0 50 100 150 200 250 300 350 400
0 100 200 300 400 500 600 700 800 900 1000
Temperature (C)
CO2 concentration (ppm)
Fig. 7.10 Thermogram of Aldrich activated carbon at a temperature ramping rate at 20 C/min
0 20 40 60 80 100 120 140 160 180
0 200 400 600 800 1000
temp (C)
CO2 conc (ppm)
Figure 7.11 The thermogram of the sediment from Boston Harbor with a ramping rate at 20 C/min.
0 50 100 150 200
0 100 200 300 400
0 200 400 600 800
935 C 1000
C O 2 c on c ( pp m ) Te m p ( C )
20 C /min
375 C for 2 hours
2 C /min
Time (min)
Figure 7.12 The variation of the emitted CO2 concentration and the temperature with time for the sediment from Boston Harbor with a temperature ramping rate at 2 C/min before 375 C, and at 20 C/min up to 935 C after holding at 375 C for 2 hours.
7.6. Summary
The experimental method can efficiently monitor the temperature under which the carbonaceous compounds are converted to carbon dioxide. The precision of the quantification of the carbon mass is acceptable. However, the recovery of the carbon mass is not consistent. A post-furnace catalytic combustion oven may improve the recovery.
Charring is a problem in removing the organic carbon. Reducing the grain size of the sodium citrate or lowering the temperature ramping rate did not lower the possibility of charring. More tests are needed for different sizes, different materials and different temperature ramping rate.
Fingerprints of BCs (thermograms) can be established by using this method, which may help to identify the origins of different BC samples and also help to predict the sorption behavior of them.
7.7. Further evaluation of the methods
Some tests should be done to improve the method.
A. Tests of the pretreatment efficiency and charring on different materials and powder sizes,
B. Repeated experiments on the BC sample with the same temperature program to investigate the method repeatability,
C. Experiments on the BC sample with different temperature ramping rates to look for the completely-oxidizing ramping rate,
D. Experiments using composite samples containing two or more reference BC materials to investigate the ability of the method to differentiate different carbon species,
7.8. Future research
Future research may include
A. Investigating the BC speciation in field samples from different land-use types and establishing the regional and global BC inventory.
B. Categorizing BC species according to their thermo-oxidation characteristics and examining the sorption behavior of each BC group toward HOCs including the sorption equilibrium isotherms and the sorption/desorption rates.
C. Verifying of the multi-component sorption model by comparing the model simulation and the experimental results.
References
Currie L. A. et al., A Critical Evaluatin of Interlaboratory Data on Total, Elemental , and Isotopic Carbon in the Carbonaceous Particle Reference Material, NIST SRM
1649a, J. Res. Natl. Stand. Technol. in press.
Gustafsson, O., Haghseta, R., Chen, C., MacFarlane, J. and Gschwend, P. M.
Quantification of the Dilute Sedimentary Soot Phase: Implications for PAH Speciation and Bioavailability, Environ, Sci. Technol. 1997, 31, 203-209.
Kuhlbusch, T. A. J. Methods for Determining Black Carbon in Residues of Vegetation Fires, Environ, Sci. Technol. 1995, 29, 2695-2702.
Middelburg, J. J., Nieuwenhuize, J., van Breugel, P., Black carbon in marine sediments, Marine Cemistry, 1999, 65, 245-252.
Nelson and Sommers, Total Carbon, Organic Carbon, and Organic Matter, in Methods
of Soil Analysis Part 2 Chemical and Microbiological Properties, Page A. L. Eds,
1982Others (see Table 1)
Chapter 8 Molecular Dynamics Simulations of the Sorption of VOCs in Humic Acid
8.1 Introduction
The sorption behavior of volatile organic compounds (VOCs) in humic substances plays important roles in pollutant fate modeling and remediation of contaminated sites.
There were many studies of mechanisms of sorption process. Previous works (Chang et al., 1997; Piatt and Brusseau, 1997; Shih and Wu, 2002a and 2002b) have focused on examining sorption kinetics of VOCs with different types of environmental sorbents.
These prior studies focused on measuring rates of sorption and diffusion in complex organo-mineral aggregates or humic substances at larger length scales. Various rate models have been used to simulate contaminant uptake and release in a macroscopic scale. As to the mechanism controlling sorption dynamics, it still remains unclear due to the difficulty in observing its dynamic behavior at the microscopic level (Brusseau and Rao, 1989).
Recently, molecular modeling techniques based on the development of fundamental physical theories and their applications to numerical simulation techniques are applied to environmental issues. For example, some researchers applied the molecular modeling techniques to explain the environmental phenomena such as the persistency of toxaphene in mammals (Vetter and Scherer, 1999) the prediction of polychlorinated hydrocarbons from municipal waste incinerators (Iino et al., 2001), the sorption mechanisms of organic chemicals adsorbed onto clays and minerals (Kubicki et al., 1997; Teppen et al., 1998; Luo and Farrell, 2003) and the interactions of natural organic matter and contaminants (Kubicki and Apitz, 1999). But to our knowledge there is very little molecular modeling work involved in sorption kinetics and thermodynamics study of a contaminant in humic substances.
Therefore, in this work we will first use the molecular dynamics simulation technique to verify our method by comparing the kinetic results of simulation with the real experimental data under dry conditions. Then we will identify the possible mechanism governing the interaction between toluene and humic acid. Finally we will evaluate the thermodynamic property of toluene in humic acid.
8.2 Computational Methods and Structural Model
Structural Model and Molecular Dynamics Simulation. The building block
structure of humic acid was described previously (Davis et al., 1997; Kolla et al., 1998).The amide-linked humic acid helical structure is stable and the amide linkage is in agreement with spectroscopic evidence. In this work the basic building block structure of humic acid was produced using the C2 Builder modules (MSI Inc.). The humic acid model consisting of seven monomers was built by adding one monomer after another gradually and at the same time the energy minimization calculations was carried out to assure its stable confirmation. Finally, the target compound, i.e. toluene molecule, was inserted into the open space formed among chains within humic acid to establish our structural model as shown in Figure 8-1.
Diffusion Coefficient. The calculation for the diffusion coefficient is based on the
statistical mechanical principles. In short, the diffusion coefficient is extracted from the proportionality constant according to the Einstein form of diffusion,( ( 0 ) ( ) )
2Considering a binary system consisting of a natural polymer, humic acid, and a penetrant, toluene molecule, mean square displacement from the dynamics trajectory is equal to the average of the displacement in equation (8-1) for the calculation of diffusion coefficient.
Activation Energy. The effect of temperature on the diffusion coefficient can be
described by the Arrhenius equation (Chang et al., 1997; Shih and Wu, 2002; Crank and Park, 1968)where D0 is the diffusion coefficient of the reference state and E is the activation energy of diffusion process. The activation energy for sorption was found by plotting ln D versus 1/T.
8.3 Results and Discussion
8.3.1 Diffusivity of Toluene inside Humic Acid
The proposed humic acid model (Figure 8-1) is based on the combination of the average structural unit of humic acid obtained from chemical analysis. Our structural model of humic acid has very similar feature by gradually adding some monomers to