Filter Samples Research Articles

Pollution Characteristics of Heavy Metals in PM1 and Source-Specific Health Risks in the Tianjin Airport Community, China.

The airport and its surrounding areas are home to a variety of pollution sources, and air pollution is a recognized health concern for local populated regions. Submicron particulate matter (PM1 with an aerodynamic diameter of <1 mm) is a typical pollutant at airports, and the enrichment of heavy metals (HMs) in PM1 poses a great threat to human health. To comprehensively assess the source-specific health effects of PM1-bound HMs in an airport community, PM1 filter samples were collected around the Tianjin Binhai International Airport for 12 h during the daytime and nighttime, both in the spring and summer, and 10 selected HMs (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The indicatory elements of aircraft emissions were certified as Zn and Pb, which accounted for more than 60% of the sum concentration of detected HMs. The health risks assessment showed that the total non-cancer risks (TNCRs) of PM1-bound HMs were 0.28 in the spring and 0.23 in the summer, which are lower than the safety level determined by the USEPA, and the total cancer risk (TCR) was 2.37 × 10-5 in the spring and 2.42 × 10-5 in the summer, implying that there were non-negligible cancer risks in the Tianjin Airport Community. After source apportionment with EF values and PMF model, four factors have been determined in both seasons. Consequently, the source-specific health risks were also evaluated by combining the PMF model with the health risk assessment model. For non-cancer risk, industrial sources containing high concentrations of Mn were the top contributors in both spring (50.4%) and summer (44.2%), while coal combustion with high loads of As and Cd posed the highest cancer risk in both seasons. From the perspective of health risk management, targeted management and control strategies should be adopted for industrial emissions and coal combustion in the Tianjin Airport Community.

Functionalization of Filter Media for Improved Crystalline Silica Analysis Using Raman Spectroscopy

ABSTRACTRespirable crystalline silica (RCS) poses significant health risks in workplaces, including underground coal mines, metal and nonmetal mining, construction sites, fire suppression, and oil and gas industries. Raman spectroscopy offers a promising avenue for direct analysis of RCS on sample filters with minimal pretreatment. However, the presence of organic compounds (OC) in the samples can generate fluorescence signals that interfere with RCS measurements, potentially saturating the detector even at short integration times, particularly when using portable Raman instruments. This study explores a novel approach to address these challenges by functionalizing filters with a hydroxyapatite/silver bromide/titanium dioxide (HAT) photocatalyst, facilitating the oxidation and removal of OC using the Raman excitation laser. Photocatalytic degradation experiments conducted on polyvinyl chloride (PVC) filters preloaded with HAT and anatase titanium dioxide (TiO2) particles demonstrated that HAT significantly enhances the degradation of OC, such as oxalic acid, under visible light irradiation compared to TiO2. Additionally, fluorescence interferences were reduced for coal dust samples analyzed on functionalized silver filters using a portable Raman instrument. The efficacy of HAT in OC photocatalytic degradation on silver filters was further confirmed using a benchtop micro‐Raman system. Filter functionalization had minimal impact on filtration efficiencies and pressure drops, indicating the feasibility of this approach for improving RCS analysis while maintaining filter performance.

A high-throughput, turbulent-mixing, condensation aerosol concentrator for direct aerosol collection as a liquid suspension

Trace measurement of aerosol chemical composition in workplace atmospheres requires the development of high-throughput aerosol collectors that are compact, hand-portable, and can be operated using personal pumps. We describe the design and characterization of a compact, high flow, Turbulent-mixing Condensation Aerosol-in-Liquid Concentrator (TCALC) that allows direct collection of aerosols as liquid suspensions, for off-line chemical, biological, or microscopy analysis. The TCALC unit, measuring approximately 12 × 16 × 18 cm, operates at an aerosol sample flowrate of up to 10 L min−1, using rapid mixing of a hot flow saturated with water vapor and a cold aerosol sample flow, thereby promoting condensational growth of aerosol particles. We investigated the effect of operating parameters such as vapor temperature, growth tube wall temperature, and aerosol sample flowrate, along with the effect of particle diameter, inlet humidity, aerosol concentration, and operation time on TCALC performance. Nanoparticles with an initial aerodynamic diameter ≥25 nm could grow to droplet diameters >1400 nm with an efficiency ≥80%. Good droplet growth efficiency was achieved for sampled aerosol relative humidity ≥9%. We measured complete aerosol collection for concentrations of ≤3 × 105 cm−3. The results showed good agreement between the particulate mass collected through the liquid collector and direct filter collection. The TCALC eliminates the need for sample preparation and filter digestion during chemical analysis, thereby increasing sample recovery and substantially improving the limit of detection and sensitivity of off-line trace analysis of collected liquid samples.

Lowering reporting limit values for respirable crystalline silica analysis by X-ray diffraction in preparation of the 0.025 mg/m3 occupational exposure limit.

Internationally, respirable crystalline silica (RCS) occupational exposure limits (OELs) are being reassessed and, in some jurisdictions, lowered, putting pressure on the capabilities of the analytical techniques used to achieve robust analyses and reliable detection limits. In preparation of a lower OEL, options for lowering the limit of detection (LoD) for RCS analysis have been assessed. Using a Direct-on-Filter X-Ray Diffraction (XRD) analysis under reduced scan speeds in combination with low-noise RCS sampling filters, an LoD of 0.25 µg/filter and a limit of quantification (LoQ) of 0.82 µg/filter can be achieved. Both limits would translate in an LoD of 0.24 µg/m3 and LoQ of 0.78 µg/m3 when sampling respirable dust for 8 h at 2.2 L/min, providing a technical solution to monitor exposures at the proposed OEL of 0.025 mg/m3 (25 µg /m3) and below, with general sampling conditions as typically applied in Australia. This is the first report showing that the OEL of 0.025 mg/m3 (25 µg /m3) is measurable by one of the standardized, direct-on-filter XRD methods.

Capturing clinically relevant Campylobacter attributes through direct whole genome sequencing of stool.

Campylobacter is the leading bacterial cause of infectious intestinal disease, but the pathogen typically accounts for a very small proportion of the overall stool microbiome in each patient. Diagnosis is even more difficult due to the fastidious nature of Campylobacter in the laboratory setting. This has, in part, driven a change in recent years, from culture-based to rapid PCR-based diagnostic assays which have improved diagnostic detection, whilst creating a knowledge gap in our clinical and epidemiological understanding of Campylobacter genotypes - no isolates to sequence. In this study, direct metagenomic sequencing approaches were used to assess the possibility of replacing genome sequences with metagenome sequences; metagenomic sequencing outputs were used to describe clinically relevant attributes of Campylobacter genotypes. A total of 37 diarrhoeal stool samples with Campylobacter and five samples with an unknown pathogen result were collected and processed with and without filtration, DNA was extracted, and metagenomes were sequenced by short-read sequencing. Culture-based methods were used to validate Campylobacter metagenome-derived genome (MDG) results. Sequence output metrics were assessed for Campylobacter genome quality and accuracy of characterization. Of the 42 samples passing quality checks for analysis, identification of Campylobacter to the genus and species level was dependent on Campylobacter genome read count, coverage and genome completeness. A total of 65% (24/37) of samples were reliably identified to the genus level through Campylobacter MDG, 73% (27/37) by culture and 97% (36/37) by qPCR. The Campylobacter genomes with a genome completeness of over 60% (n=21) were all accurately identified at the species level (100%). Of those, 72% (15/21) were identified to sequence types (STs), and 95% (20/21) accurately identified antimicrobial resistance (AMR) gene determinants. Filtration of stool samples enhanced Campylobacter MDG recovery and genome quality metrics compared to the corresponding unfiltered samples, which improved the identification of STs and AMR profiles. The phylogenetic analysis in this study demonstrated the clustering of the metagenome-derived with culture-derived genomes and revealed the reliability of genomes from direct stool sequencing. Furthermore, Campylobacter genome spiking percentages ranging from 0 to 2% total metagenome abundance in the ONT MinION sequencer, configured to adaptive sequencing, exhibited better assembly quality and accurate identification of STs, particularly in the analysis of metagenomes containing 2 and 1% of Campylobacter jejuni genomes. Direct sequencing of Campylobacter from stool samples provides clinically relevant and epidemiologically important genomic information without the reliance on cultured genomes.

Prolonged Dark Chemical Processes in Secondary Organic Aerosols on Filters and in Aqueous Solution.

Secondary organic aerosol (SOA) represents a large fraction of atmospheric aerosol particles that significantly affect both the Earth's climate and human health. Laboratory-generated SOA or ambient particles are routinely collected on filters for a detailed chemical analysis. Such filter sampling is prone to artifactual changes in composition during collection, storage, sample workup, and analysis. In this study, we investigate the chemical composition differences in SOA generated in the laboratory, kept at room temperature as aqueous extracts or on filters, and analyzed in detail after a storage time of a day and up to 4 weeks using liquid chromatography coupled to high-resolution mass spectrometry. We observe significantly different temporal concentration changes for monomers and oligomers in both extracts and on filters. In SOA aqueous extracts, many monomers increase in concentration over time, while many dimers decay at the same time. In contrast, on filters, we observe a strong and persistent concentration increase of many dimers and a decrease of many monomers. This study highlights artifacts arising from SOA chemistry occurring during storage, which should be considered when detailed organic aerosol compositions are studied. The particle-phase reactions on filters can also serve as a model system for atmospheric particle aging processes.

Validation of the method for determining priority phthalates by GC-MS at trace concentrations in surface water with a background pollution level

A methodology for determining priority phthalates (PP) at trace concentrations in surface waters with a background pollution level has been validated. Lake Baikal, the largest natural reservoir that retains up to 20% of the world’s fresh surface waters, was chosen as a natural model for the study. Baikal is characterized by a minimal content of suspended organic matter, a low degree of mineralization and a background level of organic pollutants. Four priority phthalates were found in Baikal water: dimethyl phthalate, diethyl phthalate di-n-butyl phthalate and di-(2-ethylhexyl) phthalate present in the concentration range from 0.01 to 0.66 μg/liter. The method for phthalate determination includes a single liquid-liquid extraction of phthalates (Vsample = 1 liter) and direct analysis of the extracts by GC-MS. Considering the minimum content of suspended particles in water and the use of high sensitivity of the mass spectrometry, the stages of sample filtration and concentration of extracts are excluded from the procedure. Deuterated phthalates are used as surrogate internal standards for the quantitative determination of priority phthalates. The laboratory background of phthalates was assessed using reagent-blank methods, exhaustive multiple extraction and subsequent exclusion of the resulting value from the determination result as a systematic error. The limits of phthalate determination (0.01 – 0.17 μg/liter) and the error of determination (±δ from 12 to 38%) were assessed in the concentration range from 0.01 to 0.66 μg/liter. The method was validated during monitoring of persistent organic pollutants in water of Lake Baikal for the period 2015 – 2023.

Exploring the Effect of Particle Loading Density on Respirable Dust Classification by SEM-EDX

Exposure to respirable coal mine dust (RCMD) still poses health risks to miners. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) is a powerful tool for RCMD characterization because it provides particle-level data, including elemental ratios (via the EDX signals) that can enable classification by inferred mineralogy. However, if the particle loading density (PLD) is high on the analyzed substrate (filter sample), interference between neighboring particles could cause misclassification. To investigate this possibility, a two-part study was conducted. First, the effect of PLD on RCMD classification was isolated by comparing dust particles recovered from the same parent filters under both low- and high-PLD conditions, and a set of modified classification criteria were established to correct for high PLD. Second, the modified criteria were applied to RCMD particles on pairs of filters, with each pair having one filter that was analyzed directly (frequently high PLD) and another filter from which particles were recovered and redeposited prior to analysis (frequently lower PLD). It was expected that application of the modified criteria would improve the agreement between mineralogy distributions for paired filters; however, relatively little change was observed for most pairs. These results suggest that factors other than PLD, including particle agglomeration, can have a substantial effect on the particle EDX data collected during direct-on-filter analysis.

Composition and Sources of Organic Aerosol in Two Megacities in Western China Using Complementary Mass Spectrometric and Statistical Techniques.

Over 300 daily PM2.5 filter samples were collected in two western Chinese megacities, Xi'an and Chongqing, from October 2019 to May 2020. Their aqueous extracts were nebulized simultaneously to an aerosol mass spectrometer (AMS) and a recently developed extractive electrospray ionization (EESI) mass spectrometer, for bulk and near-molecular organic aerosol (OA) composition, respectively. Carbonate was quantified using EESI and a total organic carbon analyzer to separate inorganic carbon from dust. Via isotopically-labelled internal standards and positive matrix factorization, seven water-soluble sources were quantified separately using the AMS- and EESI-based analyses, with consistent types, concentrations, and correlations. These include dust, solid fuel combustion (SFC)-related, nitrogen- (and sulfur-) containing, summer/winter oxygenated OAs, and a cigarette-related OA only in EESI. When accounting for water-solubility, SFC-related OAs were the largest (53%) sources in Chongqing, while dust (consisting of 77% OA and 23% carbonates) was the largest (30%) source in Xi'an. Overall, this study presents one of the first times that complementary mass spectrometric techniques independently resolved consistent OA sources-with added chemical information-over multiple seasons and locations of complex pollution. The methods and quantified sources are essential for subsequent chemical, modelling, and health studies, and policy making for air pollution mitigation.

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 .