Investigating the differences between receptor and dispersion modeling for concentration prediction and health risk assessment of volatile organic compounds from petrochemical industrial complexes.

Receptor and dispersion models both provide important information to help understand the emissions of volatile organic compounds (VOCs) and develop effective management strategies. In this study, differences between the predicted concentrations of two models and the associated impacts on the estimated health risks due to different theories behind two models were investigated. Two petrochemical industrial complexes in Kaohsiung city of southern Taiwan were selected as the sites for this comparison. Although the study compares the approaches by applying the methods to this specific area, the results are expected to be adopted for other areas or industries. Ninety-nine VOC concentrations at eight monitoring sites were analyzed, with the effects of diurnal temperature and seasonal humidity variations being considered. The Chemical Mass Balance (CMB) receptor model was used for source apportionment, while the Industrial Source Complex (ISC) dispersion model was used to predict the VOC concentrations at receptor sites. In the results of receptor modeling, 54% ± 11% and 49% ± 20% of the monitored concentrations were contributed by process emissions in two complexes, whereas the numbers increased to 78% ± 41% and 64% ± 44% in the results of dispersion modeling. Significant differences were observed between two model predictions (p < 0.05). The receptor model was more reproducible given the smaller variances of its results. The effect of seasonal humidity variation on two model predictions was not negligible. Similar findings were observed given that the cancer and non-cancer risks estimated by the receptor model were lower but more reproducible. The adverse health risks estimated by the dispersion model exceeded and were 75.3%-132.4% of the values estimated by using the monitored data, whereas the percentages were lowered to the range from 27.4% to 53.8% when the prediction was performed by using the receptor model. As the results of different models could be significantly different and affect the final health risk assessment, it is important to carefully choose an appropriate model for prediction and to evaluate by monitoring to avoid providing false information for appropriate management.

Optimizing the monitoring strategy of wastewater treatment plants by multiobjective neural networks approach.

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (micro) that represents the level of aspiration achieved in one objective and the conjugate of micro, (1 - micro), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN(-), SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying micro (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN(-) and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.

Watershed-based point sources permitting strategy and dynamic permit-trading analysis.

Permit-trading policy in a total maximum daily load (TMDL) program may provide an additional avenue to produce environmental benefit, which closely approximates what would be achieved through a command and control approach, with relatively lower costs. One of the important considerations that might affect the effective trading mechanism is to determine the dynamic transaction prices and trading ratios in response to seasonal changes of assimilative capacity in the river. Advanced studies associated with multi-temporal spatially varied trading ratios among point sources to manage water pollution hold considerable potential for industries and policy makers alike. This paper aims to present an integrated simulation and optimization analysis for generating spatially varied trading ratios and evaluating seasonal transaction prices accordingly. It is designed to configure a permit-trading structure basin-wide and provide decision makers with a wealth of cost-effective, technology-oriented, risk-informed, and community-based management strategies. The case study, seamlessly integrating a QUAL2E simulation model with an optimal waste load allocation (WLA) scheme in a designated TMDL study area, helps understand the complexity of varying environmental resources values over space and time. The pollutants of concern in this region, which are eligible for trading, mainly include both biochemical oxygen demand (BOD) and ammonia-nitrogen (NH3-N). The problem solution, as a consequence, suggests an array of waste load reduction targets in a well-defined WLA scheme and exhibits a dynamic permit-trading framework among different sub-watersheds in the study area. Research findings gained in this paper may extend to any transferable dynamic-discharge permit (TDDP) program worldwide.

Multicriteria relocation analysis of an off-site radioactive monitoring network for a nuclear power plant.

Due to increasing environmental consciousness in most countries, every utility that owns a commercial nuclear power plant has been required to have both an on-site and off-site emergency response plan since the 1980s. A radiation monitoring network, viewed as part of the emergency response plan, can provide information regarding the radiation dosage emitted from a nuclear power plant in a regular operational period and/or abnormal measurements in an emergency event. Such monitoring information might help field operators and decision-makers to provide accurate responses or make decisions to protect the public health and safety. This study aims to conduct an integrated simulation and optimization analysis looking for the relocation strategy of a long-term regular off-site monitoring network at a nuclear power plant. The planning goal is to downsize the current monitoring network but maintain its monitoring capacity as much as possible. The monitoring sensors considered in this study include the thermoluminescence dosimetry (TLD) and air sampling system (AP) simultaneously. It is designed for detecting the radionuclide accumulative concentration, the frequency of violation, and the possible population affected by a long-term impact in the surrounding area regularly while it can also be used in an accidental release event. With the aid of the calibrated Industrial Source Complex-Plume Rise Model Enhancements (ISC-PRIME) simulation model to track down the possible radionuclide diffusion, dispersion, transport, and transformation process in the atmospheric environment, a multiobjective evaluation process can be applied to achieve the screening of monitoring stations for the nuclear power plant located at Hengchun Peninsula, South Taiwan. To account for multiple objectives, this study calculated preference weights to linearly combine objective functions leading to decision-making with exposure assessment in an optimization context. Final suggestions should be useful for narrowing the set of scenarios that decision-makers need to consider in this relocation process.

Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images.

Soil erosion associated with non-point source pollution is viewed as a process of land degradation in many terrestrial environments. Careful monitoring and assessment of land use variations with different temporal and spatial scales would reveal a fluctuating interface, punctuated by changes in rainfall and runoff, movement of people, perturbation from environmental disasters, and shifts in agricultural activities and cropping patterns. The use of multi-temporal remote sensing images in support of environmental modeling analysis in a geographic information system (GIS) environment leading to identification of a variety of long-term interactions between land, resources, and the built environment has been a highly promising approach in recent years. This paper started with a series of supervised land use classifications, using SPOT satellite imagery as a means, in the Kao-Ping River Basin, South Taiwan. Then, it was designed to differentiate the variations of eight land use patterns in the past decade, including orchard, farmland, sugarcane field, forest, grassland, barren, community, and water body. Final accuracy was confirmed based on interpretation of available aerial photographs and global positioning system (GPS) measurements. Finally, a numerical simulation model (General Watershed Loading Function, GWLF) was used to relate soil erosion to non-point source pollution impacts in the coupled land and river water systems. Research findings indicate that while the decadal increase in orchards poses a significant threat to water quality, the continual decrease in forested land exhibits a potential impact on water quality management. Non-point source pollution, contributing to part of the downstream water quality deterioration of the Kao-Ping River system in the last decade, has resulted in an irreversible impact on land integrity from a long-term perspective.

Optimal expansion of water quality monitoring network by fuzzy optimization approach.

River reaches are frequently classified with respect to various mode of water utilization depending on the quantity and quality of water resources available at different location. Monitoring of water quality in a river system must collect both temporal and spatial information for comparison with respect to the preferred situation of a water body based on different scenarios. Designing a technically sound monitoring network, however, needs to identify a suite of significant planning objectives and consider a series of inherent limitations simultaneously. It would rely on applying an advanced systems analysis technique via an integrated simulation-optimization approach to meet the ultimate goal. This article presents an optimal expansion strategy of water quality monitoring stations for fulfilling a long-term monitoring mission under an uncertain environment. The planning objectives considered in this analysis are to increase the protection degree in the proximity of the river system with higher population density, to enhance the detection capability for lower compliance areas, to promote the detection sensitivity by better deployment and installation of monitoring stations, to reflect the levels of utilization potential of water body at different locations, and to monitor the essential water quality in the upper stream areas of all water intakes. The constraint set contains the limitations of budget, equity implication, and the detection sensitivity in the water environment. A fuzzy multi-objective evaluation framework that reflects the uncertainty embedded in decision making is designed for postulating and analyzing the underlying principles of optimal expansion strategy of monitoring network. The case study being organized in South Taiwan demonstrates a set of more robust and flexible expansion alternatives in terms of spatial priority. Such an approach uniquely indicates the preference order of each candidate site to be expanded step-wise whenever the budget limitation is sensitive in the government agencies.