For studying environmental pollution locally performed analyses are non-representative as the pollutants are distributed heterogeneously. Thus, comprehensive pollution information can only be obtained by means of spatially probing the entire area of interest. Optical spectroscopy is a powerful method because active and passive sensing is feasible over large distances. Recent experimental techniques like light detection and ranging (lidar), differential optical absorption spectroscopy (DOAS), open-path FTIR spectroscopy and chemical imaging are discussed. However, sophisticated experimental techniques alone are not sufficient to establish a successful analytical method - innovative statistical data evaluation (chemometric) techniques are also needed for reliable sensor calibration and data analyses. All spectroscopic open-path techniques are inherently hampered by unpredictable and changing measurement conditions as well as by incomplete chemical knowledge during calibration. Thus, new methods are presented, which calibration models and prevent artifacts from impacting quantification of pollutants. Novel chemical imaging techniques perform spatially resolved spectroscopy and enable multicomponent analyses at unprecedented spatial and temporal resolution. However, spectroscopic imaging produces humongous data sets and thus imposes new challenges as data storage and evaluation is a computational burden and extremely time consuming. Recent data compression algorithms utilize multidimensional wavelet transformations for shrinking data sets considerably prior to chemometric evaluation and archiving.
Keywords: Remote pollution sensing, Optical spectroscopy, Chemical imaging, Optimized chemometrics, Robust quantification
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