The application of system dynamics in different practices of a waste management system

The continuous increase in waste generation warrants global management of waste to reduce the adverse economic, social, and environmental impact of waste while achieving goals for sustainability. The complexity of waste management systems due to different waste management practices renders such systems difficult to analyze. System dynamics (SD) approach aids in conceptualizing and analyzing the structure, interactions, and mode of behavior of the complex systems. The impact of the underlying components can therefore be assessed in an integrated way while the impact of possible policies on the system can be studied to implement appropriate decisions. This review summarizes various applications of SD pertinent to the waste management practices in different countries. Practices may include waste generation, reduction, reuse/recovery, recycling, and disposal. Each study supports regional-demanding targets in environmental, social, and economic scopes such as expanding landfill life span, implementing proper disposal fee, global warming mitigation, energy generation/saving, etc. The interacting variables in the WMS are specifically determined based on the defined problem, ultimate goal, and the type of waste. Generally, population and gross domestic product can increase the waste generation. An increase in waste reduction, source separation, and recycling rate could decrease the environmental impact, but it is not necessarily profitable from an economic perspective. Incentives to separate waste and knowledge about waste management are variables that always have a positive impact on the entire system.

     

Environmental planning based on reduce,reuse, recycle and recover using artificial intelligence

Waste disposal was a significant challenge faced by the community and government. Customers buy and use goods that produce a considerable amount of waste. Waste management is a major problem since the number of consumers increased due to high waste generation. This has resulted in a huge amount of waste, which calls for enormous waste-management policies. Reduce; Reuse, Recycle, and Recover are the tools to reduce the adverse implications of retailing and manufacturing on the environment. In this paper, Artificial Intelligence based Hybridized Intelligent Framework (AIHIF) has been proposed for automated recycling to optimizing the waste management process. The system will optimize waste collection with a short distance by utilizing machine learning and graph theory. AI design technology, which helps different approaches adapted to interest groups, collecting their specific information and greatly improving environmental planning and urban management performance, accuracy, and efficiency. The experimental results show that the proposed method enhances performance and accuracy when compared to other existing methods.     

Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling approach

Introduction: There have been a number of studies that have led to the development of safety risk assessment models to quantify the probability of crash frequencies on roadway facilities (both at micro- and macro-levels), over a specified time period. However, past research has rarely focused on heterogeneous traffic conditions in developing countries. Method: This paper puts forward several models related to the traditional count approach to estimate crash frequency at a micro-level in a non-lane based bi-directional heterogeneous traffic environment. The paper shows the results of dispersion, zero-inflation, and random heterogeneity effects of different exogenous factors by comparing Poisson (P); Negative Binomial (NB); random and fixed parameter Zero-Inflated Poisson (ZIP); and Latent Class Models (LCM). The empirical analysis is based on data from a section of a major national highway in Bangladesh. The performance of the models was validated using different statistical goodness-of-fit measures that compared the estimated and observed average crash frequencies at individual locations. With the identification of the most significant influencing factors, the paper discusses the practical policy implications using partial effects analysis and spatial distribution. Results: It was found that the Zero-Inflated Random Parameter model gives a slightly better statistical fit when compared to alternative approaches. Practical applications: This micro-level modeling approach would be useful to identify significant crash risk factors; to prioritize road sections according to their safety level; to select site-specific appropriate counter-measures; and devise proactive target oriented safety management strategies. Thus, the results shown here could be a point of reference in the planning, designing, maintaining, and managing two-lane highway sections in developing countries.     

A stochastic approach for risk analysis in vapor cloud explosion

A stochastic approach for evaluating the risk of vapor cloud explosions is proposed in this work. The proposed methodology aims to incorporate the effect of uncertainty into the risk analysis to produce a better overall view for the risk. Some stochastic variables are used to estimate the probability of vapor cloud explosions: frequency of the release, the probability of not having an immediate ignition, the probability of delayed ignition and the probability of a vapor cloud explosion given a delayed ignition, as well as different possible meteorological conditions. These stochastic variables are represented with probability distribution curves. Different curves for the frequencies of releases from process equipment types (steel process pipes, flanges, manual valves, actuated valves, etc.), different equipment diameters and different leak sizes are also used in this analysis. Monte Carlo simulation is performed to obtain the risk as a probability distribution using the Analytic Solver Platform. Then the risk distribution curve obtained by Monte Carlo simulation is used to estimate the probability of satisfying the risk tolerance criterion.     

Assessing the social cost of housing projects on the built environment: Analysis and monetization of the adverse impacts incurred on the neighbouring communities

Construction projects generate serious environmental adverse impacts on the adjacent residents. All those harmful consequences and damages that third parties or the community sustain due to the implementation of construction processes are called social costs. Although, the presence of social costs is widely mentioned in the literature, in the projects' initial cost estimation practices, the social costs are not estimated and included. Whereas since these costs are not compensated, problems can be emerged by the community. It is a truism that the majority of the models proposed to quantify the social costs have been concentrated on construction, repair, and maintenance of the infrastructure projects namely; utilities, roads, and highways. On the other hand, up to the present, a limited number of attempts has been made to quantify residential housing construction associated social costs. Thus, this research aims to expand and/or contribute to the existing body of knowledge via estimating how much social cost society surrounding residential housing construction sites are subjected to. For this purpose, a social cost estimation model is developed to assist industry professionals on how to estimate social costs in residential construction projects. The social cost estimation model is developed to provide guidance for phase by phase monetization of the residential construction associated social costs. In this paper, the model proposed for social cost estimation is validated via a case study in Turkey.     

Research on flow assurance of deepwater submarine natural gas pipelines: Hydrate prediction and prevention

The formation of hydrate will lead to serious flow assurance problems in deepwater submarine natural gas transmission pipelines. However, the accurate evaluation model of the hydrate blocking risk for submarine natural gas transportation is still lacking. In this work, a novel model is established for evaluating the hydrate risk in deepwater submarine gas pipelines. Based on hydrate growth-deposition mechanism, the mathematical model mainly consists of mass, momentum and energy conservation equations. Meantime, the model results are obtained by finite difference method and iterative technique. Finally, the model has been applied in the production of deepwater gas field (L Gas Field) in China, and the sensitivity analysis of relevant parameters has been carried out. The results show that: (a). The mathematical model can well predict the hydrate blockage risk in deepwater natural gas pipelines after verification. (b). Hydrate is easily formed at the intersection of horizontal pipeline and vertical riser, and the maximum blocking position often occurs in middle of the riser. (c). The hydrate blockage degree and length of hydrate formation region (HFR) decrease with the increase of gas transport rate. (d). The hydrate blockage degree and length of HFR decrease with the increase of gas transport temperature. (e). The hydrate blockage degree and length of HFR increase with the extension of horizontal pipeline. (f). Injecting inhibitors can effectively inhibit hydrate formation and blockage, but the improvement of transmission measures can significantly reduce the dosage of inhibitor. It is concluded that measures such as increasing gas transportation rate and temperature, shortening horizontal pipeline length, optimizing inhibitor injection point and injection rate can play a safe, economic and efficient role in hydrate preventing and controlling.     

Integrating ecosystem services in spatial planning and strategic environmental assessment: The role of the cascade model

Over the last decade, the ecosystem service (ES) approach has gained increasing attention because it offers important advantages for enhancing decision-making. However, a key and remaining challenge is how to implement this approach in real-world decision problems. This challenge is particularly relevant for governance and policy instruments, such as spatial planning and strategic environmental assessment (SEA), where including the ES approach is recognized as a great opportunity for achieving sustainable development goals. Consequently, this opinion paper proposes the use of the ES cascade model, as the basis to develop a framework that makes explicit the links among development objectives, sustainability goals, and the overall dependency on the ES supply. The main reflections address the need for a collaborative work between policy and science as a cross-cutting aspect for an interactive, participative and transparent research process that allows 1) the development of spatial indicators of ecosystem structures, 2) valuation and spatial modeling of ES, 3) identification of benefits and networks of actors, 4) the definition of values and, 5) generation of scenarios for a trade-off assessment of development alternatives. Finally, in addition to the information and knowledge generation regarding multi-scale relationships of the different components of the ES cascade, real-world evidence is urgently needed.     

Impact of floodplain and Stage 0 stream restoration on flood attenuation and floodplain exchange during small frequent storms

River flooding impacts human life and infrastructure, yet provides habitat and ecosystem services. Traditional flood control (e.g., levees, dams) reduces habitat and ecosystem services, and exacerbates flooding elsewhere. Floodplain restoration (i.e., bankfull floodplain reconnection and Stage 0) can also provide flood management, but has not been sufficiently evaluated for small frequent storms. We used 1D unsteady Hydrologic Engineering Center's River Analysis System to simulate small storms in a 5 km-long, second-order generic stream from the Chesapeake Bay watershed, and varied % channel restored (starting at the upstream end), restoration location, restoration bank height (distinguishes bankfull from Stage 0 restoration), and floodplain width/Manning's n. Stream restoration decreased (attenuated) peak flow up to 37% and increased floodplain exchange by up to 46%. Floodplain width and % channel restored had the largest impact on flood attenuation. The incremental effects of new restoration projects on flood attenuation were greatest when little prior restoration had occurred. By contrast, incremental effects on floodplain exchange were greatest in the presence of substantial prior restoration, setting up a tradeoff. A similar tradeoff was revealed between attenuation and exchange for project location, but not bank height or floodplain width. In particular, attenuation and exchange were always greater for Stage 0 than for bankfull floodplain restoration. Stage 0 thus may counteract human impacts such as urbanization.     

Visible light photocatalytic degradation of microplastic residues with zinc oxide nanorods

Microplastics have recently become a major environmental issue due to their ubiquitous distribution, uncontrolled environmental occurrences, small sizes and long lifetimes. Actual remediation methods include filtration, incineration and advanced oxidation processes such as ozonation, but those methods require high energy or generate unwanted by-products. Here we tested the degradation of fragmented, low-density polyethylene (LDPE) microplastic residues, by visible light-induced heterogeneous photocatalysis activated by zinc oxide nanorods. The reaction was monitored using Fourier-transform infrared spectroscopy, dynamic mechanical analyser and optical imaging. Results show a 30% increase of the carbonyl index of residues, and an increase of brittleness accompanied by a large number of wrinkles, cracks and cavities on the surface. The degree of oxidation was directly proportional to the catalyst surface area. A mechanism for polyethylene degradation is proposed.