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Soil Sampling for Volatile Organic Compounds
Published in L.G. Wilson, Lorne G. Everett, Stephen J. Cullen, Handbook of Vadose Zone Characterization & Monitoring, 2018
Timothy E. Lewis, L. Gray Wilson
Comparability is a qualitative parameter expressing the confidence with which one data set can be compared with another. Data sets obtained from the same site using identical sampling methodologies may erroneously be compared if the environmental conditions extant at the time of sampling are not similar. Similarly, comparing two data sets, one at the start and one at some time after remedial treatment has been in progress, may falsely lead to the conclusion that the cleanup was successful. In the first data set, the laboratory extraction procedure was very rigorous, providing near 100% recovery of the total analyte concentration from the real-world matrix. In the second data set, a recovery method was used that was only 20% efficient. The “apparent” lower concentration in the post-remediation data set is actually due to the poorer extraction efficiency of the method. The two sets of data are nevertheless considered comparable because both laboratory methods exhibited good precision and comparable analyte recoveries (accuracy) for freshly spiked soil audit material and internal surrogates.
Design and testing of a personalized noise monitoring system
Published in Journal of Occupational and Environmental Hygiene, 2023
Oliver Stroh, Geb Thomas, Thomas M. Peters, Marcus Tatum
The wearable monitor incorporated an inexpensive (∼$30) external noise dosimeter (Zuidema et al. 2019). The dosimeter reports an average A-weighted sound pressure levels each second through a serial connection. This dosimeter is accurate within ±2 dBA of a Class 2 meter in a range between 75 and 94 decibels using pink noise (Hallett et al. 2018). Above this threshold, the device will underestimate dBA, with a soft floor around 60 dBA. While the dosimeter is not a Class 2 instrument, its comparability to a Class 2 instrument suggests that it can still provide relevant data regarding noise exposure. Given the verified range of the device, the top end noise exposure might cause underestimation of total exposure, but it will accurately identify noises that are unsafe. The monitor’s smartphone application started when the noise dosimeter was connected to the device with an “On-The-Go” cable. The smartphone application read the external dosimeter’s measurements, added a time stamp, and, once per minute, stored the accumulated values in a text file on an SD card inside the smartphone. The smartphone application can still function without the extra storage space provided by the external SD card.
Beyond text comprehension: exploring items’ characteristics and their effect on foreign students’ disadvantage in mathematics
Published in International Journal of Mathematical Education in Science and Technology, 2022
Clelia Cascella, Chiara Giberti
The Rasch model is a logistic one that provides, for all items and subjects, an estimation of (item) difficulty and (person) ability used to scale both items and subjects according to the same latent trait. In fact, in the Rasch model, it is hypothesized that the probability of giving a correct answer depends on a student’s relative ability (i.e. his/her ability compared to item difficulty) exclusively: other variables (such as students’ personal features, like citizenship status or gender) should not affect the probability of answering an item successfully. The Rasch model is especially adequate for pursuing our paper’s goals because it has the property of measurement invariance. As stated by Rasch, ‘The comparison between two stimuli should be independent of which particular individuals were instrumental for the comparison. Symmetrically, a comparison between two individuals should be independent of which particular stimuli within the class considered were instrumental for comparison’ (1960, p. 332). Measurement invariance, a characteristic of the Rasch model and of this class of models specifically (Andrich & Marais, 2019, p. 329), thus allows groups the comparability that is the crux of our study.
Water quality evaluation of a lacustrine water body in the Mediterranean based on different water quality index (WQI) methodologies
Published in Journal of Environmental Science and Health, Part A, 2020
Ioanna Zotou, Vassilios A. Tsihrintzis, Georgios D. Gikas
As mentioned in the previous sections, the indices which were calculated in this study were the: Prati’s Index of Pollution, Bhargava’s Index, Oregon WQI, Dinius’ Second Index and the Weighted Arithmetic WQI. Based on the measured physicochemical parameters, each WQI was calculated separately for each monthly sampling campaign at each of the three sampling sites (P1, P2, P3) and the two sampling depths (near the surface and close to the bottom). Then, for each WQI value, the corresponding quality class from 1 to 5 (where 1 indicates the worst quality and 5 the best one) was selected in accordance with the classification system of each methodology. The above-mentioned process was applied separately for each of the five WQIs computed in this study, in order to produce the variation of the water quality over the monitoring period for each of them. It should be noted that, to enable comparability among the results, the number of the quality class (1 to 5) was selected for the illustration of the water quality variation in the examined water body instead of the numerical value of each index itself, since two of the examined indices, i.e., Prati’s Index of Pollution and the Weighted Arithmetic WQI, are expressed in inverse and non-percentage numerical scales. The determination of the quality class variation over the entire period according to each methodology was followed by the classification of the examined water body on the basis of its overall water quality. For this purpose, the “worst quality scenario” was considered, which means that the lowest WQI value recorded over the entire monitoring period and across all the sampling sites was taken as representative of the overall quality, and thus, was utilized to determine the overall quality class into which the water body should be categorized. Apart from the indices computed in the present study, the results of two additional WQIs, i.e., NSF-WQI and CCME-WQI, as computed by Alexakis et al.,[31] were also incorporated in the final comparison to give a better view of the deviations that may occur in the classification when applying different WQI methodologies.