The aim of this study was to apply a spectral reflectance approach to account for small amounts of sediment dust in occupied homes. We examined the method's ability to predict the gravimetric weight of sediment dust particles solely from the reflectance data (1250-2400 nm). Multivariate data analysis based on Partial Least Squares (PLS) regression was run to predict the dust loads solely from reflectance data. Use of difference spectral index in the PLS analyses was found to demonstrate the best pre-treatment in PLS modeling. In this study, 92 measurements of dust settled on glass traps in living and bed room environments were performed, in 90 buildings within Tel-Aviv city. A map of dust distribution, based both, on the reference gravimetric and spectrally predicted weight values was generated. Reasonable explanation was provided to the found distribution that can be easily used by decision makers to improve the indoor life quality. We conclude that this methodology (simple and rapid in-situ spectral measurements with appropriate analyses), can be employed to assess dust in both indoor and outdoor environments (in small and high dust environment). This information can be used for initial decision making, improving indoor conditions, and tracking dust contamination following environmental change. This method can be further used to assess on-line very small amounts of dust and accordingly to identify shade on the environmental air quality on regular non dusty-days.
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.