The theory of neighbourhood decline due to pariah land uses: Regaining control of the downward cycle

Some neighbourhood environments have become dumping grounds for locally unwanted land uses (LULUs) that middle-class Americans do not want near their homes. LULUs may combine with other pariah land uses to collectively undermine the quality of the local environment, reduce investment, government services, the proportion of middle-income people and associated businesses. As important neighbourhood attributes are lost, illegal activities, derelict structures, trash-strewn lots and the concentration of poor and unhealthy people may increase. This paper describes the theory behind the impact of pariah land uses through examples of the downward spiral experienced by Camden, New Jersey and the south Bronx, New York. It then documents the experience of one community, Elizabethport, New Jersey, in reversing that downward spiral. Success in Elizabethport came from the synergistic activities of local, state and federal governments, community groups, and not-for-profit organizations as they struggled to regain control through local environmental management, rebuilding, and reducing crime. Social capital was also strengthened by using a local community health concern—that of childhood asthma. Efforts to reverse the downward spiral of urban decay from pariah land uses should be multi-faceted, spurred by local efforts that address local concerns.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Cultivating hybrid collectives: research methods for enacting community food economies in Australia and the Philippines

Across the globe, groups are experimenting with initiatives to create alternatives to the dominant food system. What role might research play in helping to strengthen and multiply these initiatives? In this paper we discuss two research projects in Australia and the Philippines in which we have cultivated hybrid collectives of academic researchers, lay researchers and various non-human others with the intention of enacting community food economies. We feature three critical interactions in the “hybrid collective research method”: gathering, which brings together those who share concerns about community food economies; reassembling, in which material gathered is deliberatively rebundled to amplify particular insights; and translating, by which reassembled ideas are taken up by other collectives so they may continue to “do work”. We argue that in a climate-changing world, the hybrid collective research method fosters opportunities for a range of human and non-human participants to act in concert to build community food economies.