Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.     

Development of a Local-Scale Spatially Refined Multimedia Fate Model (LSRMFM) for Urban-Scale Risk Assessment: Model Formulation, GIS-Based Preprocessing, and Case Study

A local-scale spatially refined multimedia fate model (LSRMFM) was developed to evaluate in detail the multimedia transport of organic compounds at a spatial level. The model was derived using a combination of an advection?Cdispersion?Creaction partial differential equation, a steady-state multimedia fugacity model, and a geographical information system. The model was applied to predicting four major volatile organic compounds that are produced as emissions (benzene, toluene, xylene, and styrene) in an urban and industrial area (the 50?×?50-km area was divided into 0.5?×?0.5-km segments) in Korea. To test the accuracy of the model, the LSRMFM was used to predict the extent of dispersion and the data compared with actual measured concentrations and the results of a generic multimedia fate model (GMFM). The results indicated that the method developed herein is appropriate for predicting long-term multimedia pollution. However, the comparison study also illustrated that the developed model has some limitations (e.g., steady-state assumption) in terms of explaining all the observed concentrations, and additional verification and study (e.g., validation using a large observed data set, integration with a more accurate runoff model) would be desirable. In comparing LSRMFM and GMFM, discrepancies between the LSRMFM and GMFM outputs were found, as the result of geographical effects, even though the environmental parameters were identical. The geographical variation for LSRMFM output indicated the existence of considerable local human and ecological risks, whereas the GMFM output indicated less average risk. These results demonstrate that the model has the potential for improving the management of pollutant levels under these refined spatial conditions.     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

Assessment of waters and sediments impacted by drainage at the Young Dong coal mine site, South Korea

Introduction  

This study focused on the assessment of the geochemistry and hydrology of the Imgok Creek–Young Dong tributary for the design of a field coal mine drainage treatment system.     

The impacts of the waterfront development in Iskandar Malaysia

The increasing number of waterfront projects shows that the concept of waterfront development has contributed to the advancement of the Malaysian construction industry. This study seeks to assess the impacts of the implementation of waterfront development in Iskandar Malaysia eliciting the perceived relative importance of these impacts. A comprehensive literature review has been conducted to collate the potential impacts of the waterfront development. A total of 363 sets of questionnaires were distributed to the contractors and developers registered under G7 category of the Construction Industry Development Board in Malaysia. Results show that the most significant influence of the waterfront development is related to economic benefits, while environmental impacts are not considered as a priority for this category of stakeholders. In addition, this study also indicates that the implementation of the waterfront projects has generated numerous job opportunities within the developed region. Moreover, exchanging knowledge in the planning and managing of the waterfront development is imperative to the development of the waterfront projects. Nevertheless, the shortcomings of these developments, such as the negative environmental impact on natural beaches and the coastline (i.e. pollution) as well as human-related issues (i.e. excessive human settlement), should not be taken lightly. This research confirms that the reclamation of land for waterfront development is closely correlated with the destruction of these natural structures.     

Distribution and hydrodynamic model of the Keumdong oil spill in Kwangyang Bay, Korea

Oil concentrations in the seawater, seabed sediments, and shoreline sediments were measured from the samples collected 165 days after the Keumdong oil spill accident, which had occurred in Kwangyang Bay of the South Sea, Korea in September 1993. Distribution patterns of the concentrations in the seabed and shoreline sediments have allowed us to hindcast the transport and fate of the spilled oil. Overall, the patterns agree better with the southward (seaward) surface water circulation than with the northward (landward) bottom water circulation over the region. Rapid, initial dispersal of the spilled oil to the south and its subsequent grounding onto the intertidal seabed sediments, as well as onto the shorelines, appear to have made ineffective the subsurface oil transport by the bottom current.     

Phylogenetic diversity of NO reductases,new tools for nor monitoring,and insights into N2O production in natural and engineered environments

● 548 representative nor genes were collected to create complete phylogenetic trees. ● The distribution of nor and nod genes were detected in 18 different phyla. ● The most conserved amino acids in NOR were located adjacent to the active site. ● nor-universal and Clade-specific primers were designed, suggested, and tested. Nitric oxide reductases (NORs) have a central role in denitrification, detoxification of nitric oxide (NO) in host-pathogen interactions, and NO-mediated cell-cell signaling. In this study, we focus on the phylogeny and detection of qNOR and cNOR genes because of their nucleotide sequence similarity and evolutionary relatedness to cytochrome oxidases, their key role in denitrification, and their abundance in natural, agricultural, and wastewater ecosystems. We also include nitric oxide dismutase (NOD) due to its similarity to qNOR. Using 548 nor sequences from publicly accessible databases and sequenced isolates from N₂O-producing bioreactors, we constructed phylogenetic trees for 289 qnor/nod genes and 259 cnorB genes. These trees contain evidence of horizontal gene transfer and gene duplication, with 13.4% of the sequenced strains containing two or more nor genes. By aligning amino acid sequences for qnor + cnor, qnor, and cnor, we identified four highly conserved regions for NOR and NOD, including two highly conserved histidine residues at the active site for qNOR and cNOR. Extending this approach, we identified conserved sequences for: 1) all nor (nor-universal); 2) all qnor (qnor-universal) and all cnor (cnor-universal); 3) qnor of Comamonadaceae; 4) Clade-specific sequences; and 5) nod of Candidatus Methylomirabilis oxyfera. Examples of primer performance were confirmed experimentally.     

Assessing the Removal Efficiency of Murine Norovirus 1, Hepatitis A Virus,and Human Coronavirus 229E on Dish Surfaces Through General Wash Program of Household Dishwasher

Food and Environmental Virology - The performance of dishwashers in removing live viruses is an important informative value in practical applications. Since foodborne viruses are present in...     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.