Development of the hybrid cells in series model to simulate ammonia nutrient pollutant transport along the Umgeni River

Discharge of organic waste results in high nutrient pollution of the water bodies which is a major menace to the environment. A high quantity of nutrients such as ammonia causes a reduction in the dissolved oxygen level and induces algal growth in the water bodies. Water quality models have been the tools to evaluate the rate at which streams can disperse the pollutants they receive. Many water quality models are flawed either because of their inadequacy to completely simulate the advection component of the pollutant transport, or because of the limited application of the models, due to inaccurate estimation of model parameters. The hybrid cell in series (HCIS) developed by Ghosh et al. (2004) has been able to overcome such difficulties associated with the mixing cell-based models. Thus, the current study focuses on developing an analytical solution for the pollutant transport of the ammonia concentration through the plug flow, the first and second well-mixed cells of the HCIS model. The HCIS model coupled with the first order kinetic equation for ammonia nutrient was developed to simulate the ammonia pollutant concentration in the water column. The ammonia concentration at various points along the river system was assessed by considering the effects of the transformation of ammonia to nitrite, the uptake of ammonia by the algae, the respiration rate of the algae and the input of benthic source to the ammonia concentration in the water column. The proposed model was tested using synthetic data, and the HCIS-NH₃ model simulations for spatial and temporal variation of ammonia pollutant transport were analysed. The simulated results of the HCIS-NH₃ model agreed with the Fickian-based advection-dispersion equation (ADE) for simulating ammonia concentration solved using an explicit finite difference scheme. The HCIS-NH₃ model also showed a good agreement with the observed data from the Umgeni River, except during rainy periods.     

Value chain for next-generation biofuels: resilience and sustainability of the product life cycle

Multiple factors including climate change, price uncertainties, and geopolitical instability have prompted many industries to investigate the feasibility of replacing traditional petroleum-based fuels with biofuel alternatives. However, to make this transition successful, these new biofuels must be environmentally sustainable and the necessary support infrastructure must be in place to make the production, distribution, and storage of these biofuels technologically feasible and cost effective. Developing a value chain, spanning from feedstock production to distribution to end users, requires garnering buy-in from multiple stakeholders by demonstrating environmental, economic, and social benefits and incentives. Two critical factors are the environmental benefits achieved from the use of the biofuel technology and the degree of resilience of the value chain to emergent conditions to ensure steady supply to consumers. Moreover, different biofuel pathways have different costs, benefits, and risks which must be compared. In this paper, we describe how environmental sustainability can be modeled using life cycle assessment (LCA) and how the resilience of value chain initiatives can be modeled using a scenario-based decision model. We then describe how sustainability and resilience assessments can be integrated in an iterative, anticipatory LCA framework. These assessments can be used as the basis for a business case for various investments, as well as a means for promoting responsible innovations, with the aviation industry used as a case study.     

Technology and science: innovation at the International Symposium on Sustainable Systems and Technology

While science advances technology, it is also true that technology advances science. Thus, the two bodies of knowledge are increasingly recognized as interdependent. Nonetheless, recent advances in information communication technology (ICT), in particular, may have profound implications for the norms by which science is conducted. For example, treatment of data will increasingly be in terms of probability distributions, rather than as point estimates, because the cost of computation is now so low that more robust approaches to treatment of uncertainty are within the grasp of normal science. Perhaps more importantly, the techniques by which scientists collaborate, communicate, and curate science are undergoing a rare period of rapid change. The papers in this special section exemplify the evolution of the relationship between technology and science in these two important ways: (1) treatment of data uncertainties and (2) participation in novel methods of publication and review. The latter was fostered by the 2016 meeting of the International Symposium on Sustainable Systems and Technologies (ISSST), at which all of the papers in this special section were presented. This editorial summarizes some of the trends in ICT that are the subject of experimentation in ISSST and introduces the idea that scientific societies in the information age are more likely to take the form of a network than they are likely to look like the societies of the last several centuries.     

Simulated climate adaptation in stormwater systems: evaluating the efficiency of adaptation strategies

Adaptations in infrastructure may be necessitated by changes in temperature and precipitation patterns to avoid losses and maintain expected levels of service. A roster of adaptation strategies has emerged in the climate change literature, especially with regard to timing: anticipatory, concurrent, or reactive. Significant progress has been made in studying climate change adaptation decision making that incorporates uncertainty, but less work has examined how strategies interact with existing infrastructure characteristics to influence adaptability. We use a virtual testbed of highway drainage crossings configured with a selection of actual culvert emplacements in Colorado, USA, to examine the effect of adaptation strategy and culvert characteristics on cost efficiency and service level under varying rates of climate change. A meta-model approach with multinomial regression is used to compare the value of better climate change predictions with better knowledge of existing crossing characteristics. We find that, for a distributed system of infrastructural units like culverts, knowing more about existing characteristics can improve the efficacy of adaptation strategies more than better projections of climate change. Transportation departments choosing climate adaptation strategies often lack detailed data on culverts, and gathering that data could improve the efficiency of adaptation despite climate uncertainty.     

The development of a method to determine the burden of climate change on different health outcomes at a local scale: a case study in Osaka Prefecture,Japan

Climate change impacts human health in a variety of ways. Variables including the climate-related risk factor, the health outcome and location all determine the nature and extent of the impact. The existence of different pathways and endpoints presents a problem for quantifying and comparing impacts. Disability-adjusted life year (DALY) provides a common scale, whereby the impact of climate change on both acute and chronic health outcomes can be compared. This study presents a methodology to calculate the impact of climate change on human health at a local scale, using cardiovascular disease (CVD) and meteorological disaster-related injuries (DRIs) in Osaka Prefecture, Japan, as applied case studies. An additional very fine scale assessment of CVD conducted at the neighbourhood level to demonstrate the importance of conducting risk assessments at a local level. The comparative results calculated the impact of climate change in 2050 to be 16.866 DALY/100,000 population for CVD and 0.645 DALY/100,000 for meteorological DRIs. The actual impact of climate change by 2050 on CVD is judged to be higher, although the relative risk was projected to be lower (1.006, compared to 1.263 for meteorological DRIs). The fine scale assessment revealed the variations in the projected impact of climate change on CVD for all administrative zones in Osaka Prefecture. The range of impacts varied from 0 to 114.29 DALY/100,000. The results demonstrate the applicability of using DALY to quantify the impact of climate change on different health outcomes, using a transferable methodology, and provide information that enables evidence-based prioritisation of climate change adaptation strategies at a local scale.     

Optimizing angles of rooftop photovoltaics,ratios of solar to vegetated roof systems,and economic benefits,in Portland,Oregon, USA

This study analyzed insolation data to account for multiple scattering in calculating optimal tilt angles for stationary and seasonally moving photovoltaics on three different roof types in the US Pacific Northwest: vegetated roofs, white roofs, and dark roofs. Using these results, we modeled the energy savings for vegetated roofs and roofs covered in varying numbers of photovoltaic panels. We then calculated the net present value, internal rate of return, and other economic measures for all possible combinations of covering rooftops in mixes of photovoltaic arrays and vegetation, accounting for installation costs, proposed carbon taxes, stormwater management discounts, and other relevant factors. Our results quantify how, in the US Pacific Northwest and similar locations, photovoltaics produce higher returns on investment than do vegetated roofs for new buildings, while vegetated roofs produce better returns on investment than do photovoltaics for older buildings. This is important because in many areas, some buildings have photovoltaics when a vegetated roof would have been more cost and energy efficient, while other buildings have vegetated roofs when photovoltaics would have been more cost and energy efficient. Potential applications include modifying incentive programs and other policies to account properly for building age, use, and other relevant factors to ensure building owners make the most energy-efficient decisions between photovoltaic versus vegetated roof installation. Our research also demonstrates how positive returns on investment can be realized in the US Pacific Northwest and similar regions through vegetated roofs and photovoltaics provided they are each installed optimally.     

Trends and applications of multi-criteria decision analysis in environmental sciences: literature review

Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature.     

A Method of Predicting Point and Path- Averaged Ambient Air VOC Concentrations,Using Meteorological Data

A method of predicting point and path-averaged ambient air VOC concentrations is described. This method was developed for the case of a plume generated from a single point source, and is based on the relationship between wind directional frequency and concentration. One-minute means of wind direction and wind speed were used as inputs to a Gaussian dispersion model to develop this relationship.

Both FTIR spectrometry and a whole-air sampling method were used to monitor VOC plumes during simulated field tests. One test set was also conducted using only whole-air samplers deployed in a closely-spaced network, thus providing an evaluation of the prediction technique free of any bias that might exist between the two analytical methods.

Correlations between observed point concentrations and wind directional frequencies were significant at the 0.05 level in most cases. Predicted path-integrated concentrations, based on observed point concentrations and meteorological data, were strongly correlated with observed values. Predicted point concentrations, based on observed path-integrated concentrations and meteorological data, accurately reflected the location and magnitude of the highest concentrations from each test, as well as the shape of the concentration-versus-crosswind distance curve.     

The Effects of Hypoxia on Sediment Nitrogen Cycling in the Baltic Sea

Primary production in the eutrophic Baltic Sea is limited by nitrogen availability; hence denitrification (natural transformation of nitrate to gaseous N₂) in the sediments is crucial in mitigating the effects of eutrophication. This study shows that dissimilatory nitrate reduction to ammonium (DNRA) process, where nitrogen is not removed but instead recycled in the system, dominates nitrate reduction in low oxygen conditions (O₂ <110 μM), which have been persistent in the central Gulf of Finland during the past decade. The nitrogen removal rates measured in this study show that nitrogen removal has decreased in the Gulf of Finland compared to rates measured in mid-1990s and the decrease is most likely caused by the increased bottom water hypoxia.