Evaluation of a Depth-Based Multivariate k

A nonparametric simulation model (k-nearest neighbor resampling, KNNR) for water quality analysis involving geographic information is suggested to overcome the drawbacks of parametric models. Geographic information is, however, not appropriately handled in the KNNR nonparametric model. In the current study, we introduce a novel statistical notion, called a “depth function,” in the classical KNNR model to appropriately manipulate geographic information in simulating stormwater quality. An application is presented for a case study of the total suspended solids throughout the entire United States. The stormwater total suspended solids concentration data indicated that the proposed model significantly improves the simulation performance compared with the existing KNNR model

Robust Priority for Strategic Environmental Assessment with Incomplete Information Using Multi-Criteria Decision Making Analysis

This study investigates how the priority rankings for dam construction sites vary with multi-criteria decision making (MCDM) techniques and generation approaches for incomplete information. Strategic environmental assessment (SEA) seeks to recommend sustainable dam construction sites based on their environmental and ecological impacts in a long-term plan for dam construction (LPDC) in South Korea. However, if specific information is missing, the SEA is less useful for choosing a dam construction site. In this study, we applied AHP, ELECTRE III, PROMETHEE II and Compromise Programming as MCDM techniques, and used binomial and uniform distributions to generate missing information. We considered five dam site selection situations and compared the results as they depended on both MCDM techniques and information generation methods. The binomial generation method showed the most obvious priorities. All MCDM techniques showed similar priorities in the dam site selection results except for ELECTREE III. The results demonstrate that selecting an appropriate MCDM technique is more important than the data generation method. However, using binomial distribution to generate missing information is more effective in providing a robust priority than uniform distribution, which is a commonly used technique

Development of Models for Prompt Responses from Natural Disasters

This study aims to provide an enhanced model for rapid responses from natural disasters by estimating the maximum structural displacement. The linear regression, support vector machine, and Gaussian process regression (GPR) models were applied to obtain displacement estimates. Further, normalization (NM) and standardization (SD) of variables, and principal component analysis (PCA) were applied to improve model performance. The k-fold cross-validation approach was used to assess the results from the models based on the root-mean-square error and the R-squared indices. According to the results, the GPR model with NM and SD tended to provide the best estimates among the three models. The model that was based on a PCA value of 97% yielded better displacement estimation than the models with PCA values of 95% and 100%. Based on the displacement estimation, the maximum inter-story drift ratio was used to produce the fragility curve that can be used for risk assessment. The fragility curve parameters obtained from the actual numerical and predicted models were investigated and yielded similar responses. The proposed model can thus provide accurate and quick responses in disaster case by rapidly predicting the structural damage information

Sustainability Index Evaluation of the Rainwater Harvesting System in Six US Urban Cities

This study investigated the sustainability of the rainwater harvesting system (RWHS) by analyzing six urban city sites with different rainfall statistics in the United States. We developed a new RWHS performance model by modifying a spreadsheet-based storage, treatment, and overflow runoff model (SS STORM) and verified its performance by comparing with another analytical RWHS model. The sustainability index (SI) evaluation method was used for a reservoir system and applied to the RWHS, employing modified resilience and vulnerability evaluation methods due to the different characteristics of a reservoir and the RWHS. The performance of modified SS STORM is very similar to that of the analytical method, except in Los Angeles, which is characterized by long inter-event times and low rainfall event depths due to low annual rainfall. The sustainability indices were successfully evaluated depending on both RWHS size and water demand and vary over a wide range as annual rainfall increases. This study proposes a new RWHS performance model and sustainability index evaluation method. Further study should confirm the proposed approach in regions with widely different rainfall characteristics

Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

Landslide susceptibility models are important for public safety, but often rely on inaccessible or unaffordable software and geospatial data. Thus, affordable and accessible landslide prediction systems would be especially useful in places that lack the infrastructure for acquiring and analyzing geospatial data. Current landslide susceptibility models and existing methodologies do not consider such issues; therefore, this study aimed to develop an accessible and affordable landslide susceptibility modeling application and methodology based on open-source software and geospatial data. This model used TRIGRS (asc format) and QGIS (Digital Elevation Models (DEMs) extracted from GeoTIFF format) with widely accessible environmental parameters to identify potential landslide risks. In order to verify the suitability of the proposed application and methodology, a case study was conducted on Lantau Island, Hong Kong to assess the validity of the results, a comparison with 1999 landslide locations. The application developed in this study showed a good agreement with the four previous landslide locations marked as highly susceptible, which proves the validity of the study. Therefore, the developing model and the cost-effective approach, in this study simulated the landslide performance well and suggested the new approach of the landslide prediction system

Optimal copula models for the observed discharge and nitrate concentration in the Lower Illinois River

Investigating the complex relationships between water quality parameters and river discharge has been an area of active research for decades. The objective of this study is to apply bivariate distributions to river nitrate concentration and discharge and to suggest the procedure of choosing the best bivariate distribution in the selected models. Nitrate concentration (NO3) data were measured from 1972 to 2012 at five stations in the Lower Illinois River basin, USA. The bivariate distribution was represented by applying three copula models to explore the dependence between discharge and nitrate concentration. The marginal distributions for the copula models include the generalized Pareto (GPA), generalized extreme value (GEV), two-parameter lognormal (LN2), and three-parameter lognormal (LN3) distributions as well as the three copula models of Clayton, Frank, and Gumbel, respectively. Each procedure was tested by the robust diagnostic, the probability plot correlation coefficient (PPCC) test, and the goodness-of-fit test ( statistic). Consequently, the Frank copula model with GPA for stream flow–GPA for nitrate concentration performed more accurately for data from the mainstream stations, Havana (D-31) and Valley City (D-32) and for those from a station on one tributary, Oakford station along the Sangamon River (E-25). However, along other tributaries such as the La Moine River at Ripley (DG-01) and Spoon River at Seville (DJ-08), the Clayton copula model with NL3 for stream flow–GPA for nitrate concentration showed better fit. This study enabled us to develop a procedure to determine an optimal copula for application to the environmental sciences and suggest a case study for the application of copula models to discharge, as a hydrologic variable, and nutrient concentration as a water quality variable

Spatial and Temporal Variations in Reference Crop Evapotranspiration in a Mountainous Island, Jeju, in South Korea

This study aims to assess the spatial and temporal variability of reference crop evapotranspiration (ET) over the mountainous Jeju Island, South Korea. In this mountainous region, only limited observed, station-based meteorological data are available, and thus statistical approaches are used to construct monthly reference crop ET maps. The maximum and minimum temperatures, wind speed, and relative humidity are gap filled using principal component regression (PCR) or multiple linear regression (MLR) and are then spatially interpolated using the hybrid Kriging method to construct monthly maps of reference crop ET at a resolution of 100 m. This study reveals various reference crop ET characteristics for Jeju Island that have not been investigated in previous studies. With increasing elevation and distance from the coast, the air temperature decrease and relative humidity (RH) increase. Therefore, the reference crop ET generally decreases. An increasing trend until the mid-2000s is present in the annual average reference crop ET values, and most of this increase arises from increasing trends in spring and summer. Summer reference crop ET values exhibit increasing trends over time below 1000 m a.s.l. and decreasing trends over time above 1000 m a.s.l

Assessment of an MnCe-GAC Treatment Process for Tetramethylammonium-Contaminated Wastewater from Optoelectronic Industries

Nitrogen-containing wastewater is an important issue in optoelectronic and semiconductor industries. Wastewater containing nitrogen compounds such as ammonium, monoethanolamine (MEA), and tetramethylammonium hydroxide (TMAH) must be properly treated due to concerns about health and environmental effects. MnCe-GAC (granular activated carbon) processes were developed in this study for the treatment of TMAH-contaminated wastewater in high-tech industries. The MnCe-GAC processes could effectively remove ammonium, MEA, and TMAH from aqueous solutions. The removal efficiencies of ammonium and MEA by these processes were better than observed for TMAH. Parameters affecting TMAH removal such as type of process, type of wastewater (synthetic or real), pH, salts, and t-butanol were investigated. In general, removal efficiencies of TMAH by various processes were in the following order: MnCe-GAC/O3/H2O2 > MnCe-GAC/O3 > MnCe-GAC/H2O2 > MnCe-GAC > GAC. The negative effect of sulfate and nitrate on pollutant removal might be due to the salting-out effect. Based on t-butanol experiments, the main degradation mechanisms of TMAH by the MnCe-GAC/O3/H2O2 process likely involved hydroxyl radicals. The process proposed in this study could be an effective alternative method for the treatment of high-tech industrial wastewater to meet the new TMAH discharge limit

Robust Priority for Strategic Environmental Assessment with Incomplete Information Using Multi-Criteria Decision Making Analysis

This study investigates how the priority rankings for dam construction sites vary with multi-criteria decision making (MCDM) techniques and generation approaches for incomplete information. Strategic environmental assessment (SEA) seeks to recommend sustainable dam construction sites based on their environmental and ecological impacts in a long-term plan for dam construction (LPDC) in South Korea. However, if specific information is missing, the SEA is less useful for choosing a dam construction site. In this study, we applied AHP, ELECTRE III, PROMETHEE II and Compromise Programming as MCDM techniques, and used binomial and uniform distributions to generate missing information. We considered five dam site selection situations and compared the results as they depended on both MCDM techniques and information generation methods. The binomial generation method showed the most obvious priorities. All MCDM techniques showed similar priorities in the dam site selection results except for ELECTREE III. The results demonstrate that selecting an appropriate MCDM technique is more important than the data generation method. However, using binomial distribution to generate missing information is more effective in providing a robust priority than uniform distribution, which is a commonly used technique

Electrochemical Removal of Ammonium Nitrogen and COD of Domestic Wastewater using Platinum Coated Titanium as an Anode Electrode

Biological treatment systems face many challenges in winter to reduce the level of nitrogen due to low temperatures. The present work aimed to study an electrochemical treatment to investigate the effect of applying an electric voltage to wastewater to reduce the ammonium nitrogen and COD (chemical oxygen demand) in domestic wastewater. This was done by using an electrochemical process in which a platinum-coated titanium material was used as an anode and stainless steel was used as a cathode (25 cm2 electrode area/500 mL). Our results indicated that the removal of ammonium nitrogen (NH4+–N) and the lowering of COD was directly proportional to the amount of electric voltage applied between the electrodes. Our seven hour experiment showed that 97.6% of NH4+–N was removed at an electric voltage of 5 V, whereas only 68% was removed with 3 V, 20% with 1.2 V, and 10% with 0.6 V. Similarly, at 5 V, the removal of COD was around 97.5%. Over the seven hours of the experiment, the pH of wastewater increased from pH 7.12 to pH 8.15 when 5 V was applied to the wastewater. Therefore, electric voltage is effective in the oxidation of ammonium nitrogen and the reduction in COD in wastewater