Dispersion Modeling of Polycyclic Aromatic Hydrocarbons from

Jun 23, 2006 - Laboratory for Earth Surface Processes, College of Environmental Sciences, Peking University, Beijing 100871, China, Department of Geos...
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Environ. Sci. Technol. 2006, 40, 4586-4591

Dispersion Modeling of Polycyclic Aromatic Hydrocarbons from Combustion of Biomass and Fossil Fuels and Production of Coke in Tianjin, China S H U T A O , * ,†,‡ X I N R O N G L I , † Y U Y A N G , † RAYMOND M. COVENEY, JR.,‡ XIAOXIA LU,† HAITAO CHEN,§ AND WEIRAN SHEN| Laboratory for Earth Surface Processes, College of Environmental Sciences, Peking University, Beijing 100871, China, Department of Geosciences and Center for Applied Environmental Research, University of Missouri, Kansas City, Missouri 64110-2499, Computer Science Department, Peking University, Beijing 100871, China, and Tianjin Environmental Protection Bureau, Tianjin, 100039, China

A USEPA, procedure, ISCLT3 (Industrial Source Complex Long-Term), was applied to model the spatial distribution of polycyclic aromatic hydrocarbons (PAHs) emitted from various sources including coal, petroleum, natural gas, and biomass into the atmosphere of Tianjin, China. Benzo[a]pyrene equivalent concentrations (BaPeq) were calculated for risk assessment. Model results were provisionally validated for concentrations and profiles based on the observed data at two monitoring stations. The dominant emission sources in the area were domestic coal combustion, coke production, and biomass burning. Mainly because of the difference in the emission heights, the contributions of various sources to the average concentrations at receptors differ from proportions emitted. The shares of domestic coal increased from ∼43% at the sources to 56% at the receptors, while the contributions of coking industry decreased from ∼23% at the sources to 7% at the receptors. The spatial distributions of gaseous and particulate PAHs were similar, with higher concentrations occurring within urban districts because of domestic coal combustion. With relatively smaller contributions, the other minor sources had limited influences on the overall spatial distribution. The calculated average BaPeq value in air was 2.54 ( 2.87 ng/m3 on an annual basis. Although only 2.3% of the area in Tianjin exceeded the national standard of 10 ng/m3, 41% of the entire population lives within this area.

Introduction In recent decades rapid economic growth and urbanization have led to a substantial increase in energy consumption in China. Meantime, energy-related contamination becomes a * Corresponding author phone and fax: 0086-10-62751938; email: [email protected]. † College of Environmental Sciences, Peking University. ‡ University of Missouri. § Computer Science Department, Peking University. | Tianjin Environmental Protection Bureau. 4586

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serious challenge to scientists and policy makers. Among many pollutants, polycyclic aromatic hydrocarbons (PAHs) are a group of potentially hazardous substances that may have toxic effects on living organisms (1, 2). Recently, severe PAH contamination has been reported in China (3-5). According to the results of an assessment on persistent toxic substances in China and neighboring countries, PAHs are particularly important contaminants both regionally and globally (6). Tianjin, one of the fastest growing areas in China with a total population of 9.2 million, experiences severe contamination of PAHs (7). Annual consumption of coal and petroleum in Tianjin are about 23 million and 21 million tons, respectively (9). In addition, burning of straw to produce energy is a common practice in rural Tianjin (10). Estimates indicate that approximately 175 tons of PAHs were emitted in Tianjin in 2003 with emission densities as high as 15.4 kg/km2‚y (8). Li et al. have conducted a multi-pathway exposure study in Tianjin and reported that the average daily exposures of child, youth, and adult to total PAHs were 4.3, 3.8, and 3.1 µg/kg‚d, respectively, in 2003 (11). However, differences among individual exposures would be expected and improved spatial resolution would be more meaningful. Respiration, contributing around 20% of the total exposure in Tianjin, is one of the key exposure pathways showing considerable spatial variation (11). PAH concentrations in vegetables and grains, which accounted for over 50% of the total exposure, are positively correlated to air concentration (12). Further data on concentrations of PAHs with sharper spatial resolution are needed for accurate evaluation of exposure rates. Dispersion models are useful tools for estimating spatial distribution patterns of pollutants in the atmosphere and have been widely used (13, 14). An atmospheric dispersion model from the United States Environmental Protection Agency (USEPA), Industrial Source Complex Long-term (ISCLT3), is commonly used for this purpose (15, 16). The aim of this study was to evaluate the spatial distribution pattern of PAHs in the atmosphere of Tianjin using ISCLT3. All major emission sources were taken into consideration. Sixteen parent PAHs included in the study were naphthalene (NAP), acenaphthylene (ACY), acenaphthene (ACE), fluorene (FLO), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLA), pyrene (PYR), benz[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), dibenz[a,h]anthracene (DahA), benzo[g,h,i]perylene (BghiP), and indeno[1,2,3-cd]pyrene (IcdP). The results are presented either as the concentrations of the individual compounds or as the total concentration of the 16 species (PAH16). The exposure risk is addressed on the basis of the calculated BaP equivalent concentrations (BaPeq).

Methodology Emission Characterization. Major PAH emission sources taken into consideration were coal used for power generation, gasification, central heating, coke production, and other purposes; petroleum consumed in industry and transportation; natural gas combustion; and biomass burning (10). All sources except domestic coal combustion, vehicular petroleum combustion, and biomass burning were point sources; the locations, emission heights, and fuel consumptions were tallied individually for each use (17, 18). Emissions from motor vehicles on highways and major roads were considered as linear sources described by road network and traffic flow patterns (19). Transportation emission from local 10.1021/es060220y CCC: $33.50

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FIGURE 1. Major point (left) and linear (middle) emission sources and the two monitoring stations (red dots, middle) at Baodi and Xinli. Urban districts are separated by the outer ring road from the suburban and rural areas. Locations of Tianjin and neighboring Beijing and Hebei are also presented (right). roads was treated as an area source, characterized by total road length in each of the 14 × 20 grids on a 1:380 000 transportation atlas (19). Other emission sources in the Tianjin area included biomass burning and domestic coal combustion. The total emissions from these area sources were allocated to 3113 grid sectors of 2 × 2 km2 each. Coal used for residential heating was allocated based on the population density (10, 20) and the commercially used coal was allocated to urban districts and various towns based on tertiary productions (10). In Tianjin, the use of biomass fuel for cooking is largely restricted to rural areas. Therefore, the total biomass has been allocated based on rural population and evenly divided among 12 months. Figure 1 presents all point (left) and linear (middle) emission sources as well as the locations of the two monitoring stations(Baodi and Xinli) used to validate the model (Figure 1). Emissions of PAHs from various sources were derived by multiplying the consumed quantities and emission factors (8). Dispersion Modeling. A total of 3113 receptor sectors were constructed providing grid coverage of the entire area of Tianjin. Concentrations of the 16 individual PAH compounds at the ground level (2 m) were modeled at steadystate. Based on a stability array, a Gaussian sector-average plume equation was applied to model dispersion, dry deposition, and degradation of volatile PAHs. Previous multimedia fate modeling of Lindane confirmed that the dry precipitation flux in Tianjin is one order of magnitude higher than the wet precipitation flux as would be expected for an area with less than 500 mm annual precipitation (10, 21). Modeling was conducted separately for two emission scenarios: the nonheating season from March 15th to November 15th and the heating season from November 15th to March 15th. The annual mean concentrations were calculated as duration-weighted averages. PAH concentrations were modeled in both gaseous and particulate phases. Since each source was modeled individually for all receptor sectors, the results could be used to determine the contribution pattern of various sources to the receptors. Parameter Identification. Information for characterizing the sources and receptors by the year 2003 was recorded. The study area is located on a level alluvial plain where absolute elevations of 95% of the area vary from 0 to 50 m. Hence the terrain could be assumed to be flat for purposes of modeling. The total emissions from each source were divided into gaseous and particulate phases based on the partition coefficients and particle mass ratios provided by USEPA (22). Degradation rate constants for gaseous PAHs from the literature were adopted as linear decay factors (23, 24). Data on particle density, size distribution, and mass fraction were also collected from the literature for various emission sources (25, 26). Previous fugacity fate modeling indicated an input

flux of phenanthrene from advective air flow from peripheral regions into Tianjin was several orders of magnitude lower than local emission flux. Therefore, the input of phenanthrene at the boundary was assumed to be negligible (27). For long-term modeling, the seasonally averaged meteorological data from the Tianjin Meteorological Administration were organized into joint frequencies of occurrence of particular wind speed classes, wind directions, and stability categories in order to calculate the mixing height, surface roughness height, and stability class (28). Default values from the model were used for Monin-Obukhov length, friction velocity, wind profile exponent, and vertical potential temperature gradient (29). Model Validation. Two monitoring stations provided the only information on atmospheric PAH concentrations in Tianjin for gaseous and particulate PAHs [Figure 1, Baodi (39°27′45′′ N, 117°27′19′′ E) and Xinli (39°04′51′′ N, 117°19′34′′ E)]. Both air and particle samples were collected for measurement of 16 PAHs in November 2004 for a week continuously before the heating season began. Multiple samples were collected using air pumps (TMP1500, Eltong, China) coupled with an assembled cartridge having glass PUF holders and metalline screens (Supelco) at a calibrated flow rate of 1.2 L/min for 24 h. Low volume polyurethane foam (PUF plugs from Supelco, 22 mm o.d. × 7.6 cm length) and quartz fiber filters (QFFs, 22 mm in diameter) were employed for gaseous and particulate phase collection. At each station, samples were collected and measured in triplicate. The PUF plugs and the quartz filters were extracted with a 1:1 mixture of n-hexane and cyclohexane using Soxhlet extractors for 4 and 8 h, respectively. The 16 PAHs were analyzed on a HP 6890 gas chromatograph connected to a HP 5973 mass selective detector (Agilent). Detailed descriptions for sampler preparation, sample collection, storage, extraction, analysis, and quality control can be found elsewhere (30). It should be pointed out that the insufficient data used for the model validation is a limitation of this study. Risk Assessment. The carcinogenic potency of PAH exposure through inhalation can be estimated by calculating a BaP equivalent concentration (BaPeq) based on the toxic equivalent factors (TEFs) of the individual compounds (31). BaPeq values in the study area were computed based on the modeled PAH concentrations for risk assessment against the national standard of 10 ng/m3 (32).

Results and Discussion Model Validation. Measured concentrations of the 16 PAH compounds in gaseous and particulate phases generally correlate with predicted nonheating season concentrations (Figure 2). For gaseous PAHs, underestimation of the model can be seen at both sampling sites particularly for the higher molecular weight compounds in gaseous phase (blue dots). VOL. 40, NO. 15, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Comparison between the measured and the predicted PAH concentrations in gaseous (left) and particulate (right) phases at the two monitoring stations.

FIGURE 3. Comparison between the measured and the predicted PAH profiles in gaseous and particulate phases at the two monitoring stations. Each compound is presented as the percentage of the total PAHs It is expected that soil-to-air diffusion, which was not included in the model, could make up some difference, particularly for the lower molecular weight PAHs. It was demonstrated by a fugacity model that the flux of soil-to-air diffusion of PHE in Tianjin was significantly higher than the flux of airto-soil diffusion (27). The apparent underestimation could also be partially caused by analytical errors since the measured concentrations of higher molecular species were often close to or even below the detection limits. For particulate PAHs, measured values were closer to the predicted concentrations than gaseous PAHs were. It should be pointed out that the diagrams in Figure 2 are in log-scale, and the actual differences between the measured and the modeled concentrations were quite large. Fortunately, the exposure risk is predominantly contributed by the higher molecular weight compounds, the distribution of which could be better predicted by the model. The model-predicted results are also validated in terms of PAH profiles. The relative abundances of the 16 PAH compounds are compared between measured and calculated results as the percentages of the total PAHs (Figure 3). Similar patterns occurred at both stations. The majority of the lower molecular weight compounds appeared to partition preferentially to the vapor phase. For gaseous PAHs, NAP contributed to more than 70% of the total, followed by PHE, ACY, and FLO. For the particulate phase, 3- to 4-ring compounds dominated and the measured concentrations of several higher molecular weight compounds were below the detection limits. In general, the model seems to effectively predict the PAH profiles in air. Figure 4 presents the measured and the calculated gaseous/particulate PAH ratios (averages of the two sites). The ratios of the lower molecular weight compounds were 4588

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FIGURE 4. Comparison between the measured and the predicted gaseous/particulate PAH ratios. The results are the averages of the two monitoring sites similar to each other. The deviation of the higher molecular weight compounds from the 1:1 line may be a result of particulate PAHs of some samples being below the detection limits. Contributions of Various Sources to Atmospheric PAHs. Dispersion modeling was conducted for each emission source individually and the final concentration of a PAH compound at a receptor sector was the sum of the predicted concentrations derived from various sources. Therefore, it was possible to construct the relative contributions of various sources to the PAHs at all receptors, which were averaged and compared with the contributions of these sources to the total emission in Figure 5. The dominant PAH emission sources in Tianjin are domestic coal combustion (43.3%), the coking industry (22.6%), biomass burning (21.3%), and coal gasification (9.0%). Xu et al. have estimated the PAH emission in China in 2003 and found that the biomass burning, domestic coal combustion, and the coking industry were the major sources (8).

FIGURE 5. Relative contributions of various sources to PAHs emitted at the sources (left) and PAHs accumulated at the receptors (right).

FIGURE 6. Contributions of various sources to individual PAH compounds (gaseous and particulate phases) at the receptors.

FIGURE 7. Model-predicted spatial variations of PAH16 from various sources in the nonheating and the heating seasons. The maps are log-scaled. The emission pattern was substantially different from the receiving pattern. Although domestic coal combustion still ranked first contributing to the accumulated PAHs at the receptors, the contribution increased from 43.3% at the sources to 56.0% at the receptors. Meanwhile, the contributions of coke production and coal gasification decreased from 22.6% and 9.0% to 6.6% and 2.6% from the sources to the receptors, respectively. The primary cause of such differences can be explained to a certain extent by the fact that PAHs emitted at various elevations of sources, while the receptors were set at 2 m. For example, the chimney heights of the two coal gasification and coking sources were 50 and 60 m (the injection heights were computed by the program) respectively, while the chimney heights of domestic coal combustion and traffic petroleum were 10 and 1 m, respectively. On the other hand, PAHs from domestic coal combustion and vehicle emission dispersed immediately at the ground level, increasing their proportions in receptor profiles. Other factors leading to the changes in the profile from the sources to the receptors included differences in PAH profile and particle size distribution, differences in degradation rates of individual compounds, and difference in precipitation of particles of various sizes. Figure 6 presents the contributions of various sources to individual PAH compounds. Although the general patterns of the two seasons were similar, domestic coal combustion accounted for 76% of the total PAHs in the heating season,

compared to 21% in the nonheating season. During the nonheating season, when domestic coal consumption rate was low, biomass burning was the major source of several lower molecular weight compounds, while industry-used petroleum was the major contributor to a range of higher molecular weight species from FLA to BghiP. Spatial Distribution Pattern of PAHs in the Atmosphere. Both gaseous and particulate PAHs displayed typical lognormal distributions. The calculated particulate PAH concentrations ranged from 3.3 to 186 ng/m3 with a mean value of 16.1 ng/m3, while the gaseous PAH concentrations varied between 1.9 and 1152 ng/m3 with a mean value of 41.7 ng/ m3. Figure 7 presents the calculated spatial distributions of PAH16 as gaseous and particulate phases in the two seasons. The gaseous and particulate PAHs have the same points of origin and consequently similar distribution patterns. However, the gaseous PAHs were less dispersed than the particulate ones. It appears that the rate of degradation of the gaseous PAHs exceeds the rate of precipitation of particulates. PAH concentrations in the heating season were significantly higher than those in the nonheating season, but the spatial distribution patterns were similar to each other. An extensive survey was conducted several years ago in which 188 surface soil samples were collected and measured for PAHs (33). A visual comparison of the PAH concentrations between air (Figure 7) and surface soil revealed similarity in spatial distribution patterns with high VOL. 40, NO. 15, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 8. Model-predicted spatial variations of annual mean PAH16 at 2 m from four typical sources. The maps are log-scaled.

FIGURE 9. Spatial distribution of BaPeq (left), cumulative frequencies of BaPeq (middle) against area (blue line) and population density (red line) and the relationship between BaPeq and population density (right). The map is in log-scale. concentrations in urban districts. Another peak in the contours appeared in the southeast suburb where the largest coking and coal gasification company is located. Along the southern border toward prevailing wind, concentrations of both gaseous and particulate PAHs were relatively low. The examples of typical linear (vehicular exhaust), point (coking), and area (domestic coal and biomass) sources are presented in Figure 8. The areas most heavily influenced by motor vehicle emission were the urban districts and a corridor along the Beijing-Tianjin-Tanggu expressway. Coke production from the two point sources accounted for 22.6% of the total emission. As indicated previously, PAHs emitted from these sources at 50-60 m heights had little influences on the receptors at 2 m. Still, the emissions resulted in localized heavy contamination (Figure 7). Ranking first in the relative contributions both at the sources and receptors, domestic coal combustion showed strong influence on the total concentration maps. PAHs emitted from biomass burning were evenly distributed in the area with a gradient toward the southeast direction following the pattern of rural population. Exposure Risk. Because of the heavy contamination, high PAH levels imposed severe health threat. Annual mean values of BaPeq at all receptor sectors were calculated and mapped in Figure 9 (left). The mean BaPeq was 2.54 ( 2.87 ng/m3, with 1.19 ( 1.25 ng/m3 and 5.22 ( 6.29 ng/m3 in the nonheating and the heating seasons, respectively. For most parts of the area, air BaPeq values were lower than the national standard of 10 ng/m3 (32). Of the 3113 receptor sectors in the model domain, only 72 exceeded 10 ng/m3 on an annual basis, accounting for 2.3% of the total area (blue contour line in Figure 9). Although there was only a small fraction of the area exceeding the standard, these places happened to be the urban districts with high population density. If the exceeding rate were evaluated on a population basis, around 41% of the total population is exposed to PAHs concentrations above the national standard. 4590

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In Figure 9 (right), BaPeq is plotted against population density. A significant correlation (p < 0.00001) was revealed for the urban sectors (red dots). A positive correlation between atmospheric PAHs and population has been revealed globally (34). In contrast, data points in rural areas (yellow squares) were scattered randomly. In terms of exposure risk, contributions of various sources to BaPeq at all receptors were calculated and it was revealed that domestic coal alone contributed to 66% of the total BaPeq and industrial petroleum accounted for 19%. Coal plays a central role in Chinese energy strategy. Although the strong dependence on coal is unlikely to change soon (35), more efficient coal usage will certainly help to reduce emissions (35). According to the previous discussion, domestic coal combustion contributed to 43.3% of the total PAH emission at the sources, 56.0% at the receptors, and 66% of the total health risk. This is a typical situation for northern Chinese cities where coal is burned in furnaces and stoves widely and inefficiently distributed among individual city blocks and buildings. No doubt risks from PAH exposure in Tianjin can be reduced considerably just by cutting the emission from domestic coal combustion. In fact, efforts to do this have been made in several Chinese cities by replacing coal with natural gas or by centralizing heating facilities. In Beijing, for example, consumption of domestic coal decreased by 32.5% from 1990 to 2003 (35). Projections indicate that the usage of centralized heating systems in China will increase from the current 27% to 40% in 2010 (36). Fortunately, Tianjin is also on its way toward this direction (37).

Acknowledgments The funding of this study was provided by National Science Foundation of China (Grant 40332015/40021101), National Basic Research Program (2003CB415004), the Ministry of Education, and the University of Missouri-Kansas City.

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Received for review January 31, 2006. Revised manuscript received April 20, 2006. Accepted May 22, 2006. ES060220Y

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