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.

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.

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.

A Low-Cost Programmable Reversing Flow Column Apparatus for Investigating Mixing Zones

This note describes the development and testing of a novel, programmable reversing flow 1D (R1D) experimental column apparatus designed to investigate reaction, sorption, and transport of solutes in aquifers within dynamic reversing flow zones where waters with different chemistries mix. The motivation for constructing this apparatus was to understand the roles of mixing and reaction on arsenic discharging through a tidally fluctuating riverbank. The apparatus can simulate complex transient flux schedules similar to natural flow regimes The apparatus uses an Arduino microcontroller to control flux magnitude through two peristaltic pumps. Solenoid valves control flow direction from two separate reservoirs. In-line probes continually measure effluent electrical conductance, pH, oxidation-reduction potential, and temperature. To understand how sensitive physical solute transport is to deviations from the real hydrograph of the tidally fluctuating river, two experiments were performed using: (1) a simpler constant magnitude, reversing flux direction schedule (RCF); and (2) a more environmentally relevant variable magnitude, reversing flux direction schedule (RVF). Wherein, flux magnitude was ramped up and down according to a sine wave. Modeled breakthrough curves of chloride yielded nearly identical dispersivities under both flow regimes. For the RVF experiment, Peclet numbers captured the transition between diffusion and dispersion dominated transport in the intertidal interval. Therefore, the apparatus accurately simulated conservative, environmentally relevant mixing under transient, variable flux flow regimes. Accurately generating variable flux reversing flow regimes is important to simulate the interaction between flow velocity and chemical reactions where Brownian diffusion of solutes to solid-phase reaction sites is kinetically limited.

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.

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.

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.

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.

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.

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.

Improvement and application of qPCR assay revealed new insight on early warning of Phaeocystis globosa bloom

Phaeocystis globosa bloom develops from its early solitary cells, providing clues for early warning of its bloom and timely responding to possible consequences. However, the early prediction requires quantification of the solitary cells for a thorough understanding of bloom formation. Therefore, we developed an accurate, sensitive, and specific qPCR assay for this need. Results show that the accuracy of qPCR was significantly enhanced by ameliorating DNA barcode design, improving genomic DNA extraction, and introducing a strategy of internal amplification control (IAC). This approach reached a quantification limit of 1 cell/reaction, making low-abundance cells (10¹-10³ cells/L) detection possible, and we also observed a plunge in the abundance of the solitary cells before the bloom outbreak in two winters in 2019 and 2020 for the first time, which is quite unique from laboratory results showing an increase instead. The plunge in solitary-cell abundance might be associated with the attachment of solitary cells to solid matrices to form non-solitary attached aggregate, the precursor of colonies, which gains supports from other studies and needs more investigations in the future. Therefore, as the plunge in solitary-cell abundance is a sign of colony formation, it can be used as an early warning indicator to P. globosa bloom.

Remote monitoring of minewater rebound and environmental risk using satellite radar interferometry

The cessation of dewatering following coalfield abandonment results in the rise of minewater, which can create significant changes in the local and regional hydrogeological regime. Monitoring such change is challenging but essential to avoiding detrimental consequences such as groundwater contamination and surface flooding. Inverse modelling methods using satellite radar interferometry (InSAR) have proven capable for retrospectively mapping minewater level changes, however, there is a need for the capability to remotely monitor changes as they occur. In this study, ground deformation measurements obtained from InSAR are used to develop a method to remotely monitor the spatio-temporal rise of minewater, which could be implemented in near real-time. The approach is demonstrated over the Horlivka mining agglomeration, Ukraine, where there is no other feasible approach possible due to a lack of safe ground access. The results were blindly validated against in-situ measurements before being used to forecast the time until minewater will reach the natural water table and Earth's surface. The findings reveal that, as a result of military conflict in Donbas, an environmental catastrophe could occur where potentially radioactive minewater is forecast to reach the natural water table between May and August of 2024.

Development and validation of an open-source four-pole electrical conductivity, temperature, depth sensor for in situ water quality monitoring in an estuary

Most recent implementations of low-cost electrical conductivity (EC) sensors intended for water quality measurements are based on simple two-pole designs. However, in marine settings, EC often exceeds the range where two-pole sensors provide reliable results. We have developed a simple four-pole EC sensor that relies exclusively on analog-to-digital measurements made using readily available circuit boards (pyboard v.1.1 or Raspberry Pi Pico 2040) programmed using MicroPython. Other than resistors and graphite or wire electrodes, no other electronic components are required for the EC sensor. When combined with a pressure/temperature sensor (MS5803-05), an optional NTC thermistor, batteries, and a waterproof housing constructed using a PVC pipe and a 3-D-printed cap, the device becomes a working conductivity-temperature-depth sensor capable of extended field deployments. Construction is sufficiently simple that undergraduate science students can construct one during three 3-h lab periods. Lab calibrations performed on several prototypes at ECs between 0.18 and 45 mS/cm show that confidence limits as good as about ±3% of EC are possible. Re-calibration of several prototypes 1 year after initial calibration shows that long-term calibration drift is modest. Data collected by the prototypes over several tidal cycles in the Duwamish River, Washington, USA, are in agreement with data from a co-located commercial YSI-EX03 conductivity probe. When distributed across a constructed off-channel wetland in the Duwamish system, the sensors documented large amounts of spatial and temporal variability in EC, highlighting the importance of such wetlands for providing unique temperature/salinity environments potentially valuable for outmigrating juvenile salmon.

Satellite data track spatial and temporal declines in European beech forest canopy characteristics associated with intense drought events in the Rhön Biosphere Reserve, central Germany

The increasing intensity and frequency of droughts under climate change demands effective ways to monitor drought impacts. We sought to determine how different satellite remote sensing sources influence our ability to identify temporal and spatial impacts on European beech forest canopy health during intense drought events. Imagery from three satellite series (MODIS, Landsat and Sentinel-2) was used to observe changes in canopy health during the intense droughts of 2003 and 2018 in the Rhön Biosphere Reserve, central Germany. Monthly normalized difference vegetation index (NDVI) anomalies were calculated for each satellite between 2000-2020 and compared against temperature, precipitation and the standardized precipitation evapotranspiration index (SPEI). Severe canopy impacts in 2003 and 2018 were associated with low NDVI in August and September. At the stand-scale, Sentinel-2 data allowed a spatially detailed understanding of canopy-level impacts, while MODIS provided the clearest temporal progression of the drought's impacts on the forest canopy. Low NDVI values were not exclusively associated with extremes of either temperature and precipitation individually; however, low canopy NDVI in August was associated with SPEI values below -1.5. Although the intense drought of 2018, as defined by meteorological parameters, peaked in July, canopy NDVI did not decline until August, highlighting that our ability to detect canopy impact during drought events is sensitive to the timing of image acquisition. No single satellite sensor affords a full picture of the temporal or spatial progression of drought impacts. Consequently, using sensors in tandem provides the best possible representation of canopy health during intense drought events.

Mobile application using DCDM and cloud-based automatic plant disease detection

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country's people. In India, the agricultural production of the country is directly connected to the country's economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world's agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant's visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants' information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant's name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average.

Colorimetric Systems for the Detection of Bacterial Contamination: Strategy and Applications

Bacterial contamination is a public health concern worldwide causing enormous social and economic losses. For early diagnosis and adequate management to prevent or treat pathogen-related illnesses, extensive effort has been put into the development of pathogenic bacterial detection systems. Colorimetric sensing systems have attracted increasing attention due to their simple and single-site operation, rapid signal readout with the naked eye, ability to operate without external instruments, portability, compact design, and low cost. In this article, recent trends and advances in colorimetric systems for the detection and monitoring of bacterial contamination are reviewed. This article focuses on pathogen detection strategies and technologies based on reaction factors that affect the color change for visual readout. Reactions used in each strategy are introduced by dividing them into the following five categories: external pH change-induced pH indicator reactions, intracellular enzyme-catalyzed chromogenic reactions, enzyme-like nanoparticle (NP)-catalyzed substrate reactions, NP aggregation-based reactions, and NP accumulation-based reactions. Some recently developed colorimetric systems are introduced, and their challenges and strategies to improve the sensing performance are discussed.

Evaluating the effectiveness of drones for quantifying invasive upside-down jellyfish (Cassiopea sp.) in Lake Macquarie, Australia

Upside-down jellyfish (Cassiopea sp.) are mostly sedentary, benthic jellyfish that have invaded estuarine ecosystems around the world. Monitoring the spread of this invasive jellyfish must contend with high spatial and temporal variability in abundance of individuals, especially around their invasion front. Here, we evaluated the utility of drones to survey invasive Cassiopea in a coastal lake on the east coast of Australia. To assess the efficacy of a drone-based methodology, we compared the densities and counts of Cassiopea from drone observations to conventional boat-based observations and evaluated cost and time efficiency of these methods. We showed that there was no significant difference in Cassiopea density measured by drones compared to boat-based methods along the same transects. However, abundance estimates of Cassiopea derived from scaling-up transect densities were over-inflated by 319% for drones and 178% for boats, compared to drone-based counts of the whole site. Although conventional boat-based survey techniques were cost-efficient in the short-term, we recommend doing whole-of-site counts using drones. This is because it provides a time-saving and precise technique for long-term monitoring of the spatio-temporally dynamic invasion front of Cassiopea in coastal lakes and other sheltered marine habitats with relatively clear water.

Metal-Organic Framework Based Gas Sensors

The ever-increasing concerns over indoor/outdoor air quality, industrial gas leakage, food freshness, and medical diagnosis require miniaturized gas sensors with excellent sensitivity, selectivity, stability, low power consumption, cost-effectiveness, and long lifetime. Metal-organic frameworks (MOFs), featuring structural diversity, large specific surface area, controllable pore size/geometry, and host-guest interactions, hold great promises for fabricating various MOF-based devices for diverse applications including gas sensing. Tremendous progress has been made in the past decade on the fabrication of MOF-based sensors with elevated sensitivity and selectivity toward various analytes due to their preconcentrating and molecule-sieving effects. Although several reviews have recently summarized different aspects of this field, a comprehensive review focusing on MOF-based gas sensors is absent. In this review, the latest advance of MOF-based gas sensors relying on different transduction mechanisms, for example, chemiresistive, capacitive/impedimetric, field-effect transistor or Kelvin probe-based, mass-sensitive, and optical ones are comprehensively summarized. The latest progress for making large-area MOF films essential to the mass-production of relevant gas sensors is also included. The structural and compositional features of MOFs are intentionally correlated with the sensing performance. Challenges and opportunities for the further development and practical applications of MOF-based gas sensors are also given.

Measurement of circular intensity differential scattering (CIDS) from single airborne aerosol particles for bioaerosol detection and identification

The circular intensity differential scattering (CIDS), i.e. the normalized Mueller matrix element -S₁₄/S₁₁, can be used to detect the helical structures of DNA molecules in biological systems, however, no CIDS measurement from single particles has been reported to date. We report an innovative method for measuring CIDS phase functions from single particles individually flowing through a scattering laser beam. CIDS signals were obtained from polystyrene latex (PSL) microspheres with or without coating of DNA molecules, tryptophan particles, and aggregates of B. subtilis spores, at the size of 3 μm in diameter. Preliminary results show that this method is able to measure CIDS phase function in tens of microseconds from single particles, and has the ability to identify particles containing biological molecules.

Comparison of Minimally Invasive Monitoring Methods and Live Trapping in Mammals

The conservation and management of wildlife requires the accurate assessment of wildlife population sizes. However, there is a lack of synthesis of research that compares methods used to estimate population size in the wild. Using a meta-analysis approach, we compared the number of detected individuals in a study made using live trapping and less invasive approaches, such as camera trapping and genetic identification. We scanned 668 papers related to these methods and identified data for 44 populations (all focused on mammals) wherein at least two methods (live trapping, camera trapping, genetic identification) were used. We used these data to quantify the difference in number of individuals detected using trapping and less invasive methods using a regression and used the residuals from each regression to evaluate potential drivers of these trends. We found that both trapping and less invasive methods (camera traps and genetic analyses) produced similar estimates overall, but less invasive methods tended to detect more individuals compared to trapping efforts (mean = 3.17 more individuals). We also found that the method by which camera data are analyzed can significantly alter estimates of population size, such that the inclusion of spatial information was related to larger population size estimates. Finally, we compared counts of individuals made using camera traps and genetic data and found that estimates were similar but that genetic approaches identified more individuals on average (mean = 9.07 individuals). Overall, our data suggest that all of the methods used in the studies we reviewed detected similar numbers of individuals. As live trapping can be more costly than less invasive methods and can pose more risk to animal well-fare, we suggest minimally invasive methods are preferable for population monitoring when less-invasive methods can be deployed efficiently.

COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios

Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue-Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.

Low-cost Gent type sampler constructed for urban atmospheric aerosol sampling

The importance of studying the atmospheric pollution due to its effects on human health and other ecosystems, the inexistence of national production of equipment for air sample collection, and the high cost of the imported equipment (especially in developing countries) led the authors of the present work to construct a low-cost Gent type sampler. The construction of the sampler was carried out by combining low-cost materials with good mechanical strength (such as nylon 6.0), hydraulic piping PVC, and the use of a 3D printer. The innovation of the present work is the employment of a 3D printer using ABS polymer to create the grids that cannot be machined. In addition to the sampler, the system is composed of a vacuum pump, a gas meter, and a rotameter. The total cost of the sampling system amounted at about 1200 USD, and the cost of the manufactured Gent type sampler did not reach 100 USD. The results obtained while using this set for sampling atmospheric aerosol for a period of 11 months were compared with the mass concentration of PM₁₀ obtained from the official environmental company, CETESB of São Paulo State, Brazil, showing good correlation with those from CETESB - which confirmed its effectiveness and suitability for use. The low cost, easy operation, and versatility of the built Gent type sampler enable its use for scientific and academic purposes. The equipment can be useful in environmental monitoring networks, in low-income regions, and as an instrument for environmental education used in universities.

Erosion and deposition vulnerability of small (<5,000 km2) tropical islands

The tropics are naturally vulnerable to watershed erosion. This region is rapidly growing (projected to be 50% of the global population by 2050) which exacerbates erosional issues by the subsequent land use change. The issue is particularly of interest on the many (~45,000) small tropical (<5,000 km²) islands, and their >115M residents, where ecotourism and sediment intolerant ecosystems such as coral reefs are the main driver of their economies. However, vulnerability to erosion and deposition is poorly quantified in these regions due to the misclassification or exclusion of small islands in coarse global analyses. We use the only vulnerability assessment method that connects watershed erosion and coastal deposition to compare locally sourced, high-resolution datasets (5 x 5 m) to satellite-collected, remotely sensed low-resolution datasets (463 x 463 m). We find that on the island scale (~52 km²) the difference in vulnerability calculated by the two methods is minor. On the watershed scale however, low-resolution datasets fail to accurately demonstrate watershed and coastal deposition vulnerability when compared to high-resolution analysis. Specifically, we find that anthropogenic development (roads and buildings) is poorly constrained at a global scale. Structures and roads are difficult to identify in heavily forested regions using satellite algorithms and the rapid, ongoing rate of development aggravates the issue. We recommend that end-users of this method obtain locally sourced anthropogenic development datasets for the best results while using low resolution datasets for the other variables. Fortunately, anthropogenic development data can be easily collected using community-based research or identified using satellite imagery by any level of user. Using high-resolution results, we identify a development trend across St. John and regions that are both high risk and possible targets for future development. Previously published modeled and measured sedimentation rates demonstrate the method is accurate when using low-resolution or high-resolution data but, anthropogenic development, watershed slope, and earthquake probability datasets should be of the highest resolution depending on the region specified.

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Modeling hyperspectral normalized water-leaving radiance in a dynamic coastal ecosystem

Next-generation satellite sensors such as the Ocean Color Instrument (OCI) aboard the NASA Plankton, Aerosols, Cloud and ocean Ecosystem (PACE) satellite and the proposed Surface Biology and Geology (SBG) sensor will provide hyperspectral measurements of water-leaving radiances. However, acquiring sufficiently accurate in situ validation data in coastal ecosystems remains challenging. Here we modeled hyperspectral normalized water-leaving radiance ([L_W(λ)]_N) in a dynamic coastal ecosystem using in situ inherent optical properties (IOPs) as inputs to the Hydrolight radiative transfer model. By reducing uncertainty of modeled hyperspectral [L_W(λ)]_N (%RMSE ≤ 21%) relative to [L_W(λ)]_N derived from in situ radiometric measurements (%RMSE ≤ 33%), we introduce modeling as an alternative or complementary method to in-water radiometric profilers for validating satellite-derived hyperspectral data from coastal ecosystems.

Transport of Aerosols in Underground Mine Workings in Terms of SARS-CoV-2 Virus Threat

This paper presents a method of implementation and the results of aerosol dispersion tests in underground mine workings. Numerous tests were carried out to determine the potential risk of SARS-CoV-2 coronavirus infection in the underground environment of the mines. The influence of selected parameters of mine air on the possibility and method of aerosol transmission through ventilation routes was experimentally determined in real conditions. The concentration of additional aerosols in the class of ultrafine and fine aerosols increased with the distance from the generator, while the concentration of coarse particles decreased. Assuming the consumption of the solution with which aerosols were generated, even at a small level of 1 cm3/min., the number of additional aerosols was several hundred particles in one cubic centimeter of air at a distance of 50-70 m from the generator. The concentration of ultrafine particles in the range of 40-20,000 nm increased from 122 particles/cm3 to 209 particles/cm3 at air temperature of 12 °C and relative humidity of 95-96%, and from 90 particles/cm3 to 243 particles/cm3 at air temperature of 17 °C and relative humidity of 76-82%, with the increasing distance from the generator (10 m to 50 m).

Concurrent assessment of personal, indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices

The intensity, frequency, duration, and contribution of distinct PM2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM2.5 and PM1 monitoring using novel low-cost sensing (LCS) devices, AS-LUNG. GRIMM-comparable observations were acquired by the corrected AS-LUNG readings, with R2 up to 0.998. Twenty-six non-smoking healthy adults were recruited in Taiwan in 2018 for 7-day personal, home indoor, and home outdoor PM monitoring. The results showed 5-min PM2.5 and PM1 exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m3 , respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM2.5 increments of 76.5 and 183.8 µg/m3 (1 min), respectively. Incense burning had the highest mean PM2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM2.5 increments (15.0 µg/m3 ) to indoor levels, among all single sources. Certain events accounted for 14.0%-39.6% of subjects' daily exposures. With the high resolution of AS-LUNG data and detailed time-activity diaries, the impacts of sources and ventilations were assessed in detail.

Optimized workflow for unknown screening using gas chromatography high-resolution mass spectrometry expands identification of contaminants in silicone personal passive samplers

RATIONALE

Silicone wristbands have emerged as valuable passive samplers for monitoring of personal exposure to environmental contaminants in the rapidly developing field of exposomics. Once deployed, silicone wristbands collect and hold a wealth of chemical information that can be interrogated using high-resolution mass spectrometry (HRMS) to provide a broad coverage of chemical mixtures.

METHODS

Gas chromatography coupled to Orbitrap™ mass spectrometry (GC/Orbitrap™ MS) was used to simultaneously perform suspect screening (using in-house database) and unknown screening (using vendor databases) of extracts from wristbands worn by volunteers. The goal of this study was to optimize a workflow that allows detection of low levels of priority pollutants, with high reliability. In this regard, a data processing workflow for GC/Orbitrap™ MS was developed using a mixture of 123 environmentally relevant standards consisting of pesticides, flame retardants, organophosphate esters, and polycyclic aromatic hydrocarbons as test compounds.

RESULTS

The optimized unknown screening workflow using a search index threshold of 750 resulted in positive identification of 70 analytes in validation samples, and a reduction in the number of false positives by over 50%. An average of 26 compounds with high confidence identification, 7 level 1 compounds and 19 level 2 compounds, were observed in worn wristbands. The data were further analyzed via suspect screening and retrospective suspect screening to identify an additional 36 compounds.

CONCLUSIONS

This study provides three important findings: (1) a clear evidence of the importance of sample cleanup in addressing complex sample matrices for unknown analysis, (2) a valuable workflow for the identification of unknown contaminants in silicone wristband samplers using electron ionization HRMS data, and (3) a novel application of GC/Orbitrap™ MS for the unknown analysis of organic contaminants that can be used in exposomics studies.

Satellite Observations and Malaria: New Opportunities for Research and Applications

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate–malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

On-field test and data calibration of a low-cost sensor for fine particles exposure assessment

BACKGROUND

Accurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements.

OBJECTIVE

To assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality.

METHOD

We conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM_2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration.

RESULTS

Strong correlations (≥0.82) and relatively small relative standard deviations (16-21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56-0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent.

CONCLUSIONS

Our study validated low-cost optical PM sensors under a wide range of PM_2.5 concentrations (8-167 μg/m³). Our findings indicated potential applicability of PM sensors in PM_2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.

Development of a personal passive air sampler for estimating exposure to effective chlorine while using chlorine-based disinfectants

With an increasing use of indoor disinfectants such as chlorine (Cl₂ ) and hypochlorous acid, a convenient sampler for estimating exposure to oxidants, such as effective chlorine, is necessary. Here, we developed a personal passive air sampler (PPAS) composed of a redox dye, o-dianisidine, in a polydimethylsiloxane (PDMS) sheet. o-Dianisidine readily reacts with gaseous oxidants generated by bleach usage, and its color changes as the reaction progresses; hence, personal exposure to effective chlorine could be easily detected by the naked eye, while cumulative exposure could be determined by measuring concentrations of o-dianisidine reacting with it. The PPAS was calibrated, and a sampling rate of 0.00253 m³ /h was obtained using a small test chamber. The PPAS was tested with the help of ten volunteers whose personal exposure to Cl₂ -equivalent gas was estimated after bathrooms were cleaned using spray and liquid-type household disinfection products, and the accumulated exposure-gas concentrations ranged from 69 to 408 ppbv and 148 to 435 ppbv, respectively. These PPAS-derived exposure concentrations were approximately two orders lower than those estimated using ConsExpo, suggesting a significant overestimation by prevailing screening models, possibly due to the ignorance of transformation reactions.

Monitoring changes in human activity during the COVID-19 shutdown in Las Vegas using infrasound microbarometers

While studies of urban acoustics are typically restricted to the audio range, anthropogenic activity also generates infrasound (<20 Hz, roughly at the lower end of the range of human hearing). Shutdowns related to the COVID-19 pandemic unintentionally created ideal conditions for the study of urban infrasound and low frequency audio (20-500 Hz), as closures reduced human-generated ambient noise, while natural signals remained relatively unaffected. An array of infrasound sensors deployed in Las Vegas, NV, provides data for a case study in monitoring human activity during the pandemic through urban acoustics. The array records a sharp decline in acoustic power following the temporary shutdown of businesses deemed nonessential by the state of Nevada. This decline varies spatially across the array, with stations close to McCarran International Airport generally recording the greatest declines in acoustic power. Further, declines in acoustic power fluctuate with the time of day. As only signals associated with anthropogenic activity are expected to decline, this gives a rough indication of periodicities in urban acoustics throughout Las Vegas. The results of this study reflect the city's response to the pandemic and suggest spatiotemporal trends in acoustics outside of shutdowns.

High-sensitivity biomedical sensor based on photoacoustic and cavity enhanced absorption spectroscopy with a new software platform for breath analysis

An easy-to-use highly sensitive sensor is reported for trace gas analysis. A near-infrared fiber-coupled external cavity diode laser in combination with a photoacoustic spectroscopy cell and a cavity enhanced absorption spectroscopy cell is used for analysis of trace gases. A software platform for direct absorption and wavelength modulation spectroscopy is developed in order to identify and quantify the molecules and to achieve a higher signal-to-noise ratio. Considering all of these features, the main advantage of our system is to analyze two different samples simultaneously and quickly. Trace gas measurement is assessed, and a detection limit of 1.5 ppb at 6528.76cm^-1 for ammonia is demonstrated. Furthermore, the sensor with our software platform can be easily used outside of the laboratory, for example, in hospitals.

Aerosol emission of adolescents voices during speaking, singing and shouting

Since the outbreak of the COVID-19 pandemic, singing activities for children and young people have been strictly regulated with far-reaching consequences for music education in schools and ensemble and choir singing in some places. This is also due to the fact, that there has been no reliable data available on aerosol emissions from adolescents speaking, singing, and shouting. By utilizing a laser particle counter in cleanroom conditions we show, that adolescents emit fewer aerosol particles during singing than what has been known so far for adults. In our data, the emission rates ranged from 16 P/s to 267 P/s for speaking, 141 P/s to 1240 P/s for singing, and 683 P/s to 4332 P/s for shouting. The data advocate an adaptation of existing risk management strategies and rules of conduct for groups of singing adolescents, like gatherings in an educational context, e.g. singing lessons or choir rehearsals.

Comparative field study on bioassay responses and micropollutant uptake of POCIS, Speedisk and SorbiCell polar passive samplers

Routine water quality monitoring is generally performed with chemical analyses of grab samples, which has major limitations. First, snapshot samples will not give a good representation of the water quality. Second, it is not sufficient to analyze only a limited number of (priority) pollutants. These limitations can be circumvented by an alternative environmental risk assessment that combines time-integrated passive sampling (PS) with effect-based methods. This study aimed to select which of three polar PS devices was best suited for effect-based monitoring strategies. In the first part of this study, Speedisk, SorbiCell and POCIS polar PS devices were compared by simultaneous deployment at five sites. Chemical analyses of 108 moderately polar compounds (-1.82 < log D < 6.28) revealed that highest number of compounds, with the widest range of log K_OW, log D and pKa, were detected in extracts of POCIS, followed by Speedisk. SorbiCell samplers accumulated the lowest numbers and concentrations of compounds, so they were not further investigated. In a follow-up study, bioassay responses were compared in extracts of POCIS and Speedisk devices deployed at eight sites. The passive sampler extracts were subjected to bioassays for non-specific toxicity, endocrine disruption, and antibiotics activities. More frequent and higher responses were induced by POCIS extracts, leading to more exceedances of effect-based trigger values for environmental risks. As POCIS outperformed Speedisk, it is better suited as PS device targeting polar compounds for semi-quantitative effect-based water quality monitoring.

Comparison of samplers collecting airborne influenza viruses: 1. Primarily impingers and cyclones

Researchers must be able to measure concentrations, sizes, and infectivity of virus-containing particles in animal agriculture facilities to know how far infectious virus-containing particles may travel through air, where they may deposit in the human or animal respiratory tract, and the most effective ways to limit exposures to them. The objective of this study was to evaluate a variety of impinger and cyclone aerosol or bioaerosol samplers to determine approaches most suitable for detecting and measuring concentrations of virus-containing particles in air. Six impinger/cyclone air samplers, a filter-based sampler, and a cascade impactor were used in separate tests to collect artificially generated aerosols of MS2 bacteriophage and swine and avian influenza viruses. Quantification of infectious MS2 coliphage was carried out using a double agar layer procedure. The influenza viruses were titrated in cell cultures to determine quantities of infectious virus. Viral RNA was extracted and used for quantitative real time RT-PCR, to provide total virus concentrations for all three viruses. The amounts of virus recovered and the measured airborne virus concentrations were calculated and compared among the samplers. Not surprisingly, high flow rate samplers generally collected greater quantities of virus than low flow samplers. However, low flow rate samplers generally measured higher, and likely more accurate, airborne concentrations of Infectious virus and viral RNA than high flow samplers. To assess airborne viruses in the field, a two-sampler approach may work well. A suitable high flow sampler may provide low limits of detection to determine if any virus is present in the air. If virus is detected, a suitable lower flow sampler may measure airborne virus concentrations accurately.

Soil dissipation of sugarcane billet seed treatment fungicides and insecticide using QuEChERS and HPLC

Chemical treatment of sugarcane seed with fungicides and insecticides prior to planting increases yields of cane and sugar for the perennial, annually harvested crop. However, the fate of the applied chemicals is unknown. Therefore, the purpose of this study was to measure the aerobic dissipation of selected billet seed treatment chemicals in a mineral sugarcane soil from Louisiana. Soil samples from the surface 15 cm were treated with either thiamethoxam, azoxystrobin, fluxapyroxad, propiconazole, or pyraclostrobin and monitored over 100 days under laboratory conditions. Insecticide and fungicide levels were determined by high performance liquid chromatography. Dissipation data were fitted to four kinetic models: simple first-order (SFO), first order multi-compartment (FOMC), double-first order in parallel (DFOP), and hockey-stick (HS). The dissipation half-life (DT₅₀) of thiamethoxam, azoxystrobin, fluxapyroxad, propiconazole, or pyraclostrobin were 275, 100, 144, 74, and 39 d, respectively. Overall, the DT₅₀ for the pesticides in the study indicated medium to long persistence in soil under the conditions of the experiment. This is the first report for several of these pesticides related to the aerobic dissipation in soils used to grow sugarcane.

Enriched Aerosol-to-Hydrosol Transfer for Rapid and Continuous Monitoring of Bioaerosols

Bioaerosols, including infectious diseases such as COVID-19, are a continuous threat to global public safety. Despite their importance, the development of a practical, real-time means of monitoring bioaerosols has remained elusive. Here, we present a novel, simple, and highly efficient means of obtaining enriched bioaerosol samples. Aerosols are collected into a thin and stable liquid film by the unique interaction of a superhydrophilic surface and a continuous two-phase centrifugal flow. We demonstrate that this method can provide a concentration enhancement ratio of ∼2.4 × 10⁶ with a collection efficiency of ∼99.9% and an aerosol-into-liquid transfer rate of ∼95.9% at 500 nm particle size (smaller than a single bacterium). This transfer is effective in both laboratory and external ambient environments. The system has a low limit of detection of <50 CFU/m³_air using a straightforward bioluminescence-based technique and shows significant potential for air monitoring in occupational and public-health applications.

Passive sampling of toluene (and benzene) in indoor air using a semipermeable membrane device

The distribution and concentration of organic compounds in the environment have attracted great interest mainly due to their capability of bioaccumulation, dispersion, and danger to living organisms. Factors such as urbanization, population growth, and the emergence of new technologies contribute to the increase in pollutant emissions, especially volatile organic compounds (VOCs), such as benzene, toluene, ethylbenzene, and xylenes (BTEX). These compounds are emitted by several sources, becoming more common in work environments, influencing indoor air quality (IAQ), which can cause health damage, in addition to increasing the likelihood of cancer development. In this context, we developed a semipermeable membrane device (SPMD), consisting of low density polyethylene membrane (8 cm long × 3 cm wide), filled with 3 mL of acetonitrile, for passive sampling of toluene (and benzene) in gas phase. With this configuration, the SPMD needed 24 h exposure to the indoor air in order to achieve equilibrium. The target compounds were quantified in the acceptor phase by HPLC-DAD. The optimized SPMD was tested for the collection of toluene and benzene in six chemistry laboratories at Fluminense Federal University and in five nail salons in the city of Niterói, in the state of Rio de Janeiro, Brazil. The developed sampling method was able to identify the analytes in the indoor air of the studied environments, and was easy to operate, with no need to clean up the extracts, allowing their direct injection into the chromatographic system.

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

An integrated system of air sampling and simultaneous enrichment for rapid biosensing of airborne coronavirus and influenza virus

Point-of-care risk assessment (PCRA) for airborne viruses requires a system that can enrich low-concentration airborne viruses dispersed in field environments into a small volume of liquid. In this study, airborne virus particles were collected to a degree above the limit of detection (LOD) for a real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). This study employed an electrostatic air sampler to capture aerosolized test viruses (human coronavirus 229E (HCoV-229E), influenza A virus subtype H1N1 (A/H1N1), and influenza A virus subtype H3N2 (A/H3N2)) in a continuously flowing liquid (aerosol-to-hydrosol (ATH) enrichment) and a concanavalin A (ConA)-coated magnetic particles (CMPs)-installed fluidic channel for simultaneous hydrosol-to-hydrosol (HTH) enrichment. The air sampler's ATH enrichment capacity (EC) was evaluated using the aerosol counting method. In contrast, the HTH EC for the ATH-collected sample was evaluated using transmission-electron-microscopy (TEM)-based image analysis and real-time qRT-PCR assay. For example, the ATH EC for HCoV-229E was up to 67,000, resulting in a viral concentration of 0.08 PFU/mL (in a liquid sample) for a viral epidemic scenario of 1.2 PFU/m3 (in air). The real-time qRT-PCR assay result for this liquid sample was "non-detectable" however, subsequent HTH enrichment for 10 min caused the "non-detectable" sample to become "detectable" (cycle threshold (CT) value of 33.8 ± 0.06).

Factors affecting in situ analysis of total petroleum hydrocarbons in contaminated soils by using a mid-infrared diffuse reflectance spectroscopy

The hand-held mid-infrared diffuse reflectance infrared Fourier transform (MIR-DRIFT) spectrometer was used to assess the applicability of on-site and real time monitoring of total petroleum hydrocarbons (TPH) in contaminated soils during site characterization and remediation. Field measurement devices (MIR-DRIFT and turbidimetric screening test kits) were used to analyze reference soils with concentration ranging from 713 to 54790 mg/kg and compared with the results by a gas chromatography/mass spectrometry method (GC/MS). In situ field measurement of 147 petroleum-contaminated soil samples from 11 contaminated sites was correlated with laboratory-determined soil TPH levels by GC/MS. The concentrations of TPH by MIR-DRIFT were significantly correlated to the concentrations of TPH by GC/MS. Detection of TPH by the MIR spectrometer was not affected by the weathering effects of diesel-contaminated soils. Soils contaminated by mixed fuels with high content of gasoline constituents may cause the potential interference in MIR measurement. In field practice, interference may be attributed to soil moisture, soil organic matter, and soil texture. Soil moisture below 5% is required to reduce variation of infrared beam reflected from high level of surface liquid. When measuring the contaminated soil with a high organic matter content, the results may be overestimated due to the possible effects of surface reflection and interference. Clay and partial silty clay soils were not suitable for MIR spectrometer detection due to a potential shielding effect to reduce the infrared radiation absorbed by TPH. Future research is warranted to reduce the variation caused by soil texture and heterogeneity in TPH prediction.

Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

Environmentally-friendly technology for rapid identification and quantification of emerging pollutants from wastewater using infrared spectroscopy

The monitoring of emerging pollutants in wastewaters is nowadays an issue of special concern, with the classical quantification methods being time and reagent consuming. In this sense, a FTIR transmission spectroscopy based chemometric methodology was developed for the determination of eight of these pollutants. A total of 456 samples were, therefore, obtained, from an activated sludge wastewater treatment process spiked with the studied pollutants, and analysed in the range of 200 cm^-1 to 14,000 cm^-1. Then, a k-nearest neighbour (kNN) analysis aiming at identifying each sample pollutant was employed. Next, partial least squares (PLS) and ordinary least squares (OLS) modelling approaches were employed in order to obtain suitable prediction models. This procedure resulted in good prediction abilities regarding the estimation of atrazine, desloratadine, paracetamol, β-estradiol, ibuprofen, carbamazepine, sulfamethoxazole and ethynylestradiol concentrations in wastewaters. These promising results suggest this technology as a fast, eco-friendly and reagent free alternative methodology for the quantification of emerging pollutants in wastewaters.

Bay watch: Using unmanned aerial vehicles (UAV's) to survey the box jellyfish Chironex fleckeri

Biological investigations on free ranging marine species are regarded as challenging throughout the scientific community. This is particularly true for ‘logistically difficult species’ where their cryptic natures, low abundance, patchy distributions and difficult and/or dangerous sampling environments, make traditional surveys near impossible. What results is a lack of ecological knowledge on such marine species. However, advances in UAV technology holds potential for overcoming these logistical difficulties and filling this knowledge gap. Our research focused on one such logistically difficult species, the Australian box Jellyfish (Chironex fleckeri), and we investigated the capacity of consumer grade UAV technology to detect this, highly venomous, target species in the inshore waters of Northern Queensland Australia. At two sites in the Weipa area, we utilized video analysis, visual count comparisons with a netted animal tally, and evaluated the role of associated environmental conditions, such as wind speed, water visibility and cloud cover on jellyfish detection rates. In total fifteen, 70 meter transects were completed between two sites, with 107 individuals captured. Drone success varied between the two sites with a significant difference between field and post-field (laboratory) counts. Animal size and cloud cover also had significant effects on detection rates with an increase in cloud cover and animal size enhancing detection probability. This study provides evidence to suggest drone surveys overcome obstacles that traditional surveys can’t, with respect to species deemed logistically difficult and open scope for further ecological investigations on such species.

Sensitive detection of NO using a compact portable CW DFB-QCL-based WMS sensor

This paper introduces a compact and portable sensor based on mid-infrared absorption spectroscopy for NO detection employing a room-temperature continuous wave (CW) distributed feedback quantum cascade laser (DFB-QCL) emitting at 1900.08cm^-1. A software-based digital signal generator and lock-in amplifier, in combination with the wavelength modulation spectroscopy (WMS) technique, were used for the concentration measurement of NO. In addition, a Gabor filter denoising method was developed to improve the performance of the measurement system. As a result, a minimum detection limit of 42 ppbv can be achieved at 3 s integration time, and a measurement precision of 450 ppbv can be reached with a time resolution of 0.1 s. The performance of the compact portable sensor was verified by a series of experiments, denoting great potential of field application for sensitive NO sensing.

Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Ambient air pollution in urban cities in sub-Saharan Africa (SSA) is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM2.5). On most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24 h concentration measured across all sites was 38 µg/m3 with the highest PM2.5 period average concentration of 91 µg/m3 measured in Kampala, Uganda and lowest concentrations of 15 µg/m3 measured in Faraja, The Gambia. Kampala in Uganda and Nnewi in Nigeria recorded the longest periods with concentrations >250µg/m3. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM2.5 concentrations in SSA countries. Such data are crucially needed to raise public, societal and policymaker awareness about air pollution across SSA.

Development of a bio-optical model for the Barents Sea to quantitatively link glider and satellite observations

A bio-optical model for the Barents Sea is determined from a set of in situ observations of inherent optical properties (IOPs) and associated biogeochemical analyses. The bio-optical model provides a pathway to convert commonly measured parameters from glider-borne sensors (CTD, optical triplet sensor-chlorophyll and CDOM fluorescence, backscattering coefficients) to bulk spectral IOPs (absorption, attenuation and backscattering). IOPs derived from glider observations are subsequently used to estimate remote sensing reflectance spectra that compare well with coincident satellite observations, providing independent validation of the general applicability of the bio-optical model. Various challenges in the generation of a robust bio-optical model involving dealing with partial and limited quantity datasets and the interpretation of data from the optical triplet sensor are discussed. Establishing this quantitative link between glider-borne and satellite-borne data sources is an important step in integrating these data streams and has wide applicability for current and future integrated autonomous observation systems. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.