Status of Dieldrin in vegetable growing soils across a peri-urban agricultural area according to an adapted sampling strategy

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.

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

Atmosphere ◽

10.3390/atmos12030384 ◽

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.

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

Heritage Science ◽

10.1186/s40494-020-00454-4 ◽

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.

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

Contemporary Clinical Trials Communications ◽

10.1016/j.conctc.2021.100730 ◽

2021 ◽

Vol 21 ◽

pp. 100730

Author(s):

David Pogorzelski ◽

Uyen Nguyen ◽

Paula McKay ◽

Lehana Thabane ◽

Megan Camara ◽

...

Keyword(s):

Sampling Strategy ◽

Work Flow

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Evaluation of Seasonal Groundwater Quality Changes Associated with Groundwater Pumping and Level Fluctuations in an Agricultural Area, Korea

Water ◽

10.3390/w13010051 ◽

2020 ◽

Vol 13 (1) ◽

pp. 51

Author(s):

Kyoochul Ha ◽

Eunhee Lee ◽

Hyowon An ◽

Sunghyun Kim ◽

Changhui Park ◽

...

Keyword(s):

Water Quality ◽

Groundwater Quality ◽

Groundwater Level ◽

Irrigation Water ◽

Agricultural Area ◽

Water Type ◽

Quality Changes ◽

Groundwater Pumping ◽

Rice Paddies ◽

Groundwater Levels

This study was conducted to evaluate seasonal groundwater quality due to groundwater pumping and hydrochemical characteristics with groundwater level fluctuations in an agricultural area in Korea. Groundwater levels were observed for about one year using automatic monitoring sensors, and groundwater uses were estimated based on the monitoring data. Groundwater use in the area is closely related to irrigation for rice farming, and rising groundwater levels occur during the pumping, which may be caused by the irrigation water of rice paddies. Hydrochemical analysis results for two separate times (17 July and 1 October 2019) show that the dissolved components in groundwater decreased overall due to dilution, especially at wells in the alluvial aquifer and shallow depth. More than 50% of the samples were classified as CaHCO3 water type, and changes in water type occurred depending on the well location. Water quality changes were small at most wells, but changes at some wells were evident. In addition, the groundwater quality was confirmed to have the effect of saltwater supplied during the 2018 drought by comparison with seawater. According to principal component analysis (PCA), the water quality from July to October was confirmed to have changed due to dilution, and the effect was strong at shallow wells. In the study areas where rice paddy farming is active in summer, irrigation water may be one of the important factors changing the groundwater quality. These results provide a qualitative and quantitative basis for groundwater quality change in agricultural areas, particularly rice paddies areas, along with groundwater level and usage.

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

Remote Sensing ◽

10.3390/rs13050907 ◽

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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Historical Changes in Land Use and Suitability for Future Agriculture Expansion in Western Bahia, Brazil

Remote Sensing ◽

10.3390/rs13061088 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1088

Author(s):

Fernando Martins Pimenta ◽

Allan Turini Speroto ◽

Marcos Heil Costa ◽

Emily Ane Dionizio

Keyword(s):

Land Use ◽

Land Cover ◽

Land Suitability ◽

Agricultural Area ◽

Landsat Images ◽

Historical Changes ◽

Cropland Area ◽

Irrigated Area ◽

Agriculture Expansion ◽

Forest Code

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands.

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