On the nature and origins of visibility-reducing aerosols in the los angeles air basin

ON THE NATURE AND ORIGINS OF VISIBILITY-REDUCING AEROSOLS IN

THE LOS ANGELES AIR BASIN

W. H. WHITE* and P. T. ROBERTS?

W. M. Keck Laboratories, California Institute of Technology, Pasadena, CA 91125, U.S.A.

(First received 26 July 1976 and in revised form 21 December 1976)

A~~ct--Simui~neous measurements of the light-s~tte~ng coefficient and chemical ~m~sition of ambient aerosols were made during selected smog episodes in the Los Angeles air basin. These data are statistically analyzed to determine the effective scattering efficiencies of the major secondary aerosol species. The individual scattering efficiencies are then used to estimate the contributions of major sources of reactive gases to the reduction of visibility in Los Angeles.

Sulfate and nitrate compounds appear to have scattered more light at a given mass concentration than did other chemical fractions of the aerosol. The observed relationship of SO, and NO, concentrations to the concentrations of tracers for major source types was consistent with existing inventories of SO, and NO, emissions in the basin. Because of the high scattering efficiency of sulfates, the estimated contribution of large stationary sources of SO, to the reduction of visibility was comparable with that of the automobile.

INTRODIJCTION

“Tfiis duy thrq’ satn; on land great signnt smokes. It is a good land in u~~earun~e, and there are great valleys, and in the interior there are ~zjgli ridges.”

The Los Angeles air basin as seen from offshore in 1542 by the Cabrillo expedition (Ferrel, 1542).

“Objects on the plain helow us dwindled till it began to look like arz airman’s map; hut though the bungalows became white specks among their tufted orchards, they seemed to retain their sharpness of outline, and every tree stood like a toy on a little platform of darker green shadow. One began to realize a little more clearly what the lucid air of California might mean to the astronomer.”

The Los Angeles air basin as seen from the road to Mount Wilson in 1917 by the English poet Alfred Noyes (Noyes, 1927).

Modern inhabitants of the Los Angeles air basin sel- dom enjoy the magnificent views that once charmed explorers, poets, and astronomers. The deterioration of the visual environment characteristic of smoggy days is due primarily to the scattering of light by a complex atmospheric suspension of fine solid and liquid particles. This aerosol is a mixture of natural components such as fog and dust, anthropogenic primary particulates from mobile and stationary sources, and secondary material produced in the atmosphere from gases. To develop a control strategy which will improve visibility, it is first necessary to relate the optics of the atmosphere to the emissions of aerosols and their gas-phase precursors.

The most important optical parameter of the urban atmosphere is the total scattering coefficient, b,,,,, which determines the rate (fraction per distance) at

* Present address: 1180 N. Chester Avenue, Pasadena, CA 91104, U.S.A.

t Present address: Chevron Research Co., P.O. Box 1627, Richmond, CA 94802, U.S.A.

which a beam of light is scattered in all directions. Light scattered into an observer’s line-of-sight by the ambient aerosol is perceived as a visible haze obscur- ing distant objects. While some features of this haze depend in an essential way on the angular distribution of the scattered light (Husar and White, 1976) or on its polarization (White, 1975), prevailing visibility within the haze is described adequately by the expression

VISIBILITY = 2.9/b,,,,.

(Samuels et al., 1973). An aerosol is most often characterized for control

purposes by its mass concentration, MASS, so that it is convenient to relate visibility to the aerosol through the aerosol’s scatter~izg e~c~e~cy, which is just the ratio b,,,,/MASS. Because of the variability of this ratio for different aerosols, however, a model which simply scales the light-scattering coefficient to the ambient mass concentration is not an adequate base for a control strategy (Samuels et al., 1973). If the ambient aerosol were simply a mixture of directly emitted primary aerosols, then the scattering efficiency of each component aerosol could be measured at the source, and the optical effects of specific control measures could readily be calculated. Unfortunately, much of the Los Angeles smog aerosol is formed in the atmosphere, and modelling studies (Gartrell and Friedlander, 1975; White and Husar, 1976) have identified this secondary material as a major contributor to reduced visibility; its importance can be judged from the fact that the scattering efficiencies of Los Angeles smog aerosol are much higher than the scattering efficiencies generally measured for primary aerosols (Findley and Rossano, 1972; Elder et ul., 1974).

Most of the secondary aerosol is added to existing particles (Husar et al., 1976), and cannot be isolated

803

804 W. H. WHITE and P. T. Rotn~rs

for direct irk sirlr measurement. Moreover. the light- scattering efficiency of a particte depends strongly on its size (van de H&t, 1957). so that the optical contributions of individual aeroso1 species cannot be recon- structed accurateiy from the chemical analysis of a filter sample. ln view of these experimental difficulties, it is natural to look for a statistical relationship between the scattering efficiency of the ambient aerosol and its chemical composition. In this paper we derive effective scattering eficiencies for the major secondary aerosol species. through the statistical analysis of data from the 1973 Aerosol Chancteriza- tion Experiment (ACHEX) in Los Angeles (Hidy YT tri.. 1974). We then use these individual scattering effi- ciencics to estimate the contributions of major sources of reactive gases to the reduction of visibility in Los Angeles.

DATA BASE AND STATISTICAL

PROCEDURES

During summer and early fall of 1973. an instru- mented mobile van was used to monitor aerosol cot& position. scattering coefficients, and gas co&en- t&ions during selected smog episodes in the Los Angeles air basin. The data base for this paper is summarized in Appendix 1; a more detailed descrip- tion is given in Hidy et ul. (1974).

Our conciusions are based primarily on empirical relationships obtained by stepwise multiple linear regressions performed with a standard computer rou- tine. In general, regression of a variable Yon variables X ,...., X,, determines coefficients et ,..., c, which minimize the variance 6’ of the quantity Y - CiciXi. The result is an approximate linear relationship

Y = c* + C,“,X, + t

with a standard error oft. If the linear model is physi- cally appropriate, then the coefficients ci can be determined with arbitrary accuracy by taking enough samples, even though the uncertainty in the relationship may be large for individual samples. In most of the regressions reported here, the coefficients are estimated accurate to within 20”/,; coefficients whose calculated standard error exceeds ZOy{> are marked with an asterisk (*). Coeificients are set to zero unless the F test shows the corresponding variables to be statistically significant at the 95’!< confidence level in reducing 8. In some of the regression models, the constant term is constrained to be zero on physical grounds.

The notation (fl, .+. t.) at the end of a regression relationship means that regression was performed on II observations for which the mean value of the dependent variable was J;. and produced a multiple correlation coefficient of I’. The number of observations can fluctuate slightly with changes in the set of variables. due to missing data. The square of the correlation coefticient is the proportion of the variance of the

dependent variable which is explained by the rcgres- sion relationship:

Here (T’ is the original variance of the dependent variable and E’ is the variance remaining after regression.

AEROSOL COMPOSiTIOh

In terms of mass concentration. the three most important elemental and ionic species found in the aerosols sampled were NO, ” SO;-, and C. In this section we discuss the probable nature of the nitrate. sulfate, and carbonaceous compounds these represented in the aerosol, and describe their distribution with respect to particle size.

Likely nitrate and sulFaate compounds in the aerosol include ammonium salts formed by absorption of ammonia from the gas phase. and sodium salts fortned by replacement of chloride in sea salt. A mole balance on the 24 h hi-vol samples indicates that most of the nitrate and sulfate ion can hc accounted for in this way:

+ I 74 (SO$ .-- (96146) Na,,, ,,,,, ) _ .~ .._-- ~-~~ 96

-.---- k 0.05

(II = I I. J = 0.43, I’ = 0.981. (i)

This relationship was determined by regression of NH: on NO, and (SO;- - (96%) Na,,,,;,,,), where

Na,,, ..III is calculated from sodium and aluminum concentrations as in Miller cf rrl. (1972). The quantity 96/46 Na,,,, ,n,( in the sulfate term accounts for the conversion of all marine sodium chloride to sodium sulfate. Other possible models for regression. in which sodium chloride is converted to sodium bisulfate or sodium nitrate, produce slightly poorer fits.

In (1) the regression relationship is expressed with ionic concentrations divided by ionic weights, so that the empirical coefficients of the nitrate and sulfate terms have direct stoichiomctric significance. These coefficients. and the small degree of scatter in the relationship, suggest that nearly all of the nitrate is ammonium nitrate. and that most of the suii&e not associated with sea salt is ammonium sulfate. The molecular weight of ammonium nitrate is approxi- mately 1.3 times the weight of the nitrate ion. and we will assume in what follows that

N ITRATES:NO, = I ._I

The same compound-to-ion ratio (C!I) is representa- tive of the likely atmospheric mix of (NH& SO, (C/I = 1.3X), (NHJHSO, (C’,!l = 1.70). Na,SO, (C/I = 1.48), NaHSO, (C;J = 1.25), and H2S04 (C?I : l.02); WC will aIs0 assume that

SI ILFATES:SO; ::: 1.3.

Visibility-reducing aerosols in Los Angeles air basin 805

Table 1. Estimated breakdown of aerosol mass concentration for ACHEX 2 h aerosol samples

Average mass concentration (p(g/m3) total of 199 pg/m3 derived from:

All Sources Gasoline Fuel & Crude Oil

Nitrate Compounds 41 32 9 Sulfate Compounds 33 5 28 Organic Compounds 30 30 0 Sum 104 67 37

The bulk of the carbon in the aerosol was con-

tained in organic compounds (Appel et al., 1976). In analyses of hi-vol filter samples taken at Pasadena during the period of ACHEX, Grosjean and Fried- lander (1975) found that the carbon content of these compounds ranged from 67% for the polar fractions to 85% for the nonpolar fractions, with an average for their data of 73’4. The reciprocal of 0.73 is ap- proximately 1.4. and we will assume that

ORGANICS/C = 1.4.

Table 1 gives the average mass concentration of nitrates, sulfates, and organics in the ACHEX 2 h samples, based on the scaling factors estimated above. These three components account for over half of the average aerosol mass. Judging from the eleven 2 h

samples analyzed for aluminum, soil dust accounts for 3540% of the remainder, leaving an average of about 60 pg/m3 to be attributed to water and primary anthropogenic aerosols. The figures in Table 1 summarize data taken at four locations over a limited experimental period, and do not represent true basinwide or yearly averages.

The light-scattering effectiveness of an aerosol depends not only on its chemical composition, but on the distribution of particle sizes as well. Table 2 shows the average mass fraction of each aerosol component which was found in particles less than 0.5 pm in diameter.

It is evident from Table 2 that the chemical composition of the aerosol varies with particle size. The smaller particles were found to be enriched with carbon, while nitrate and aluminum were concentrated in the larger particles. Although its effect cannot be quantified from our data, ambient relative humidity appears as an important determinant of particle size (cf. Sinclair et al., 1974). Nitrate and sulfate size distri-

butions are particularly affected by relative humidity; this is consistent with the known hygroscopicity of the probable nitrate and sulfate compounds in the aerosol (e.g. Charlson et al., 1974), but may also re-

flect humidity-dependent formation mechanisms for these compounds.

RELATIONSHIP OF THE LIGHT-

SCATTERING COEFFICIENT TO AEROSOL COMPOSITION

The scattering due to a given mass concentration of aerosol depends on the size, shape, and composi-

tion of the aerosol particles. If the particles are uniform, homogeneous spheres of diameter D,, density

p, and refractive index m, then the ratio of aerosol scattering coefficient to aerosol mass concentration is given by:

&,/MASS = W,)

= (312~~~) / KW,/~, m)f@) dl (2)

where K is the Mie scattering function and f(1) is the normalized wavelength distribution of solar radi- ation. The function G(D,), plotted in Fig. 1, peaks sharply when particle diameters are near the wave- lengths of visible light.

Ambient aerosols are distributed with respect to particle size, and b,,,,/MASS for such an aerosol is obtained by averaging:

b,,,,/MASS = s

W,)ma=(D,) dD, (3)

where G(D,) is the ratio for a monodisperse aerosol, given in (2), and mass (D,) is the normalized aerosol mass distribution.

Table 2. Ratio of mass on after filter (particle diameter less than 0.5 pm) to mass on total filter (particle diameter less than 20pm) for individual aerosol components

(Average and standard deviation of ACHEX 2 h aerosol samples.)

AF/TF Average

Aerosol Component

Total aerosol Nitrate Sulfate Carbon Aluminum

Overall

0.37 f 0.10 0.33 f 0.16 0.49 + 0.20 0.72 + 0.13 0.08 k 0.09

RH < 60% RH > 60%

0.43 + 0.06 0.30 + 0.09 0.39 + 0.10 0.28 + 0.19 0.64 f 0.12 0.34 * 0.14

XOh W. H. WHITE and P. T. ROBER’IS

Fig. I. Ratio of light scattering coeficient to mass concentration for uniform spherical particles of unit density.

refractive index 1.5. and diameter Op.

Previous studies by Charlson et ul. (1969) and Samuels et al. (1973) have shown that this ratio has a fairly narrow dist~bution for ambient Los Angeles aerosols. Figure 2 illustrates the approximate propor- tionality between h,,,, and MASS in the ACHEX 2-h samples; the average B,,,JMASS for these samples was ,032 with a standard deviation of ,009,

h,,,,/MASS = .03? & ,009, (4)

which is consistent with the earlier measurements. The variability in the ratio h,,,JMASS is of course

due to the variable composition of the ambient aerosol. Regression shows that much of the deviation in (4) was statistically associated with the sulfate and nitrate fractions of the aerosol:

b,,,,/MASS = ,020 + .056 SO:-/MASS

+ ,0~~2NO~~ASS & .006 (5)

(n = 58, j = .032, r = .71).

Here p denotes the ambient relative humidity: p = ‘/RH/lOO. As shown in Fig. 3, the ratio b,,,J MASS tended to be higher than average for aerosols rich in sulfates and nitrates. and lower than average for aerosols poor in these compounds.

We can rewrite (5) in a suggestive form if we use the conclusion of the last section that NITRATES/ NO; = SULFATES/SC& = 1.3. Simple algebraic

manipuIation of (5) then gives

h,,,,/MASS = .062 SULFATES~MASS

i- (.020 + ,049 p*) NITRATES/MASS

-t ,020 (MASS - SULFATES (6)

- NITRATES)/MASS + ,086.

(VI = 58. r = ,032. I’ = .72).

In (6) A,,,JMASS is presented as the average of three different values, weighted according to the composition of the aerosol.

The form of regression (6) can he derived theoreti- cally from the observation that the (llntlormalized) aerosol mass distribution is the sum of the ~unnor~d-

lized) component nws tlistributwns:

mass(D,) = 1 mass,(D,)MASS,, 17)

where MASSi is the mass concentration of the ith component and mass,(D,) ix the normalized component mass distribution. Ilsing Equation (7), we can rewrite Equation (3) to resemble regression (6):

h_, ‘MASS = 2 C‘; MASS, MASS. #a)

where

Comparison of Equations (Xb) and (3) shows that the coefficients Ci of (8b) are analogues of the ratio h,,.,jMASS for individual fractions of the aerosol. It is natural, then. to identify C‘, as the scattering efi- ciency of the i”’ aerosol cnmponent. If each component of the aerosol possessed a characteristic normalized mass distr~b~~tioll which depended only on the relative humidity. so that the c(~rnpone~lt scattering efficiencies Ci = ti Q) were independent of the

component mass concentrations MASS,, then C,MASS, would be the contribution of the i”‘ component to the light-scattering coethcient. The success of regression (6) indicates that this is to some extent the case. The functional relationship between Ci and relative humidity is in general dificult to predict on

theoretical grounds. and we do not attribute any physical significance to the specific form of the nitrate coefficient beyond the observation that it is nonlinear, as might be expected for :I dcliquescent material.

The coeflicients of regression (6) are empirical esti- mators for the C,. so the colltribtjtion of the 8 aerosol component to the light-scattering coefficient can be judged from these cocflicients and the measured component rndss concentrations. On this basis, sulfates and nitrates together account for more than half

MO%?, pqiim’

Fig. 1. Correlation of iight scattering coefficient with aerosol mass ~oncent~tion rn ACHEX 2 h aerosol samples.

Solid line shows average ratio h,,,JMASS.


4

\ SO;/ mass

Fig. 3. Ratio b&MASS from ACHEX 2 h aerosol samples, plotted as a function of SOT/MASS, NOT/MASS, and a relative humidity. Values indicated at each point are the observed b,,,,/MASS

ratios. Sloping lines indicate the b,,,,/MASS ratio predicted by the regression relationship (5).

of the light-scattering observed during the ACHEX 2 h sampling periods, as shown in Table 3. The figures in Table 3 summarize data taken at four locations

over a limited experimental period, and do not represent true basinwide or yearly averages.

DISCUSSION OF THE AEROSOL

SCA’ITERING RELATIONSHIP

There is some scatter in the b,,,, values observed at a given level of SULFATES, NITRATES, and MASS. The coefficients of (6) are chosen to minimize this scatter relative to aerosol mass concentration; the coefficients which minimize the absolute scatter are only slightly different:

b,,,, = 062 SULFATES

+ (.022 + .050 $) NITRATES

+ .022 (MASS - SULFATES

- NITRATES) f 1.0

(n = 58, j = 6.4, r = .96).

(9)

Because the strong correlation of b,,,, with MASS has not been factored out of (9), the correlation coefficient for this regression is much more impressive than the correlation coefficient for (6). On the other hand, precisely because the correlation of b,,,, with MASS has been factored out of (6), the evidence from this regression for the optical importance of sulfates and nitrates is the more convincing.

The functional relationships in (6) and (9) are but two of scores which were tested in regressions. Although carbon was a major constituent of the aerosol, in its light-scattering effectiveness it could not be distinguished statistically from the rest of the nonsul- fate, nonnitrate fraction. The inclusion of nickel and lead as tracers for primary aerosols did not signifi- cantly improve the relationships. Of the functional forms tested for the relative-humidity-dependent coefficients Ci (p), quadratics generally gave the best fit to the data. No significant nonlinear relationships were found between light scattering and any of the

Table 3. Estimated breakdown of light scattering coefficient for ACHEX 2-h aerosol samples

Average contribution to b,,,t(10-4m-‘) total of 6.4 x 10-4m-’ derived from:

All Sources Gasoline Fuel & Crude Oil

Nitrate Compounds 1.7 1.4 0.3 Sulfate Compounds 2.0 0.3 1.7 Organic Compounds 0.6 0.6 0.0 Sum 4.3 2.3 2.0

A.E. 11;9--A

x0x W. H. WHITE and P. T. ROIMTS

aerosol components. Regression of MASS on aerosol composition indicated that the sulfate and nitrate terms in (6) and (9) do represent sulfate and nitrate compounds. and do not include significant contributions from unrelated material statistically associated with SO, and NO, (White. 1976).

An important chemical factor contributing to the superior scattering efficiency of sulfates and nitrates is the affinity of these compounds for water. Much of the scattering statistically associated with sulfate and nitrate may have been due to unbound water

which was not measured gravimetrically due to losses in sampling and equilibration. In the case of nitrate compounds. this hypothesis is supported by the relative-

humidity dependence of the empirical scattering efficiency. No such dependence could be identified for sulfate compounds: even the deletion of all data taken at relative humidities greater than 70”; does not sig- nificantly change the coefficient of the sulfate term in regression (9). This is surprising. since the distribution of sulfates with respect to particle size does

depend on relative humidity, as Table 2 shows. The importance of sulfate compounds for visibility

at all relative humidities probably stems chiefly from their distribution with respect to particle size. The size resolution of the impactor was not sufficient for a direct calculation of the efficiencies C,, but the afterfilter/total-filter ratios in Table 2 show that the mass median particle diameter of sulfates averaged a little over 0.5 ,ttm. right in the middle of the particle size range producing the most scattering for a given mass concentration. For comparison. the mass median diameter of carbon was well below 0.5 pm; many of the carbonaceous particles were too small to scatter effec- tively. The difference between the complex refractive index of much of the carbonaceous material and the essentially real refractive index of the sulfates may havse augmented the difference in their effective scat-

tering efficiencies.

The aerosol-scattering relationship prcsentcd here was fitted to a limited set of data from a limited geographic area. Pending further measurements to test its generality. it should be applied to other situations only with considerable caution. It is worth noting. however. that the reciprocal of the sulfate coefficient in (6) and (9) falls within the range of sulfate/h,_, ratios reported by Waggoner uf (I/. (I 976) for a variety of sulfate-dominated aerosols. The distribution of sulfate compounds with respect to particle size is gov- erned largely by the mechanics of the SO, ~SOi conversion process. and may be self-preserving (Fricd-

lander, 1977). In this case one might expect the scattering ehiciency of sulfates to be more or less univcr- sal.

SOLRCES OF AEROSOL PRECIIRSORS

Mass balances based on known emissions of primary aerosol account for only a small fraction of the nitrates. sulfates, and organics found in the Los Angeles smog aerosol, and the hulk of each of these species is thought to result from gas-to-particle conversion in the atmosphere (Heisler et al.. 1971: Gar- trell and Friedlander. 1975). The mechanisms in-

volved are poorly understood. but the presumed precursors of these species in the gas phase are the nitrogen and sulfur oxides and certain hydrocarbons. particularly the C, and larger olefins and some aro- matics. Table 4 presents an abbreviated inventory ol the sources of these gases.

Table 4 shows that two broadly defined groups ol sources, with mutually distinct geographical distributions and characteristic emissions. are responsible for nearly all of the production of reactive gases in Los Angeles, The gasoline sources arc distributed fairly uniformly over a large area of the basin, and produce nearly all of the reactive hydrocarbons. The fuel and crude oil sources arc clustered along the coast and

‘Table 4. Inventory of recent inventories by

estimated reactive gas emissions (tonnes per day) in the Los Angeles Basm compiled from most local air pollution control districts: Los Angeles (1973). Orange County (1972). San Bernardino

(1972). and Riverside (1970)

Source type NO,(TId) SO,& T/d) RI-ICY’*’ (Tjd)

I. Gasoh~ I040 40 IO70

Motor vehicle exhaust and blow-by? Evaporation

I040 JO 770 300

2. Fwl oil md crude oil 430 345 I 0

Power plants Industry and commerce Ships. railroads, jets, diesel vehicles Petroleum refining and sulfur recovery

150 I90 160 70 90 70 5 30 II5 i

3 Other 10 55 70

* Reactive hydrocarbons as defined by the APCD’s include some species, such as the light olefins, which are not converted to aerosol in significant amounts.

t Based on the seven-mode-cycle.


produce most of the sulfur oxides. In this section we identify and validate chemical tracers for the emissions of these two source groups.

Lead aerosol and carbon monoxide are important constituents of the exhaust from gasoline-powered motor vehicles. This single source is estimated to account for 97-98% of all emissions of these species in the Los Angeles air basin, about 9 tons/day of airborne lead (estimated from Huntzicker et al., 1974) and 10,COO tons/day of carbon monoxide (from APCD figures). The reliability of lead and carbon monoxide as tracers can be judged from their correlation in the ACHEX 2 h samples:

CO = 874 + 954Pb f 555

(n = 53, y = 3775, r = .96). (10)

The lead coefficient in (10) is close to the estimated mass ratio of carbon monoxide emissions to lead emissions, 10000/9 = 1111.

Nickel and vanadium are, like sulfur, trace constituents of the heavier fractions of petroleum, and are exhausted to the atmosphere in aerosols during the regeneration of refinery catalyst and the combus- tion of fuel oil. It is estimated that, nationwide, more vanadium is released to the atmosphere in this man- ner than is consumed in all metallurgical applications combined (Fischer, 1973). Nickel correlates with vanadium in the eleven 2-h samples analyzed for

vanadium and in the 24-h hi-vol samples:

V = .Oll + .72*Ni f .014

(n = 11, jj = .051, r = .83) (2-h) (1 la)

V = .006 + .91 Ni f .008 (n = 11, y = ,030, r = .91) (24-h)

(lib)

The ratio’of Ni to V in fuel and crude oils fluctuates greatly from batch to batch, and we are unable to estimate the ratio of the emissions of these elements for comparison with the Ni coefficients of (11).

Of the four source tracers discussed above, only lead and nickel were monitored for all 2 h samples taken at the mobile van and satellite stations. Figure 4 shows the geographical distribution of the nickel/ lead ratio calculated from the average values of lead and nickel during the sea breeze regime. The limited data suggest a nickel-rich plume originating in the area between El Segundo and Alamitos Bay and following wind streamlines to the north and east.

Oxides of nitrogen and sulfur were measured during the 2-h sampling periods at the mobile van. If we set NT = NNO, + NNO; and ST = Sso2 + Ssor, and regress N, and S, on Pb and Ni, we find:

N,= -11+34Pb+_32

(n = 17, j = 145, r = .95)

before 10:00 PST

(12a)

I KILOMETERS

0 ,”

I

A MAJOR POWER PLANTS

0 MAJOR REFINERIES

1

Fig. 4. Patterns of pollutant transport in afternoon. Arrows show most frequent surface wind directions on 16-point compass, with percent frequency of three adjacent directions given by numbers at tails. (One-hour readings 120&1800 PDT, collected from APCD’s and National Weather Service by Meteor- ology Research, Inc.) Bold numbers give ratios 100 Ni/Pb. (From average Ni and Pb in 2 h aerosol

samples taken 1200-1800 PDT at ACHEX mobile van and satellite stations.)

XI0 W. H. WHITE and P. T. ROBEKTS

NT = 7 + 17Pb + 199*Ni j, 10

(fl = 43, _C = 57, r = .88)

after IO : 00 PST

(L2b)

$=4‘+746Nik 11

(n=l4,y=45,r=.95)

before to:00 PST

UW

S, = 25 + 430*Ni k 16

(n = 30, 3 = 49. r = 35) (IW

after 1O:OO PST

These relationships are in quali~tive agreement with the inventory, linking nitrogen primarily to gasoline sources and sulfur to fuel and crude oil sources.

The scatter in both morning relationships is small, and we can use the coefficient of (13a), together with known sulfur emissions, to estimate emissions of nickel in the Los Angeles air basin: [(32/64) 385 tons/ day]/746 2 $ ton/day. The coefficient of lead in (12a) is close to the estimated ratio of nitrogen emissions to lead emissions for motor vehicles, [(14/46) 1040]/9 = 35. There is a good deal of scatter in the afternoon relationships, and the coefficients of Pb and Ni are lower than they are in the morning. These lower coefhcients may reflect the Ioss of nitrogen and sulfur oxides to atmospheric and surface sinks.

SOURCE CONTRIBUTIONS TO

AEROSOL

In this section we develop rough estimates for the contributions of the two source groups identified in the preceding section to ambient levels of particulate nitrates. sulfates, and organics. To simplify notation, let X,, X,. and X3 represent secondary nitrates, sulfates, and organics, and let [Xj], be the concentration, in a given air parcel, of Xj produced from the emissions of source group i. If source group i is considered as a single emitter, then [Xjli is given formally by:

[X.]. z j:.u”,,. I 1 ” v “’

where eij (tons/day) is the emission rate of Xj precursor by source group i. yi (days) is the fraction of source group i’s daily emissions which is exhausted into the air parcel, V(m3) is the volume of the air parcel, and .fii @g/ton) is the mass of X,i produced from a ton of precursor emitted by source group i.

We have no way of measuring .fii, and will assume for simplicity that fij = fii; for each species, the fraction of the gas-phase precursor converted to particulate is independent of the precursor source. If we assume also that the two source groups of Table 4 account for all emissions of Xj precursor, then the fraction of Xj attributable to each source group is:

114)

where w = (p2j’p1. The quantities eij are given in Table 4 and w can be determine from the source tracers identified in the preceding section:

= 36Ni/Pb (IS)

The quantity [X,] is just the measured concentration of particulate nitrates, sulfates, or organics, if we neg- lect the contributions of primary aerosols.

Table 1 gives average values for [XJ,, calculated from (14) and (15) using concentrations of compounds and tracers sampled during ACHEX. Table 3 presents corresponding breakdowns of the aerosol light scattering coefhcient. The calculations are not very sensi- tive to the crude assumptions on which they are based, because most nitrogen oxides and reactive hydrocarbons come from gasoline sources, while most sulfur oxides come from fuel and crude oil sources. Indeed, the estimated total contributions of the two source groups are not very different if we simply attribute all nitrates and organics to gasoline sources and a11 sulfates to fuel and crude oil sources.

It should be noted that. while the sources itemized in Table 4 are involved in producing a large fraction of the ambient aerosol, they are not the sole agents of this production. For exampIe, sulfur oxides are essential to the formation of sulfate aerosol, but the process is facilitated by the presence of ammonia and reactive hydrocarbons. The major sources of sulfur oxides are not major sources of ammonia and reactive hydrocarbons, so that it is somewhat misleading to attribute all effects of sulfates to the sources of sulfur oxides.

GEC$XtAPHlCAL VARIATIONS IN

AEROSOL COMPOSITION

ACHEX revealed substantial geographic vltriation in aerosol concentration and composition. The rcia- tive cont~butions of in~vidual aerosol components to light scattering therefore depended strongly OKI

location, as shown in Fig. 5. Generally, sulfates and oil-based sources were more important in the western portion of the basin, and nitrates were more important in the eastern portion. The sampling periods at the different sites were overlapping but not coinci- dent, so that some of the observed differences in average concentration and composition may be due to temporal, rather than spatial, variations.

Figure 5 includes data from stations other than the mobile van. Since relative humidity was not monitored at these stations, the breakdown of h,,,, is based on the following regression, rather than regression (6):

h,,,,/MASS = ,015 (MASS - SULFATES

- NITRATES)/MASS

+ .071 SULFATES/MASS

+ ,050 NIT~ATES~ASS f .007

(n = 58, E = .032, r = .60).


DOMINGUEZ HILLS

KILOMETERS

Fig. 5. Geographical distribution of light scattering coefficient (10-4m-‘) and estimated breakdown by component. (Average of 2-h aerosol samples at ACHEX mobile van and satellite stations.)

This regression, based on mobile van data, gives a relationship which is “averaged” over the distribution of relative humidities experienced at the van.

AcknowiedgementeThe California Aerosol Characteriza- tion Study was sponsored by the State of California Air Resources Board. The analysis presented in this paper was supported by National Institutes of Enviro~ental Health Sciences Training Grants 3Ml ESOOO4-13Sl and 3TOl ES~80-0751 and by Meteorology Research, Inc. Some of the results were presented as paper No. 7.5-28.6 at the 68th Annual Meeting of the Air Pollution Control Associ- ation in Boston, June 1975. L. Hashimoto and S. L. Heisler assisted in the preparation of figures and tables. The authors are particularly grateful to Professor S. K. Fried- lander for helpful advice which strongly influenced the finished work.

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APPENDIX 1. NOTATION, UNITS

AND ANALYTICAL TECHNIQUE

h.,,, (10-4m-r)

MASS (kg/m”)

NO3 (pg,;m")

SO, (fig/m’)

C @g/m?

NITRATES

SULFATES

aerosol light scattering coefficient, nephelometer aerosol mass concentration, gravimetric particulate nitrate concentration. wet chemistry particulate sulfate concentration. wet chemistry particulate carbon concentration. flame ionization detector and ashing (pg/m3) estimated concentration of particulate nitrate compounds, NITRATES = 1.3 NO; (pgim’) estimated concentration of particulate sulfate compounds, SUL- FATES = 1.3 SO,

ORGANICS

NH; (pgy!mJ)

Na (ng/m3)

Al @g/m”)

Na,, ., ..,,,

Pb Wd)

Ni (a/m?

V Wm”)

CO Wm3)

(h(g;m”) estimated concentratton of particulate organic compounds. ORGANJCS = 1.4 C particulate ammonium ion conccn- tration, wet chemistry particulate sodium concentration. neutron activation particulate aluminum concentration. neutron activation (/{g/m”) estimated concentration ol particulate sodium due to sea salt.

N%,, \.I// m= Na - 1.025/.0X2) Al particulate lead concentration. X-ray tluoresccncc particulate nickel conccntratton. X-ray Ruorescencc particulate vanadtum concentratton. neutron activation carbon monoxide concentration. gas chromatography followed by flame ionization detector nitrogen oxides concentration, chcm- lumencsence detector sulfur dioxide concentratton. gas chromatography followed by tlamc photometric detector mtrogen concentration, N, = 573 % NO, + (14/46) x NO, sulfur concentration. S, = 1309 x SO2 + (32196) x SO,

Except for those presented in Figures 4 and 5. all data are from the ACHEX mobile van, which was operated at Dominauez Hills. West Covina. Pomona. and Riverside. Gravimetric and chemical analyses were performed on three types of filters. Unless otherwise noted. the data used in this paper are taken from 2-h samples on a total filter with an upper cutoff at about D, = 2Opm. This filter was operated in parallel with a Lundgren impactor having an afterfilter designed to collect particles with D, I 0.5 nm. Certain analyses, such as that for ammonium. were performed only on 24 h hi-vol filter samples.

Documents

On the nature and origins of visibility-reducing aerosols in the los angeles air basin