The performance of simple slit impactors for air sampling of mold contamination was compared under field conditions. Samples were collected side-by-side, outdoors in quadruplicates with Burkhard (ambient sampler) and Allergenco MK3 spore traps and with two identical Allergenco slit cassettes operated at diverse flow rates of 5 and 15 L/min, respectively. The number and types of mold spores in each sample were quantified by microscopy. Results showed all four single-stage slit impactors produced similar spore yields. Moreover, paired slit cassettes produced similar outcomes despite a three-fold difference in their sampling rate. No measurable difference in the amount or mix of mold spores per m(3)of air was detected. The implications for assessment of human exposures and interpretation of indoor/outdoor fungal burden are discussed. These findings demonstrate that slit cassettes capture most small spores, effectively and without bias, when operated at a range of flow rates including the lower flow rates used for personal sampling. Our findings indicate sampling data for mold spores correlate for different single stage impactor collection methodologies and that data quality is not deteriorated by operating conditions deviating from manufacturers' norms allowing such sampling results to be used for scientific, legal, investigative, or property insurance purposes. The same conclusion may not be applied to other particle sampling instruments and mulit-stage impactors used for ambient particulate sampling, which represent an entirely different scenario. This knowledge may help facilitate comparison between scientific studies where methodological differences exist.
The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollens. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. Automated analyses of pollen extracts are being explored as an alternative to traditional pollen counting. METHODS: Extracts of ambient air-sampled pollen from Munich in 2016 and 2017 were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and classification models, using traditional machine learning and deep learning algorithms, were trained to recognize patterns in the metabolites or NMR spectra, based on expertidentified pollen counts. RESULTS: Regression and decision-tree based algorithms using the concentration of metabolites, measured from the NMR spectra, outperformed using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models performed better than regression models for NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). CONCLUSIONS: This study demonstrates that NMR spectra of airsampled pollen extracts could be used in an automated fashion to provide genus and type-specific measures of the pollen count. The classification algorithms can accurately differentiate the low/medium/high category standards of the National Allergy Board.
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