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Severe air pollution episodes in Florence: data analysis and forecasting operational method A. Barbaro,* M. Bazzani,* F.Giovannim,* P. Nannini* "Environmental Physics Unit, Environmental ProtectionAgency of Tuscany (ARPAT),via diSan Salvi 12, 50144 Florence, Italy ^Environmental Unit, Provincia di Firenze, via Lorenzo il Abstract An operational method has been developed to forecast the maximum hourly concentration of NO% in Florence urban area during the current day. The situations that can lead to high concentrations of NC>2 have been identified and described by means of a statistical analysis of the environmental conditions measured. The forecasting method has been developed through the following tasks: preliminarystudy to select the most significant environmental parameters and synoptic conditions; characterisation of each day using the data measured till 10 am; selection of the best algorithm to compare the current day with the past events and to individuate the days alike; • use of statistical methods to calculate the probability to exceed the NOz warning limits during the next hours. 1 Introduction Since 1991, Italian laws require the air pollution monitoring in the most populated cities. [1 ] In Florence urban area (450,000 inhabitants) a monitoring network was been setup in 1993. Eight stations measure the concentrations of some primary and secondary pollutants (CO, NO, NO%, O?, TSP, SOz, HC), while two stations measure the surface meteorological parameters (wind speed and direction, temperature, rainfall, total and net radiation, atmospheric pressure). This network was set up by Florence District (Provincia di Firenze) and it is technically managed by Environmental Protection Agency of Tuscany (ARPAT). The eight air pollution stations are classified into 4 types: • 1 of type A, placed in an urban park, farfrom the most important pollution Transactions on Ecology and the Environment vol 8, © 1996 WIT Press, www.witpress.com, ISSN 1743-3541

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Page 1: 538 Air Pollution Monitoring, Simulation and Control€¦538 Air Pollution Monitoring, Simulation and Control sources (roads, factories, etc), for the background concentration measurement;

Severe air pollution episodes in Florence: data

analysis and forecasting operational method

A. Barbaro,* M. Bazzani,* F. Giovannim,* P. Nannini*

"Environmental Physics Unit, Environmental Protection Agency of

Tuscany (ARPAT), via di San Salvi 12, 50144 Florence, Italy

^Environmental Unit, Provincia di Firenze, via Lorenzo il

Abstract

An operational method has been developed to forecast the maximum hourlyconcentration of NO% in Florence urban area during the current day. Thesituations that can lead to high concentrations of NC>2 have been identified anddescribed by means of a statistical analysis of the environmental conditionsmeasured. The forecasting method has been developed through the following

tasks:• preliminary study to select the most significant environmental parameters and

synoptic conditions;• characterisation of each day using the data measured till 10 am;• selection of the best algorithm to compare the current day with the past

events and to individuate the days alike;• use of statistical methods to calculate the probability to exceed the NOz

warning limits during the next hours.

1 Introduction

Since 1991, Italian laws require the air pollution monitoring in the mostpopulated cities. [1 ] In Florence urban area (450,000 inhabitants) a monitoringnetwork was been set up in 1993. Eight stations measure the concentrations ofsome primary and secondary pollutants (CO, NO, NO%, O?, TSP, SOz, HC),while two stations measure the surface meteorological parameters (wind speedand direction, temperature, rainfall, total and net radiation, atmosphericpressure). This network was set up by Florence District (Provincia di Firenze)and it is technically managed by Environmental Protection Agency of Tuscany(ARPAT).

The eight air pollution stations are classified into 4 types:• 1 of type A, placed in an urban park, far from the most important pollution

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sources (roads, factories, etc), for the background concentrationmeasurement;

• 3 of type B, placed in residential areas, but far from the most importantroads;

• 3 of type C, placed near the most important roads, at 2 -6 m distance fromthe traffic lane;

• 1 of type D, placed outside the urban area, to measure photochemical smog

Concerning the two meteorological stations, one is placed on a buildingroof in the centre of the city (76 m on sea level), the other up a hill near thetown (325 m on sea level). Further, the traffic flow is measured near one of thetype C station: the data obtained are quite representative of the total traffic inthe urban area.

A more recent law states that Local Authorities must take action againstemissions of pollutants whenever its concentrations exceed then stated dailywarning limits. [2 ] In Florence area NOz is the pollutant that more frequentlyexceeds its limit (200 |ig/nf, equivalent to 0.106 ppm at 25°C and 1013 hPa,expressed as the highest hourly concentration in a day). Whenever this limit isexceeded in half of the type A and B stations (2 in Florence), Local Authoritiesannounce a 'caution condition" and during the next 24 hours can stop thecirculation of cars which are not equipped with catalytic converters (60- 70% oftotal cars are not equipped). In Florence these situations occurred 24 timesduring the last 3 winter periods (from November to March). The emergencymeasures are effective only when they match the severe pollution episodes: it isthen necessary to forecast if NOi warning limits will be exceeded (at least24 48 hours before).

The methods usually employed to carry out this kind of forecast are basedon statistical techniques which relate meteorological parameters with pollutantsconcentrations. [3 ,4,5] Even if this approach needs a large amount of datacollected in the same monitoring stations, it has the advantage to providespecific forecast for the monitored urban area. In order to successfully forecaststhe severe episodes it is necessary to carefully choose the variables that can bestdescribe critical environmental conditions. For this purpose we must take intoaccount that severe episodes occur during periods characterized by highatmospheric stability, i.e. by low mixing-height and low wind speed. [6 ] Theseweather conditions are generally related to typical synoptic situations. [7 , 8 ]

2 Preliminary statistical analysis

The NC>2 concentrations and the meteorological data measured during the last 3winter periods (1993/94, 1994/95 and 1995/96: 454 daily records) have beenemployed. To choose a NOz indicator, the daily maximum hourly concentrationof each A and B station has been considered. The indicator is obtained bychoosing the second highest value among the 4 ones. When this indicatorexceeds 200 ng/m^ the day is declared to be in 'caution condition". In thefollowing this indicator is named NO2ab. Note that in Florence usually the dailyhighest NOz concentrations occur from 12 am to 5 pm.

Specific indicators have been chosen for the other environmental variables

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(i.e. meteorological parameters, traffic count, and precursors concentrationsmeasured in type B stations). Among these indicators special attention has beendedicated to those using the data till 10 am. In fact daily ARPAT is obliged toinform Local Authorities earlier than 11 am about air quality and its short andlong-term forecasts.

Standard statistical tests (/-Student and Wilcoxon-Mann-Withney) havebeen used to identify the most significative meteorological indicators. To thispurpose the whole sample has been split in 2 sets: one contains the daily recordswith NO2ab>180 g/m (UP group), the other daily records are collected in thesecond set (DOWN group) Some indicators have statistically different meanvalues for the 2 sets. It is the case of the indicators relating to the wind speedand to the temperature inversion (calculated as the hourly temperaturedifference between the two meteorological stations). The significance of theseindicators has been also confirmed by a correlation and partial correlationanalysis. The results of this preliminary study are consistent with the physics ofthe problem.

Moreover, the synoptic weather conditions on Italy for each day of theinvestigated period have been. They have been classified between cyclonic (L)and anticyclonic (H) situations, with a further distinction inside the two classesbased on the prevalent local wind direction. [9 ]

The results show that the 53% of the UP group days belongs to the HWclass, characterized by an anticyclonic area expanding from the Atlantic Oceanover the middle-western Mediterranean Sea. Sometime a secondary anticyclonicarea takes pace on North Africa, driving on Italian peninsula warmer and drierair from Sahara desert. This synoptic condition yields over Italian peninsulabreezes or West low winds, clear sky, land or subsidence inversions, highstability of low layers. During the winter this situation causes the pollutanttrapping in a very low mixing layer (up to 10CM50 m).

High atmospheric stability conditions of short duration can occur alsoduring other synoptic conditions, belonging to anticyclonic as well as cyclonicclasses. In particular the analysis has shown that the 23% of the UP group daysbelongs to the L classes. These conditions can occur just before the arrival of anatmospheric disturbance, proceeded by a warm front moving over the colderearth surface.

This preliminary analysis shows that using local environmental data andsynoptic informations it is possible to identify the days characterized by weatherconditions that can lead to peak NOi concentrations in Florence urbanarea. [10] However, for an operative forecast, a large number of specificindicators is needed, as well as the comparison of their informative contents.

3 Forecasting methods

As previously discussed, severe air pollution episodes forecast needs the use ofpredictive models which can handle many indicators at the same time. Usually,multivariate techniques are used.

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3.1 Statistical models obtained by means of segmentation and groupingtechniques

A set of models has been identified using the Chi-squared Automatic InteractionDetector (CHAID) analysis, which performs a segmentation modelling. The aimis to divide a statistical population into groups that differ from each other withrespect to the values of a designated variable. [11 ] The CHAID analysis dividesa population into two or more distinct groups based on categories of the 'best"predictor of a dependent variable or criterion; the segmentation can be repeatedon each of these groups, using a different predictor. This splitting processcontinues until no more statistically significant predictor can be found. TheCHAID method analyses the predictor-criterion contingency table for eachvariable, by means of a log-linear statistical model The explicating capability ofeach predictor is estimated by grouping the predictor categories in everypossible ways in order to maximize the dependence relation between predictorand criterion. At the end of the process a set of groups is obtained (named finalstates in the following), having its own criterion distribution. The cases insideeach final state are characterized by the same predictors values.

It is necessary to point out that the CHAID method can be used only withvariables whose values are classified into distinct groups (categorical variables).So, continuous variables have to be properly converted into categorical ones bygrouping adjacent values.

In this application of the CHAID method the criterion is the NO2abindicator, whose values have been split into the followingcategories: Dl[NO2ab<100], D2[100<NO2ab<180], Ul[180<NO2ab<220],U2[NO2ab>220]. The Ul and U2 categories distinguish cases of less and moreseverity respectively. In the same way, the predictors have been chosen amongthe environmental indicators available earlier than 10 am, properly categorizedobserving the range of each variable and its phenomenological behaviour withrespect to NO2ab (scatter plots).

The data belonging to the first two winter periods have been used to set upthe models, while the third winter period as test set. Besides, the preliminarystatistical analysis, confirmed by preparatory CHAID applications, has shownthat NO2ab did not ever exceed 200 jug/m3 either on holidays or on rainydays. [12] This is easily understood taking into account the traffic reductionduring the holidays mornings, and the chemical-physical reactions which formNO2 in atmosphere [13 ] For this reason, this kind of days has been rejectedfrom the initial data set.

Nine models have been set up using this final data set. As explanatoryexample, the tree diagram of one of these is shown in Figure 1. The firstpredictor of this model is the number of hours with temperature inversion in thetime interval from 12 am of the day before to 10 am of the analysed day (named77 in Figure 1). The second predictor is the mean wind speed of the first 10hours of the analysed day measured at the hill meteorological station (namedWS in Figure 1). The model has four final states. The states 1 and 3 arecharacterized by zero expected frequencies into the Ul and U2 categories ofNO2ab. The state 4 contains a great number of critical events (66% of casesinto the Ul and U2 categories). The state 2 shows more uncertainty, with 70%of cases into the D2 category and 25% of cases into the Ul and U2 category.

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D1D2U1U2

III cat.

29 66%15 34%0 0%0 0%

NA,1Ki

$

r

D1D2U1U2

D1D2U1U2

JO2ab

38 28%70 52%17 13%10 7%

in

III cat.

9 14%46 72%8 13%1 2%

2,3

•f

ill cat.

D1 0 0% 1D2 9 33% 1U1 9 33% IU2 9 33% 1

Final State 1 JWS Final State 4

^WScat.NA,1 WS cat. 2,3

Final State 2 Final State 3

Figure 1. Tree diagram of a CHAID model. The temperature inversion hoursnumber predictor (TI) is classified into the following categories:NA [not available data], 1[TI=0], 2[1<TI<6], 3[7<TI<12], 4[TI>13]. The meanwind speed at the hill station predictor (WS) is classified into the followingcategories: NA [not available data], 1[WS<1.5], 2[1.5<WS<3], 3[WS>3]. Foreach final state, the number of cases and the relative percentages of cases insideevery category of NO2ab are indicated.

The model performance in the 72 working-days without rain in the lastwinter period (1995/96) is summarized in Table 1. During this period 13 criticalepisodes (11 belonging to the Ul category and 2 to the U2 one) occurred: themodel has classified 11 of these (including the 2 belonging to the U2 category)into the final state 4, 1 into the state 2, and 1 into the state 3. Moreover, themodel has classified into the final state 4 only 4 cases belonging to the Dl andD2 categories.

Note that the two episodes belonging to the Ul category -that the modelshowed in Figure 1 has classified into the final state 2 and 3- are correctlyclassified by some of the other 9 models which use different indicators aspredictors. These results suggest to use several models, and the to compare

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their results to solve this kind of uncertainties. Of course the CHAID modelsperformances can be improved including the last winter period in the data usedfor their set up.

Table 1. Performance of one of the CHAID models in test period(working-days without rain in winter 1995/96)

NO2ab categoriesDlD2UlU2

totalpercentage of casesinto Ul and U2

expected percentageof cases into Ul andU2 (see Figure 1)

final statesstate 119800

27

state 31081019

2%

0%

state 2281011

9%

24%

state 4139215

73%

67%

total322711272

Using this set of 9 models, it is possible to forecast daily the probability thatNO2ab exceeds the warning limit not later than 10 am of the same day. Themodels that use only meteorological predictors can be employed with forecastvalues of the same variables (obtained by a meteorological Limited Area Model,LAM) to extend their forecast over the next days.

3.2 Statisticaltechnique

models obtained by means of ^-nearest-neighbours

The second way used to develop a forecast method is based on the so called^-nearest-neighbours technique. Each daily record is located into a^-dimensional space whose coordinates are n environmental indicators. If theseones are properly selected, the daily records distribution shows areas withNO2ab similar values; in particular the severe episodes (NO2ab>180 ng/W)gather together. To forecast the NO2ab value in a given day, whose values ofthe n environmental coordinates are known, the t-nearest-neighbours events aresearched. This operation is equivalent to select the first k past daily records,having similar environmental indicators values.

The simplest method to search and to select the events is to calculate theirdistance from the n-dimensional point in which NO2ab has to be forecast,assuming that the space is endowed with the Euclidean metric (Figure 2).Theoretically this assumption means that the chosen ^-coordinates areorthogonal. When this assumption is not satisfied, a generalized distance isused, such as the Mahalanobis distance, which takes into account correlatedvariables. In this case the distance involves a coordinate transformation bymeans of the variance-covariance matrix. [14 ] The Mahalanobis distance has notbeen used in this first application. After statistical standardization of the

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indicators, the Euclidian distance was employed.

• NO2ab>220 (U2)

+ 180<NO2ab<220(U1)

O 100<NO2ab<180(D2)

X N02ab<100(D1)

"0+x

X0

X

X

o

mean wind speedin city (within 10 am) hours of temperature

inversion (within 10 am)

Figure 2. Example of similar events search in 3-dimensional space: 18 January1996 is the day in which NO2ab has to be forecast.

The data set and the environmental indicators used are the same as inCHAID application. This method has been tested in several spaces havingdifferent coordinates, both as number and type. The search of the^-nearest-neighbours has been repeated for each of the 72 daily recordsbelonging to the winter period 1995/96, used as test set. At the end of the^-nearest-neighbours search, the forecast result can be expressed either by anestimated NO2ab value or by the probability that NO2ab belongs to the UPgroup. This last solution has been preferred because of the easier comparisonwith the CHAID method results.

Anyway, the k value has to be indicated: in this study k=\3 was empiricallychosen. Moreover it has preferred to compute the probability to be into the UPgroup Drawing from the data set a k size random sample without replacement,the probability to find m events in the UP group is expressed by thehypergeometric distribution PR (m, k-tn, NUP, N-Nup), where NUP is the numberof cases into the UP group and N is the total number of daily records.[15 ] Inthis case the probability to draw more than 4 events in the UP group is ?4=26%,and to draw more than 6 events is Pe=3%. Of course the selection obtained bythe -nearest-neighbours method is not randomly carried out. So, the probabilitythat NO2ab exceeds warning limit is very high when more than 6 events in theUP group are found; on the contrary it is very low when less than 4 events in

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the UP group are found. In this way three classes of probability has beenidentified.

In Table 2 is summarized the performance of one of the employed13-nearest-neighbours models. The model uses 7 indicators: mean wind speedsat the city station and at hill one (time interval: 0-10 am), mean temperatureinversions (at 10 am, 0-10 am, 12 am-12 pm of the day before), car counting(at 8-9 am). The model has identified correctly 10 of the 13 critical episodes(high probability class), while 3 events belonging to the DOWN group werewrongly identified. The 3 severe episodes left has been classified as uncertainones.

Table 2. Performance of one of the 13-nearest-neighbours models in the testperiod (working-days without rain in winter 1995/96)

NO2ab groups

DOWNUPtotal

percentage ofcases into the UP

group

probability that NO2ab exceeds warning limitlow

(0-4 UP events)55055

0%

uncertainly(5-7 UP events)

134

23%

high(8- 13 UP events)

31013

77%

The performances of this method too can be improved including the lastwinter period in the data base. In the final operative version the search shouldbe carried out updating daily the data base.

Finally, it has to be pointed out that the 6-nearest-neighbours technique issuccessfully used in avalanches forecast too. [16 ]

4 Operative application

The two forecasting methods shown previously allow to forecast -within 10 amof the same day- the probability that NO2ab exceeds warning limit, using theknown environmental indicators. The forecast is expressed as probability toreach the "caution condition" as defined by the Italian law.

To carry out a long term forecast (i.e. in the next 2-K3 days), the timechanges of the selected environmental indicators have to be taken into accountwith particular attention to the meteorological ones. To this purpose, forecastsynoptic conditions on Europe can be used to understand if the situation evolvestowards a critical one, as identified by preliminary statistical analysis. Theseinformations can be received from the European Centre for Medium-RangeWeather Forecasts (ECMWF) or from other Meteorological Services. Forecastsynoptic charts for 34-6 days are daily at disposal on ECMWF WWW site.

The meteorological indicators employed in the two forecasting method canbe predicted using a LAM having adequate space and time resolution. FlorenceDistrict chose LAMBO, the limited area model set up by Meteorological

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Service of Emilia-Romagna Region (SMR-ER).[17 ] This LAM runs daily onthe C94/2128 super-computer of North-Est University Computing Centre(CINECA): the forecasts are at disposal on its WWW site. LAMBO uses theECMWF forecasts as boundary conditions: through two consecutiveintegrations a spatial resolution of 20 km is obtained. The time resolution is 6hours for the next 72 hours, starting each day from 0 am.

At present LAMBO is employed daily to obtain high resolution weatherforecasts on Florence area, with special attention to wind speed and direction,temperature profile, rainfall. However, the use of obtained variables into theCHAID and ^-nearest-neighbours models will be soon tested to extend theirforecasts over the next days. During the last winter (1995/96) ARPAT has dailycarried out forecasts about the probability that NOz exceeds warning limit usingthe two shown methods and the synoptic informations. In this period, the trafficwas stopped on three days

A special success was obtained on 18 and 19 January 1996, when high NO2concentrations were occurred. Till 17 January, the weather conditions werecharacterized by strong North-East wind and low temperature, which maintainNC>2 concentrations at low values. During the night from 17 and 18 January, theextension of an anticyclonic area over Italy caused a sudden calm of wind and astrong land temperature inversion. This episode was been correctly forecastedthree days before, so the emergency measures were coincident with it

Acknowledgements - The authors wish to acknowledge Dr P. Battini, head ofEnvironmental Physics Unit (ARPAT), for useful technical discussions; GeneralDirection of ARPAT and Environmental Unit of Provincia di Firenze to havesupported this study.

References

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2 . Decreto del Ministro deir Ambiente 15 aprile 1994, Norme tecniche inmateria di livelli e di stati di attenzione e di allarme per gli inquinantiatmosferici nelle aree urbane, ai sensi degli articoli 3 e 4 del decreto delPresidente della Repubblica 24 maggio 1988, n.203, e delFart.9 del decretoministeriale 20 maggio 1991, Gazzetta Ufficiale della Repubblica Italiana,serie generale, 1994, 107, 101-103.

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6 . Pasquill, F. & Smith, F.B., Atmospheric Diffusion. Study of the dispersionofwindborne material from industrial and other sources, pp. 351-378, EllisHorwood Ltd, Chichester, 1983.

7 . Kallos, G, Kassomenos, P. & Pielke, R.A., Synoptic and mesoscaleweather conditions during air pollution episodes in Athens, Greece,Boundary-Layer Meteorology, 1993, 62, 163-184.

8 . Hernandez, E, Garcia, R. & Finzi, G, The SO 2 Pollution in Madrid. 1-AStudy of the Meteorological and Statistical Aspects, II Nuovo Cimento,1983, 6C, 595-603.

9 . Bernacca, E., La previsione del tempo e i climi della terra e d'ltalia,Chapter 13, / principali tipi di tempo suiritalia, Editrice La Scuola,Brescia, 1972.

10 Barbaro, A., Giovannini, F & Nannini, P., Un primo studio sulle relazionifra inquinamento da NOz e condizioni meteorologiche nell'area urbana diFirenze, j«2K, 1994,6,4-9.

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16 .Bolognesi, R, Buser, O & Good, W., La previsione locale delle valanghein Svizzera: strategia e strumenti, Neve e Valanghe, 1995, 24, 18-21.

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