'I'he methodologies and concepts de- scribed above were tested through ap- nlication to real data bases. and used Lo. 1;;;;. ~ikra. 12. Wcsl 1,os Angeles.
How Many Stations Are Required to Estimate the Design Value and the Expected Number of Exceedawces of the Ozone Standard in an Urban Area?
F. L. Ludwig, H. S. Javilz, and A. Valdes SRI international Menlo Park. CA
A methodology lor determining regional ozone design values and the expected number of ex-
fail outside the range hounded by the smallest and largest values in the input data list. Rather, the analysis scheme fits second degree polynomials to the data so that maxima a ~ i dminima can be specified that are not. coincident with the observation sites in the input data set. The technique is based on one developed by R. L. Mancux)Vor the analysis of meteorological variables. Its application to ozone monitoring data is described in detail hy Ludwig e l nI.1 Such interpolation schemes can be sensitive to had data, especially unT h e establishment and operation of air puterized technique that is described realistically high values. Therefore, the quality monitoring stations is very exbriefly herein (details, including a ) m data had to be carefi~llyscreened before pensive so it is desirable to use only as puler programs and user's manuals, the techniques were applied. many as are necessary to meet the obhave heen described elsewhere).' The method divides the regiol~of injectives of the monitoring. In the case of terest into cells that are small enough so ozone, a frequent objective is to deterProcedures for Determining that the ozone concentrations within mine the numher of exceedances that Exceedances and Design Value from them can he characterized by a single can be expected in an area and a design Observed Data value. The philosophy was adopted that value that can he used in the developthis is important only in "critical" cells ment of control strategies. This paper 1,udwig et nl.' have described two where the highest design values, or most describes the application of some comgeneral methods for estimating design exceedances, are to be found. Thus, the puterized analytical techniques to four values and exceedances from ozone method begins with relatively large cells special ozone monitoring networks. The monitoring data. ' n ~ e yrefer to these as that are characterized by the ozone four networks were chosen hecause they "isopleth modeling" and "probabilistic concentration estimates a t their center include an unusually large numher of modeling." The two aljproaches give and proceeds to generate smaller cells in monitoring stations, which allows the substantially similar results, so only the effects of reducing the number of monconceptually simpler isopleth tech~~ique those areas where the design value is high or the number of exceedances is itoring stations to be assessed quantiwill be discussed here. Basically, the great. The method starts with a grid of tatively. T h e cities examined are: isopleth met:hodology generates ozone Houston, Los Angeles, Philadelphia, large cells, subdivides those with tlie concentration estimates for each cell in and St. Louis. There is great diversity in most exceedances or the highest estia network of cells that overlie the area of mat.ed ozone values, and reljeats the the geographical location, clin~ateand interest. 'This is done for all days in the process until further subdivision does types of e~nissionsrepresented by these data set when it is likely that exceednot change the results appreciably. four cities, so the results of this study a i m s may have occurred x~inewherein Figure 1 shows a schematic represhould have a fairly wide application. the region. These estimated values are sentation of the nested grid approach T h e results described below are enused to determine the number ol' exwhich was used as part of the isopleth couraging, as they suggest that esti1nat.e~ ceedances and the design value for each methodology. Figure l(a) shows the of design value are substantially the cell. same for the fnll network of stations, as inilial grid where cont:ent.rations arc 'I'he isonleth inodelincr aweroach nses estimated by sea)nd degree interpolathey are for ;I well designed suhser (in most cases) which includes only about tion for t.he center of each cell (indicated one third of the full net.work. The design by the points in the figure) for each day not linear int.erpolat.ion or a simple inwhen cxceedances of the 120 pph stanvalue and tlie cxpect.ed ilumher of exverse distance weighting approach. Silclr ceedances ~vereest.i~nat.edby a cornschemes cannot. reproduce values that (',>sri::l,, l!IR:l ,iir l~,>ll,,,,,>,>l.k siles usina data from lhc, summer and autismn oI' 1977. . Silc
P s ilirluilpd i l l i.nsist.ent \vitIl ICI'A ]philosul,hy. ~
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Conclusions
10 28 28 22
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Stmthmann. F".A. Schiermeier, Ilocurnenlation of the Regional Air 1'01liltion Study (RAI'S) and lielaled invcsliaations ia the St. Louis Air Quality Cont.ro1 liegioa,': ISI'A lieport 60014-79076, I1.S. Env~mnmental 1'mlect.ion
value, bul, this discrepancy has heen shown t o be a n artifad, of the methodology. ~
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This study ruas conducted under National Coouerative I-Sirliwav IZesearch I'ragram liroject 20-I.&. 'Tile ol,inions and findinrcs exnressed or imolied in this paper are iilosiof the authors. They are not necessarily those of the 'I'ransportation Ilesearch tloard, t h e National Academy of Sciences, t h e I>edcral I-fighway Adrninistratio~~, the American Association US S t a t e I-iighway a n d 'l'rnnspiirtalion Officials, nor of tlie individual states part.icipat.ing in the Na1.ional (?ooperativc I-fighwny liesearcli l'rogra~n. Valuahlr 1 t 1 rvcre pr>' Messrs. 1)ale Convent.ry, Normarr splleric Science Center and lllii 1);itil I'ossiel, Willia~nI.el,artmenl, Slil Si~mmcrhaysof t.hc l1.S. ICnviron~rrentaI Inlcv,~ationiil,:i:i:H~x\~cns~~~ood i Avel'rol,eel,ior~ A ~ ; ~ ! I I Mr. C ~ ; ( h ~ r y'~an1i~1liiIl liiic. Meilkl l'arlc. ('A 94025. 'This o S I?:I~~;III ('orl~or:~t,ion;Mr. I)
