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The feasibility of detecting explosives in the atmosphere at concentrations as low as 0.01 ppq hinges on the poorly known question of what interfering species exist at these or higher concentrations. To clarify the issue, hundreds of samples of ambient air, either clean or loaded with explosives (from lightly contaminated environments) have been collected in fiberglass/stainless steel filters coated with Tenax-GR, thermally desorbed at variable temperature, and ionized with Cl via secondary electrospray (SESI). They are analyzed with a narrow-band mobility filter (SEADM's P5 DMA) and a triple quadrupole mass spectrometer (Sciex's 5500), configured in series to transmit precursor and fragment ions of the explosives Nitroglycerin, PETN, RDX, and TNT. Blanks were sampled outdoors at a rural site (Boecillo, Valladolid, Spain), and loads were sampled at diverse locations. For RDX and TNT, atmospheric background inhibits detection below 1 part/trillion (ppt) without mobility filtering. This interference was drastically reduced by the DMA, allowing detection up to 1 part/quadrillion (ppq). Further sensitivity increase was achieved by scanning over a mobility region several percent around that of the target explosive, to separate various isobaric compounds by Gaussian deconvolution. (i) All four MS/MS channels analyzed exhibit several background peaks within the narrow mobility intervals investigated. At least one of these interferents is much stronger than the instrument background at the explosive's mobility, making DMA separation most helpful. (ii) For Nitroglycerin and PETN the combined filtering techniques have not lowered ambient chemical noise down to 0.01 ppq. (iii) Interferents are greatly reduced for TNT and RDX, resulting in minimal chemical noise: 322 blank tests for RDX yielded mean signal of 0.0012 ppq and standard deviation σ = 0.0035 ppq (mean + 3σ detection limit of 0.01 ppq).

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The simultaneous determination of carbendazim, fuberidazole and thiabendazole was accomplished by cross-section (CS) fluorimetry in combination with multivariate calibration algorithms. The total luminescence information of the compounds was used to optimise the linear trajectories of the CS. A comparison between principal component regression (PCR) and two partial least squares (PLS) algorithms, PLS-1 and PLS-2, with different pre-processing methodologies was made. The final model, which applied the PLS-1 method, built using pesticide standard and emission spectra, was successfully used for the determination of these compounds in synthetic mixtures. However, a different PLS-1 multivariate calibration model, based on CS through the total luminescence spectroscopic data, was necessary for determining the cited pesticides in water samples. Mean centring was the best pre-processing technique in both PLS-1 models. This later calibration model was built from ultra-pure water samples spiked with known carbendazim, fuberidazole and thiabendazole concentrations, after solid-phase extraction (SPE). The method, which had a precision better than 5%, was shown to be suitable for carbendazim, fuberidazole and thiabendazole monitoring in water samples at trace levels.