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Fate Modeling of Phenanthrene with Regional Variation in Tianjin, China S H U T A O , * †,‡ H O N G Y I N G C A O , † WENXIN LIU,† BENGANG LI,† JUN CAO,† FULIU XU,† XUEJUN WANG,† RAYMOND M. COVENEY, JR.,‡ WEIRAN SHEN,§ BAOPING QIN,§ AND REN SUN§ Department of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Peking University, Beijing 100871, China, Department of Geosciences, University of Missouri, Kansas City, Missouri 64110-2499, and Tianjin Environmental Protection Bureau, Tianjin 300191, China
A multimedia fate model with spatially resolved air and soil phases was developed and evaluated. The model was used for calculation of phenanthrene concentrations in air, water, soil, and sediment in Tianjin area and transport fluxes between the adjacent bulk phases under steadystate assumption. Both air and soil phases were divided into 3113 individual compartments of 4 km2 each to assess the spatial variation of phenanthrene concentrations and fluxes. Independently measured phenanthrene concentrations in air, water, and soil were used for model validation. The spatial variation in soil was validated using a set of measured phenanthrene concentrations of 188 surface soil samples collected from the area. Most data used either for model calculation or for model validation were collected during the last 5 years. As the results of the model validation, the calculated mean values for phenanthrene concentrations in various bulk phases are in fair agreement with those independently observed and are very close to those calculated using the model without spatial variation. The absolute difference between the calculated and the measured mean concentrations are 0.14, 0.48, and 0.13 logunits (mol/m3) for air, water, and soil, respectively. The spatial distribution patterns of phenanthrene in both air and soil were well modeled. Spatially, however, the model overestimated the soil phenanthrene level at low concentration range and underestimated it at high concentration range. The calculated distribution of phenanthrene in the air matches well with the emission from fossil fuel combustion, while the calculated distribution pattern in the soil is similar to that observed.
Introduction Polycyclic aromatic hydrocarbons (PAHs) are among the most important environmental contaminants in China; they result mainly from incomplete combustion of fossil fuels including the exhaust from motor vehicles (1, 2). Many compounds belonging to PAHs were confirmed to be mutagenic and carcinogenic (3, 4). As one of the fastest growing areas in * Corresponding author phone and fax: 0086-10-62751938, email:
[email protected]. † Peking University. ‡ University of Missouri. § Tianjin Environmental Protection Bureau. 10.1021/es021023b CCC: $25.00 Published on Web 04/25/2003
2003 American Chemical Society
coastal China, Tianjin also suffers from severe contamination of PAHs from various sources (5, 6). As one of the most abundant PAHs, the mean concentration of phenanthrene in 188 topsoil samples from the Tianjin area is 84.1 µg/kg (7). Among them, the average of nine top soils from the urban area of Tianjin is 279.9 µg/kg (7), which is much higher than the average concentration of 190 µg/kg in temperate urban top soils around world (8). The large amount of coal used appears to be the most important reason for the prevalence of phenanthrene in the region (5). Because of their high mobility especially in the air, strong affinity to organic matter, and high persistence, PAHs occur in every media in the environment including air, water, soil, and sediment. To predict their fate, therefore, multimedia modeling is suitable. Among various multimedia models, the fugacity model has been developed and applied to predictions of the fate of organic chemicals including PAHs on regional scale (9-14). As part of a systematic effort to investigate the fate of PAHs on a regional scale in Tianjin, Wang et al. have modeled the fate of benzo[a]pyrene in the area using a level III fugacity model and found that there is a very small portion of benzo[a]pyrene in the air while soil is the dominant sink (15). Significant amounts of benzo[a]pyrene accumulate in agricultural products every year. The fate of phenanthrene in the same area has also been modeled using a very similar approach, and the results of model validation indicated a fair agreement between the predicted and the observed concentrations in the bulk phases including air, water, and soil. One important assumption of applying fugacity models is that the bulk phases are completely mixed with homogeneous concentrations of the modeled chemical. A mean concentration is calculated for predicting the level of the chemical. Statistics on frequency distribution are sometimes provided as an indicator to model uncertainty. As a result, valuable information on spatial distribution of the modeled chemicals, which is often desirable for regional exposure modeling and risk assessment, cannot be provided. Relatively large uncertainty is also induced simply by ignoring the spatial variation. Multimedia models with regional segments were developed for large, spatially heterogeneous areas in several studies (16-18). Recently, 188 surface soil samples were collected from Tianjin for measurement of persistent organic pollutants (POPs) including PAHs (7). The measured mean concentration of phenanthrene in soil is 84.1 µg/kg with a standard deviation of 112.6 µg/kg, indicating a fairly large variation among sampling locations. The actual deviation among samples is even larger than the calculated results when following factors are taken into account: (1) composite samples (a mixture from five subsamples) were collected at each sampling location, and (2) duplicate measurements were performed for each composite sample and the statistics were derived from the means of the duplicates (7). Since emission from fossil fuel combustion provides the major input of phenanthrene to air and the emission rate depends very much on population density and location of industry, spatial variation in phenanthrene concentration in air is also expected. This study included an effort on development and evaluation of a spatially resolved multimedia model. The model was tested for fate modeling of phenanthrene in Tianjin. Among four bulk phases, spatial variations of phenanthrene concentration in air and soil were taken into consideration. The results were compared with those from VOL. 37, NO. 11, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Study area (most industrial facilities are located in the urban area and satellite towns of Hangu, Tanggu, and Dagang). a model simulation without spatial variation as well as with available field observations. It was expected that the model with spatial resolution would not only reduce the uncertainty of the conventional multimedia model but also provide information on spatial variation.
Methodology Study Area. Tianjin is selected as a “prototype region” of northern China. Urban Tianjin is one of the largest industrial cities in China. The annual mean temperature is 12 °C with prevailing south winds (19). A large portion of the land in the surrounding area is used for agriculture and staple crops including rice, wheat, and vegetables (20). Air, water, soil, and sediment in the area were heavily contaminated by many
kinds of pollutants including phenanthrene and other PAHs (7, 21). The main pollution sources of the area are industrial discharge, wastewater irrigation, coal burning, and motor vehicle emission (5, 6, 22, 23). Approximately 25 million tons of coals are consumed each year both by industry and for domestic heating and electric power (5). The amount of wastewater used for irrigation totals around 1 billion tons per year (6), and such irrigated fields are located mainly in the farmland surrounding the urban areas of Tianjin, Hangu, Tanggu, and Dagang as shown in Figure 1. Model with Spatial Variation. Air, water, soil, and sediment were defined as four bulk compartments for modeling the fate of phenanthrene in the area. Spatial variations in the air and soil were taken into consideration and they were further divided into 3113 individual cells, each 2 × 2 km2 in area. Each of the total 6228 compartments (3113 air cells, 3113 soil cells, 1 water phase, and 1 sediment phase) was assumed to be thoroughly mixed with mass-balanced input and output fluxes under steady-state assumption. The cells of air and soil in direct contact are considered to be paired. Water is treated as a single compartment in contact with all air cells through average interface area (The total water surface area divided by 3113). Sediment is regarded as being in direct contact with water. Because most cells at boundary of the area were included (those with more than 0.8 km2 within the boundary), the modeled area (12 452 km2) is slightly larger than the actual size of the Tianjin municipality, 11 300 km2 (5). Like other fugacity models, the subcompartments included in the bulk compartments are as follows: air and particles (solids) in air; water and suspended solids in water; air, water, and solids in soil; and water and solids in sediment. A level-III fugacity model was applied to characterize the fate of phenanthrene in these compartments under a steady-state assumption (24). The major transfer and transformation processes modeled are defined in Figure 2, and additional details are given in the
FIGURE 2. Transfer processes between adjacent compartments and compartment cells. For air and soil, there are 3113 cells, respectively, and for water and sediment, there is only one compartment for each phase. The processes are designated as Tijk. The subscripts i and j represent various media (0 for outside of the area including local emission and discharge from human activities and 1, 2, 3, and 4 for compartments of air, water, soil, and sediment, respectively). The subscript k indicates process category (t, h, d, p, w, s, l, e, and m for advective flow, human activity including discharge, emission, and irrigation, diffusion, dry and wet precipitation, sedimentation, erosion as solute or suspended solids, and degradation, respectively). 2454
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FIGURE 3. Comparison of the observed and the calculated phenanthrene concentrations in the bulk phases in Tianjin. The calculated results were derived either from a model with four bulk phases of air, water, soil, and sediment (model 1, previous study) or from a model with multiple air and soil phases and single water and sediment phases (model 2, this study). Phenanthrene concentrations were not available for sediments. FIGURE 5. Spatial distribution of calculated (a) and observed (b) phenanthrene concentrations in surface soil of Tianjin. The calculated results were mapped based on the 3113 predicted values in a grid of 2 × 2 km2 cells using kriging interpolation without smoothing. The map of the observed phenanthrene concentration was generated from the measured results of 188 discrete sampling locations using kriging interpolation with matrix smoothing.
FIGURE 4. Spatial distributions of calculated phenanthrene concentration in air (a) and emission of phenanthrene from fossil fuel combustion (b). The former was generated from 3113 predicted values in a grid of 2 × 2 km2 cells. The emission was mapped based on spatial distribution of the amount of coal combustion and emission factors. Supporting Information. A total of 6228 mass balance equations were established, and a more detailed description of the model with spatial resolution can be found in the Supporting Information. Parameter Identification. Parameters used in the model calculations are listed in Table C in the Supporting Information. Only a single value was adopted for most parameters, and these were derived from the literature. 3113 values were used for each of three parameters, which are believed to be key factors governing the spatial variation of phenanthrene in air and soil: (1) local emission flux of phenanthrene to air, (2) soil organic carbon content, and (3) degradation rate in soil. For either soil organic matter or emission flux to air, 3113 values were interpolated based on discrete data sets. 188 composite surface soil samples were collected using stainless steel scoops from Tianjin area in May of 2001. At each sample location, five samples were collected from a 100 × 100 m plot, and these were thoroughly mixed to form composite samples. The samples were transferred to precleaned amber glass containers and maintained at 4 °C prior to chemical analysis during June of 2001. The soil organic matter contents were determined using a Shimadzu 5000A TOC analyzer (7). Emissions from various locations were calculated based on coal consumption data from 1996 to 2000 and the estimated emission factor (6, 25, 26). The method of inverse distance to a power of 2 was applied for interpolation of both soil organic matter and emission data. Surfer
v.7.0 was used for the interpolation calculation (27). Wastewater irrigation also varies spatially. However, without detailed maps, these spatial variations could not be incorporated in our study. Others (28) have previously reported that degradation of organic chemicals in soil is inhibited by the presence of organic matter, but this effect has not been quantified. In our study, a linear negative correlation was assumed between degradation rate and soil organic carbon content. There are also independently observed data on concentrations in air, soil, and water from the literature for model validation (Supporting Information). As mentioned previously, the concentrations of phenanthrene in 188 composite surface soil samples were measured in our laboratory (7). For phenanthrene analysis, 20 g of soil sample (70 mesh) was extracted with accelerated solvent extractor (DIONEX ASE 300) using 1:1 n-hexane/acetone at 140 °C and 1500 psi for 5 min. For cleanup, the extract was transferred with cyclohexane (2 mL × 2) to a silica gel column pre-eluted with 40 mL of pentane. The column was first eluted with 20 mL of pentane (discarded) followed by 50 mL of pentane/dichloromethane (3:2). The extracts were analyzed using a Agilent 6890 GC coupled with a Agilent 5973 mass spectrometer. A 30 m × 0.25 mm i.d. × 0.25 µm film thickness HP-5MS capillary column (Agilent Technology) was used. According to the result of a preliminary experiment, the coefficient of variation of six duplicate measurements for a soil sample with a mean phenanthrene concentration of 0.23 µg/g was only 0.35%, while the recovery rate was 120% (29).
Results and Discussion Model Validation. The model was validated in two ways: comparison between the calculated and independent observed phenanthrene concentrations in bulk phases and comparison of the calculated results between the two models with (current study) or without (previous study) spatial variation. As can be seen in Figure 3, the log-transformed phenanthrene concentrations (original concentrations in parentheses) in air, water, soil, and sediment calculated by the model are -8.32 (4.79 × 10-9), -3.92 (1.20 × 10-4), -3.31 (4.90 × 10-4), and -1.86 (1.38 × 10-2) mol/m3, respectively. For air and soil, the values are means of 3113 calculated concentrations, respectively, while only a single value was VOL. 37, NO. 11, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Spatial distribution of measured organic matter content in surface soil of Tianjin. The map was interpolated based on data of 188 discrete sampling locations using kriging with matrix smoothing. generated for either water or sediment. The independently measured mean phenanthrene concentrations are -8.46 (3.47 × 10-9), -3.44 (3.63 × 10-4), and -3.18 (6.61 × 10-4) mol/m3 for air, surface water, and soil, respectively, after logtransformation (original concentration in parentheses). Data on sediment for Tianjin are not available. The absolute differences between the calculated and the measured concentrations are 0.14, 0.48, and 0.13 log-units, respectively, compared to 0.45, 0.42, and 0.55 for a model without spatial variation. The decrease in the gaps between the calculated and the observed concentrations may not necessarily indicate a meaningful improvement in the model accuracy when
uncertainty of the model inputs is taken into consideration. However, our results do suggest the advisability of dividing the soil and air phases into subcompartments to model the fates of pollutants. Spatial Distribution of Phenanthrene in Air. The concentration of phenanthrene in air was mapped based on the calculated concentrations in the 3113 cells. The results are presented as an image map in Figure 4a. In Figure 4b, spatial distribution of emission rate of phenanthrene is illustrated. The patterns shown in the two maps in Figure 4 are very similar, in fact practically identical, to each other. Only two processes bring phenanthrene into air (Figure 2): emission from human activity (shown in Figure 4b) and diffusion from soil and water (shown in Figure 9a). The mean rates of the two processes in the area are 0.32 and 0.0052 mol/h‚km2, respectively, indicating that human activity is the dominant contributor (more than 98%) of the total input to air. The only recognizable difference between parts a and b of Figure 4 is the indistinct pattern of the strip in the south-north direction of the concentration map, suggesting a slight influence of advective flow in the direction of the prevailing wind (south to north). The standard deviation of the calculated phenanthrene concentrations in the air is 8.4 × 10-10 mol/m3 with a mean value of 4.8 × 10-9 mol/m3, accounting for a large portion of the uncertainty in the calculated results using a model without spatial variation. Spatial Distribution of Phenanthrene in Surface Soil. During a preliminary modeling, only the influence of soil organic matter on adsorption equilibrium (Z33) was taken into consideration, while its effect on degradation rate (Km3) was not accounted for. The resulting distribution pattern of phenanthrene concentration in soil was just like that in the air and was totally different from that of the observed results (Figure 5b). It implied that the affinity of soil organic matter for phenanthrene or thermodynamic equilibrium is not the primary governing factor for the observed positive correlation between soil organic matter and phenanthrene contents. The influence of organic matter on the degradation rate was then incorporated into the model by using an individual D30mc value as a function of organic carbon content for each soil cell. As a result, the calculated soil phenanthrene concentrations for various soil cells were different, and the modelpredicted distribution pattern in surface soil (Figure 5a) is similar to that observed (Figure 5b). The distribution patterns of the two maps are similar to each other in general (Figure 5). The areas with relatively high phenanthrene concentration are those around the urban area of Tianjin and the coastal towns of Hangu and Tanggu, featuring both high input flux from air (Figure 4a) and high organic matter content in soil (Figure 6). The area to the north of the Tianjin urban districts (Wuqing County) is one of the places that has received wastewater irrigation for more than 40 years. Very high organic matter content was also
FIGURE 7. Comparison of the calculated and the observed phenanthrene concentrations in surface soil of Tianjin based on either 3113 cells (a) or 188 sampled sites (b). 1:1 lines are drawn to illustrate the prediction residuals. 2456
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FIGURE 8. Comparison of the calculated phenanthrene fluxes using the two models: model 1, with four bulk phases of air, water, soil, and sediment; and model 2, with 3113 air cells, 3113 soil cells, and single water and sediment phases. The transport processes are defined in Table A in the Supporting Information.
FIGURE 9. Spatial distribution of calculated phenanthrene fluxes from soil to air (a) and degradation in soil (b). Maps were interpolated from 3113 predicted values in a grid of 2 × 2 km2 using kriging interpolation without smoothing. detected in this area (Figure 6). It is not surprising to find that the spatial distribution pattern of the modeled soil phenanthrene matches better with soil organic matter than with the measured phenanthrene concentration. The organic matter content is the only input parameter with spatial variation that is directly related to soil compartments. To evaluate the similarity of the two maps (Figure 5), the modeled phenanthrene concentration in surface soil can be plotted against the measured data (Figure 7). Since there are 3113 calculated data and only 188 measured values and the two data sets are not superimposed to each other, interpolation of 3113 values based on the 188 numbers or vice versa is necessary. Plots using both procedures were shown in Figure 7 using either 3113 cells (a) or 188 sampling sites (b). Although the data points shown in Figure 7 follow a general 1:1 linear trend indicating a fair agreement of the spatial distribution patterns between the observed and the modeled results, significant residuals do exist. The deviation of the modeled result from the observed one follows a general trend: the model overestimated soil phenanthrene level at low concentration range and underestimated it at high concentration range. In addition to the possible presence of governing factors other than soil organic matter, including pH, temperature, moisture, texture, nutrients, and so on (30, 31, 32), one explanation is that the relationship between the degradation rate of phenanthrene and soil organic matter content may follow a nonlinear pattern rather than the linear
equation used in the model. The influence of organic matter on degradation of PAHs is complicated in terms of mechanism. Although it was demonstrated in general that decreased microbial degradation is often attributed to PAH association with the soil organic matrix (33, 34), the role of organic matter in the biodegradation of PAHs is somewhat controversial. For instance, previous workers have reported that the onset time for pyrene biodegradation by bacterial colony on a magnetite surface is dramatically shortened by the presence of humic acid (35). Ortega-Calvo and colleagues measured the degradation rate of phenanthrene in five soil samples with varied organic carbon and clay contents (36) and found that the rate and extent of degradation were statistically lower in the soil with a higher organic matter content. We tried to fit their data to a nonlinear equation with both organic matter and clay contents as independent variables. The following equation was derived from these efforts with the coefficient of determination equal to 0.901 (n ) 5)
Rd (mg/kg‚d) ) (42.4 OM -0.47)/CLAY where Rd is the degradation rate and OM(wt %) and CLAY(wt %) are the contents of organic matter and clay in soil, respectively. A nonlinear relationship between the degradation rate and the organic matter content is clearly demonstrated. However, the relationship between degradation rate and soil properties is site specific. Specific experimental study VOL. 37, NO. 11, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 10. Fluxes between phases and between Tianjin and the surrounding area. using local samples is required before the relationship can be further refined quantitatively and used for multimedia modeling. A specific value of the organic carbon was assigned for each cell without information of its variation. This could be another reason for the model uncertainty and should be carefully investigated in the future. If it is the case, the significant influence of underestimation of variability of input parameters should be taken into consideration. It should be noted that another reason for the underestimation at high concentration range could be other local sources of phenanthrene in addition to air-soil transport originated from coal combustion. Relatively high phenanthrene concentration in surface soil at several discrete spots in Tianjin can be seen (Figure 5b) but were not predicted by the model (Figure 5a). Further investigation is needed to identify the possible sources. Mass Transfer Fluxes. The transfer fluxes of phenanthrene between the adjacent phases as well as between Tianjin and the surrounding area calculated using the two models with or without spatial variation are shown in Figure 8. For the model with spatial variation, mean values were adopted when the fluxes were calculated for the 3113 individual cells of soil or air. With only a few exceptions, the fluxes between the adjacent compartments calculated using the model with spatial variation are smaller than those without spatial variation. One possible reason is the difference of the input parameters used for the two models. Although identical values were assigned to all input parameters of the two models (means for the regional resolved variables), the real inputs for the model without spatial variation are lower than those originally assigned. The model was run 10 000 times with randomly generated inputs based on normal distribution with given means and standard variations. For the parameters with relatively low values, negative numbers were occasionally generated and were discarded as programmed. As a result, the actual means of many input parameters are lower than the previously assigned with a left-hand truncated normal distribution. Despite the small systematical difference in fluxes, there is no significant difference in the overall fate of phenanthrene in the area predicted by the two models. As has been thoroughly discussed in the previous study with nonspatial modeling, soil and sediment serve as the dominant sink (15). The primary sources of phenanthrene are local emissions from fossil fuel combustion, wastewater from upstream sources, and local wastewater discharge, while the major output pathways from the system were degradation in the soil and sediment. With spatially varied inputs, transfer fluxes calculated from air or soil to other phases also varied spatially. For example, Figure 9 illustrates the spatial variation of phenanthrene transfer flux from soil to air (a) and degradation flux in soil 2458
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(b). It appears that the soil to air diffusion flux is more or less negatively correlated with soil organic matter content indicating the immobilization of the chemical by soil organic matter. The spatial distribution pattern of phenanthrene degradation flux, on the other hand, is very similar to that of the flux from air to soil (positively proportional to concentration in the air) which dominates the input to soil. The major input and output processes into and out from soil are thus balanced. The degradation flux depends on both total phenanthrene concentration and degradation rate. The latter is negatively correlated to soil organic matter content. The reason for more degradation near Tianjin with high organic matter content is that the total phenanthrene near Tianjin is very high (Figure 5), which is maintained by high air-to-soil input. Figure 10 illustrates all fluxes either between the adjacent phases or between Tianjin and the surrounding area. Among various pathways, wastewater from upstream, local emission to air, and wastewater discharge are the most important inputs of phenanthrene to Tianjin, while degradation in soil and sediment and advective water flow to downstream are the major outputs. Within the system, air to soil transport is the most important process, followed by surface to air diffusion and sedimentation from water column to bottom sediment. In addition to the emission of phenanthrene to air and soil organic matter content, many other input parameters used in the model can be characterized by spatial variation. More information is required for future improvement of the model such as meteorological information and wastewater irrigation. It is also desirable that spatial variations of all bulk phases be taken into consideration. At this stage, however, it is difficult to include spatial information for water and bottom sediment. Unlike chemical concentrations in soil and air which can be treated two-dimensionally, concentrations in river and sediment cannot be readily incorporated into the framework of the current model.
Acknowledgments Funding was provided by National Scientific Foundation of China [40031010 and 40024101].
Supporting Information Available Detailed description of the fugacity model. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review November 12, 2002. Revised manuscript received March 13, 2003. Accepted March 17, 2003. ES021023B
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