Traitement et analyse de séries chronologiques continues de ...

More documents

Recommendations

Info

Annexes to monitoring and modelling]. PhD thesis: INSA de Lyon, France, 374 p. (in French). Available at http://docinsa.insa-lyon.fr/these/pont.php?id=torres Van Bijnen M. and Korving H. (2008). Applications and results of automatic validation of sewer monitoring data. Proceedings of the 11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008, 10 p. Williamson J.H. (1968). Least-squares fitting of a straight line. Canadian Journal of Physics, 46, 1845-1847.
Annexes Assessing dry weather flow contribution in TSS and COD storm events loads in combined sewer systems M. Métadier* , ** and J. L. Bertrand-Krajewski** * Safege, 15-27 rue du Port, 92022 Nanterre cedex, France (E-mail: marjolaine.metadier@safege.fr) ** Université de Lyon, INSA Lyon, LGCIE – Laboratoire de Génie Civil et Ingénierie Environnementale, 34 avenue des Arts, F-69621 Villeurbanne cedex, France (E-mail: marjolaine.metadier@safege.fr) Abstract Continuous high resolution long term turbidity measurements along with continuous discharge measurements are now recognised as an appropriate technique for the estimation of in sewer Total Suspended Solids (TSS) and Chemical Oxygen Demand (COD) loads during storm events. In the combined system of the Ecully urban catchment (Lyon, France), this technique is implemented since 2003, with more than 200 storm events monitored. This paper presents a method for estimation of the dry weather contribution to measured total TSS and COD event loads with special attention devoted to uncertainties assessment. The method accounts for the dynamics of both discharge and turbidity time series at two minutes time step. The study is based on 180 dry weather days monitored in 2007-2008. Three distinct classes of dry weather days were evidenced. Variability analysis and quantification showed that no seasonal effect and no trend over the year were detectable. The law of propagation of uncertainties is applicable for uncertainties estimation. The method has then been applied to all measured storm events. This study confirms the interest of long term continuous discharge and turbidity time series in sewer systems, especially in the perspective of wet weather quality modelling. Keywords: COD; dry weather contribution; event loads; turbidity; TSS; uncertainties; water quality modelling INTRODUCTION As specified in the French transcription (decrees of June 22nd, 2007 and February 15th, 2008) of the European Urban Waste Water Directive of May 21 st , 1991, TSS and COD loads at significant discharge points, especially CSOs (Combined Sewer Overflow structures) in sewer systems have to be monitored. Thus, practical and efficient solutions have to be implemented, making particularly challenging the development of economically affordable solutions for a reliable continuous assessment of pollutant loads during storm events. Since a decade, surrogate continuous measurements such as turbidity or UV-visible spectrophotometry are increasingly being recognised as promising techniques for water quality measurement in sewer systems. Several research groups are working on that field worldwide (e.g. Gruber et al., 2005; Fletcher and Deletic, 2007; Muschalla et al., 2008; Ruban et al., 2008; Lacour et al., 2009; Métadier and Bertrand-Krajewski, 2009). Significant knowledge has been acquired and new methodologies for implementing these techniques were developed with a particular focus on assessing measurement uncertainties (e.g. Bertrand-Krajewski et al., 2008; Fletcher and Deletic, 2007; Ruban and Joannis, 2008; Lacour, 2009). Available continuous turbidity time series can be used for various purposes, including estimation of pollutant loads for regulatory requirements, operation, planning and rehabilitation of sewer systems, real time control and modelling. This paper deals with the use of discharge and turbidity time series for pollutant load modelling during storm events in combined sewer systems, with a special attention devoted to the estimation of the dry weather (DW) contribution to total TSS and COD loads measured during storm events. Indeed, most models of storm weather pollutant loads in combined sewer systems are based on the assumption that
Page 1:
N° d’ordre 2011ISAL0018 Année 2
Page 5 and 6:
Remerciements Je voudrais d’abord
Page 7:
Traitement et analyse de séries ch
Page 11:
Table des matières
Page 14 and 15:
Tables des matières 3.2.1 Test de
Page 16 and 17:
Tables des matières CHAPITRE 9 : P
Page 18 and 19:
Tables des matières 14.3.4 Calcul
Page 21:
Liste des abréviations DCO Demande
Page 24 and 25:
Liste des variables DTS E f f -1 du
Page 26 and 27:
Liste des variables S Pond somme po
Page 29:
Introduction générale 1
Page 32 and 33:
Introduction générale et al. (200
Page 34 and 35:
Introduction générale Structure d
Page 37:
Partie 1 : La modélisation de la q
Page 40 and 41:
Partie 1 - Chapitre 1 : Introductio
Page 42 and 43:
Partie 1 - Chapitre 1 : Introductio
Page 45 and 46:
Partie 1 - Chapitre 2 : Approches d
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57:
Page 60 and 61:
Partie 1 - Chapitre 3 : Choix des a
Page 62 and 63:
Partie 1 - Chapitre 3 : Choix des a
Page 65:
Partie 2 Test des modèles et incer
Page 68 and 69:
Partie 2 - Test des modèles et inc
Page 70 and 71:
Partie 2 - Chapitre 4 : Le concept
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 83 and 84:
Partie 2 - Chapitre 5 : Les méthod
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Partie 2 - Chapitre 6 : Test des mo
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127:
Page 131:
Partie 3 Construction de la base de
Page 135 and 136:
Partie 3 - Chapitre 7 : Présentati
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Partie 3 - Chapitre 8 : Traitement
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171:
Page 175:
Partie 4 - Analyse des données Int
Page 178 and 179:
Partie 4 - Chapitre 10 : Variabilit
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Partie 4 - Chapitre 11 : Estimation
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
Partie 4 - Analyse des données : C
Page 241:
Partie 5 Choix des modèles et mét
Page 245 and 246:
Partie 5 - Chapitre 13 : Modèles d
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
Page 270 and 271:
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Page 280 and 281:
Partie 5 - Chapitre 15 : Méthodolo
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 291:
Partie 6 - Résultats des tests Int
Page 294 and 295:
Partie 6 - Chapitre 16 : Test des m
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
Page 317 and 318:
Page 319 and 320:
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339:
Page 343 and 344:
Conclusion générale Retour sur la
Page 345 and 346:
Conclusion générale évidence la
Page 347 and 348: Conclusion générale données acqu
Page 349: Bibliographie 321
Page 352 and 353: Bibliographie Bertrand-Krajewski J.
Page 354 and 355: Bibliographie Bujon G. (1988). Pré
Page 356 and 357: Bibliographie Dorval F. 2010. Const
Page 358 and 359: Bibliographie Gupta K. et Saul Adri
Page 360 and 361: Bibliographie Kuczera G. et Parent
Page 362 and 363: Bibliographie Muschalla D., Schneid
Page 364 and 365: Bibliographie monitoring data. Proc
Page 366 and 367: Bibliographie Vrugt J. et Bouten W.
Page 369: Annexes Annexe 1 Métadier M. et Be
Page 372 and 373: Annexes INTRODUCTION Les exigences
Page 374 and 375: Annexes correspondante (équation 3
Page 376 and 377: Annexes Figure 26. Relations [MES]-
Page 378 and 379: Annexes avec m Xi le flux de pollua
Page 380 and 381: Annexes MES 2000 Masses événement
Page 382 and 383: Annexes capteurs, utilisation des r
Page 384 and 385: Annexes ANNEXE A Cette annexe prés
Page 387 and 388: Annexes From mess to mass: a method
Page 389 and 390: Annexes Sensor uncertainties. Calib
Page 391 and 392: Annexes terminated. Else Test 2 is
Page 393 and 394: Annexes hydrologic events. Event lo
Page 395 and 396: Annexes a b c 1500 TSS (kg) COD (kg
Page 397: Annexes test and apply the enhanced
Page 401 and 402: Annexes t f 2 2 2 2 2 2 u( M X )
Page 403 and 404: Annexes M M M X _ WW X X _ DW 2 (
Page 405 and 406: Annexes class 4. This regression co
Page 407 and 408: Annexes The analysis of the evoluti
Page 409: Annexes Lacour C., Joannis C. and C
show all

Traitement et analyse de séries chronologiques continues de ...

Create successful ePaper yourself

Delete template?

Save as template?