With increasingly efficient columns, eluite peaks are increasingly narrower. To take full advantage of this, choice of the detector response time and the data acquisition rate a.k.a. detector sampling frequency, have become increasingly important. In this work, we revisit the concept of data sampling from the theorem variously attributed to Whittaker, Nyquist, Kotelnikov, and Shannon. Focusing on time scales relevant to the current practice of high performance liquid chromatography (HPLC) and optical absorbance detection (the most commonly used method), even for very narrow simulated peaks Fourier transformation shows that theoretical minimum sampling frequency is still relatively low (<10 Hz). However, this consideration alone may not be adequate for real chromatograms when an appreciable amount of noise is present. Further, depending on the instrument, the manufacturer's choice of a particular data bunching/integration/response time condition may be integrally coupled to the sampling frequency. In any case, the exact nature of signal filtration often occurs in a manner neither transparent to nor controllable by the user. Using fast chromatography on a state-of-the-art column (38,000 plates), we evaluate the responses produced by different present generation instruments, each with their unique black box digital filters. We show that the common wisdom of sampling 20 points per peak can be inadequate for high efficiency columns and that the sampling frequency and response choices do affect the peak shape. If the sampling frequency is too low or response time is too large, the observed peak shapes will not remain as narrow as they really are - this is especially true for high efficiency and high speed separations. It is shown that both sampling frequency and digital filtering affect the retention time, noise amplitude, peak shape and width in a complex fashion. We show how a square-wave driven light emitting diode source can reveal the nature of the embedded filter. We discuss time uncertainties related to the choice of sampling frequency. Finally, we suggest steps to obtain optimum results from a given system.

JLW conducted analytical chemistry analyses, hydrogeology descriptions, geospatial analyses, statistical analyses, and performed quality checks and verification of all data collected; BEF, JMM, ZLH, DDC, and JLW analyzed data and provided conclusions for statistical analyses and geospatial relationships; TB and HR analyzed UOG drilling localities and common industrial practices; AFK, CPS, PH, TB, HR, CER and JLW provided comments, conclusions, and corrections on earlier versions of the manuscript; ZLH, BEF, DDC, JMM, PH and KAS wrote the paper.

Trillions of liters of wastewater from oil and gas extraction are generated annually in the US. The contribution from unconventional drilling operations (UDO), such as hydraulic fracturing, to this volume will likely continue to increase in the foreseeable future. The chemical content of wastewater from UDO varies with region, operator, and elapsed time after production begins. Detailed chemical analyses may be used to determine its content, select appropriate treatment options, and identify its source in cases of environmental contamination. In this study, one wastewater sample each from direct effluent, a disposal well, and a waste pit, all in West Texas, were analyzed by gas chromatography-mass spectrometry, inductively coupled plasma-optical emission spectroscopy, high performance liquid chromatography-high resolution mass spectrometry, high performance ion chromatography, total organic carbon/total nitrogen analysis, and pH and conductivity analysis. Several compounds known to compose hydraulic fracturing fluid were detected among two of the wastewater samples including 2-butoxyethanol, alkyl amines, and cocamide OPEN ACCESSWater 2015, 7 1569 diethanolamines, toluene, and o-xylene. Due both to its quantity and quality, proper management of wastewater from UDO will be essential.

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Height- and area-based quantitation reduce two-dimensional data to a single value. For a calibration set, there is a single height- or area-based quantitation equation. High-speed high-resolution data acquisition now permits rapid measurement of the width of a peak (W), at any height h (a fixed height, not a fixed fraction of the peak maximum) leading to any number of calibration curves. We propose a width-based quantitation (WBQ) paradigm complementing height or area based approaches. When the analyte response across the measurement range is not strictly linear, WBQ can offer superior overall performance (lower root-mean-square relative error over the entire range) compared to area- or height-based linear regression methods, rivaling weighted linear regression, provided that response is uniform near the height used for width measurement. To express concentration as an explicit function of width, chromatographic peaks are modeled as two different independent generalized Gaussian distribution functions, representing, respectively, the leading/trailing halves of the peak. The simple generalized equation can be expressed as W = p(ln h̅), where h̅ is h/h, h being the peak amplitude, and p and q being constants. This fits actual chromatographic peaks well, allowing explicit expressions for W. We consider the optimum height for quantitation. The width-concentration relationship is given as ln C = aW + b, where a, b, and n are constants. WBQ ultimately performs quantitation by projecting h from the width, provided that width is measured at a fixed height in the linear response domain. A companion paper discusses several other utilitarian attributes of width measurement.