LEADER 01199nam a2200289 a 4500 001 991003195169707536 008 080118s2007 sz a b 000 0 eng d 020 $a9783039112760 035 $ab13646850-39ule_inst 040 $aDip.to Lingue$bita 082 04$a306.44 245 00$aDiscourse and Contemporary Social Change /$cNorman Fairclough, Giuseppina Cortese & Patrizia Ardizzone (eds) 260 $aBern ;$aNew York :$bPeter Lang,$cc2007 300 $a555 p. :$bill. ;$c23 cm 440 0$aLinguistic insights ;$vv. 54 504 $aContiene riferimenti bibliografici 650 4$aSociolinguistica 650 4$aAnalisi del discorso 700 1 $aFairclough, Norman$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0254264 700 1 $aCortese, Giuseppina$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0132545 700 1 $aArdizzone, Patrizia 907 $a.b13646850$b28-01-14$c18-01-08 912 $a991003195169707536 945 $aLE012 306.44 FAI 3$g1$i2012000257900$lle012$o-$pE0.00$q-$rl$s- $t0$u9$v0$w9$x0$y.i14652444$z18-01-08 996 $aDiscourse and Contemporary Social Change$91463861 997 $aUNISALENTO 998 $ale012$b18-01-08$cm$da $e-$feng$gsz $h0$i0 LEADER 03315nam 22006975 450 001 9911007357103321 005 20250603131217.0 010 $a981-9657-77-6 024 7 $a10.1007/978-981-96-5777-3 035 $a(CKB)39160596400041 035 $a(MiAaPQ)EBC32145061 035 $a(Au-PeEL)EBL32145061 035 $a(DE-He213)978-981-96-5777-3 035 $a(OCoLC)1522470441 035 $a(EXLCZ)9939160596400041 100 $a20250603d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Science in Air Quality Monitoring /$fby Hui Liu, Yanfei Li, Zhu Duan 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (258 pages) 225 1 $aEngineering Applications of Computational Methods,$x2662-3374 ;$v23 311 08$a981-9657-76-8 327 $aChapter 1 Introduction -- Chapter 2 Data preprocessing in air quality monitoring -- Chapter 3 Data decomposition in air quality monitoring -- Chapter 4 Data identification in air quality monitoring -- Chapter 5 Data preprocessing in air quality monitoring -- Chapter 6 Data forecasting in air quality monitoring -- Chapter 7 Data interpolation in air quality monitoring. 330 $aThis book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc. 410 0$aEngineering Applications of Computational Methods,$x2662-3374 ;$v23 606 $aEnvironmental sciences$xMathematics 606 $aEnvironmental monitoring 606 $aArtificial intelligence$xData processing 606 $aEngineering$xData processing 606 $aPollution 606 $aMathematical Applications in Environmental Science 606 $aEnvironmental Monitoring 606 $aData Science 606 $aData Engineering 606 $aPollution 615 0$aEnvironmental sciences$xMathematics. 615 0$aEnvironmental monitoring. 615 0$aArtificial intelligence$xData processing. 615 0$aEngineering$xData processing. 615 0$aPollution. 615 14$aMathematical Applications in Environmental Science. 615 24$aEnvironmental Monitoring. 615 24$aData Science. 615 24$aData Engineering. 615 24$aPollution. 676 $a333.7 700 $aLiu$b Hui$0274539 701 $aLi$b Yanfei$01827456 701 $aDuan$b Zhu$01827457 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007357103321 996 $aData Science in Air Quality Monitoring$94395629 997 $aUNINA