Desert pastoralists’ negative and positive effects on rare wildlife in the Gobi

In arid regions of the developing world, pastoralists and livestock commonly inhabit protected areas, resulting in human–wildlife conflict. Conflict is inextricably linked to the ecological processes shaping relationships between pastoralists and native herbivores and carnivores. To elucidate relationships underpinning human–wildlife conflict, we synthesized 15 years of ecological and ethnographic data from Ikh Nart Nature Reserve in Mongolia's Gobi steppe. The density of argali (Ovis ammon), the world's largest wild sheep, at Ikh Nart was among the highest in Mongolia, yet livestock were >90% of ungulate biomass and dogs >90% of large‐carnivore biomass. For argali, pastoral activities decreased food availability, increased mortality from dog predation, and potentially increased disease risk. Isotope analyses indicated that livestock accounted for >50% of the diet of the majority of gray wolves (Canis lupus) and up to 90% of diet in 25% of sampled wolves (n = 8). Livestock composed at least 96% of ungulate prey in the single wolf pack for which we collected species‐specific prey data. Interviews with pastoralists indicated that wolves annually killed 1–4% of Ikh Nart's livestock, and pastoralists killed wolves in retribution. Pastoralists reduced wolf survival by killing them, but their livestock were an abundant food source for wolves. Consequently, wolf density appeared to be largely decoupled from argali density, and pastoralists had indirect effects on argali that could be negative if pastoralists increased wolf density (apparent competition) or positive if pastoralists decreased wolf predation (apparent facilitation). Ikh Nart's argali population was stable despite these threats, but livestock are increasingly dominant numerically and functionally relative to argali. To support both native wildlife and pastoral livelihoods, we suggest training dogs to not kill argali, community insurance against livestock losses to wolves, reintroducing key native prey species to hotspots of human–wolf conflict, and developing incentives for pastoralists to reduce livestock density.     

Threats to intact tropical peatlands and opportunities for their conservation

Large, intact areas of tropical peatland are highly threatened at a global scale by the expansion of commercial agriculture and other forms of economic development. Conserving peatlands on a landscape scale, with their hydrology intact, is of international conservation importance to preserve their distinctive biodiversity and ecosystem services and maintain their resilience to future environmental change. We explored threats to and opportunities for conserving remaining intact tropical peatlands; thus, we excluded peatlands of Indonesia and Malaysia, where extensive deforestation, drainage, and conversion to plantations means conservation in this region can protect only small fragments of the original ecosystem. We focused on a case study, the Pastaza‐Marañón Foreland Basin (PMFB) in Peru, which is among the largest known intact tropical peatland landscapes in the world and is representative of peatland vulnerability. Maintenance of the hydrological conditions critical for carbon storage and ecosystem function of peatlands is, in the PMFB, primarily threatened by expansion of commercial agriculture linked to new transport infrastructure that is facilitating access to remote areas. There remain opportunities in the PMFB and elsewhere to develop alternative, more sustainable land‐use practices. Although some of the peatlands in the PMFB fall within existing legally protected areas, this protection does not include the most carbon‐dense (domed pole forest) areas. New carbon‐based conservation instruments (e.g., REDD+, Green Climate Fund), developing markets for sustainable peatland products, transferring land title to local communities, and expanding protected areas offer pathways to increased protection for intact tropical peatlands in Amazonia and elsewhere, such as those in New Guinea and Central Africa which remain, for the moment, broadly beyond the frontier of commercial development.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Atrazine contamination in agricultural soils from the Yangtze River Delta of China and associated health risks

Atrazine is one of the most widely applied and persistent herbicides in the world. In view of limited information on the regional contamination of atrazine in soils in China, this study investigated the spatial distribution and environmental impacts of atrazine in agricultural soils collected from the Yangtze River Delta (YRD) as an illustrative analysis of rapidly developing regions in the country. The results showed that the concentrations of atrazine in the YRD agricultural soils ranged from <1.0 to 113 ng/g dry weight, with a mean of 5.7 ng/g, and a detection rate of 57.7 % in soils. Pesticide factory might be a major source for the elevated levels of atrazine in Zhejiang Province. The contamination of atrazine was closely associated with land use types. The concentrations and detection rates of atrazine were higher in corn fields and mulberry fields than in rice paddy fields. There was no significant difference in compositions of soil microbial phospholipids fatty acids among the areas with different atrazine levels. Positive relationship (R = 0.417, p < 0.05, n = 30) was observed between atrazine and total microbial biomass. However, other factors, such as soil type and land management practice, might have stronger influences on soil microbial communities. Human health risks via exposure to atrazine in soils were estimated according to the methods recommended by the US EPA. Atrazine by itself in all the soil samples imposed very low carcinogenic risks (<10^?6) and minimal non-cancer risks (hazard index <1) to adults and children.     

Performance evaluation of circulating fluidized bed incineration of municipal solid waste by multivariate outlier detection in China

This first nationwide survey was conducted to evaluate the overall performance of the circulating fluidized bed (CFB) incineration of municipal solid waste (MSW) in 2014-2015 in China. Total 23 CFB incineration power plants were evaluated. The data for monthly average flue gas emission of particles, CO, NO _x , SO₂ and HCl were collected over 12 consecutive months. The data were analyzed to assess the overall performance of CFB incineration by applying the Mahalanobis distance as a multivariate outlier detection method. Although the flue gas emission parameters had met the Chinese national emission standards, there were 11 total outliers (abnormal behavior) detected in 6 out of 23 CFB incineration power plants from the perspective of the MSWincineration performance. The results demonstrate that it is more important for a better performance of CFBs to reduce the frequencies of the MSW load changes, rather than the magnitudes of the MSW load changes, particularly reducing the frequencies in the range of 10% and more of the load changes, under the same and stable conditions. Furthermore, the overloading occurs more often than the underloading during the operation of the CFB incineration power plants in China. The frequent overloading is 0% to 30% of the designed capacity. To achieve the stable performance of CFBs in practice, an appropriately designed MSW storage capacity is suggested to build in a plant to buffer and reduce the frequency of the load changes.

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基于小流域尺度的目标抗生素排放量估算方法——以梅江流域为例

抗生素的大量使用和排放造成的环境污染和生态风险问题日益突出,抗生素排放量的估算是评价流域内抗生素污染程度的重要指标,但目前抗生素排放量估算方法尚不完善。本研究以梅江流域为例,建立了适合小流域尺度的典型抗生素排放量估算方法,计算了四环素类抗生素(TCs)向不同环境相的排放量并分析了其主要来源。结果表明:2016年梅江流域TCs排放量为8 558.1 kg,不同行政区抗生素排放量差异较大,其中梅江镇受人口密度及养殖密度影响,抗生素排放量最大,高达1 224.4 kg;同时流域内不同抗生素的排放量也有所不同,其大小顺序为强力霉素(DXC)土霉素(OTC)四环素(TC)金霉素(CTC);TCs受排放源、排放途径等因素影响,以进入环境水相及土壤相为主,其中进入水相中的抗生素主要来自人类及生猪粪便,进入土壤相中的抗生素主要来自生猪及三黄鸡粪便。梅江小流域抗生素具有潜在的生态风险,应加强抗生素使用管理。该研究为我国小尺度流域目标抗生素排放量的估算提供了十分有效的方法。     

有机磷农药二元混合物联合效应评价新方法的研究

以敌敌畏、乐果联合作用于线虫的毒理学实验为基础,基于FH插值法理论,通过较少的实验数据建立有机磷农药二元混合物联合效应模型,求解作用效应表达式并绘制联合效应三维曲面图;基于合并用药分析公式,计算联合作用评价q值并绘制农药浓度与q值的联合作用评价三维曲面图,结果表明敌敌畏与乐果混合物联合作用不仅表现为相加作用,而且还在局部低浓度表现为拮抗作用,与多元统计分析结果一致。上述研究表明FH插值法理论可作为二元有机磷农药联合效应分析及评价的一种新方法。     

黄、渤海滨海带大气颗粒物时空分布与来源特征

PM10作为大气污染物监测的主要指标之一,探究大气PM10浓度对大气环境质量和人体健康评价具有重要意义。黄、渤海滨海带包括京、津和辽、冀、鲁、苏等工、农业大省,区域大气PM10污染的时空分布和来源特征具有复杂性和典型性。在锦州、北京、天津、烟台、青岛、连云港和盐城7个城市布设10个采样点,含7个城市点和3个农村点,开展为期一年的大气颗粒物的采样;同时,于冬季1月和夏季7月在锦州、天津和烟台进行合计60 d的加密采样,藉以确定研究区域大气PM10的时空分布和来源特征。结果表明,黄、渤海滨海带大气年均PM10总浓度为(129’18)"g·m~(-3),单月最低值出现在2015年7月盐城农村样点15"g·m~(-3),最高值为2015年3月北京城市点307"g·m~(-3)。盐城大气PM10浓度(城市点(85’27)"g·m~(-3)和农村点(66’35)"g·m~(-3))显著低于其他样点大气PM10浓度。渤海滨海带中西部的京(140’68"g·m~(-3))、津(169’60"g·m~(-3))两市大气PM10年均浓度显著高于东部的锦州(125’41"g·m~(-3))和烟台(109’31"g·m~(-3));而且黄海滨海带大气PM10年均浓度(114"g·m~(-3))显著低于渤海滨海带年均浓度(136"g·m~(-3)),总体上表现出西高东低、北高南低的特征。黄、渤海滨海带城市点和农村点年均浓度分别为(129’18)"g·m~(-3)和(112’30)"g·m~(-3);农村点春冬季大气PM10浓度和城市点浓度相当,无显著差异,夏秋季大气PM10浓度略低于城市浓度,表明农村地区大气颗粒物污染情况也较为严重,需受到关注。区域内PM10浓度季节变化整体表现为春冬高、夏秋低。利用多元回归分析初步判断黄、渤海滨海带PM10属于复合来源,大气PM10浓度约30%的变化与降水、人均能耗和沙尘天气相关。黄、渤海滨海带大气PM10浓度的昼夜变化不大,大气PM10浓度与气温呈现正相关,与风速和降水呈现负相关,表现为受各种气象因素综合作用的影响。     

菲和镉单一及复合污染条件下在毛蚶体内的富集动力学研究

采用室内半静态双箱动力学模型实验,研究了菲和镉单一及复合污染条件下在毛蚶(Anadara subcrenata)体内的生物富集,通过对富集与释放过程中毛蚶体内菲和Cd的富集量进行非线性曲线拟合,获得了菲和Cd单一及复合污染条件下在毛蚶体内的吸收速率常数k1、释放速率常数k2、生物富集因子BCF、生物半衰期B1/2和平衡状态下最大富集量CA max等动力学参数。实验结果表明,菲和Cd在实验前期富集速率较高,8 d以后富集速率减缓,释放阶段与富集阶段相似。毛蚶对菲的BCF值为37.80,远大于Cd的BCF值13.12,且生物半衰期时间更长,菲更容易在生物体内富集。菲和Cd联合暴露条件下,在毛蚶体内的CA max和BCF值均大于单一作用,说明二者同时暴露时,毛蚶对菲和镉的吸收富集均有所增强。实验模型拟合度较好,输出值和实测值之间无显著性差异,拟合方程和拟合参数可信。