Integrated Environmental Assessment and Management — Volume 10, Number 4—pp. 489–491 © 2014 SETAC
489
Author's Reply to Van Metre and Mahler (2014) Kirk T O'Reilly,*y Jaana Pietari,z and Paul D Boehmz yExponent, Bellevue, Washington, USA zExponent, Maynard, Massachusetts, USA
(Submitted 27 May 2014; Accepted 10 June 2014)
When properly applied, receptor models and other forensic methods can be useful tools for investigating PAH sources. Although there are published reports of using receptors models such as EPA’s Chemical Mass Balance (CMB) to characterize potential sources of PAHs responsible for the background profile associated with urban sediments, the results are sensitive to the model conditions and subject to high uncertainty. Even though Van Metre and Mahler (2010) claimed to use CMB to test the Mahler hypothesis, only the results of 4 runs that appeared consistent with the hypothesis were discussed in detail. A more complete evaluation indicates that CMB results do not support the hypothesis. When used to support source control policy, scientists must describe the receptor model results and associated uncertainties in a way that is understood by nontechnical decision makers. As noted in Van Metre and Mahler’s letter, our concerns with their application of CMB include source selection and modeling approach. CMB output is valid only if the sources used as inputs are correctly identified (Gleser 1997). A fundamental problem with the common approach for modeling urban sources of PAHs lies in applying generic source profiles obtained from the literature without evaluating whether they are correct or appropriate. It does not matter if, as Van Metre and Mahler claim, “they are the best available” or it is a “common practice,” if the source profiles are incorrect or incomplete. Van Metre and Mahler obtained many of their profiles from Li et al. (2003). Given the wide range of types of underlying studies and various mathematical manipulations required to convert published information into PAH profiles, some evidence is required to demonstrate that the profiles represent the sources claimed. We can show that the source profiles used are unlikely to be adequate to accurately model the sediment systems. When PCA is applied to samples that are affected by a mixture of sources, samples that represent source chemistry should plot as
end members, and the mixtures should plot within the area bounded by these samples (Johnson et al. 2004). When we applied PCA to the data presented in Van Metre and Mahler (2010), we found very few sediment samples within the area bounded by their proposed sources (Figure 1). Another problem with source selection is that it seems incomplete. The failure to include a source associated with manufactured gas plant wastes is an obvious gap, because these materials are known to contain PAHs, the facilities were commonly situated next to water bodies, and the PAH‐rich materials have a history of being used in road construction (Hubbard and Draper 1911; Reinke and Glidden 2007) so have the potential to be widespread in the urban environment. Profiles of urban dust from their own studies, such as Van Metre and Mahler (2003) and Mahler et al. (2010), would have been useful additions. As described in Table 1 of Van Metre and Mahler (2010), at least 6 different RTS profiles were used as model inputs— 1 fresh product, 3 scrapings of RTS‐treated pavement, and 2 based on dust collected from multiple parking lots. The 4 model runs discussed in detail used only the 2 dust profiles and not those that represent a direct analysis of RTS. Since submitting O’Reilly et al. (2014), we reran Van Metre and Mahler (2010) Model A but replaced the parking‐lot dust source profile with a profile from either Mahler et al.’s (2005) RTS test plots or fresh RTS. With the test plot RTS profile, the average contribution dropped from 46% to 1.5%, and the median dropped from 46% to zero. The average and median RTS contributions, based on the source profile of fresh RTS, were 0.6% and 0%. In both cases, there was an excellent fit (R2 > 0.997) between measured and modeled PAH concentrations (Figure 2).
* To whom correspondence may be addressed: [email protected] Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ieam.1556
Figure 1. Results of PCA using Van Metre and Mahler (2010) sediment and source profiles. The observation that most of the sediment samples are outside the bounded area suggests that the correct sources were not used in CMB modeling.
Letter to the Editor
DEAR SIR: We appreciate the opportunity to reply to Van Metre and Mahler’s Letter to the Editor (this issue). This continues an ongoing technical discussion concerning the “Mahler hypothesis” (Mahler et al. 2005) that refined tar pavement sealers (RTS) are an important source of polycyclic aromatic hydrocarbons (PAHs) to urban sediments. The key points in O’Reilly et al. (2014) are:
490
Figure 2. CMB modeled PAH versus measured PAH concentrations for runs using either the RTS parking‐lot dust source profile, RTS test plot profile, or no RTS source. In each case, R2 exceeded 0.997. The error bars show the default uncertainty used in the model. The ability of the model to fit the data with an RTS profile that suggested that it was a PAH source (median contribution of the RTS lot dust ¼ 49%), an RTS profile that suggested that it was not a PAH source (median contribution of the RTS test plot ¼ 0), or without an RTS source input indicates that CMB does not provide support for the Mahler hypothesis.
Because Van Metre and Mahler (2010) stated that the purpose of their CMB study was to test the hypothesis concerning the role of RTS, sufficient results should have been presented to allow the reader to evaluate the validity of the hypothesis. At a minimum, this would include RTS‐free negative controls and results with all the RTS tested. Both our group (Figure 2) and Crane (2014) found excellent agreement (R2 > 0.99 and 0.93, respectively) between measured and modeled PAH concentration whether or not an RTS source profile is included as a CMB input. In their letter, Van Metre and Mahler state that they have since run negative controls, but it seems they plan to gloss over the implications by focusing on secondary indicators of model performance. Crane (2014) used a similar approach, and the discussion in that article appears to be an effort to defend, rather than to test, the hypothesis. We argue that slight differences in performance factors among runs of a model with inherently high uncertainty have little meaning, and these differences do not diminish the strength of the conclusion that the RTS‐free negative control results undermine the use of CMB to support the Mahler hypothesis. Van Metre and Mahler’s goal was to test the Mahler hypothesis; therefore, the statement that the all model runs are in general agreement in terms of relative importance of the sources is insufficient and, based on our application of CMB with the same source profile, incorrect. Van Metre and Mahler defend the decision to highlight only four of the 200 model runs by stating that all runs do not need to be discussed in detail. This misses our point in that, at a minimum, they should have included results that would allow readers to evaluate for themselves the hypothesis being tested. Although all 200 runs need not have been described, focusing on 4 variations of essentially the same model inputs—the 2 parking‐lot dusts with either 11 or 12 PAHs—whose results were consistent their hypothesis suggests that a goal of the article was to indict RTS. The methods applied by Van Metre and Mahler (2010) were derived from the article by Li et al. (2003), which presented the output from 9 models, showing a range of results. Given the expanded use of supplemental information sections since 2003,
Integr Environ Assess Manag 10, 2014—KT O'Reilly et al.
Van Metre and Mahler easily could have presented a more complete description of their CMB results. Although Van Metre and Mahler suggest there is a consensus on the role of RTS, most of the articles cited are their own (Mahler et al. 2005, 2012; Van Metre et al. 2009; Van Metre and Mahler 2010; Yang et al. 2010), their collaborators (Watts et al. 2010; Crane 2014), or reiterate the observation that there are chemical similarities between the PAH profile of pyrogenic sources and urban sediments (Selbig et al. 2013; Witter et al. 2014). Evaluation of their studies indicates a fatal flaw in the logic of each. As used in Mahler et al. (2005), diagnostic ratios are blunt tools for source identification, because the ratios of many potential sources overlap (O’Reilly et al. 2012), and similarity is not proof of causation. As noted in their letter, the claims made in Van Metre et al. (2009) are based in part on the then‐uncited anecdote about different patterns of sealer usage in the West compared to the central and eastern United States. Although this information is used to support the hypothesis, it ignores other regional differences that could influence background PAH profiles, such as the fact that the West has a petroleum‐powered economy, whereas coal has long been the dominant energy source in the Midwest and eastern United States. Yang et al. (2010) describes the use of petrography. Particles with elevated PAH concentrations were identified as coal tar pitch and were assumed by the authors to be associated with RTS. Only 3 lake sediment samples were analyzed, and in a finding inconsistent with the Mahler hypothesis, those samples had similar PAH concentrations, even though the presumed contributions of RTS were quite different (see Figure 3, Yang et al. 2010). The authors admit that predicting PAH concentrations based on amount of coal tar pitch results in large overestimations, but attempt to argue away this inconsistency. Our concerns with claims made by Crane (2014) and Witter et al. (2014) are described in O’Reilly (2014a, 2014b). Shifting claims between publications further weakens the arguments presented by Van Metre and Mahler. Van Metre et al. (2000) initially identified sources associated with automobile usages as primary contributors of PAHs in Lake Ballinger, near Seattle, Washington. Ballinger was used as an example of a low‐RTS Western lake (Van Metre et al. 2009), but a year later, Van Metre and Mahler (2010) claimed that RTS was a significant source to Lake Ballinger. Other work by Van Metre (Thapalia et al. 2010; Grey et al. 2013) suggests that a smelter is responsible for some contaminants found in Lake Ballinger’s sediment. Although these articles focused on metals, not PAHs, another study indicated a link between metals and PAHs in sediments affected by the same smelter (Louchouarn et al. 2012). These inconsistencies were not brought to light, even as claims about Lake Ballinger (Van Metre and Mahler 2010) became the catalyst for enactment of a statewide ban of RTS. We appreciate that the editors of Integrated Environmental Assessment and Management (IEAM) have made space available for this technical discussion concerning the issues raised in our article. The concept of postpublication peer review has sparked growing interest in a number of technical fields (Hunter 2012; Teixeira da Silva 2013). Such review can be especially useful in environmental science because of its interdisciplinary nature, the difficulty of conducting well‐controlled studies in the field, the potential for personal bias, and the inherent uncertainty of natural systems. The interdisciplinary issue can be of particular
Response of O'Reilly et al. to Van Metre and Mahler's—Integr Environ Assess Manag 10, 2014
concern as powerful computer tools become more user‐ friendly. Now that forensic models, such as CMB and PCA, have achieved “plug‐and‐play” accessibility for use by a wider spectrum of scientists, it is even more important that published results are carefully vetted so mistakes introduced by 1 research group are not propagated through the science by others. In O’Reilly et al. (2014), we discussed the challenges of parsing out individual contributors of pyrogenic PAHs in urban background. We described the state of the practice for using receptor models and other forensic methods to assess PAH sources in individual watersheds. The case study was presented as an example of what we consider an application that pushed these methods beyond their capabilities, in an effort to prove the contribution of a particular source across a range of sites. Our article highlights concerns with how the model inputs were selected and what we believe was an incomplete presentation of the results. The comments in Van Metre and Mahler’s letter do not change our conclusion.
REFERENCES Crane JL. 2014. Source apportionment and distribution of polycyclic aromatic hydrocarbons, risk considerations, and management implications for urban stormwater pond sediments in Minnesota, USA. Arch Environ Contam Toxicol 66:176–200. Gleser LJ. 1997. Some thoughts on chemical mass balance models. Chemometr Intell Lab 37:15–22. Grey JE, Pribil MJ, Van Metre PC, Borrok DM, Thapalia A. 2013. Identification of contamination in a lake sediment core using Hg and Pb isotopic compositions, Lake Ballinger, Washington, USA. Appl Geochem 29:1–12. Hubbard P, Draper CN. 1911. Naphthalene in road tar. Circular No. 96. Department of Agriculture, Office of Public Roads, Washington DC. Hunter J. 2012. Post‐publication peer review: Opening up scientific conversation. Front Computat Neurosci 6:63. Johnson GW, Ehrlich R, Full W. 2004. Principal components analysis and receptor models in environmental forensics. In: Murphy BL, Morrison RD, editors. Introduction to environmental forensics. Burlington (MA): Elsevier Academic Press. 461 p. Li A, Jan JK, Scheff PA. 2003. Application of EPA CMB8.2 Model for Source Apportionment of Sediment PAHs in Lake Calumet, Chicago. Environ Sci Technol 37:2958–2965. Louchouarn P, Kuo L, Brandenberger JM, Marcantonio F, Garland C, Gill GA, Cullinan V. 2012. Pyrogenic inputs of anthropogenic Pb and Hg to sediments of the Hood Canal, Washington, in the 20th century: Source evidence from stable Pb isotopes and PAH signatures. Environ Sci Technol 46:5772–5781. Mahler BJ, Van Metre PC, Bashara TJ, Wilson JT, Johns DA. 2005. Parking lot sealcoat: An unrecognized source of urban polycyclic aromatic hydrocarbons. Environ Sci Technol 39:5560–5566.
491
Mahler BJ, Van Metre PC, Wilson JT, Musgrove M, Burtank TL, Ennis TE, Bashara TJ. 2010. Coal‐tar‐based parking lot sealcoat: An unrecognized source of PAH to settled house dust. Environ Sci Technol 44:894–900. Mahler BJ, Van Metre PC, Crane JL, Watts AW, Scoggins M, Williams ES. 2012. Coal‐ tar‐based pavement sealcoat and PAHs: Implications for the environment, human health, and stormwater management. Environ Sci Technol 56:3039– 3045. O'Reilly KT. 2014a Article title misstates the role of pavement sealers. Environ Pollut 191:260–261. O'Reilly KT. 2014b Response to authors' reply on “Coal‐tar‐based sealcoated pavement: A major PAH source to urban stream sediments”. Environ Pollut 191:264–265. O'Reilly KT, Pietari J, Boehm P. 2012. A forensic assessment of coal tar sealants as a source of polycyclic aromatic hydrocarbons in urban sediments. Environ Forensics 13:185–196. O'Reilly KT, Pietari J, Boehm PD. 2014. Parsing pyrogenic polycyclic aromatic hydrocarbons: Forensic chemistry, receptor models, and source control policy. Integr Environ Assess Manag 10:279–285. Reinke G, Glidden S. 2007. Case study of worker exposure to coal tar containing paving materials on a routine paving project in Iowa. J Occup Environ Hyg 4(S1):228–232. Selbig WR, Bannerman R, Corsi SR. 2013. From streets to streams: Assessing the toxicity potential of urban sediment by particle size. Sci Total Environ 444:381– 391. Teixeira da Silva JA. 2013. The need for post‐publication peer review in plant science publishing. Front Plant Sci 4:485. Thapalia A, Borrok DM, Van Metre PC, Musgrove M, Landa ER. 2010. Zn and Cu isotopes as tracers of anthropogenic contamination in a sediment core from an urban lake. Environ Sci Technol 44:1544–1550. Van Metre PC, Mahler BJ, Furlong ET. 2000. Urban sprawl leaves its PAH signature. Environ Sci Technol 34:4064–4070. Van Metre PC, Mahler BJ. 2003. The contribution of particles washed from rooftops to contaminant loading to urban streams. Chemosphere 52:1727– 1741. Van Metre PC, Mahler BJ. 2010. Contribution of PAHs from coal‐tar pavement sealcoat and other sources to 40 U.S. lakes. Sci Total Environ 409:334– 344. Van Metre PC, Mahler BJ, Wilson JT. 2009. PAHs underfoot: Contaminated dust from coal‐tar sealcoated pavement is widespread in the United States. Environ Sci Technol 43:20–25. Watts AW, Ballestero TP, Roseen RM, Houle JP. 2010. Polycyclic aromatic hydrocarbons in stormwater runoff from sealcoated pavements. Environ Sci Technol 44:8849–8854. Witter A, Nguyen MH, Baidar S, Sak PB. 2014. Coal‐tar‐based sealcoated pavement: A major PAH source to urban stream sediments. Environ Pollut 185:59–68. Yang Y, Van Metre PC, Mahler BJ, Wilson JT, Ligouis B, Razzaque M, Schaeffer C, Werth C. 2010. The influence of coal‐tar sealcoat and other carbonaceous materials on polycyclic aromatic hydrocarbon loading in an urban watershed. Environ Sci Technol 44:1217–1233.
489
Author's Reply to Van Metre and Mahler (2014) Kirk T O'Reilly,*y Jaana Pietari,z and Paul D Boehmz yExponent, Bellevue, Washington, USA zExponent, Maynard, Massachusetts, USA
(Submitted 27 May 2014; Accepted 10 June 2014)
When properly applied, receptor models and other forensic methods can be useful tools for investigating PAH sources. Although there are published reports of using receptors models such as EPA’s Chemical Mass Balance (CMB) to characterize potential sources of PAHs responsible for the background profile associated with urban sediments, the results are sensitive to the model conditions and subject to high uncertainty. Even though Van Metre and Mahler (2010) claimed to use CMB to test the Mahler hypothesis, only the results of 4 runs that appeared consistent with the hypothesis were discussed in detail. A more complete evaluation indicates that CMB results do not support the hypothesis. When used to support source control policy, scientists must describe the receptor model results and associated uncertainties in a way that is understood by nontechnical decision makers. As noted in Van Metre and Mahler’s letter, our concerns with their application of CMB include source selection and modeling approach. CMB output is valid only if the sources used as inputs are correctly identified (Gleser 1997). A fundamental problem with the common approach for modeling urban sources of PAHs lies in applying generic source profiles obtained from the literature without evaluating whether they are correct or appropriate. It does not matter if, as Van Metre and Mahler claim, “they are the best available” or it is a “common practice,” if the source profiles are incorrect or incomplete. Van Metre and Mahler obtained many of their profiles from Li et al. (2003). Given the wide range of types of underlying studies and various mathematical manipulations required to convert published information into PAH profiles, some evidence is required to demonstrate that the profiles represent the sources claimed. We can show that the source profiles used are unlikely to be adequate to accurately model the sediment systems. When PCA is applied to samples that are affected by a mixture of sources, samples that represent source chemistry should plot as
end members, and the mixtures should plot within the area bounded by these samples (Johnson et al. 2004). When we applied PCA to the data presented in Van Metre and Mahler (2010), we found very few sediment samples within the area bounded by their proposed sources (Figure 1). Another problem with source selection is that it seems incomplete. The failure to include a source associated with manufactured gas plant wastes is an obvious gap, because these materials are known to contain PAHs, the facilities were commonly situated next to water bodies, and the PAH‐rich materials have a history of being used in road construction (Hubbard and Draper 1911; Reinke and Glidden 2007) so have the potential to be widespread in the urban environment. Profiles of urban dust from their own studies, such as Van Metre and Mahler (2003) and Mahler et al. (2010), would have been useful additions. As described in Table 1 of Van Metre and Mahler (2010), at least 6 different RTS profiles were used as model inputs— 1 fresh product, 3 scrapings of RTS‐treated pavement, and 2 based on dust collected from multiple parking lots. The 4 model runs discussed in detail used only the 2 dust profiles and not those that represent a direct analysis of RTS. Since submitting O’Reilly et al. (2014), we reran Van Metre and Mahler (2010) Model A but replaced the parking‐lot dust source profile with a profile from either Mahler et al.’s (2005) RTS test plots or fresh RTS. With the test plot RTS profile, the average contribution dropped from 46% to 1.5%, and the median dropped from 46% to zero. The average and median RTS contributions, based on the source profile of fresh RTS, were 0.6% and 0%. In both cases, there was an excellent fit (R2 > 0.997) between measured and modeled PAH concentrations (Figure 2).
* To whom correspondence may be addressed: [email protected] Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ieam.1556
Figure 1. Results of PCA using Van Metre and Mahler (2010) sediment and source profiles. The observation that most of the sediment samples are outside the bounded area suggests that the correct sources were not used in CMB modeling.
Letter to the Editor
DEAR SIR: We appreciate the opportunity to reply to Van Metre and Mahler’s Letter to the Editor (this issue). This continues an ongoing technical discussion concerning the “Mahler hypothesis” (Mahler et al. 2005) that refined tar pavement sealers (RTS) are an important source of polycyclic aromatic hydrocarbons (PAHs) to urban sediments. The key points in O’Reilly et al. (2014) are:
490
Figure 2. CMB modeled PAH versus measured PAH concentrations for runs using either the RTS parking‐lot dust source profile, RTS test plot profile, or no RTS source. In each case, R2 exceeded 0.997. The error bars show the default uncertainty used in the model. The ability of the model to fit the data with an RTS profile that suggested that it was a PAH source (median contribution of the RTS lot dust ¼ 49%), an RTS profile that suggested that it was not a PAH source (median contribution of the RTS test plot ¼ 0), or without an RTS source input indicates that CMB does not provide support for the Mahler hypothesis.
Because Van Metre and Mahler (2010) stated that the purpose of their CMB study was to test the hypothesis concerning the role of RTS, sufficient results should have been presented to allow the reader to evaluate the validity of the hypothesis. At a minimum, this would include RTS‐free negative controls and results with all the RTS tested. Both our group (Figure 2) and Crane (2014) found excellent agreement (R2 > 0.99 and 0.93, respectively) between measured and modeled PAH concentration whether or not an RTS source profile is included as a CMB input. In their letter, Van Metre and Mahler state that they have since run negative controls, but it seems they plan to gloss over the implications by focusing on secondary indicators of model performance. Crane (2014) used a similar approach, and the discussion in that article appears to be an effort to defend, rather than to test, the hypothesis. We argue that slight differences in performance factors among runs of a model with inherently high uncertainty have little meaning, and these differences do not diminish the strength of the conclusion that the RTS‐free negative control results undermine the use of CMB to support the Mahler hypothesis. Van Metre and Mahler’s goal was to test the Mahler hypothesis; therefore, the statement that the all model runs are in general agreement in terms of relative importance of the sources is insufficient and, based on our application of CMB with the same source profile, incorrect. Van Metre and Mahler defend the decision to highlight only four of the 200 model runs by stating that all runs do not need to be discussed in detail. This misses our point in that, at a minimum, they should have included results that would allow readers to evaluate for themselves the hypothesis being tested. Although all 200 runs need not have been described, focusing on 4 variations of essentially the same model inputs—the 2 parking‐lot dusts with either 11 or 12 PAHs—whose results were consistent their hypothesis suggests that a goal of the article was to indict RTS. The methods applied by Van Metre and Mahler (2010) were derived from the article by Li et al. (2003), which presented the output from 9 models, showing a range of results. Given the expanded use of supplemental information sections since 2003,
Integr Environ Assess Manag 10, 2014—KT O'Reilly et al.
Van Metre and Mahler easily could have presented a more complete description of their CMB results. Although Van Metre and Mahler suggest there is a consensus on the role of RTS, most of the articles cited are their own (Mahler et al. 2005, 2012; Van Metre et al. 2009; Van Metre and Mahler 2010; Yang et al. 2010), their collaborators (Watts et al. 2010; Crane 2014), or reiterate the observation that there are chemical similarities between the PAH profile of pyrogenic sources and urban sediments (Selbig et al. 2013; Witter et al. 2014). Evaluation of their studies indicates a fatal flaw in the logic of each. As used in Mahler et al. (2005), diagnostic ratios are blunt tools for source identification, because the ratios of many potential sources overlap (O’Reilly et al. 2012), and similarity is not proof of causation. As noted in their letter, the claims made in Van Metre et al. (2009) are based in part on the then‐uncited anecdote about different patterns of sealer usage in the West compared to the central and eastern United States. Although this information is used to support the hypothesis, it ignores other regional differences that could influence background PAH profiles, such as the fact that the West has a petroleum‐powered economy, whereas coal has long been the dominant energy source in the Midwest and eastern United States. Yang et al. (2010) describes the use of petrography. Particles with elevated PAH concentrations were identified as coal tar pitch and were assumed by the authors to be associated with RTS. Only 3 lake sediment samples were analyzed, and in a finding inconsistent with the Mahler hypothesis, those samples had similar PAH concentrations, even though the presumed contributions of RTS were quite different (see Figure 3, Yang et al. 2010). The authors admit that predicting PAH concentrations based on amount of coal tar pitch results in large overestimations, but attempt to argue away this inconsistency. Our concerns with claims made by Crane (2014) and Witter et al. (2014) are described in O’Reilly (2014a, 2014b). Shifting claims between publications further weakens the arguments presented by Van Metre and Mahler. Van Metre et al. (2000) initially identified sources associated with automobile usages as primary contributors of PAHs in Lake Ballinger, near Seattle, Washington. Ballinger was used as an example of a low‐RTS Western lake (Van Metre et al. 2009), but a year later, Van Metre and Mahler (2010) claimed that RTS was a significant source to Lake Ballinger. Other work by Van Metre (Thapalia et al. 2010; Grey et al. 2013) suggests that a smelter is responsible for some contaminants found in Lake Ballinger’s sediment. Although these articles focused on metals, not PAHs, another study indicated a link between metals and PAHs in sediments affected by the same smelter (Louchouarn et al. 2012). These inconsistencies were not brought to light, even as claims about Lake Ballinger (Van Metre and Mahler 2010) became the catalyst for enactment of a statewide ban of RTS. We appreciate that the editors of Integrated Environmental Assessment and Management (IEAM) have made space available for this technical discussion concerning the issues raised in our article. The concept of postpublication peer review has sparked growing interest in a number of technical fields (Hunter 2012; Teixeira da Silva 2013). Such review can be especially useful in environmental science because of its interdisciplinary nature, the difficulty of conducting well‐controlled studies in the field, the potential for personal bias, and the inherent uncertainty of natural systems. The interdisciplinary issue can be of particular
Response of O'Reilly et al. to Van Metre and Mahler's—Integr Environ Assess Manag 10, 2014
concern as powerful computer tools become more user‐ friendly. Now that forensic models, such as CMB and PCA, have achieved “plug‐and‐play” accessibility for use by a wider spectrum of scientists, it is even more important that published results are carefully vetted so mistakes introduced by 1 research group are not propagated through the science by others. In O’Reilly et al. (2014), we discussed the challenges of parsing out individual contributors of pyrogenic PAHs in urban background. We described the state of the practice for using receptor models and other forensic methods to assess PAH sources in individual watersheds. The case study was presented as an example of what we consider an application that pushed these methods beyond their capabilities, in an effort to prove the contribution of a particular source across a range of sites. Our article highlights concerns with how the model inputs were selected and what we believe was an incomplete presentation of the results. The comments in Van Metre and Mahler’s letter do not change our conclusion.
REFERENCES Crane JL. 2014. Source apportionment and distribution of polycyclic aromatic hydrocarbons, risk considerations, and management implications for urban stormwater pond sediments in Minnesota, USA. Arch Environ Contam Toxicol 66:176–200. Gleser LJ. 1997. Some thoughts on chemical mass balance models. Chemometr Intell Lab 37:15–22. Grey JE, Pribil MJ, Van Metre PC, Borrok DM, Thapalia A. 2013. Identification of contamination in a lake sediment core using Hg and Pb isotopic compositions, Lake Ballinger, Washington, USA. Appl Geochem 29:1–12. Hubbard P, Draper CN. 1911. Naphthalene in road tar. Circular No. 96. Department of Agriculture, Office of Public Roads, Washington DC. Hunter J. 2012. Post‐publication peer review: Opening up scientific conversation. Front Computat Neurosci 6:63. Johnson GW, Ehrlich R, Full W. 2004. Principal components analysis and receptor models in environmental forensics. In: Murphy BL, Morrison RD, editors. Introduction to environmental forensics. Burlington (MA): Elsevier Academic Press. 461 p. Li A, Jan JK, Scheff PA. 2003. Application of EPA CMB8.2 Model for Source Apportionment of Sediment PAHs in Lake Calumet, Chicago. Environ Sci Technol 37:2958–2965. Louchouarn P, Kuo L, Brandenberger JM, Marcantonio F, Garland C, Gill GA, Cullinan V. 2012. Pyrogenic inputs of anthropogenic Pb and Hg to sediments of the Hood Canal, Washington, in the 20th century: Source evidence from stable Pb isotopes and PAH signatures. Environ Sci Technol 46:5772–5781. Mahler BJ, Van Metre PC, Bashara TJ, Wilson JT, Johns DA. 2005. Parking lot sealcoat: An unrecognized source of urban polycyclic aromatic hydrocarbons. Environ Sci Technol 39:5560–5566.
491
Mahler BJ, Van Metre PC, Wilson JT, Musgrove M, Burtank TL, Ennis TE, Bashara TJ. 2010. Coal‐tar‐based parking lot sealcoat: An unrecognized source of PAH to settled house dust. Environ Sci Technol 44:894–900. Mahler BJ, Van Metre PC, Crane JL, Watts AW, Scoggins M, Williams ES. 2012. Coal‐ tar‐based pavement sealcoat and PAHs: Implications for the environment, human health, and stormwater management. Environ Sci Technol 56:3039– 3045. O'Reilly KT. 2014a Article title misstates the role of pavement sealers. Environ Pollut 191:260–261. O'Reilly KT. 2014b Response to authors' reply on “Coal‐tar‐based sealcoated pavement: A major PAH source to urban stream sediments”. Environ Pollut 191:264–265. O'Reilly KT, Pietari J, Boehm P. 2012. A forensic assessment of coal tar sealants as a source of polycyclic aromatic hydrocarbons in urban sediments. Environ Forensics 13:185–196. O'Reilly KT, Pietari J, Boehm PD. 2014. Parsing pyrogenic polycyclic aromatic hydrocarbons: Forensic chemistry, receptor models, and source control policy. Integr Environ Assess Manag 10:279–285. Reinke G, Glidden S. 2007. Case study of worker exposure to coal tar containing paving materials on a routine paving project in Iowa. J Occup Environ Hyg 4(S1):228–232. Selbig WR, Bannerman R, Corsi SR. 2013. From streets to streams: Assessing the toxicity potential of urban sediment by particle size. Sci Total Environ 444:381– 391. Teixeira da Silva JA. 2013. The need for post‐publication peer review in plant science publishing. Front Plant Sci 4:485. Thapalia A, Borrok DM, Van Metre PC, Musgrove M, Landa ER. 2010. Zn and Cu isotopes as tracers of anthropogenic contamination in a sediment core from an urban lake. Environ Sci Technol 44:1544–1550. Van Metre PC, Mahler BJ, Furlong ET. 2000. Urban sprawl leaves its PAH signature. Environ Sci Technol 34:4064–4070. Van Metre PC, Mahler BJ. 2003. The contribution of particles washed from rooftops to contaminant loading to urban streams. Chemosphere 52:1727– 1741. Van Metre PC, Mahler BJ. 2010. Contribution of PAHs from coal‐tar pavement sealcoat and other sources to 40 U.S. lakes. Sci Total Environ 409:334– 344. Van Metre PC, Mahler BJ, Wilson JT. 2009. PAHs underfoot: Contaminated dust from coal‐tar sealcoated pavement is widespread in the United States. Environ Sci Technol 43:20–25. Watts AW, Ballestero TP, Roseen RM, Houle JP. 2010. Polycyclic aromatic hydrocarbons in stormwater runoff from sealcoated pavements. Environ Sci Technol 44:8849–8854. Witter A, Nguyen MH, Baidar S, Sak PB. 2014. Coal‐tar‐based sealcoated pavement: A major PAH source to urban stream sediments. Environ Pollut 185:59–68. Yang Y, Van Metre PC, Mahler BJ, Wilson JT, Ligouis B, Razzaque M, Schaeffer C, Werth C. 2010. The influence of coal‐tar sealcoat and other carbonaceous materials on polycyclic aromatic hydrocarbon loading in an urban watershed. Environ Sci Technol 44:1217–1233.
