@article{apte2017high,
  added-at = {2018-09-11T08:52:49.000+0200},
  author = {Apte, Joshua S and Messier, Kyle P and Gani, Shahzad and Brauer, Michael and Kirchstetter, Thomas W and Lunden, Melissa M and Marshall, Julian D and Portier, Christopher J and Vermeulen, Roel CH and Hamburg, Steven P},
  biburl = {https://www.bibsonomy.org/bibtex/2f7376175007734d8b0b4f4f78b33c2ba/msteininger},
  interhash = {0ccf62d3573ca54565a060252250022e},
  intrahash = {f7376175007734d8b0b4f4f78b33c2ba},
  journal = {Environmental Science \& Technology},
  keywords = {Oakland bc california dataset no no2 street use view},
  number = 12,
  pages = {6999--7008},
  publisher = {ACS Publications},
  timestamp = {2018-09-11T08:52:49.000+0200},
  title = {High-resolution air pollution mapping with Google street view cars: exploiting big data},
  url = {https://pubs.acs.org/doi/pdf/10.1021/acs.est.7b00891},
  volume = 51,
  year = 2017
}

@article{spinelle2015field,
  added-at = {2017-11-15T23:25:07.000+0100},
  author = {Spinelle, Laurent and Gerboles, Michel and Villani, Maria Gabriella and Aleixandre, Manuel and Bonavitacola, Fausto},
  biburl = {https://www.bibsonomy.org/bibtex/2851f3c9dd0d1b8be026e4d9538264e24/becker},
  doi = {https://doi.org/10.1016/j.snb.2015.03.031},
  interhash = {31b7eeab169addf08d67edecb7634856},
  intrahash = {851f3c9dd0d1b8be026e4d9538264e24},
  issn = {0925-4005},
  journal = {Sensors and Actuators B: Chemical},
  keywords = {air ann calibration multiple network neural nn no no2 nose o3 p2map pollution quality sensor sensors},
  number = {Supplement C},
  pages = {249 - 257},
  timestamp = {2017-11-15T23:25:07.000+0100},
  title = {Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide},
  url = {http://www.sciencedirect.com/science/article/pii/S092540051500355X},
  volume = 215,
  year = 2015
}

@article{Juhos20081488,
  abstract = {The main aim of this paper is to predict NO and NO2 concentrations four days in advance comparing two artificial intelligence learning methods, namely, Multi-Layer Perceptron and Support Vector Machines on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO2 concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged in order to build a model for predicting NO and NO2 concentrations several hours in advance. The prediction of NO and NO2 concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO2. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, Multi-Layer Perceptron and Support Vector Regression are used to provide efficient non-linear models for NO and NO2 times series predictions. Multi-Layer Perceptron is widely used to predict these time series, but Support Vector Regression has not yet been applied for predicting NO and NO2 concentrations. Grid search is applied to select the best parameters for the learners. To get rid of the curse of dimensionality of the spatial embedding of the time series Principal Component Analysis is taken to reduce the dimension of the embedded data. Three commonly used linear algorithms were considered as references: one-day persistence, average of several-day persistence and linear regression. Based on the good results of the average of several-day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO2 the improvement of the prediction is considerable; however, it is less notable than for NO.},
  added-at = {2013-03-28T17:00:57.000+0100},
  author = {Juhos, István and Makra, László and Tóth, Balázs},
  biburl = {https://www.bibsonomy.org/bibtex/270d6cf3c171445620c5024658516ac44/becker},
  description = {ScienceDirect.com - Simulation Modelling Practice and Theory - Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis},
  doi = {10.1016/j.simpat.2008.08.006},
  interhash = {b8e240cb2c8bb4d0f42aeda944a3ed15},
  intrahash = {70d6cf3c171445620c5024658516ac44},
  issn = {1569-190X},
  journal = {Simulation Modelling Practice and Theory},
  keywords = {calibration eva everyaware machines networks neural no no2 support svm vector},
  number = 9,
  pages = {1488 - 1502},
  timestamp = {2013-03-28T17:00:57.000+0100},
  title = {Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis},
  url = {http://www.sciencedirect.com/science/article/pii/S1569190X08001585},
  volume = 16,
  year = 2008
}

@article{hlwoodcock:Xie2000,
  abstract = {Since symmetry breaking occurs in the ONNO+ cation with the self-consistent field (SCF) method and inverse symmetry breaking occurs when density functional theory (DFT) methods are used, Brueckner double excitation coupled cluster methods (BD) and ED plus perturbative triple-excitation contributions [BD(T)] have been used to study the geometries and vibrational frequencies for the trans and cis structures of the ONNO+ cation. Double-zeta plus polarization (DZP) basis sets and triple-zeta plus double polarization with f functions (TZ2Pf) basis sets were utilized. The ground state of the trans-ONNO cation is of (2)A(g) symmetry, which has a slightly (0.36 kcal mol(-1)) lower energy than the cis conformer ((2)A(1)). The controversial vibrational frequency corresponding to the asymmetric N-O stretching mode for both trans and cis structures is predicted as about 1600 cm(-1). This value is discussed in the context of the many (sometimes variant) experimental assignments.},
  added-at = {2006-06-16T05:03:46.000+0200},
  author = {Xie, Y. M. and Schaefer, H. F.},
  biburl = {https://www.bibsonomy.org/bibtex/20dd92ccca74f70aa21306ed5921f26a2/hlwoodcock},
  citeulike-article-id = {569524},
  comment = {Times Cited: 4 Article English Cited References Count: 21 341ew},
  interhash = {3ab177f1d33e0ff3777812fc0ea658b7},
  intrahash = {0dd92ccca74f70aa21306ed5921f26a2},
  journal = {Molecular Physics},
  keywords = {neon argon no cis bibtex-import no2 solid anions dimer ions},
  number = 14,
  pages = {955--959},
  priority = {2},
  timestamp = {2006-06-16T05:03:46.000+0200},
  title = {The puzzling infrared spectra of the nitric oxide dimer radical cation: a systematic application of brueckner methods},
  volume = 98,
  year = 2000
}