Environmental sampling strategy and design

2014 ◽  
pp. 46-75
Author(s):  
Mark Shibata ◽  
Natasha Hausmann
Keyword(s):  
Sampling Strategy ◽  
Environmental Sampling

Why a Standardization of Strontium Isotope Baseline Environmental Data Is Needed and Recommendations for Methodology

2017 ◽  
Vol 5 (2) ◽  
pp. 184-195 ◽  
Author(s):  
Deanna N. Grimstead ◽  
Selin Nugent ◽  
Jean Whipple
Keyword(s):  
Sampling Strategy ◽  
Baseline Data ◽  
Strontium Isotope ◽  
Environmental Data ◽  
Semiarid Region ◽  
Environmental Sampling ◽  
Sr Isotope ◽  
Data Collections ◽  
Field Planning

ABSTRACTSince initial applications of strontium isotope human sourcing in the early 1990s, the use of the method has steadily increased in archaeology and in anthropology more broadly. Despite this trend, the collection of necessary baseline environmental data has not been standardized and sometimes does not occur at all. A thorough environmental sampling strategy will ensure that all the variability within a selected region is documented, which is a critical step to improving the accuracy of sourcing studies. Furthermore, shared strontium baseline data collections are needed to improve the intercomparability of datasets and results. This paper provides a case study from a semiarid region in northwestern New Mexico, USA, highlighting the need for a bottom-up approach to baseline data collection (from bedrock to animal) and describes the methods of pre-field planning and collecting, including rationales for what samples to collect for Sr isotope baseline data. The authors hope that this paper will lay a foundation for the implementation and standardization of Sr isotope baseline data collecting, which does not currently exist.

Automated radon monitoring system for continuous environmental sampling

1990 ◽  
Vol 25 (12) ◽  
pp. 1259-1263
Author(s):  
Domenica Paoletti ◽  
Giuseppe Schirripa Spagnolo
Keyword(s):  
Monitoring System ◽  
Environmental Sampling ◽  
Radon Monitoring

scholarly journals Dried urine spots as sampling technique for multi-mycotoxin analysis in human urine

2021 ◽  
Author(s):  
Jessica Schmidt ◽  
Viktoria Lindemann ◽  
Monica Olsen ◽  
Benedikt Cramer ◽  
Hans-Ulrich Humpf
Keyword(s):  
Ochratoxin A ◽  
Human Urine ◽  
Sampling Strategy ◽  
Sampling Technique ◽  
Human Biomonitoring ◽  
Urine Samples ◽  
Isotope Dilution Analysis ◽  
Tenuazonic Acid ◽  
Stable Isotope Dilution Analysis ◽  
Mycotoxin Analysis

AbstractA simple and effective approach for HPLC-MS/MS based multi-mycotoxin analysis in human urine samples was developed by application of dried urine spots (DUS) as alternative on-site sampling strategy. The newly developed method enables the detection and quantitation of 14 relevant mycotoxins and mycotoxin metabolites, including citrinin (CIT), dihydrocitrinone (DH-CIT), deoxynivalenol (DON), fumonisin B1 (FB1), T-2 Toxin (T-2), HT-2 Toxin (HT-2), ochratoxin A (OTA), 2′R-ochratoxin A (2′R-OTA), ochratoxin α (OTα), tenuazonic acid and allo-tenuazonic acid (TeA + allo-TeA), zearalenone (ZEN), zearalanone (ZAN), α-zearalenol (α-ZEL), and β-zearalenol (β-ZEL). Besides the spotting procedure, sample preparation includes enzymatic cleavage of glucuronic acid conjugates and stable isotope dilution analysis. Method validation revealed low limits of detection in the range of pg/mL urine and excellent apparent recovery rates for most analytes. Stability investigation of DUS displayed no or only slight decrease of the analyte concentration over a period of 28 days at room temperature. The new method was applied to the analysis of a set of urine samples (n = 91) from a Swedish cohort. The four analytes, DH-CIT, DON, OTA, and TeA + allo-TeA, could be detected and quantified in amounts ranging from 0.06 to 0.97 ng/mL, 3.03 to 136 ng/mL, 0.013 to 0.434 ng/mL and from 0.36 to 47 ng/mL in 38.5%, 70.3%, 68.1%, and 94.5% of the samples, respectively. Additional analysis of these urine samples with an established dilute and shoot (DaS) approach displayed a high consistency of the results obtained with both methods. However, due to higher sensitivity, a larger number of positive samples were observed using the DUS method consequently providing a suitable approach for human biomonitoring of mycotoxin exposure.

scholarly journals GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 384
Author(s):  
Yaroslav Bezyk ◽  
Izabela Sówka ◽  
Maciej Górka ◽  
Jan Blachowski
Keyword(s):  
Seasonal Variability ◽  
Sampling Strategy ◽  
Climate Impacts ◽  
Air Pollutant ◽  
Urban Atmosphere ◽  
Atmospheric Co2 ◽  
Spatial Characteristic ◽  
Interpolation Methods ◽  
Co2 Levels ◽  
The City

Understanding the magnitude and distribution of the mixes of the near-ground carbon dioxide (CO2) components spatially (related to the surface characteristics) and temporally (over seasonal timescales) is critical to evaluating present and future climate impacts. Thus, the application of in situ measurement approaches, combined with the spatial interpolation methods, will help to explore variations in source contribution to the total CO2 mixing ratios in the urban atmosphere. This study presents the spatial characteristic and temporal trend of atmospheric CO2 levels observed within the city of Wroclaw, Poland for the July 2017–August 2018 period. The seasonal variability of atmospheric CO2 around the city was directly measured at the selected sites using flask sampling with a Picarro G2201-I Cavity Ring-Down Spectroscopy (CRDS) technique. The current work aimed at determining the accuracy of the interpolation techniques and adjusting the interpolation parameters for estimating the magnitude of CO2 time series/seasonal variability in terms of limited observations during the vegetation and non-vegetation periods. The objective was to evaluate how different interpolation methods will affect the assessment of air pollutant levels in the urban environment and identify the optimal sampling strategy. The study discusses the schemes for optimization of the interpolation results that may be adopted in areas where no observations are available, which is based on the kriging error predictions for an appropriate spatial density of measurement locations. Finally, the interpolation results were extended regarding the average prediction bias by exploring additional experimental configurations and introducing the limitation of the future sampling strategy on the seasonal representation of the CO2 levels in the urban area.

scholarly journals Identifying VOCs in exhibition cases and efflorescence on museum objects exhibited at Smithsonian’s National Museum of the American Indian-New York

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Alba Alvarez-Martin ◽  
John George ◽  
Emily Kaplan ◽  
Lauren Osmond ◽  
Leah Bright ◽  
...  
Keyword(s):  
New York ◽  
American Indian ◽  
Solid Phase ◽  
Construction Materials ◽  
Sampling Strategy ◽  
Sampling Technique ◽  
Direct Analysis ◽  
National Museum ◽  
Structural Adhesive ◽  
Speed Of Analysis

AbstractTwo mass spectrometry (MS) methods, solid-phase microextraction gas chromatography (SPME–GC–MS) and direct analysis in real time (DART-MS), have been explored to investigate widespread efflorescence observed on exhibited objects at the Smithsonian’s National Museum of the American Indian in New York (NMAI-NY). Both methods show great potential, in terms of speed of analysis and level of information, for identifying the organic component of the efflorescence as 2,2,6,6-tetramethyl-4-piperidinol (TMP-ol) emitted by the structural adhesive (Terostat MS 937) used for exhibit case construction. The utility of DART-MS was proven by detecting the presence of TMP-ol in construction materials in a fraction of the time and effort required for SPME–GC–MS analysis. In parallel, an unobtrusive SPME sampling strategy was used to detect volatile organic compounds (VOCs) accumulated in the exhibition cases. This sampling technique can be performed by collections and conservation staff at the museum and shipped to an off-site laboratory for analysis. This broadens the accessibility of MS techniques to museums without access to instrumentation or in-house analysis capabilities.

scholarly journals Managing work flow in high enrolling trials: The development and implementation of a sampling strategy in the PREPARE trial

2021 ◽  
Vol 21 ◽  
pp. 100730
Author(s):  
David Pogorzelski ◽  
Uyen Nguyen ◽  
Paula McKay ◽  
Lehana Thabane ◽  
Megan Camara ◽  
...  
Keyword(s):  
Sampling Strategy ◽  
Work Flow

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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