(2) The zinc and lead concentrations in the six main components of these background samples decreased in the order heavy minerals > organic debris > conglomerate particles > calcitic shell fragments > quartz > aragonitic shell fragments. The various shell fragments comprised the largest single component by weight, and they contained the largest proportion of the total heavy metal in the samples. The heavy metals appeared to be present within the shells rather than on their surfaces. In or near sea grass beds, the organic debris comprised a significant reservoir of heavy metal a t relatively high concentrations, whereas the heavy minerals accounted for only a few percent of the total heavy metal. (3) In the background samples, zinc and lead were distributed between the components in proportions similar to iron. The affinity for the metals differed between components, but for any one component the affinity was similar for zinc, lead, and iron. (4) Samples within -30 km of the Port Pirie smelter but outside the intertidal zone had zinc and lead concentrations -30 times higher, on the average, than the background samples, and they were slightly more enriched in zinc than in lead. (5) The average lead and zinc enrichment ratios in the contaminated sediments were higher for particles in the size range 10-1000 pm than they were for finer particles. (6) Comparison of the contaminated and background samples also showed that the enrichment ratio for zinc in the various density subfractions decreased in the order heavy minerals > aragonitic shells > calcitic shells > conglomerates > organic debris. For Iead, the order was aragonitic shells > calcitic shells = heavy minerals > conglomerates > organic debris. (7) Shell fragments in sediment samples from near the smelter had lead and zinc concentrations -30 times higher than shell fragments ‘from uncontaminated areas. Because of their large capacity for heavy metals and their abundance in the sediments, shells were the main reservoir for heavy metals. (8) On a weight basis, the calcitic shells took up more heavy metal than the aragonitic shells. However, the aragonitic shells were more contaminated in the sense that their metal enrichment ratio was larger. (9) Most of the zinc, lead, and cadmium in the contaminated
shell fragments was removed by 0.15 M HCl. However, only -50% of the cadmium was extracted from the organic debris, compared with -80% of the zinc and lead. Acknowledgment
We thank Dr. K. G. Tiller for supplying samples of the Spencer Gulf sediments and Mr. E. S. Pilkington for performing the AAS cadmium analyses. Literature Cited (1) Cartwright, B.; Merry, R. H.; Tiller, K. G. Aust. J. Soil Res. 1976,
15,69.
( 2 ) Tiller, K. G., CSIRO Division of Soils, personal communication,
1977. (3) Dossis, P.; Warren, L. J. In “Contaminants and Sediments”; Baker, R. A,, Ed.; Ann Arbor Science Publishers: Ann Arbor, MI, 1980; Vol. 1,pp 119-39. (4) Pilkington, E. S.; Warren, L. J. Enuiron. Sci. Technol. 1979.13, 295. (5) Ward, T. J.; Young, P. C . Aust J . Mar. Freshwater Res., in press. (6) Warren, L. J. In “Effects of Heavy Metals on Aquatic Life” (First Annual Progress Report of the CSIRO Heavy Metals Task Force, Jan-Dec 1978);Commonwealth Scientific and Industrial Research Organization: Canberra, Australia, 1979; p 27. (7) Chester, R.; Aston, S. R. In “Chemical Oceanography”, 2nd ed.; Riley, J. P., Chester, R., Eds.; Academic Press: London, 1976;Vol. 6, pp 281-390. (8) Taylor, D. Estuarine Coastal Mar. Sci. 1974,2,417. (9) Calvert, S. E. In “Chemical Oceanography”, 2nd ed.; Riley, J. P., Chester, R., Eds.; Academic Press: London, 1976; Vol. 6, pp 187280. (10) Helmke, P. A.; Koons, R. D.; Schomberg, R. J.; Iskander, I. K. Enuiron. Sci. Technol. 1977,11,984. (11) Forstner, U. In “Interactions Between Sediments and Fresh Water”; Golterman, H. L., Ed.; Junk: The Hague, 1977; pp 94103. (12) Sturesson,U. Ambio 1976,5,253. (13) Sturesson, U. Ambio 1978,7,122. (14) Lande, E. Enuiron. Pollut. 1977,12,187. (15) Phillips, D. J. H. Enuiron. Pollut. 1977,13, 281. (16) Ireland, M. P.; Wooton, R. J. Enuiron. Pollut. 1977,12,27. (17) Luoma, S. N.; Bryan, G. W. J. Mar. Biol. Assoc. U. K.1978,58, 793. (18) 0.95. The difference between the absolute values obtained by the two methods is attributed to the fact that the continuous monitor used in this study was not equipped with any flow measuring device. Therefore, any decrease from the factory set flow rate, which could not be noted during normal operation, might be the reason for the poor absolute agreement between the two measurements.
Diurnal Variation of Suspended Particulate Matter. It has long been recognized (6, 7) that most of the soil elements-Si, Ca, Ti, and Fe-occur as large particles, while Pb, S, and Br emitted from combustion sources occur as small particles. Because of the health aspects of respirable particles, it is interesting to examine the diurnal variations in TSP and RSP. The diurnal variations in T S P and RSP for three consecutive days in March 1979 are shown in Figure 4.The variations in TSP are similar to the variations in particle number density. The maximum TSP mass concentration was easily associated with morning and afternoon rush hours. Typical maximal TSP concentrations measured during the 1978/79 winter season were in the range of 80-120 pg/m3. At nighttime, TSP concentration dropped to a minimum level, usually 20-30 yg/m3. Also, rain washout of particulate matter was observed during the period presented in Figure 4;this is probably the reason for the missing morning value on March 26. Relatively little diurnal variations in the RSP mass concentration were observed during our study. As can be seen from the data presented in Figure 4,the morning and afternoon maximums are indistinguishable. The only diurnal variation in RSP is that the average daytime concentration is within the range of 25-35 pg/m3 and the average nighttime value is 15-25 pg/m3. The fact that the diurnal variations in RSP are small as compared with those of the T S P is not
P.
250
I
t
i
\
m
h
9.0
1
"3
t
NUMBER (L
0
+
3
z
h
I
%?
0
Lc
U
t-
4
\
/
I
"OLUME
h
-
t 0
t
number
5 50
100
150
200
0.001
0.01
0. 1
1. 0
10. 0
300
PARTICLE DIAMETER ( m i c r o n s ) Figure 5. Comparison of submicron particles, number density, surface,
Figure 3. Comparison of 24 averages of suspended particulate matter concentrations as obtained by high-volume sampler and by automatic
and volume size distribution. The solid line represents typical data obtained in Jerusalem. The dashed line represents data obtained in Pasadena, CA, by Whitby et ai. (5).
HIGH VOLUME SAMPLER
250
(ug/m3)
air particulate monitor. 100
. . , . .
: :
. . : : . : : : : . . . ;
" " " : : : : .
0.01-1 pm. The number density distribution function shown in this figure is defined as
,-.
f(N) = AN/NT Alog D
0
3.25.79
ie
18
18 SUNDAY
MONDAY
3.26. 79
TUESDAY
0
3.27. 79
Figure 4. Presentation of 3-day concentration variations of particulate matter (TSP, solid line) and respirable particulate matter (RSP,dashed line).
unexpected. The reason is that the gravitational influence on the respirable particles is much smaller compared to the similar effect on the nonrespirable fraction (TSP minus RSP). However, it was interesting to see that the nighttime suspended particulate matter is composed mainly of particles in the respirable size range. Since particles emitted from combustion sources are known to be in the submicron range, the fact that the T S P and RSP nighttime levels are similar suggests that only a fraction of T S P comes from combustion. The other part of the TSP, which is 1-3 times heavier, measured during daytime, consists of particles in the nonrespirable range. Therefore, i t can be concluded that the reason for the correlation between high TSP levels and the traffic density is not due to direct emission, but due to mechanical turbulence caused by motor vehicles resulting in entrainment of natural dust into the atmosphere. Since large particles tend to settle rapidly, the difference between RSP and TSP is reduced a t night and also a t midday when traffic density is lower. This conclusion is consistent with the finding of two earlier reports on suspended particulate matter near highways in Texas (8) and Oregon (9). In these reports it was found that a large fraction of the maw of the particles near highways contains elements which are commonly found in soil and could not be associated with emission from combustion. Particle Size Distribution. Information on particle size distribution in the submicron range was obtained mainly from the electrical aerosol size analyzer. A typical plot of particle distribution is presented in Figure 5. This figure shows number, volume, and surface distribution in the range of
(5)
where is the number of particles at a given size range, NT is the total number of particles, and D is the particle diameter. A similar distribution function can be defined for the particle surface distribution of f(S)and particle volume distribution f( V). Figure 5 also shows a similar particle distribution curve from data as measured by Whitby et al. (10)in Pasadena, CA. Two major differences can be seen by comparing the Pasadena and Jerusalem atmospheric particle distribution curves: (1) the maximum in number density in the Jerusalem case is shifted toward larger sizes; (2) in the Jerusalem air, there is no evidence for the bimodal nature of the volume distribution curve which is typical of Pasadena "smoggy" air. The differences in particle number and particle volume distribution provide evidence that the main source for particles is different. In Pasadena a major source for particle production is the photochemical smog reaction. The bimodal nature of the volume distribution curve is typical of areas in which photochemical smog is significant (11).However, in the case of Jerusalem air, there is little atmospheric photochemical activity during the wintertime. Most, if not all, of the particulate matter in the atmosphere is a result of direct emission from combustion (motor vehicles and home heating furnaces). Mean Particle Diameter. Estimations of the diurnal variations in the mean particle diameter have been carried out during this study by two different methods. By the first method, the mean diameter of the particles, from the EASA data, was calculated with the equation Dm(EASA) = CNiDi/N
(6)
where Dm is the mean diameter, Ni is the number of particles a t a given size fraction, Di is the average diameter of the particles a t a given size fraction, and N is the total number of particles. The mean particle diameter was also estimated by converting the AMM readings, which were obtained in pg/m3, to the total volume of particles (this procedure would provide reasonable answers, if the average particle density is approximately 1 g/cm3):
VT(cm3) = 10-12(AMM)(pg/m3)/p(g/cm3)
(7)
where VT is the total volume of particles in 1cm3 of air and p is the average particle density (assuming p = 1).The mean diameter of the particles was then obtained from the equation Volume 15, Number 12, December 1981 1459
n
8 . 5 . : " : ' ~ ' . . : " : :
:
,
:
.
.
.
:
:
:
:
:
.
,
'
:
:
'
:
-
0
* -
0
o
-
..
EASA APM/CNC
0 E
"
..
K W
t-
.
0.3-
W
5
'.
2
0
z
4 W
..
I: W _J
:
0. 1..
..
b-
L1: 4
a
6
18
6"'
'
I8
' . . ' E
'
'
18"
"
consistent with Joachim et al. ( I ) , who reported that in most dust storms particles measured under similar conditions &re in the range of 1-20 ym. The mean diameter obtained from the AMM and CNC data during the dust storm increased from 0.1 hm on a typical day to 0.8 ym under some storm conditions. Although the AMM-CNC method can only provide an indication of the actual mean particle diameter, our experimental evidence suggests that a significant fraction of the desert aerosol is in the submicron range. The mean diameter obtained from the EASA measurements also increased under storm conditions, but only up to 0.25 ym. While the D , (EASA) value ignores the contribution of large particles to the mean diameter, it also suggests that a substantial fraction of the desert particles are in the submicron range. The mean particle diameter size of
I
Figure 6. Diurnal variation in mean particle diameter obtained by EASA (asterisks)and by the AMM-CNC method (circles).
particle diameter during a typical dust storm, is more consistent with our experimental data. Acknowledgment
D,(AMM/CNC) = ( ~ V T / [ T ( C N C ) ] ) ~ ' ~
(8)
A continuous presentation of mean particle diameter as estimated by both methods for a period of 3 consecutive days is presented in Figure 6. The comparison between the results from the two methods is rather interesting. Throughout most of the day, with the exception of the late night hours, the agreement between the two methods is reasonable to good. However, during the late night hours and also during dust storms, no correlation between the diameter calculated from the EASA data and the AMM-CNC data could be found. While the EASA data suggested that the mean particle diameter is always in the range of 0.06-0.15 pm, the AMM-CNC methods estimated that during late night hours D , is usually from 0.2 to 0.4 ym and in some cases even higher. This discrepancy is thought to be the result of the late-night formation of large particles due to coagulation. Some of the Iarge particles were too large to be measured by the submicron size analyzer, and their contribution to the mean diameter has not been take into account. It should also be noted that the accuracy of the late-night measurements is reduced simply because of low concentration of particulates in the air. Dust Storms. On several occasions during this study, a moderate dust-storm episode occurred. During such an episode, the T S P concentration increased from an average 30min daily maximum of 80 pg/m3 to more than 2000 pg/m3 for a period of several hours. The automatic dust monitor was not ready for such episodes, which requires a much shorter sampling time, and in many cases the information obtained was incomplete. From the partial information, which proved to be valid, it was found that the large fraction of the particle mass (from 40% to 80%)was in the respirable range. This is
We thank Mr. M. Turner, the head of the Municipal Environmental Protection Unit, for his long and hard effort of initiating the Air Quality Control program in this city, Mr. B. Malenky for his technical assistance, and Mr. A. Conway for his assistance in preparing this manuscript for press. Literature Cited (I) Joachim, J. H.; Alexander,M.; Ashbel,D. J. Appl. Meteorol. 1973, 12, 792. (2) Ganor, E. Ph.D. Dissertation, The Hebrew University, Jerusalem, Israel, 1975. (3) Aerosol Technology Committee, ATHA Guide for Respirable Mass Sampling. A m . Znd. Hyg. Assoc. J . 1970,31, 133. (4) Fuchs, N. A. "Mechanism of Aerosols"; Pergamon Press: New York, 1964; p 27. (5) Husar, R. B.; Whitby, K. T.; Lin, B. Y. H. J. Collord Interface Sci.
1972,39, 177. (6) Parker, R. D.; Buzzard, G. H.; Dzubay, T. G.; Bell, J. P. Atmos. Environ. 1977,11,617. (7) Cahill, T. A.; Ashbaugh, L. L.; Barone, J. B.; Elder, R. A.; Feeney, P. J.; Flocchini, R. G.; Goodart, C.; Shadoan, D. J.; Wolfe,G. W. J . Air Pollut Control Assoc. 1977,27,675. (8) Bullin, J. A.; Moe, R. D.; Miculka, J. P. "The Measurements and Analysis of Resuspended Dust from Roadways in Texas"; College Station, TX, 1979,Texas Transportation Institute Research report 528-IF. (9) Baum, E. J.; Pitter, R. L. "The Impact of Emission from Transportation Sources on Air Quality's Atmospheric Aerosol"; State of Oregon, Highway Division, Air Quality Study 5149-621-10,1976. (10) Whitby, K. T.; Husar, R. B.; Lin, B. H. Y. J. Colloid Interface Sci. 1972,39, 177. (11) Corn, M. "Air Pollution"; Stern, A. C., Ed.; Academic Press: New York, 1976; Chapter 3. Received for review August 1, 1980. Revised manuscript received March 23,1981. Accepted J u l y 17,1981. This work was supported by resources from the Municipality of Jerusalem for which we are grateful.
Correction 1980, Volume 14 Yoshiaki Ishizu: General Equation for the Estimation of Indoor Pollution. Page 1255. The last term of eq 9 should be
1460
Environmental Science & Technology
