均浓度呈下降趋势或变化较小。2个站点HCFC-132b浓度相对较低,但2019年相比2018年有明显升高。结合风向进行分析,发现上甸子站HCFC-22、HCFC-141b、HCFC-142b和HCFC-132b高浓度水平主要由西南扇区(北京城区方向)的WSW、SSW及SW方向贡献,而HCFC-124和HCFC-133a在各风向上的浓度和载荷差异较小。临安站HCFC-124高浓度水平主要由SSE和NNE方向贡献,分别对应金华和湖州方向;其他5种HCFCs的高浓度水平主要由东北扇区的ENE方向贡献,对应杭州城区方向。     

Using global sensitivity analysis of demographic models for ecological impact assessment

Population viability analysis (PVA) is widely used to assess population‐level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input‐parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input‐parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea‐level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions.     

A spatially explicit estimate of avoided forest loss

With the potential expansion of forest conservation programs spurred by climate-change agreements, there is a need to measure the extent to which such programs achieve their intended results. Conventional methods for evaluating conservation impact tend to be biased because they do not compare like areas or account for spatial relations. We assessed the effect of a conservation initiative that combined designation of protected areas with payments for environmental services to conserve over wintering habitat for the monarch butterfly (Danaus plexippus) in Mexico. To do so, we used a spatial-matching estimator that matches covariates among polygons and their neighbors. We measured avoided forest loss (avoided disturbance and deforestation) by comparing forest cover on protected and unprotected lands that were similar in terms of accessibility, governance, and forest type. Whereas conventional estimates of avoided forest loss suggest that conservation initiatives did not protect forest cover, we found evidence that the conservation measures are preserving forest cover. We found that the conservation measures protected between 200 ha and 710 ha (3-16%) of forest that is high-quality habitat for monarch butterflies, but had a smaller effect on total forest cover, preserving between 0 ha and 200 ha (0-2.5%) of forest with canopy cover >70%. We suggest that future estimates of avoided forest loss be analyzed spatially to account for how forest loss occurs across the landscape. Given the forthcoming demand from donors and carbon financiers for estimates of avoided forest loss, we anticipate our methods and results will contribute to future studies that estimate the outcome of conservation efforts.