Function Approximation and Documentation of Sampling Data Using Artificial Neural Networks

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field.Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors.BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.     

脱氮硫杆菌处理垃圾填埋场渗滤污水的研究

在填充不同粒径硫磺的固定床反应器中,研究脱氮硫杆菌去除垃圾填埋场消化渗滤污水中硝酸盐的可行性,结果表明,当ＨＲＴ为５．７１ｈ,硫磺粒径为２．８－５．６ｍｍ时,最高处理浓度达４００ｍｇ／Ｌ,达以反硝化所需的最小停留时间取决于硫磺粒径及进水硝酸盐浓度。     

Impact of photocatalytic remediation of pollutants on urban air quality

In the recent years, photocatalytic self-cleaning and “depolluting” materials have been suggested as a remediation technology mainly for NO _x and aromatic VOCs in urban areas. A number of products incorporating the aforementioned technology have been made commercially available with the aim to improve urban air quality. These commercial products are based on the photocatalytic properties of a thin layer of TiO₂ at the surface of the material (such as glass, pavement, etc.) or embedded in paints or concrete. The use of TiO₂ photocatalysts as an emerging air pollution control technology has been reported in many locations worldwide. However, up to now, the effectiveness measured in situ and the expected positive impact on air quality of this relatively new technology has only been demonstrated in a limited manner. Assessing and demonstrating the effectiveness of these depolluting techniques in real scale applications aims to create a real added value, in terms of policy making (i.e., implementing air quality strategies) and economics (by providing a demonstration of the actual performance of a new technique).

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Field Trial of Biosparging with Oxygen for Bioremediation of Volatile Organic Compounds

Xpert Design & Diagnostics, LLC (XDD), & Conestoga‐Rovers and Associates (CRA) conducted a biosparging field trial at a Superfund site in New Jersey. The biosparging field trial proved that biosparging with oxygen was very effective in promoting the biological destruction of benzene. The approximately 265‐day period of oxygen injection successfully reduced benzene concentrations by several orders of magnitude, or even to non‐detect values, at least 40 feet from the point of injection. Through co‐precipitation of arsenic with oxidized iron, biosparging also effectively reduced total concentrations of arsenic and iron in groundwater. Based on the results of the biosparging field trial, the final remedy for the site has been amended to include the use of biosparging technology as an alternative to groundwater pumping and aboveground treatment in select locations. © 2001 John Wiley & Sons,Inc.     

How to assess the feeding activity in ecotoxicological laboratory tests using enchytraeids?

The risk assessment of pesticides on soil fauna is an issue to protect agroecosystem sustainability. Enchytraeids are recognized as relevant soil bioindicators of chemical stress in agroecosystems. In laboratory, the reproduction test was found to be sensitive to reveal chemical impacts on enchytraeids. However, it does not allow to assess the impacts on ecological functions in which enchytraeids are involved. The objectives of this study were (i) to explore the feasibility of the bait-lamina test with enchytraeids under laboratory conditions and (ii) to compare its sensitivity with the Enchytraeid Reproduction Test. We exposed individuals of Enchytraeus albidus to two commercial formulations of fungicides widely used in Europe. The EC50 reproduction for the Swing^® Gold (50 g L^?1 epoxiconazole and 133 g L^?1 dimoxystrobin) and the Cuprafor micro^® (50% copper oxychloride) were respectively estimated at 1.66?±?0.93 times the recommended dose and >?496 mg kg^?1 of copper. However, no impact was found on the feeding activity of enchytraeids. The bait-lamina test thus appeared less sensitive than the Enchytraeid Reproduction Test to the tested fungicides. Despite that, this test which is achievable under laboratory conditions and allows assessing indirect effects of pesticides is quick, cheap, and easy to perform. It would deserve to be used to explore longer-exposure effects through the repeated addition of bait-lamina sticks.     

Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5 °C future

Whilst future air temperature thresholds have become the centrepiece of international climate negotiations, even the most ambitious target of 1.5 °C will result in significant sea-level rise and associated impacts on human populations globally. Of additional concern in Arctic regions is declining sea ice and warming permafrost which can increasingly expose coastal areas to erosion particularly through exposure to wave action due to storm activity. Regional variability over the past two decades provides insight into the coastal and human responses to anticipated future rates of sea-level rise under 1.5 °C scenarios. Exceeding 1.5 °C will generate sea-level rise scenarios beyond that currently experienced and substantially increase the proportion of the global population impacted. Despite these dire challenges, there has been limited analysis of how, where and why communities will relocate inland in response. Here, we present case studies of local responses to coastal erosion driven by sea-level rise and warming in remote indigenous communities of the Solomon Islands and Alaska, USA, respectively. In both the Solomon Islands and the USA, there is no national government agency that has the organisational and technical capacity and resources to facilitate a community-wide relocation. In the Solomon Islands, communities have been able to draw on flexible land tenure regimes to rapidly adapt to coastal erosion through relocations. These relocations have led to ad hoc fragmentation of communities into smaller hamlets. Government-supported relocation initiatives in both countries have been less successful in the short term due to limitations of land tenure, lacking relocation governance framework, financial support and complex planning processes. These experiences from the Solomon Islands and USA demonstrate the urgent need to create a relocation governance framework that protects people’s human rights.     

Ecological changes and local knowledge in a giant honey bee (<Emphasis Type="Italic">Apis dorsata</Emphasis> F.) hunting community in Palawan,Philippines

One of the traditional livelihood practices of indigenous Tagbanuas in Palawan, Philippines is wild honey hunting and gathering from the giant honey bee (Apis dorsata F.). In order to analyze the linkages of the social and ecological systems involved in this indigenous practice, we conducted spatial, quantitative, and qualitative analyses on field data gathered through mapping of global positioning system coordinates, community surveys, and key informant interviews. We found that only 24% of the 251 local community members surveyed could correctly identify the giant honey bee. Inferential statistics showed that a lower level of formal education strongly correlates with correct identification of the giant honey bee. Spatial analysis revealed that mean NDVI of sampled nesting tree areas has dropped from 0.61 in the year 1988 to 0.41 in 2015. However, those who correctly identified the giant honey bee lived in areas with high vegetation cover. Decreasing vegetation cover limits the presence of wild honey bees and this may also be limiting direct experience of the community with wild honey bees. However, with causality yet to be established, we recommend conducting further studies to concretely model feedbacks between ecological changes and local knowledge.