Ibrahim Alameddine

On the application of multilevel modeling in environmental and ecological studies

This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

An evaluation of automated structure learning with Bayesian networks: An application to estuarine chlorophyll dynamics

We develop a Bayesian network (BN) model that describes estuarine chlorophyll dynamics in the upper section of the Neuse River Estuary in North Carolina, using automated constraint based structure learning algorithms. We examine the functionality and usefulness of the structure learning algorithms in building model topology with real-time data under different scenarios. Generated BN models are evaluated and a final model is selected. Model results indicate that although the effect of water temperature and river flow on chlorophyll dynamics has remained unchanged following the implementation of the nitrogen Total Maximum Daily Load (TMDL) program; the response of chlorophyll levels to nutrient concentrations has been altered. The results stress the importance of incorporating expert defined constraints and links in conjunction with the automated structure learning algorithms to generate more plausible structures and minimize the sensitivity of the learning algorithms. This hybrid approach towards structure learning allows for the incorporation of existing knowledge while limiting the scope of the learning algorithms to defining the links between environmental variables for which the expert has little or no information.

Multilevel regression models describing regional patterns of invertebrate and algal responses to urbanization across the USA

Abstract Multilevel hierarchical regression was used to examine regional patterns in the responses of benthic macroinvertebrates and algae to urbanization across 9 metropolitan areas of the conterminous USA. Linear regressions established that responses (intercepts and slopes) to urbanization of invertebrates and algae varied among metropolitan areas. Multilevel hierarchical regression models were able to explain these differences on the basis of region-scale predictors. Regional differences in the type of land cover (agriculture or forest) being converted to urban and climatic factors (precipitation and air temperature) accounted for the differences in the response of macroinvertebrates to urbanization based on ordination scores, total richness, Ephemeroptera, Plecoptera, Trichoptera richness, and average tolerance. Regional differences in climate and antecedent agriculture also accounted for differences in the responses of salt-tolerant diatoms, but differences in the responses of other diatom metrics (% eutraphenic, % sensitive, and % silt tolerant) were best explained by regional differences in soils (mean % clay soils). The effects of urbanization were most readily detected in regions where forest lands were being converted to urban land because agricultural development significantly degraded assemblages before urbanization and made detection of urban effects difficult. The effects of climatic factors (temperature, precipitation) on background conditions (biogeographic differences) and rates of response to urbanization were most apparent after accounting for the effects of agricultural development. The effects of climate and land cover on responses to urbanization provide strong evidence that monitoring, mitigation, and restoration efforts must be tailored for specific regions and that attainment goals (background conditions) may not be possible in regions with high levels of prior disturbance (e.g., agricultural development).

Climate change and temperature rise: Implications on food- and water-borne diseases

This study attempts to quantify climate-induced increases in morbidity rates associated with food- and water-borne illnesses in the context of an urban coastal city, taking Beirut-Lebanon as a study area. A Poisson generalized linear model was developed to assess the impacts of temperature on the morbidity rate. The model was used with four climatic scenarios to simulate a broad spectrum of driving forces and potential social, economic and technologic evolutions. The correlation established in this study exhibits a decrease in the number of illnesses with increasing temperature until reaching a threshold of 19.2 °C, beyond which the number of morbidity cases increases with temperature. By 2050, the results show a substantial increase in food- and water-borne related morbidity of 16 to 28% that can reach up to 42% by the end of the century under A1FI (fossil fuel intensive development) or can be reversed to ~0% under B1 (lowest emissions trajectory), highlighting the need for early mitigation and adaptation measures.

A Bayesian changepoint-threshold model to examine the effect of TMDL implementation on the flow-nitrogen concentration relationship in the Neuse River basin.

In-stream nutrient concentrations are well known to exhibit a strong relationship with river flow. The use of flow measurements to predict nutrient concentrations and subsequently nutrient loads is common in water quality modeling. Nevertheless, most adopted models assume that the relationship between flow and concentration is fixed across time as well as across different flow regimes. In this study, we developed a Bayesian changepoint-threshold model that relaxes these constraints and allows for the identification and quantification of any changes in the underlying flow-concentration relationship across time. The results from our study support the occurrence of a changepoint in time around the year 1999, which coincided with the period of implementing nitrogen control measures as part of the TMDL program developed for the Neuse Estuary in North Carolina. The occurrence of the changepoint challenges the underlying assumption of temporal invariance in the flow-concentrations relationship. The model results also point towards a transition in the river nitrogen delivery system from a point source dominated loading system towards a more complicated nonlinear system, where non-point source nutrient delivery plays a major role. Moreover, we use the developed model to assess the effectiveness of the nitrogen reduction measures in achieving a 30% drop in loading. The results indicate that while there is a strong evidence of a load reduction, there still remains a high level of uncertainty associated with the mean nitrogen load reduction. We show that the level of uncertainty around the estimated load reduction is not random but is flow related.

Robust multivariate outlier detection methods for environmental data.

Outliers are an inevitable concern that needs to be identified and dealt with whenever one analyzes a large data set. Today’s water quality data are often collected on different scales, encompass several sites, monitor several correlated parameters, involve a multitude of individuals from several agencies, and span over several years. As such, the ability to identify outliers, which may affect the results of the analysis, is crucial. This note presents several statistical techniques that have been developed to deal with this problem, with particular emphasis on robust multivariate methods. These techniques are capable of isolating outliers while overcoming the effects of masking that can hinder the effectiveness of common outlier detection techniques such as Mahalanobis distances (MD). This note uses a comprehensive national metadata set on lake water quality as a case study to analyze the effectiveness of three robust outlier detection techniques, namely, the minimum covariance determinant (MCD), the min...

Hollow fiber vs. flat sheet MBR for the treatment of high strength stabilized landfill leachate.

The Membrane Bioreactor (MBR) technology is increasingly becoming a prominent process in the treatment of high-strength wastewater such as leachate resulting from the decomposition of waste in landfills. This study presents a performance comparative assessment of flat sheet and hollow fiber membranes in bioreactors for the treatment of relatively stable landfill leachate with the objective of defining guidelines for pilot/full scale plants. For this purpose, a laboratory scale MBR system was constructed and operated to treat a leachate with Chemical Oxygen Demand (COD) (3900-7800mg/L), Biochemical Oxygen Demand (BOD5) (∼440-1537mg/L), Total Phosphorus (TP) (∼10-59mg/L), Phosphate (PO4(3)(-)) (5-58mg/L), Total Nitrogen (TN) (1500-5200mg/L), and ammonium (NH4(+)) (1770-4410mg/L). Both membranes achieved comparable BOD (92.2% vs. 93.2%) and TP (79.4% vs. 78.5%) removals. Higher PO4(3)(-) removal efficiency or percentage (87.3% vs. 81.3%) and slightly higher, but not statistically significant, COD removal efficiency were obtained with the hollow fiber membrane (71.4% vs. 68.5%). On the other hand, the flat sheet membrane achieved significantly higher TN and NH4(+) removal efficiencies (61.2% vs. 49.4% and 63.4% vs. 47.8%, respectively), which may be attributed to the less frequent addition of NaOCl compared to the hollow fiber system.

Risk assessment of oil spills along the Mediterranean coast: A sensitivity analysis of the choice of hazard quantification

Oil pollution in the Mediterranean represents a serious threat to the coastal environment. Quantifying the risks associated with a potential spill is often based on results generated from oil spill models. In this study, MEDSLIK-II, an EU funded and endorsed oil spill model, is used to assess potential oil spill scenarios at four pilot areas located along the northern, eastern, and southern Mediterranean shoreline, providing a wide range of spill conditions and coastal geomorphological characteristics. Oil spill risk assessment at the four pilot areas was quantified as a function of three oil pollution metrics that include the susceptibility of oiling per beach segment, the average volume of oiling expected in the event of beaching, and the average oil beaching time. The results show that while the three pollution metrics tend to agree in their hazard characterization when the shoreline morphology is simple, considerable differences in the quantification of the associated hazard is possible under complex coastal morphologies. These differences proved to greatly alter the evaluation of environmental risks. An integrative hazard index is proposed that encompasses the three simulated pollution metrics. The index promises to shed light on oil spill hazards that can be universally applied across the Mediterranean basin by integrating it with the unified oil spill risk assessment tool developed by the Regional Marine Pollution Emergency Response Centre for the Mediterranean (REMPEC).

Carbon Monoxide and Volatile Organic Compounds as Indicators of Indoor Air Quality in Underground Parking Facilities

The provision of underground parking facilities (UPFs) has become a necessity particularly in densely populated urban areas and business districts. Indoor air quality (IAQ) in such facilities is of great concern due to vehicle-induced emissions. This paper presents an IAQ assessment at six representative UPFs in Beirut using carbon monoxide and volatile organic compounds as IAQ indicators. For this purpose, concentrations of both pollutants were measured at different time intervals throughout the day. Vehicular emission factors (EFs) were estimated using the EMFAC7F1.1 model. A transient mass balance model was then applied to construct concentration profiles. The ventilation rates required to maintain pollutant concentrations within acceptable standards were estimated under maximum UPF occupancy, and a sensitivity analysis was conducted to evaluate the effect of changes in EMFAC7F1.1 input parameters on EFs and ventilation rates.

The role of the water tankers market in water stressed semi-arid urban areas:Implications on water quality and economic burden

Population growth and development are associated with increased water demand that often exceeds the capacity of existing resources, resulting in water shortages, particularly in urban areas, where more than 60% of the worlds population resides. In many developing communities, shortages often force households to depend on water tankers amongst other potential sources for the delivery of water for domestic and/or potable use. While water tankers have become an integral part of the water supply system in many countries, the sector is often unregulated and operates with little governmental supervision. Users are invariably unaware of the origin or the quality of purchased water. In an effort to better assess this sector, a field survey of water vending wells and tankers coupled with a water quality sampling and analysis program was implemented in a pilot semi-arid urban area (Beirut, Lebanon) to shed light on the environmental and socio-economic impacts of the water tanker sector. Total dissolved solids (TDS), chloride (Cl-), and microbial loads exceeded drinking water quality standards. While TDS and Cl- levels were mostly due to saltwater intrusion in coastal wells, tankers were found to be a significant source of total coliforms. Delivered water costs varied depending on the tanker size, the quality of the distributed water, and pre-treatment used, with a markup of nearly 8-24 folds of the public water supply and an equivalent economic burden of 16% of the average household income excluding environmental externalities of water quality. The study concludes with a management framework towards consumer protection under integrated supply and demand side measures.