Source-Receptor Reconciliation of Routine Air Monitoring Data for Trace Metals: An Emission Inventory Assisted Approach Glen R. Cam* and Gregory J. McRae Environmental Quality Laboratory 206-40, California Institute of Technology, Pasadena, California 9 1125
Inventory procedures for fine-particle trace-metals emissions are developed that assist aerosol source apportionment by receptor modeling techniques. It is shown how sparse routine air monitoring data sets on a very few trace elements can be used in chemical element balance calculations once emission inventory data have shown that a very few source signatures do complete a mass balance on those chemical elements that were measured. Methods developed are tested in the South Coast Air Basin of California for the year 1976, where it is shown that over 80% of the fine lead emissions comes from highway traffic, 81% of the nickel arises from fuel oil fly ash, and more than 90% of the iron and manganese comes from soil-like materials. With use of readily available trace element data from local and Federal monitoring networks, it is found that most monitoring sites are exposed to aerosol containing about 20% highway vehicle exhaust, 1-2% fuel oil fly ash, 20-50% soil dust or road dust, with sulfates and nitrates each present at about 15% of total mass. Airborne particulate matter concentrations have been measured routinely by high-volume sampling at hundreds of locations in the United States by state and local air pollution control agencies and by the National Air Surveillance Network (NASN). In addition to total aerosol mass, the concentrations of a very few easily measured trace elements, such as lead and iron, often are available. Because of their extensive spatial and temporal coverage, these data sets form a very important foundation for particulate-pollutant abatement plans. If these trace-element data could be used to support chemical mass balance receptor modeling studies, then rollback models could be replaced by a more accurate means for defining particulate control strategies. One current deterrent to this approach is that the number of chemical elements measured often is small, and key marker elements such as aluminum or silicon for soil dust are not determined. The objective of this research is to develop techniques that relate source contributions to ambient particulate air quality that are adapted to data characteristic of routine air quality monitoring programs. The approach taken is to employ emissions inventory data to assess the ability of those elements that were measured to serve as tracers for major source types. Then source contribution assignments will be obtained from a variety of alternative chemical element balance and tracer calculation procedures. Chemical element balance results will be checked against multivariate statistical methods that take advan0013-936X/83/0917-0129$01.50/0
tage of the time series nature of the ambient data and the tremendous numbers of ambient samples available. Methods developed will be tested on both local agency and NASN data taken in the Los Angeles area.
Chemical Mass Balance Approach The chemical element signatures of specific emission sources can be used to trace the relative contribution of each source type to the particulate matter measured in an ambient aerosol (1-5). The method consists of an element-by-element source-receptor chemical balance. Assume that chemical element emissions from each source are averaged over the particle size distribution and that each source type emits a characteristic pattern of chemical elements or compounds. Then the mass concentration ci of each element i = 1, 2, ..., n in an ambient sample can be related to the aerosol sources by the expression (5)
where ajjis the fraction of chemical species i in the particulate emissions from source j , sj is the mass concentration of material from sources j = 1, 2, ..., m observed at the receptor site, and f i j is the coefficient of fractionation, representing the fraction of species i from source j that appears at the sampling site. Fractionation may result from the effects of gravitational settling, in which elements concentrated in large-particle sizes (e.g., some of the lead in auto exhaust aerosol) are preferentially removed during transport from the emission source to a receptor site. Thus if the chemical composition of the emission from the major particulate sources is known, along with the chemical composition of an ambient sample, eq 1 defines a series of simultaneous relationships that may be solved for the relative source contributions, SF The solution of an overdetermined system of equations based on (1) is given by
s = [ATWA]-IATW c
(2)
where s is a vector of estimated source contributions to the ambient samples, c is a vector of the concentrations of species i = 1,2, ..., n measured at the monitoring site, A is the matrix filaijappearing in eq 1, and W is a diagonal matrix of weighting factors. The weighting factors commonly employed are l/Q, where ui is the standard deviation of single determination of the concentration of species i in an ambient sample (4). Elements like iron and manganese might differ in absolute mass concentration by 2 orders of magnitude, yet both be measurable with the
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Environ. Scl. Technol., Vol. 17, No. 3, 1983 129
Flguro 1. L o c a h of m k o r i n g sites within the South Coast Air Basin.
eame relative accuracy, say f 10%. If q 2 were estimated without the use of a weighting matrix, W ,then the least-squares fitting process will force a close fit to the high-abundance elements like iron while disregarding equally valuable data on the concentration of low-abundance elements like manganese. When Wis formed from a diagonal matrix of l/u:'s, the effect is to normalize the mass balance equation for each element by the accuracy with which that element can be measured. In the hypothetical case just examined, iron and manganese data being of equal relative accuracy would then affect the leastsquares fit equally. Chemical elements not included in the least-squares fitting procedure can be examined to check the accuracy of the chemical element balance (4). Chemical element balance or tracer techniques have been demonstrated in the Los Angeles area (I-3,5,6),New York City (7,8),Chicago (9).Washington, DC (4), and in Portland, OR (IO). Additional work is reviewed by Cooper and Watson (11)and by Gordon (12). Historical Air Quality Monitoring Data Base Total suspended particulate matter (TSP) concentrations have been measured in the Los Angeles area by the South Coast Air Quality Management District (SCAQMD) and independently by the National Air Surveillance Network (NASN). Data at ten sampling stations have been chosen for use in this study, at the sites indicated in Figure 1. The South Coast Air Quality Management District network began operation in Aug 1965 at a few locations and has expanded over the years. Twenty-four-hour average particulate data were collected on glass fiber fdtera by high-volume sampling. TSP concentrations were measured gravimetrically, and sulfates and nitrates were determined by wet chemical methods. Seven trace metals (Cu, Cr, Fe, Mn, Ni, Pb, and !An) were measured by atomic absorption spectrophotometry after direct extraction of a filter section in nitric acid. Sampling and analytical methods are described in ref 13-16. A representative set of data taken at the downtown Los Angeles (SCAQMD) monitoring station is shown in Figure 2. The SCAQMD trace-metal sampling program for metals other than lead 130 Envkon. Sd. TBchnol.. Vol. 17, No. 3. 1983
was terminated in early 1978. As a result, we will analyze data taken through the year 1977,the last full year of trace-metal network operation. Sample collection and analysis procedures used by the National Air Surveillance Network are described in ref 17 and 18. The NASN stations operated by collecting 2 4 h particulate samples on glass fiber filters by high-volume sampling. Quarterly average trace-metals concentrations for ten elements (Cd, Cr, Co, Cu, Fe, Ph, Mn, Sn, Ti, and V) will be studied here. These elements were measured with an emission spectrograph after extrading a composite formed from portions of all Hi-Vol filters taken at a single station in each quarter of each year. Although total mass, sulfates, nitrates, and ammonium ion data are available for single filter samples, we have averaged all such determinations for each quarter year at each NASN site in order to place those data into the same time frame as the tracemetals data Since the downtown L a Angeles NASN high-volume sampler was located with a SCAQMD station, this site was selected as a common point for comparison of the two independent sampling systems. The Anaheim and San Bemardino NASN stations were chosen to expand the geographic coverage of this study to the south and east of Los Angeles County. Trace Element Emissions in the South Coast Air Basin An inventory of aerosol emissions was assembled in the Los Angeles area for each of the trace metals and ionic species measured by the SCAQMJJ and NASN networks. The objective was to determine those source types that dominate the emissions of each chemical substance within the geographic region shown in Figure 1. The year 1976 was chosen as the base time period for inventory calculations in order to capitalize on the emissions data base of Taback et al. (19). The inventory results are summarized in Tables 1-111, and a complete description of this inventory is presented in ref 20. Information on the size distribution and chemical composition of the particulate emissions from more than 60 major source types was compiled from the work of Miller et al. (2), Taback et al. (19),Watson (IO),and others. The
1 t
33l
a
IO
0
YEW
1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 19/6 :977 lEW
i E
E
P
io., 0.I
0 1
0
1965 1966 1967 1968 1969 1970 1971 1972 1973 197U 1975 1976 1977
lEW
YEW
YEW
YEW
Flgure 2. Particulate air quality data at Los Angeles.
source composition profiles were used to subdivide the basinwide inventory of total particulate emissions into coarse (dp> 10 pm) and fine particle (dp I10 pm) fractions. The fine-particle emissions inventory was further subdivided into separate inventories for each of the trace metals within the geographic region of interest. Fineparticle emissions are emphasized because most of the particles emitted in sizes larger than about 10 pm settle out of the atmosphere rapidly and thus do not reach the regional monitoring sites (21). Use of source profiles generated after removing the emissions of large settleable particulate matter provides one means of accommodating in eq 1). the effect of atmospheric fractionation The fine-particle trace metals emissions inventory is summarized in Table I11 and in Figure 3 for those elements present in the ambient monitoring data base. Emissions of fine-particle iron, manganese, and titanium are dominated by soil-dust-like sources, with greater than 90% of these emissions derived from crustal material. In a similar fashion, 83% of the airborne lead is emitted from engines burning leaded gasoline. Another 9% of the fine lead emissions comes indirectly from vehicle exhaust in the form of resuspended road dust. Over 80% of fine nickel emissions are estimated to arise from residual fuel oil combustion. Nickel appears to be a better tracer for fuel oil combustion than vanadium in Los Angeles. As seen
vij
21150 Kg/DAY
6791
573
160
Kg/DAY
Kg/DAY
Kg/DAY
~~. Kg/DAY
296
248
119
25
2205
Kg/DAY
Kg/DAY
Kg/DAY
Kq/DAY
KglDAY
Kg/DAY
Figure 3. Flne-particle trace-metals emissions by element (1976).
in Figure 3, vanadium emissions are split between fuel oil combustion and soil-like crustal sources. These source assignments, made on the basis of emissions data, are consistent with the inferences drawn from air quality measurements made in Pasadena by Hammerele and Pierson (6). They attributed iron, manganese, and titanium to soil dust and lead to leaded gasoline use and noted that vanadium is correlated with nickel but displays a size distribution half-way between the Pb, Br, Ni, and Zn group (which are all in very small particles) and the Fe, Mn, and Environ. Sci. Technol., Voi. 17, No. 3, 1983
131
Table I. Emissions Estimates for Fuel Combustion Sources
electric utilities natural gas residual oil (0.50% S) landfill and digester gas refinery fuel natural gas refinery gas residual oil nonrefinery industrial fuel natural gas LPG residual oil distillate oil digester gas (IC engines) coke oven gas residential/commercial natural gas LPG residual oil distillate oil
mass estimated emission fraction pro- fuel usage. factor. emissions. < 10 km, file lo9 BTUiday kg/109 BTU kg/day % Stationary Sources
fine-particle emissions, kg/day
27 64 27
227.45 993.42 0.82
1.081 21.619 1.081
246 21477 1
95.0 97.0 95.0
233.6 20832.4 0.8
27 27 1
93.03 395.95 32.97
9.080 9.080 21.619
845 3595 713
95.0 95.0 87.0
802.5 3415.5 620.1
27 27 1 2 50 27
421.64 2.74 53.42 42.74 6.30 37.53
7.567 7.567 21.619 23.520 20.430 7.567
3191 21 1155 1005 129 284
95.0 95.0 87.0 98.0 99.0 95.0
3031.0 19.7 1004.8 985.1 127.4 269.8
51 51 1 2
1181.92 18.08 22.19 22.19
8.071 8.071 21.619 21.619
9539 146 480 480
95.0 100.0 100.0 98.0
9062.3 145.9 479.7 470.1 subtotal 41500.8
788 47463 9947 8058
100.0 60.0 60.0 96.0
788.1 28477.6 5968.4 7736.0
733 28
100.0 60.0
733.0 16.8
Mobile Sources highway vehicles catalyst autos and light trucks noncatalyst autos and light trucks medium and heavy gasoline vehicles diesel vehicles civil aviation jet aircraft aviation gasoline commercial shipping residual oil-fired ship boilers diesel ships railroad diesel oil military gasoline diesel oil jet fuel residual oil (bunker fuel) miscellaneous off-highway vehicles
53 54 54 52
368.78 1255.16 228.17 125.52
55 54
44.56 1.29
U S . EPA
1 52
29.41 17.43
85.386 49.102
2511 856
87.0 96.0
2184.7 821.6
52
19.94
81.837
1632
96.0
1566.6
54 52 55 1
6.03 17.81 16.71 0.27
43.596 78.564 83.386
263 1399 659 23
60.0 96.0 100.0 87.0
157.7 1343.3 659.0 19.6
52
39.73
78.564
3121
96.0
2.137 37.814 43.596 64.200 9.08"
U.S. EPA
2996.5
subtotal 53468.9 a
g/LTO cycle.
Ti group (soil-like materials that appear in larger particle sizes). The remaining metals under study at our monitoring sites include Zn, Cd, Cr, Co, and Cu. Most of these metals arise from small contributions from a large number of diverse sources that have been grouped together in Figure 3. Zinc is particularly interesting. In past receptor modeling studies it has been attributed mostly to tire dust or to municipal incineration (see, for example, ref 3 and 4). In contrast, emission inventory tables similar to Tables I and I1 constructed for zinc alone show that zinc is emitted from 40 separate source classes in Los Angeles (20). Unless a large number of source signatures (and elements) are used in the data reduction process, it is almost impossible to account for zinc sources correctly in Los Angeles. Chromium, copper, and cadmium likewise are emitted from a diversity of sources. The cobalt inventory is suspect since the source tests used were carried out on naturalgas-burning combustion equipment that may have been contaminated by a previous history of oil burning. Emission inventory results for trace metals are compared to ambient air monitoring data in Figure 4. Since the 132 Environ. Sci. Technoi., Vol. 17, No. 3, 1983
emissions data are distributed over the whole Los Angeles urban area, the air monitoring sites shown for comparison are those farthest downwind of the city within the Los Angeles urban plume. The relative abundance of trace metals in both the emissions and air quality data sets are well matched, except for copper and cobalt. The ambient copper measurements are known to be contamined by copper worn from high-volume sampler motors (22). The cobalt emission estimates are suspect, as mentioned previously. Key Emissions Sources The particulate composition data available at routine monitoring sites are not detailed enough to account for all of the major aerosol sources. Data on the best soil dust tracers, silicon and aluminum, are unavailable. In a similar fashion, we lack data on sodium as a sea salt tracer and calcium as a key component of cement and gypsum dust. There are, however, several likely candidate tracer elements that can be associated with major source types. From Figure 3 it can be seen that fuel oil fly ash dominates the nickel emissions, auto exhaust controls the suspended
Table 11. Industrial and Fugitive Emissions
stationary industrial process point sources petroleum industry refining paving and roofing materials other (calcining mineral) organic solvent use surface coating printing storage loss other chemical plants metallurgical industry metals, general primary metals secondary metals nonferrous metals other metal fabrication nonferrous metals other mineral industry glass furnaces rock, stone, other waste burning at point sources wood and paper burning food and agriculture food and kindred grain mill and bakery vegetable oil other miscellaneous industrial iron and steel foundry nonferrous metals other unspecified
mass pro- emissions, fraction file kg/day histidine > glycine Tris. Canine serum is more effective as a leaching agent than one would predict on the basis of its concentrations of citrate and histidine, so that other biological chelators, possibly cysteine, appear to be important leaching agents. For the trace elements Zn, Mn, Cr, Ni; and Cu, the initial leaching rates with 0.5 M HC1 range from 350 to 850 pug of metal per gram of ash per day (ppm/day). The rates drop by 1-2 orders of magnitude within 24 h and then level off at 1-10 ppm/day. The initial rates with EDTA and citric acid are also high, 100-400 ppm/day, but they fall off even more rapidly than the HCl leaching rates. The leaching of vanadium is exceptionally rapid, with initial rates of 1000-3000 ppm/day. In addition, EDTA and citric acid leach over 50% of the acidsoluble vanadium compared to only 10-35% of the other transition metals,
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N
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Introduction Because of dwindling supplies of other fossil fuels, the use of coal in this country is likely to increase dramatically 0013-936X/83/0917-0139$01.50/0
in the near future. Even with modern pollution abatement equipment, the general population in many areas will be exposed to higher levels of particulate emissions from coal combustion. Since coal fly ash contains significant concentrations of a wide variety of heavy metals, there are potential health problems associated with the inhalation of larger quantities of fly ash. Coal fly ash consists primarily of amorphous aluminosilicates, with lower levels of heavy metals and organic compounds (1-3). However, previous leaching (4) and spectroscopic studies ( 5 , 6 )have shown that many of the metals are concentrated on the surface of the aluminosilicate core, rather than evenly distributed throughout the particle. Many elements, including As, Cd, Mo, Sb, Se, W, and Zn, are present almost exclusively in this outer layer of heavy metals ( 4 ) . There are still other elements such as Co, Cr, Cu, V, and Pb for which more than 50% of the total particle concentration is present in this outer layer ( 4 ) . This enhanced surface concentration has two implications with respect to possible health effects. First, these metals are readily available for leaching without the necessity of dissolving the relatively inert aluminosilicate core of the particle. More importantly, this enhancement results in higher heavy-metal concentrations in the smaller particles, due in part to their larger surface-to-mass ratio. These smaller particles are more likely to pass through the
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