of the seasonal forcing of Pacific interan-nual variability. If this theory can be vali-dated, the ENSO cycle might be estab-lished as an example of low-order chaos in ahighly complex physical system.

REFERENCES AND NOTES

1. M. J. Suarez and P. S. Schopf, J. Atmos. Sci. 45,3283 (1988).

2. N. E. Graham and W. B. White, Science240, 1293(1988).

3. D. S. Battisti and A. C. Hirst, J. Atmos Sci. 46,1687 (1989)

4. M. A. Cane, M Munnich, S. E. Zebiak. ibid 47,1562 (1990).

5. M. Munnich, M A. Cane, S. E Zebiak, ibid. 48,1238 (1991)

6 E Rasmusson and T. Carpenter, Mon WeatherRev. 110,354 (1982).

7. M. A Cane and S. E. Zebiak, Science 228,1085(1985); S E. Zebiak and M Cane, Mon. WeatherRev. 115, 2262 (1987).

8. F.-F Jin, J D Neelin, M. Ghil, Science 264, 70(1994)

9. J. Bjerknes, Mon. Weather Rev. 97,163 (1969)10 S G Philander, EI Nino. La Nina, and the South-

ern Oscillations (Academic Press, San Diego, CA,1990).

11. The model parameters are CK = L/(2.3 months),CR = CK/3, a = 1/(180 days), b = 1/(120 days),and c = 1/(138 days) Unless indicated otherwise,A(h) is as in (5) with a+ = 1, a- = 1, b+ = 2.0.and b- = -b+/5. Our heuristic model equation

produces regular ENSO oscillations when runwithout the seasonal forcing term. Equation 1 wasintegrated by use of a high-accuracy, variable-

order, variable-step Adams method using routineD02CBF in Fortran Library Manual, Mark II (Numerical Algorithms Group, 1984).

12. P. Bak, T. Bohr, M. H. Jensen, Phys. Scr. T9, 5((1985); T. Bohr, P. Bak, M. H. Jensen, Phys. ReI-A 30, 1970 (1984).

13. G. K. Vallis, Science 232, 243 (1986); J. GeophysRes. 93C, 13979 (1988).

14. M. A. Cane, S. E. Zebiak, S. C. Dolan, Nature321827 (1986)

15. P. Grassberger and I. Procaccia, Physica D 9189 (1983).

16. J.-P. Eckmann and D. Ruelle, ibid 56, 185 (1992)17. J Theiler, B Galdrikian, A. Longtin, S Eubank, J

D. Farmer, in Nonlinear Prediction and ModelingM. Casdagli and S Eubank, Eds. (Addison Wesley, Redwood City, CA, 1991), pp. 163-188 Thenumber of model data points used to obtain Fig,is sufficient to determine the correlation dimensiorof the system based on the theoreticallimitation~given in (16) In contrast, a real ENSO time serie~of 100 years at most is probably too shon for thedimension estimate

18 M. A. Cane, S. E. Zebiak, Y. Xue, Eds., Proceedings of the Workshop on Decade to Cenlul}Time Scales of Nalural Climate Variability, Climate Research Committee, National Academy 01Sciences, Irvine, CA, 21 to 24 September 1992(National Academy Press, Washington, DC. irpress)

19. A M. Fraser and H. L. Swinney, Phys Rev. A 331134 (1986)

20. We thank M Munnich and Y. Xue for their helpM.AC was supponed by grant NA16-RC-o43203 from the National Oceanic and AtmosphericAdministration and grant OCE.90-00127 frorrNSF

1 July 1993; accepted 31 January 1994

Growth of Continental-Scale Metro-Agro-Plexes,Regional Ozone Pollution, and

World Food Production

W. L. Chameides,* P. S. Kasibhatla,t J. Yienger, H. Levy IIThree regions of the northern mid-latitudes, the continental-scale metro-agro-plexespresently dominate global industrial and agricultural productivity. Although these regionscover only 23 percent of the Earth's continents. they account for most of the world'scommercial energy consumption, fertilizer use, food-crop production, and food exportsThey also account for more than half of the world's atmospheric nitrogen oxide (NOJemissions and, as a result, are prone to ground-level ozone (03) pollution during thesummer months. On the basis of a global simulation of atmospheric reactive nitrogercompounds, it is estimated that about 10 to 35 percent of the world's grain productiormay occur in parts of these regions where ozone pollution may reduce crop yieldsExposure to yield-reducing ozone pollution may triple by 2025 if rising anthropogenicNOx emissions are not abated.

The unprecedented increase in the stan-dard of living of humanity since the Indus-trial Revolution can be attributed in part to

tWo factors: the development of high-in-put-high-yield agriculture, capable of feed.ing an increasingly urban population, andan urban-industrial infrastructure, heavilydependent on fossil fuels for the productiol1and transport of manufactured goods (1).The correlation betWeen agriculture andfossil fuel burning is most pronounced il1three regions of the northern mid-latitudc~(Fig. I): (i) eastern North America (250 to500N and 1050 to 6O0W); (ii) Europe (36'to 700N and 100W to ~E); and (iii)

W. L Chameides and P. S. Kasibhatla, School of Earthand Atmospheric Sciences, Georgia Institute of Tech-nology, Atlanta, GA 30332, USA.J. Yienger and H. Levy II, Geophysical Fluid DynamicsLaboratory, Princeton University, Princeton, NJ 08542,USA.

I

driving seasonal frequency: The maxima ofthe 12-month running average of the modeltime series (representing the ENSO warmevents (Fig. ID, upper panel») occur every 2to 5 years and always within the same 4months of the calendar year-precisely theENSO characteristic missing in the existingsimplified delay-oscillator ENSO models.Earlier examinations of EN SO as a low-orderchaotic system either have used simplifiedmodels lacking the essential equatorial wavedynamics (although they discussed the lock-ing to the seasonal cycle) (13) or did notfully realize the importance of mode lockingto the seasonal cycle and the mechanism ofresonance overlapping (5).

Is the irregularity of ENSO indeed due tolow-order chaos and not to random forcing(2)? The instrumental record of the realENSO data, which extends over slightlymore than 100 years, is too short to identifychaos in an observed time series. Instead, weanalyzed the results of the CZ ENSO model(7), which has been used to predict severalENSO events (14).

The diagnostic tool we used to identifychaotic model behavior was the calculationof the phase-space correlation dimension(15) from monthly averaged East PacificSST from a IO24-year run. The correlationdimension for this run is about d = 3.5 (Fig.2A), which suggests a chaotic dynamic sys-tem with a small Is(2d + I») number ofdegrees of freedom.

The correlation dimension calculationsare prone to various artifacts (16), and inorder to reduce this possibility we used acontrol time series of surrogate data (17)with the same characteristics (number ofpoints, power spectrum) as the CZ modeltime series. We chose a time series from alinear Markov model built from the CZmodel and driven by random forcing (18).The dimension estimate for the Markovmodel (Fig. 2B) indicates that this timeseries is random and distinguishable fromthe low-order dimension found with the CZmodel. This result is consistent with thesuggestion that the irregularity of ENSOevents (at least in the CZ model) is not dueto random noise (such as ocean weatherphenomena present in the CZ model).

We suggest that the natural oscillator ofthe equatorial Pacific ocean-atmosphere sys-tem can enter into nonlinear resonance withthe seasonal cycle at several periods of theoscillator (mostly 2 to 5 years). The coexist-ence of these resonances results in chaoticbehavior that is due to the jumping of thesystem among the different resonances. Thisis a feature of the quasi-periodicity route tochaos (12).

Much additional work is needed to fur-ther examine the relevance of these ideas tothe observed ENSO characteristics and toclarify the spatial and temporal mechanisms

*To whom correspondence should be addressed.tVisiting scientist at Geophysical Fluid Dynamics Lab-oratory, Princeton University, Princeton, NJ 08542.USA.

74 SCIENCE. YOLo 264 .1 APRIL 1994

greatly increased the spatial scale of photo-chemical smog in the CSMAPs. RegionalOJ pollution, often associated with sum-mertime high-pressure systems, can extendover thousands of kilometers and encom-pass agricultural as well as urban areas (13).The repetition of episodes of OJ pollutionover a growing season produces a pattern ofchronic exposure that ultimately reducescrop yields.

The specific relation between OJ dosageand crop yield is complex and depends onseveral parameters, such as the species anddevelopmental stage of the crop, the envi-ronmental conditions, and the pattern andduration of OJ exposure (10, 11). In gen-eral, crop yield reductions of 5 to 10%result when OJ reaches some thresholdconcentration, and these reductions in-crease as the OJ concentration increasesabove the threshold. For cumulative expo-sures over a growing season, the thresholdranges from -50 parts per billion by volume(ppbv) for sensitive crops, such as severaltypes of winter wheat, to 70 ppbv forinsensitive crops, such as rice (6, 11).

~ eastern China and japan (250 to 45°N and1000 to 146°E). Within each of these re-gions. intense urban-industrial and agricul-rural activities tend to cluster together intoa single large network. or plexus. of landsaifected by human activity. We use here theterm continental-scale metro-agro-plexes(CSMAPs) as shorthand for these threeregions to characterize their size and inter-lliingling of agricultural and urban-industri-al activities.

Although these regions comprise only23% of the Earth's continents. the threeCSMAPs account for about 75% of theworld's consumption of commercial energyand fertilizers and about 60% of the world'sfood crop production and food exports (2-4). They are also major source regions foratmospheric pollutants such as nitrogenoxides (NOx = NO + NOz). More than50% of global NOx emissions originate inCSMAPs. and within CSMAPs anthropo-genic emissions make up more than 75% ofthis total (Table I). Anthropogenic emis-sions arise primarily from the burning offossil fuels. However. microbial emissions

from fertilized soils are also significant.Our calculations with the GeophysicalFluid Dynamics Laboratory (GFDL) three-dimensional, global chemical transportmodel (GCTM) suggest that photochem-ical smog has become ubiquitous inCSMAPs because of these NOx emissions,and as a result many of the world's mostproductive agricultural regions are probablyexposed to harmful concentrations of ozone(03) (5-7).

Photochemical smog refers to the mix ofnoxious gases and particles produced nearthe Earth's surface from the photooxidationof VOC (hydrocarbons and other volatileorganic compounds) and CO in the pres-ence of NOx (8). Although photochemicalsmog produces several phytotoxins, we fo-cus here on 03, a component of smogwhose effects on vegetation have been welldocumented (9-12). Concentrations of 03tend to be highest in and around urbanareas. However, suburban sprawl, growingnumbers of automobiles and expandingroadway networks, as well as an increasingreliance on nitrogenous fertilizers, has

Tolal grain production (g m-2 year-I)

goON

BOoN

300N

EO

30°$

60°$ -..1

00°$ =::::::::===~:::=::::::====::==== =sJ180'W 120'W 6O0W GM 6O"E 12ooe 180oe

Fossil fuel NOx emissions (g N m-2 year-I) SCW growing season [NOy- NOxJ (ppbv)>1 >5goON

GOoN

goON

600N

3O0N

EO!I

30°8 I

60°8

90°5180oW

1

0.5

I:

.10.2i'l~ 0.1 2

;-:~, ~

i

0.05 A115:. ~ -~"~ ~

" "'0

300N

EO

30"5 l =::=::~=::==::- - 60°5 -

90°5 -.

180"W 120OW 600W GM 6O0E 1200E

Fenilizer-induced NOx emissions (9 N m-2 year-1)

120.W 60.W GM 60.E 120.E

RCW growing season [NOy -NOx] (ppbv)

~'E

l>0.5 >5

gooN

GooN10.5

0.2

0.1

I:

2

3O.N i

EOL0.05 3O.S'.'

60.S0.02 OO.S .

1SO.W 120.W OO.W GM OO;;E~20.E 180.e. 0

Fig. 2. Growing season (24) average surface [NOy -NOx] calculatedby the model with NOx emissions for the present and for 2025 forslowly changing world (SCW) and rapidly changing world (RCW)scenarios

--~

OOON

60oN

3OoN!

EoL30"8

60'S -

OO'S .180OW 120OW SO'W GM sooE 120oE 180oE. 0

Fig. 1. Geographic distributions of cereal crop production (28, 29, 32,33), NO x emissions from fossil fuel burning for the mid-1980s (16), andfertilizer -induced soil emissions of NO x for the late 1980s (30, 31, 34). GM,Greenwich meridian; EO, equator

Tha

Table 1. Emission rates for NO" (kilotons of N per day) during the growing season (24). Estimatesare given for the present (P) and for 2025 assuming slowly changing world (SCW) and rapidlychanging world (RCW) scenarios (that is, scenarios with modest and rapid expansion in the globaleconomy, respectively.) Emissions for 2025 are estimated from regional projections for fossil fueland N fertilizer usage with an econometric model (35). "Other" refers to NO" emissions sourcesfrom natural (nonfertilized) soils (34), biomass burning (36), lightning (17), aircraft (37), andstratospheric N2O oxidation (38). For sources of fossil fuel emissions and fertilizer-induced soilemissions, see (16, 34).

Emission rates for CSMAPs in northern mid-latitudesGlobal emission

ratesSource ofemissions

Eastern NorthAmerica Europe Eastern Asia

SCW RCWp p SCW RCW p SCW RCW p SCW RCW0.1 1 10

INO,- NO) (ppbv)

Fig. 3. Cumulative percentage of cereal cropsgrown in areas with a seasonally averaged[NOy -NO,J above a given concentrationbased on model-calculated [NOy -NO,J andcrop distribution in Fig. 1. Estimates are shownfor the present (P), for the present withoutfertilizer-induced emissions (No fert.), for thepresent without fossil fuel or fertilizer-inducedemissions (No fert., no FF), and for 2025 as.suming slowly changing world (SCW) and rap-idly changing world (RCW) scenarios. Concen.trations of 03 are from the empirical formula ofTrainer et al. (23).

18 16 18 18 213.6 4.5 4.4 6.4 13

2513

6~8 13 211.5 3.1 3.1

5815

7733

11033

Fossil fuelsFertilizer-

induced soilemissions

OtherTotal

3.4 3.4 3.4 4.3 4.3 4.3 2.9 2.9 2.9 4025 24 26 29 38 42 11 19 27 113

40150

40183

ger in poor countries, where the price elas-ticity of demand for staple foods remainshigh. Clearly, the full economic, environ-mental, and human costs of the effect of OJpollution on crops is a complex issue thatwill require careful assessment.

Moreover, although the current loss incrop yields because of OJ pollution appeanto be only a few percent of the total, thiswill likely change in the coming decades.The NOx emissions projected for 2025 notonly intensify pollution over two of theCSMAPs but also enhance pollution inagricultural regions of the developingworld. For 2025, we estimate that as muchas 30 to 75% of the world's cereals may begrown in regions with OJ above the 50 to70 ppbv threshold, which suggests that theagricultural losses may increase significant-ly. Additionally, this' increased pollutioneffect will be occurring at a time whengrowing populations in developing nation!will be straining food production capacities.Benefits may therefore result from reducinfuse of fossil fuels, limiting losses of nitrogerfertilizers from soils, implementing NO,emission controls, and developing °J-resis-tant crops. Enhanced networks for monitor-ing air quality throughout the developecand developing worlds to assess the extentand severity of OJ pollution on continentalscales will aid in evaluating the benefits 0:these mitigating strategies.

na where concentrations approach 1 ppbv.The increase in emissions projected for2025 intensifies the pollution in the Euro-pean and Asian CSMAPs and also producespockets of [NO, -NOxl pollution innorthern Africa, the Indian subcontinent,and South America.

Our results suggest that a sizable por-tion of the world's food crops are presentlyexposed to high [NO, -NOxl (Fig. 3). Ifthe empirical formula of Trainer et al. (23)relating 03 concentration to [NO, -

NOxl (with a slope of 8.5) were appropri-ate for all CSMAPs, our calculationswould imply that 9 to 35% of the world'scereal crops are presently exposed over thegrowing season to average 03 concentra-tions above the threshold of 50 to 70 ppbv(25). These percentages would drop to 6to 30%, if there were no emissions fromfertilizers, and to 0%, if there were noemissions from fossil fuels and ferrilizers.Typical 03 exposure-yield relations (6)suggest that the losses in crop yields fromthe current exposure are of the order of afew percent of the world's production ofcereals and other crops (26).

Industrialized countries typically restrictagricultural supply through policies to elim-inate surplus (27). Thus, a loss of a fewpercent in food production could easily bemade up by adopting compensating measuresin these countries to maintain productivityat an appropriate level to meet demand.Such compensating measures might includethe use of additional fertilizers, pesticides,and herbicides or the cultivation and irriga-tion of relatively marginal lands, with asso-ciated economic and environmental costs.On the other hand, if such compensatingmeasures are not taken to increase foodsupply, 03 pollution could cause an increasein international agricultural prices. Such anincrease in prices would likely increase hun-

SCIENCE. YOLo 264 .I APRIL 1994

We estimated the areal extent of 03pollution and its correlation with agricul-tural activity from simulations of NOx andtotal reactive nitrogen (NO,) (14) with theGFDL 3D GCTM (15-21). (A dearth ofdata on air quality on a continental scaleprecludes a direct detennination from 03observations.) On regional scales, 03 pro-duction is typically found to be limited bythe availability of NOx (22, 23), and inrural areas, the summertime 03 concentra-tion has been observed to vary linearly withthe concentration of the products of NOxoxidation (19, 21, 23)-that is,

[031 = a + b[NO, -NOxl (I)

where a represents the nominal background03 concentration in air not directly influ-enced by anthropogenic emissions (-35ppbv) , and b represents the number of 03molecules produced for each NOx moleculeemitted into the atmosphere. This latterparameter depends on the local concentra-tions ofVOC and NOx and generally variesfrom 5 to 13. These results suggest that[NO, -NOxl is a reasonable diagnostic forestimating 03 concentrations on regionalscales.

Figure 2 illustrates the average [NO, -

NOxl during the growing season (24) cal-culated for present-day NOx emissions aswell as for those projected for 2025 underscenarios for a slowly changing world(SCW) and a rapidly changing world(RCW) (Table I). Here, the influence ofanthropogenic emissions is apparent. Forpresent-day emissions, [NO, -NOxl con-centrations in excess of 2 ppbv cluster inthe CSMAPs, whereas concentrations out-side the CSMAPs generally fall well belowI ppbv. In the absence of anthropogenicNOx emissions, we find that [NO, -NOx)concentrations in the CSMAPs never ex-ceed 0.5 ppbv, except in northeastern Chi-

REFERENCES AND NOTES

1. G 0 Ness. in Population-Environment DynamicsIdeas and Observalions. G. D. Ness. W. D. Orakl'S. R. Brechin. Eds. (Univ of Michigan Press. AnrArbor, 1993). pp 33-56.

;;E%itilii,\:"",;;;:.1

tt ~ fl'):"

~

may cause larger crop reductions than thoseestimated here (12), the latter effect is probablynegligible Application rates of N fertilizers toagricultural plots are generally more than 100 kgof N ha-' year-' (2), whereas NOx emissionsfrom fossil fuel combustion and soil emissions inCSMAPs are 10 kg of N ha-1 year-' or less

(Table 1).27 P. Foster, The World Food Problem (Lynne Rien-

ner, Boulder, CO, 1992); R L. Naylor, Provisioningthe Cities into the 21"' Century (Institute for Inter-national Studies, Stanford University, Stanford,

CA, 1993)28. Agricultural Statistics 1992 (U.S Department of

Agriculture, Government Printing Office, Washing-ton, DC, 1992); World Agricultural Production(U.S. Department of Agriculture, Circular SeriesWAP 8-92, Washington, DC, 1992); additionalstatistics supplied by the Production Estimatesand Crop Assessment Division of the U.S. Depart-ment of Agriculture, Washington, DC.

29. E Mathews, Atlas of Archived Vegetation, Land-Use and Seasonal Albedo Data Sets, NASA Tech.Memo 86199 (Goddard Space Flight Center, In-stitute for Space Studies, New York, 1985).

30. W A. Kaplan, S C. Wofsy, M. Keller, J M. daCosta, J Geophys. Res 93, 1389 (1988); P. S.Bakwin et al., ibid. 95, 16755 (1990).

31. E. J. Williams, A Guenther, F. C. Fehsenfeld, ibid97,7511 (1992); M F. Shepard, S Barzetti, D RHastie, Atmos Environ 25A, 1961 (1991).

32 Cereal crops are the total of the production ofwheat, rice, and coarse grains such as maize,millet, oats, rye, and barley

33 Cereal production was derived from country-by-country statistics for 1991 (2), except for the UnitedStates, Canada, China, and the former Soviet Union,where 1991 statistics at the state or province levelwere used (28) These production statistics weregeographically disaggregated onto a 10 by 10 gridwith the land use data of Mathews (29) renormalizedso that the total cultivated area within a given coun-try did not exceed the value reponed by the Food

and Agriculture Organization (2).

34. The NO. soil source was estimated for 10 genericbiomes (water, ice, desert, tundra, scrubland,grassland, woodland, forest, rainforest, and agri-cultural lands) and geographically distributedwith renormalized land use data (29) Emissionsare assumed to be 0 for water, ice, desert, andscrubland and 3 and 05 ng of N per square meterper second for rainforests in the dry and wetseasons, respectively (30) Emissions for all otherbiomes are given by A'exp(0.071 T), where T istemperature (in degrees Celsius) and A (nano-grams of N per square meter per second) is afitting parameter for each biome (31). For agricul-turallands, A is 0 28(F) during the growing sea-son (where Fis the fertilization rate in kilograms ofN per hectare per month) (31) and is equal to thatfor grasslands during the nongrowing seasonThe fertilizer-induced soil source is the flux ob-tained with N fertilization rates reported by theFood and Agriculture Organization (2) minus theflux obtained assuming all agricultural lands emitin a manner similar to that of grasslands

35. 0 A. Lashof and D. A. Tirpak, Policy Options forStabilizing Global Climate (Hemisphere, NewYork, 1990)

36 H Levy II, W. J. Moxim, P. S Kasibhatla. J. A.Logan, in Global Biomass Buming Atmospheric,Climatic, and Biospheric Implications, J. S.Levine, Ed (MIT Press, Cambridge, MA. 1991),pp 363-369

37. P. S Kasibhatla, Geophys Res Lel/ 98, 1707

(1993).38. -, H. Levy II, W J Moxim, W. L Chameides,

J GeophYS. Res 96, 18631 (1991)39. This research was supported in part by the NSF

under grant A TM-9213643 and by the NationalOceanic and Atmospheric Administration undergrant NA36GPO250 We thank R Naylor and J ALogan for their helpful comments and sugges-tions, E Mathews for supplying the global landuse data set, and T Rocke for supplying cropstatistics

4 November 1993; accepted 15 February 1994

Unexpected Square Symmetry Seen byAtomic Force Microscopy in Bilayer Films

of Disk-Like Molecules

Nicholas C. Maliszewskyj, Paul A. Heiney,* Jack Y. Josefowicz,John P. McCauley Jr.,t Amos B. Smith III

Thin films of disk-shaped molecules are expected to display anisotropic optical and transportproperties, leading to applications in optical display or sensor technologies. Bilayer lang-muir-Blodgett films of monomeric triphenylene mesogens have been studied by atomic forcemicroscopy. The triphenylene cores of the constituent molecules tend to promote the for-mation of columnar structures in the plane of the substrate and along the- direction ofdeposition of the film. Atomic force microscopy images of bilayer Langmuir-Blodgett filmsrevealed two types of structure, one corresponding to an aligned columnar structure and theother to an unusual square lattice, which may result from the superposition of columnarstructures in adjacent layers that intersect at near right angles. Annealing such bilayers nearthe melting point of the bulk compound improved the structural ordering by reducing theangular spread of orientations associated with the well-<1eveloped columnar structure insome areas and by producing a more distinct square lattice in other areas of the sample.

.2. Agrostat-PC Production (Computerized Informa-

tion Series, Food and Agriculture Organization,United Nations, Rome, 1991).

3. 1990 Energy Statistics Yearbook (Bureau of Sta-tistics, United Nations, New York, 1992).

4 Foreign Trade by Commodities (Statistics Direc-torate, Organization for Economic Cooperationand Development, Paris, 1993).

5. Other studies have found significant reductions incrop yields in the United States and in the Kantodistrict of Japan as a result of 03 pollution (6, 7).

6 R. MAdams, J D Glyer, S. L Johnson, B A.McCarl, J Air Waste Manag 39, 960 (1989)

7. K. Kobayashi, in Tropospheric Ozone and theEnvironment ", R L. Bergland, Ed. (Air and WasteManagement Association, Pittsburgh, 1992), pp537-551

8. A. J. Haagen-Smit, Ind. Eng Chem. 44, 1362(1952); J H. Seinfeld et al., Rethinking the OzonePol/ution Problem in Urban and Regional OzonePol/ution (National Academy Press, Washington,DC, 1991)

9 W. W. Heck, J A Dunning, I J. Hindawi, Science151, 577 (1966)

10. V. C. RuneckJes and B. I. Chevone, in Surface-Level Ozone Exposures and Their Effecls onVegetation, A. S. LeFohn, Ed. (Lewis, Chelsea, MI,1992), pp 189-270.

11 D. T. Tingey, W. E. Hogsett, E. H. Lee, in Tropo-spheric Ozone and the Environment, R. L. Berg-land, D Lawson, D McKee, Eds. (Air and WasteManagement Association, Pittsburgh, 1989), pp272-288

12. C. A. Ennis, J Smith, A. L. lBZrus, Tel/us 458, 40(1993). .

13. F. M. Vukovich, W. D Bach, B W. Crissman, W. J.King, Atmas. Environ. 11,967 (1977); R Guicheritand H. Van Dop, ibid., p. 145; J. A. Logan, J.GeophYS. Res. 94, 8511 (1989); J. Fishman, F. M.Vukovich, D R. Cahoon, M. C Shipham, J. Clim.Appi. Meteorol. 26, 1638 (1987).

14. NOydenotes total reactive N and is equal to NOxplus the N-containing products derived from theoxidation of NOx-

15 The GFDL GCTM calculates time-dependent dis-tributions of NOr NOx, HN03, and peroxyacetyl-nitrate with a simplified photochemical schemeand 12 months of 6-hour wind, temperature, andprecipitation fields from a parent global circula-tion model (16, 17). The model has 11 layers inthe vertical direction and a horizontal resolution of-265 km. Table 1 lists the NOx sources includedin our application of the model; the resulting (NOy-NO,J values generally agree to within 1 IT SD ofdaytime averaged observations at a number ofcontinental and remote locations (18-21).

16. P. S. Kasibhatla, H. Levy II, W. J. Moxim, J.Geophys. Res 98,7165 (1993).

17. H. Levy II, W. B. Maxim, P. S. Kasibhatla, in TheTropospheric Chemistry of 03 in Polar Regions, H.Niki and K. H. Becker, Eds. (NATO AdvancedStudy Institute Series, Springer-Verlag, Berlin,1993), vol. 17, pp. 77-88.

18. D. D. Parrish et al., J. Geophys. Res. 98, 2927

(1993).19. T. Wang, thesis, Georgia Institute of Technology,

Atlanta (1992).20. S. T. Sandholm et al.. J. Geophys. Res. 97, 16481

(1992); E. L. Atlas etal.. ibid.. p.10449. .

21. A. Volz- Thomas et al.. in Proceedings of EURO-

TRAC Symposium '92, P. M. Borrell, Ed. (SPBAcademic, The Hague, 1993).

22. W. L. Chameides et al., J. Geophys. Res 97,6037

(1992); S. Sillman, J. A. Logan, S. C. Wofsy, ibid.95,5731 (1990).

~3. M. Trainer et al.. ibid. 98, 2917 (1993).4. The growing season is defined as November

through February for goo to 23"5, all year for 23°S2 to 23°N, and May through August for 23° to gooN.5. Virtual~ identical exposure statistics were calculat-

ed with the distribution of all f(Xxj crops surm1ed2 together on the basis of their cakJric content.6. This estimate does not account for synergistic

effects between 03 and other pollutants, such asS02' or for the fertilizing effects of NO,depositionAlthough the synergistic effects of other pollutants

rod-like molecules, disk-shaped mesogensexhibiting columnar liquid crystalline phases(3) have been shown to form Langmuir (4)and LB films (5, 6). The electronic conduc-tivity of doped bulk discotic mesophases ishighly anisotropic, with most of the conduc-

Langmuir-Blodgett (LB) films (1, 2) havelong been of intetest both as model systemsfor tWo-dimensional (20) physics and fortheir promise in technological applications.Although most research in this area hasconcentrated on LB films of amphiphilic

SCIENCE. YOLo 264 .1 APRIL 199477