Characterizing sources of nitrate leaching from an irrigated dairy farm in Merced County, California

Dairy farms comprise a complex landscape of groundwater pollution sources. The objective of our work is to develop a method to quantify nitrate leaching to shallow groundwater from different management units at dairy farms. Total nitrate loads are determined by the sequential calibration of a sub-regional scale and a farm-scale three-dimensional groundwater flow and transport model using observations at different spatial scales. These observations include local measurements of groundwater heads and nitrate concentrations in an extensive monitoring well network, providing data at a scale of a few meters and measurements of discharge rates and nitrate concentrations in a tile-drain network, providing data integrated across multiple farms. The various measurement scales are different from the spatial scales of the calibration parameters, which are the recharge and nitrogen leaching rates from individual management units. The calibration procedure offers a conceptual framework for using field measurements at different spatial scales to estimate recharge N concentrations at the management unit scale. It provides a map of spatially varying dairy farming impact on groundwater nitrogen. The method is applied to a dairy farm located in a relatively vulnerable hydrogeologic region in California. Potential sources within the dairy farm are divided into three categories, representing different manure management units: animal exercise yards and feeding areas (corrals), liquid manure holding ponds, and manure irrigated forage fields. Estimated average nitrogen leaching is 872 kg/ha/year, 807 kg/ha/year and 486 kg/ha/year for corrals, ponds and fields respectively. Results are applied to evaluate the accuracy of nitrogen mass balances often used by regulatory agencies to assess groundwater impacts. Calibrated leaching rates compare favorably to field and farm scale nitrogen mass balances. These data and interpretations provide a basis for developing improved management strategies.     

Influence of tobacco smoke on carcinogenic PAH composition in indoor PM10 and PM2.5

Because of the mutagenic and/or carcinogenic properties, Polycyclic Aromatic Hydrocarbons (PAH), have a direct impact on human population. Consequently, there is a widespread interest in analysing and evaluating the exposure to PAH in different indoor environments, influenced by different emission sources. The information on indoor PAH is still limited, mainly in terms of PAH distribution in indoor particles of different sizes; thus, this study evaluated the influence of tobacco smoke on PM₁₀ and PM_2.5 characteristics, namely on their PAH compositions, with further aim to understand the negative impact of tobacco smoke on human health. Samples were collected at one site influenced by tobacco smoke and at one reference (non-smoking) site using low-volume samplers; the analyses of 17 PAH were performed by Microwave Assisted Extraction combined with Liquid Chromatography (MAE–LC). At the site influenced by tobacco smoke PM concentrations were higher 650% for PM₁₀, and 720% for PM_2.5. When influenced by smoking, 4 ring PAH (fluoranthene, pyrene, and chrysene) were the most abundant PAH, with concentrations 4600–21 000% and 5100–20 800% higher than at the reference site for PM₁₀ and PM_2.5, respectively, accounting for 49% of total PAH (Σ_PAH). Higher molecular weight PAH (5–6 rings) reached concentrations 300–1300% and 140–1700% higher for PM₁₀ and PM_2.5, respectively, at the site influenced by tobacco smoke. Considering 9 carcinogenic PAH this increase was 780% and 760% in PM₁₀ and PM_2.5, respectively, indicating the strong potential risk for human health. As different composition profiles of PAH in indoor PM were obtained for reference and smoking sites, those 9 carcinogens represented at the reference site 84% and 86% of Σ_PAH in PM₁₀ and PM_2.5, respectively, and at the smoking site 56% and 55% of Σ_PAH in PM₁₀ and PM_2.5, respectively. All PAH (including the carcinogenic ones) were mainly present in fine particles, which corresponds to a strong risk for cardiopulmonary disease and lung cancer; thus, these conclusions are relevant for the development of strategies to protect public health.     

Isoprene emissions and climate

Biogenic volatile organic compounds (BVOCs) play an important role in atmospheric chemistry and the carbon cycle. Isoprene is quantitatively the most important of the non-methane BVOCs (NMBVOCs), with an annual emission of about 400–600 TgC; about 90% of this is emitted by terrestrial plants. Incorporating a mechanistic treatment of isoprene emissions within land-surface schemes has recently become a focus for the modelling community, the aim being to quantify the potential magnitude of associated climate feedbacks. However, these efforts are hampered by major uncertainties about why plants emit isoprene and the relative importance of different environmental controls on isoprene emission. The availability and reliability of observations of isoprene fluxes from different types of vegetation is limited, and this also imposes constraints on model development. Nevertheless, progress is being made towards the development of mechanistic models of isoprene emission which, in conjunction with atmospheric chemistry models, will ultimately allow improved quantification of the feedbacks between the terrestrial biosphere and climate under past and future climate states.     

Operational decisions in response to a performance-based regulation to reduce organic waste impacts near Atlantic salmon farms in British Columbia,Canada

Nutrient enrichment of the seafloor (benthos) has been a primary subject of investigation since the beginning of commercial salmon farming. Waste feed and feces passing through the containment nets can create zones of organic enrichment to the benthos in the vicinity of the pen cages. Early studies of salmon farming in British Columbia Canada (500–1200 metric ton production farms) showed that these effects varied greatly and were reduced where conditions of adequate water depth and strong tidal currents were present. More recent studies of farms producing in excess of 2000 tons of fish have documented measurable organic benthic impacts.The implementation of a performance-based waste regulation in British Columbia in 2002 has caused operational changes to achieve and maintain compliance. This paper describes field monitoring data for four representative farm operations including the waste impact levels prior to the implementation of regulatory standards and the effect of adaptive management actions taken to reduce organic enrichment such as modified feed delivery, relatively small position changes to the cages or reducing site production to achieve continuing compliance.Monitoring over several full production cycles at selected farms indicated that sediment impacts at maximum production levels have decreased while maintaining, and in some cases increasing overall production of salmon.     

Estimating carbon in savanna ecosystems: rational distribution of effort

Minimising the cost of repeatedly estimating C (C) stocks is crucial to the financial viability of projects that seek to sell C credits. Depending on the price of C, this may imply less or more sampling effort than would be applied for science objectives. In systems with heterogeneous C pools, such as savannas, this translates into a variable-effort sampling strategy that maximises the marginal additional C that can be claimed per incremental unit of effort expended. Analysis of a savanna in north-eastern South Africa indicates relatively modest returns per hectare due to the small C quantities and low sequestration rates. Under these conditions, areas in excess of 1,000 ha and infrequent sampling frequencies of 5–10 years are required to make such projects financially viable. For such projects the sample variance, number of samples, cost per sample and establishment costs have negligible impacts on financial viability. It was also found that the soil-C pool contributes up to three times the net returns of the aboveground C pool and provides a strong argument to monitor soil C for certification and market trading. The financial viability estimates, however, do not include the management or opportunity costs incurred in changing the land use. The economies of scale identified in this study combined with the massive area covered by savannas indicate that these additional costs can be covered. Further research is recommended to quantify these costs and interrogate the feasibility of large scale (in excess of 10,000 ha) C-sink projects in savanna systems.     

海水中痕量元素测定及其在重金属污染源判别方面的应用

利用电感耦合等离子体质谱(ICP-MS)研究了胶州湾及莱州湾这两个海域中痕量元素污染水平,发展了ICP-MS海水环境中痕量元素的测定方法,同时找出海水环境中具有保守性的数种元素作为污染示踪元素.达到对于海水环境中痕量污染物被动示踪的目的.对18个真实样品进行了识别率试验,识别率可达90%以上,在选取3个海水样品及1个河水样品混合后的模拟分担率实验,也取得了满意的结果,其误差平均值仅有1.17%.     

Impact of wildflower strips on biological control of cabbage lepidopterans

In a 2-year experiment we investigated whether wildflower strips can be used to enhance the control of cabbage moth, Mamestra brassicae L., and cabbage white butterfly, Pieris rapae L. At two sites, including six organically cultivated fields, M. brassicae egg parasitism and predation rates were determined along with an assessment of larval parasitism rates in M. brassicae and P. rapae using a DNA-based approach. Within each field, plots with and without wildflower strips were sampled and a grid design of 3 m × 3 m was used to analyze the spatial pattern of parasitism. The provision of wildflower strips provided an idiosyncratic effect on the control of lepidopterans: parasitism rates in M. brassicae eggs and larvae were not affected, whereas parasitism rates of larval P. rapae were significantly enhanced by the wildflower strips at one of the two sites. Moreover, at one site predation rates on M. brassicae eggs were significantly enhanced in the wildflower strip plots. Geostatistical analysis showed no distinct spatial patterns in parasitism rates. These results demonstrate that the provision of wildflower strips does not necessarily enhance biological control of lepidopteran cabbage pests and suggest that site-specific environmental factors strongly affect the impact of wildflower strips.     

Distribution patterns of plants explained by human movement behaviour

Distribution patterns of plants are affected by human activities such as creation, destruction or modification of habitats. However, another important question is to what extent humans shape plant distributions by acting as dispersal vectors. In order to answer this question we developed a simulation model for the spread of plant species between human settlements. This was done on the basis of extensive sociological and ecological data on a regional scale. With regard to the sociological data, human movement behaviour defined the amount of exchange between the settlements. Gardening types represented the potential habitat in our model. The ecological data was derived from a vegetation survey carried out in 2003, which was a repeat of a survey between 1974 and 1981 along the same transects. From these surveys, we studied the distributions of 13 species in 67 settlements. In our model, the earlier survey provided the data for the initial distribution. The simulated pattern was consequently compared with the distribution pattern in 2003. In the model, dispersal kernels based on patterns of human movement between settlements led to a better match with the distribution patterns than a null model simulating pure distance dependent dispersal for all species. This was statistically significant for seven of the thirteen species. A striking result was that alien species seem to benefit more from human dispersal than native species. We emphasize the importance of the sociological data on human movement behaviour in parameterizing our regional scale model. This study provides quantitative evidence on the impact of human movement behaviour on the distribution of plant species in suburban areas.     

Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics

Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios.We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results.