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Environmental Engineering is a branch of engineering focused on designing, implementing, and managing systems and technologies to protect and improve the environment. It encompasses the study of air, water, and soil quality, waste management, and sustainable practices to mitigate pollution and promote ecological health.
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Five Misunderstandings about Case-Study Research
by Bent Flyvbjerg

2006, Qualitative Inquiry, vol. 12, no. 2, pp. 219-245

This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot... more
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Large Area Hydrologic Modeling and Assessment Part II: Model APPLICATION1
by Tharacad Ramanarayanan

1998, Jawra Journal of the American Water Resources Association

A conceptual, continuous time model called SWAT (Soil and Water Assessment Tool) was developed to assist water resource managers in assessing the impact of management on water supplies and nonpoint source pollution in watersheds and large... more
Figure 2. Components of the Hydrologic Balance Simulated Within a SWAT Subbasin.
Figure 4. Nitrogen Forms and Transformations Simulated with a SWAT Subbasin. Fertilization. Fertilizer applications can also be scheduled by the user or automatically applied by the model. The user-scheduled option requires the user to input the application date, total amount of N and P, fraction of organic and inorganic N and P, and the soil layer of application. The model adds the amount of fertilizer to the proper nutrient pool (organic and inorganic) and to the specified soil layer. The auto- matic fertilization option requires the user to input the plant nitrogen stress level (0-1) to trigger fertil- ization, the amount of NOs in the soil profile after fer- tilization, the soil layer of the application, the maximum amount of NO3 that can be applied in one year, and the minimum time between fertilizer appli- cations.
Figure 5. Pesticide Transformations Simulated Within a SWAT Subbasin.
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Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset
by Tim Osborn
This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were... more
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Insights into the modeling of adsorption isotherm systems
by Araceli Sánchez López
Concern about environmental protection has increased over the years from a global viewpoint. To date, the prevalence of adsorption separation in the environmental chemistry remains an aesthetic attention and consideration abroad the... more
Lists of adsorption isotherms models. energy distribution, with most of its sites has an adsorption energy lower than the peak (maximum) or mean value [23]. Table 1 in homogeneous or heterogeneous systems due to its versatility [22]. Typically, a minimization procedure is adopted in solving the equations by maximizing the correlation coefficient between the experimental data points and theoretical model predictions with solver add-in function of the Microsoft excel [26]. In the limit, it approaches Freundlich isotherm model at high concentration (as the exponent # tends to zero) and is in accordance with the low concentration limit of the ideal Langmuir condition (as the 6 values are all close to one) [67].
Lists of error functions. Table 2 with relative pressure ranges from 0.05 to 0.30 corresponding to a monolayer coverage lying between 0.50 and 1.50. Its extinction model related to liquid-solid interface is exhibited as: As a note, the empirical isotherm is reasonable fit to Frenkel—Halsey-Hill (FHH) or MacMillan—-Teller (MET) isotherms for relative pressures higher than 0.8 and approximately Brunauer-Emmett-Teller (BET) isotherm for relative pressures lower than 0.35.
Previous researches of the linear and nonlinear isotherm studies. Table 3
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Microbial Fuel Cells: Methodology and Technology†
by Bruce Logan

2006, Environ. Sci. …

Microbial fuel cell (MFC) research is a rapidly evolving field that lacks established terminology and methods for the analysis of system performance. This makes it difficult for researchers to compare devices on an equivalent basis. The... more
FIGURE 1. Operating principles of a MFC (not to scale). A bacterium in the anode compartment transfers electrons obtained from an electron donor (glucose) to the anode electrode. This occurs either through direct contact, nanowires, or mobile electron shuttles (small spheres represent the final membrane associated shuttle). During electron production protons are also produced in excess. These protons migrate through the cation exchange membrane (CEM) into the cathode chamber. The electrons flow from the anode through an external resistance (or load) to the cathode where they react with the final electron acceptor (oxygen) and protons (26).
FIGURE 2. Types of MFCs used in studies: (A) easily constructed system containing a salt bridge (shown by arrow) (27); (B) four batch-type MFCs where the chambers are separated by the membrane (without a tube) and held together by bolts (7); (C) same as B but with a continuous flow-through anode (granular graphite matrix) and close anode—cathode placement (75); (D) photoheterotrophic type MFC (76); (E) single-chamber, air-cathode system in a simple “tube” arrangement (30); (F) two-chamber H-type system showing anode and cathode chambers equipped for gas sparging (23). resistance or power densities derived from polarization curves taken using different methods, has made it difficult to interpret and compare resu ts among these systems (26). The variation in reported data has created a need to clarify methods of data collection and reporting. We have individu- ally received many requests construction of MFCs and for rom researchers for details on names of providers of materials and equipment, indicating t' here is a need in the literature for a paper that provides a more comprehensive source of this information. In this paper, we therefore review existing H-shape systems are acceptable for basic parameter research, such as examining power production using new materials, or types of microbial communities that arise during the degradation of specific compounds, but they typically produce low power densities. The amount of power that is generated in these systems is affected by the surface area of the cathode relative to that of the anode (28) and the surface of the membrane (29). The power density (P) produced by these systems is typically limited by high internal resistance
FIGURE 3. MFCs used for continuous operation: (A) upflow, tubular type MFC with inner graphite bed anode and outer cathode (35); (B) upflow, tubular type MFC with anode below and cathode above, the membrane is inclinated (36); (C) flat plate design where a channel is cut in the blocks so that liquid can flow in a serpentine pattern across the electrode (17); (D) single-chamber system with an inner concentric air cathode surrounded by a chamber containing graphite rods as anode (34); (E) stacked MFC, in which 6 separate MFCs are joined in one reactor block (25).
FIGURE 4. Electrochemical analysis of microbial fuel cells. (A) Anodic (blue solid line) and cathodic (red dashed line) voltage profiles over time when applying the current interrupt method for determination of the ohmic resistance of an MFC. Sections 2 and 3 indicate the voltage differences related to the ohmic resistance; sections 1 and 4 indicate voltage losses caused by the activation overpotentials. (B) Cyclic voltammogram (solid line) of an elec- trochemically active mixed microbial community. The dashed lines connect the oxidation and reduction peaks of redox active compounds (6).
4Calculated from Gibbs free energy data tabulated in ref 67. % Note that an MFC with an acetate oxidizing anode (HCO;- = 5 mM, CH;COO- = 5 mM, pH = 7) and an oxygen reducing cathode (pO2 = 0.2, pH = 7) has a cell emf of 0.805 — 0.296= 1.101 V. TABLE 1. Standard Potentials Ey and Theoretical Potentials for Typical Conditions in MFCs Eyre (Enrc Was Calculated Using Eq 5 and Half Cell Values from Ref 57; All Potentials Are Shown against NHE)
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Review on thermal energy storage with phase change: materials, heat transfer analysis and applications
by Muhammad Nadjib

2003, Applied Thermal Engineering

Thermal energy storage in general, and phase change materials (PCMs) in particular, have been a main topic in research for the last 20 years, but although the information is quantitatively enormous, it is also spread widely in the... more
Fig. 1. Areas of research in thermal storage systems [1]. equipment or in installations with reduced prices for electrical energy consumed during off-peak hours. Nowadays, security of energy supply is often achieved with extra equipment. The use of phase change materials (PCMs) could either avoid or reduce this extra equipment. As it will be seen later in this paper, thermal inertia and thermal protection is the area where the PCMs have achieved a higher penetration in the market. Low mama wrens an oo eaviraw AP ao¢isdéan dan Geaw «oth "TEO aoadnnw whaon nhawewsa ewotacsale Tha
Fig. 2. Classification of energy storage materials [1].
Fig. 3. Enthalpy variation with temperature.
Inorganic substances with potential use as PCM
Table 1 (continued)
Table 1 (continued) n.a.: not available. B. Zalba et al. | Applied Thermal Engineering 23 (2003) 251-283 a a Pa as As far as concerns the storage temperature or phase change, the heat transfer in accumulators can be improved choosing the PCM in such a way that its phase change temperature optimises the thermal gradient with respect to the substance with which the heat is being exchanged (Farid [46], Hassan [64], Strub [65]). For example, with paraffins and alkanes it is possible to vary the number of carbon atoms or form different molecular alloys which allows a practically continuous varia- tion of the phase change temperature within certain ranges.
Non-eutectic mixtures of inorganic substances with potential use as PCM % in weight; n.a.: not available.
Organic substances with potential use as PCM
Organic eutectics with potential use as PCM Table 5 on observation over experimental set-ups [78,79]. Porisini [80] tested the corrosion of four com- mercially available salt hydrates used as PCMs in 1988. Recently, Cabeza et al. [81-84] studied corrosion resistance of five common metals (aluminium, brass, copper, steel and stainless steel) in contact with molten salt hydrates (zinc nitrate hexahydrate, sodium hydrogen phosphate do- decahydrate, calcium chloride hexahydrate, sodium carbonate, potassium hydrogen carbonate, potassium chloride, water, sodium acetate trihydrate, and sodium thiosulphate pentahydrate) in an immersion corrosion test. ot @ a ee a | ay i rr ee o a, a, er, ee ey
Fatty acids with potential use as PCM % in weight; n.a.: not available.
Commercial PCMs available in the market n.a.: not available.
Comparison of organic and inorganic materials for heat storage Table 8 Important characteristics of energy storage materials
3. Heat transfer
B. Zalba et al. | Applied Thermal Engineering 23 (2003) 251-283 In the past, the earliest works basically concentrated on analysing pure substances, often water (for ice stores). Studies that can be cited include London [98], Goodman [162], Lazaridis [100] and Saitoh [163]. In subsequent years, and even quite recently and due to the implementation on a commercial and industrial scale of the so-called ice storage systems, abundant information re- lating to water as a storage substance has also appeared i in the literature. we ite: Pn a aa Pa ee qe
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Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation
by Greg McCabe

1999, Water Resources Research

Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the "goodness-of-fit" of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values... more
WHT EM VVELU GE SELULOsS LM MIC Gi LU Ue CHU Le PCeLivy of the evaluation. Note, however, that the coefficient of deter- mination is limited in that it standardizes for differences be- tween the observed and predicted means and variances since it only evaluates linear relationships between the variables. It can be easily demonstrated that if P, = (AO, + B) for any nonzero value of A and any value of B, then R? = 1.0. Thus R? is insensitive to additive and proportional differences be- tween the model simulations and observations [see Willmott, 1984]. Large values of R? can be obtained even when the model-simulated values differ considerably in magnitude (i.e., values of B that differ significantly from 0.0) and variability (i.e., values of A that differ significantly from 1.0). Clearly, in such cases, a model would exhibit serious flaws that should preclude the attribution of a “perfect” designation. These lim- itations in the coefficient of determination and other correla- tion-based measures are well documented [cf. Willmott, 1981; Moore, 1991; Kessler and Neas, 1994; Legates and Davis, 1997], although such measures still have been used recently to pro- vide, for example, an assessment of climate change detection [e.g., Santer et al., 1995; Hegerl et al., 1996; Santer et al., 1996] and hydrological and hydroclimatological applications (see McCuen and Snyder [1975] and Willmott [1984] for some ex- amples).
where j represents an arbitrary power (i.e., a positive integer). Note that the original index of agreement d developed by Willmott [1981] becomes d, using this notation.
where O’ is the baseline value of the time series against which the model is to be compared. It usually is a function of time and, in some applications, may be a function of other variables as well. Consequently, d{, is useful in that its interpretation is more conventional, as it more closely follows the interpretation Garrick et al. [1978, p. 376] further argued that the assump- tion of comparing the model to the observed mean was “un- necessarily primitive.” Better methods exist to define the base- line against which a model should be compared. For example, persistence or averages that vary by season or another time period (i.e., a climatology) may provide a more appropriate baseline for most hydrological or hydroclimatological studies than simply the average of the entire time series. Thus both E, and d, can be rewritten in a “baseline adjusted” form as
Figure 2. Scatterplots of the Thornthwaite, Jensen-Haise, van Bavel, and climatological (monthly mean) estimates versus the observed (coefficient adjusted pan evaporation) data pre- sented in Figure 1. An examination of Pearson’s correlation coefficient (r) or the coefficient of determination (R*) yields a misleading pic-
Figure 1. Time series of monthly potential evapotranspiration estimated by the Thornthwaite [1948], Jensen and Haise [1963], and van Bavel [1966] methods compared with observations (measured pan evaporation multiplied by a pan coefficient of 0.76). Data were taken from January 1981 through December 1983 for southern Louisiana (data from McCabe and Muller [1987]).
Figure 4. Scatterplot of the observed and modeled monthly runoff (precipitation runoff modeling system) data presented in Figure 3.
Figure 3. Observed and modeled monthly runoff (precipitation runoff modeling system) for the East River Basin in southwestern Colorado from October 1972 through September 1989 (data from McCabe and Hay [1995]).
Table 1. Means and Standard Deviations of Observed and Simulated Monthly Potentia Evapotranspiration for Baton Rouge, Louisiana, for the Period From January 1981 Through December 1983, and Statistics Comparing the Observed and Simulated Time Series
Table 2. Means and Standard Deviations of Observed and Simulated Monthly Runoff in the East River Basin in Southwestern Colorado for the Period October 1972 Through September 1989, and Statistics Comparing the Observed and Simulated Time Series
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Wind power in power systems
by wanki emmanuel

2005

Figure 8.1 Examples of: a) frequency variations in the system during one day; b) frequency distribution in the system during one week (Source: Hirvonen, 2000).
Figure 8.2 Example of actual load in the system during 3 hours compared to forecasted load. (Source Holttinen, 2003)
Figure 8.3 Activation of power reserves and frequency of power system as a function of time, for a situation where a large power plant is disconnected from the power system. (Source: Hirvonen, 2000).
For power systems, the relevant information on wind power production is the probability distribution, the range and seasonal or diurnal patterns of the production, as well as the magnitude and the frequency of the variations (ramp rates). Figure 8.4 Example of large-scale wind power production: Denmark (both Zeeland and Jutland) in January 2000. Average wind power production in January was 687 MW and in 2000 all year 485 MW (~24 % of the installed capacity). (Source: Holttinen, 2003)
Figure 8.5 Increased resources and geographical spreading lead to a flattened duration curve of wind power production. Example of year 2000 hourly data, where wind power production from turbines throughout the four Nordic countries, Denmark, Finland, Norway and Sweden, is compared with one of the wind farms and one of the areas (Denmark, West). (Source: Holttinen, 2003) The duration curve of dispersed wind power production in figure 8.5 illustrates that: the production from one wind turbine is zero for 10-20 % of the time, and at nominal capacity 1-5 % of the time, whereas the production from large-scale wind power production is in this example rarely below 5 % or above 75 % of capacity.
Figure 8.6 Seasonal variations of wind power production. Example from Finland for the years 1992— 2002. Average monthly production in 1992—2002 is shown (solid line) together with the electric consumption in 2002 (dotted line). (Source: Holttinen, 2003)
Wind is driven by weather fronts. It may also follow a daily pattern caused by the sun. Depending on what is prevalent in the region, there is either a strong or hardly any diumal pattern in the production. There are many sites where the wind often starts to blow in the morning and calms down in the evening (Hurley and Watson, 2002; Ensslin et al, 2000). Ir Northern Europe, this is most pronounced during the summer (see Figure 8.7). Diurnal variation can also be due to local phenomena. An example would be the mountain ranges ir California with morning and evening peaks, when wind blows from the desert to the see and in the opposite direction, respectively. Figure 8.7 Diurnal variations of wind power production are larger during the summer, example fror Denmark.
The larger the area, the longer the periods of time over which the smoothing effec extends. Figure 8.8 shows the decreasing correlation’ of the variations for different time scales (Ernst, 1999). The correlation is here calculated for the differences betwee consecutive production values (AP). For the time series of production values, the correlation does not decrease as rapidly as shown here. Within one wind farm, gust: (seconds) will not effect all wind turbines at exactly the same moment. However, the hourl} wind power production will follow approximately the same ups and downs. In a larger are covering several hundreds of square kilometres, the weather fronts causing high winds wil not pass simultaneously over the entire regions. However, high and low wind months wil coincide for the whole area. Figure 8.8 Variations will smooth out faster when the time scale is small. Correlation of variations for different time scales, example from Germany (Ernst, 1999).
Figure 8.9. Variation of wind power production from one hour to the next. Duration curve of variations, as a percentage of installed capacity, for Denmark (Jutland) and for the theoretical Nordic wind power production assuming equal production in each of the four countries, year 2000. (Source: Holttinen, 2003)
When studying the impact of wind power on power systems, we refer to an area that is larger than only one wind farm. According to the impact that is analysed, we have to look at the power system area that is relevant. For voltage management, only areas near wind power plants should be taken into account. Even though there should be enough reactive power reserve in the system during disturbances, the reserve should mainly be managed locally. For intra-hour variations, frequency control for load following, we should look at the area of the synchronously operated system. DC links connecting synchronously operated areas can also be automised to be used for primary power control®. However, their power reserve capacity is usually only allocated as emergency power supply. For the day-ahead hourly production, a relevant area would be the electricity market. The Nordic power market, for instance, includes countries that are situated in different synchronous systems. Large interconnected areas lead to substantial benefits, unless there are bottlenecks in transmission’. Figure 8.10 Power system impacts of wind power, causing integration costs. Some of the impacts can be beneficial for the system, and wind power can provide a value, not only costs (Source Holttinen, 2003).
Figure 8.11 Correlation of temperature to wind power production and load in a cold climate, example of Finland, geographically dispersed wind power. There were 48 hours (0.1 % of time) below —23 °C and 549 hours (1.6 % of time) below —14°C during the years 1999-2002. (Source: Holttinen, 2003) Nevertheless, variable sources can save thermal capacity. Since no generating plant is completely reliable, there is always a finite risk of not having enough capacity available. Variable sources may be available at the critical moment when demand is high and many other units fail. Fuel source diversity can also reduce risk.
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Multifractal detrended fluctuation analysis of sunspot time series
by M Movahed

2006, Journal of Statistical Mechanics-theory and Experiment

We use multifractal detrended fluctuation analysis (MF-DFA), to See query 1 study sunspot number fluctuations. The result of the MF-DFA shows that there are three crossover timescales in the fluctuation function. We discuss how the... more
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Macropores and water flow in soils
by Peter Germann

1982, Water Resources Research

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Fully coupled “online” chemistry within the WRF model
by Georg Grell

2005, Atmospheric …

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Techniques of trend analysis for monthly water quality data
by arun bharathi

1982, Water resources research

The user has requested enhancement of the downloaded file.
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Lignin valorization: improving lignin processing in the biorefinery
by Richard Chandra

2014, Science (New York, N.Y.)

Research and development activities directed toward commercial production of cellulosic ethanol have created the opportunity to dramatically increase the transformation of lignin to value-added products. Here, we highlight recent advances... more
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Empirical equations for some soil hydraulic properties
by Roger B Clapp

1978, Water Resources Research

with a power function relating soil moisture and hydraulic conductivitymis used to derive a formula for the wetting front suction required by the Green-Ampt equation. Representative parameters for the moisture charac- teristic, the... more
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Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States
by Michael Halbleib

2008, International Journal of Climatology

Spatial climate data sets of 1971-2000 mean monthly precipitation and minimum and maximum temperature were developed for the conterminous United States. These 30-arcsec (∼800-m) grids are the official spatial climate data sets of the U.S.... more
Table III. Number of stations used in the mapping process, and native averaging interval. See text for definitions of acronyms.
Figure 1. Locations of (a) surface precipitation stations and (b) surface temperature stations used in the interpolation.
Figure 2. Western U.S. coastal proximity grids for (a) temperature (TW) and (b) precipitation (PW). Coastal proximity describes the optimal path length from the coastline in kilometres, accounting for terrain blockage. Trajectory describes the straight-line path length from the coastline in kilometres, modified by terrain blockage. Topographic facet guidance is provided to PRISM through a series of topographic facet grids (Table ID). The current method for delineating topographic facets is described in Gibson et al. (1997) and Daly et al. (2002). Facet grids are constructed for six DEM smoothing levels, or scales (Daly et al., 1994, 2002). The smoothed DEM for each level is prepared by applying the Gaussian filter (Barnes, 1964) to the precipitation DEM. The horizontal filtering cut-off distance (i.e. how far out the A simple trajectory model was designed to assess rela- tive moisture availability to guide precipitation mapping (abbreviated PW; Table I). The trajectory model is simi- lar to the coastal advection model in that it accounts for changes in moistu re content due to path length and ter- rain effects. It differs in that the air parcel trajectories are straight line and invariant throughout the simulation, as might be the case within large-scale synoptic circula- tions that produce significant precipitation. The premise here is that the mean potential for precipitation expe- rienced at a site is the result of (1) loss of moisture through rainout, which accumulates as the path length over land increases, and (2) enhancement and suppres- sion of precipitation caused by adiabatic cooling and warming during gence is not consi bearing air flow al southwesterly, wit flow over terrain obstacles. Conver- dered in this simple model. Moisture- ong the West Coast is most commonly h some variation around that direction. Therefore, the trajectory model was run for three flow directions: 230, 24 aged as 0.2(230) 4 O, and 250° true, and the results aver- + 0.6(240) + 0.2(250). Algorithms for, and operation of, the trajectory model are given in Daly
Figure 3. Topographic facet grid for the conterminous United States at a filtering distance of 36 km. A simple method was developed to spatially distribute the approximate height of the top of a climatologically persistent inversion layer, should it exist, for use in Our analyses of rawinsonde data from several cities in the United States with persistent climatological inver- sions, mostly during winter, indicated that the inver- sion top typically occurred at roughly 200-300 m above ground level. Therefore, 250 m was added to the base elevation at each grid cell to obtain the potential inver- sion height above sea level. While this is only a rough approximation of the inversion height, it has been effective for modelling purposes (Table II). The poten- tial inversion height grid for the contiguous United States is illustrated in Figure 4. Valleys, plains, and other low-lying areas tend to fall within the bound- ary layer, while local ridge tops and other elevated
Figure 4. Potential inversion height grid for the conterminous United States, illustrated as the difference between the inversion height and the 800-m DEM elevation. Terrain rising above the inversion height (in layer 2) is shaded, and terrain falling below this height (in layer 1) is unshaded.
Figure 5. Topographic index grid for the conterminous United States. The scale indicates metres above the local base terrain. PHYSIOGRAPHICALLY SENSITIVE MAPPING OF CLIMATOLOGICAL TEMPERATURE AND PRECIPITATION
Figure 6. Effective terrain grid for the conterminous United States. Black areas denote terrain features that are expected to produce the strongest terrain-induced (3D) precipitation patterns, and grey areas denote features expected to have some influence on precipitation patterns (2D/3D transition). Unshaded grid cells as far as 100 km away from the shaded areas may also be considered 3D or 2D/3D transition areas. See text and Appendix C for discussion.
Figure 7. Conterminous U.S. maps of PRISM 1971-2000 mean annual precipitation, January minimum temperature, and July maximum temperature. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 8. PRISM mean absolute cross-validation errors (MAE) for 1971—2000 mean: (a) annual precipitation; (b) January minimum temperature; and (c) July maximum temperature. Station density was too high to be shown clearly, requiring that 80% of the stations be randomly omitted from the plots.
Figure 9. Seventy percent prediction intervals (PI70) for: (a) mean annual precipitation; (b) January mean minimum temperature; and (c) July mean maximum temperature. PI70 for precipitation is expressed as a percentage of the pixel’s value.
Figure 10. (a) Scatterplot of the relationship between C-V MAE and PI70 for mean annual precipitation (given in percentage of the estimated grid cell value) when averaged over 300 x 300-km blocks in the western United States; and (b) R? of the linear relationships between CV-MAE and PI70 for mean annual precipitation, January minimum temperature, and July maximum temperature at block sizes ranging from 100 to 500 km in the western United States. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 11. Daymet/PRISM and WorldClim/PRISM ratio maps (expressed as percentage) of mean annual precipitation for the conterminous United States. This figure is available in colour online at www.interscience.wiley.com/ijoc Basin RO + ET values in Figure 14 (upper left panel) show the sharp decline in moisture between windward and leeward basins. A comparison of basin-mean precip- itation for the three data sets is presented in the remaining three panels of Figure 14, and a profile of the com- parisons is shown in Figure 15. Daymet simulated the windward basins well, but at the expense of the leeward side, which was substantially overestimated. WorldClim was quite low on the windward side, and somewhat high on the leeward side. PRISM simulated both the windward
Figure 12. Daymet — PRISM and WorldClim — PRISM difference maps of mean January minimum temperature for the conterminous United States. This figure is available in colour online at www. interscience.wiley.com/ijoc
Figure 13. Daymet — PRISM and WorldClim — PRISM difference maps of mean July maximum temperature for the conterminous United States. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 14. Upper left panel: annual RO + ET (runoff + evapotranspiration), a water balance approximation of total precipitation, for selected basins in Washington’s Olympic Mountains, colour-coded following the precipitation legend. ET was roughly estimated at 500 mm/yr for all basins. Remaining panels: WorldClim, Daymet, and PRISM mean annual precipitation grids, with basin overlays, also colour-coded following the precipitation legend. To agree with the basin water balance estimates, the mean grid colour within a basin should match that shown in the upper left panel. Stations used in the PRISM analysis are shown as black dots. This figure is available in colour online at www. interscience.wiley.com/ijoc
Figure 15. Estimated mean annual precipitation from WorldClim, Daymet, PRISM, and RO + ET (runoff + evapotranspiration), a water balance approximation of total precipitation, for river basins on the windward and leeward slopes of the Olympic Mountains. ET is assumed to be 500 mm for all watersheds. Runoff periods of record are 1971-2000, except for 1975-2000 on the Queets and 1995-2000 on the Quilcene. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 16. PRISM diagnostic grids associated with mean annual precipitation for the Olympic Mountains, Washington: (a) topographic facet grid at a filtering distance of 36 km; (b) PW coastal proximity, which describes a straight line, approximately southwesterly path length from the coastline in km, modified by terrain blockage; (c) vertical precipitation gradient, expressed as a percentage of the pixel’s value per kilometre: and (d) 70% prediction interval (PI70), expressed as a percent of the pixel’s value. in the protected Gunnison Valley and adjacent valleys (Figure 17). PRISM diagnostic grids show an estimated SRRRE Aone tewourn mes Muwemeemn Figen wns emenreated vinta TSR NT So OR OOS cx EN STE NST SPITE ONT ENTREES) RE AUR ST NEZE NNR in Sem ROSETTE TRS IS ge oe fe ENR Cold biases of the Daymet and WorldClim data sets in western Colorado are illustrated in Figure 19. Here, 369 stations were grouped into four topographic index
Figure 17. Comparison of PRISM, Daymet, and WorldClim January mean minimum temperature grids for the Gunnison Valley region. CO. Stations used in the PRISM interpolation are shown as red dots in the PRISM panel. This figure is available in colour online a www.interscience.wiley.com/ijoc
Figure 19. Biases between grid-cell estimates and observed 1971-2000 mean January minimum temperatures (estimate — observation), grouped by topographic index, for 369 stations in western Colorado. Stations with high topographic indexes are on locally elevated terrain, and those with low indexes are in local valley bottoms. Stations with moderate indexes (300—500) are neither in local valley bottoms nor on locally elevated terrain. Figure 18. PRISM diagnostic grids associated with January mean minimum temperature interpolation for the Gunnison Valley region, CO: (a) inversion layer; (b) topographic index; (c) vertical temperature gradient; and (d) 70% prediction interval (PI70).
Figure 20. Comparison of PRISM, Daymet, and WorldClim July mean maximum temperature grids for the central coast, CA. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 21. PRISM diagnostic grids associated with July mean maximum temperature interpolation for the central coast, CA: (a) summer inversion layer, given as layer | (within inversion) or layer 2 (above inversion); (b) western temperature (TW) coastal proximity, expressed as the optimal! path length from the coastline in kilometres, accounting for terrain blockage; (c) vertical temperature gradient; and (d) 70% prediction interval! (PI70).
Figure 22. Bias between grid-cell estimates and observed 1971—2000 mean July maximum temperatures (estimate — observation), grouped by coastal proximity (Figure 2(a)), for 1201 stations in California. Stations with low coastal proximity indexes experience a generally strong marine temperature influence during summer, and those with high indexes experience much less of this influence (see Figures 2(a) and 21(b) for spatial context).
Figure Al. Mean absolute error of the adjusted mean July maximum temperature from the known 30-year mean versus number of anchor stations used to compute the adjusted mean. A total of 84 stations were tested using short-term periods of record of 5, 11, and 21 years. This figure is available in colour online at www.interscience.wiley.com/ijoc
Table I. Methods for preparation of PRISM input grids, part 1. the basis for most of the other physiographic input grids to PRISM (Section 4). The DEM used in this study was derived from the 3-arcsec (~80-m) National Elevation Database (NED) (NED; http://gisdata.usgs.gov/NED/). The NED elevation data was found to be superior to other DEMs for the conterminous United States, such as GTOPO30, Digital Terrain Elevation Database (DTED), and Space Shuttle Radar Topography Mission (SRTM), for this application. The GTOPO30 and DTED DEMs had noticeable elevation breaks, or ‘seams’ along some of the original U.S.Geological Survey (USGS) quad boundaries. The SRTM DEM had numerous grid cells with missing data in areas of steep terrain, and possessed significant ground clutter associated with low microwave signal-to-noise ratios. This 30-arcsec (~800-m) DEM was used for interpolat- ing maximum and minimum temperature. An additional filtering step was performed to obtain a DEM suitable for precipitation interpolation. The Gaussian filter was applied to the 30-arcsec (~800-m) DEM to filter out ter- rain features up to 3.75 arcmin (~7 km) in extent, while retaining the 30-arcsec (~800-m) grid resolution. The direct effects of elevation on precipitation appear to be most important at scales of 5-10 km or greater, owing to a number of mechanisms, including the advective nature of moisture-bearing airflow, the viscosity of the atmo- sphere, delays between initial uplift and subsequent rain- out, and the movement of air around terrain obstacles (Daley, 1991; Daly et al., 1994; Funk and Michaelsen, 2004; Sharples et al., 2005). In this paper, the two DEMs are hereafter referred to as the temperature and precipi- tation DEMs (Table II).
Table II. Methods for preparation of PRISM input grids, part 2.
able IV. PRISM algorithms and associated physiographic climate forcing factors, inputs to the algorithms, and references on their formulation and use. Methods used to prepare the gridded model inputs are summarized in Tables I and 2.
Table V. Vertical gradients in temperature and precipitation, summarized by effective terrain height (precipitation) and layer (temperature).
Table VI. PRISM cross-validation and PI70 statistics for precipitation and minimum and maximum temperature for the western, central, and eastern regions. MAE is the mean absolute (unsigned) difference between the prediction and observation during single-deletion jackknife cross-validation, and PI70 is the mean 70% prediction interval of the PRISM climate-elevation regression function.
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Photocatalytic degradation pathway of methylene blue in water
by Chantal Guillard

2001, Applied Catalysis B: Environmental

The TiO 2 /UV photocatalytic degradation of methylene blue (MB) has been investigated in aqueous heterogeneous suspensions. In addition to a prompt removal of the color, TiO 2 /UV-based photocatalysis was simultaneously able to oxidize... more
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Chemical Treatments of Natural Fiber for Use in Natural Fiber-Reinforced Composites: A Review
by Lope Tabil Jr.

2007, Journal of Polymers and The Environment

Studies on the use of natural fibers as replacement to man-made fiber in fiber-reinforced composites have increased and opened up further industrial possibilities. Natural fibers have the advantages of low density, low cost, and... more
Maleated coupling agents are widely used to strength- en composites containing fillers and fiber reinforce- ments [51-54]. The difference with other chemical treatments is that maleic anhydride is not only used to modify fiber surface but also the PP matrix to achieve better interfacial bonding and mechanical properties in composites [55-57]. The PP chain permits maleic anhydride to be cohesive and produce maleic anhy- dride grafted polypropylene (MAPP). Then the treat- ment of cellulose fibers with hot MAPP copolymers provides covalent bonds across the interface (Fig. 1). The mechanism of reaction of maleic anhydride with PP and fiber can be explained as the activation of the
Most permanganate treatments are conducted by using potassium permanganate (KMnO,) solution (in acetone) in different concentrations with soaking duration from 1 to 3 min after alkaline pre-treatment [10, 23, 48, 49, 54]. Paul et al. [10] dipped alkaline- treated sisal fibers in permanganate solution at con- centrations of 0.033, 0.0625 and 0.125% in acetone for 1 min. As a result of permanganate treatment, the hydrophilic tendency of the fibers was reduced, and thus, the water absorption of fiber-reinforced composite decreased. The hydrophilic tendency of fiber decreased as the KMnO, concentrations increased. But at higher KMnO, concentrations of 1%, degradation of cellulosic Mohanty et al. [59] used MAPP as coupling agent for the surface modification of jute fibers. It has been found that a 30% fiber loading with 0.5% MAPP concentration in toluene and 5 min impregnation time with 6 mm average fiber lengths gave the best results. A 72.3% increase in flexural strength was observed with the treated composites. In addition to PP matrix, Mishra et al. [60] reported that maleic anhydride treatment reduced the water absorption to a great extent in banana, hemp and sisal fiber-reinforced novolac composites. Mechanical properties like Young’s modulus, flexural modulus, hardness and impact strength of plant fiber-reinforced composites increased after maleic anhydride treatment.
Fig. 2 The reaction pathway for the preparation of urethane derivative of cardanol [23]
Sodium chlorite (NaClO,) usually is used in bleaching fibers; however, it could delignify ligno- cellulosics. Studies have been conducted wherein it was used in fiber surface treatment for composites. Mishra et al. [65] dipped untreated sisal fiber, for use in sisal-PS biocomposites, in sodium _ chlorite Joseph and Thomas [23] studied the chemical treatment of cardanol derivative of toluene diisocya-
composition may differ with the growing condition and test methods even for the same kind of fiber. Cellulose is a semicrystalline polysaccharide made up of pD- glucopyranose units linked together by f-(1-4)-gluco- sidic bonds [19]. And the large amount of hydroxyl group in cellulose gives natural fiber hydrophilic properties when used to reinforce hydrophobic matri- ces; the result is a very poor interface and poor resistance to moisture absorption [20]. Hemicellulose is strongly bound to cellulose fibrils presumably by hydrogen bonds. MHemicellulosic polymers are branched, fully amorphous and have a significantly lower molecular weight than cellulose. Because of its open structure containing many hydroxyl and acetyl groups, hemicellulose is partly soluble in water and hygroscopic [16]. Lignins are amorphous, highly com- plex, mainly aromatic, polymers of phenylpropane units [19] but have the least water sorption of the natural fiber components [16]. Rananca tha law: tnftartacial nranartiac hoatrnan Ghar optimize the interface of fibers. Chemicals may acti- vate hydroxyl groups or introduce new moieties that can effectively interlock with the matrix. The devel- opment of a definitive theory for the mechanism of bonding by chemicals in composites is a complex problem. Generally, chemical coupling agents are molecules possessing two functions. The first function is to react with hydroxyl groups of cellulose and the second is to react with functional groups of the matrix. Bledzki and Gassan [9] outlined several mechanisms of coupling in materials, namely: (a) elimination of weak boundary layers; (b) production of a tough and flexible layer; (c) development of a highly crosslinked inter- phase region with a modulus intermediate between that of substrate and of the polymer; (d) improvement of the wetting between polymer and substrate; (e) formation of covalent bonds with both materials; and (f) alteration of acidity of substrate surface. Chemical modifications of natural fibers aimed at improving the adhesion with a polymer matrix were investigated by a number of researchers [4, 21-24]. However, different chemicals were used and very limited papers [9, 18] took a partial review on the Because the low interfacial properties between fiber and polymer matrix often reduce their potential as reinforcing agents due to the hydrophilic nature of natural fibers, chemical modifications are considered to
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Response of mean annual evapotranspiration to vegetation changes at catchment scale
by Glen Walker

2001, Water Resources Research

It is now well established that forested catchments have higher evapotranspiration than grassed catchments. Thus land use management and rehabilitation strategies will have an impact on catchment water balance and hence water yield and... more
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Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
by Claudia Tebaldi

2007, International Journal of Climatology

There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method... more
Figure 2. Map showing re-gridded RCM cells on the CRU 0.5° by 0.5° grid for the UK. The two regions used in this study are shown in grey: North West England (dark) and South East England (light). Figure 1. Flow diagram showing steps in the methodology used to produce probabilistic scenarios of climate change impacts for the River Eden. This figure is available in colour online at www.interscience.wiley.com/ijoc
Figure 3. Percentage seasonal change in precipitation projected under the 2070-2100 SRES A2 emissions scenario from the control period, 1961-1990, for two UK regions: North West England (black) and South East England (red). Notations for seasons are winter (DJF, December to February), spring (MAM, March to May), summer (JJA, June to August) and autumn (SON, September to November). The projection from the IPCC AR4 suite of 14 GCMs for all UK is shown in green for comparison. sensible results, being designed to handle an arbitrary number of data points, one caveat of the Bayesian approach is that independence is assumed between ensemble members. This may be an oversimplification, as many climate models share internal numerical and parameterization schemes. Here in particular, as some RCMs are driven by lateral boundary schemes from the same GCM, this assumption may be even less defensible and generate overly confident estimates of the uncertainty in the changes. However, results for two UK regions from the set of RCMs are comparable to the results for the entire UK from the set of IPCC-AR4 GCMs (Figures 3 and 4), suggesting that these RCMs are in fact representative of the IPCC-AR4 results. There is noticeable agreement in the location and even the spread of the pdfs for all seasons but summer, where the GCM
Figure 4. Percentage seasonal change in temperature projected under the 2070-2100 SRES A2 emissions scenario from the control period, 1961-1990, for two UK regions: North West England (black) and South East England (red). Notations for seasons are winter (DJF, December to February), spring (MAM, March—May), summer (JJA, June—August) and autumn (SON, September—November). The projection from the IPCC AR4 suite of 13 GCMs for all UK is shown in green for comparison.
Figure 5. The Eden catchment in northwest England (to Temple Sowerby). . Here, we use the same methodology as Kilsby et al. (2007b) to derive a suite of CFs for each of the six PRUDENCE RCMs for a catchment in northwest Using the calibrated hydrologic model, 100 synthetic 30-year daily flow sequences were then produced for the baseline and each of the six possible futures (one from
Figure 6. Projected percentage changes in flow statistics for the 2070-2100 period (change from 1961-1990): (a) mean flow, (b) standard deviation of flow, (c) 5th percentile of flow, (d) 95th percentile of flow. Seasonal change probabilities are given for winter (DJF, black), spring (MAM, red), summer (JJA, green) and autumn (SON, blue).
Figure 7. Analysis of downscaling literature using the ISI Web of Knowledge: ‘downscaling’ included in title or abstract
Table I. Comparative summary of the relative merits of statistical and dynamical downscaling techniques (adapted from Wilby and Wigley, 1997).
Table II. Examples of recent statistical downscaling studies. This list is not intended to be exhaustive but illustrative of the geographical distribution of downscaling studies and the methods employed. Abbreviations used in this table are: REG - linear regression methods, ANN - artificial neural networks, CCA — canonical correlation analysis, OTH — other, SCA -— scaling methods, SVD — singular value decomposition, WG — weather generators, WT — weather typing. For predictands, T — temperature, P — precipitaion, PE — potential evapotranspiration, H — humidity variables. performance of NCEP reanalysis variables in the sta- tistical downscaling of daily precipitation using ANNs and principal components analysis (PCA). Downscaling methods had the greatest skill in mid-latitude locations for the cool/dry season and showed the largest errors in the wet season. The poorest geographical performance was in the Tropics. However, this may be due to the dominance of convective processes which are harder to predict or due to poor quality of the reanalysis data in this region rather than deficiencies in downscaling method. example, the downscaling skill for mean daily temper- ature in central Europe was found to be dependent upon station elevation, with statistical downscaling methods showing more skill at higher elevation stations, and with increased skill at continental compared to mar- itime sites (Huth, 1999). In contrast, Kleinn et al. (2005) found that for dynamically downscaled and then bias- corrected precipitation and temperature outputs there was less skill at high elevations. Similarly, seasonal and geo- graphical differences in downscaling skill were noted by Cavazos and Hewitson (2005) who examined the
Table IV. Seasonal mean i values for the North West England region of the UK for the six selected regional climate models for (a) precipitation, and (b) temperature. Note that A values for all six models have been standardized to add to 1.0 for each season. (a) results are significantly less warm and less dry than the RCM results. they can be applied as such in the downscaling step. Since the Bayesian analysis produces joint posterior pdfs of the lambda parameters, we use the posterior mean of the distribution as our best estimate of model weights (Table IV).
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Sustainable bio-composites from renewable resources: opportunities and challenges in the green materials world
by Lawrence Drzal

2002, Journal of Polymers and the …

Sustainability, industrial ecology, eco-efficiency, and green chemistry are guiding the development of the next generation of materials, products, and processes. Biodegradable plastics and bio-based polymer products based on annually... more
Fig. 1. Concept of “sustainable” bio-based product.
Fig. 2. Carbon dioxide sequestration.
Fig. 3. Classification of natural/biofibers.
Fig. 4. Structure of cellulose. Plastics are lighter but are not fit for load-bearing application because of the lack of sufficient strength, stiffness, and dimensional stability. However, fibers pos- sess high strength and are sufficiently stiff but cannot be used for load-bearing applications because of their fibrous nature. In fiber-reinforced composites, the fibers serve as reinforcements by giving strength and stiffness to the composite structure. Natural/biofibers may be classified Biopolymers (biodegradable polymers; classifica- tion shown in Fig. 8) may be obtained from renewable resources, synthesized microbially, or synthesized from
Fig. 5. Phenolic precursors that form the lignin.
Fig. 7. Modulus and specific modules: E-glass versus some natural fibers.
Fig. 9. Aliphatic polyesters— structure of some important biopolyesters.
n= 400-750; R= H (Celluiose), acetyl (Cellulose acetate), acetyland propionyl (cellulose acefat propionate), or acetyl and butyry! (Cellulose acetate butyrate)
Fig. 11. Classification of bio-composites.
Fig. 8. Classification of biopolymers. Biodegradable polymers need to be developed so as to make them suitable as matrix polymers for composite applications. Traditional plastics, such as polypropylene, polyethylene, polyester, and epoxy, have undergone con- siderable development and wide use in composite applica- tions. Originally, biopolymers were intended to be used in packaging industries, farming, and other applications with minor strength requirements. Performance limita- tions and the high cost of biopolymers are major barriers for their widespread acceptance as substitute for tradi- tional nonbiodegradable polymers. The high performance
Fig. 13. Concept on design of engineered natural/biofiber. The significant attraction of biofibers is their low cost; surface treatments that avoid using organic solvents i.e., water-based sizing, alkali treatment, and silane treatments, are logical approachs [29] to making reactive biofiber. Superior-strength bio-composites can be obtained through application of the reactive engineered natural fibers concept [30]. Design of engineered natural/ Biofiber is schematically represented in Fig. 13. Engi- neered biofibers are defined as the suitable blend of sur- face-treated bast (e.g., kenaf, hemp) and a leaf fiber (e.g., pineapple leaf fiber [PALF]). Selection of blends of bio- fibers is based on the fact that the correct blend achieves
Fig. 12. Challenges to fabricate superior bio-composites.
Fig. 14. Reacter of biofiber and maleated polymer— mechanism for compatibilization.
Table I. Modulus Comparison of E-Glass and Some Important Natural Fibers
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A literature and practice review to develop sustainable business model archetypes
by Nancy Bocken and 
1 more
Steve Evans

2014, Journal of Cleaner Production

Eco-innovations, eco-efficiency and corporate social responsibility practices define much of the current industrial sustainability agenda. While important, they are insufficient in themselves to deliver the holistic changes necessary to... more
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Acid mine drainage remediation options - a review JOHNSON 20
by Machuki Jr.
Acid mine drainage (AMD) causes environmental pollution that affects many countries having historic or current mining industries. Preventing the formation or the migration of AMD from its source is generally considered to be the... more
Fig. 2. Layout of a “dry cover” for minimising production of mine spoil effluents. Underwater storage has been used for disposing and storing mine tailings that are potentially acid- producing (Li et al., 1997). Again, the objective is to prevent contact between the minerals and dissolved oxygen. Shallow water covers may be used, and their Another suggested approach for minimising AML production is to blend acid-generating and acid consuming materials, producing environmentally benign composites (Mehling et al., 1997). A varian on this theme is to add solid-phase phosphates (sucl as apatite) to pyritic mine waste in order to precipitate iron (III) as ferric phosphate, thereby reducing it: potential to act as an oxidant of sulfide minerals However, inhibition of pyrite oxidation using thi:
Fig. 1. Various approaches that have been evaluated to prevent or minimise the generation of mine drainage waters.
Fig. 3. Biological and abiotic strategies for remediating acid mine drainage waters. Finally, in as much as lithotrophic (“rock eating”) iron- and sulfur-oxidising bacteria have a pivotal role in generating AMD, various laboratory- and field- scale tests using biocides have been carried out to inhibit their activities in mineral spoils and tailings. This has generally involved the application of anionic surfactants such as sodium dodecyl sulfate (SDS), which are highly toxic to this group of microorgan- isms. However, the effectiveness of biocide applica- tions has been found to be highly variable affording, Given the practical difficulties entailed in inhibit- ing the formation of AMD at source, often, the only alternative is to minimise the impact that this polluting water has on receiving streams and rivers, and the wider environment; such an approach involves “migration control” measures. Quite often, these have been divided into “active” and “passive” processes, the former generally (though not exclusively) referring to the continuous application of alkaline materials to neutralise acidic mine waters and precipitate metals, and the latter to the use of natural and constructed wetland ecosystems. Passive systems have the advant- age of requiring relatively little maintenance (and recurring costs) than active systems, although they may be expensive and/or impractical to set up in the first place. In reality, all “passive” treatment technol- ogies require a certain amount of maintenance costs. A more useful subdivision is between those remedia- tion technologies that rely on biological activities and
Fig. 4. Abiotic ferrous iron oxidation as a function of pH, calculated from Stumm and Morgan (1981) for an aqueous solution of 10 mM ferrous iron containing 8 mg O,/l. arsenic, originating primarily from the oxidative dissolution of arsenopyrite (FeAsS) present in mine waste materials. Soluble arsenic, which is present mostly as anionic As (V) (AsO?) in mine waters, may be removed mainly by adsorption onto positively charged ferric iron colloids and, in theory, by the formation of scorodite (FeAsO,). Interestingly, nove strains of Thiomonas-like bacteria that oxidise arsenic (III) to arsenic (V), as well as oxidising ferrous iron and reduced sulfur compounds, have been isolated from mine waters (Battaglia-Brunet et al., 2002; Coupland et al., 2003). These bacteria probably contribute to removal of arsenic in these waters and have potential for use in fixed bed bioreactor systems similar to those described in Section 4.6 for the oxidation and precipitation of iron.
Fig. 5. Schematic layout of a reducing and alkalinity producing system (RAPS), redrawn after Younger et al. (2003). Water flowing through the compost layer is stripped of dissolved oxygen, while both this layer and the underlying limestone add alkalinity to the AMD. i I a a “An important engineering variant on the basic compost bioreactor theme is the reducing and alkalinity producing system (RAPS) layout (Younger et al., 2003), which is also referred to as successive alkalinity producing system (SAPS; Kepler and McCleary, 1994). In this type of system (Fig. 5), AMD flow moves downwards through a layer of Permeable reactive barriers (PRBs) are being used increasingly to treat a wide range of polluted ground- waters. Those that have been installed to bioremediate AMD operate on the same basic principles as compost bioreactors, described in Section 4.3 (Benner et al., 1997). Construction of PRBs involves digging of a trench or pit in the flow path of contaminated groundwater, filling the void with reactive materials
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Validity of Cubic Law for fluid flow in a deformable rock fracture
by shura tadesse

1980, Water Resources Research

The validity of the cubic law for laminar flow of fluids through open fractures consisting of parallel planar plates has been established by others over a wide range of conditions with apertures ranging down to a minimum of 0.2/zm. The... more
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Chemical and mechanistic aspects of the selective catalytic reduction of NO x by ammonia over oxide catalysts: A review
by Guido Busca

1998, Applied Catalysis B-environmental

The open literature concerning chemical and mechanistic aspects of the selective catalytic reduction of NO by ammonia (SCR process) on metal oxide catalysts is reviewed. Catalytic systems based on supported V 2 O 5 (including the... more
Scheme 1. Proposed structures for monomeric vanadyl species [46-49] and polymeric metavanadate species [51] in their dehydrated forms on the surface of VO5-TiO> catalysts.
Fig. 1. NO conversion versus temperature over: (a) WO3(9)/TiO3; (b) V205(0.78)/TiO2; (c) V205(1.4)/TiO2; (d) V205(0.78)-WO3(9)/TiO2; (e) V205(1.4)-WO3(9)/TiO>. Experimental conditions: catalyst weight, 160 mg (60-100 mesh); P=1 atm; flow rate=60 Ncc/min; feed: He+800 ppm NH3+800 ppm NO +1% O32. Reprinted with permission from [14].
Fig. 2. (A) Concentration profiles of NO, NH3, N2 and N2O over the V20s;-WO3-TiO; catalyst at a space velocity of 7.41 g! h—!. The inlet concentration of NO and NH; are 500 Vppm; balance He with 2% O>. (B) Concentration profiles of NO, NH3, Nz and NO over the Fe,03— SiO, catalyst at a space velocity of 1601 g~' h”!. The inlet concentration of NO and NH; are 500 Vppm; balance He with 2% O5. Reprinted with permission from [91]. tract the reactant NH; (the case of reactions (7) and
Fig. 3. Comparison of the conversion of NO in copper samples (%%wt CuO) on various oxide supports. Reprinted with permission from [95]. Several kinetic studies, mostly obtained under “real’’ conditions, have been reported for the SCR reaction. These are based on both merely empirical expressions (e.g. power-law kinetics) and/or mechan- istic approaches (e.g. Langmuir—Hinshelwood or The same features have been reported on catalysts with different active cations [12] like, e.g. on Mn- based catalysts (MnO,—AlO3, MnO,-WO3-AlLO3 [26,96] and MnO,-TiO>, [79]), on a number of Cu- expressions (e.g. power-law kinetics) and/or mechan- Parallel behaviors, with a temperature range with maximum NO conversion upon SCR and the competi- tion of SCR and ammonia oxidation reactions, have been found on any vanadia-based catalyst, like for V,05-ZrO, catalysts [93]. However, very similar behavior has also been reported for catalysts with very different formulations. In Fig. 2, a comparison is shown between the behavior of a V30;-WO3-TiO, catalyst and a Fe.O3—SiO, catalyst [91]. Again, this behavior can be found for supported CuO-based catalysts [12,21,94,95], as shown in Fig. 3.
Fig. 4. FT-IR spectra of the adsorbed species arising from adsorp- tion of ammonia over a V,0;-WO;/TiO2 sample (WO3=9% (w/w) and V,0; =1.47% (w/w)) and subsequent outgassing at RT (a), 400 K (b), 523 K (c), and 623 K (d). The spectra of the activated catalyst have been subtracted. Reprinted with permission from [133]. The thermal stability of NH3 adsorbed species has also been investigated by FTIR: it has been found that Lewis bonded molecular species are thermally more stable than ammonium ions (Fig. 4) [122]. It has also been reported that water competes with ammonia on
Scheme 2. Schemes of the generation of the Brgénsted acid sites from surface vanadyl centers.
Scheme 3. Proposed structures for ammonia adsorbed on V,0;—TiO3: (a) Lewis-bonded NH; at Ti sites; (b) H-bonded NH; on oxide sites; (c) Lewis-bonded NH3 at vanadyl sites; (d) ammonium ions bonded at V Br¢gnsted acid sites.
Fig. 5. FT-IR spectra of the adsorbed species arising from NH; adsorption (20 torr) over CuO-TiO, at (a) r.t. and subsequent outgassing at (b) 373 K, (c) 423 K, (d) 523 K, and (e) 623 K. Reprinted with permission from [19]. G. Busca et al./Applied Catalysis B: Environmental 18 (1998) 1-36
Fig. 6. FT-IR spectra of the adsorbed species arising from the interaction of NO (10 torr) with adsorbed ammonia on a model V205;-WO3-— TiO, catalyst, at different temperatures: (a) 300 K; (b) 373 K; (c) 403 K; (d) 423 K; (e) 453 K; (f) 523 K.
Fig. 7. Results of transient response experiments performed at 493 K over a V20s;-WO;/TiO> sample (WO3=9% (w/w) and V205=1.47% (w/ w)). Section (A): outlet reactor concentration following a step addition of NO (700 ppm) in He+1% Op, (squares) and of NH; (700 ppm) in He+1% Op (triangles). The theoretical inlet step is also reported (dashed line). Section (B): outlet reactor concentrations following a step addition of NH3 (700 ppm) in He+NO (700 ppm)-+O, 1%. The N, and H,O concentrations have not been reported. Section (C): outlet reactor concentrations following a step addition of NO (700 ppm) in He+NHs3 (700 ppm)+O, 1%. The Nz and HO concentrations have not been reported.
Fig. 8. Mechanism of the NO—NH; reaction on vanadium oxide catalysts proposed by Inomata et al. [68] in the presence of oxygen. Reprinted with permission from [68](c).
Fig. 9. Mechanism of the NO-—NH; reaction on supported vanadium oxide catalysts proposed by Janssen et al. [71](b) in the presence of oxygen. Reprinted with permission from [71](b).
Fig. 10. Mechanism of the NO—NH3 reaction on vanadium oxide catalysts proposed by Ozkan et al. [73](b) in the presence of oxygen.
Fig. 11. Mechanism of the NO-—NH; reaction on supported vanadium oxide catalysts proposed by Ramis et al. [123] in the presence of oxygen.
Fig. 12. Scheme illustrating the catalytic cycle of the SCR reaction over vanadia/titania catalyst in the presence of oxygen proposed by Topsge et al. [173]. Reprinted with permission from [173](c).
Fig. 13. Scheme illustrating the catalytic cycle of the SCR reaction over Cu-ZSM5 catalyst in the presence of oxygen proposed by Komatsu et al. [29]. Reprinted with permission from [29].
Fig. 14. Scheme illustrating the mechanism of the NO—NHs3 reaction over Cu-based catalyst in the presence of oxygen proposed by Centi et : [21]. Reprinted with permission from [21]. where NH species are produced by NH; dissociation. This mechanism is, however, only tentative since it is
Proposed reactant species, intermediates and active sites in different mechanisms and/or kinetic schemes for SCR on vanadia-based catalysts Table |
Proposed reactant species, intermediates and active sites in different mechanisms and/or kinetic schemes for SCR on vanadia-based catalysts Table 2
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Industrial applications of microbial lipases
by abdulhameed hameed

2006, Enzyme and Microbial Technology

Lipases are a class of enzymes which catalyse the hydrolysis of long chain triglycerides. Microbial lipases are currently receiving much attention with the rapid development of enzyme technology. Lipases constitute the most important... more
Fig. 1. Global enzyme markets by application sectors, through 2009 ($ millions).
the synthesis of esters has been recognized nearly 70 years ago [15]. Enzymes produced by Bacillus sp 3. Industrially used enzymes
Isolation of Lipases from various microorganisms
Source: Business Communications Company, Inc. Global enzyme markets by application sectors, through 2009 ($ millions)
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Neuregulin 1 and Susceptibility to Schizophrenia
by Omar Omar

2002, American Journal of Human Genetics

The cause of schizophrenia is unknown, but it has a significant genetic component. Pharmacologic studies, studies of gene expression in man, and studies of mouse mutants suggest involvement of glutamate and dopamine neurotransmitter... more
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Does Corporate Social Responsibility Affect the Cost of Capital
by Dev Mishra

2010

Recent years have seen a considerable increase in the literatures concerning the separate areas of corporate social responsibility and corporate tax aggressiveness. However, comparatively little scholarly attention has been paid to the... more
Fig. 2. P : aay ea ‘ti LS structural model with significant path coefficients. ***p <0.01, **p <0.05, *p < The results of the PLS structural model analysis are depicted in Figure 2. The GoF was computed, yielding GoF = 0.4223, indicating acceptable validity for the model.
The results of the measurement model for the full sample are summarized in Tables 2-4. As Table 2 shows, the industry construct was removed from the model and not used i analysis because of its low item loadings and lack of composite reliability and conve n further rgent validity. All the items of final model loaded on their respective constructs and were greater than 0.5. The factor loadings from the final PLS measurement model are presented in Table 3. Convergent validity appeared adequate for all constructs, as AVE exceeded 0.5 in all The discriminant validity of the constructs is presented in Table 4, which shows that diagonal AVE values exceed all other scores, suggesting sufficient discriminant validity.
Estimation of the measurement model parameters (full sample, n = 83).
The results from the structural model, presented in Table 5, indicate how the different dimensions of corporate social responsibility affect tax aggressiveness. The findings reveal a positive and significant relationship between CSR_S and TAG (0.2899, t = 1.8566, p <0.1), providing support for hypothesis H;. Indeed, the more the firms engaged in human resources activities, human rights in workplaces, and community involvement, the higher their ETRs were and, thus, the lower the likelihood that they would be tax aggressive in nature. The results also show a negative and significant relationship between CSR_ECO and TAG (- 0.3554, t = 2.5659, p < 0.05), suggesting that the more firms engaged in business behavior efforts, the lower their ETRs were and, thus, the higher the likelihood that they would be tax aggressive in nature. Hence, hypothesis H3 is supported. Moreover, the findings show no significant relationship between CSR_CG, and TAG, thus rejecting hypothesis H», Finally, the results also reveal no significant relationship between CSR_ENV and TAG, rejecting hypothesis H,.
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Three-dimensional stochastic analysis of macrodispersion in aquifers
by Lynn Gelhar

1983, Water Resources Research

The dispersive mixing resulting from complex flow in three-dimensionally heterogeneous porous media is analyzed using stochastic continuum theory. Stochastic solutions of the perturbed steady flow and solute transport equations are used... more
Fig. 10. Comparisons of theory (A\,; = o/7)/7) with Monte Carlo simulations.
Equation (22) is the tensorial expression of the Fickian- type gradient transport relationship. In order to evaluate the macroscopic dispersivity tensor Aj, it is necessary to relate the spectrum of the local flow variation to that of the local log hydraulic conductivity perturbations. This relationship, which depends on the type of covariance function used to describe the heterogeneity of the medium, is developed for several cases in the following sections. where S_q, is the cross spectrum of c and q; and Sj, is the cross spectrum of the j and i components of the specific discharge. Equation (21) relates the spectra of flow variation with that of the dispersive flux. To evaluate the mean dispersive flux, we integrate its spectrum,
Fig. 1. Logarithmic plot of longitudinal (A,,) and transverse (A22) macrodispersivities for a statistically isotropic medium with correlation scale \ and isotropic local dispersivity a. With q2 = q3 = 0 and a statistically isotropic In K field, the transverse mean hydraulic gradients J, = J; = 0 (see section 5); then, by using the representation theorem with (28), the spectrum of the specific discharge becomes
diffusion, then (29) takes on a relatively simple form which can be evaluated exactly; this involves extensive manipula- tions which are available from Gelhar and Axness [1981]. The results, are seen to agree, to lowest order in ¢, with the asymptotic results (33) and (36), when a, = az. Figure 1 shows a comparison of the exact and aproximate results; this indi- cates that the approximate solution is adequate for « < 0.01. Note that the ratio of transverse to longitudinal macroscopic dispersion is ¢/3, which will be very small under typical conditions.
Fig. 2. Longitudinal (A,,) and transverse (A33) macrodispersivi- ties with flow parallel to bedding: A. = o/l;7/ary, 1, is the correlation scale parallel to bedding, and /; is that perpendicular to bedding; a7 is the local transverse dispersivity. All results use the modified exponential covariance (6), except (47), the dot-dashed line, which is based on the exponential covariance (4). The dashed lines show the asymptotic result (42) for Aj.
The F; integral is transformed from the (k,, k2, k3) coordinate system to the (k;’, kz’, k3') system by k; = ajk;’, where a, = cos (x;’, x;) denotes the matrix of direction cosines; noting that ko = ky? + ky? + ky? = ky'2 + ky’? + ky!” = k’? and that the Jacobian of the transformation matrix is 1, Fig. 3. Coordinate systems for the case x2 = x,'; the dashed ellipse represents the e' level of correlation in the covariance function (4), the x,’ direction being that of bedding. The x,’ coordi- nates are the principal axes of the effective hydraulic conductivity tensor and the x,* coordinates are the principal axes of the macrodis- persivity tensor.
3. 4a. This shows g), and g2. as a function of A,/A3 and Aj/A2. Equations (53) and (54) are shown in Figure 4 along with the general numerical results. A two-dimensional flow system results if \; — © or p> 0
Fig. 4b. This shows g33 as a function of A,/A3 and Aj/A2. in the g,,,, integrals; straightforward integrations in that case produce
Fig. 5. Effective hydraulic conductivities parallel (K,,) and per- pendicular (K33) to bedding; the solid lines are the generalization (60) and the dashed lines are the first order results (59): A, = Ao. from the neglect of higher order terms in the perturbation analysis. Since some natural media have o/ this large, it may be useful to consider a generalization which properly ap- proaches the limiting case of one-dimensional flow parallel and perpendicular to layers. One possible way of generaliz- ing (59) is to consider the quantities in parenthesis as the first two terms of the Taylor series expansion for e*. The general- ization of (59) for of > 1 then becomes,
Fig. 6. Anisotropy of effective hydraulic conductivity as a func- tion of the variance of In K, 7, and aspect ratio A,/A3 for the case A, — and y = q/KJ;. When using (63) it is easily seen that Ay = Aji, Since Rijmn = Rjimn, thus confirming that the macroscopic
. 7. (a) Principal angle, 7. (6) Principal components of macro- dispersivity tensor. Both for case 1 with A; = Az. To illustrate the structure of (65), the dependence of A; on 6 was calculated for a specified medium (8 = K33'/K1)' = 4, Ai/ h3 = 5, and of = 2.58 (from (60)). Figure 7 shows the principal components of Aj, A,\*, A22*, and A33* in normal- ized form, along with the angle y. The most notable feature of these results is the fact that 7» is negative when @ is positive. In other words, the principal axis associated with the maximum principal component of Aj is deflected in a direction opposite to that of the stratification. Note also that the transverse dispersivities Az.* and A33* are of similar magnitude and attain a maximum of about 8% of A,,*. For 0 = 0° and 90°, Ax* = A33* = 0, as would be expected, since local dispersion was neglected in the asymptotic analysis leading to (65). These cases with flow parallel or perpendicu- lar to stratification are similar to those analyzed in sections 3 and 4 including the effect of local dispersion, where it was found that the transverse dispersivity was of order ofa7// (see (36) and Figure 2). Typically, for these special cases the transverse macrodispersivities will be several orders of
Fig. 8. Coordinate systems for case 2 (x3 = x,'); the dashed ellipse represents the e ' level of correlation in the covariance function (4), the x,’ direction being that of maximum correlation scale in the horizontal. The x;’ coordinates are the principal axes of the effective hydraulic conductivity tensor, and the x,* coordinates are the principal axes of the macrodispersivity tensor.
Fig. 9a. Configuration of a dispersing pulse and bedding for item D of Table 3 with mean flow inclined to bedding (case 1); A, = 10 m, of = 2.5, \y/dz = 1, Ay/A3 = 20; x, is the mean flow direction.
Fig. 9b. Configuration of dispersing pulse for item J of Table 3 with horizontal anisotropy (case 2); A, = 10 m, of = 2.5, A,/d2 = 5, A /A3 = 20; x, is the mean flow direction.
APPENDIX: GENERAL SOLUTION OF THE P, Qj, Rymn INTEGRALS The stochastic theory developed here provides a specific predictive model of large-scale field dispersion. If the scien- tific community is to develop reliable predictions of large- scale, long-term transport of solutes in the subsurface envi- ronment, this and competing theories must be tested under large-scale controlled field conditions. This will require carefully designed tracer experiments which are monitored over several years at several different sites representative of important geologic settings (fluvial deposits, glacial out- wash, shoreline sandstone, alluvial basin fill). Detailed mea- surements of the variability of hydraulic conductivity will be required at each site.
Acknowledgments. This research was supported in part by grants from the National Science Foundation (ENG75-14184) and the Environmental Protection Agency (R-806092). Part of the work was done while the first author held visiting appointments with the Sonderforschungsberiech 80 (a special research program on trans- port processes in flow) at the Universitat Karlsruhe, West Germany, and the Centre D’Informatique Geologique (Fontainebleau), Ecole des Mines de Paris, France. The support of the Geophysical Research Center at New Mexico Institute of Mining and Technology is also acknowledged. Discussion with A. L. Gutjahr and G. de Marsily have influenced this work.
TABLE 1. Summary of Asymptotic Results for the Macroscopic Dispersivity Tensor A, Notation: of = variance of In K; \,, J, = correlation scales (see (4), (6)); az, ar = local dispersivities; y = q/K, J;
TABLE 2. Calculated Macrodispersivities for the Quadra Sand, Vancouver, British Columbia of = 0.71, \y = dg = 5m, dy = 0.38 m, a, = 10 a7 = 1 cm, Ki'/K33' = 1.80 (equation (60)
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Review of Technologies for Oil and Gas Produced Water Treatment
by Alireza Pendashteh and 
1 more
Luqman Chuah Abdullah

2009, Journal of Hazardous …

Produced water is the largest waste stream generated in oil and gas industries. It is a mixture of different organic and inorganic compounds. Due to the increasing volume of waste all over the world in the current decade, the outcome and... more
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Analytical prediction of stability lobes in milling
by Y. Altintas and 
1 more
Erhan Budak

1995

Figure 1: Dynamic model of milling with two degrees of freedom
The dynamic milling expression (8) is therefore re- duced to the following where the integrated functions are given as:
Figure 2: A single single degree of freedom system: Comparison of the predicted chatter limit with the published data (Minis and Yanu- shevsky [1993], analog data is from Opitz [1968] and Sridhar et al. [1968]).
Milling with a 2-DOF Cutter : A two DOF milling system example is taken from Smith and Tlusty [12]. This is a half immersion-up milling of Figure 3: Analytical, multi frequency and time domain stability limit predictions for a case an- alyzed by Smith and Tlusty [1990].
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The green, blue and grey water footprint of crops and derived crop products
by Arjen Hoekstra

2010, American Journal of Hematology

This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating... more
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Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment
by albert westermeier

2002, International Journal of Climatology

We present a dataset of daily resolution climatic time series that has been compiled for the European Climate Assessment (ECA). As of December 2001, this ECA dataset comprises 199 series of minimum, maximum and/or daily mean temperature... more
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A critical review of data on field-scale dispersion in aquifers
by Lynn Gelhar

1992, Water Resources Research

A critical review of dispersivity observations from 59 different field sites was developed by compiling extensive tabulations of information on aquifer type, hydraulic properties, flow configuration, type of monitoring network, tracer,... more
Fig. 1. Longitudinal dispersivity versus scale of observation identified by type of observation and type of aquifer. The data are from 59 field sites characterized by widely differing geologic materials.
Fig. 2. Longitudinal dispersivity versus scale with data classified by reliability.
Fig. 3. Longitudinal dispersivity versus scale of observation with adjustments resulting from reanalyses. Arrows indicate reported values at tails and corresponding values from reanalyses at heads. Dashed line connects two dispersivity values determined at the Hanford site.
Fig. 5. Vertical transverse dispersivity as a function of observation scale.
ontal and vertical transverse dispersivities; largest symbols are high reliability and smallest symbols are low reliability. Vertical dashed lines connecting two points indicate sites where all three principal components of the dispersivity tensor have been measured. Horizontal dashed line indicates a ratio of A; /Ay = 1/3. which has been widely used in numerical simulations. Fig. 6. Ratio of longitudinal to horiz When the data on longitudinal dispersivity are classified according to reliability, the pattern regarding scale depen- dence of dispersivity is less clear (see Figure 2). There are no
*TDS denotes total dissolved solids; EC, electrical conductivity; PFB, pentafluorobenzoate; MTFMB, metatrifluoromethylbenzoate; MEB, metafluorobenzoate; Para-FB, parafluorobenzoate; RhWT, rhodamine-WT dye; and SCN, thiocyanate. +A, denotes longitudinal dispersivity; A, horizontal transverse dispersivity; and A y, vertical transverse dispersivity. Reported values for A;, Ay, and Ay are separated by slashes. Absence of slashes means that values were reported for A; only. A comma or a dash separating entries means that multiple values or a range of values, respectively, were reported for a particular dispersivity component. +For description of classification criteria, see text. 8B. E. Adams and L. W. Gelhar, Field study of dispersion in a heterogeneous aquifer: Spatial moments analysis (submitted to Water Resources Research, 1991). ||Porosity-corrected dispersivity value.
1. The tracer test was either ambient flow with known input, diverging radial flow, or a two-well pulse test (without recirculation). These three test configurations produce break- through curves which are sensitive to the dispersion coefficient and appear to work well in field applications [Welty and Gelhar, 1989]. The radial converging flow test is generally considered less satisfactory than the diverging test because breakthrough curves at the pumping well for the converging test frequently exhibit tailing, which complicates the interpre- tation of these tests. Some researchers attribute this behavior to two or more discrete geologic layers and try to reproduce the observed breakthrough curve by superposition of break- through curves in each layer, where the properties of each layer may differ [e.g., Ivanovitch and Smith, 1978, Sauty, 1977]. The problem with this interpretation is that there are typically numerous heterogeneities on a small scale that cannot be attributed solely to identifiable layers. One possible expla- nation of the tailing in radial convergent tests is sometimes termed ‘‘borehole flushing,’’ where the tail of the breakthrough curve is attributed to the slow flushing of the input slug of tracer out of the injection borehole by the ambient groundwater flaw. Goblet [1982] measured the slow flushing of tracer out of the values that do not fall into the high or low groups. These classifications do not place strict numerical confidence limits on reported dispersivities, but rather are intended to provide an order-of-magnitude estimate of the confidence we place on a given value. In general, we consider high-reliability dispersivity values to be accurate within a factor of 2. Low-reliability values are considered to be no more accurate than within 1 or 2 orders of magnitude. Intermediate reliabil- ity falls somewhere between the extremes. We wish to make a distinction between the judgment of the reliability of the reported dispersivity and the worth of a study. Often, the Purpose of a study was for something other than the deter- mination of dispersivity. Our classification of dispersivity is not intended as a judgment on the quality of a study as 2 whole, but rather to provide us with some criteria with which to screen the large number of data values obtained. By then examining the more reliable data, conclusions which evolve from the data will be more soundly based and alternative interpretations may’ become apparent.
TABLE 2. Numbers of Dispersivities for Different Types of Tests and Media se cen
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The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models
by N Quinn

1991, Hydrological Processes

The accuracy of the predictions of distributed hydrological models must depend in part on the proper specification of flow pathways. This paper examines some of the problems of deriving flow pathways from raster digital terrain data in... more
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The influence of autocorrelation on the ability to detect trend in hydrological series
by Sheng Yue

2002, Hydrological Processes

This study investigated using Monte Carlo simulation the interaction between a linear trend and a lag-one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial... more
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Underestimating Costs in Public Works Projects: Error or Lie?
by Bent Flyvbjerg

2002, Journal of the American Planning Association, vol. 68, no. 3, pp. 279-295

This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and... more
Figure 3. Inaccuracy of cost estimates in transportation projects over time, 1910-1998 (fixec
lable 1. Inaccuracy of transportation project cost estimates by type of project (fixed prices)
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A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils
by Timothy Ginn and 
1 more
Bernard Cosby

1984, Water Resources Research

Stochastic modeling of soil water fluxes in the absence of measured hydraulic parameters requires a knowledge of the expected distribution of the hydraulic parameters in different soil types. Predictive relationships describing the... more
Fig. 1. (a) The USDA soil texture triangle. (b) Reclassification of the texture categories into four broad regions for the two and four group discriminant analyses.
Fig. 2. Values of the F ratios from the one-way analyses of variance. The dashed line represents a significant result (p = 0.10).
Fig. 3. Percent of variance explained by each factor in the two- way analyses of variance. The dashed line represents the percent of variance explained by texture alone.
Fig. 4. Plots of the mean values of the hydraulic parameters for each textural class versus the most important variable (percent sand, silt, or clay) determined from the multiple linear regression analysis. The solid line is the univariate regression line.
Fig. 5. Plots of the standard deviations of the hydraulic parame- ters within each textural class versus the most important variable (percent sand, silt, or clay) determined from the multiple linear regres- sion analysis. The solid line is the univariate regression line.
Fig. 6. F ratios from the one-way analysis of variance for each parameter, before (a) and after normalization of the parameter values using (b) the univariate expressions of Table 5 and (c) the multivariate expressions of Table 4. All F ratios are significant (p = 0.10).
Fig. 7. Plot of the two significant discriminant function scores for the four group analysis. The modified USDA textural triangle is superimposed on the discriminant space. A similar plot of the discriminant scores based on the 11 group analysis is presented in Figure 8. A distorted version of the textural triangle (Figure 1a) is superimposed on the dis- criminant space. Again, the similarity between the two spaces is striking. While the relative areas of the textural classes have changed, the neighbor-to-neighbor relationship is identical in the two spaces. The only textural class which falls outside its expected region is the sandy clay class. It should be noted, however, that this class was represented by only 16 samples in the total sample population of 1448.
Fig. 8. Plot of the two most important discriminant function scores for the eleven group analysis. A distorted USDA textural trian- gle is superimposed on the discriminant space.
TABLE 1. List of Sample and Site Descriptors and the Classes for Each Descriptor The number in parentheses is the number of samples in each classi- fication. Texture, land use, and horizon were available for all samples. Other descriptors were not always available for each sample. Un- classified samples were not included in statistical analyses using that descriptor.
TABLE 2. Values of Percent Silt, Sand, and Clay Content Used for Each Textural Class in the Regression Analyses The percentages were obtained from midpoint values of each tex- tural class using the USDA textural triangle.
TABLE 3. Means and Standard Deviations for the Four Hydraulic Parameters in Each Textural Class Parameters: b is the slope of log Y versus log (@/®,) regression, Y in centimeters H,O; log '¥, is the intercept of log ¥ versus log (@/@,) regression, in centimeters H,O; log K, is the log of the saturated hydraulic conductivity in inches per hour; ©, is the saturated water content in percent (volume/volume).
TABLE 4. Results of Multiple Linear Regression Analyses on the Means and Standard Deviations of the Parameters
TABLE 5. Results of the Univariate Regressions of the Hydraulic Parameters on Percent Sand, Silt, or Clay
TABLE 6. Correlation Coefficients (r) Between Canonical Discriminant Function Scores and the Hydraulic Parameter Values
TABLE 7. Correlation Coefficients (r) Between Discriminant Function Scores clays (the coefficients are —0.99 for both correlations). This suggests that we might interpret the second discriminant func- tion as a silt axis. The first function for each group might likewise be interpreted as a clay-sand axis. The two discrimi- nant functions can be used to define a planar parameter space similar to the sand, silt, clay planar space defined by the USDA triangle. Figure 7 shows a plot of the two discriminant function scores for the four group case. The functions were evaluated for each group using the mean of the parameter values within each group. Superimposed on the discriminant space is the modified USDA triangle from Figure 1b. The similarity between the classification scheme based on weighted combinations of the hydraulic parameters and the classifi- cation scheme based on particle size distributions of the soils is striking.
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Climatic warming in the Tibetan Plateau during recent decades
by XIAODONG LIU

2000, International Journal of Climatology

Adequate knowledge of climatic change over the Tibetan Plateau (TP) with an average elevation of more than 4000 m above sea level (a.s.l.) has been insufficient for a long time owing to the lack of sufficient observational data. In the... more
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The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes
by Erik van den Elsen

2002, Hydrology and Earth System Sciences

A Surface Energy Balance System (SEBS) is proposed for the estimation of atmospheric turbulent fluxes and evaporative fraction using satellite earth observation data, in combination with meteorological information at proper scales. SEBS... more
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Statistics of extremes in hydrology
by Richard Katz

2002, Advances in Water Resources

The statistics of extremes have played an important role in engineering practice for water resources design and management. How recent developments in the statistical theory of extreme values can be applied to improve the rigor of... more
Fig. 1. Annual maximum of daily precipitation amount at Fort Col- lins, CO, USA, 1900-1999.
Fig. 2. Annual cycle in effective design value (100-yr return period) for fitted GEV distribution for monthly maximum of Fort Collins daily precipitation. Observed values of monthly maximum of daily precipi- tation indicated by circles.
Fig. 4. Q-Q plot for fit of GEV distribution to annual peak flow of Potomac River (line of equality indicates perfect fit). Fig. 3. Annual peak flow of Potomac River at Point of Rocks, MD. USA, 1895-2000. (Fig. 4) indicates that the fit is reasonably adequate, even in the upper tail. In Section 5.2.2, another annual peak flow time series will be analyzed for which the fit of the GEV distribution does not appear to be acceptable.
Fig. 6. Q-Q plot for fit of GP distribution to US annual damage from floods (line of equality indicates perfect fit).
Fig. 7. Annual maximum sea level at Freemantle, Western Australia, 1897-1989. Trend in median of fitted GEV distribution given by dashed line.
Fig. 8. Scatter plot of January maximum of daily precipitation amount at Chico, CA, USA, vs. January mean pressure. (dependence of the shape parameter on pressure would be permissible as well). Katz and Parlange [54] originally analyzed this same precipitation and pressure data, but for conditional stochastic modeling of the time series of daily precipitation amounts (1.e., not just extremes). The following results can be viewed as an extension of those in Katz [51], in which extreme precipitation was mod- eled conditional on only two pressure states (below or above average).
Fig. 9. Annual peak flow for Salt River near Roosevelt, AZ, USA. 1924-1999. When the GEV distribution is Salt River peak flow data, a rather parame shown) indicates very poor fit for tions (j = 0.677 for penalized 7=0.4 relative for PWM, quite a bit sma y high values). Alternative fitted by ML to the high estimated shape er is obtained (7 = 0.859) and a Q-O plot (not the highest observa- ikelihood [16] and ler than ML but still y, a GP distribution is assumed instead, with ML yielding an estimated shape parameter of = 0.279 (i.e., consistent with that ob- tained by Anderson and Meerschaert [3] for monthly
Fitted GEV distribution (by POT approach with threshold of 40 mm) to maximum daily precipitation (mm) in January at Chico, CA, US/ conditional on pressure covariate (mb — 1000) observed pressure to 23.6 yr for the highest; for a 20-yr return period, from about 8.1-44.9 yr. Table 1
Fig. 10. Q—-Q plot for fit of GP distribution to annual peak flow of Salt River conditional on Darwin pressure (line of equality indicates perfect fit). When the conditional GP distribution is fitted by ML to the Salt River peak flow, with the log-transformed scale parameter being linearly related to the Darwin pressure (i.e., Inag(y) =o) +1), the estimated slope of 6, =0.0450 (corresponding to an increase of about 4.61% per mb) is statistically significant (P- value = 0.003 for likelihood ratio test of ¢; = 0). In this model, the estimated shape parameter is smaller ye (? = 0.156) with only very weak evidence of a heavy tai (P-value ~ 0.234 for likelihood ratio test of y= 0). A residual Q-O plot (Fig. 10) for the GP distribution based on Eq. (8) appears satisfactory, even somewha improved upon the corresponding Q—Q plot without the Darwin pressure as a covariate. Given a 10-yr long-run return period, the effective return period varies from
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Recycling and recovery routes of plastic solid waste (PSW): A review
by murali chaitanya
Plastic solid waste (PSW) presents challenges and opportunities to societies regardless of their sustainability awareness and technological advances. In this paper, recent progress in the recycling and recovery of PSW is reviewed. A... more
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Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation
by Daniel Loucks

1982, Water Resources Research

Three criteria for evaluating the possible performance of water resource systems are discussed. These measures describe how likely a system is to fail (reliability), how quickly it recovers from failure (resiliency), and how severe the... more
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Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model
by Hongliang Zhang
Source apportionment is typically performed on chemical composition data derived from particulate matter (PM) samples. However, many common sources no longer emit significant amounts of characteristic trace elements requiring the use of... more
Fig. 1. Map showing the locations of the major roadways, the Eastman Kodak complex and the monitoring site in Rochester, NY Semi-volatile organic vapors can be adsorbed on the quartz filters and create a positive bias in measured MM concentrations. Those semi-volatile organic compounds may also be evaporated from the filter during the sampling, resulting in a negative artifact (Turpin et al., 2000). Since denuders were not used to correct for sampling artifacts, it is difficult to quantify them in this study (Park The New York State Department of Environmental Conservation (NYS DEC) maintains a monitoring site in Rochester, NY (43°08’46” N, 77°32'54” W, Elevation = 137 m, U.S. EPA site code 36-055-
Fig. 3. PMF factor profiles for organic compounds.
Fig. 5. Day of week plot for the PMF factor PM2.5 contributions. Gasoline vehicles and diesel emissions factors were identified by the presence of EC, OC, hopanes, and cholestanes. The latter two are makers for motor vehicle emissions (Schauer et al., 2002) and are present specifically in heavy petroleum distillates such as motor oil (Simoneit et al., 1999). The diesel emissions factors contained The Saturday and Sunday factor contributions were slightly lower compared to the weekdays, which may be attributed to the local nitrate formed from diesel emissions. Zhao et al. (2007) found that urban sites had higher nitrate compared to rural sites indicating local nitrate formation.
Fig. 4. PMF factor PM2.5 contributions.
Fig. 6. Seasonal variations of the PMF factor PM2.5 contributions.
PMp25 species quantified in this study. Table 1 (continued) Table 1
Std. Dev.: Standard deviation; BDL: Below detection limit.
Comparison of the two apportionments of PM2.5 mass (unit: pg m73).
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Possible methods for biodiesel production
by Alanoud Alnaqabi
Biodiesel production is a very modern and technological area for researchers due to the relevance that it is winning everyday because of the increase in the petroleum price and the environmental advantages. In this work it is made a... more
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A natural gradient experiment on solute transport in a sand aquifer: 3. Retardation estimates and mass balances for organic solutes
by Mark Goltz

1986, Water Resources Research

Laboratory investigations were conducted to determine whether the observed field retardation of bromoform, carbon tetrachloride, tetrachloroethylene, 1,2‐dichlorobenzene, and hexachloroethane at the Borden field site could be explained by... more
Fig. 1. Experimental site. Rectangle within the sand quarry illus- trates the location of the transport experiment and matches the frame of Figure 5 (top). Also shown is the approximate extent of contami- nation from the landfill in 1979, as delineated by a 10 mg/L chloride isopleth. Rectangles 2 and 3 mark the locations of previous smaller- scale tracer tests [Sudicky et al., 1983, and Sutton and Barker, 1985, respectively].
Fig. 2. Approximate vertical geometry of aquifer along section AA’ (Figure 1). Rectangle illustrates the vertical zone ir which the experiment was conducted, which is above the landfill leachate plume (denoted by 10 mg/L chloride isoplett from 1979 data).
Fig. 5. Locations of multilevel samplers and injection wells as of January 1986: (top) plan view and (bottom) approxi- mate vertical distribution of sampling points (plus signs) projected onto cross-section AA’ (vertical exaggeration = 4.6). Positions labeled UW-1 and UW-2 are the locations of piezometer nests and core samples discussed in the text. Position labeled UW-3 is the center of a set of 32 cores taken in two intersecting and orthogonal lines by Sudicky [this issue]. Solid vertical lines in the bottom frame indicate the location and screen length of the injection wells.
Fig. 6. Relative concentration distribution of chloride on August 24, 1982, 1 day after injection.
fig. 7. Vertical profiles of relative concentration of chloride and carbon tetrachloride at several sampling locations.
Fig. 8. Vertically averaged concentration distribution of two solutes: (a) chloride ion, 1, 85, 462, and 647 days after injection (averaging interval: z = —1.5 to z = —7.5 m) and (b) carbon tetrachloride, 16, 380, and 633 days after injection (averaging interval: z = —1.5 to z = —6.0m).
Fig. 9. Approximate concentration distributions of chloride (1 and 462 days after injection) along vertical cross section AA’, the horizontal axis of the plume (vertical exaggeration = 3). Contours depicted for the 1- and 462-day plumes are 10, 100, 300, and 600 mg/L and 10, 30, 100, and 300 mg/L, respectively. Figure 9 presents approximate concentration distributions of the chloride plume in vertical sections on or near the longi- tudinal axis of the plume (1 and 462 days after injection). The vertical scale of the figure is exaggerated by a factor of 3 for clarity. Because such vertical representations cannot be pro- Figure 8 shows that the mobility of the carbon tetrachloride is significantly less than that of chloride, providing qualitative evidence of retardation due to sorption. The retardation of the other organic solutes was even greater [Roberts et al., this issue], generally in accord with their hydrophobicity (Table 3).
*Determined by X-ray diffraction. TABLE 1. Mineralogy of a Bulk Sample of the Aquifer Material
TABLE 2. Background Groundwater Characteristics Source one, Nicholson et al. [1983]; results have been converted from mM to mg/L, except for alkalinity. Source two, this study.
TABLE 3. Injected Solutes and Their Properties Minus sign, little potential for biotransformation; plus sign, moderate potential for biotransformation; double plus sign, good potential for biotransformation; question mark, potential for biotransformation unknown at onset of this work. *Methanogenic conditions.
TABLE 4. Summary of Synoptic Monitoring Program
TABLE 5. Summary of Time Series Monitoring Program
TABLE 6. Comparison of Methods for Estimating Average Linear Groundwater Velocity *Temperature corrections have been made to adjust hydraulic con- ductivity values to 10°C. tCalculated using estimated hydraulic conductivity and assuming the mean hydraulic gradient and aquifer porosity are 0.0043 and 0.33, respectively.
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Rationality and Power: Democracy in Practice
by Bent Flyvbjerg

1998

Back cover text: If the new fin de siècle marks a recurrence of the real, Bent Flyvbjerg’s Rationality and Power epitomizes that development and sets new standards for social and political inquiry. The Danish town of Aalborg is to... more
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Application of chitin and chitosan based materials
by nhâttam nhattam
As functional materials, chitin and chitosan offer a unique set of characteristics: biocompatibility, biodegradability to harmless products, nontoxicity, physiological inertness, antibacterial properties, heavy metal ions chelation, gel... more
Fig. 1. Structure of chitin, chitosan and cellulose.
Some of the more important industrial applications of immobilized enzyme systems [1-3,5]
Some of the most frequently studied enzymes for enzyme-based biosensors [3,10- 12] Table 2
Enzymes immobilized on chitin- and chitosan-based materials
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Heterogeneous photocatalysis Transition metal ions in photocatalytic systems
by Marta I Litter

1999, Applied Catalysis B: Environmental

The presence of transition metal ions in photocatalytic reactions is reviewed according to two main approaches: (a) the influence of transition metal ions on the rate of photocatalytic reactions (mainly oxidation) and (b) the... more
Fig. 1. Simplified diagram of the heterogeneous photocatalytic processes occurring on an illuminated semiconductor particle.
A great discussion exists nowadays about the oxida- tive pathway, which could be performed by direct hole attack or mediated by HO® radicals, in their free or adsorbed form. The oxidative pathway leads, in many cases, to complete mineralization of an organic sub- strate to CO2 and H20. Generally, A is dissolved Oz, which is transformed in superoxide radical anion (O2°—) and can lead to the additional formation of HO®: ! The values of the standard redox potentials cited in this paper are referred to NHE and taken from [4].
Fig. 2. Positions of the redox potentials of various metallic couples related to the energy levels of the conduction and valence bands of Ti02 Degussa P-25 at pH 0.
Fig. 3. Schematic diagram for the photocatalytic deposition of a metal on TiO3.
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USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment
by Matthew MacLeod

2008, The International Journal of Life Cycle Assessment

Background, Aim and Scope. In 2005 a comprehensive comparison of LCIA toxicity characterisation models was initiated by the UNEP-SETAC Life Cycle Initiative, directly involving the model developers of CalTOX, IMPACT 2002, USES-LCA, BETR,... more
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How much complexity is warranted in a rainfall-runoff model?
by Darren Beriro

1993, Water Resources Research

Dcvclopmcnt of mathmodeh rrtattrg the precipitation incident upon a catchment to the stnamaow cmmathg from the catchment has been a mjor focus af srnface water hydrology for ~e 8. O e n a a n y , v a l u~8 f o r~~i n s u c b m o d d. m u... more
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