MODELING OF NONPOINT SOURCE POLLUTION OF NITROGEN AT THE WATERSHED SCALE1

ABSTRACT: The Watershed Nutrient Transport and Transformation (NTT-Watershed) model is a physically based, energy-driven, multiple land use, distributed model that is capable of simulating water and nutrient transport in a watershed. The topographic features and subsurface properties of the watershed are refined into uniform, homogeneous square grids. The vertical discretization includes vegetation, overland flow, soil water redistribution and groundwater zones. The chemical submodel simulates the nitrogen dynamics in terrestrial and aquatic systems. Three chemical state variables are considered (NO₃^-_-, NH₄⁺, and Org-N). The NTT-Watershed model was used to simulate the fate and transport of nitrogen in the Muddy Brook watershed in Connecticut. The model was shown to be capable of capturing the hydrologic and portions of the nitrogen dynamics in the watershed. Watershed planners could use this model in developing strategies of best management practices that could result in maximizing the reductions of nitrogen export from a watershed.     

Optimizing safety-constrained solvent selection for process systems with economic uncertainties

Solvents are very commonly used in industrial facilities for a multitude of reasons. Traditionally, solvent selection has been based on minimizing the process operating cost while satisfying a set of operational requirements. Regrettably, safety considerations have typically been overlooked during the design phase. In this paper, a systematic approach is introduced to integrate safety issues into solvent selection and provides a computationally effective method for establishing tradeoffs between the economic and safety objectives. In order to quantify the risk associated with the solvent, we focus on the potential spillage of the solvent and introduce a risk index that is a function of the amount of solvent used and stored, as well as the Permissible Exposure Limit (PEL) dictated by regulatory directives. An optimization formulation is developed and the associated mathematical program solved to select optimal solvents and blends while incorporating economic, technical, and safety considerations. Tradeoff (Pareto) curves are developed to represent the multi-objective optimization results and tradeoffs. Furthermore, economic-data uncertainty and variability over expected ranges are included in the optimization formulation to conduct an insightful sensitivity analysis. Finally, an illustrative case study is considered via increasing levels of complexity in order to evaluate the proposed optimization method which considers both operating cost and safety risk implications in the presence of economic uncertainties.     

Modelling pollutants dispersion and plume rise from large hydrocarbon tank fires in neutrally stratified atmosphere

Petrochemical industries normally use storage tanks containing large amounts of flammable and hazardous substances. Therefore, the occurrence of a tank fire, such as the large industrial accident on 11th December 2005 at Buncefield Oil Storage Depots, is possible and usually leads to fire and explosions. Experience has shown that the continuous production of black smoke from these fires due to the toxic gases from the combustion process, presents a potential environmental and health problem that is difficult to assess. The goals of the present effort are to estimate the height of the smoke plume, the ground-level concentrations of the toxic pollutants (smoke, SO₂, CO, PAHs, VOCs) and to characterize risk zones by comparing the ground-level concentrations with existing safety limits. For the application of the numerical procedure developed, an external floating-roof tank has been selected with dimensions of 85 m diameter and 20 m height. Results are presented and discussed. It is concluded that for all scenarios considered, the ground-level concentrations of smoke, SO₂, CO, PAHs and VOCs do not exceed the safety limit of IDLH and there are no “death zones” due to the pollutant concentrations.     

Numerical simulation of LNG dispersion under two-phase release conditions

The use of LNG (liquefied natural gas) as fuel brings up issues regarding safety and acceptable risk. The potential hazards associated with an accidental LNG spill should be evaluated, and a useful tool in LNG safety assessment is computational fluid dynamics (CFD) simulation. In this paper, the ADREA-HF code has been applied to simulate LNG dispersion in open-obstructed environment based on Falcon Series Experiments. During these experiments LNG was released and dispersed over water surface. The spill area is confined with a billboard upwind of the water pond. FA1 trial was chosen to be simulated, because its release and weather conditions (high total spill volume and release rate, low wind speed) allow the gravitational force to influence the cold, dense vapor cloud and can be considered as a benchmark for LNG dispersion in fenced area. The source was modeled with two different approaches: as vapor pool and as two phase jet and the predicted methane concentration at sensors' location was compared with the experimental one. It is verified that the source model affect to a great extent the LNG dispersion and the best case was the one modeling the source as two phase jet. However, the numerical results in the case of two phase jet source underestimate the methane concentration for most of the sensors. Finally, the paper discusses the effect of neglecting the ?9.3° experimental wind direction, which leads to the symmetry assumption with respect to wind and therefore less computational costs. It was found that this effect is small in case of a jet source but large in the case of a pool source.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Development of a database for accidents and incidents in the Greek petrochemical industry

The present paper describes the development of a database that comprises all incidents from the Greek petrochemical industry for the period 1997–2003. This database includes industrial incidents, accidents, operational accidents and near misses from all petrochemical sites in Greece and Cyprus. The design of the database has been conceived in a user-friendly way with additional possibilities for its further use, such as: statistical analysis of the data, calculation of safety indicators, accident reports and human factors analysis. The database allows the various participating industries to compare the analysis of indicators in their own installations with the national average, as the database comprises data from the entire Greek petrochemical industry. Special care has been given to include data from near misses too.     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Risk informed optimization of a hazardous material multi-periodic transportation model

This paper presents a systematic framework toward the development of a Transportation Model for Hazardous Materials (HazMat). In practice, the proposed modeling framework is realized through an appropriate generalization of the traditional transportation network problem in the presence of safety constraints that need to be satisfied. The objective is to minimize transportation cost while reducing risks at the desired levels.In particular, the present research study identifies and evaluates different risk factors that influence the HazMat transportation network. Next, the transportation model is depicted graphically using nodes and arcs and optimal conditions are identified by solving the associated minimum cost flow network problem. The results show safety levels that help making informed decisions on choosing the optimal transportation configuration for hazardous material shipments.Within the proposed methodological context, appropriately parameterized simulation studies elucidate the effects of occurrence probabilities of the different risk events on transportation cost. Furthermore, as the appropriate management decisions must consider the effect of actions in one time period on future periods, the proposed model is structured as a multi-periodic model.Finally, the proposed methodological approach is employed to demonstrate the utility of proper analytical tools in decision making and particularly in ensuring that scientifically informed safety procedures are in place while transporting goods that can be potentially proven dangerous to the public and the surroundings.     

A guide to qualitative attribution methods for evaluation in conservation

Knowledge of what conservation interventions improve biodiversity outcomes, and in which circumstances, is imperative. Experimental and quasi-experimental methods are increasingly used to establish causal inference and build the evidence base on the effectiveness of interventions, but their ability to provide insight into how and under what conditions an intervention should be implemented to improve biodiversity outcomes faces limitations. A suite of attribution methods that leverage qualitative methods for causal inference is available but underutilized in conversation impact evaluation. This article provides a guide to 5 such qualitative attribution methods: contribution analysis, process tracing, realist evaluation, qualitative comparative analysis, and most significant change. It defines and introduces each method and then illustrates how they could be applied through a case study of community conservancies in Namibia. This guide provides examples of how qualitative attribution methods can advance knowledge of what works, in which contexts, and why in biodiversity conservation.