Sampling efficiency and nucleic acid stability during long-term sampling with different bioaerosol samplers.

Aerosol microbiome studies have received increased attention as technological advancements have made it possible to dive deeper into the microbial diversity. To enhance biomass collection for metagenomic sequencing, long-term sampling is a common strategy. While the impact of prolonged sampling times on microorganisms' culturability and viability is well-established, its effect on nucleic acid stability remains less understood but is essential to ensure representative sample collection. This study evaluated four air samplers (SKC BioSampler, SASS3100, Coriolis µ, BioSpot-VIVAS 300-P) against a reference sampler (isopore membrane filters) to identify nucleic acid stability during long-term sampling. Physical sampling efficiencies determined with a fluorescent tracer for three particle sizes (0.8, 1, and 3 µm), revealed high efficiencies (> 80% relative to reference) for BioSampler, SASS3100, and BioSpot-VIVAS for all particle sizes, and for Coriolis with 3 µm particles. Coriolis exhibited lower efficiency for 0.8 µm (7%) and 1 µm (50%) particles. During 2-h sampling with MS2 and Pantoea agglomerans, liquid-based collection with Coriolis and BioSampler showed a decrease in nucleic acid yields for all test conditions. BioSpot-VIVAS displayed reduced sampling efficiency for P. agglomerans compared to MS2 and the other air samplers, while filter-based collection with SASS3100 and isopore membrane filters, showed indications of DNA degradation for 1 µm particles of P. agglomerans after long-term sampling. These findings show that long-term air sampling affects nucleic acid stability in both liquid- and filter-based collection methods. These results highlight bias produced by bioaerosol collection and should be considered when selecting an air sampler and interpreting aerosol microbiome data.

The use of a portable X-ray fluorescence spectrometer for measuring nickel in plants: sample preparation and validation.

X-ray fluorescence is a fast, cost-effective, and eco-friendly method for elemental analyses. Portable X-ray fluorescence spectrometers (pXRF) have proven instrumental in detecting metals across diverse matrices, including plants. However, sample preparation and measurement procedures need to be standardized for each instrument. This study examined sample preparation methods and predictive capabilities for nickel (Ni) concentrations in various plants using pXRF, employing empirical calibration based on inductively coupled plasma optical emission spectroscopy (ICP-OES) Ni data. The evaluation involved 300 plant samples of 14 species with variable of Ni accumulation. Various dwell times (30, 60, 90, 120, 300 s) and sample masses (0.5, 1.0, 1.5, 2.0 g) were tested. Calibration models were developed through empirical and correction factor approaches. The results showed that the use of 1.0 g of sample (0.14 g cm-2) and a dwell time of 60 s for the study conditions were appropriate for detection by pXRF. Ni concentrations determined by ICP-OES were highly correlated (R2 = 0.94) with those measured by the pXRF instrument. Therefore, pXRF can provide reliable detection of Ni in plant samples, avoiding the digestion of samples and reducing the decision-making time in environmental management.

A smartphone-assisted photoelectrochemical POCT method via Z-scheme CuCo2S4/Fe3O4 for simultaneously detecting co-contamination with microplastics in food and the environment.

As emerging contaminants, microplastics threaten food and environmental safety. Dibutyl phthalate (DBP, released from microplastics) and benzo[a]pyrene (BaP, adsorbed on microplastics) coexisted in food and the environment, harming human health, requesting a sensitive and simultaneous testing method to monitor. To address current sensitivity, simultaneousness, and on-site portability challenges during dual targets in complex matrixes, CuCo2S4/Fe3O4 nanoflower was designed to develop a smartphone-assisted photoelectrochemical point-of-care test (PEC POCT). The carrier transfer mechanism in CuCo2S4/Fe3O4 was proven via density functional theory calculation. Under optimal conditions, the PEC POCT showed low detection limits of 0.126, and 0.132 pg/mL, wide linearity of 0.001-500, and 0.0005-50 ng/mL for DBP and BaP, respectively. The smartphone-assisted PEC POCT demonstrated satisfied recoveries (80.00%-119.63%) in real samples. Coherent results were recorded by comparing the PEC POCT to GC-MS (DBP) and HPLC (BaP). This novel method provides a practical platform for simultaneous POCT for food safety and environment monitoring.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Research on high performance combustible gas concentration sensor based on pyramid beam splitter matrix.

Combustible gas concentration detection faces challenges of increasing accuracy, and sensitivity, as well as high reliability in harsh using environments. The special design of the optical path structure of the sensitive element provides an opportunity to improve combustible gas concentration detection. In this study, the optical path structure of the sensitive element was newly designed based on the Pyramidal beam splitter matrix. The infrared light source was modulated by multi-frequency point signal superimposed modulation technology. At the same time, concentration detection results and confidence levels were calculated using the 4-channel combustible gas concentration detection algorithm based on spectral refinement. Through experiment, it is found that the sensor enables full-range measurement of CH4, at the lower explosive limit (LEL, CH4 LEL of 5%), the reliability level is 0.01 parts-per-million (PPM), and the sensor sensitivity is up to 0.5PPM. The sensor is still capable of achieving PPM-level detections, under extreme conditions in which the sensor's optical window is covered by 2/3, and humidity is 85% or dust concentration is 100mg/m3. Those improve the sensitivity, robustness, reliability, and accuracy of the sensor.

Development of a novel DGT passive sampler for measuring polycyclic aromatic hydrocarbons in aquatic systems.

Polycyclic aromatic hydrocarbons (PAHs) are priority pollutants and need to be measured reliably in waters and other media, to understand their sources, fate, behaviour and to meet regulatory monitoring requirements. Conventional water sampling requires large water volumes, time-consuming pre-concentration and clean-up and is prone to analyte loss or contamination. Here, for the first time, we developed and validated a novel diffusive gradients in thin-films (DGT) passive sampler for PAHs. Based on the well-known DGT principles, the sampler pre-concentrates PAHs with typical deployment times of days/weeks, with minimal sample handling. For the first time, DGT holding devices made of metal and suitable for sampling hydrophobic organic compounds were designed and tested. They minimize sorption and sampling lag times. Following tests on different binding layer resins, a MIP-DGT was preferred - the first time applying MIP for PAHs. It samples PAHs independent of pH (3.9 -8.1), ionic strength (0.01 -0.5 M) and dissolved organic matter < 20 mg L-1, making it suitable for applications across a wide range of environments. Field trials in river water and wastewater demonstrated that DGT is a convenient and reliable tool for monitoring labile PAHs, readily achieving quantitative detection of environmental levels (sub-ng and ng/L range) when coupled with conventional GC-MS or HPLC. ENVIRONMENTAL IMPLICATIONS: PAHs are carcinogenic and genotoxic compounds. They are environmentally ubiquitous and must be monitored in waters and other media. This study successfully developed a new DGT passive sampler for reliable in situ time-integrated measurements of PAHs in waters at the ng/L level. This is the first time to use passive samplers for accurate measurements of hydrophobic organic contaminants in aquatic systems without calibration, a big step forward in monitoring PAHs. The application of this new sampler will enhance our understanding of the sources, fate, behavior and ecotoxicology of PAHs, enabling improved environmental risk assessment and management of these compounds.

Paper-based sensors: affordable, versatile, and emerging analyte detection platforms.

Paper-based sensors, often referred to as paper-based analytical devices (PADs), stand as a transformative technology in the field of analytical chemistry. They offer an affordable, versatile, and accessible solution for diverse analyte detection. These sensors harness the unique properties of paper substrates to provide a cost-effective and adaptable platform for rapid analyte detection, spanning chemical species, biomolecules, and pathogens. This review highlights the key attributes that make paper-based sensors an attractive choice for analyte detection. PADs demonstrate their versatility by accommodating a wide range of analytes, from ions and gases to proteins, nucleic acids, and more, with customizable designs for specific applications. Their user-friendly operation and minimal infrastructure requirements suit point-of-care diagnostics, environmental monitoring, food safety, and more. This review also explores various fabrication methods such as inkjet printing, wax printing, screen printing, dip coating, and photolithography. Incorporating nanomaterials and biorecognition elements promises even more sophisticated and sensitive applications.

Internet-of-things-integrated molecularly imprinted polymer-based electrochemical nano-sensors for pesticide detection in the environment and food products.

To ensure environmental and health safety, relevant pollutants such as pesticides must be screened thoroughly to set their permissible limit. Various approaches have been used to identify pesticides such as capillary electrophoresis, gas and liquid-liquid chromatography, high-performance liquid chromatography, and enzyme-linked immune-absorbent tests. However, these techniques have some drawbacks, including time-consuming difficult steps, expensive bulky equipment, expert personnel, and a lack of selectivity. Recent advances in the field of biosensing have introduced biosensors for the onsite detection of pesticides which offer several advantages including rapid, simple, selective, sensitive, low-cost operation, and on-site detection. With the advent of molecularly imprinted polymer which substituted the traditional biorecognition elements (BREs) such as enzymes and antibodies, biosensors' sensitivity, selectivity, and reproducibility enhanced many folds. Molecularly imprinted polymers (MIP) are artificial polymer molecules that resemble natural BREs. They are synthesized when functional monomers are polymerized in the presence of a target analyte. Owing to the advantages of MIP, in this paper, the development of MIP-based electrochemical biosensors for pesticide detection is reviewed critically. A brief introduction to pesticides and the use of MIPs-based electrochemical sensors for pesticide detection is presented along with pros and cons. Further, Internet of Things (IoT) integrated MIP-based nanosensors for pesticide detection and information distribution have been discussed. In the end, future perspectives and challenges while implementing MIP-based nanosensors for onsite pesticide recognition have eventually been highlighted.

Feasibility of residential air quality monitoring to address asthma outcomes.

Improving asthma outcomes for underserved populations can be addressed through interventions to improve indoor air quality (IAQ). New protocol for measuring IAQ and health outcomes are imperative given advances in IAQ monitoring technology and challenges in conducting intervention research in homes. In this pilot study HEPA air purifiers and HEPA vacuum cleaners were provided to five homes with children with asthma. For 6 weeks, eight common components of air quality were measured using a low-cost multi-channel air quality monitoring device, with data conveyed directly from participant homes via Wi-Fi connection. In conjunction with periodic surveys on asthma control, impact of asthma on quality of life and intervention compliance, outcomes compared IAQ, home characteristics, and asthma-related measures. This pilot study demonstrates the feasibility of a protocol to evaluate a dual component intervention to improve IAQ in homes, as measured with a low-cost air quality monitoring device.

Recent advancements of smartphone-based sensing technology for diagnosis, food safety analysis, and environmental monitoring.

The emergence of computationally powerful smartphones, relatively affordable high-resolution camera, drones, and robotic sensors have ushered in a new age of advanced sensible monitoring tools. The present review article investigates the burgeoning smartphone-based sensing paradigms, including surface plasmon resonance (SPR) biosensors, electrochemical biosensors, colorimetric biosensors, and other innovations for modern healthcare. Despite the significant advancements, there are still scarcity of commercially available smart biosensors and hence need to accelerate the rates of technology transfer, application, and user acceptability. The application/necessity of smartphone-based biosensors for Point of Care (POC) testing, such as prognosis, self-diagnosis, monitoring, and treatment selection, have brought remarkable innovations which eventually eliminate sample transportation, sample processing time, and result in rapid findings. Additionally, it articulates recent advances in various smartphone-based multiplexed bio sensors as affordable and portable sensing platforms for point-of-care devices, together with statistics for point-of-care health monitoring and their prospective commercial viability.

Comparing Antoine parameter sources for accurate vapor pressure prediction across a range of temperatures.

Determining the vapor pressure of a substance at the relevant process temperature is a key component in conducting an exposure assessment to ascertain worker exposure. However, vapor pressure data at various temperatures relevant to the work environment is not readily available for many chemicals. The Antoine equation is a mathematical expression that relates temperature and vapor pressure. The objective of this analysis was to compare Antoine parameter data from 3 independent data sources; Hansen, Yaws, and Custom data and identify the source that generates the most accurate vapor pressure values with the least bias, relative to the referent data set from the CRC Handbook of Chemistry and Physics. Temperatures predicted from 3 different Antoine sources across a range of vapor pressures for 59 chemicals are compared to the reference source. The results show that temperatures predicted using Antoine parameters from the 3 sources are not statistically significantly different, indicating that all 3 sources could be useful. However, the Yaws dataset will be used in the SDM 2.0 because the data is readily available and robust.

A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing.

Microplastics (MPs), the emerging pollutants appeared in water environment, have grabbed significant attention from researchers. The quantitative method of spherical MPs is the premise and key for the study of MPs in laboratory researches. However, the manual counting is time-consuming, and the existing semi-automated analysis lacked of robustness. In this study, a highly accurate quantification method for spherical MPs, called VS120-MC was proposed. VS120-MC consisted of the digital slide scanner VS120 and the MPs image processing software, MPs-Counter. The full-area scanning photography was employed to fundamentally avoid the error caused by random or partition sampling modes. To accomplish high-performance batch recognition, the Weak-Circle Elimination Algorithm (WEA) and the Variable Coefficient Threshold (VCT) was developed. Finally, lower than 0.6% recognition error rate of simulated images with different aggregated indices was achieved by MPs-Counter with fast processing speed (about 2 s/image). The smallest size for VS120-MC to detect was 1 µm. And the applicability of VS120-MC in real water body was investigated. The measured value of 1 µm spherical MPs in ultra-pure water and two kinds of polluted water after digestion showed a good linear relationship with the Manual measurements (R2 = 0.982,0.987 and 0.978, respectively). For 10 µm spherical MPs, R2 reached 0.988 for ultra-pure water and 0.984 for both of the polluted water. MPs-Counter also showed robustness when using the same set of parameters processing the images with different conditions. Overall, VS120-MC eliminated the error caused by traditional photography and realized an accurate, efficient, stable image processing tool, providing a reliable alternative for the quantification of spherical MPs.

Tyrosinase-based nanobiosensor for environmental monitoring of hormones in river water.

This study explores the application of a tyrosinase cantilever nanobiosensor for detecting 17ß-estradiol and estrone in typical water systems. The physical-chemical parameters of water were evaluated within the Tigre River micro-basin in Erechim, RS, to determine water potability for urban populations. Water clarity, conductivity, and pH levels were essential markers, adhering to recognized standards for water quality and human consumption. The cantilever nanobiosensor demonstrated strong sensitivity and a broad linear range, with a limit of detection (<0.00051 ppb) surpassing other enzymatic biosensors and covering a range of 0.0001-100 ppb. The real water sample quality investigated in relation to contamination with 17ß-estradiol and estrone by nanobiosensor showed values below the LOD for both compounds. Recovery studies demonstrated the reliability of the nanobiosensor. Selectivity tests indicated minimal interference from structurally similar substances. This study validates the nanobiosensor's potential for environmental monitoring and hormone detection, aligning with standard practices.

Recent Developments and Applications of Microbial Electrochemical Biosensors.

This chapter provides a comprehensive overview of microbial electrochemical biosensors, which are a unique class of biosensors that utilize the metabolic activity of microorganisms to convert chemical signals into electrical signals. The principles and mechanisms of these biosensors are discussed, including the different types of microorganisms that can be used. The various applications of microbial electrochemical biosensors in fields such as environmental monitoring, medical diagnostics, and food safety are also explored. The chapter concludes with a discussion of future research directions and potential advancements in the field of microbial electrochemical biosensors.

The Smart Drifter Cluster: Monitoring Sea Currents and Marine Litter Transport Using Consumer IoT Technologies.

The study of marine Lagrangian transport holds significant importance from a scientific perspective as well as for practical applications such as environmental-pollution responses and prevention (e.g., oil spills, dispersion/accumulation of plastic debris, etc.). In this regard, this concept paper introduces the Smart Drifter Cluster: an innovative approach that leverages modern "consumer" IoT technologies and notions. This approach enables the remote acquisition of information on Lagrangian transport and important ocean variables, similar to standard drifters. However, it offers potential benefits such as reduced hardware costs, minimal maintenance expenses, and significantly lower power consumption compared to systems relying on independent drifters with satellite communication. By combining low power consumption with an optimized, compact integrated marine photovoltaic system, the drifters achieve unlimited operational autonomy. With the introduction of these new characteristics, the Smart Drifter Cluster goes beyond its primary function of mesoscale monitoring of marine currents. It becomes readily applicable to numerous civil applications, including recovering individuals and materials at sea, addressing pollutant spills, and tracking the dispersion of marine litter. An additional advantage of this remote monitoring and sensing system is its open-source hardware and software architecture. This fosters a citizen-science approach, enabling citizens to replicate, utilize, and contribute to the improvement of the system. Thus, within certain constraints of procedures and protocols, citizens can actively contribute to the generation of valuable data in this critical field.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs' lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.

Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions.

An Optimized Active Sampling Procedure for Aerobiological DNA Studies.

The Earth's atmosphere plays a critical role in transporting and dispersing biological aerosols. Nevertheless, the amount of microbial biomass in suspension in the air is so low that it is extremely difficult to monitor the changes over time in these communities. Real-time genomic studies can provide a sensitive and rapid method for monitoring changes in the composition of bioaerosols. However, the low abundance of deoxyribose nucleic acid (DNA) and proteins in the atmosphere, which is of the order of the contamination produced by operators and instruments, poses a challenge for the sampling process and the analyte extraction. In this study, we designed an optimized, portable, closed bioaerosol sampler based on membrane filters using commercial off-the-shelf components, demonstrating its end-to-end operation. This sampler can operate autonomously outdoors for a prolonged time, capturing ambient bioaerosols and avoiding user contamination. We first performed a comparative analysis in a controlled environment to select the optimal active membrane filter based on its ability to capture and extract DNA. We have designed a bioaerosol chamber for this purpose and tested three commercial DNA extraction kits. The bioaerosol sampler was tested outdoors in a representative environment and run for 24 h at 150 L/min. Our methodology suggests that a 0.22-µm polyether sulfone (PES) membrane filter can recover up to 4 ng of DNA in this period, sufficient for genomic applications. This system, along with the robust extraction protocol, can be automated for continuous environmental monitoring to gain insights into the time evolution of microbial communities within the air.

Target-triggered double fluorescent biosensors for rapid and sensitive detection of long-chain perfluorinated compounds using DNA probe and lysozyme fiber.

Perfluorinated compounds (PFCs) are useful man-made chemicals and serve as new emerging organic pollutants due to their environmental and health concerns. Chromatography-mass detection methods often need complex procedure and are also too expensive, so there is a critical demand to develop rapid, inexpensive, easy-to-operate and sensitive methods for PFCs detection. In this work, double fluorescent biosensors ('DT sensor' and 'FT sensor') have been designed to quantitatively detect long-chain perfluorinated compounds (PFCs), due to their strong hydrophobic interaction with DNA probe or lysozyme fiber. The ratio and rapid fluorescence responses offered more obvious signal changes, and high sensitivity with a limit of detection (LOD) of 0.16 µM (98.2 ppb) for perfluorododecanoic acid (PFDoA). For three PFCs with longer perfluoroalkyl chain (CF2), increased detection sensitivity was achieved due to a stronger hydrophobicity. The fluorescent biosensors showed a good selectivity for long-chain PFCs and served as cross-reactive sensors to differentiate three different long-chain PFCs. The biosensors also had robust signal response in tap water or serum samples, and the LOD can be further lowered to pM (ppt) level after sample preconcentration.