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Vicinities of manufactured gas plants were often contaminated with solid iron–cyanide complexes
as a result of the coal gasification process. During the remediation of affected soils, knowledge about contaminant concentrations is crucial, but laboratory methods are often expensive and time consuming. Rapid and non-destructive field methods for contaminant determination permit an analysis of large sample numbers and hence, facilitate identification of ‘hot spots’ of contamination. Diffuse near infrared reflectance spectroscopy has proven to be a reliable analytical tool in soil investigation. In order to determine the feasibility of a Polychromix Handheld Field Portable Near-Infrared Analyzer (FP NIR), various sample preparation methods were examined, including homogenizing, sieving, drying, and grinding. Partial least squares calibration models were developed to determine near infrared (NIR) spectral responses to the cyanide concentration in the soil samples. As a control, the contaminant concentration
was determined using conventional flow injection analysis. The experiments revealed that portable
near-infrared spectrometers could be a reliable device for detecting cyanide concentrations >2,400 mgkg−1 in the field and >1,750 mgkg−1 after sample preparation in the laboratory.We found that portable NIR spectrometry cannot replace traditional laboratory analyses due to high limits of detection, but that it could be used for identification of contamination ‘hot spots’.
Normalized Difference Vegetation Indices (NDVIs) are typically determined using satellite or airborne remote sensing, or field portable spectrometers, which give an averaged signal on centimetre to metre scale plots. Biological soil crust (BSC) patches may have smaller sizes, and ecophysiological, hydrological as well as pedological processes may be heterogeneously distributed within this level of resolution. A ground-based NDVI imaging procedure using low-cost equipment (Olympus Camedia 5000z digital camera equipped with a Hoya R72 infrared filter) was developed in this study to fill this gap at the level of field research, where carrying costly and bulky equipment to remote locations is often the limiting factor for data collection. Method principle and field data are presented, and the field experiment was deepened comparing NDVI measurements and CO2 turnover of soil crust samples in the laboratory, backing the reliability of the approach.
A commercially available colour rendition chart with known red (600–700 nm) and NIR (800–900 nm) reflectances was placed into each scene and used for calibration purposes on a per-image basis. Generation of NDVI images involved (i) determination of red and NIR reflectances from the pixel values of the red and NIR channels, respectively, and (ii) calculation and imaging of the NDVI, where NDVI values of −1 to +1 were mapped to grey values of 0 to 255. The correlation between NDVI values retrieved from these images and NDVI values determined using field spectrometry was close (r2 = 0.91), the 95% confidence interval amounted to 0.10 NDVI units. The pixel resolution was 0.8 mm in the field and 0.2 mm in the laboratory, but can still be improved significantly with closer distance to the crust or with higher camera resolution. NDVI values obtained using the new method were related to the net CO2 uptake of BSCs, where both slope and correlation coefficient of the respective regression function conformed with literature data. Geostatistical analysis revealed that both spatial variability of net CO2 uptake as well as size of individual hot spots of this parameter increased with crust development. The latter never exceeded 4 mm in the investigated crusts, which points to the necessity of high resolution imaging for linking remote sensing with ecophysiology. Perspectively, the new method could be used for field monitoring of both biological soil crusts and vascular vegetation.
Dew formation was investigated in three developmental stages of biological soil crusts (BSC), which were collected along a catena of an inland dune and in the initial substrate. The Penman equation, which was developed for saturated surfaces, was modified for unsaturated surfaces and used for prediction of dewfall rates. The levels of surface saturation required for this approach were predicted using the water retention functions and the thicknesses of the BSCs. During a single event, dewfall increased with crust development from 0.08 kg m−2 for the initial substrate to 0.10, 0.20 and 0.25 kg m−2 for crusts stages 1 to 3, respectively, which was well reflected by the model response. The suggested mechanism of dew formation involves a delay in water saturation in near-surface soil pores and EPS where the crusts were thicker and where the water capacity was high, resulting in elevated vapor flux towards the surface. The results also indicate that the amount of dewfall was too low to observe water flow into deeper soil. Analysis of the soil water retention curves revealed that, despite the sandy mineral matrix, moist crusts with clogged by swollen EPS pores exhibited a clay-like behavior. It is hypothesized that BSCs gain double benefit from suppressing their competitors by runoff generation and from improving their water supply by dew collection. Despite higher amounts of dew, the water availability to the crust community decreases with crust development, which may be compensated by ecophysiological adaptation of crust organisms, and which may further suppress higher vegetation or mosses.