Geochemistry of tin (Sn) in Chinese coals

Environmental Geochemistry and Health - Based on 1625 data collected from the published literature, the geochemistry of tin (Sn) in Chinese coals, including the abundance, distribution, modes of...     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

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.     

Absorption characteristics of potassium carbonate-based solutions with rate promoters and corrosion inhibitors

In this research, absorbents for CO₂ capture were prepared by blending 30 wt% potassium carbonate, 3 wt% of a rate promoter, and 1 wt% of a corrosion inhibitor. Pipecolic acid, sarcosine, and diethanolamine were chosen as rate promoter candidates. Based on a rate promoter screening test for CO₂ loading capacity and absorption rate, pipecolic acid and sarcosine were selected to be used as rate promoters. 1,2,3-benzotriazole and ammonium thiocyanate were chosen as corrosion inhibitors, and they were mixed with a 30 wt% potassium carbonate-based absorbent mixture containing one of the rate promoters. The absorption rates for four absorbent solutions (30 wt% potassium carbonate?+?3 wt% pipecolic acid?+?1 wt% 1,2,3-benzotriazole, 30 wt% potassium carbonate?+?3 wt% pipecolic acid?+?1 wt% ammonium thiocyanate, 30 wt% potassium carbonate?+?3 wt% sarcosine?+?1 wt% 1,2,3-benzotriazole, and 30 wt% potassium carbonate?+?3 wt% sarcosine?+?1 wt% ammonium thiocyanate) were measured, tabulated, and graphically displayed. These types of absorbents can be used for capturing CO₂ under high temperature and pressure conditions, such as those found in coal-fired power plants.     

辽宁省机动车污染防治对策探讨

辽宁省机动车保有量大、增长速度快，机动车污染物排放总量不断增加，给城市环境空气质量改善带来巨大压力，本文根据辽宁省机动车污染实际情况，提出了辽宁省机动车污染的防治对策。     

Low-temperature CO oxidation over Au-doped 13X-type zeolite catalysts: preparation and catalytic activity

Au-supported 13X-type zeolite (Au/13X) was synthesized using a common deposition–precipitation (DP) method with a solution of sodium carbonate as a precipitate agent. Further testing was conducted to test for catalytic oxidation of CO. A study was conducted on the effects of different preparation conditions (i.e., chloroauric acid concentration, solution temperature, pH of solution, and calcinations temperature) on Au/13X for CO oxidation. In respect to the catalytic activity, the relationship between different the preparation conditions and gold particles in 13X zeolite was analyzed using X-ray diffraction, TEM and XPS. The activity of Au/13X catalysts in CO oxidation was dependent on the chloroauric acid concentration. From XRD results, a higher chloroauric acid concentration induced larger gold nanoparticles, which resulted in lower catalytic activity. Results revealed that higher temperatures induced higher Au loading, homogeneous deposit, and smaller gold clusters on the support of 13X, resulting in higher CO activity. Furthermore, a pH of 5 or 6 generated greater amounts of Au loading and smaller Au particles on 13X than at a pH of 8 or 9. This may be a result of an effective exchange between Au(OH)2Cl2- and Au(OH)₃Cl^- on specific surface sites of zeolite under the pH’s 5 and 6. The sample calcined at 300°C showed the highest activity, which may be due to the sample’s calcined at 200°C inability to decompose completely to metallic gold while the sample calcined at 400°C had larger particles of gold deposited on the support. It can be concluded from this study that Au/13X prepared from a gold solution with an initial chloroauric acid solution concentration of 1.5 × 10^-3 mol·L^-1 gold solution pH of 6, solution temperature of around 90°C, and a calcination temperature of 300°C provides optimum catalytic activity for CO oxidation.     

Influence of filtration velocity on DON variation in BAF for micropolluted surface water treatment

Biological aerated filters (BAFs) are widely used for the treatment of micropolluted surface water. However, the biological process produces dissolved organic nitrogen (DON), which, as precursors of nitrogenous disinfection by-products, pose potential threats to drinking water safety. Therefore, to control DON in BAF effluent, it is necessary to study the influence of BAF operation parameters on DON production. In this study, the influence of filtration velocity in a BAF on DON production was investigated. Under different filtration velocity (0.5, 2, and 4 m/h) conditions, profiles of DON concentrations along the media layer were measured. The profile at a filtration velocity of 0.5 m/h showed a decreasing trend, and the ones under filtration velocities of 2 and 4 m/h fluctuated in a small range (from 0.1 to 0.4 mg/L). Moreover, the relatively high filtration velocities of 2 and 4 m/h resulted in a lower level of DON concentration. Additionally, 3D excitation-emission matrix fluorescence spectroscopy was used to characterize DON. It is found that the patterns of DON at a relatively high filtration velocity condition (4 m/h) were obviously different from the ones under low filtration velocity conditions (0.5 and 2 m/h).     

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.