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.

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.

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 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.

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.

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.

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.

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.

Detecção de flavivirus e alphavirus em mosquitos (diptera: culicidae) capturados em região próxima à Três Lagoas ­ Ms / Detection of flavivirus and alphavirus in mosquitoes (diptera: culicidae) captured in a region close to Tres Lagoas ­ Ms / Detección de flavivirus y alphavirus en mosquitos (diptera: culicidae) capturados en una región cercana a Tres Lagoas ­ Ms

Introdução: Arbovírus são causadores de doenças humanas, sendo que mudança ecológicas e aumento do contato humano-vetor aumenta a possibilidade de surtos. Objetivo: Detectar, identificar e caracterizar arbovírus presentes em mosquitos vetores capturados em regiões de mata próximas a Três Lagoas, MS. Metodologia: Mosquitos foram capturados utilizando armadilhas de luz em regiões de mata circunvizinha a Três Lagoas. Os mosquitos capturados foram classificados por gênero (chave morfológica) e agrupados em pools com até 20 espécimes, e utilizados através da reação de RT-PCR com posterior sequenciamento e análise filogenética. Resultados: Foram capturados 851 dos gêneros: Culex spp. (11 pools); Aedes spp. (13 pools); Haemagogus spp. (7 pools) e outros gêneros não identificados. Sequencias de vírus Dengue (DENV) foram amplificadas de 2/13 (15,38%) pools de Aedes spp. e uma sequência de vírus Mayaro (MAYV) 1/7 (7,7%) foi amplificada de pools de Haemagogus spp. As análises filogenéticas mostraram que as sequências de DENV agrupava-se no clado de DENV1 e DENV2. A sequência de MAYV agrupou-se junto a sequências de amostras de infecções humana por MAYV do grupo L. Conclusão: Estes resultados reforçam a circulação de DENV, que é causador de surtos anuais de doenças febris agudas no município, e detecção, por primeira vez na região, a circulação de MAYV, reforçando a necessidade de monitoramento viral constante nessa região.

Introduction: Arboviruses cause human diseases, and ecological changes and increased human-vector contact increase the possibility of outbreaks. Objective: To detect, identify and characterize arboviruses present in mosquito vectors captured in forest regions close to Tres Lagoas, MS. Methodology: Mosquitoes were captured using light traps in forest regions surrounding Tres Lagoas. The captured mosquitoes were classified by gender (morphological key) and grouped into pools with up to 20 specimens and used through the RT-PCR reaction with subsequent sequencing and phylogenetic analysis. Results: 851 of the genera were captured: Culex spp. (11 pools); Aedes spp. (13 pools); Haemagogus spp. (7 pools) and other unidentified genera. Dengue virus (DENV) sequences were amplified from 2/13 (15.38%) pools of Aedes spp. and a Mayaro virus (MAYV) sequence 1/7 (7.7%) were amplified from pools of Haemagogus spp. Phylogenetic analyzes showed that one of the DENV sequences clustered in the DENV1 and DENV2 clade. The MAYV sequence was grouped together with sequences from samples of human MAYV infections of the L group. Conclusion: These results reinforce the circulation of DENV, which causes annual outbreaks of acute febrile illnesses in the municipality, and detection, for the first time in the region, the circulation of MAYV, reinforcing the need for constant viral monitoring in this region.

Introducción: Los arbovirus causan enfermedades humanas, y los cambios ecológicos y el mayor contacto humano-vector aumentan la posibilidad de brotes. Objetivo: Detectar, identificar y caracterizar arbovirus presentes en mosquitos vectores capturados en regiones de selva próximas a Tres Lagoas, MS. Metodología: Los mosquitos fueron capturados utilizando trampas de luz en las regiones forestales que rodean Tres Lagoas. Los mosquitos capturados fueron clasificados por género (clave morfológica) y agrupados en pools de hasta 20 ejemplares, y utilizados mediante la reacción RT-PCR con posterior secuenciación y análisis filogenético. Resultados: Se capturaron 851 de los géneros: Culex spp. (11 pools); Aedes spp. (13 pools); Haemagogus spp. (7 pools) y otros géneros no identificados. Las secuencias del virus del dengue (DENV) se amplificaron a partir de 2/13 (15,38 %) grupos de Aedes spp. y una secuencia de virus Mayaro (MAYV) 1/7 (7,7%) de pools de Haemagogus spp. Los análisis filogenéticos mostraron que una de las secuencias de DENV se agrupaba en el clado DENV1 y DENV2. La secuencia de MAYV se agrupó con secuencias de muestras de infecciones humanas de MAYV del grupo L. Conclusión: Estos resultados refuerzan la circulación de DENV, causante de brotes anuales de enfermedades febriles agudas en el municipio, y la detección, por primera vez en la región, la circulación de MAYV, reforzando la necesidad de un monitoreo viral constante en esta región.

Identificação e condução de manifestação oftálmica de mpox pela vigilância epidemiológica: relato de experiência / Identification and management of ophthalmic manifestation of mpox by epidemiological surveillance: experience report / Identificación y manejo de la manifestación oftálmica del mpox mediante vigilancia epidemiológica: informe de una experiencia

Objetivo: descrever o processo de condução de um caso com manifestação oftálmica pela mpox, destacando o trabalho da enfermagem na prevenção de complicações da doença. Método: relato de experiência da condução de um caso de mpox ocorrido em setembro de 2022 com manifestação oftálmica. Resultados: a pronta identificação da complicação oftálmica, com a presença de conjuntivite e edema palpebral, apresentada por um paciente com suspeita de mpox pela equipe de enfermagem da vigilância epidemiológica durante as ações de monitoramento, e o envolvimento da equipe com outras equipes de diversos níveis da assistência à saúde permitiram a instituição precoce do tratamento com antiviral recomendado pelo Ministério da Saúde, o que contribuiu para um melhor desfecho. Conclusão: é importante que se dissemine o conhecimento sobre as manifestações oftálmicas associadas à mpox para que a equipe de enfermagem, que atua nos diversos níveis de atenção à saúde, esteja atenta para implementar, de forma precoce, medidas de prevenção, diagnóstico e tratamento adequados.

Objective: to describe the process of conducting a case with ophthalmic manifestation by mpox, highlighting the work of nursing in preventing complications of the disease. Method: experience report of managing a case of mpox that occurred in September 2022 with ophthalmic manifestation. Results: the prompt identification of the ophthalmic complication, with the presence of conjunctivitis and eyelid edema, presented by a patient with suspected mpox by the epidemiological surveillance nursing team during monitoring actions and the team's involvement with other teams from different levels of the health care allowed the early initiation of antiviral treatment recommended by the Ministry of Health, which contributed to a better outcome. Conclusion: it is important to disseminate knowledge about ophthalmic manifestations associated with mpox for the nursing team that works at different levels of health care to be attentive to implement preventive measures, diagnosis and adequate and early treatment.

Objetivo: describir el proceso de manejo de un caso con manifestación of- talmológica por mpox, destacando la labor de enfermería en la prevención de complica- ciones de la enfermedad. Material y método: relato de experiencia de manejo de un caso de mpox ocurrido en septiembre de 2022 con manifestación oftálmica. Resultados: la rápida identificación de la complicación oftálmica, con la presencia de conjuntivitis y edema de párpados, presentada por un paciente con sospecha de mpox por el equipo de enfermería de vigilancia epidemiológica durante las acciones de monitoreo y la partici- pación del equipo con otros equipos de diferentes niveles de la asistencia sanitaria per- mitió el inicio precoz del tratamiento antiviral recomendado por el Ministerio de Salud, lo que contribuyó a un mejor resultado. Conclusión: es importante difundir el conoci- miento sobre las manifestaciones oftálmicas asociadas a la viruela del mono para que el equipo de enfermería que actúa en los diferentes niveles de atención a la salud esté atento a implementar medidas preventivas, diagnósticas y de tratamiento adecuado y precoz.

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.

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.

Addressed Combined Fiber-Optic Sensors as Key Element of Multisensor Greenhouse Gas Monitoring Systems.

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry-Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen-Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of -60…+300 °C with an accuracy of 0.1-0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1-0.5%.

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.

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.

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.