IMPACT ASSESSMENT OF EMISSIONS FROM A MUNICIPAL WASTE INCINERATOR

IMPACT ASSESSMENT OF EMISSIONS FROM A MUNICIPAL WASTEINCINERATOR

S. M. KOBLANTZ1, D. G. TEIGER1, M. E. KITTO1;2, V. A. DUTKIEWICZ1;2,J. M. MATUSZEK1;2 and L. HUSAIN1;2�

1 Wadsworth Center, New York State Department of Health, Albany, New York 12201–0509, U.S.A.;2 Department of Environmental Health and Toxicology, School of Public Health, State University of

New York at Albany, Albany, New York 12201–0509, U.S.A.

(Received: October 1995; revised: February 1996)

Abstract. Emissions from a refuse-derived fuel steam generating plant in downtown Albany, NY,have been a subject of public concern during, and since cessation of, operation of the plant. Aerosolsamples routinely collected every sixth day at four air quality monitoring sites (three PM10 and oneTSP) in the environs of the plant were analyzed for fourteen trace metals and three combustion-relatedinorganic anions to detect contributions of the incinerator to the ambient burden in Albany. Statisticaland correlative comparisons of the analyte concentrations were made using direct comparison ofmonthly, quarterly and annual arithmetic and geometric means, enrichment-factor analysis, factoranalysis and correlation with wind direction, precipitation and tonnage of refuse burned. Theseseveral comparisons reveal that trace-metal and anion concentrations in the fallout of emissions fromthe plant are extremely low and are indistinguishable from the corresponding ambient concentrationsat Albany. Factor analyses and wind-direction correlations indicate that contaminants at Albany werecomponents of mixed air masses with contributions from a variety of regionally distributed sources.

1. Introduction

Oftentimes, public or policy issues arise concerning the potential impact of airborneemissions from a large emission source, such as a municipal incinerator, sometimeseven after the facility has ceased operations. We provide here an example of a situ-ation where archived samples from a routine air-quality monitoring program wereextensively analyzed for trace metals and combustion-related anions to address justsuch a case. The analytical results were then subjected to various statistical andcorrelative comparisons to ascertain whether the incinerator emissions could bedistinguished from other sources in and around an urban setting.

The ANSWERS (Albany New York Solid Waste Energy Recovery System)plant, a refuse-derived fuel (RDF) steam generation plant located in downtownAlbany, began operation in 1981, and discontinued municipal waste incinerationon January 29, 1994. The plant and an adjoining oil-fueled plant had repeatedparticulate emission problems which caused concern to local residents and promp-ted requests to study the potential health impact of the emission on the neighbor-hood.

Emissions from burning approximately 100 to 500 tons per day of RDF at theANSWERS plant were vented through a 60-m stack with base elevation approx-imately 20–25 m above mean sea level (MSL). Plume elevation is likely to be

Environmental Monitoring and Assessment 45: 21–??, 1997.c 1997 Kluwer Academic Publishers. Printed in the Netherlands.

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22 S. M. KOBLANTZ ET AL.

extended due to the normal dry-gas exit velocity of 3800 m3 min�1 and tem-perature of 230 �C (Kitto, 1992), so the surrounding low buildings (6- to 10-mhigh) should not affect initial plume dispersion. An average of 320 tons of RDFwas burned daily during the study period with no clearly identifiable seasonaldependence on daily tonnage.

Stack emission tests conducted by the New York State Department of Environ-mental Conservation (DEC) in 1984 showed that trace metals, especially chromi-um, nickel, zinc, lead, manganese, cadmium and vanadium, were emitted from theANSWERS plant. Zoller et al (1974) indicated that direct atmospheric collectionof these trace substances provides an advantage over deposited samples such assnow or ice, because the investigator was aware of the (meteorological) history ofthe atmospheric samples. Consequently, we postulated that measurement and eval-uation of trace metals and combustion-related anions in ambient aerosols upwindand downwind of the plant, during RDF burning and after cessation, might providea means of evaluating any impact from the emissions.

An impact assessment was initiated in which 467 archived air-particulate sam-ples collected locally during 1993 and 1994 were analyzed for fourteen tracemetals (arsenic, barium, beryllium, cadmium, calcium, undifferentiated chromi-um, copper, iron, lead, manganese, mercury, nickel, vanadium, and zinc) and threecombustion-related anions (chloride, nitrate and sulfate).

2. Air Particulate Samples

Air particulate samples are routinely collected every sixth day using high-volumesamplers (approximately 1600 m3 per 24-h period) mounted on the roofs of struc-tures at four sites in Albany County as part of the approximately 110-site Statewideambient air monitoring network operated by DEC. The four sites in Albany Countyhave operated since 1986 and are spatially distributed along the west bank of theHudson River valley (Figure 1) in accordance with historical meteorological data.

Inhalable particulates (PM10, particle size <10 �m) were collected on quartzfilters atop three of the sites: the 6-m high Goodyear Tire Warehouse at the Portof Albany (site 0101-10), about 3 km south of the ANSWERS plant and baseapproximately 6 m above MSL; the 6-m high Albany County Health buildingat Green and Ferry Streets (site 0101-13), about 1.5 km south of the plant andbase approximately 6 m above MSL; and the 4-m high air-monitoring trailer atthe Loudonville Reservoir at Shaker Road and Shaker Park Drive (site 0101-33),about 2.5 km north of the plant and base approximately 105 m above MSL. Ananemometer stands approximately 4 m above the sample collector at site 0101-33.Total suspended particulates (TSP, particle size <100 �m) were collected on glass-fiber filters atop the 12-m high Albany Public Library (site 0101-31), about 0.4 kmwest of the plant and base approximately 60 m above MSL. Building height is not

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Table IAnalytical methods and extraction media for evaluation of tracemetal and anion concentrations on TSP and PM10 filters

Method Extraction matrix Analyte(s)

ICPa Nitric acid/ Ba, Cu, Fe, Mn,Hydrochloric acid V, Zn. Ca

AASa Nitric acid/HF/ Total CrHydrogen peroxide

CVAASa Permanganate/ HgPersulfate/ and nitric acid

ICa Distilled deionized water SO4=, NO3

�, Cl�

ICP-MSb Nitric acid As, Be, Cd, Ni, Pb

a Standard Methods for the Examination of Water and WasteWater, 18th Edition, APHA, Washington, DC, 1992 (Methods3120B, 3111B, 3112B and 4110B, respectively).b Test Methods for Evaluating Solid Waste, Volume II: FieldManual of Physical/Chemical Methods, SW846 (Third Edition)U.S. Environmental Protection Agency, Washington, DC, 1986(Method 6020).

more than 550 km long) formed through the Appalachian Mountains by the LakeGeorge basin and Hudson River flowing south into the Atlantic Ocean and the LakeChamplain and Richelieu River flowing north into Canada and the St. LawrenceRiver. The peaks of the Catskill, Adirondack, Green and Berkshire Mountains (allsubunits of the Appalachians) rise to as high as 1,650 m above MSL, while the riverand lake levels are at or below the elevations of the two lakes, both of which averageapproximately 30 m above MSL. Such a restricted space for regional wind flowhelps to define the representativeness of the anemometer readings at approximately115 m elevation even to the valley floor.

3. Chemical Analyses

Portions of each filter were extracted in a microwave oven using various acid/oxidantmixtures and the extractant analyzed by inductively coupled plasma emission spec-trometry (ICP), inductively coupled plasma mass spectrometry (ICP-MS), flameatomic absorption spectrometry (AAS), or cold vapor atomic absorption spec-trometery (CVAAS) (Table I). Separate portions of each filter were extracted withdistilled deionized water and the extractant analyzed for sulfate, nitrate and chlo-ride ions using ion chromatography (IC). Analyses to differentiate hexavalent andtrivalent chromium ions were discontinued when it was found that there was sig-nificant conversion of hexavalent chromium to the trivalent state during extractionfrom the high-volume filters; undifferentiated, total chromium concentration wasmeasured instead.

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EMISSIONS FROM A MUNICIPAL WASTE INCINERATOR 25

Samples for trace-metal analyses were prepared in batches from portions offilters from various sampling locations. Each batch consisted of 19 samples, aduplicate of one of the samples, a spiked sample, an urban dust QC sample, afilter blank and a separate QC sample for beryllium. The urban dust QC sampleis SRM # 1648 Urban Particulate Matter from the National Institute of Scienceand Technology (NIST). Reference and spike solutions and spiked filter stripswere prepared and submitted by an independent internal laboratory. Concentrationvalues for the blank samples established a minimum reportable limit (MRL) byanalyte for each batch of filters.

Batches for chloride, sulfate and nitrate analyses were prepared separately andeach consisted of 25 samples, one spiked filter strip, two duplicates (one each fromtwo of the samples), and one blank strip.

Analytical accuracy (bias), determined for the metals from the NIST SRMsample accompanying each batch, varied by analyte with means relative to therespective NIST SRM values falling in the range of 76% (for V) to 104% (forAs); relative standard deviations about the respective means ranged from 3% to7%. Analytical precision, from comparison of the duplicate results, also varied byanalyte with mean differences ranging between 3% and 14% for both metals andanions. Mean analytical recoveries of spikes for both metals and anions ranged from91% (for Cd) to 118% (for Mn), except for Hg with a mean of 65%; relative standarddeviations for spikes were up to six-fold greater than those for the respective NISTurban dust QC samples.

4. Results and Discussion

A variety of methods are available to test whether a particular contaminant sourceimpacts its environs. In this case, the lack of emissions data for the assessmentperiod led us to attempt a broad-based search for potential pollutants generatedlocally. This particular effort began with the premise that the samples of opportunitypresented by the routinely collected air-quality filters available from the final yearof operation (1993) and the year immediately following shutdown could signalimpact of the ANSWERS plant. Statistical and correlative comparisons of nearly10,000 analyte and QA concentration measurements and approximately 600 fielddata were used to assess potential plant impact.

Only As and SO4= concentrations always exceeded the MRL, for both years

and all four sites. With only a few exceptions, Fe and Pb concentrations exceededthe respective MRLs. Be and chloride concentrations were seldom above the MRLduring the study and are not discussed. At various sites and times approximatelyhalf or more of the concentration measurements for Cr, Mn, Hg, and Ca werebelow the respective MRLs. Three- to fivefold variations in initial concentrationson some filters batches caused some of the Cd and Ni measurements to sufferdiffering MRLs within a specific data set.

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4.1. COMPARISON OF CONCENTRATION MEANS

On the assumption that baseline contributions from other sources and climatologi-cal effects on contributing factors (e.g., dust, washout, etc.) would be similar for thetwo years, we attempted an initial comparison by analyte of monthly, quarterly andannual arithmetic and geometric means of analyte concentrations, with correspond-ing standard deviations, for 1993 and 1994. The numbers of concentration valuesbelow the respective MRLs varied by analyte, site, and filter batch. Comparisonsmade using MRL or one-half-MRL values in combination with real values hadlittle effect on the conclusions derived; the high degree of statistical variability inthe data precluded more elaborate constructs. Examples of the monthly arithmeticmeans of four trace metal concentrations are provided in Figure 2: As and Cd, as thetwo most toxic trace metals per DEC’s air quality control guidelines for New York,the 1991 Ambient Guideline Concentrations (AGC); Ni, as a toxic trace metal andan indicator of combustion products; and Fe, as an indicator of soil-derived crustalparticulate matter. The AGCs are not regulatory standards, but rather are used forpermitting purposes; they are based on consideration of the possible effects ofchronic exposure and are expressed as annual average concentrations. Concentra-tions of crustal elements on TSP filters range from twofold (Fe) to two magnitudes(Ca) greater than the corresponding values on the PM10 filters. The monthly meanconcentration values for each of the four sites during the operating year 1993 areindistinguishable from post-operational values.

Table II provides a comparison of the geometric means (GM), geometric stan-dard deviations (GSD) and range of values for site 0101-33. For the most part,the respective geometric means and standard deviations are the same during bothyears, as are the observed ranges. Ratios of 1993 GM to 1994 GM are within �20% of unity except for Cd, Ni and Hg at 1.3, 0.6 and 2.3, respectively. The slightlyhigh ratio for the Cd measurements appears to result largely from use of an MRL of0.58 ng m�3 for half of 1993 and an MRL of 0.12 ng m�3 for the remainder, whilean MRL of 0.26 ng m�3 was used for half of 1994 and an MRL of 0.10 ng m�3

for the remainder, the aforementioned blank problem. With Ni, the ratio appearsto be less than unity largely from the use of MRLs of 3.3 and 1.8 ng m�3 duringparts of 1993 versus 5.8 and 1.7 ng m�3 during parts of 1994. The Hg results donot appear to be influenced by any particular analytical artifact: 40 measurementsfor 1993 exceed the singular MRL of 0.03 ng m�3 during 1993 where only 22 doso during 1994; the maximum value is threefold greater in 1993 than in 1994; fivevalues during 1993 exceed the maximum 1994 concentration; and samples for bothyears were stored in the same manner and location. Hg volatility during collectionmay contribute to the difference between respective annual values, but quantitativedata to that effect are not available.

Wind distributions for the 1993 and 1994 sampling periods were nearly iden-tical, so the data have been composited into a single histogram (Figure 3). The

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spatial restrictions on regional wind flow discussed previously explain the closecomparability of the annual wind distributions.

The temporal variability of each analyte concentration in PM10 samples collect-ed in both years is similar in magnitude among the data sets. The distributions andstandard deviations about the means of the respective concentration measurementsin the PM10 data sets are similar in value and skewness to those for the analyti-cal quality control samples as well. This is the case whether the data are treatedas normally or log-normally distributed, indicating that collection and instrumen-tal/analytical uncertainties may affect the distributions (Blackwood, 1995; Ott,1995).

For both years the monthly as well as annual arithmetic and geometric con-centration means of As at all four sites always exceeded the 1991 AGC, but Asconcentrations are much the same as have been found at other times and at otherlocations in and around the State, including at the remote summit of WhitefaceMountain (Dutkiewicz et al., 1987; Parekh and Husain, 1981). The monthly con-centration mean of Cd at the sites exceeded the 1991 AGC with two-fold greaterfrequency during 1994, but with the greatest value at site 0101-33 during 1993.The monthly concentration mean of Ni at site 0101-31 met or exceeded the 1991AGC during three months when RDF was burned and once when not. Annual aver-age concentrations of Cd and Ni did not exceed the respective 1991 AGC values.Concentrations of other analytes fell well below the respective 1991 AGC values,some by several magnitudes. The annual mean concentration of Pb for both yearswas more than two magnitudes less than the Federal (and State) ambient air qualitystandard (Code of Federal Regulation, 1994) and approximately one-tenth the 1991AGC.

Concentration means at site 0101-13 for Cd, Mn, Ni, V and Zn during 1993 and1994 are substantially the same as those reported for 1982–1987 (Department ofHealth, 1994) within the respective standard deviations. Those for Cr are approx-imately tenfold greater than, and for Pb approximately one-tenth of, the earliervalues. Site 0101-13 is the only station common to both evaluations.

4.2. ENRICHMENT FACTOR ANALYSIS

For convenience in subsequent discussion we categorize the trace metals as crustal,oil-combustion or anthropogenic/toxic in origin; however, all trace metals andanions on aerosols are mixtures of particles from crustal and anthropogenic sources.Concern about the contribution of crustal loading led to the application of enrich-ment factor (EF) analysis for differentiating possible sources (Zoller et al., 1974).Enrichment factors using Fe as reference were calculated for the trace metalsobserved on the filters to determine deviations in the particulate composition rela-tive to crustal soil using Taylor’s crustal abundance pattern (Taylor and McLennan,1985).

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The EF values for the three PM10 sites differentiated between elements ofprimarily crustal origin (Mn, Ba, Ca) and those with a significant anthropogeniccomponent (including As, Cd, Hg, and Pb). Average EF values for the TSP filtersat site 0101-31 indicate a large crustal contribution to all elemental concentrationsat that site. The respective average EF values are the same whether or not theANSWERS plant was operating.

4.3. FACTOR ANALYSIS

While EF values can be used to identify elements as crustal or anthropogenicin origin, they cannot resolve the sources. However, atmospheric concentrationsof elements from common sources and areas will correlate in the air mass andon collected particles. Factor analysis (FA) provides a multiple linear regressiontechnique which can be used to study the correlations among a large number of data(Hopke, 1985). An example of factor analysis using BMDP 4M (Dixon and Brown,1994) is provided as Table III for site 0101-33, which with its elevated suburbanlocation is one of the four sites to be least influenced by dust re-entrainment fromurban traffic or other potential confounding sources such as heating exhausts or asewage-sludge incinerator between sites 0101-10 and 0101-13.

Dependent variables (e.g., trace metal or anion concentrations) or independentvariables (e.g., wind direction and speed or day-of-week) for each sample arenormalized against the mean for that variable for the entire data set, thus givingequal weight to each variable. The normalization produces a total system varianceproportional to the number of variables. The BMDP 4M output clusters correlatedvariables into a specified number of factors according to the amount of eachvariable’s variance from the respective mean and with the degree of correlation forthat variable within the factor represented by a numerical score (factor loading).Summing the squared loadings of a variable across factors produces a communalityvalue which expresses the degree to which the variance for a given variable is“explained” by the distribution across factors, ranging in the example from excellent(82% of the variable variance explained) for Pb to extremely poor (13% of thevariable variance explained) for Cd. The total system variance accounted withina factor (% Variance) is computed as the sum of the squares of the loadings(SSL) divided by the number of variables and converted to percentage, with factorreliability increasing with increasing SSL. Factors are rank ordered in the outputaccording to SSL. Reliability of a given FA is optimized by minimizing the systemvariance through deletion of poorly-defined variables and factors. For clarity, theBMDP 4M output does not list loadings <0.25 (shown as dashes in Table III).Typically, only factor loadings >0.30 (>9% overlap in variance between the variableand factor) are considered eligible for interpretation (Tabachnik and Fidell, 1983).Comrey (1973) has suggested a qualitative scale for the reliability of the factorloading of a particular variable, ranging in variance overlap increments of 10%

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Table IIISorted rotated factor loadings by year for site 0101-33. The BMDP4M output lists all loadings <0.25 as default values of zero; theseare replaced by dashes (–) here to highlight factor loadings abovethe default value

Variable Factor 1 Factor 2 Factor 3 Communality

1993

Pb 0.81 – 0.39 0.82SO4 0.81 – – 0.80As 0.79 – 0.26 0.78Zn 0.79 0.30 0.38 0.77Ba 0.74 0.44 – 0.76Fe 0.67 0.62 – 0.74NO3 0.60 – – 0.73Ca – 0.77 0.29 0.64Mn 0.40 0.75 – 0.63Cu – 0.54 0.29 0.53Winda – –0.28 –0.72 0.51Ni 0.48 0.38 0.62 0.50V 0.48 0.40 0.61 0.45Hg – – 0.61 0.41Cr – – –0.49 0.28Cd 0.26 –0.31 – 0.13

%Variance 29 17 15

1994

Ba 0.90 – – 0.82Fe 0.88 – – 0.78Mn 0.83 – 0.28 0.77Pb 0.81 0.38 – 0.79Zn 0.80 0.38 0.29 0.87As 0.80 – – 0.63Ca 0.57 – –0.42 0.49Ni 0.53 0.43 0.52 0.73Winda –0.51 – –0.41 0.42V 0.34 0.71 0.45 0.83NO3 0.44 0.71 – 0.69Cd – 0.68 – 0.46Hg – 0.67 –0.28 0.53SO4 – – 0.75 0.57Cu – – 0.65 0.43Cr 0.34 –0.42 – 0.30

%Variance 35 17 14

aWind = wind direction

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from excellent (>0.71 loading, or >50% variance overlap) to poor (>0.32 loading,or >10% variance overlap).

For this example, some of the variables which might otherwise be deleted toreduce system variance are retained. Be and Cl� were not included as variablesbecause of the poor and incomplete data sets obtained at all sites, but Ca, Cd, Cr,Cu, Mn and Hg were retained for all data sets despite the varying quality of data setsat some sites and periods. Wind direction is retained as variable, although windspeed and day-of-week were eliminated after preliminary calculations indicatedthat none of the independent variables had a significant effect on the distributionof the dependent variables.

The 1993 data set for site 0101-33 (Table III) indicates that Factor 1 accountsfor 29% of the total system variance, with excellent to very good correlation forPb, SO4

=, As, Zn, Ba and Fe, and NO3�. Correlations within Factor 1 for V,

Ni and Mn are only fair to poor. V and Ni, apparently due to oil-combustionproducts (Husain, 1986; Dzubay et al., 1988), are highly correlated with oneanother, diminishing the likelihood that Ni concentrations were related to RDFcombustion at the ANSWERS plant. The highly correlated V and Ni variables arespread across all factors, indicating that there is no common local source for thesetrace elements. Factor 2 provides poor correlation for Fe, Ba, V and Ni (Cu isnot considered reliable, because it may be a product of the motor brushes in thesampling unit). The excellent correlation between Ca and Mn in Factor 2 is likelydue to the large number of MRLs in each data set and is not significant. The goodcorrelation of V, Ni and Hg in Factor 3 is accompanied by a strong anti-correlationwith Wind (direction). The poorly defined anti-correlation for Cr may be an artifactof the many concentration values for that variable which fall below the MRL. Thefailure of Cd to correlate in any factor is likely due to the large variance in the MRLvalues from the highly differing background levels in the batches of filters used atthe site. Therefore, we conclude that there is no dominant local source for thesemetals, a particularly important consideration towards excluding Pb, As, Hg andNi as significantly impacting on the site as emissions from the ANSWERS plant.

The last conclusion is further supported by comparing to the FA for 1994, whenthe ANSWERS plant was not operating except for a period in January. Again Ba,Fe, Pb, Zn and As exhibit excellent correlation within the predominant Factor 1(35% of total system variance explained). Ni and V are again closely correlatedwith one another and spread across factors. Wind direction is anti-correlated. Hgis correlated with Ni and V in Factor 2, as well as with Cd; the better quality ofthe Cd concentration data for 1994 appears to provide correlation where the 1993data set could not. In both the 1993 and 1994 FA, the greatest percentage of systemvariance is explained by the principal Factor 1. Factor 2 and 3 account for nearlyequal variance in each case, so the ordering of these two factors is not important.The result of this FA is that we cannot identify a dominant local source impactingon site 0101-33 whether or not the ANSWERS plant was burning RDF.

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The three-factor analysis provided as an example is used for all sites becauseit is consistent with sorted and shaded diagrammatic output of BMDP 4M whichindicates for all sites a dominant factor of 4 to 6 or 7 variables, a weak secondaryfactor (Ni and V) and poorly defined mix among the remaining variables. Four-factor analyses were tested with data sets for all four sites in an attempt to separatesome of the components in the principal factor. However, no additional separa-tion developed with the fourth factor. Instead, the fourth factor served primarily toaccommodate the poorly defined variables, with only minor redistribution of someweakly-defined variables or readjustment of factor loadings. Furthermore, the addi-tional factor contributed 10% or less to the amount of system variance explained.So the three-factor analysis is considered more representative of conditions at allthe sites during the defined periods.

Comparison of three-factor analyses of 1993 and 1994 data sets for the otherPM sites, 0101–10 and 0101-13, provide much the same conclusions as are derivedfor site 0101-33. The TSP site 0101-31 produced slightly more complex factorloadings, especially for the 1994 data set, but in general the conclusions which wederive are as discussed for site 0101-33.

A comparison of the FA across sites for 1993 is another approach to identifyingANSWERS plant emissions (Table IV). At all four sites, the principal factor (25–29% of variance) exhibits a fair to excellent degree of correlation among Pb, Zn,Ba, Fe and Mn, with Ca, SO4

= and As also correlating, but more weakly so. Ni andV remain strongly associated, but appear to be spread less across factors than forthe site 0101-33. Most of the variables exhibit a fairly large spread across factors.The association of Hg, Cd and Pb in the least defined Factor 3 in the FA for sites0101-10 and 0101-13 is not likely to be related to ANSWERS plant operation (seefollowing discussions of wind dependent analyses). Other potential associationsare much like those for site 0101-33. Thus, any impact from the ANSWERS plantoperation during 1993 is simply not apparent at any of the sites.

Incorporating wind direction (or speed) as an independent variable in this FAprovides only qualitative information (e.g., wind direction is anti-correlated toconcentration changes), because the available meteorological data are not sufficientto isolate specific sources. Gatz (1978) has used BMDP 4M to sort data setsobtained with a field sampling program which was designed to provide the type ofmeteorological data necessary to allow FA to identify specific pollutant sources.With shutdown of the ANSWERS plant, a sampling program more specificallydesigned for FA analysis could not be developed.

The FA performed for this assessment provides a variety of information, themost important of which complements the conclusions drawn from the statisticalaverages described earlier. Where the mean values are indistinguishable betweenoperating and non-operating years or PM10 sites, the FA takes the assessmentsignificantly beyond for it evaluates the degree and direction (higher or lower)from the mean that the concentration values fall in each sample. Although the EFanalysis found differences in origin, we find with a high degree of certainty from

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Table IVSorted, rotated factor loadings by site for 1993 samples; otherwise, as in Table III

Factor FactorVariable 1 2 3 Variable 1 2 3

Site 0101-33 Site 0101-10Pb 0.81 – 0.39 Fe 0.92 – –SO4 0.81 – – Mn 0.89 – –As 0.79 – 0.26 Ba 0.85 0.34 –Zn 0.79 0.30 0.38 Ca 0.68 0.47 –Ba 0.74 0.44 – Pb 0.62 0.44 0.48Fe 0.67 0.61 – Cu 0.53 – –NO3 0.60 – – Ni – 0.94 –Ca – 0.77 0.29 V – 0.94 –Mn 0.40 0.74 – SO4 0.31 0.62 –Cu – 0.54 – Zn 0.53 0.59 0.34Wind – –0.28 –0.72 Cd – – 0.90Ni 0.48 0.38 0.62 Hg – – 0.88V 0.48 0.40 0.61 As 0.38 0.39 0.43Hg – – 0.61 NO3 – 0.32 0.26Cr – – –0.49 Wind 0.39 – –Cd 0.26 –0.31 – Cr – 0.29 –

%Variance 29 17 15 %Variance 26 22 14

Site 0101-13 Site 0101-31Fe 0.92 – – Ba 0.88 – –Ba 0.91 0.26 – Zn 0.86 – –Mn 0.90 – – Mn 0.85 0.29 –Ca 0.71 0.49 – Ca 0.81 – –Cr 0.55 –0.39 – Fe 0.70 0.32 –V – 0.86 0.29 NO3 – 0.87 –Ni – 0.86 0.29 Cd 0.34 0.79 –Wind – –0.66 – SO4 – 0.73 –0.40SO4 0.40 0.53 – Cr – 0.63 –0.33Cd – – 0.82 Ni – 0.50 0.75Pb 0.56 0.32 0.62 V – 0.49 0.75Hg –0.32 – 0.60 Wind – – –0.69Cu 0.41 – 0.51 Cu – – 0.65Zn 0.46 0.33 0.39 As 0.31 0.49 0.44As 0.46 0.41 – Pb 0.47 0.44 0.38NO3 – 0.42 – Hg 0.38 – –

%Variance 29 21 13 %Variance 25 22 18

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FA that in those aerosol samples where Pb and As concentrations are high (orlow), the corresponding Fe, Ba and Zn concentrations are correspondingly high (orlow). Also, the urban and suburban, and the PM10 and TSP, sites produce muchthe same correlations. We find that concentrations of all the trace metals are notcorrelated (or are anti-correlated) with wind direction. So we can conclude witha reasonable degree of certainty that the observed Pb concentrations develop notfrom the ANSWERS plant but from widely distributed sources.

The strong correlation of V and Ni, and of the pair with SO4= and NO3

�,indicates that these variables are most likely from oil-combustion sources, andstrongly supports a conclusion that observed Ni concentrations are not related toANSWERS plant operations. The strong anti-correlation with wind direction ofthis associated group of variables further supports a conclusion that the sourcesare widely distributed and, especially because of the locations of sites 0101-10 and0101-33 relative to one another and to the ANSWERS plant, from sources outsidethe urban area. The issue of how wind direction affects the data is explored morespecifically in the following sections of this paper.

4.4. WIND DEPENDENT CONCENTRATIONS

The assessments described so far require large data sets to determine potentialimpacts, which in this case makes them dependent on averaging over fairly longtime periods. Plant emissions apparently were so small that ambient concentrationsmasked any impact over those extended times. With some sacrifice of statisticalpower, subsets of the accumulated data can be organized to target emissions froma particular source by comparing analyte concentrations in samples at downwindsites against those for upwind sites on the same day. Alternatively, one can compareconcentrations in downwind samples during operation to those for days when thesame site is downwind, but the plant is not operating.

The PM10 sites used for this comparison lie more or less along a north–southline passing through the ANSWERS plant (Figure 1). Site 0101-31 was not usedin this comparison, because the wind rarely blew toward the site and a greatercrustal component might mask contribution from the plant. The three PM10 sites,on the other hand, reduce the crustal contribution while remaining sensitive to the<10 �m particles which are expected from this type of incinerator (Carroll, 1994;Kitto, 1992). In an attempt to distinguish any signal from the ANSWERS plant, wehave used meteorological data from site 0101-33 to characterize the meteorologicalconditions at all sites on each day for which samples were collected and analyzed.Similar meteorological data are not available for the other sites. Figure 3 showsa histogram of wind direction for the assessment days. The data set has threemodes: winds from approximately 35�, 150� and 215�. These are designated asnortheasterly, southeasterly and southwesterly winds, respectively.

On days with southeasterly or southwesterly winds, there exists the probabilityof having emissions from the ANSWERS plant impact site 0101-33. For simplicity,

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trace metals (As and Pb), the crustal reference element (Fe) and combustion-relatedanions (SO4

= and NO3�) do not suffer this limitation.

That the close correlation between receptor and control data sets can be attributedto regional air-mass concentrations is shown in Figure 5: As, Cd, Ni and Feare exhibited for reasons cited before; Pb for concentration values relative tothe 1991 AGC; and SO4

= as a combustion-related anion. Although a signal ofANSWERS plant emissions would appear as a predominance of data above theoptimal correlation between sites (respective increasing diagonals), the reverse istrue. Only SO4

= concentrations are reasonably well correlated between the twosites. With so many values at or near the MRL, the Cd concentrations do not exhibitany distinguishing correlations or anticorrelations. The preponderance of the highvalues for As, Pb, Ni and Fe occurs at site 0101-10, the upwind control site forthese meterological conditions, indicating that the sources are outside the Albanyurban area and the ANSWERS plant is not the principal source of the contributingaerosols.

A similar analysis was performed for sites 0101-10 and 0101-13, creating thistime a subset with winds from 0� � 45� (designated as northerly) and for the dayswhen the ANSWERS plant was burning RDF. There were 14 and 13 sampled daysfitting these conditions at site 0101-10 and site 0101-13, respectively. There were24 and 22 days at site 0101-10 and site 0101-13, respectively, when the winds werefrom the north and the ANSWERS plant was not burning RDF. The latter providerespective control sets for the days with burning. Another control set is that forsite 0101-33 during days with northerly winds and the plant operating. For noneof elements are the data sets for the receptor sites when ANSWERS was burningRDF significantly different from the control data sets.

With northerly winds, As, Ni and SO4= concentrations at receptor sites 0101-

10 and 0101-13 correlate with the respective concentrations at the upwind controlsite 0101-33. Pb and Cd concentrations are skewed, with high values occurringmore at the upwind control site 0101-33 than at the receptor sites, again indicatingsources outside the urban area. The Fe correlation is the reverse, with concentrationsskewed toward high values at both downwind sites, consistent with dust becomingre-entrained within the urban area.

Figure 5 provides graphic illustration of the previous statistically-derived anti-correlation between wind direction and concentration when using BDMP 4M forfactor analysis. The unillustrated discussion for northerly winds impacting sites0101-10 and 0101-13 lends further example.

These analyses indicate that measurable fallout from the RDF burning could notbe identified at downwind sites regardless of wind direction. In fact, the correlationsshow the anthropogenic pollutants impacting more heavily at the upwind controlsites. Thus, this highly directed method of data analysis indicates that trace elementsin emissions from the ANSWERS plant were too slight during the assessmentperiod to be identified above ambient levels introduced from outside the urbanarea.

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4.5. CONCENTRATION VERSUS RDF BURNED

We also attempted to correlate the concentration values against RDF tonnage burnedfor the 58 aforementioned days with southerly winds. Because of its suburban set-ting, elevation relative to the plant stack and high likelihood of being downwindof the ANSWERS plant, site 0101-33 seemed the one most likely to provide acorrelation, if one existed. It is also within the area of impact postulated previously,although at a value one-fifth to one-third of the projected maximum impact point(Department of Health, 1994). Figure 6 provides examples of the attempted cor-relations for the six species illustrated previously. Concentration means are shownas horizontal lines. While the singularly high value for Pb is not correlated withvalues at the other sites on that day, it is of similar magnitude to Pb concentrationsat site 0101-33 at other times not isolated by wind-sectored analysis, whether ornot the plant was operating. Also, note in Figure 5 that eight values at the controlsite 0101-10 exceed 10 ng m�3 as opposed to only this single value at site 0101-33, indicating that sources outside the area are more likely to be contributing toelevated Pb concentrations than is the ANSWERS plant. Concentrations of otherelements and anions as a function of RDF burned fall within the range of distrib-utions of the six examples, having much the same widely ranging values even onnon-operational days (zero tonnage) and scattered, generally low concentrationson operational days. Precipitation was recorded at site 0101-33 on six of 28 daysduring which RDF was burned and six of 30 non-operational days (see highlightedsquared points in Figure 6). The arithmetic or geometric concentration means forAs, Cd, Ni and SO4

= for either six-day set are indistinguishable from the respec-tive concentration means for days without precipitation, as expected for regionalair pollutants. The concentration mean for Fe during days with precipitation wasapproximately half that of days without, as might be expected if larger particlesfrom re-entrained dust are contributing. The arithmetic or geometric concentrationmeans for days with both precipitation and RDF burning are indistinguishable fromthose with precipitation, but without RDF burning.

The fact that there appears to be no correlation in these targeted data sub-sets between concentration and quantity of RDF burned supports the conclusionfrom the other comparisons that the emissions from the ANSWERS plant are notsignificant.

5. Conclusions

The cardinal assessment concerning the impact of ANSWERS plant is that theconcentrations in Albany air of combustion-related compounds from ANSWERSemissions are so small that concentrations of many analytes were often belowthe respective MRLs even when state-of-the-art analytical systems are applied.Concentrations above the MRLs were indistinguishable from ambient levels in

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Albany air, despite the application of several analytical and statistical techniquesdesigned to enhance identification of emissions from the ANSWERS plant.

Data show that the monthly mean concentrations of all species studied werethe same, within experimental and climatological uncertainties, in 1993 when theincinerator was operating and in 1994 after it ceased operation. Factor analysisshows that there is no dominant local source for the toxic trace metals. The con-centration means of trace metals and combustion-related anions at sites up- anddownwind of the incinerator were the same, within the experimental uncertainties.Wind-sectored correlations between up- and downwind sites indicated a greaterfrequency of elevated Pb concentrations at the upwind control sites, regardless ofwind direction. A wind-sectored comparison of concentrations on days when theincinerator consumed RDF with those days when no RDF was burned failed toidentify ANSWERS-related emissions and indicated impact from sources outsidethe study area.

From these data, we conclude that during the assessment period the contributionto the atmospheric burden of the trace elements and combustion-related anions fromRDF combustion at the ANSWERS plant was negligible.

Acknowledgement

The New York State Department of Environmental Conservation provided archivedsamples, local meteorological data and much of the plant-related information.

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IMPACT ASSESSMENT OF EMISSIONS FROM A MUNICIPAL WASTE INCINERATOR