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ABSTRACT Macrophytes are a key biological component in many fluvial ecosystems. In altered streams, they can become highly dominant features, covering extensive parts of the channel with cascading effects on stream conditions and biological composition. The aim of this study is to examine the possibility of using information collected by aerial digital photography-spectral analysis (ADP-SA) as an alternative, cost-effective methodology to the commonly used spectral procedure at a large (section) scale, and to the ground level visual survey (gridded frame) at a smaller (organism) scale. Cladophora glomerata and Nasturtium officinale, were selected as the targeted macrophyte species and classified at the same time (species relative cover) by using the three methodologies. Our findings show that the ADP-SA methodology was able to detect species and relative cover in similar accuracy (≤10% differences) at the two spatial scales. ADP-SA had a better spatial resolution than both the hyperspectral and the visual methodology (4 cm vs. 1 m and 10 cm, respectively) and was capable to differentiate submerged from emergent plants. However, on a smaller scale, ground level work is suitable and essential for detecting rarer species and is not hindered by weather, canopy cover or multi-layered plant composition. ADP-SA can therefore add a cost-effective and nonsubjective practice to the existing tool kit of macrophyte surveys, particularly in small streams, which require high spatial resolution. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper systematically derives and analyses the generic phenomenon of space-invariant predictability of spatio-temporal observation fields using past multitemporal observations. We focus on thermal infrared remote sensing as a non-trivial example illustrating the predictability concept. The phenomenon and the systematic analysis thereof are experimentally demonstrated to be productive for developing effective automated anomaly detection and classification methods operating under the assumption of dynamic environment and sensor response. Using a simple preliminary experiment involving uncalibrated tower-based high-resolution thermal infrared surveillance, we test the conceptual validity of the space-invariant multitemporal prediction and exemplify its potential applications. In addition, we use a MODIS thermal image sequence and the task of hot anomaly detection to demonstrate the applicability of the approach for monitoring the status of large territories from space-borne platforms. [ABSTRACT FROM AUTHOR]

Visible–near-infrared–shortwave-infrared (VNIR–SWIR) spectroscopy is one of the most promising sensing techniques to meet ever-growing demands for soil information and data. To ensure the successful application of this technique in the field, efficient methods for tackling detrimental moisture effects on soil spectra are critical. In this paper, mathematical techniques for reducing or removing the effects of soil moisture content (SMC) from spectra are reviewed. The reviewed techniques encompass the most common spectral pre-processing and algorithms, as well as less frequently reported methods including approaches within the remote sensing domain. Examples of studies describing their effectiveness in the search for calibration model improvement are provided. Moreover, the advantages and disadvantages of the different techniques are summarized. Future research including further studies on a wider range of soil types, in-field conditions, and systematic experiments considering several SMC levels to enable the definition of threshold values for the effectiveness of the discussed methods is recommended. [ABSTRACT FROM AUTHOR]

Abstract: Imaging Spectroscopy (IS) is a promising tool for studying soil properties in large spatial domains. Going from point to image spectrometry is not only a journey from micro to macro scales, but also a long stage where problems such as dealing with data having a low signal-to-noise level, contamination of the atmosphere, large data sets, the BRDF effect and more are often encountered. In this paper we provide an up-to-date overview of some of the case studies that have used IS technology for soil science applications. Besides a brief discussion on the advantages and disadvantages of IS for studying soils, the following cases are comprehensively discussed: soil degradation (salinity, erosion, and deposition), soil mapping and classification, soil genesis and formation, soil contamination, soil water content, and soil swelling. We review these case studies and suggest that the IS data be provided to the end-users as real reflectance and not as raw data and with better signal-to-noise ratios than presently exist. This is because converting the raw data into reflectance is a complicated stage that requires experience, knowledge, and specific infrastructures not available to many users, whereas quantitative spectral models require good quality data. These limitations serve as a barrier that impedes potential end-users, inhibiting researchers from trying this technique for their needs. The paper ends with a general call to the soil science audience to extend the utilization of the IS technique, and it provides some ideas on how to propel this technology forward to enable its widespread adoption in order to achieve a breakthrough in the field of soil science and remote sensing. [Copyright &y& Elsevier]

A significant urban heat island (UHI) was identified in Tel-Aviv on a stable winter day. The UHI detection was performed using air temperatures at both the roof and the street levels (using fixed-station and car-traverse measurements) and at the surface level (using an airborne thermal video radiometer [TVR]). Whereas the complex microscale characteristics of the UHI studied by the TVR at the surface level showed variations of up to 10 deg. C, at the street level, air temperature variations of 3-5 deg. C were identified between the city center and the surrounding areas. It was found that during the nighttime, thewarm Mediterranean Sea has a moderating effect on the roof-level temperatures, whereas, at the street level, the urban heat island is more pronounced. The combined method of monitoring the UHI from different levels and on different scales for the first time enabled a spatialassessment of the city's UHI and its diverse thermal coverage characteristics. The thermal differences of neighborhoods, urban activity and urban components were compared. It was demonstrated that the city cover plays an important role in the thermal activity of Tel-Aviv. A similar UHI spatial pattern was obtained using isotherm maps, generated from the air temperatures at street level, and thermal images, generated by the TVR at the surface level. It was concluded that there are differences in the magnitude of the UHI at different levels of thecanopy layer and at different times, but the UHI pattern has similartrends." [ABSTRACT FROM AUTHOR]

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible–near infrared (vis–NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of. [ABSTRACT FROM AUTHOR]

Abstract: This study examines whether converting the fossil fuel of the Tel Aviv power station from oil to gas influences air pollution in the local urban environment. To this end, the spectral properties of accumulated dust on tree leaves and paper bags were assessed before (2004) and after (2006) the conversion. The sampling site was a garden in a neighborhood located 2700m downwind of the power station. In addition, air pollution concentrations and particulate matter parameters recorded by a local meteorological station were analyzed (PM10, NO x , NO2, NO, and SO2). Although differences in the average monthly concentration of pollution parameters are mostly insignificant between the two periods, the accumulated particulate matter exhibits considerably different spectral patterns. All first period samples exhibit a distinctly concave slope in the spectral region between 400 and 1400nm, indicative of high amounts of soot, most likely due to the combustion products of fuel oil exhausted by the power plant. In contrast, the second period samples exhibit spectra that indicate reduced soot content and even appear slightly convex, evidencing the presence of dust of mineral origin, a feature likely masked by the soot in the first period. Thus, the spectral data support that the power plant conversion results in less pollution. More generally, this study corroborates that VIS-NIR-SWIR spectroscopy characterizes key properties of the particulate layer accumulating on sampled surfaces and thus, is a powerful method for monitoring the urban environment. [Copyright &y& Elsevier]

This paper presents a multi-sensor remote sensing study of physically crusted soil with various moisture contents by using millimetre-wave backscatter radiation. The backscattered radiation was used to generate an empirical model to estimate soil moisture at the thin surface crust scale by using a set of soils with known moisture content as a reference. The model was then used to assess the moisture content of a new set of soils from the same group. Real-time measurement of soil moisture in the top layer beneath the soil crust is impractical at best and impossible using traditional methods. The proposed method, therefore, paves the way to assessing soil moisture content immediately beneath the surface crust as well as in a micro-profile at depths of 0-3 cm. It can be combined with other active and passive remote sensing methods to provide a micro-profile measurement of soil moisture to the depth of the entire upper root zone (0-30 cm) and of the plough layer (0-20 cm) under undisturbed conditions. The method is based on the emerging technology of millimetre waves, which provide improved spatial resolution of the subsurface concurrent with surface mapping. Further development and use of the method described herein will make it simple and practical to analyse and understand the processes governing the soil-water interface. The major processes for which this information is important are soil crusting, water budgets (infiltration, runoff and evaporation) and soil erosion. [ABSTRACT FROM AUTHOR]

Abstract: We study hyperspectral images of the Bodélé Depression in Northern Chad, acquired by the Hyperion sensor onboard EO-1 spacecraft. Relative abundances of four major mineral components are obtained on a pixel-by-pixel basis and we report on the comparison of images of a dust storm with the same areas on a calm day. Minerals lifted and suspended particles downwind of a dust source are thus identified. Linear Spectral Unmixing (LSU) decomposition results for the calm condition match those of our field study. LSU calm vs. stormy comparison, based on absorbance features, highlight the spectral contrast. Despite low contrast above bright areas, morphological dissimilarity is evident via the wave and tongue-like features, aligned with the prevailing northeasterly winds. We analyze the longest part of shortwave infra-red (2080–2380nm) wavelengths where the atmosphere is transparent, optical properties are stable, and absorption features of hydroxyl-bearing minerals, sulfates, and carbonates are pronounced. The results of our spectral analyses reveal that clay minerals may be used as tracers for atmospheric dust monitoring even above bright areas. Such clay minerals include kaolinite, illite-moscovite, and Fe-rich nontronite. [Copyright &y& Elsevier]

Background: Populations of Olea europaea subsp. europaea var. sylvestris, the ancestor of cultivated olives, are scattered across the Mediterranean Basin. However, after millennia of possible hybridization with cultivated varieties, the genetic identity of many of these populations remain questionable. In the southern Levant, the plausible primary domestication center of olives, many of the naturally growing olive (NGOs) are considered feral, having developed from nearby olive groves. Here, we investigated the genetic identity of NGOs population in the Carmel region, hypothesizing that their specific location, which limit anemophily, provided an opportunity for the persistence of genuine var. sylvestris., Results: We mapped more than 1,000 NGOs on the Kurkar ridge along the Carmel coast, within and outside the residential area of Atlit and used simple sequence repeats of 14 loci to assess the spatial genetic structure of 129 NGOs. Genetic diversity parameters and genetic distances between NGO and cultivated olives, as well as phenotypic and morphometric analyses of their oil content and pits, respectively, indicated the presence of a genuine var. sylvestris population. However, NGOs within the residential area of Atlit and old settlements showed an intermediate admix genetic structure, indicating on hybridization with local varieties, a consequence of their proximity to cultivated trees., Conclusions: Integrating the results of genetic and phenotypic analyses we provide crucial evidence of the presence of a genuine var. sylvestris population in the southern Levant, in close geographical proximity to archaeological sites with the earliest evidence of olive exploitation in the ancient world. We supplement the results with recommendations for a conservation program that combines municipal requirements and the urgent need to preserve the largest population of var. sylvestris in the southern Levant., (© 2024. The Author(s).)

The collection and analysis of large amounts of information on a plant-by-plant basis contributes to the development of precision fertigation and may be achieved by combining remote-sensing technology with high-throughput phenotyping methods. Here, lettuce plants (Lactuca sativa) were grown under optimal and suboptimal nitrogen and irrigation treatments from seedlings to harvest. A Plantarray system was used to calculate and log weights, daily transpiration, and momentary transpiration rates throughout the experiment. From 15 d after planting until experiment termination, the entire array of plants was imaged hourly (from 09.00 h to 14.00 h) using a hyperspectral moving camera. Three vegetation indices were calculated from the plants' reflectance signal: red-edge chlorophyll index (RECI), photochemical reflectance index (PRI), and water index (WI), and combined treatments, physiological measurements, and vegetation indices were compared. RECI values differed significantly between nitrogen treatments from the first day of imaging, and WI values distinguished well-irrigated from drought-treated groups before detecting significant differences in daily transpiration rate. The PRI, calculated hourly during the drought-treatment phase, changed with the momentary transpiration rate. Thus, hyperspectral imaging might be used in growing facilities to detect nitrogen or water shortages in plants before their physiological response affects yields., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Quartz is the most abundant mineral on the earth's surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible-near-infrared-shortwave-infrared (Vis-NIR-SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis-NIR-SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis-NIR-SWIR spectral region (R 2  = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis-NIR-SWIR region provided poor results (R 2  = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000-2450 nm spectral range (atmospheric window) was used (R 2  = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000-2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible-near infrared (vis-NIR: 350-2500 nm) and X-ray fluorescence (XRF: 0.02-41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis-NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis-NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis-NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis-NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models' accuracies as compared with the single vis-NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis-NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

Potassium is a macro element in plants that is typically supplied to crops in excess throughout the season to avoid a deficit leading to reduced crop yield. Transpiration rate is a momentary physiological attribute that is indicative of soil water content, the plant's water requirements, and abiotic stress factors. In this study, two systems were combined to create a hyperspectral-physiological plant database for classification of potassium treatments (low, medium, and high) and estimation of momentary transpiration rate from hyperspectral images. PlantArray 3.0 was used to control fertigation, log ambient conditions, and calculate transpiration rates. In addition, a semi-automated platform carrying a hyperspectral camera was triggered every hour to capture images of a large array of pepper plants. The combined attributes and spectral information on an hourly basis were used to classify plants into their given potassium treatments (average accuracy = 80%) and to estimate transpiration rate (RMSE = 0.025 g/min, R 2 = 0.75) using the advanced ensemble learning algorithm XGBoost (extreme gradient boosting algorithm). Although potassium has no direct spectral absorption features, the classification results demonstrated the ability to label plants according to potassium treatments based on a remotely measured hyperspectral signal. The ability to estimate transpiration rates for different potassium applications using spectral information can aid in irrigation management and crop yield optimization. These combined results are important for decision-making during the growing season, and particularly at the early stages when potassium levels can still be corrected to prevent yield loss.

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR: 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R 2  = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R 2  = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis-NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Reflection spectroscopy, in the visible-near-infrared-shortwave infrared region (Vis-NIR-SWIR, 350-2500 nm), is a useful technology to extract chemical and physical properties of materials, but might be useless in identifying the spectral features of transparent or dark opaque liquids. Low reflectance values of a liquid reduce the ability to identify characteristic absorption features at specific wavelengths in the reflectance spectrum. In this study, we present a rapid and easy-to-use method to increase the measured reflectance spectrum and expose characteristic absorption features of a liquid. This was done by mixing the liquid with a white enhanced substance (WES). For this purpose, we used aluminum oxide (Al 2 O 3 ) powder-a very bright (high albedo) substance and featureless across the entire Vis-NIR-SWIR region. The reflectance spectrum of the mixture-liquid and WES-was measured using a spectroradiometer. This procedure enabled to identify characteristic spectral features of the liquids that would have not been observed in the reflectance spectrum measured from the liquid alone.

Petroleum hydrocarbons are contaminants of great significance. The commonly used analytic method for assessing total petroleum hydrocarbons (TPH) in soil samples is based on extraction with 1,1,2-Trichlorotrifluoroethane (Freon 113), a substance prohibited to use by the Environmental Protection Agency. During the past 20 years, a new quantitative methodology that uses the reflected radiation of solids has been widely adopted. By using this approach, the reflectance radiation across the visible, near infrared-shortwave infrared region (400-2500 nm) is modeled against constituents determined using traditional analytic chemistry methods and then used to predict unknown samples. This technology is environmentally friendly and permits rapid and cost-effective measurements of large numbers of samples. Thus, this method dramatically reduces chemical analytical costs and secondary pollution, enabling a new dimension of environmental monitoring. In this study we adapted this approach and developed effective steps in which hydrocarbon contamination in soils can be determined rapidly, accurately, and cost effectively solely from reflectance spectroscopy. Artificial contaminated samples were analyzed chemically and spectrally to form a database of five soils contaminated with three types of petroleum hydrocarbons (PHCs), creating 15 datasets of 48 samples each at contamination levels of 50-5000 wt% ppm (parts per million). A brute force preprocessing approach was used by combining eight different preprocessing techniques with all possible datasets, resulting in 120 different mutations for each dataset. The brute force was done based on an innovative computing system developed for this study. A new parameter for evaluating model performance scoring (MPS) is proposed based on a combination of several common statistical parameters. The effect of dividing the data into training validation and test sets on modeling accuracy is also discussed. The results of this study clearly show that predicting TPH levels at low concentrations in selected soils at high precision levels is viable. Dividing a dataset into training, validation, and test groups affects the modeling process, and different preprocessing methods, alone or in combination, need to be selected based on soil type and PHC type. MPS was found to be a better parameter for selecting the best performing model than ratio of prediction to deviation, yielding models with the same performance but less complicated and more stable. The use of the "all possibilities" system proved to be mandatory for efficient optimal modeling of reflectance spectroscopy data.

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing "more crop per drop" (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involvingcrop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m³/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m³) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fractionby reference ET. The ETfraction was determined using Landsat thermal imagery by selecting the "hot" pixels (zero ET) and "cold" pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m³) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m³, 11% of the area having WP in range of 0.30-0.36 kg/m³, and only 2% of the area with WP greater than 0.36 kg/m³. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.

The aim of this study was to apply a novel sensitive technique, involving reflectance spectroscopy in the 400-2500-nm region, to assess dust loads. A spectral library was created to enable identification of the possible sources of settled dust in indoor samples -- mineral versus organic-anthropogenic. Two field experiments were carried out at different dates, the first in dwellings and the second in office environments. Two main spectral patterns were found. Type A spectra indicate a high proportion of minerals in the sample and are characteristic of dust samples taken from the dwelling environment during April (when there were 5 dust storm events). Type B spectra denote a high proportion of organic matter in the sample and are characteristic of the dust samples taken from the offices during March (when there were only 2 dust storm events). The spectral shape within the visible range can be used to estimate the relative amount of mineral and organic components in the sample. Multivariate data analysis, based on Partial Least Squares (PLS) regression, was utilized to predict the relationship between the reflectance of a dust sample and its mass. The relative Root Mean Square Error of Predictions (%RMSEP) generated for the dust sampled in dwellings (6.5%) and offices (7.0%) are quite impressive considering the relatively small amounts of settled dust and its precise gravimetric weight accurate to +/-0.01 mg (min and max values are 0.1-3.2 mg). In addition, PLS regression analysis was used to identify which variables influence dust load. Possible applications of the proposed method are discussed.

Cutting spinal nerves just distal to the dorsal root ganglion (DRG) triggers, with rapid onset, massive spontaneous ectopic discharge in axotomized afferent A-neurons, and at the same time induces tactile allodynia in the partially denervated hindlimb. We show that secondary transection of the dorsal root (rhizotomy) of the axotomized DRG, or suppression of the ectopia with topically applied local anesthetics, eliminates or attenuates the allodynia. Dorsal rhizotomy alone does not trigger allodynia. These observations support the hypothesis that ectopic firing in DRG A-neurons induces central sensitization which leads to tactile allodynia. The question of how activity in afferent A-neurons, which are not normally nociceptive, might induce allodynia is discussed in light of the current literature.