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The soils of western Haleakalā are incredibly diverse due to two primary reasons: 1) they receive varying levels of rainfall based on location relative to Haleakalā, and 2) volcanic vents upwind of the area indicate that volcanic ash has blanketed much of western Haleakalā in the past. Ash can weather to form short-range-order materials, which contribute to the classification of andic soil properties. Due to their structure, short-range-order materials impart soil behavior that creates benefits and challenges in land management. Therefore, an understanding of how these soils formed and differ, and where andic soils occur is crucial. The objectives of this study were to observe soil properties to determine how they vary across the precipitation gradients of the study area and how they relate to soil formation and diversity; and to utilize spatial statistics to predict where andic soils may occur to improve soil mapping. Sixteen soils were sampled on the same mapped substrate of northwest-facing slopes < 5% across gradients in elevation (7-1362 m) and mean annual precipitation (283-2768 mm MAP). Soils were described by horizon to at least 1-m depth, or a rootrestricting layer, and rock fragments at the base of each soil pit were sampled. Soil chemical, physical, and mineralogical properties were performed, and soil data across depth and location were compared. Using climate raster data and soil property data used to classify andic soils for the 16 soils sampled, we created and validated models to predict the areal extent of andic soils in the study area. Soil organic carbon increased with increasing precipitation before decreasing at 2300 mm MAP. Base saturation and pH remain high until a threshold at 1500 mm MAP before decreasing abruptly. The degree of mineral weathering and soil development increased with increasing MAP. The lowland soils of central Maui are likely much older than soils of upcountry Maui. Ash-influenced soils of upcountry Maui were less developed, more likely to have andic soil properties, and had disrupted pedogenic thresholds for pH and base saturation. Spatial predictions of soil properties used to classify andic soils accurately classified Natural Resources Conservation Service soils to 88.9%.

In this study, a 150 mJ laser-induced breakdown spectroscopy (LIBS) system was assessed to determine calcium content in infant formula (IF) samples. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers advantages over conventional methods such as real-time analyses, little to no sample preparation and ease of use. The aim of this work was to evaluate the feasibility of LIBS as an at-line tool for IF manufacturing. To this end, IF mixtures with varying content of calcium were prepared over a range (approx. 1.5–7 mg/g of calcium) selected to be in conformity with the guidelines provided by the Codex Alimentarius Commission. Atomic Absorption Spectroscopy (AAS) was used as the reference method for mineral content determination in IF. Partial least squares regression (PLSR) was applied to the LIBS spectral data to develop a calibration model for calcium content quantification. A validation approach was then carried out to investigate the robustness of the model, which showed a good fit with values of R 2 cv of 0.90 for cross-validation and a R 2 p 0.85 for external validation. Low values for root mean square errors of cross-validation (RMSECV) and prediction (RMSEP) of 0.62 mg/g and 0.68 mg/g were obtained. Furthermore, this study also illustrated the possibility of LIBS to provide mineral prediction maps as a useful tool for testing sample homogeneity.

In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landform classification). Furthermore land cover data from the CORINE dataset is added. The response variable is maximum wind speed, measurements are provided by a network of weather stations. The area of interest is Switzerland, a country which suits perfectly for this study because of its highly dynamic orography and various landforms.

Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.