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Three methods for quantifying proximity of air sampling sites to spatially resolved emissions of semi-volatile organic contaminants
Atmospheric Environment 44 (2010) 4380e4387
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Three methods for quantifying proximity of air sampling sites to spatially resolved emissions of semi-volatile organic contaminants J.N. Westgate a, b, C. Shunthirasingham a, b, C.E. Oyiliagu a, b, H. von Waldow c, F. Wania a, b, * a
Department of Chemistry, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 c Institute for Chemical and Bioengineering, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093 Zürich, Switzerland b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 January 2010 Received in revised form 23 July 2010 Accepted 26 July 2010
Passive air samplers have made it possible to measure long-term average air concentrations of semivolatile organic contaminants (SVOCs) at a large number of sampling sites. In order to use the results of such measurement networks in the derivation of empirical measures of long-range transport, a method is required that quantitatively expresses the proximity of air sampling sites to spatially distributed emissions. We propose three increasingly sophisticated tiers for quantifying proximity to emissions. The ‘static’ method assumes that a sampling site is only influenced by emission taking place in the same 1 of latitude by 1 of longitude cell in which it is located. The ‘dispersion’ method additionally accounts for the influence of emissions in neighboring cells by adding the emissions into each cell weighted by the distance between the cell’s center and the center of the cell containing the sampling site. The ‘air-shed’ method quantifies proximity to emissions by combining the emissions in each cell with the probability that air arriving at the sampling site passed through each cell. The probability is calculated for each sampling site by aggregating a large number of air mass back-trajectories. These new proximity gauges were contrasted against the remoteness index RI, which is derived from global atmospheric tracer transport modeling. The four methods were used to quantify the proximity of the sampling sites of the Global Atmospheric Passive Sampling (GAPS) study to global Polycyclic Aromatic Hydrocarbon (PAH) emissions. The proximity gauges produce markedly different results primarily for sites located near steep gradients in population, such as occur in coastal areas or at the feet of mountain ranges. The dispersion method produces quite similar results to the air-shed method using drastically less computational power and input data, but application of the air-shed method may be necessary where winds are strongly directional. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Semi-volatile organic contaminant Passive air sampling Proximity gauge Air-shed Remoteness index
1. Introduction Semi-volatile Organic Contaminants (SVOCs) are able to undergo transport via the atmosphere and are often present in the air at sites distant from their sources. Most of the chemicals classified as Persistent Organic Pollutants (POPs) are SVOCs and Article 16 of the Stockholm Convention on POPs stipulates that efforts be made to monitor the efficacy of the measures taken to reduce the amounts of harmful organic chemicals in air (United Nations Environment Programme, 1998). While Polycyclic Aromatic Hydrocarbons (PAHs) are not part of the Stockholm Convention, they are listed in the Long-Range Transboundary Air Pollution (LRTAP) Aarhus Protocol on POPs (United Nations Economic * Corresponding author. Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4. E-mail address: [email protected] (F. Wania). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.07.051
Commission for Europe, 1998). While measuring the global air concentrations of SVOCs is a daunting task, some of the logistical and financial hurdles encountered using electrically powered samplers can be mitigated by the employment of Passive Air Samplers (PASs) (Lohmann et al., 2001). PASs are relatively low cost, simple devices that require no electrical power and can be deployed with little technical expertise in nearly any environment on Earth’s surface, including remote areas which may be difficult and expensive to access (Pozo et al., 2006). They consist of a sorbent material that is physically protected by a housing that allows SVOCs to diffuse to the sorbent. Poly-Urethane Foam (PUF) plug samplers can gather measureable quantities of SVOCs from the atmosphere with little or no breakthrough when deployed from a few weeks to months at a time (Shoeib and Harner, 2002), while divinylbenzene-styrene copolymer (XAD-2) resin based samplers can be deployed for periods from a few months to more than a year (Wania et al., 2003). These samplers provide average air concentrations integrated over the
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deployment time of the sampler. The low cost and relatively long deployment time make it possible to install large numbers of PASs over a wide area to gather a global picture of the long-term, average air concentrations of gas-phase POPs and other SVOCs (e.g. Pozo et al., 2006, 2009; Klanova et al., 2007; Liu et al., 2008). Air concentrations of SVOCs measured by PASs may be used to garner some information about the atmospheric transport behavior of these chemicals by comparing the proximity of the samplers to areas of emissions. However, spatially resolved estimates of emissions of SVOCs are required to make such comparisons. Several workers have produced spatially resolved global estimates of emissions of SVOCs. Li et al. (2000, 2003) have produced a global emission database for a- and b-hexachlorocyclohexanes (HCHs) on a 1 of latitude by 1 of longitude grid for 1980, 1990, and 2000. Breivik et al. (2002a,b, 2007) recently updated a similar data set for polychlorinated biphenyls (PCBs) that includes predictions of future emissions as well as historical ones. While no such data set for PAHs was located, Zhang and Tao (2009) have published emissions estimates of 16 PAHs by country for 2004. Those data were combined with population density information from United Nations Environmental Program Global Resource Information Database (UNEP/GRID) (Li, 2003) to yield 1 1 resolved estimates of PAH emissions. Three methods of increasing sophistication were developed to empirically quantify the proximity of air sampling sites to these gridded emissions estimates as a cell specific value called a pertingency index (PI). The ‘static’ gauge assumes that the effect on a cell is from emissions into that cell only. The ‘dispersion’ gauge includes emissions from neighboring cells that are weighted using the distances between cell centers. The ‘air-shed’ gauge includes emissions into all cells weighted by the recent spatial history of air masses that arrive in each cell. Differences between PIs calculated by the three methods suggest differing degrees of the influence of transport paths on air concentrations. Regressions of calculated PIs against measured air concentrations from PASs ought to yield clues about the atmospheric transport behavior of individual SVOCs, and can potentially be used to estimate PIs for contaminant species with similar physicochemical properties to those measured. 2. Methods 2.1. The ‘static’ method The static method of deriving a PI value from gridded emissions of SVOCs assumes that a cell is influenced only by emissions into that 1 1 cell. That is:
Eja PIS;ja ¼ P Ea
(1)
where PIS,ja is the PI of cell j to sources of chemical a from the static proximity gauge, Eja is the estimated emission of chemical a into cell j and SEa is the sum of emissions of chemical a in all cells. PIS,ja is thus the fraction of the total globally emitted amount of chemical a that is emitted to cell j. 2.2. The ‘dispersion’ method
PID;ja
Pn E W ¼ Pi ¼ 1 Pia i Ea W
Eia is the emission of chemical a into cell i, and Wi is a weighting factor of the impact that cell i has on cell j. As the denominator is the product of the sums of all emissions and all weights globally PID,ja is normalized and dimensionless. The weight is described by:
f ; isj 2 Wi ¼ di;j 1; i ¼ j
(2)
where PID,ja is the PI of cell j to sources of chemical a from the dispersion proximity gauge, n is the number of cells with emissions,
(3)
where di,j is the distance between the centers of cells i and j, and f is a scaling factor that, in part, accounts for the choice of distance units but can also serve to describe a contaminant’s travel distance. The square of the distance between cell centers was chosen as this corresponds to the change in area with the radius of a circle. For this work two values were used for f: ‘short-dispersion’ used a value of 111, the approximate distance between the centers of cells that share a side at Earth’s equator in kilometers; ‘long-dispersion’ used a value of 4107, or one third of the square of 111. 2.3. The ‘air-shed’ method The air-shed proximity gauge calculates PIs by taking both the gridded estimation of emission and an approximate spatial history of the air mass being sampled into account. This is done by amassing a set of air mass back-trajectories for the location of the sampler. The back-trajectories used were generated by the Air Quality Models and Applications section of the Weather and Environmental Prediction and Services Branch of Environment Canada using the Trajectory Model and the output of the Global Environmental Multi-scale Model (GEM) in diagnostic mode (D’Amours and Pagé, 2001). A back-trajectory is composed of a series of points above Earth’s surface representing the locations of an air mass at regular intervals backward in time; for an air mass which arrives at a location at time t these points are Lagrangian approximations of the positions of that air mass at t h, t 2h, t 3h etc. to t nh, where h is some time constant and nh is the time at which the uncertainty in the approximation becomes unacceptably large. For this work h is 6 h e the time between model output data in diagnostic mode e and nh is 120 h, or 5 days. To construct an air-shed for a PAS, back-trajectories specific to the surface location of the sampler were calculated for every six hours of the deployment of the sampler for air arriving at three different altitudes: 50 m, 100 m and 200 m. These were then ‘binned’ together by rounding them to the nearest cell center. Each cell was then assigned a value corresponding to the number of points that fell within it. After normalizing to the total number of points that value can be considered as the probability that a given air mass arriving at the sampling site passed through that cell; conversely it can be considered as the contribution that a given cell makes to the cell containing the sampler. The air-shed can then be overlaid upon the emission estimate to give a measure of the relation between the distances from sources to the sampler weighted by the approximate history of the air masses being sampled to give:
PIA;ja ¼
The dispersion proximity gauge accounts for the effect that neighboring cells have on each other. It is described by:
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Pn iP ¼ 0 Eia Di Ea
(4)
where PIA,ja is the PI of cell j to sources of chemical a by the air-shed method, Ei is the estimated emission of chemical a in cell i and Di is the probability density value for cell i from the air-shed map for cell j. This value is normalized to the total emission of chemical a, as D is already normalized. For the purpose of correlating PIA,ja to measured levels of SVOCs the back-trajectories were generated for
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the actual deployment periods of the air samplers in the GAPS study, or the first 365 days thereof for samplers that were deployed for more than one year. As back-trajectory generation is computationally expensive and the air-shed method requires many of them, air-sheds were only generated for cells in which XAD-2 based PASs were deployed during the first two years of the GAPS study.
2.4. The ‘remoteness index’ RI In a recent paper von Waldow et al. (2010) describe a location specific parameter, the Remoteness Index (RI) that describes the remoteness of a location from a distributed source of SVOCs. To calculate RI, a global atmospheric circulation model with a spatial resolution of 4 of latitude by 5 of longitude is employed to calculate atmospheric steady-state concentrations for a set of hypothetical tracers that have atmospheric residence times, seff, ranging from 2 to 2000 days and are emitted according to a particular emission scenario. The resulting data set is used to fit a simple empirical equation that describes modeled air concentrations as a function of location and seff. That empirical equation contains one location dependent fitting parameter which serves as Remoteness Index, RI. A change of RI with factor a, ceteris paribus, corresponds to a change of the quantity ln(Cair/Cmax) with factor a, where Cmax is the steady-state concentration that would result from a sufficiently large atmospheric residence time to achieve a nearly homogeneous global distribution of air concentrations at steady-state. RIs of the 39 GAPS sites were calculated from the PAH emission estimates described in section 2.5. For the purpose of comparison, the negative Remoteness Index, NRI d RI, serves as a proximity gauge.
2.5. Gridded PAH emission data The three methods for deriving a relationship between PAS measurements of SVOCs and their proximities to sources are based on a division of Earth’s surface into 1 of latitude by 1 of longitude cells. As a first approximation of PAH emissions on this grid, population density of humans was used as a surrogate. The population data were taken from the UNEP/GRID Sioux Falls website (Li, 2003) and cells belonging to more than one nation were randomly assigned to one nation with the combined populations from the nations. Then a species-specific approximation of PAH emissions was made using estimates of national emissions of 4 PAHs e benz [a]anthracene (BaA), chrysene (CHR), fluoranthene (FLR) and pyrene (PYR) (Figure S1) e taken from Zhang and Tao (2009). These were combined with the population data set so that the national estimate was distributed on a per capita basis:
Ea;i ¼ Ea;N
Qi QN
(5)
where Ea,i is the emission of chemical a in cell i, Ea,N is the emission of chemical a in nation N, Qi is the population in cell i, and QN is the population of nation N.
2.6. GAPS measurements Atmospheric PAH concentrations were measured for two approximately one-year periods by XAD-2 PAS. The sampler locations are detailed in Table S1 and Figure S2. Descriptions of the sampler preparation, extraction and analysis, and quality control measures can be found in section 2 of the supplementary information (SI).
3. Results and discussion 3.1. Results using PAHs 3.1.1. Comparisons of the proximity gauges Fig. 1 depicts the global distribution of the PIS and PID,Short and of RI for FLR. The highest pertingency values fall in India, East Asia and Nigeria, reflecting not only relatively large national estimated emissions of FLR, but very high population densities as well. The simple static proximity gauge in Fig. 1a shows adjacent cells with very different values. Those cells over oceans and the Earth’s great deserts, and thus zero population, are assigned zero pertingency. The main action of the dispersion proximity gauge, which smoothes the values over space, is immediately apparent when Fig. 1a is compared to Fig. 1b. Whereas these two gauges give a set of values for all cells in a single calculation, the air-shed proximity gauge calculates a value only for a single cell (see Figures S3eS4). Thus, PIs by the air-shed method are only available for those cells in which XAD-2 PAS were deployed plus one additional cell for which an airshed was generated in previous work (Chen et al., 2008). In order to compare the PIs calculated using the three proximity gauges, the PIs for each cell and method were re-normalized to the sum of the PIs by that method for the cells for which PIA had been calculated. Figs. 2 and 3 compare these re-normalized PI values for FLR for the cells containing XAD-2 sites that were active during year 1 of GAPS. The three sites that lie furthest from the 1:1 lines in Fig. 2a and b are the sites at Huayna Potosi, Bolivia, Indaiatuba, Brazil and Toronto, Canada. Possible reasons for the differences suggest themselves upon examination of the locations of the sites. Maps depicting the model outputs can be found in Fig. 1 and Figures S3eS6 in the SI. The datum for site Huayna Potosi lies below and to the right of the 1:1 line in Fig. 2a, indicating that that PIS is calculated to be relatively higher than PID. The site is located on a mountain outside of La Paz, Bolivia, but sufficiently close that the sampling site and the city are in the same cell. The static proximity gauge, which calculates a PI based only on the emissions of FLR in the relatively densely populated cell in which the sampler was deployed gives a higher value than the dispersion method, which includes the effects on that cell by the less densely populated cells surrounding it; this demonstrates that although the dispersion model does not calculate dilution due to transport out of a cell per se, the presence of neighboring cells with lower emissions decreases the calculated PI relative to the whole set of cells. PIS is much lower for the Indaiatuba site than PID: the cell in which the site is located is in a sparsely populated mountainous region and is assigned a small portion of the Brazilian emissions of FLR. The cell immediately to the South contains Sao Paulo, the most populated city in the country, and the dispersion method captures that in its calculation, giving a higher PI to the cell with the sampler than the static method does. The Greater Toronto Area, in Canada, is the most populous area in that country, with approximately 17% of the nation’s people living there (Statistics Canada, 2007). PIS is thus relatively high for the cell with the sampler in Toronto. North of Toronto, the population density drops quickly and thus PID is relatively lower than PIS. Fig. 2b is quite similar to Fig. 2a, and similar arguments for the deviations from the 1:1 line can be made in both cases. The differences between the dispersion and air-shed proximity gauges are more clearly displayed in Fig. 3a. The PIs calculated by these two methods are generally quite similar but a few sites show clear differences. The air-shed maps for Huayna Potosi and Indaiatuba can be found in Figures S7 and S8 in the SI; the former shows that the air-shed for site Huayna Potosi is largely over the land, and that the air is more often from the land to the Northwest than the ocean to the Southwest, causing the air-shed proximity gauge to assign
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Fig. 1. Mollweide projection map representing PIs for fluoranthene (FLR) calculated by static proximity gauge method. Redder colours represent greater relative pertingency, whereas white background colour represents zero pertingency. Blue-and-yellow dots are sampling sites from year 1 of GAPS in which XAD-2 PASs were deployed (a). Map representing FLR PIs calculated by short-dispersion proximity gauge method; very small values were rounded to zero (b). Map of RI for FLR (c). [For interpretation of colours referred in this figure legend, the reader is referred to web version of the article.]
a greater PI than the simpler dispersion method that assigns equal weight to cells in all directions. The air-shed map for site Indaiatuba shows that the air above the ocean makes a greater contribution to this site than the air above the land, thus the assigned PIA is lower for this cell than the PID, for which contributions by air above land and above ocean are split nearly equally.
The dispersion and air-shed methods produce drastically different results for the cell containing Chengdu, Sichuan Province, China, where the air-shed is strongly directional owing to the extreme local topography (not shown). Chengdu is situated in a basin below the Tibet-Qinghai plateau which causes the wind to arrive primarily from the densely populated areas to the East, so PIA
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Fig. 2. Comparison of PIs, in arbitrary units, for fluoranthene by static and ‘short-dispersion’ (f ¼ 111) proximity gauges for all cells in which XAD-2 based PASs were deployed for first year of the GAPS study (a) a comparison of PIs from static and the air-shed methods (b). Black solid line represents a 1:1 agreement. Differences for sites named Huayna Potosi, Indaiatuba and Toronto are discussed in main text. Complete list of sites as well as their coordinates, appears in Table S1 in supplementary information.
is large. The dispersion method gives equal weight to the East and the sparsely populated plateau to the West, and thus PID is relatively lower. Fig. 3b shows a comparison of the PID,Short and PID,Long, with a finer scale than Figs. 2 or 3a for ease of comparison. Most sites fall quite close to the 1:1 line, indicating that f does not contribute strongly to the relative PI values, generally. Those sites below and to the right give lower PID,Long to sites whose relative PI is decreased by greater proportions of contributing cells being oceanic, such as the Huayna Potosi site, or having low population densities, such as Toronto. For the site in Manila, Philippines, the calculated PID,Short is lower than PID,Long; China is estimated to have high PAH emissions and the steeper drop in weight with distance for short-dispersion prevents China from having a strong influence, while long-dispersion allows it. For the site which is named Accra but is actually North of the densely populated coastal areas of West Africa, the relatively greater PID,Long is likely caused by increased weight assigned to cells in Nigeria, which has a high emission estimated for FLR. The seeming trend for the lower PI value sites for greater PID,Long than PID,Short values is an artifact of the normalization scheme: the large differences for sites Huayna Potosi and Toronto are balanced against all other sites’ differences in the opposite direction so the sums of PIs by both sets are unity.
Fig. 4 shows a comparison of the standardized NRI values with the PIA values (a) and with PID,Short values (b). Linear relationships were achieved by log-transforming the PIs, which exhibit a rightskewed distribution whereas the NRIs are symmetrically distributed. NRIs appear to be well correlated with PIs by these other two proximity gauges, but statistical comparisons were reserved for those gauges that produce values globally, and appear in the SI (Table S2). The locations characterized as closer to sources, that is, have higher NRI than PIA are the Alaskan sites Barrow and Dyea, and Accra in Ghana. The Australian sites Cape Grim and Darwin, as well as the South African sites De Aar and Kalahari are characterized to be more remote by their NRIs than by their PIAs. The deviations of the PIs from NRI exhibit a similar pattern for both the air-shed and the short-dispersion proximity gauges suggesting that wind patterns have a larger effect on NRIs than on PIA values. Figure S9 in the SI displays the differences between NRIs and the PIs in relation to the proximity to sources measured by the short-dispersion proximity gauge. There is a trend of increasing positive differences with a decreasing proximity, implying that for remote locations, the von Waldow et al. gauge assumes a larger contaminant influx over large distances than the air-shed gauge. To derive RIs, long-term means of global circulations patterns are considered that might differ considerably from the air-shed for
Fig. 3. Comparisons of PIs in arbitrary units calculated by dispersion and air-shed proximity gauges (a) and dispersion proximity gauge using weighting factor (f) of 111 (short) and 4107 (long) (b) for fluoranthene on all but two highest value cells in which samplers were deployed for first year of GAPS study. Sites named Accra, Huayna Potosi, Indaiatuba, Manila and Toronto are discussed in main text.
2
a
Cape_Grim
−2
−1
0
0
Tudor_Hill Dyea
−3
−3
Barrow −3
Darwin
−2
Dyea
Accra
Cape_Grim
−1
Darwin −1
b DeAar
Accra
ln(PID.Short)st
0
DeAar Kalahari
−2
ln(PIA)st
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1
2
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1
2
NRIst
Barrow −3
−2
−1
0
1
2
NRIst
Fig. 4. Comparison of standardized NRI with standardized logarithm of PIA (a) and with the standardized logarithm of PID,Short (b) at all year 2 XAD-2 GAPS sites. The proximity gauges are based on the fluoranthene emission scenario.
a particular year or from geographical distances from sources from which PID,Shorts are calculated. Thus for RIs the dominant direction of contaminant delivery to the Alaskan sites is from the West through the polar jet stream, which connects these sites to the high estimated emissions in Asia. In the years considered for the calculation of the PIAs however, the air-shed for Barrow extended predominantly eastward (Figure S10) and the Dyea air-shed indicates a dominance by local influences, perhaps due to the steep local terrain (Figure S11). The dearth of significant local sources but a possible significant influx over large distances explains the higher NRI compared to PIA in these cases. This highlights another difference between approaches, as the air-shed method uses only relatively surficial air masses e even at five days backwards in time the average endpoint height for air masses arriving at 200 m is around 1000 m e while RI is arrived at by considering the entire troposphere (von Waldow et al., 2010). The deviations of Cape Grim on the other hand likely stem from the air-shed that connects this site to the strong sources on the Australian mainland in the north (Figure S12), while the NRI is dominated by the southern hemisphere polar jet which brings clean air from the Indian Ocean and effectively decouples Tasmania from the Australian mainland. All three PI methods assigned a lower relative source proximity to Accra, Ghana than the corresponding NRI would suggest. In the long-term average of global circulation used for calculating NRIs, this site is dominated by the easterly Trade Winds. Thus, this gauge makes Accra not very remote from Nigeria, which is assigned a large NRI for FLR in the emission scenario. Neither the static nor dispersion proximity gauges include the Trade Winds and the airshed for Accra for that year lies primarily to the South, indicating the air arriving at Accra that year was chiefly of oceanic origin: all three methods assign low PIs for FLR. 3.1.2. Comparisons with measured data To determine how PIs and NRIs might be helpful in understanding atmospheric measurements of SVOCs, the logarithm of the air concentrations of the four PAHs measured by XAD-2 PAS in the first year of GAPS were regressed against corresponding values from the four proximity gauges. Non-detects were removed prior to regression. Fluoranthene was the compound with the fewest nondetects as well as the only compound that exhibited clearly significant correlations with the proximity gauges. The correlations with PYR were weak and no significant relationship between proximity gauges and BaA or CHR was detected (Tables S3eS5). Considerable difficulty stems from using measurements of PAHs, only four of which were quantified when sampled using XAD-2 and which have multiple point and diffuse sources: it can be problematic to locate a sampler such that it is not influenced by point
sources and thus accurately represents the average air concentration in a 10 000 km2 cell. This is clearly evident in the very large differences in measured PAH concentrations between the two sites in Delhi, India (Table S1). Possible insights into the transport behavior of PAHs are briefly discussed in S3.1.2 of the SI. The correlation of FLR concentrations with PIS values is weak, but highly significant for the other three gauges (Figure S13). The PID,Long, PIA and NRI values exhibit an equally good fit and the measurement sites form a very similar pattern in relationship to the regression lines, the exceptions noted in section 3.1.1 notwithstanding. This indicates that long-term global circulation patterns can often be used in lieu of back-trajectories to derive proximity gauges, even for substances with a relatively short residence time in air, like PAHs. The residuals of the regression can be used to evaluate for which sites the individual proximity gauges do not describe proximity to sources well. Figure S14 shows these residuals individually for the GAPS sites, grouped according to the proximity gauge method used. For instance, the static method contains the assumption that emissions, or lack thereof, in neighboring cells can be disregarded when assigning proximity to sources. When the PIS for FLR by this method are fit against measurements, the point for Toronto, Canada lies quite far from the curve, suggesting this assumption is not sound for this site. For Indaiatuba, Brazil, the static method appears to describe the proximity to sources of FLR well. The dispersion method, on the other hand, which accounts for the proximity to neighboring cells and emissions therein, describes the proximity of Toronto to sources of FLR well, but the point for Indaiatuba lies further from the curve. A range of values for f from Eqn (3) were explored revealing that for values greater than w1000 very little change in the goodness of fit of PIDs to measured air concentrations of FLR is seen. This and a more detailed discussion of the relative strengths and weaknesses of the methods revealed by the comparisons to measurements appear in S3.1.2 of the SI. Production and use of polychlorinated biphenyls (PCBs) are prohibited by international agreement, and thus, unlike PAHs, generally have only a few diffuse sources with little short-scale variation in air concentrations. Figures S15 and S16 display regressions of PIS and PID for six individual PCB congeners, using the mid-level estimate from Breivik et al. (2007), against the mean atmospheric concentrations for 2004e2008 derived from active air measurements taken in the European Monitoring and Evaluation Program (EMEP) (Hjellbrekke, 2009) for the 8 stations for which data were available (Table S6). The air-shed method is not strictly applicable to these measurements (samples are taken for 24e48 h per week) and was not included; for a time series of measurements statistical and probabilistic methods may instead be applied to
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back-trajectories (e.g. Stohl, 1996; Hoh and Hites, 2004; Hafner et al., 2007; Wang et al., 2010). Using r2 as an indicator of correlation, the dispersion method is a distinct improvement over the static method for PCB-28 and PCB-52, but the static method is a sufficient, or perhaps even a superior, gauge of proximity to sources for PCB153 and PCB180. Von Waldow et al. (2010) regressed the same EMEP data against the RI calculated from the high-level emission scenario from Breivik et al. (2007), and found highly significant relationships for all congeners except PCB-28 and PCB52. The sampling methods employed by EMEP collect both gas and particle phase contaminants. It may then be surmised that these regressions indicate that for PCBs 28 and 52, it is important to account for emissions within neighboring cells because they have relatively large travel distances due to their higher vapour pressure, whereas PCBs 153 and 180, which are perhaps more particle bound, have travel distances much shorter than the grid cell size and local sources dominate air concentrations. The results of the PIto-measurement comparisons that were not conclusive are discussed in the SI.
for instance, a citizen living on a subsistence farm in a developing nation is assumed to produce the same amount and species of PAH emissions as a person living in a city of that same nation. Furthermore, in nations where biomass burning is the main source of PAH emissions those emissions are attributed to the populous cities, not the rural areas where such burning often occurs. For another example, a person living in the state of Alaska, USA, largely north of 60 latitude, is assumed to be responsible for the same emissions as a person in the state of Texas, USA, at 30 latitude, or even the tropical island state of Hawaii. This is likely a poor assumption and it must introduce some error. Of course, no correct inventories of PAH emissions are available, so the effect of these errors on the interpretation of the results can only be speculated. Globally there are likely a few areas with very high or very low emissions of PAHs, and many areas of intermediate emissions which would lead to a similar spatial variation of emissions as those used, even if they are not in the correct locations. As the proximity gauges all are calculated with the same emission scenario, similar differences between them would still appear if the actual emissions were available and used as input.
3.2. Possible sources of error/areas for improvement 3.2.1. The pole problem All of the methods of assessing PI rely on a grid of cells of 1 of latitude by 1 of longitude. It is important to note that these cells decrease in area from the equator to the poles, and thus emissions in a cell cannot strictly be considered per unit area or volume when comparing cells; oceanographers and meteorologists refer to this as the Pole Problem (e.g. Williamson, 2007). However, the vast majority of people e and thus the chemicals they produce and use e resides at latitudes between 50 degrees North and South. The grid cell area of the 1 1 grid differs maximally by 35% in that region, so that the error introduced is negligible for this work. 3.2.2. The use of an arbitrary grid The 1 1 grid provides a useful framework for performing calculations, but national boundaries do not generally follow these arbitrary lines. About 9% of cells on land contain borders, so the population estimate from UNEP/GRID contains many cells that are assigned to multiple nations, with separate data for each nation, which is not suitable for the applications in this work. The populations for such cells were summed and the cell randomly assigned to one of the nations sharing the cell. For the estimation of emission of a PAH in that cell, that nation’s estimated per capita emission was multiplied by the summed population; however, that nation’s population e and thus the per capita emission e was not adjusted by the number of additional humans from assigning the cell to that nation. Conveniently, adjacent countries likely produce similar amounts and species of PAHs on a per capita basis, and have similar population densities. These last two assumptions may be poor for some parts of Africa where the quality of life can vary widely from nation to nation, and even within nations, on a small spatial scale. Additionally, the chance position of the divisions between cells can place a cell outside of its primary source of influence; for instance, if the sampler at Delhi C was about 9 kilometers (km) west of its real location e or if the observatory at Greenwich, England were 9 km east of its real location e it would cross a line of longitude and the PI as calculated by the static proximity gauge would be an order of magnitude lower. 3.2.3. The use of a “first approximation” emission scenario It is assumed in the input to these proximity gauges that every citizen of a nation contributes the same fraction of the national PAH emission. Some error must be introduced in this approximation, as,
3.2.4. The use of air-sheds When employing air-sheds for understanding contaminant movement three important caveats should be considered: first, unlike a watershed, an air-shed has no equivalent air-shed divide; second, unlike watersheds, air-sheds can change on a very short time-scale; third, some error will be introduced by h ¼ 6 h, since an air mass may pass through a cell but not be counted because it is not present in the cell at the 6 h endpoint. The third point is important near the poles, where the cells are quite narrow, and for faster moving winds at high altitudes, but may also lead to the underestimation of the contribution to PI made by emissions into the cell in which the sampler was deployed, especially if the sampler is near the edge of the cell. The bias effect of the small polar cells is likely alleviated somewhat by the lack of sources of contaminants at high latitudes. The reliability of the air-shed is also reduced by the assumption that the air mass retains its composition as it travels, which is a supposition of the Trajectory Model that generates the back-trajectories from which the air-shed is composed (D’Amours and Pagé, 2001). Further difficulties arise from balancing the use of realistic arrival heights for the backtrajectories e the samplers were only w2 m above the surface e against the errors introduced when the Trajectory Model calculates that trajectories intersect the ground. Also, air mass location error increases with back-trajectory length, These limitations are important when considering a small number of back-trajectories, such as when trying to elucidate a specific source point, but if it is assumed that these errors are approximately random, their importance is reduced by integrating thousands of trajectories into an air-shed in which individual trajectory endpoints have very little effect on the final PI. 4. Conclusion Three methods for quantifying distances to multiple sources of SVOCs as pertingency indices were examined: a simple ‘static’ gauge, and the increasingly sophisticated ‘dispersion’ and ‘air-shed’ gauges. The three methods gave largely similar PIs to 1 of latitude by 1 of longitude cells when the PIs were normalized for comparison. PIA and PID were also quite similar to the negative remoteness index NRI, calculated from an atmospheric circulation model. Where steep gradients in the emission estimate existed between neighboring cells, the simplest method produced markedly different e and likely less sensible e values of PI from the more sophisticated methods. All of the methods are however subject to
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any errors contained in the initial emission estimate or the arbitrary location of cell divisions. Provided a suitably large (>1000) value is used for f, the dispersion proximity gauge provides a good balance of realism and ease of computation, as PIs by this method can be determined for the whole globe in a matter of hours using a personal computer. In areas within a few hundred kilometers of strong sources and where winds are strongly directional, the application of the back-trajectory based air-shed method appears to become necessary. In comparison with the previously established remoteness index RI, the air-shed method yields more realistic results for conditions that differ from the long-term average of the wind fields, and is less influenced by long-range transport patterns. Acknowledgements The authors wish to thank the Air Quality Models and Applications section of the Weather and Environmental Prediction Services branch of Environment Canada for generating the backtrajectories, Sum Chi Lee, Karla Pozo and Tom Harner for logistical handling of the GAPS samplers, and the Natural Science and Engineering Research Council of Canada for funding. Appendix. Supplementary information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.atmosenv.2010.07.051. References Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2002a. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach 1. Global production and consumption. Science of the Total Environment 290, 181e198. Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2002b. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach 2. Emissions. Science of the Total Environment 290, 199e224. Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2007. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach-3. An update. Science of the Total Environment 377, 296e307. Chen, D.Z., Liu, W.J., Liu, X.D., Westgate, J.N., Wania, F., 2008. Cold-trapping of persistent organic pollutants in the mountain soils of Western Sichuan, China. Environmental Science & Technology 42, 9086e9091. D’Amours, R., Pagé, P., 2001. Atmospheric transport models for environmental emergencies. Available from: http://collaboration.cmc.ec.gc.ca/cmc/cmoi/ product_guide/docs/lib/model-eco_urgences_e.pdf (accessed 23.07.10). Hafner, W.D., Solorzano, N.N., Jaffe, D.A., 2007. Analysis of rainfall and fine aerosol data using clustered trajectory analysis for National Park sites in the Western US. Atmospheric Environment 41, 3071e3081. Hjellbrekke, A., 2009. EMEP measurement data online. http://tarantula.nilu.no/ projects/ccc/emepdata.html. Hoh, E., Hites, R.A., 2004. Sources of toxaphene and other organochlorine pesticides in North America as determined by air measurements and potential source
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contribution function analyses. Environmental Science & Technology 38, 4187e4194. Klanova, J., Kohoutek, J., Cupr, P., Holoubek, I., 2007. Are the residents of former Yugoslavia still exposed to elevated PCB levels due to the Balkan wars? Part 2: passive air sampling network. Environment International 33, 727e735. Li, Y.-F., 2003. Global Population Distribution Database. Sioux Falls. http://na.unep. net/. Li, Y.F., Scholtz, M.T., van Heyst, B.J., 2000. Global gridded emission inventories of alpha-hexachlorocyclohexane. Journal of Geophysical Research-Atmospheres 105, 6621e6632. Li, Y.F., Scholtz, M.T., van Heyst, B.J., 2003. Global gridded emission inventories of b-hexachlorocyclohexane. Environmental Science & Technology 37, 3493e 3498. Liu, S.Z., Tao, S., Liu, W.X., Dou, H., Liu, Y.N., Zhao, J.Y., Little, M.G., Tian, Z.F., Wang, J.F., Wang, L.G., Gao, Y., 2008. Seasonal and spatial occurrence and distribution of atmospheric polycyclic aromatic hydrocarbons (PAHs) in rural and urban areas of the North Chinese Plain. Environmental Pollution 156, 651e656. Lohmann, R., Corrigan, B.P., Howsam, M., Jones, K.C., Ockenden, W.A., 2001. Further developments in the use of semipermeable membrane devices (SPMDs) as passive air samplers for persistent organic pollutants: field application in a spatial survey of PCDD/Fs and PAHs. Environmental Science & Technology 35, 2576e2582. Pozo, K., Harner, T., Lee, S.C., Wania, F., Muir, D.C.G., Jones, K.C., 2009. Seasonally resolved concentrations of persistent organic pollutants in the global atmosphere from the first year of the GAPS study. Environmental Science & Technology 43, 796e803. Pozo, K., Harner, T., Wania, F., Muir, D.C.G., Jones, K.C., Barrie, L.A., 2006. Toward a global network for persistent organic pollutants in air: results from the GAPS study. Environmental Science & Technology 40, 4867e4873. Shoeib, M., Harner, T., 2002. Characterization and comparison of three passive air samplers for persistent organic pollutants. Environmental Science & Technology 36, 4142e4151. Statistics Canada, 2007. Population dwelling counts, for census metropolitan areas, 2006 and 2001 censuses. http://www.statcan.gc.ca/. Stohl, A., 1996. Trajectory statistics e a new method to establish source-receptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe. Atmospheric Environment 30, 579e587. United Nations Economic Commission for Europe, 1998. UN ECE Protocol on Persistent Organic Pollutants under the 1979 Convention on Long-Range Transboundary Air Pollution. United Nations Environment Programme, 1998. UNEP Preparation of an International Legally Binding Instrument Implementing International Action on Certain Persistent Organic Pollutants. von Waldow, H., MacLeod, M., Scheringer, M., Hungerbühler, K., 2010. Quantifying remoteness from emission sources of persistent organic pollutants on a global scale. Environmental Science & Technology 44, 2791e2796. Wang, R., Tao, S., Wang, B., Yang, B., Lang, C., Zhang, Y., Hu, J., Ma, J., Hung, H., 2010. Sources and pathways of polycyclic aromatic hydrocarbons transported to Alert, the Canadian High Arctic. Environmental Science & Technology 44, 1017e1022. Wania, F., Shen, L., Lei, Y.D., Teixeira, C., Muir, D.C.G., 2003. Development and calibration of a resin-based passive sampling system for monitoring persistent organic pollutants in the atmosphere. Environmental Science & Technology 37, 1352e1359. Williamson, D.L., 2007. The evolution of dynamical cores for global atmospheric models. Journal of the Meteorological Society of Japan 85B, 241e269. Zhang, Y.X., Tao, S., 2009. Global atmospheric emission inventory of polycyclic aromatic hydrocarbons (PAHs) for 2004. Atmospheric Environment 43, 812e819.
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Three methods for quantifying proximity of air sampling sites to spatially resolved emissions of semi-volatile organic contaminants J.N. Westgate a, b, C. Shunthirasingham a, b, C.E. Oyiliagu a, b, H. von Waldow c, F. Wania a, b, * a
Department of Chemistry, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 c Institute for Chemical and Bioengineering, ETH Zürich, Wolfgang-Pauli-Strasse 10, 8093 Zürich, Switzerland b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 January 2010 Received in revised form 23 July 2010 Accepted 26 July 2010
Passive air samplers have made it possible to measure long-term average air concentrations of semivolatile organic contaminants (SVOCs) at a large number of sampling sites. In order to use the results of such measurement networks in the derivation of empirical measures of long-range transport, a method is required that quantitatively expresses the proximity of air sampling sites to spatially distributed emissions. We propose three increasingly sophisticated tiers for quantifying proximity to emissions. The ‘static’ method assumes that a sampling site is only influenced by emission taking place in the same 1 of latitude by 1 of longitude cell in which it is located. The ‘dispersion’ method additionally accounts for the influence of emissions in neighboring cells by adding the emissions into each cell weighted by the distance between the cell’s center and the center of the cell containing the sampling site. The ‘air-shed’ method quantifies proximity to emissions by combining the emissions in each cell with the probability that air arriving at the sampling site passed through each cell. The probability is calculated for each sampling site by aggregating a large number of air mass back-trajectories. These new proximity gauges were contrasted against the remoteness index RI, which is derived from global atmospheric tracer transport modeling. The four methods were used to quantify the proximity of the sampling sites of the Global Atmospheric Passive Sampling (GAPS) study to global Polycyclic Aromatic Hydrocarbon (PAH) emissions. The proximity gauges produce markedly different results primarily for sites located near steep gradients in population, such as occur in coastal areas or at the feet of mountain ranges. The dispersion method produces quite similar results to the air-shed method using drastically less computational power and input data, but application of the air-shed method may be necessary where winds are strongly directional. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Semi-volatile organic contaminant Passive air sampling Proximity gauge Air-shed Remoteness index
1. Introduction Semi-volatile Organic Contaminants (SVOCs) are able to undergo transport via the atmosphere and are often present in the air at sites distant from their sources. Most of the chemicals classified as Persistent Organic Pollutants (POPs) are SVOCs and Article 16 of the Stockholm Convention on POPs stipulates that efforts be made to monitor the efficacy of the measures taken to reduce the amounts of harmful organic chemicals in air (United Nations Environment Programme, 1998). While Polycyclic Aromatic Hydrocarbons (PAHs) are not part of the Stockholm Convention, they are listed in the Long-Range Transboundary Air Pollution (LRTAP) Aarhus Protocol on POPs (United Nations Economic * Corresponding author. Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4. E-mail address: [email protected] (F. Wania). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.07.051
Commission for Europe, 1998). While measuring the global air concentrations of SVOCs is a daunting task, some of the logistical and financial hurdles encountered using electrically powered samplers can be mitigated by the employment of Passive Air Samplers (PASs) (Lohmann et al., 2001). PASs are relatively low cost, simple devices that require no electrical power and can be deployed with little technical expertise in nearly any environment on Earth’s surface, including remote areas which may be difficult and expensive to access (Pozo et al., 2006). They consist of a sorbent material that is physically protected by a housing that allows SVOCs to diffuse to the sorbent. Poly-Urethane Foam (PUF) plug samplers can gather measureable quantities of SVOCs from the atmosphere with little or no breakthrough when deployed from a few weeks to months at a time (Shoeib and Harner, 2002), while divinylbenzene-styrene copolymer (XAD-2) resin based samplers can be deployed for periods from a few months to more than a year (Wania et al., 2003). These samplers provide average air concentrations integrated over the
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deployment time of the sampler. The low cost and relatively long deployment time make it possible to install large numbers of PASs over a wide area to gather a global picture of the long-term, average air concentrations of gas-phase POPs and other SVOCs (e.g. Pozo et al., 2006, 2009; Klanova et al., 2007; Liu et al., 2008). Air concentrations of SVOCs measured by PASs may be used to garner some information about the atmospheric transport behavior of these chemicals by comparing the proximity of the samplers to areas of emissions. However, spatially resolved estimates of emissions of SVOCs are required to make such comparisons. Several workers have produced spatially resolved global estimates of emissions of SVOCs. Li et al. (2000, 2003) have produced a global emission database for a- and b-hexachlorocyclohexanes (HCHs) on a 1 of latitude by 1 of longitude grid for 1980, 1990, and 2000. Breivik et al. (2002a,b, 2007) recently updated a similar data set for polychlorinated biphenyls (PCBs) that includes predictions of future emissions as well as historical ones. While no such data set for PAHs was located, Zhang and Tao (2009) have published emissions estimates of 16 PAHs by country for 2004. Those data were combined with population density information from United Nations Environmental Program Global Resource Information Database (UNEP/GRID) (Li, 2003) to yield 1 1 resolved estimates of PAH emissions. Three methods of increasing sophistication were developed to empirically quantify the proximity of air sampling sites to these gridded emissions estimates as a cell specific value called a pertingency index (PI). The ‘static’ gauge assumes that the effect on a cell is from emissions into that cell only. The ‘dispersion’ gauge includes emissions from neighboring cells that are weighted using the distances between cell centers. The ‘air-shed’ gauge includes emissions into all cells weighted by the recent spatial history of air masses that arrive in each cell. Differences between PIs calculated by the three methods suggest differing degrees of the influence of transport paths on air concentrations. Regressions of calculated PIs against measured air concentrations from PASs ought to yield clues about the atmospheric transport behavior of individual SVOCs, and can potentially be used to estimate PIs for contaminant species with similar physicochemical properties to those measured. 2. Methods 2.1. The ‘static’ method The static method of deriving a PI value from gridded emissions of SVOCs assumes that a cell is influenced only by emissions into that 1 1 cell. That is:
Eja PIS;ja ¼ P Ea
(1)
where PIS,ja is the PI of cell j to sources of chemical a from the static proximity gauge, Eja is the estimated emission of chemical a into cell j and SEa is the sum of emissions of chemical a in all cells. PIS,ja is thus the fraction of the total globally emitted amount of chemical a that is emitted to cell j. 2.2. The ‘dispersion’ method
PID;ja
Pn E W ¼ Pi ¼ 1 Pia i Ea W
Eia is the emission of chemical a into cell i, and Wi is a weighting factor of the impact that cell i has on cell j. As the denominator is the product of the sums of all emissions and all weights globally PID,ja is normalized and dimensionless. The weight is described by:
f ; isj 2 Wi ¼ di;j 1; i ¼ j
(2)
where PID,ja is the PI of cell j to sources of chemical a from the dispersion proximity gauge, n is the number of cells with emissions,
(3)
where di,j is the distance between the centers of cells i and j, and f is a scaling factor that, in part, accounts for the choice of distance units but can also serve to describe a contaminant’s travel distance. The square of the distance between cell centers was chosen as this corresponds to the change in area with the radius of a circle. For this work two values were used for f: ‘short-dispersion’ used a value of 111, the approximate distance between the centers of cells that share a side at Earth’s equator in kilometers; ‘long-dispersion’ used a value of 4107, or one third of the square of 111. 2.3. The ‘air-shed’ method The air-shed proximity gauge calculates PIs by taking both the gridded estimation of emission and an approximate spatial history of the air mass being sampled into account. This is done by amassing a set of air mass back-trajectories for the location of the sampler. The back-trajectories used were generated by the Air Quality Models and Applications section of the Weather and Environmental Prediction and Services Branch of Environment Canada using the Trajectory Model and the output of the Global Environmental Multi-scale Model (GEM) in diagnostic mode (D’Amours and Pagé, 2001). A back-trajectory is composed of a series of points above Earth’s surface representing the locations of an air mass at regular intervals backward in time; for an air mass which arrives at a location at time t these points are Lagrangian approximations of the positions of that air mass at t h, t 2h, t 3h etc. to t nh, where h is some time constant and nh is the time at which the uncertainty in the approximation becomes unacceptably large. For this work h is 6 h e the time between model output data in diagnostic mode e and nh is 120 h, or 5 days. To construct an air-shed for a PAS, back-trajectories specific to the surface location of the sampler were calculated for every six hours of the deployment of the sampler for air arriving at three different altitudes: 50 m, 100 m and 200 m. These were then ‘binned’ together by rounding them to the nearest cell center. Each cell was then assigned a value corresponding to the number of points that fell within it. After normalizing to the total number of points that value can be considered as the probability that a given air mass arriving at the sampling site passed through that cell; conversely it can be considered as the contribution that a given cell makes to the cell containing the sampler. The air-shed can then be overlaid upon the emission estimate to give a measure of the relation between the distances from sources to the sampler weighted by the approximate history of the air masses being sampled to give:
PIA;ja ¼
The dispersion proximity gauge accounts for the effect that neighboring cells have on each other. It is described by:
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Pn iP ¼ 0 Eia Di Ea
(4)
where PIA,ja is the PI of cell j to sources of chemical a by the air-shed method, Ei is the estimated emission of chemical a in cell i and Di is the probability density value for cell i from the air-shed map for cell j. This value is normalized to the total emission of chemical a, as D is already normalized. For the purpose of correlating PIA,ja to measured levels of SVOCs the back-trajectories were generated for
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the actual deployment periods of the air samplers in the GAPS study, or the first 365 days thereof for samplers that were deployed for more than one year. As back-trajectory generation is computationally expensive and the air-shed method requires many of them, air-sheds were only generated for cells in which XAD-2 based PASs were deployed during the first two years of the GAPS study.
2.4. The ‘remoteness index’ RI In a recent paper von Waldow et al. (2010) describe a location specific parameter, the Remoteness Index (RI) that describes the remoteness of a location from a distributed source of SVOCs. To calculate RI, a global atmospheric circulation model with a spatial resolution of 4 of latitude by 5 of longitude is employed to calculate atmospheric steady-state concentrations for a set of hypothetical tracers that have atmospheric residence times, seff, ranging from 2 to 2000 days and are emitted according to a particular emission scenario. The resulting data set is used to fit a simple empirical equation that describes modeled air concentrations as a function of location and seff. That empirical equation contains one location dependent fitting parameter which serves as Remoteness Index, RI. A change of RI with factor a, ceteris paribus, corresponds to a change of the quantity ln(Cair/Cmax) with factor a, where Cmax is the steady-state concentration that would result from a sufficiently large atmospheric residence time to achieve a nearly homogeneous global distribution of air concentrations at steady-state. RIs of the 39 GAPS sites were calculated from the PAH emission estimates described in section 2.5. For the purpose of comparison, the negative Remoteness Index, NRI d RI, serves as a proximity gauge.
2.5. Gridded PAH emission data The three methods for deriving a relationship between PAS measurements of SVOCs and their proximities to sources are based on a division of Earth’s surface into 1 of latitude by 1 of longitude cells. As a first approximation of PAH emissions on this grid, population density of humans was used as a surrogate. The population data were taken from the UNEP/GRID Sioux Falls website (Li, 2003) and cells belonging to more than one nation were randomly assigned to one nation with the combined populations from the nations. Then a species-specific approximation of PAH emissions was made using estimates of national emissions of 4 PAHs e benz [a]anthracene (BaA), chrysene (CHR), fluoranthene (FLR) and pyrene (PYR) (Figure S1) e taken from Zhang and Tao (2009). These were combined with the population data set so that the national estimate was distributed on a per capita basis:
Ea;i ¼ Ea;N
Qi QN
(5)
where Ea,i is the emission of chemical a in cell i, Ea,N is the emission of chemical a in nation N, Qi is the population in cell i, and QN is the population of nation N.
2.6. GAPS measurements Atmospheric PAH concentrations were measured for two approximately one-year periods by XAD-2 PAS. The sampler locations are detailed in Table S1 and Figure S2. Descriptions of the sampler preparation, extraction and analysis, and quality control measures can be found in section 2 of the supplementary information (SI).
3. Results and discussion 3.1. Results using PAHs 3.1.1. Comparisons of the proximity gauges Fig. 1 depicts the global distribution of the PIS and PID,Short and of RI for FLR. The highest pertingency values fall in India, East Asia and Nigeria, reflecting not only relatively large national estimated emissions of FLR, but very high population densities as well. The simple static proximity gauge in Fig. 1a shows adjacent cells with very different values. Those cells over oceans and the Earth’s great deserts, and thus zero population, are assigned zero pertingency. The main action of the dispersion proximity gauge, which smoothes the values over space, is immediately apparent when Fig. 1a is compared to Fig. 1b. Whereas these two gauges give a set of values for all cells in a single calculation, the air-shed proximity gauge calculates a value only for a single cell (see Figures S3eS4). Thus, PIs by the air-shed method are only available for those cells in which XAD-2 PAS were deployed plus one additional cell for which an airshed was generated in previous work (Chen et al., 2008). In order to compare the PIs calculated using the three proximity gauges, the PIs for each cell and method were re-normalized to the sum of the PIs by that method for the cells for which PIA had been calculated. Figs. 2 and 3 compare these re-normalized PI values for FLR for the cells containing XAD-2 sites that were active during year 1 of GAPS. The three sites that lie furthest from the 1:1 lines in Fig. 2a and b are the sites at Huayna Potosi, Bolivia, Indaiatuba, Brazil and Toronto, Canada. Possible reasons for the differences suggest themselves upon examination of the locations of the sites. Maps depicting the model outputs can be found in Fig. 1 and Figures S3eS6 in the SI. The datum for site Huayna Potosi lies below and to the right of the 1:1 line in Fig. 2a, indicating that that PIS is calculated to be relatively higher than PID. The site is located on a mountain outside of La Paz, Bolivia, but sufficiently close that the sampling site and the city are in the same cell. The static proximity gauge, which calculates a PI based only on the emissions of FLR in the relatively densely populated cell in which the sampler was deployed gives a higher value than the dispersion method, which includes the effects on that cell by the less densely populated cells surrounding it; this demonstrates that although the dispersion model does not calculate dilution due to transport out of a cell per se, the presence of neighboring cells with lower emissions decreases the calculated PI relative to the whole set of cells. PIS is much lower for the Indaiatuba site than PID: the cell in which the site is located is in a sparsely populated mountainous region and is assigned a small portion of the Brazilian emissions of FLR. The cell immediately to the South contains Sao Paulo, the most populated city in the country, and the dispersion method captures that in its calculation, giving a higher PI to the cell with the sampler than the static method does. The Greater Toronto Area, in Canada, is the most populous area in that country, with approximately 17% of the nation’s people living there (Statistics Canada, 2007). PIS is thus relatively high for the cell with the sampler in Toronto. North of Toronto, the population density drops quickly and thus PID is relatively lower than PIS. Fig. 2b is quite similar to Fig. 2a, and similar arguments for the deviations from the 1:1 line can be made in both cases. The differences between the dispersion and air-shed proximity gauges are more clearly displayed in Fig. 3a. The PIs calculated by these two methods are generally quite similar but a few sites show clear differences. The air-shed maps for Huayna Potosi and Indaiatuba can be found in Figures S7 and S8 in the SI; the former shows that the air-shed for site Huayna Potosi is largely over the land, and that the air is more often from the land to the Northwest than the ocean to the Southwest, causing the air-shed proximity gauge to assign
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Fig. 1. Mollweide projection map representing PIs for fluoranthene (FLR) calculated by static proximity gauge method. Redder colours represent greater relative pertingency, whereas white background colour represents zero pertingency. Blue-and-yellow dots are sampling sites from year 1 of GAPS in which XAD-2 PASs were deployed (a). Map representing FLR PIs calculated by short-dispersion proximity gauge method; very small values were rounded to zero (b). Map of RI for FLR (c). [For interpretation of colours referred in this figure legend, the reader is referred to web version of the article.]
a greater PI than the simpler dispersion method that assigns equal weight to cells in all directions. The air-shed map for site Indaiatuba shows that the air above the ocean makes a greater contribution to this site than the air above the land, thus the assigned PIA is lower for this cell than the PID, for which contributions by air above land and above ocean are split nearly equally.
The dispersion and air-shed methods produce drastically different results for the cell containing Chengdu, Sichuan Province, China, where the air-shed is strongly directional owing to the extreme local topography (not shown). Chengdu is situated in a basin below the Tibet-Qinghai plateau which causes the wind to arrive primarily from the densely populated areas to the East, so PIA
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Fig. 2. Comparison of PIs, in arbitrary units, for fluoranthene by static and ‘short-dispersion’ (f ¼ 111) proximity gauges for all cells in which XAD-2 based PASs were deployed for first year of the GAPS study (a) a comparison of PIs from static and the air-shed methods (b). Black solid line represents a 1:1 agreement. Differences for sites named Huayna Potosi, Indaiatuba and Toronto are discussed in main text. Complete list of sites as well as their coordinates, appears in Table S1 in supplementary information.
is large. The dispersion method gives equal weight to the East and the sparsely populated plateau to the West, and thus PID is relatively lower. Fig. 3b shows a comparison of the PID,Short and PID,Long, with a finer scale than Figs. 2 or 3a for ease of comparison. Most sites fall quite close to the 1:1 line, indicating that f does not contribute strongly to the relative PI values, generally. Those sites below and to the right give lower PID,Long to sites whose relative PI is decreased by greater proportions of contributing cells being oceanic, such as the Huayna Potosi site, or having low population densities, such as Toronto. For the site in Manila, Philippines, the calculated PID,Short is lower than PID,Long; China is estimated to have high PAH emissions and the steeper drop in weight with distance for short-dispersion prevents China from having a strong influence, while long-dispersion allows it. For the site which is named Accra but is actually North of the densely populated coastal areas of West Africa, the relatively greater PID,Long is likely caused by increased weight assigned to cells in Nigeria, which has a high emission estimated for FLR. The seeming trend for the lower PI value sites for greater PID,Long than PID,Short values is an artifact of the normalization scheme: the large differences for sites Huayna Potosi and Toronto are balanced against all other sites’ differences in the opposite direction so the sums of PIs by both sets are unity.
Fig. 4 shows a comparison of the standardized NRI values with the PIA values (a) and with PID,Short values (b). Linear relationships were achieved by log-transforming the PIs, which exhibit a rightskewed distribution whereas the NRIs are symmetrically distributed. NRIs appear to be well correlated with PIs by these other two proximity gauges, but statistical comparisons were reserved for those gauges that produce values globally, and appear in the SI (Table S2). The locations characterized as closer to sources, that is, have higher NRI than PIA are the Alaskan sites Barrow and Dyea, and Accra in Ghana. The Australian sites Cape Grim and Darwin, as well as the South African sites De Aar and Kalahari are characterized to be more remote by their NRIs than by their PIAs. The deviations of the PIs from NRI exhibit a similar pattern for both the air-shed and the short-dispersion proximity gauges suggesting that wind patterns have a larger effect on NRIs than on PIA values. Figure S9 in the SI displays the differences between NRIs and the PIs in relation to the proximity to sources measured by the short-dispersion proximity gauge. There is a trend of increasing positive differences with a decreasing proximity, implying that for remote locations, the von Waldow et al. gauge assumes a larger contaminant influx over large distances than the air-shed gauge. To derive RIs, long-term means of global circulations patterns are considered that might differ considerably from the air-shed for
Fig. 3. Comparisons of PIs in arbitrary units calculated by dispersion and air-shed proximity gauges (a) and dispersion proximity gauge using weighting factor (f) of 111 (short) and 4107 (long) (b) for fluoranthene on all but two highest value cells in which samplers were deployed for first year of GAPS study. Sites named Accra, Huayna Potosi, Indaiatuba, Manila and Toronto are discussed in main text.
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Fig. 4. Comparison of standardized NRI with standardized logarithm of PIA (a) and with the standardized logarithm of PID,Short (b) at all year 2 XAD-2 GAPS sites. The proximity gauges are based on the fluoranthene emission scenario.
a particular year or from geographical distances from sources from which PID,Shorts are calculated. Thus for RIs the dominant direction of contaminant delivery to the Alaskan sites is from the West through the polar jet stream, which connects these sites to the high estimated emissions in Asia. In the years considered for the calculation of the PIAs however, the air-shed for Barrow extended predominantly eastward (Figure S10) and the Dyea air-shed indicates a dominance by local influences, perhaps due to the steep local terrain (Figure S11). The dearth of significant local sources but a possible significant influx over large distances explains the higher NRI compared to PIA in these cases. This highlights another difference between approaches, as the air-shed method uses only relatively surficial air masses e even at five days backwards in time the average endpoint height for air masses arriving at 200 m is around 1000 m e while RI is arrived at by considering the entire troposphere (von Waldow et al., 2010). The deviations of Cape Grim on the other hand likely stem from the air-shed that connects this site to the strong sources on the Australian mainland in the north (Figure S12), while the NRI is dominated by the southern hemisphere polar jet which brings clean air from the Indian Ocean and effectively decouples Tasmania from the Australian mainland. All three PI methods assigned a lower relative source proximity to Accra, Ghana than the corresponding NRI would suggest. In the long-term average of global circulation used for calculating NRIs, this site is dominated by the easterly Trade Winds. Thus, this gauge makes Accra not very remote from Nigeria, which is assigned a large NRI for FLR in the emission scenario. Neither the static nor dispersion proximity gauges include the Trade Winds and the airshed for Accra for that year lies primarily to the South, indicating the air arriving at Accra that year was chiefly of oceanic origin: all three methods assign low PIs for FLR. 3.1.2. Comparisons with measured data To determine how PIs and NRIs might be helpful in understanding atmospheric measurements of SVOCs, the logarithm of the air concentrations of the four PAHs measured by XAD-2 PAS in the first year of GAPS were regressed against corresponding values from the four proximity gauges. Non-detects were removed prior to regression. Fluoranthene was the compound with the fewest nondetects as well as the only compound that exhibited clearly significant correlations with the proximity gauges. The correlations with PYR were weak and no significant relationship between proximity gauges and BaA or CHR was detected (Tables S3eS5). Considerable difficulty stems from using measurements of PAHs, only four of which were quantified when sampled using XAD-2 and which have multiple point and diffuse sources: it can be problematic to locate a sampler such that it is not influenced by point
sources and thus accurately represents the average air concentration in a 10 000 km2 cell. This is clearly evident in the very large differences in measured PAH concentrations between the two sites in Delhi, India (Table S1). Possible insights into the transport behavior of PAHs are briefly discussed in S3.1.2 of the SI. The correlation of FLR concentrations with PIS values is weak, but highly significant for the other three gauges (Figure S13). The PID,Long, PIA and NRI values exhibit an equally good fit and the measurement sites form a very similar pattern in relationship to the regression lines, the exceptions noted in section 3.1.1 notwithstanding. This indicates that long-term global circulation patterns can often be used in lieu of back-trajectories to derive proximity gauges, even for substances with a relatively short residence time in air, like PAHs. The residuals of the regression can be used to evaluate for which sites the individual proximity gauges do not describe proximity to sources well. Figure S14 shows these residuals individually for the GAPS sites, grouped according to the proximity gauge method used. For instance, the static method contains the assumption that emissions, or lack thereof, in neighboring cells can be disregarded when assigning proximity to sources. When the PIS for FLR by this method are fit against measurements, the point for Toronto, Canada lies quite far from the curve, suggesting this assumption is not sound for this site. For Indaiatuba, Brazil, the static method appears to describe the proximity to sources of FLR well. The dispersion method, on the other hand, which accounts for the proximity to neighboring cells and emissions therein, describes the proximity of Toronto to sources of FLR well, but the point for Indaiatuba lies further from the curve. A range of values for f from Eqn (3) were explored revealing that for values greater than w1000 very little change in the goodness of fit of PIDs to measured air concentrations of FLR is seen. This and a more detailed discussion of the relative strengths and weaknesses of the methods revealed by the comparisons to measurements appear in S3.1.2 of the SI. Production and use of polychlorinated biphenyls (PCBs) are prohibited by international agreement, and thus, unlike PAHs, generally have only a few diffuse sources with little short-scale variation in air concentrations. Figures S15 and S16 display regressions of PIS and PID for six individual PCB congeners, using the mid-level estimate from Breivik et al. (2007), against the mean atmospheric concentrations for 2004e2008 derived from active air measurements taken in the European Monitoring and Evaluation Program (EMEP) (Hjellbrekke, 2009) for the 8 stations for which data were available (Table S6). The air-shed method is not strictly applicable to these measurements (samples are taken for 24e48 h per week) and was not included; for a time series of measurements statistical and probabilistic methods may instead be applied to
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back-trajectories (e.g. Stohl, 1996; Hoh and Hites, 2004; Hafner et al., 2007; Wang et al., 2010). Using r2 as an indicator of correlation, the dispersion method is a distinct improvement over the static method for PCB-28 and PCB-52, but the static method is a sufficient, or perhaps even a superior, gauge of proximity to sources for PCB153 and PCB180. Von Waldow et al. (2010) regressed the same EMEP data against the RI calculated from the high-level emission scenario from Breivik et al. (2007), and found highly significant relationships for all congeners except PCB-28 and PCB52. The sampling methods employed by EMEP collect both gas and particle phase contaminants. It may then be surmised that these regressions indicate that for PCBs 28 and 52, it is important to account for emissions within neighboring cells because they have relatively large travel distances due to their higher vapour pressure, whereas PCBs 153 and 180, which are perhaps more particle bound, have travel distances much shorter than the grid cell size and local sources dominate air concentrations. The results of the PIto-measurement comparisons that were not conclusive are discussed in the SI.
for instance, a citizen living on a subsistence farm in a developing nation is assumed to produce the same amount and species of PAH emissions as a person living in a city of that same nation. Furthermore, in nations where biomass burning is the main source of PAH emissions those emissions are attributed to the populous cities, not the rural areas where such burning often occurs. For another example, a person living in the state of Alaska, USA, largely north of 60 latitude, is assumed to be responsible for the same emissions as a person in the state of Texas, USA, at 30 latitude, or even the tropical island state of Hawaii. This is likely a poor assumption and it must introduce some error. Of course, no correct inventories of PAH emissions are available, so the effect of these errors on the interpretation of the results can only be speculated. Globally there are likely a few areas with very high or very low emissions of PAHs, and many areas of intermediate emissions which would lead to a similar spatial variation of emissions as those used, even if they are not in the correct locations. As the proximity gauges all are calculated with the same emission scenario, similar differences between them would still appear if the actual emissions were available and used as input.
3.2. Possible sources of error/areas for improvement 3.2.1. The pole problem All of the methods of assessing PI rely on a grid of cells of 1 of latitude by 1 of longitude. It is important to note that these cells decrease in area from the equator to the poles, and thus emissions in a cell cannot strictly be considered per unit area or volume when comparing cells; oceanographers and meteorologists refer to this as the Pole Problem (e.g. Williamson, 2007). However, the vast majority of people e and thus the chemicals they produce and use e resides at latitudes between 50 degrees North and South. The grid cell area of the 1 1 grid differs maximally by 35% in that region, so that the error introduced is negligible for this work. 3.2.2. The use of an arbitrary grid The 1 1 grid provides a useful framework for performing calculations, but national boundaries do not generally follow these arbitrary lines. About 9% of cells on land contain borders, so the population estimate from UNEP/GRID contains many cells that are assigned to multiple nations, with separate data for each nation, which is not suitable for the applications in this work. The populations for such cells were summed and the cell randomly assigned to one of the nations sharing the cell. For the estimation of emission of a PAH in that cell, that nation’s estimated per capita emission was multiplied by the summed population; however, that nation’s population e and thus the per capita emission e was not adjusted by the number of additional humans from assigning the cell to that nation. Conveniently, adjacent countries likely produce similar amounts and species of PAHs on a per capita basis, and have similar population densities. These last two assumptions may be poor for some parts of Africa where the quality of life can vary widely from nation to nation, and even within nations, on a small spatial scale. Additionally, the chance position of the divisions between cells can place a cell outside of its primary source of influence; for instance, if the sampler at Delhi C was about 9 kilometers (km) west of its real location e or if the observatory at Greenwich, England were 9 km east of its real location e it would cross a line of longitude and the PI as calculated by the static proximity gauge would be an order of magnitude lower. 3.2.3. The use of a “first approximation” emission scenario It is assumed in the input to these proximity gauges that every citizen of a nation contributes the same fraction of the national PAH emission. Some error must be introduced in this approximation, as,
3.2.4. The use of air-sheds When employing air-sheds for understanding contaminant movement three important caveats should be considered: first, unlike a watershed, an air-shed has no equivalent air-shed divide; second, unlike watersheds, air-sheds can change on a very short time-scale; third, some error will be introduced by h ¼ 6 h, since an air mass may pass through a cell but not be counted because it is not present in the cell at the 6 h endpoint. The third point is important near the poles, where the cells are quite narrow, and for faster moving winds at high altitudes, but may also lead to the underestimation of the contribution to PI made by emissions into the cell in which the sampler was deployed, especially if the sampler is near the edge of the cell. The bias effect of the small polar cells is likely alleviated somewhat by the lack of sources of contaminants at high latitudes. The reliability of the air-shed is also reduced by the assumption that the air mass retains its composition as it travels, which is a supposition of the Trajectory Model that generates the back-trajectories from which the air-shed is composed (D’Amours and Pagé, 2001). Further difficulties arise from balancing the use of realistic arrival heights for the backtrajectories e the samplers were only w2 m above the surface e against the errors introduced when the Trajectory Model calculates that trajectories intersect the ground. Also, air mass location error increases with back-trajectory length, These limitations are important when considering a small number of back-trajectories, such as when trying to elucidate a specific source point, but if it is assumed that these errors are approximately random, their importance is reduced by integrating thousands of trajectories into an air-shed in which individual trajectory endpoints have very little effect on the final PI. 4. Conclusion Three methods for quantifying distances to multiple sources of SVOCs as pertingency indices were examined: a simple ‘static’ gauge, and the increasingly sophisticated ‘dispersion’ and ‘air-shed’ gauges. The three methods gave largely similar PIs to 1 of latitude by 1 of longitude cells when the PIs were normalized for comparison. PIA and PID were also quite similar to the negative remoteness index NRI, calculated from an atmospheric circulation model. Where steep gradients in the emission estimate existed between neighboring cells, the simplest method produced markedly different e and likely less sensible e values of PI from the more sophisticated methods. All of the methods are however subject to
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any errors contained in the initial emission estimate or the arbitrary location of cell divisions. Provided a suitably large (>1000) value is used for f, the dispersion proximity gauge provides a good balance of realism and ease of computation, as PIs by this method can be determined for the whole globe in a matter of hours using a personal computer. In areas within a few hundred kilometers of strong sources and where winds are strongly directional, the application of the back-trajectory based air-shed method appears to become necessary. In comparison with the previously established remoteness index RI, the air-shed method yields more realistic results for conditions that differ from the long-term average of the wind fields, and is less influenced by long-range transport patterns. Acknowledgements The authors wish to thank the Air Quality Models and Applications section of the Weather and Environmental Prediction Services branch of Environment Canada for generating the backtrajectories, Sum Chi Lee, Karla Pozo and Tom Harner for logistical handling of the GAPS samplers, and the Natural Science and Engineering Research Council of Canada for funding. Appendix. Supplementary information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.atmosenv.2010.07.051. References Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2002a. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach 1. Global production and consumption. Science of the Total Environment 290, 181e198. Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2002b. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach 2. Emissions. Science of the Total Environment 290, 199e224. Breivik, K., Sweetman, A., Pacyna, J.M., Jones, K.C., 2007. Towards a global historical emission inventory for selected PCB congeners e a mass balance approach-3. An update. Science of the Total Environment 377, 296e307. Chen, D.Z., Liu, W.J., Liu, X.D., Westgate, J.N., Wania, F., 2008. Cold-trapping of persistent organic pollutants in the mountain soils of Western Sichuan, China. Environmental Science & Technology 42, 9086e9091. D’Amours, R., Pagé, P., 2001. Atmospheric transport models for environmental emergencies. Available from: http://collaboration.cmc.ec.gc.ca/cmc/cmoi/ product_guide/docs/lib/model-eco_urgences_e.pdf (accessed 23.07.10). Hafner, W.D., Solorzano, N.N., Jaffe, D.A., 2007. Analysis of rainfall and fine aerosol data using clustered trajectory analysis for National Park sites in the Western US. Atmospheric Environment 41, 3071e3081. Hjellbrekke, A., 2009. EMEP measurement data online. http://tarantula.nilu.no/ projects/ccc/emepdata.html. Hoh, E., Hites, R.A., 2004. Sources of toxaphene and other organochlorine pesticides in North America as determined by air measurements and potential source
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