Filter Samples Research Articles

Semi-supervised human action recognition via dual-stream cross-fusion and class-aware memory bank

Human action recognition, a crucial task for understanding complex human behavior from sensor data, faces significant challenges. This study specifically addresses two of these challenges: the complex interplay between joint positions and their velocities in action data, and improving model generalizability in the context of limited labeled data. To this end, we present the dual-stream cross-fusion and class-aware memory bank (DSCF-CAMB) framework, a novel semi-supervised approach. It employs a dual-stream cross-fusion (DSCF) mechanism to independently encode joint and velocity data streams, and through cross-fusion, enriches the action representation by exploring correlations between joint positions and their velocities. Meanwhile, to address the challenges posed by unlabeled data, we have developed a contrastive loss based class-aware memory bank (CAMB) that carefully selects high-confidence positive and negative sample embeddings and filters out potential noise-contributing samples. This strategy significantly improves the robustness and accuracy of the framework in action recognition. The experimental results show that our method outperforms the state-of-the-art methods by an average of 2.7% and 1.5% in top-1 accuracy on the renowned NTU RGB+D and NW-UCLA datasets, respectively, demonstrating the superiority of our method in human action recognition.

Residential radon decay products are associated with cough and phlegm in patients with COPD

Radon decay products attach to particulate matter (referred to as particle radioactivity, PR) has been shown to be potential to promote airway damage after inhalation. In this study, we investigated associations between PR with respiratory symptoms and health-related quality of life (HRQL) in patients with COPD. 141 male patients with COPD, former smokers, completed the St. George's Respiratory Questionnaire (SGRQ) after up to four 1-week seasonal assessments (N=474) of indoor (home) and ambient (central site) particulate matter ≤ 2.5 µm in diameter (PM2.5) and black carbon (BC). Indoor PR was measured as α-activity (radiation) on PM2.5 filter samples. The ratio of indoor/ambient sulfur in PM2.5 (a ventilation surrogate) was used to estimate α-PR from indoor radon decay. SGRQ responses assessed frequent cough, phlegm, shortness of breath, wheeze, and chest attacks in the past 3 months. Multivariable linear regression with generalized estimating equations accounting for repeated measures was used to explore associations, adjusting for potential confounders. Median (IQR) indoor α-PR was 1.22 (0.62) mBq/m3. We found that there were positive associations between α-PR with cough and phlegm. The strongest associations were with estimated α-PR of indoor origin for cough (31.1 % increase/IQR, 95 %CI: 8.8 %, 57.8 %), and was suggestive for phlegm (13.0 % increase/IQR, 95 %CI: −2.5 %, 31.0 %), similar adjusting for indoor BC or PM2.5. α-PR of indoor origin was positively associated with an increase in SGRQ Symptoms score [1.2 units/IQR; 95 %CI: −0.3, 2.6] that did not meet conventional levels of statistical significance. Our results suggested that exposure to indoor radon decay products measured as particle radioactivity, a common indoor exposure, is associated with cough, and suggestively associated with phlegm and worse HRQL symptoms score in patients with COPD.

Contrastive Learning Joint Regularization for Pathological Image Classification with Noisy Labels

The annotation of pathological images often introduces label noise, which can lead to overfitting and notably degrade performance. Recent studies have attempted to address this by filtering samples based on the memorization effects of DNNs. However, these methods often require prior knowledge of the noise rate or a small, clean validation subset, which is extremely difficult to obtain in real medical diagnosis processes. To reduce the effect of noisy labels, we propose a novel training strategy that enhances noise robustness without prior conditions. Specifically, our approach includes self-supervised regularization to encourage the model to focus more on the intrinsic connections between images rather than relying solely on labels. Additionally, we employ a historical prediction penalty module to ensure consistency between successive predictions, thereby slowing down the model’s shift from memorizing clean labels to memorizing noisy labels. Furthermore, we design an adaptive separation module to perform implicit sample selection and flip the labels of noisy samples identified by this module and mitigate the impact of noisy labels. Comprehensive evaluations of synthetic and real pathological datasets with varied noise levels confirm that our method outperforms state-of-the-art methods. Notably, our noise handling process does not require any prior conditions. Our method achieves highly competitive performance in low-noise scenarios which aligns with current pathological image noise situations, showcasing its potential for practical clinical applications.

Analysis of PM2.5, black carbon, and trace metals measurements from the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS)

ABSTRACT Communities near transportation sources can be impacted by higher concentrations of particulate matter (PM) and other air pollutants. Few studies have reported on air quality in complex urban environments with multiple transportation sources. To better understand these environments, the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS) was conducted in three neighborhoods in Southeast Kansas City, Kansas. This area has several emissions sources including transportation (railyards, vehicles, diesel trucks), light industry, commercial facilities, and residential areas. Stationary samples were collected for 1-year (October 24, 2017, to October 31, 2018) at six sites using traditional sampling methods and lower-cost air sensor packages. This work examines PM less than 2.5 μm in diameter (PM2.5), black carbon (BC), and trace metals data collected during KC-TRAQS. PM2.5 filter samples showed the highest 24-h mean concentrations (9.34 μg/m3) at the sites located within 20–50 m of the railyard. Mean 24-h PM2.5 concentrations, ranging from 7.96 to 9.34 μg/m3, at all sites were lower than that of the nearby regulatory site (9.83 μg/m3). Daily maximum PM2.5 concentrations were higher at the KC-TRAQS sites (ranging from 25.31 to 43.76 μg/m3) compared to the regulatory site (20.50 μg/m3), suggesting short-duration impacts of localized emissions sources. Across the KC-TRAQS sites, 24-h averaged PM2.5 concentrations from the sensor package (P-POD) ranged from 3.24 to 5.69 µg/m3 showing that, out-of-the-box, the PM sensor underestimated the reference concentrations. KC-TRAQS was supplemented by elemental and organic carbon (EC/OC) and trace metal analysis of filter samples. The EC/OC data suggested the presence of secondary organic aerosol formation, with the highest mean concentrations observed at the site within 20 m of the railyard. Trace metals data showed daily, monthly, and seasonal variations for iron, copper, zinc, chromium, and nickel, with elevated concentrations occurring during the summer at most of the sites. Implications: This work reports on findings from a year-long air quality study in Southeast Kansas City, Kansas to understand micro-scale air quality in neighborhoods impacted by multiple emissions sources such as transportation sources (including a large railyard operation), light industry, commercial facilities, and residential areas. While dozens of studies have reported on air quality near roadways, this work will provide more information on PM2.5, black carbon, and trace metals concentrations near other transportation sources in particular railyards. This work can also inform additional field studies near railyards.

Association of ischemic placental disease in a Southern California Birth Cohort and PM2.5 chemical species and oxidative potential markers

Road traffic is a significant source of particulate matter pollution, whose exposure is a significant risk factor in pregnancy-related health outcomes. The exact mechanisms behind the relationship between traffic-related air pollution (TRAP) exposure and adverse pregnancy outcomes remain unclear. We aim to assess the relationship between exposure to brake and tire wear-associated metals and oxidative potential and ischemic placental disease (IPD). Data were assembled from a final population of 178 women who sought specialized prenatal care at UCLA between 2016 and 2019 in Los Angeles, CA. Modeled first trimester exposures to chemical constituents and oxidative stress potential of PM2.5, black carbon, and PM2.5 mass concentration. Speciated measurements included tracers of brake wear (barium), tire wear (zinc), and oxidative potential markers based on metal concentrations (KM-SUB-ELF ROS) or laboratory assays (DTT loss, OH radical formation). Exposures were modeled by integrating data from filter samples, a low-cost PM2.5 sensor network, and land-use data. We used logistic regression to estimate the associations between air pollution exposures and IPD, adjusting for covariates assessed through medical records and interviews. Scaled to the interquartile range, odds ratios (95% CI) were as follows: barium OR: 1.7 (1.1, 2.7), zinc OR: 1.4 (.86, 2.4), and oxidative potential markers, both modeled as well as measured through DTT loss and OH formation assays (ORs ranging from 1.1-2.0). Point estimates of effect sizes for PM2.5 and black carbon were lower than most measurements (ORs: 1.3-1.4). mass and black carbon. Our findings suggest two key points: (i) metals associated with brake and tire wear, currently unregulated, may play a role in the relationship between TRAP and adverse pregnancy outcomes, and (ii) reducing tailpipe emissions may not be sufficient to protect pregnant women from TRAP.

On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators

In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios.

Hardware and Software Design of Programmable Medium and High-Speed Data Acquisition (DAQ) Board of Fiber Optic Signal for Partial Discharge Acquisition

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz.

Annual variation of source contributions to PM10 and oxidative potential in a mountainous area with traffic, biomass burning, cement-plant and biogenic influences

Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.

Versatile filter membrane for effective sampling and real-time quantitative detection of airborne pathogens

Construction of air filter membranes bearing prominent collecting and transferring capability is highly desirable for detecting airborne pathogens but remains challenging. Here, a hyaluronic acid air filter membrane (HAFM) with tunable heterogeneous micro-nano porous structures is straightforwardly constructed through the ethanol-induced phase separation strategy. Airborne pathogens can be trapped and collected by HAFM with high performance due to the ideal trade-off between removal efficiency and pressure drop. By exempting the sample elution and extraction processes, the HAFM after filtration sampling can not only directly disperse on the agar plate for colony culture but also turn to an aqueous solution for centrifugal enrichment, which significantly reduces the damage and losses of the captured microorganisms. The following combination with ATP bioluminescence endows the HAFM with a real-time quantitative detection function for the captured airborne pathogens. Benefiting from high-efficiency sampling and non-traumatic transfer of airborne pathogens, the real-world bioaerosol concentration can be facilely evaluated by the HAFM-based ATP assay. This work thus not only provides a feasible strategy to fabricate air filter membranes for efficient microbial collection and enrichment but also sheds light on designing advanced protocols for real-time detection of bioaerosols in the field.

Efficient wastewater sample filtration improves the detection of SARS-CoV-2 variants: An extensive analysis based on sequencing parameters.

During the SARS-CoV-2 pandemic, many countries established wastewater (WW) surveillance to objectively monitor the level of infection within the population. As new variants continue to emerge, it has become clear that WW surveillance is an essential tool for the early detection of variants. The EU Commission published a recommendation suggesting an approach to establish surveillance of SARS-CoV-2 and its variants in WW, besides specifying the methodology for WW concentration and RNA extraction. Therefore, different groups have approached the issue with different strategies, mainly focusing on WW concentration methods, but only a few groups highlighted the importance of prefiltering WW samples and/or purification of RNA samples. Aiming to obtain high-quality sequencing data allowing variants detection, we compared four experimental conditions generated from the treatment of: i) WW samples by WW filtration and ii) the extracted RNA by DNase treatment, purification and concentration of the extracted RNA. To evaluate the best condition, the results were assessed by focusing on several sequencing parameters, as the outcome of SARS-CoV-2 sequencing from WW is crucial for variant detection. Overall, the best sequencing result was obtained by filtering the WW sample. Moreover, the present study provides an overview of some sequencing parameters to consider when optimizing a method for monitoring SARS-CoV-2 variants from WW samples, which can also be applied to any sample preparation methodology.

Dual Dynamic Threshold Adjustment Strategy

Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitates the incorporation of additional hyperparameters, notably the threshold, which defines whether the sample pair is informative. The threshold provides a stable numerical standard for determining whether to retain the pairs. It is a vital parameter to reduce the redundant sample pairs participating in training. Nonetheless, finding the optimal threshold can be a time-consuming endeavor, often requiring extensive grid searches. Because the threshold cannot be dynamically adjusted in the training stage, we should conduct plenty of repeated experiments to determine the threshold. Therefore, we introduce a novel approach for adjusting the thresholds associated with both the loss function and the sample mining strategy. We design a static Asymmetric Sample Mining Strategy (ASMS) and its dynamic version, the Adaptive Tolerance ASMS (AT-ASMS), tailored for sample mining methods. ASMS utilizes differentiated thresholds to address the problems (too few positive pairs and too many redundant negative pairs) caused by only applying a single threshold to filter samples. The AT-ASMS can adaptively regulate the ratio of positive and negative pairs during training according to the ratio of the currently mined positive and negative pairs. This meta-learning-based threshold generation algorithm utilizes a single-step gradient descent to obtain new thresholds. We combine these two threshold adjustment algorithms to form the Dual Dynamic Threshold Adjustment Strategy (DDTAS). Experimental results show that our algorithm achieves competitive performance on the CUB200, Cars196, and SOP datasets. Our codes are available at https://github.com/NUST-Machine-Intelligence-Laboratory/DDTAS .

Loss of micropollutants on syringe filters during sample filtration: Machine learning approach for selecting appropriate filters

Prefiltration before chromatographic analysis is critical in the monitoring of environmental micropollutants (MPs). However, in an aqueous matrix, such monitoring often leads to out-of-specification results owing to the loss of MPs on syringe filters. Therefore, this study investigated the loss of seventy MPs on eight different syringe filters by employing Random Forest, a machine learning algorithm. The results indicate that the loss of MPs during filtration is filter specific, with glass microfiber and polytetrafluoroethylene filters being the most effective (<20%) compared with nylon (>90%) and others (regenerated-cellulose, polyethersulfone, polyvinylidene difluoride, cellulose acetate, and polypropylene). The Random Forest classifier showed outstanding performance (accuracy range 0.81–0.95) for determining whether the loss of MPs on filters exceeded 20%. Important factors in this classification were analyzed using the SHapley Additive exPlanation value and Kruskal–Wallis test. The results show that the physicochemical properties (LogKow/LogD, pKa, functional groups, and charges) of MPs are more important than the operational parameters (sample volume, filter pore size, diameter, and flow rate) in determining the loss of most MPs on syringe filters. However, other important factors such as the implications of the roles of pH for nylon and pre-rinsing for PTFE syringe filters should not be ignored. Overall, this study provides a systematic framework for understanding the behavior of various MP classes and their potential losses on syringe filters.

Evaluation of conditions to minimize mercury losses from leaching test eluates

A series of experiments are described that provide recommendations of modifications to leaching test specifications to minimize losses of mercury from eluates during leaching tests and management of analytical samples. EPA methods of the Leaching Environmental Assessment Framework (LEAF) were used as a reference point for what steps are most likely to see losses, including filtration, storage of unpreserved mercury contaminated material, and materials of construction for vessels. The potential sorption of mercury to borosilicate glass and PTFE lab materials was tested to determine what materials are suitable to replace HDPE and polypropylene. The experimental findings indicate that bottles constructed of PTFE and Type I (borosilicate) glass are recommended as extraction and containment vessels for mercury-containing liquids. Type I syringe filtration of aqueous mercury samples at pH 2 through 9 through 0.45 µm PTFE filters housed in polypropylene is acceptable; however, sorption losses were observed to occur at pH > 9. Storage of unpreserved liquids containing mercury over extended leaching test intervals did not lead to significant losses of mercury when coupled with minimized headspace. All mercury-containing eluates should include preservation with Optima HNO3 to a 1 % concentration and gold (III) standard to a concentration of 200 µg/L, followed by refrigeration at <6 °C prior to analysis.

Nearly polarization-insensitive angular filters enabled by metal-dielectric photonic crystal in the visible region

We report on the design and fabrication of nearly polarization-insensitive angular filters, which have been developed through the optimization of one-dimensional Ag/MgF2 photonic crystals (PCs). We evaluate different initial systems for optimization and compare their results in terms of both the wavelength and angular selectivity. Our findings reveal that relaxing the strict periodic condition of initial photonic crystals with a small number of lattices has enabled improvement in the angular selectivity via Fabry–Perot resonances in dielectric layers, achieving a transmission as high as 81% at normal incidence by optimizing the dielectric layer thickness. The simulation results demonstrate that the transmitted beam through the angular filtering sample at 633 nm has allowable angles within 29° and 33° for TE and TM polarization, respectively, with a transmission over 80% at normal incidence. This proposed and demonstrated angular filter represents what we believe is a novel way to utilize 1D metal-dielectric PCs as polarization-insensitive angular filters, overcoming the main drawback of a low transmission. This angular filter will have significant applications in lighting, beam manipulation, optical coupling, and optical detectors.

Efficiencies of filtration sampling and extraction for recovery of viable Staphylococcus aureus bioaerosols

Airborne Staphylococcus aureus is detected in various locations and linked to human infection. Reliable quantification of viable S. aureus bioaerosols by filter-based samplers helps characterize personal exposure, requiring efficient sampling and post-sampling processing. However, the efficiencies of filtration sampling and cell extraction are undetermined for viable S. aureus. In coupled with quantitative PCR and propidium monoazide, the performance of three widely-used samplers (IOM, Button, and Cassette) loaded with polycarbonate (PC) or gelatin filters was evaluated over 30–270 min of sampling and compared to that of BioSampler containing deionized water (DW). Effects of sampler type, filter type, and sampling time on cell recovery efficiency of sampling methods were assessed. Methods to extract cells from filters were also studied. The 1.5-min vortexing in peptone-Tween mixture and 10-min heating in DW were respectively granted optimal for cell extraction from PC and gelatin filters with extraction efficiencies averaged 1.0–1.76 (n = 4). Both Button and IOM with 3-μm gelatin filter performed best to capture S. aureus, significantly greater than Button with 0.8-μm PC by a factor of 9–11 (P < 0.05) and Cassette or IOM with 0.2-μm PC by a factor of 15–79 (P < 0.05). Cassette and IOM with 0.2-μm PC also showed less efficiencies than BioSampler/DW by a factor of 4–16 (P < 0.05). Cell recovery efficiency was not affected by sampling time except for the Button with 0.8-μm PC. Overall, filter type is the most critical factor governing cell recovery efficiency. Button and IOM with gelatin filter and 10-min heating in DW are considered the most efficient filtration sampling and extraction methods for viable S. aureus.

Primer set evaluation and sampling method assessment for the monitoring of fish communities in the North‐western part of the Mediterranean Sea through eDNA metabarcoding

AbstractEnvironmental DNA (eDNA) metabarcoding appears to be a promising tool to survey fish communities. However, the effectiveness of this method relies on primer set performance and on a robust sampling strategy. While some studies have evaluated the efficiency of several primers for fish detection, it has not yet been assessed in situ for the Mediterranean Sea. In addition, mainly surface waters were sampled and no filter porosity testing was performed. In this pilot study, our aim was to evaluate the ability of six primer sets, targeting 12S rRNA (AcMDB07; MiFish; Tele04) or 16S rRNA (Fish16S; Fish16SFD; Vert16S) loci, to detect fish species in the Mediterranean Sea using a metabarcoding approach. We also assessed the influence of sampling depth and filter pore size (0.45 μm versus 5 μm filters). To achieve this, we developed a novel sampling strategy allowing simultaneous surface and bottom on‐site filtration of large water volumes along the same transect. We found that 16S rRNA primer sets enabled more fish taxa to be detected across each taxonomic level. The best combination was Fish16S/Vert16S/AcMDB07, which recovered 95% of the 97 fish species detected in our study. There were highly significant differences in species composition between surface and bottom samples. Filters of 0.45 μm led to the detection of significantly more fish species. Therefore, to maximize fish detection in the studied area, we recommend to filter both surface and bottom waters through 0.45 μm filters and to use a combination of these three primer sets.

Environmental DNA storage and extraction method affects detectability for multiple aquatic invasive species

AbstractEnvironmental DNA (eDNA) refers to genetic material released by organisms into their surrounding environment. Collecting and identifying eDNA has gained popularity for monitoring and surveillance of aquatic invasive species. Invasive species management is most successful when an invasion is identified early while population size is likely to be low, highlighting the importance of eDNA detection sensitivity. Various factors influence DNA yield recovered from environmental samples. Environmental DNA storage and extraction methods, for example, can be adjusted to maximize DNA yield, thereby improving detectability. In this study, we compared the performance of two eDNA storage and extraction methods in detecting three common aquatic invasive species (Bythotrephes longimanus, Dreissena polymorpha, and Faxonius rusticus) across five natural ecosystems of Minnesota, United States. One method involved storing filters in 95% ethanol (EtOH) and extracting DNA using a DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), whereas the other method used cetyl trimethylammonium bromide (CTAB) for storage and a phenol–chloroform–isoamyl (PCI) procedure for DNA extraction. We also investigated the effect of DNA extract volume (1 μL relative to 3 μL) in qPCR reactions on eDNA detections for the commercial kit method. The CTAB‐PCI method yielded significantly more positive detections, across all three species, compared to the EtOH‐Qiagen method. Moreover, we found that using 1 μL of DNA extract in qPCR reactions was equally effective as using 3 μL. To improve detections of aquatic invasive species, we recommend that researchers store eDNA sample filters in CTAB or a similar lysis buffer such as Longmire's solution and extract with PCI when feasible, but note that lower extract volumes might be used without negative effect when either increasing technical replicates or repurposing samples for the detection of multiple species.

Impact of COVID-19 Lockdown on Inhaled Toxic Elements in PM2.5 in Beijing: Composition Characterization and Source-Specific Health Risks Assessment

In early 2020, China experienced a mass outbreak of a novel coronavirus disease (COVID-19). With an aim to evaluate the impact of emission variations on toxic element species in PM2.5 and the health risks associated with inhalation exposure during COVID-19, we collected PM2.5 filter samples in Beijing from January 1 to February 28, 2020. Positive matrix factorization (PMF) and a health risk (HR) assessment model were used to assess the health risks of the toxic elements and critical risk sources. The total concentration of eight toxic elements (Se, Cd, Pb, Zn, As, Cu, Ni, and Cr) in Beijing showed a trend of first increasing and then decreasing: full lockdown (322.9 ng m−3) &gt; pre-lockdown (264.2 ng m−3) &gt; partial lockdown (245.3 ng m−3). During the lockdown period, stringent control measures resulted in significant reductions (6−20%) in Zn, Pb, Cd, and Ni levels, while concentrations of Se, As, Cu, and Cr were unexpectedly elevated (14−348%). A total of five sources was identified: traffic emission, coal combustion, dust emission, industrial emission and mixed source of biomass burning and firework combustion. Total carcinogenic risk (TCR) of the selected toxic elements exceeded the US EPA limits for children and adults. As and Cr (IV) were the main contributors to non-carcinogenic and carcinogenic risks, respectively. For source-resolved risks, coal combustion was the main contributor to HI (43%), while industrial emissions were the main cause of TCR (45%). Additionally, increased contributions from coal combustion, biomass burning, and firework combustion during the full lockdown elevated the HI and TCR values.

Ensuring Accuracy: Critical Validation Techniques in Geochemical Analysis for Sustainable Geothermal Energy Development

Geochemical analysis is a critical tool in geothermal exploration, providing valuable insights into reservoir characteristics. However, obtaining accurate and reliable geochemical data requires rigorous validation techniques. This review examines key factors affecting the accuracy of geochemical data and discusses best practices for ensuring quality. Proper sampling methods, including selection of representative locations, use of appropriate equipment, and adherence to robust protocols for sample collection, filtration, preservation, and storage, are essential for maintaining integrity. Analytical techniques must be carefully selected, with regular calibration and standardization of instruments using certified reference materials. Implementing comprehensive quality assurance and quality control procedures, such as analyzing blanks, duplicates, and spike samples, helps monitor precision and accuracy. Data interpretation should consider the complexities of the geological and hydrological settings, integrating multiple lines of evidence. By following established guidelines and continuously updating methods based on emerging technologies and inter-laboratory comparisons, geothermal teams can optimize the reliability of their geochemical data. Accurate and precise geochemical information, when combined with geological, geophysical, and hydrological data, enables informed decision-making and enhances the success of geothermal projects. As geothermal energy gains importance in the transition to sustainable resources, ensuring the accuracy of geochemical analysis will be crucial for effective exploration and development.

Automated SEM analysis of particles in Hanford tank waste

Multiple bench-scale filtration campaigns of Hanford tank waste supernatant on a backpulseable dead-end filtration skid have provided greater insight into the solids that cause fouling and reduce filter performance. The solids collected during each campaign were concentrated from the backpulse solutions and examined using automated particle analysis (APA) methods with scanning electron microscopy and X-ray energy dispersive spectroscopy to categorize particle types and their morphological characteristics. We show that with APA, thousands of particles can be analyzed to provide accurate insight into the phases that may be impacting filter performance.