In situ X-ray area detector flat-field correction at an operating photon energy without flat illumination. Corrigendum.

The name of an author in the article by Weng et al. (2023) [J. Synchrotron Rad. 30, 546-554] is corrected.

In situ X-ray area detector flat-field correction at an operating photon energy without flat illumination.

Flat-field calibration of X-ray area detectors is a challenge due to the inability to generate an X-ray flat-field at the selected photon energy the beamline operates at, which has a strong influence on the measurement behavior of the detector. A method is presented in which a simulated flat-field correction is calculated without flat-field measurements. Instead, a series of quick scattering measurements from an amorphous scatterer is used to calculate a flat-field response. The ability to rapidly obtain a flat-field response allows for recalibration of an X-ray detector as needed without significant expenditure of either time or effort. Area detectors on the beamlines used, such as the Pilatus 2M CdTe, PE XRD1621 and Varex XRD 4343CT, were found to have detector responses that drift slightly over timescales of several weeks or after exposure to high photon flux, suggesting the need to more frequently recalibrate with a new flat-field correction map.

Performance-based protocol for selection of economical portable sensor for air quality measurement.

An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS.

Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network.

Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 µg/m3, reduced from 18.4 ± 6.5 µg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks.

A statistical approach to correct X-ray response non-uniformity in microstrip detectors for high-accuracy and high-resolution total-scattering measurements.

An unbiased approach to correct X-ray response non-uniformity in microstrip detectors has been developed based on the statistical estimation that the scattering intensity at a fixed angle from an object is expected to be constant within the Poisson noise. Raw scattering data of SiO2 glass measured by a microstrip detector module was found to show an accuracy of 12σPN at an intensity of 106 photons, where σPN is the standard deviation according to the Poisson noise. The conventional flat-field calibration has failed in correcting the data, whereas the alternative approach used in this article successfully improved the accuracy from 12σPN to 2σPN. This approach was applied to total-scattering data measured by a gapless 15-modular detector system. The quality of the data is evaluated in terms of the Bragg reflections of Si powder, the diffuse scattering of SiO2 glass, and the atomic pair distribution function of TiO2 nanoparticles and Ni powder.

Sources of error and variability in particulate matter sensor network measurements.

The quality of mass concentration estimates from increasingly popular networks of low-cost particulate matter sensors depends on accurate conversion of sensor output (e.g., voltage) into gravimetric-equivalent mass concentration, typically using a calibration procedure. This study evaluates two important sources of variability that lead to error in estimating gravimetric-equivalent mass concentration: the temporal changes in sensor calibration and the spatial and temporal variability in gravimetric correction factors. A 40-node sensor network was deployed in a heavy vehicle manufacturing facility for 8 months. At a central location in the facility, particulate matter was continuously measured with three sensors of the network and a traditional, higher-cost photometer, determining the calibration slope and intercept needed to translate sensor output to photometric-equivalent mass concentration. Throughout the facility, during three intensive sampling campaigns, respirable mass concentrations were measured with gravimetric samplers and photometers to determine correction factors needed to adjust photometric-equivalent to gravimetric-equivalent mass concentration. Both field-determined sensor calibration slopes and intercepts were statistically different than those estimated in the laboratory (α = 0.05), emphasizing the importance of aerosol properties when converting voltage to photometric-equivalent mass concentration and the need for field calibration to determine slope. Evidence suggested the sensors' weekly field calibration slope decreased and intercept increased, indicating the sensors were deteriorating over time. The mean correction factor in the cutting and shot blasting area (2.9) was substantially and statistically lower than that in the machining and welding area (4.6; p = 0.01). Therefore, different correction factors should be determined near different occupational processes to accurately estimate particle mass concentrations.

Evaluation of low-cost electro-chemical sensors for environmental monitoring of ozone, nitrogen dioxide, and carbon monoxide.

Development of an air quality monitoring network with high spatio-temporal resolution requires installation of a large number of air pollutant monitors. However, state-of-the-art monitors are costly and may not be compatible with wireless data logging systems. In this study, low-cost electro-chemical sensors manufactured by Alphasense Ltd. for detection of CO and oxidative gases (predominantly O3 and NO2) were evaluated. The voltages from three oxidative gas sensors and three CO sensors were recorded every 2.5 sec when exposed to controlled gas concentrations in a 0.125-m3 acrylic glass chamber. Electro-chemical sensors for detection of oxidative gases demonstrated sensitivity to both NO2 and O3 with similar voltages recorded when exposed to equivalent environmental concentrations of NO2 or O3 gases, when evaluated separately. There was a strong linear relationship between the recorded voltages and target concentrations of oxidative gases (R2 > 0.98) over a wide range of concentrations. Although a strong linear relationship was also observed for CO concentrations below 12 ppm, a saturation effect was observed wherein the voltage only changes minimally for higher CO concentrations (12-50 ppm). The nonlinear behavior of the CO sensors implied their unsuitability for environments where high CO concentrations are expected. Using a manufacturer-supplied shroud, sensors were tested at 2 different flow rates (0.25 and 0.5 Lpm) to mimic field calibration of the sensors with zero air and a span gas concentration (2 ppm NO2 or 15 ppm CO). As with all electrochemical sensors, the tested devices were subject to drift with a bias up to 20% after 9 months of continuous operation. Alphasense CO sensors were found to be a proper choice for occupational and environmental CO monitoring with maximum concentration of 12 ppm, especially due to the field-ready calibration capability. Alphasense oxidative gas sensors are usable only if it is valuable to know the sum of the NO2 and O3 concentrations.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality.

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Development and calibration of a modifiable passive sampler for monitoring atmospheric tritiated water vapor in different environments.

Anthropogenic release of tritium from nuclear facilities is expected to increase significantly in the coming decades, which may cause radiation exposure to humans through the contamination of water and food chains. It is necessary and urgent to acquire detailed information about tritium in various environments for studying its behavior and assessing the potential radiation risk. In the atmosphere, although the passive sampling technique provides a low-cost and convenient way to characterize the dynamics of tritiated water vapor (HTO), a single, simple sampler configuration makes it difficult to collect sufficient and representative samples within the expected period from different environments. In this study, we systematically studied the impacts of sampler configurations on sampling performance and proposed a modifiable sampler design by scaling sampler geometry and adjusting absorbent to achieve different monitoring demands. The samplers were subsequently deployed at five sites in China and Germany for the field calibration and the measured results exhibited a good agreement between the adsorption process obtained in sites corrected with diffusion coefficient and the one calibrated in Shanghai. This suggests the feasibility of predicting sampling performance in the field based on known data. Finally, we developed a strategy for sampler modification and selection in different environments and demonstrated that using easily obtainable environmental data, our sampler can be optimized for any area without any time-consuming preliminary experiments. This work provides a scientific basis for establishing high-resolution atmospheric HTO database and expands the conventional empirical sampler design paradigm by demonstrating the feasibility of using quantitative indices for sampler performance customization.

Field calibrations of soil moisture sensors in a forested watershed.

Spatially variable soil properties influence the performance of soil water content monitoring sensors. The objectives of this research were to: (i) study the spatial variability of bulk density (ρ(b)), total porosity (θ(t)), clay content (CC), electrical conductivity (EC), and pH in the upper Makaha Valley watershed soils; (ii) explore the effect of variations in ρ(b) and θ(t) on soil water content dynamics, and (iii) establish field calibration equations for EC-20 (Decagon Devices, Inc), ML2x (Delta-T-Devices), and SM200 (Delta-T-Devices) sensors to mitigate the effect of soil spatial variability on their performance. The studied soil properties except pH varied significantly (P < 0.05) across the soil water content monitoring depths (20 and 80 cm) and six locations. There was a linear positive and a linear inverse correlation between the soil water content at sampling and ρ(b), and between the soil water content at sampling and θ(t), respectively. Values of laboratory measured actual θ(t) correlated (r = 0.75) with those estimated from the relationship θ(t) = 1 - ρ(b)/ρ(s), where ρ(s) is the particle density. Variations in the studied soil properties affected the performance of the default equations of the three tested sensors; they showed substantial under-estimations of the actual water content. The individual and the watershed-scale field calibrations were more accurate than their corresponding default calibrations. In conclusion, the sensors used in this study need site-specific calibrations in order to mitigate the effects of varying properties of the highly weathered tropical soils.

The high-altitude bird chronicles: lessons from field work with Frapps.

Over the past decade, Peter Frappell, aka Frapps, has been an integral part of an international group studying birds that migrate or reside at altitude. This research has taken the extended group from Terkhiin Tsagaan Lake on the Mongolian plateau to Chilika Lake in eastern India, Koonthankulum bird sanctuary in southern India, Lake Qinghai in Chinese Tibet, Summer Lake Wildlife and Malheur National Wildlife Refuge in Oregon, and San Pedro a Marca, Vichaycocha and Lake Titicaca National Reserve in Perú. It has been a productive project producing over 30 manuscripts, 15 of which were based on research in the field. What has not been published are the stories behind the research and the critical lessons learned along the way. Some of these are chronicled here.

Using the heteronuclear Bloch-Siegert shift of protons for B1 calibration of insensitive nuclei not present in the sample.

Indirect rf field calibration using the heteronuclear Bloch-Siegert shift is presented. This method is useful for calibrating ω1 = -γB1 for the rf channels of small volume fast-spinning probes on which direct rf calibration is practically inconvenient or difficult for insensitive low-γ nuclei. Proton signals are observed for the rf calibration of the insensitive nuclei without requiring their presence in the sample. For a linearly modulated rf field, the heteronuclear Bloch-Siegert shift is given by, ΔωBS=ω0ω12/ω02-ωirr2, where ω0 and ωirr are the Larmor and irradiation frequencies, respectively. A short protocol using full-echo acquisition of protons is described for measurement of the phase change induced by the Bloch-Siegert shift. The calibration procedure is validated by a comparison with direct 13C calibration and demonstrated for 14N rf field measurement of a 0.75 mm 100 kHz triple-resonance magic-angle spinning probe.

Field Calibration of Wind Direction Sensor to the True North and Its Application to the Daegwanryung Wind Turbine Test Sites.

This paper proposes a field calibration technique for aligning a wind direction sensor to the true north. The proposed technique uses the synchronized measurements of captured images by a camera, and the output voltage of a wind direction sensor. The true wind direction was evaluated through image processing techniques using the captured picture of the sensor with the least square sense. Then, the evaluated true value was compared with the measured output voltage of the sensor. This technique solves the discordance problem of the wind direction sensor in the process of installing meteorological mast. For this proposed technique, some uncertainty analyses are presented and the calibration accuracy is discussed. Finally, the proposed technique was applied to the real meteorological mast at the Daegwanryung test site, and the statistical analysis of the experimental testing estimated the values of stable misalignment and uncertainty level. In a strict sense, it is confirmed that the error range of the misalignment from the exact north could be expected to decrease within the credibility level.

Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments.

Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM2.5) has, to date, been limited due to the cost and logistics involved with traditional monitoring approaches. New miniaturized particle sensors are a potential strategy to gather more time- and spatially-resolved data, to address data gaps in regions with limited monitoring and to address important air quality research priorities in a more cost-effective manner. This work presents field evaluations and lab testing of three models of low-cost (< $200) PM sensors (SHINYEI: models PPD42NS, PPD20V, PPD60PV) in three locations: urban background (average PM2.5: 8 µg m-3) and roadside in Atlanta, Georgia, USA (average PM2.5: 21 µg m-3), and a location with higher ambient concentrations in Hyderabad, India (average PM2.5: 72 µg m-3). Sensor measurements were compared against reference monitors in the lab using one-minute averages and in field locations using one-hour averages. At the Atlanta sites the sensors were weakly correlated with a tapered element oscillating microbalance (TEOM) at best (R2 ≤ 0.30). In Hyderabad, the PPD20V sensors had the highest correlation with the environmental beta attenuation monitor (E-BAM) (R2 > 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R2 = ~0, < 40 µg m-3). The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications (< ~250 µg m-3). These field- testing results provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions. The inconsistent performance results underscore the need for rigorous evaluation of optical particle sensors in the laboratory and in diverse field environments.

1H NMR at Larmor frequencies down to 3Hz by means of Field-Cycling techniques.

Field-Cycling (FC) NMR experiments were carried out at 1H Larmor frequencies down to about 3Hz. This could be achieved by fast switching a high polarizing magnetic field down to a low evolution field which is tilted with respect to the polarization field. Then, the low frequency Larmor precession of the nuclear spin magnetization about this evolution field is registered by means of FIDs in a high detection field. The crucial technical point of the experiment is the stabilization of the evolution field, which is achieved by compensating for temporal magnetic field fluctuations of all three spatial components. The paper reports on some other basic low field experiments such as the simultaneous measurement of the Larmor frequency and the spin-lattice relaxation time in such small fields as well as the irradiation of oscillating transversal magnetic field pulses at very low frequencies as a novel method for field calibration in low field FC NMR. The potential of low field FC is exemplified by the 1H relaxation dispersion of water at frequencies below about 2kHz stemming from the slow proton exchange process.

Taking a look at the calibration of a CCD detector with a fiber-optic taper.

At the Structural Biology Center beamline 19BM, located at the Advanced Photon Source, the operational characteristics of the equipment are routinely checked to ensure they are in proper working order. After performing a partial flat-field calibration for the ADSC Quantum 210r CCD detector, it was confirmed that the detector operates within specifications. However, as a secondary check it was decided to scan a single reflection across one-half of a detector module to validate the accuracy of the calibration. The intensities from this single reflection varied by more than 30% from the module center to the corner of the module. Redistribution of light within bent fibers of the fiber-optic taper was identified to be a source of this variation. The degree to which the diffraction intensities are corrected to account for characteristics of the fiber-optic tapers depends primarily upon the experimental strategy of data collection, approximations made by the data processing software during scaling, and crystal symmetry.

A passive sampling method for detecting analgesics, psycholeptics, antidepressants and illicit drugs in aquatic environments in the Czech Republic.

The goal of this study was to assess the bioavailable concentrations of analgesics, psycholeptics, antidepressants and illicit drugs in the surface waters of the Czech Republic. All of the sampling sites are located within the most important water quality monitoring profiles at the Czech Hydrometeorological Institute. The total concentrations of the compounds ranged from 463 to 6,447 ng POCIS(-1) (Polar Organic Chemical Integrative Sampler). Carbamazepine (196-2,690 ng POCIS(-1)) and tramadol (160-2,250 ng POCIS(-1)) were the most abundant compounds at every site. The most polluted sites were those that received communal wastewater effluent and had a low dilution factor (ratio of wastewater effluent and river flow). The aqueous concentrations of the target compounds were estimated using sampling rate values obtained during a field calibration experiment. Patterns in the aqueous concentrations of the compounds (after back calculation from POCIS extracts) and the POCIS concentrations are different, possibly leading to discrepancies between the toxicity assessments conducted using POCIS extracts and those conducted using grab samples of water from the same location.