“My garden is an expression of me”: Exploring householders' relationships with their gardens

Domestic gardens offer immense potential as sites for native biodiversity conservation. In urban areas they often comprise the largest land use, thus presenting an accessible and immediate way for urban dwellers to connect with nature and to support and enhance native biodiversity. This paper presents findings from a study of 55 domestic gardens undertaken in Dunedin, New Zealand, which explores householders' relationships with their gardens. The study data was derived from two interviews with householders, two photo exercises (approximately a year apart), together with a number of biological studies of the gardens. Gardens proved to be very important for our householders; for physical and mental health, as an expression of ownership and identity, as sites for social relationships, for connecting with nature and as site of domestic produce production. Householders' connections with nature were idiosyncratic, multifaceted and exhibited in ways that are more complex and varied than those usually considered by those working in the natural sciences and indeed biophilia supporters. We emphasize the importance of the people side of nature in seeking to build and support positive ecological change in the urban environment and the value of combining natural and social science approaches.     

Degradation of aqueous piperazine in carbon dioxide capture

Concentrated, aqueous piperazine (PZ) is a novel solvent for carbon dioxide (CO₂) capture by absorption/stripping. One of the major advantages of PZ is its resistance to thermal degradation and oxidation.At 135 and 150 °C, 8 m PZ is up to two orders of magnitude more resistant to thermal degradation than 7 m monoethanolamine (MEA). After 18 weeks at 150 °C, only 6.3% of the initial PZ was degraded, yielding an apparent first order rate constant for amine loss of 6.1 × 10^?9 s^?1. PZ was the most resistant amine tested, with the other screened amines shown in order of decreasing resistance: 7 m 2-amino-2-methyl-1-propanol, 7 m Diglycolamine^®, 7 m N-(2-hydroxyethyl)piperazine, 7 m MEA, 8 m ethylenediamine, and 7 m diethylenetriamine. Thermal resistance allows the use of higher temperatures and pressures in the stripper, potentially leading to overall energy savings.Concentrated PZ solutions demonstrate resistance to oxidation compared to 7 m MEA solutions. Experiments investigating metal-catalyzed oxidation found that PZ solutions were 3–5 times more resistant to oxidation than MEA. Catalysts tested were 1.0 mM iron (II), 4.0–5.0 mM copper (II), and a combination of stainless steel metals (iron (II), nickel (II), and chromium (III)). Inhibitor A reduced PZ degradation catalyzed by iron (II) and copper (II).     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

The promise of acceptance as an NGO security management approach

This paper explores three questions related to acceptance as a security management approach. Acceptance draws upon relationships with community members, authorities, belligerents and other stakeholders to provide consent for the presence and activities of a non‐governmental organisation (NGO), thereby reducing threats from these actors. Little is documented about how NGOs gain and maintain acceptance, how they assess and monitor the presence and degree of acceptance, or how they determine whether acceptance is effective in a particular context. Based on field research conducted in April 2011 in Kenya, South Sudan and Uganda, we address each of these three issues and argue that acceptance must be actively sought as both a programme and a security management strategy. In the paper we delineate elements common to all three contexts as well as missed opportunities, which identify areas that NGOs can and should address as part of an acceptance approach.     

Impacts of Climatic Change On Peatland Hydrochemistry; A Laboratory-Based Experiment

A laboratory simulation of a reduction in water table height that could be anticipated from current climate change models, resulted in a change in the efficiency with which a valley-bottom wetland acted as a sink/source of nutrients. Effects were confined to the upper 10 cm of the profile, but since this depth has the greatest hydraulic conductivity, it was noted that the effects of hydrochemical changes therein would be readily transferred to receiving waterbodies. Marked changes in leachate chemistry were observed, including increases in nitrate and sulphate concentrations, while concentrations of phenolics, dissolved organic carbon, potassium, iron and ammonium decreased. These changes have implications for the quality and productivity of waters draining wetlands.