Characteristics and Source Identification of Polycyclic Aromatic Hydrocarbons (PAHs) in Urban Soils: A Review

Pedosphere 27(1): 17–26, 2017 doi:10.1016/S1002-0160(17)60293-5 ISSN 1002-0160/CN 32-1315/P c 2017 Soil Science Society of China ⃝ Published by Elsevier B.V. and Science Press

Characteristics and Source Identification of Polycyclic Aromatic Hydrocarbons (PAHs) in Urban Soils: A Review WANG Chunhui, WU Shaohua, ZHOU Shenglu∗ , SHI Yaxing and SONG Jing School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Road, Nanjing 210023 (China) (Received May 18, 2016; revised November 11, 2016)

ABSTRACT Polycyclic aromatic hydrocarbons (PAHs) are mainly produced by combustion processes and consist of a number of toxic compounds. They are always emitted as a mixture and have become a major type of pollutants in urban areas. The degree of soil contamination by PAHs is of special concern in areas immediately in proximity to cities with heavy traffic, factories, older buildings, and infrastructure. The accumulation of soil PAHs is also affected by non-anthropogenic factors, such as climate, vegetation, and soil property. This paper reviews three typical source identification techniques, including diagnostic ratios, positive matrix factorization, and principle components analysis. The advantages or disadvantages of these techniques are analyzed. It is recommended that multiple identification techniques be used to determine the sources in order to minimize the weaknesses inherent in each method and thereby to strengthen the conclusions for PAH source identification. Key Words: anthropogenic factors, diagnostic ratios, organic pollutants, positive matrix factorization, principle components analysis, soil contamination, soil property, urban environment Citation: Wang, C H, Wu S H, Zhou S L, Shi Y X, Song J. 2017. Characteristics and source identification of polycyclic aromatic hydrocarbons (PAHs) in urban soils: A Review. Pedosphere. 27(1): 17–26.

The global urban population is now greater than the rural population (Buhaug and Urdal, 2013). This urbanization has been accompanied by increased production of industrial waste, traffic pollution, and household garbage, which form many polycyclic aromatic hydrocarbons (PAHs) and other pollutants that migrate into urban soils through dry and wet atmospheric deposition. These environmental loads in urban areas cause deterioration in the soil environment, which has a limited carrying capacity. The PAHs are a diverse group of organic compounds containing two or more fused aromatic rings of carbon and hydrogen atoms (Harvey, 1991). Many PAHs are carcinogenic and mutagenic, and are listed in the 1998 Protocols on Persistent Organic Pollutants to the Convention on Long Range Transboundary Air Pollution (UNECE, 1998). The United States Environmental Protection Agency (USEPA) has identified 16 PAHs as priority pollutants. As PAHs are sparingly soluble, readily adsorbable by soil particles, and difficult to be degraded, they tend to accumulate in soils (Tang et al., 2005, 2006; Ping et al., 2007). Soils are the most important sink for PAHs in the environment. Consequently, soil sys∗ Corresponding

author. E-mail: [email protected].

tem is a good indicator of environmental pollution and environment risk for human exposure in urban areas. The spatial distribution and associated influencing factors for PAHs in urban soils have stimulated wide interest in recent years. In this paper, we review 1) characteristics and distribution of PAHs (the 16 priority PAHs) in urban soils, 2) factors that influence their accumulation, and 3) their sources and source identification techniques. Our review will provide a useful foundation for further research on PAHs. CHARACTERISTICS AND DISTRIBUTION OF PAHS IN URBAN SOILS Characteristics The PAHs are mainly produced from the incomplete combustion or pyrolysis of organic matter and an urban area is usually the main PAH source in a given region. Consequently, soil PAH concentrations in urban areas are much higher than those in areas farther away from the city center. Wilcke (2000) found that the concentration of PAHs in urban soils is frequently 10 times higher than that in natural soils as a result of stronger emission in the central urban areas

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as compared to in the more remote areas. The PAH concentration is the highest at high-traffic sites, followed by park/residential sites and suburban sites, with rural sites containing the lowest levels of PAHs (Wang et al., 2007). Wang et al. (2015) found that a gradient of contamination for soil PAHs is evident from urban to rural areas. The reason is that the distribution of contaminants in urban soils is mainly influenced by industrial activities, traffic, population density, and the elapsed time since urbanization. Peng et al. (2013) noted that residential building age, population density, road density, and distance from the urban center have significant correlations with PAHs in urban soils. Each land type has its own characteristics; e.g., the main features of industrial areas include clustering of factories, which normally create many pollutants in the environment. Roadside (traffic) and industrial areas have been identified as important emission sources of PAHs. An increasing number of studies found that these areas have the highest concentrations of PAHs in urban soils (Nadal et al., 2004; Jiang et al., 2009; Singh et al., 2012; Kwon and Choi, 2014; Suman et al., 2016). In general, roadside and industrial areas are the most heavily polluted, while rural or agricultural areas are the least polluted (Pouyat et al., 1994; Wang et al., 2007). For example, PAH concentrations are 7.4 times higher in industrial areas than in agricultural areas in Delhi, India (Singh et al., 2012) and 9.1 times higher in busy streets than in open spaces in Detroit, USA (Wang et al., 2008). The concentration of soil PAHs in urban areas is determined by their sources. Jiang et al. (2009) found that the concentration of PAHs in soil of the greenbelt area is similar to those of the park and commercial areas, while the lowest is found in the residential area. However, Liu et al. (2010) found that among 6 types (residential area, business area, classical garden, public green space, roadside area, and culture and education area) of land use, the culture and education area has the highest concentration of PAHs in soil. The concentration is moderate in the classical garden and business area, followed by the residential and roadside areas, and the lowest is in the public green space. As cities are becoming more and more industrialized, PAH pollution has become a serious problem. This can be explained by the fact that most cities have various industrial plants and heavy traffic that generate huge amounts of PAHs because of the high-temperature and continuous combustion processes (Peng et al., 2011). For example, Morillo et al. (2007) found that soil samples from Glasgow (Scotland, UK) show very high concentrations of PAHs. This city has a very strong industrial heritage with heavy traffic,

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relatively low mean temperatures, very humid conditions, and a high concentration of organic carbon in the soil. Jiang et al. (2009) reported that the mean concentration of soil PAHs in Shanghai (China) is 3 290 µg kg−1 , which can be attributed to the fact that Shanghai has many industrial plants, including the largest steel works and petrochemical complex in China. In addition, Liu et al. (2010) found that a close relation of PAH concentration with the urbanization history of Beijing urban area and inferred an increasing trend of soil PAHs with time and the age of urban area. This also illustrates that residential building age is a significant factor related to the concentration of PAHs in urban soils. The oldest district always has the largest accumulation of PAHs in soils. The reason is that older districts have been receiving emissions for longer periods of time; therefore, high levels of PAHs in these urban areas are the result of long-term accumulation. The Hutong districts are the oldest part of the city of Beijing (China), became official residences for the bureaucrats of the Emperor’s court more than six hundred years ago, and have been continuously inhabited ever since. Peng et al. (2011) found that soil samples from there contained the second highest soil PAH concentration of all samples they analyzed. Centuries of domestic cooking and home heating have left a legacy of PAHs as a result of coal and wood burning. In Lisbon (Portugal) soils, PAH concentrations are high in historical parks and gardens that are located in the city center which is close to a river and is the oldest part of the city (Cachada et al., 2012). Most of the contaminated day care centers in the city of Bergen, Norway, are located in the old central part of the city (Haugland et al., 2008). Moreover, there is a positive correlation between population density and soil PAH concentrations (Nam et al., 2009). Jensen et al. (2007) found that PAH concentrations are substantially lower in less populated areas in northern Norway than in the urbanized and much more populated areas near the city of Oslo in southern Norway. Higher concentrations of PAHs are generated from industrial activities, traffic, and other activities in more populated areas in central urban districts, whereas PAH concentrations are lower in rural areas with lower population densities. Almost all studies on PAHs in urban soils indicate a consistent relationship between human activities and PAH contamination of soils in cities. The greater the population of the city, the more the activities and thus the higher the concentration of PAHs produced there (Saltiene et al., 2002). It is obvious that the concentrations of PAHs in

SOIL PAH CHARACTERISTICS AND SOURCE IDENTIFICATION

many urban soils vary over a wide range of values. For example, in Beijing soils, the maximum concentration is over 130 times greater than the minimum concentration, with the concentration range being 93–13 141 µg kg−1 (Peng et al., 2011). In Torino (Italy) soils, the total concentrations of PAHs range from 148 to 23 500 µg kg−1 , with the maximum being 158 times the minimum (Morillo et al., 2007). Haugland et al. (2008) indicated that the levels of PAHs in day care centers in Bergen (Norway) range from not available to 200 000 µg kg−1 . Therefore, spatial heterogeneity is a significant characteristic of soil PAHs in urban areas. Composition profiles The 16 priority PAHs are divided into two groups based on the number of aromatic rings: the lower-molecular-weight (LMW) 2–3 ring PAHs and the higher-molecular-weight (HMW) 4–6 ring PAHs. In many cities, the HMW PAHs account for the majority of the total PAHs in urban soils (Zhang et al., 2006; Chung et al., 2007; Ma et al., 2009; Cachada et al., 2012; Singh et al., 2012; Wang et al., 2013). The reason is that the LMW PAHs are usually formed in low-temperature processes, while the HMW PAHs are formed in high-temperature processes such as the combustion of fuels in engines (Mostert et al., 2010). That is, the PAHs of petrogenic origins are characterized by the predominance of 2- and 3-ring PAHs, while the PAHs of pyrogenic origins are characterized by a high proportion of 4-ring or heavier PAHs (Aichner et al., 2007; Wang et al., 2009). Generally, mixed pyrogenic activities are the major contributors of PAHs in urban soils (Zakaria et al., 2002; Tang et al., 2005; Bhupander et al., 2012). Augusto et al. (2010) suggested that the LMW PAHs might have a higher proportion in the air phase than the HMW PAHs because the former have a high volatility and octanol-water partition coefficient (Kow ) and consequently would be subject to a longer distance “grasshopper” effect or “multihop” transport to areas more remote from the emission sources (Gouin et al., 2004; Zhao et al., 2015). The HMW PAHs are associated with particles that undergo “single hop” transport behavior and are likely in closer proximity to emission sources (Nam et al., 2008; Peng et al., 2011). Therefore, because of their various transport behaviors, PAHs may become fractionated during their transport through the atmosphere. NON-ANTHROPOGENIC FACTORS CING PAHS IN URBAN SOILS

INFLUEN-

Climate Soil PAHs are strongly impacted by atmospheric

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deposition of anthropogenic emissions via wet and dry mechanisms (Wilcke, 2007; Esen et al., 2008; Wang et al., 2011). In the atmosphere, PAHs are in a gaseous phase, absorbed on aerosols or partitioned between two phases depending on temperature, vapor pressure, solubility of the compound, and size and surface area of suspended particles (Baek et al., 1991). Surface deposition occurs through wet and dry processes by scavenging gas and particle phase contaminants in rain or snow (Eisenreich et al., 1981); therefore, surface deposition is vulnerable to effects from climate and seasonality. Generally speaking, particulate PAHs are more abundant at night than in the daytime, and in winter than in summer (Zhang et al., 2012). For example, Wang et al. (2011) found that the spatial distribution of PAHs is affected by seasonal variation, with greater concentrations in winter than in summer because of greater deposition at cooler temperatures. Masih and Taneja (2006) conducted a study on PAHs in surface soil in Agra (India) for a span of one year and indicated that the maximum concentrations of PAHs are found in winter. These suggest that soil temperature is a very important factor in determining the leachability or mobility of soil PAHs. In winter with low temperatures, degradation of PAHs decreases and PAHs are also less widely spread, as compared to the other seasons. Vegetation Urban trees remove gaseous air pollution via uptake into leaf stomata, absorption through cuticles (Wania and McLachlan, 2001), and interception of airborne particulate pollutants in the foliar canopy (Nowak et al., 2006). Intercepted PAHs are deposited directly on the ground, washed off trees by rain through throughfall and stemflow, or dropped along with falling leaves and twigs. The stagnant atmosphere underneath the tree canopy will likely slow down volatilization of PAHs from soils (Cousins et al., 1999). Moreover, once volatilized from the soil, PAHs may be subsequently reabsorbed by plants (Collins and Finnegan, 2010). Urban tree stands may therefore play a significant role in the fate of PAHs, decreasing their airborne half-life and transmitting them from the atmosphere to the soil (Mclachlan and Horstmann, 1998). Peng et al. (2012) reported that the type of vegetative cover affects PAHs concentrations of the soil, vegetation dominated by trees, shrubs, and herbs trap more airborne PAHs and accumulate more PAHs than grassland. Therefore, the type of vegetation is a significant factor that affects the accumulation of PAHs in urban soils. Moreover, De Nicola et al. (2015) indicated that leaves and soils accumulate high PAH concentrations in most urbanized areas. This shows that ve-

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getation leaves are also an important factor influencing the accumulation of PAHs. However, leaves mainly accumulate the lightest PAHs (De Nicola et al., 2014).

reflected by the association between black carbon and PAHs in urban soils (Yu et al., 2006; Zhang et al., 2006; Wilcke, 2007).

Soil property

SOURCES AND SOURCE IDENTIFICATION TECHNIQUES OF PAHS IN URBAN SOILS

Soil organic matter (SOM) plays a key role in the partitioning, storage, and longevity of persistent organic pollutants like PAHs; many studies found a strong correlation between concentrations of soil PAHs and SOM (Wilcke et al., 2000; Maisto et al., 2006; He et al., 2009; Wang et al., 2010). This phenomenon might be due to the strong adsorption of PAHs to SOM, especially the allochthonous carbonaceous SOM that exhibits adsorption properties much different from the autochthonous SOM (Dachs and Eisenreich, 2000). However, a poor correlation between soil PAHs and SOM was also reported in previous studies (Jones et al., 1989; Zhang et al., 2006; Yin et al., 2008; Jiang et al., 2009). The reason is that a lack of correlation should be expected in an environment with continuous inputs of fresh PAHs, at least until equilibrium is reached (Katsoyiannis, 2006). Thus, the poor correlation may be due to the result of non-equilibrium adsorption between SOM and PAHs. Moreover, some studies found that SOM is correlated only with LMW PAHs, which shows that LMW PAHs can easily achieve equilibrium adsorption than HMW PAHs (Bucheli et al., 2004; Br¨andli et al., 2008; Nam et al., 2008). Black carbon, the carbonaceous residue of incomplete combustion of biomass or fossil fuels, is ubiquitously present in soils and sediments at median levels, 4% and 9% of SOM, respectively (Cornelissen et al., 2005). Due to its high surface area and microporosity, black carbon appears to be very important to the sorption of organic compounds in the environment (Yang et al., 2012). However, the ability of sorption is various for different-molecular-weight PAHs, as well as different areas. For example, Liu et al. (2011) found that black carbon is correlated significantly with PAHs in urban areas and with heavier 4-, 5- and 6-ring PAHs in the adjacent rural plains, whereas there is no significant correlation between black carbon and any PAHs in the farther rural mountains. Agarwal and Bucheli (2011) found that black carbon has a significant positive correlation with lighter PAHs, but not with heavier PAHs in Swiss soils mostly from remotely located background sites, whereas heavier PAHs are significantly correlated with black carbon in Delhi soils from the capital city of India. They concluded that SOM governs the distribution of PAHs in background soils rich in organic matter, whereas the proximity to emission sources is

It is clear that PAHs originate from natural processes such as biomass burning, volcanic eruption, and diagenesis, as well as from anthropogenic activities such as coal and wood burning, petrol and diesel oil combustion, and industrial activities (Wang et al., 2007; Mostert et al., 2010). However, PAHs are always emitted as a mixture (Tobiszewski and Namie´snik, 2012). Therefore, appropriate methods can be used to identify particular sources of soil PAHs. Understanding the impact of particular emission sources on different ecosystems is crucial for proper risk assessment and risk management (Tobiszewski and Namie´snik, 2012). At present, many efficient methods are used for source identification of soil PAHs in urban areas. These methods include the diagnostic ratio analysis and multivariate statistical analysis including positive matrix factorization (PMF) and principle components analysis (PCA). Diagnostic ratios The PAH emission profile for a given source depends on the processes producing the PAHs (Manoli et al., 2004). Diagnostic ratios identify the origin of the contamination by comparing the relative concentrations of individual PAHs (fluoranthene (Flu), pyrene (Pyr), benz[a]anthracene (BaA), chrysene (Chr), indeno[1,2,3-cd]pyrene (InP); benzo[g,h,i]perylene (Bg-hiP), anthracene (Ant), phenanthrene (Phe), benzo[a]py-rene (BaP), etc.) with well-known references and thus qualitatively distinguishing petrogenic and pyrolytic sources. Table I lists some typical diagnostic ratios used in previous studies (Soclo et al., 2000). The ratio of LMW PAHs/HMW PAHs < 1 indicates pyrogenic sources including incomplete combustion of fossil fuels or wood and that > 1 signals petrogenic sources including spilled oil or petroleum products. The boundary value of the Flu/(Flu + Pyr) ratio for petroleum appears to be closer to 0.4 rather than 0.5, the values between 0.4 and 0.5 are more characteristic of liquid fossil fuel (vehicle and crude oil) combustion, and the values > 0.5 are characteristic of grass, wood, or coal combustion (Yunker et al., 2002). Ravindra et al. (2008) reported that the Flu/(Flu + Pyr) ratio > 0.5 indicates diesel emissions, whereas that < 0.5 indicates gasoline

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TABLE I Diagnostic ratios used and their typically reported values in literature for source identification of polycyclic aromatic hydrocarbons (PAHs) Ratioa)

Value range

Source

Reference

ΣLMW/ΣHMW

<1 >1 < 0.4 0.4–0.5 > 0.5 < 0.5 > 0.5 < 0.2 0.2–0.35 > 0.35 < 0.2 0.2–0.5 > 0.5 < 0.1 > 0.1 < 0.6 > 0.6

Pyrogenic Petrogenic Petrogenic Fossil fuel combustion Grass, wood, coal combustion Petrol emissions Diesel emissions Petrogenic Coal combustion Vehicular emissions/combustion Petroleum Petroleum combustion Coal combustion Petrogenic Pyrogenic Non-traffic emissions Traffic emissions

Zhang et al., 2008

Flu/(Flu + Pyr)

BaA/(BaA + Chr)

InP/(InP + BghiP)

Ant/(Ant + Phe) BaP/BghiP

Yunker et al., 2002

Ravindra et al., 2008 Aky¨ uz and C ¸ abuk, 2010 Yunker et al., 2002 Yunker et al., 2002

Pies et al., 2008 Katsoyiannis et al., 2007

a) ΣLMW = sum of the lower-molecular-weight (LMW) 2–3 ring PAHs; ΣHMW = sum of the higher-molecular-weight (HMW) 4–6 ring PAHs; Flu = fluoranthene; Pyr = pyrene; BaA = benz[a]anthracene; Chr = chrysene; InP = indeno[1,2,3-cd]pyrene; BghiP = benzo[g,h,i]perylene; Ant = anthracene; Phe = phenanthrene; BaP = benzo[a]pyrene.

emissions. The BaA/(BaA + Chr) ratio < 0.2 implies petroleum, from 0.2 to 0.35 either petroleum or combustion, and > 0.35 combustion (Yunker et al., 2002). The InP/(InP + BghiP) ratio < 0.2 likely implies petroleum, between 0.2 and 0.5 liquid fossil fuel (vehicle and crude oil) combustion, and > 0.5 grass, wood, and coal combustion (Yunker et al., 2002). The Ant/(Ant + Phe) ratio < 0.1 usually is taken as an indication of petroleum, while that > 0.1 indicates a dominance of combustion (Budzinski et al., 1997). The BaP/BghiP ratio > 0.6 indicates traffic emissions, while that < 0.6 indicates non-traffic emissions. When PAH ratios are used to determine the source of an emission, it is assumed that the ratios remain constant from sources to receptors (Wang et al., 2010). However, the PAH ratios can be altered significantly during the transport of the PAHs in a multimedia environment because of the physicochemical properties of the paired PAHs (Lang et al., 2008). Zhang et al. (2005) used a fugacity model in combination with filed verification to examine such changes and found that the receptor-to-source ratio (RRS ) values of two paired PAHs usually differ significantly. The PAH ratios change remarkably from the source of the emissions to various environmental media. Consequently, a site-specific correction factor, defined as the ratio of the two RRS values of the two paired PAHs for a specific PAH ratio in a given medium, was applied to adjust

the ratio changes in a multimedia environment (Zhang et al., 2005). Using the correction factor from Zhang et al. (2005), Wang et al. (2010) calibrated the Flu/(Flu + Pyr) and Ant/(Ant + Phe) ratios of soil samples. They found that the values of the Flu/(Flu + Pyr) ratio were greater than 0.5 before and after correction for all soil samples, suggesting that the main PAH sources are coal, grass and wood combustion. The Ant/(Ant + Phe) ratio changed significantly after correction because of faster degradation of Ant during transport as compared to that of Phe. For the majority of the soil samples, this ratio suggested PAHs emissions from combustion, with only a few cases of PAH emissions from petrogenic sources. These results suggested that the PAHs in soils of the study areas were primarily from coal combustion and biomass burning (Wang et al., 2010). Therefore, to overcome the problem of ratio change, the use of HMW PAH ratios is necessary because they are much more stable. The advantage of the diagnostic ratio method is that it is simple to use, but only a qualitative result can be obtained using it. Positive matrix factorization Positive matrix factorization (PMF) is a multivariate statistical analysis method that decomposes a matrix of speciated sample data into two matrices: factor contributions and factor profiles. The factor profiles

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need to be interpreted by the user, using measured source profile information and emissions or discharge inventories, to identify the source types that may be contributing to the sample (Paatero and Tapper, 1994; Paatero, 1997; US EPA, 2014). A common strategy is to search the literature for measured source profiles with characteristics similar to factor profiles. For example, Wang et al. (2013) conducted a study to identify the sources of PAHs in urban soils in Shanghai (China) using the PMF method. They compared the source profiles in detail with widely available literature values and concluded that the soil PAHs originate from petrogenic sources, coal combustion, biomass burning, creosote, coke tar-related sources and vehicular emissions. The PMF method has been used extensively for source identification of ambient particulate matter in the atmosphere, and there is an increasing tendency for its use in studies of the urban soil environment in recent years (Wang et al., 2009, 2013, 2015; Chen et al., 2013; Yang et al., 2013; Kwon and Choi, 2014). A critical process for using PMF is the determination of the number of factors required to provide clear, physically meaningful results while simultaneously reducing matrix dimensionality as much as possible (Saraga et al., 2010). The common strategy for finding the optimum number of factors in the PMF solution is to examine values of the sum of the squares of the difference between the original dataset and the PMF output (Q) for PMF solutions resulting from a range of values of the number of factors contributing to the samples (p) (Reff et al., 2007). For example, Wang et al. (2009) reported that a three-factor model produces a Q value equaling the theoretical Q, which indicates an appropriate uncertainty in the modeling input. Chen et al. (2013) conducted a modelling study and found all the runs converge to a minimum Q value in robust mode (Qrobust ), and Qrobust is equal to the true Q value, indicating no outliers impacting the Q value. In addition, most of the residuals are between +3 and −3 and are normally distributed under four factors, suggesting that the PAHs were accurately modeled. Chen et al. (2013) concluded that coal combustion, vehicular diesel and gasoline combustion, and a petroleum ¨ umqi, China. Comsource are the PAH sources in Ur¨ pared with other methods, the PMF method has many advantages. For example, Yang et al. (2013) compared the PMF model with the principal component analysis with multiple linear regression (PCA-MLR) and Unmix models and found that different receptor models provide divergent source profiles which are affected by the model itself as well as the underlying dataset.

However, the PMF model could provide better results than the other two models based on point-by-point estimates of uncertainty errors in the dataset. In addition, an advantage of PMF is that its non-negativity constraints provide for obtaining physically realistic meanings (Khairy and Lohmann, 2013). Principal component analysis Principal component analysis (PCA), also a multivariate analytical tool widely used for receptor modeling in environmental source apportionment studies, is conducted to reduce a set of original variables and to extract a small number of latent factors for analyzing relationships among the observed variables (Jolliffe, 2005; Jiang et al., 2009). The core step is that principle components (PCs) are extracted with different factor loadings by utilizing the orthogonal transformation method and each PC is further evaluated and recognized by source markers or profiles as reasonable pollution sources. This process is similar to the strategy of PMF in that the literature is searched for measured source markers or profiles with characteristics similar to factor markers or profiles. The factor loading scores are a type of correlation coefficient, and higher score values are therefore associated with greater significance (Agarwal et al., 2009). Using two principal components is common in analyzing the sources of soil PAHs in urban environment. For example, Chung et al. (2007) examined the status of PAH pollution in Hong Kong (China) soils by PCA and found two factors can explain 94% of the total variance. The study of Ma et al. (2011) also indicated that two PCs extracted from the raw data of Huizhou (China) and Zhanjiang (China) can also explain 71.33% and 80.05% of the total variance, respectively. Similar results were also found in the studies of Jiang et al. (2009), Liu et al. (2011), and Peng et al. (2011). Multiple approaches used in combination It was verified that the sources of urban soil PAHs are mixed and include pyrolytic and petrogenic origins. Beyond that, the analytic process of finding source profiles with characteristics similar to factor profiles for both PMF and PCA are speculative, which may result in inaccuracies. Moreover, it is also assumed that the PAHs remain constant from sources to receptors. Therefore, using multiple approaches in combination for source identification of PAHs in the urban soil environment provides many opportunities for future research and is more reliable than using only a single source identification method because it ensures better accuracy.

SOIL PAH CHARACTERISTICS AND SOURCE IDENTIFICATION

For example, both the diagnostic ratio method and the PMF model were used in the study of Kwon and Choi (2014). The results of the diagnostic ratio method suggested that the main sources of PAHs could be not only industrial activities but also vehicles in the industrial areas. The PMF model results indicated that diesel vehicle emission contributes to over 50% of the measured PAHs and industrial emission sources include heavy oil combustion, coke oven, and coal/biomass burning. Zuo et al. (2007) used diagnostic ratios, PCA, and multiple linear regression (MLR) to identify the sources of surface soil PAHs in Tianjin (China). They found that the contributions of the main sources are 41% from coal, 20% from petroleum, and 39% from coking and biomass, which are compatible with PAHs emissions estimated based on fuel consumption and emission factors. CONCLUSIONS AND FUTURE PERSPECTIVES The characteristics of soil PAHs in urban areas are a result of anthropogenic activities. It is obvious that the anthropogenic factors, as compared to nonanthropogenic factors, have the most impact on the accumulation of soil PAHs in urban areas. The industrial areas, the oldest urban districts, and the areas adjacent to roadways should draw considerable attention for their potential human health risk from soil PAHs. However, most of the research on urban soil PAHs mainly focuses on big cities and much less information is available on PAHs in soils from small and medium-sized cities. Thus, to understand the characteristic and sources of PAHs in soils from all types of cities, much more scientific attention should be addressed to smaller cities in the future. Additionally, the current research on urban soil PAHs mainly concentrates on the pollution level, sources, or spatial distribution, which are static problems. In fact, the trend of accumulation of soil PAHs is more crucial for environmental management departments or government departments. Therefore, estimating the dynamic database for each city or the application of the related models to simulate the accumulation trend of urban soil PAHs should be a future research direction. Diagnostic ratios, positive matrix factorization, and principle components analysis, three typical source identification techniques widely used in studying the urban soil environment, have advantages and disadvantages. Moreover, these source identification techniques are typically used for studying PAHs in the atmosphere, where they are relatively stable. It is a fact that PAHs can be altered significantly during their transport from the emission sources to the soil envi-

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ronment. Surprisingly, many researchers have ignored this problem. Therefore, using multiple approaches for source identification of PAHs in the urban soil environment is more reliable than using just one source identification method. Based on the current situation, development of new models or application of novel solutions for source identification of PAHs in an unstable soil environment or other environments should also be a direction for future research. ACKNOWLEDGEMENT The authors acknowledge the financial support from the National Natural Science Foundation of China (No. 41671085). REFERENCES Agarwal T, Bucheli T D. 2011. Is black carbon a better predictor of polycyclic aromatic hydrocarbon distribution in soils than total organic carbon? Environ Pollut. 159: 64–70. Agarwal T, Khillare P S, Shridhar V, Ray S. 2009. Pattern, sources and toxic potential of PAHs in the agricultural soils of Delhi, India. J Hazard Mater. 163: 1033–1039. Aichner B, Glaser B, Zech W. 2007. Polycyclic aromatic hydrocarbons and polychlorinated biphenyls in urban soils from Kathmandu, Nepal. Org Geochem. 38: 700–715. Aky¨ uz M, C ¸ abuk H. 2010. Gas-particle partitioning and seasonal variation of polycyclic aromatic hydrocarbons in the atmosphere of Zonguldak, Turkey. Sci Total Environ. 408: 5550–5558. Augusto S, M´ aguas C, Matos J, Pereira M J, Branquinho C. 2010. Lichens as an integrating tool for monitoring PAH atmospheric deposition: A comparison with soil, air and pine needles. Environ Pollut. 158: 483–489. Baek S O, Field R A, Goldstone M E, Kirk P W, Lester J N, Perry R. 1991. A review of atmospheric polycyclic aromatic hydrocarbons: sources, fate and behavior. Water Air Soil Poll. 60: 279–300. Bhupander K, Gargi G, Richa G, Dev P, Sanjay K, Shekhar S C. 2012. Distribution, composition profiles and source identification of polycyclic aromatic hydrocarbons in roadside soil of Delhi, India. J Earth Syst Sci. 2: 10–22. Br¨ andli R C, Bucheli T D, Ammann S, Desaules A, Keller A, Blum F, Stahel W A. 2008. Critical evaluation of PAH source apportionment tools using data from the Swiss soil monitoring network. J Environ Monit. 10: 1278–1286. ¨ 2004. PolyBucheli T D, Blum F, Desaules A, Gustafsson O. cyclic aromatic hydrocarbons, black carbon, and molecular markers in soils of Switzerland. Chemosphere. 56: 1061–1076. Budzinski H, Jones I, Bellocq J, Pi´ erard C, Garrigues P. 1997. Evaluation of sediment contamination by polycyclic aromatic hydrocarbons in the Gironde estuary. Mar Chem. 58: 85–97. Buhaug H, Urdal H. 2013. An urbanization bomb? Population growth and social disorder in cities. Global Environ Chang. 23: 1–10. Cachada A, Pato P, Rocha-Santos T, da Silva E F, Duarte A C. 2012. Levels, sources and potential human health risks of organic pollutants in urban soils. Sci Total Environ. 430: 184–192. Chen M, Huang P, Chen L. 2013. Polycyclic aromatic hydrocarbons in soils from Urumqi, China: distribution, source con-

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