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This study was aimed at developing a new sensitive approach to account for small sediment dust particles using spectral reflectance across the shortwave spectral region (1250-2400 micron). The NIRA (Near Infrared Analyses) approach was adopted in order to examine its capability to predict gravimetric weight of sediment dust particles solely from the reflectance data. In order to quantitatively characterize the dust loading process, two model composition mixtures representing homogeneity (talc powder) and heterogeneity (Environmental Protected Agency (EPA) dust) of chemical compounds were examined. A wind tunnel was constructed and used to simulate the different amounts of dust loadings over an indoor environment. Different spectral manipulations most commonly used to analyze spectral data were tested. On these manipulated spectra, a multivariate data analysis based on Partial Least Squares (PLS) regression was run and prediction modeling between NIR spectroscopy and the dust loadings was generated. For this purpose, the relationship between spectroscopic measurements and the total gravimetric weight was used. Using reflectance values in the PLS analysis was found to demonstrate the best performance in EPA dust relative to other manipulations employed (with RMSEP of 4.8%). For the talc dust, the first derivative of absorbance manipulation was found to demonstrate the best performance relative to other manipulations with RMSEP of 5.4%. Although the RMSEP might seem somewhat high, one should note that this concerns a relatively small amount of dust with a narrow gravimetric weight of ±0.0001 g. Moreover, validation and examination tests applied to the population studied have presented very significant results. This method can be further used to assess very small amounts of dust in indoor environments and accordingly to identify shade on the environmental air quality on regular non dusty-days.
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