Chronic exposure to drinking water nitrate and trihalomethanes in the French CONSTANCES cohort.

Trihalomethanes (THMs) and nitrate are widespread chemicals in drinking water. Chronic exposure has been associated with increased cancer risk despite inconclusive evidence, partly due to the challenges in long-term exposure assessment and potential exposure misclassification. We estimated concentrations of nitrate and THMs in drinking water using a public regulatory monitoring database (SISE-Eaux) for CONSTANCES, a French population-based prospective cohort. We obtained 26,322,366 measurements of drinking water parameters from 2000 to 2020. We excluded missing, implausible and duplicated measurements; we corrected or imputed missing geocodes of sampling locations; we calculated the annual median concentration of nitrate and THMs by surveillance area. To predict missing annual median concentrations, linear mixed models with random intercept using surveillance area as a clustering variable were developed for each region for nitrate and the four THM components (chloroform, chlorodibromomethane, bromodichloromethane and bromoform) separately. Concentrations in the nearest surveillance area from the household were merged per year among 75,462 participants with residential history geocoded for 2000-2020. Estimated concentrations resulting from this approach were compared with measured concentrations in 100 samples collected in Paris, Rennes and Saint-Brieuc in 2021. Median annual concentrations of total THMs and nitrate at study participants' homes for 2000-2020 were, respectively, 15.7 µg/l (IQR: 15.2) and 15.2 mg/l (IQR: 20.8). Among these, 35% were based on measurements for nitrate (16% for THMs), 44% (46%) were predicted using on linear mixed models, and 21% (38%) were based on distribution unit median values. Conditional R2 predictive models ranged from 0.71 to 0.91 (median: 0.85) for nitrate, and from 0.48 to 0.80 for THMs (median: 0.68). These concentrations will allow future association analyses with risk of breast and colorectal cancer. Our cleaning process introduced here could be adapted to other large drinking water monitoring data.

Environmental relevance monitoring and assessment of ochreous precipitates, hydrochemistry and water sources from abandoned coal mine drainage.

This study investigated the mineralogical and chemical characteristics of ochreous precipitates and mine water samples from abandoned Upper Carboniferous hard coal mines in an extensive former mining area in western Germany. Mine water characteristics have been monitored and assessed using a multi-methodological approach. Thirteen mine water discharge locations were sampled for hydrochemical analysis, with a total of 46 water samples seasonally collected in the whole study area for stable isotopic analyses. Mineralogical composition of 13 ochreous precipitates was identified by a combination of powder X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FE-SEM/EDS). Results showed that abandoned mine drainage was characterized by circumneutral pH, Eh values ranging from 163 to 269 mV, relatively low concentrations of Fe and Mn, and was dominated by HCO3- > SO42- > Cl- > NO3- and Na+ > Ca2+ > Mg2+ > K+. Goethite and ferrihydrite were the dominant precipitated Fe minerals, with traces of quartz, dolomite, and clay minerals. Some metal and metalloid elements (Mn, Al, Si, and Ti) were found in the ochreous sediments. The role of bacteria in the formation of secondary minerals was assessed with the detection of Leptothrix ochracea. The δ18O and δ2H values of mine water plotted on and close to the GMWL and LMWLs indicated local derivation from meteoric water and represented the annual mean precipitation isotopic composition. Results might help to develop strategies for the management of water resources, contaminated mine water, and public health.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Electrolyte-Gated Carbon Nanotube Field-Effect Transistor-Based Sensors for Nanoplastics Detection in Seawater: A Study of the Interaction between Nanoplastics and Carbon Nanotubes.

Plastics accumulating in the environment are nowadays of great concern for aquatic systems and for the living organisms populating them. In this context, nanoplastics (NPs) are considered the major and most dangerous contaminants because of their small size and active surface, which allow them to interact with a variety of other molecules. Current methods used for the detection of NPs rely on bulky and expensive techniques such as spectroscopy. Here we propose, for the first time, a novel, fast, and easy-to-use sensor based on an electrolyte-gated field-effect transistor (EG-FET) with a carbon nanotube (CNT) semiconductor (EG-CNTFET) for the detection of NPs in aquatic environments, using polystyrene NPs (PS-NPs) as a model material. In particular, as a working mechanism for the EG-CNTFETs we exploited the ability of CNTs and PS to form noncovalent interactions. Indeed, in our EG-CNTFET devices, the interaction between NPs and CNTs caused a change in the electric double layers. A linear increase in the corrected on current (*ION) of the EG-CNTFETs, with a sensitivity of 9.68 µA/(1 mg/mL) and a linear range of detection from 0.025 to 0.25 mg/mL were observed. A π-π interaction was hypothesized to take place between the two materials, as indicated by an X-ray photoelectron spectroscopy analysis. Using artificial seawater as an electrolyte, to mimic a real-case scenario, a linear increase in *ION was also observed, with a sensitivity of 6.19 µA/(1 mg/mL), proving the possibility to use the developed sensor in more complex solutions, as well as in low concentrations. This study offers a starting point for future exploitation of electrochemical sensors for NP detection and identification.

A bifunctional MoS2/SGCN nanocatalyst for the electrochemical detection and degradation of hazardous 4-nitrophenol.

Herein, we reported the dual functions of molybdenum disulfide/sulfur-doped graphitic carbon nitride (MoS2/SGCN) composite as a sensing material for electrochemical detection of 4-NP and a catalyst for 4-NP degradation. The MoS2 nanosheet, sulfur-doped graphitic carbon nitride (SGCN) and MoS2/SGCN were characterized using field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) spectroscopy and X-ray photoelectron spectroscopy (XPS). Electrochemical characterization of these materials with electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) in 1 mM K4[Fe(CN)6]3-/4- show that the composite has the lowest charge transfer resistance and the best electrocatalytic activity. The limit of detection (LOD) and the linear range of 4-nitrophenol at MoS2/SGCN modified glassy carbon electrode (MoS2/SGCN/GCE) were computed as 12.8 nM and 0.1 - 2.6 µM, respectively. Also, the percentage recoveries of 4-NP in spiked tap water samples ranged from 97.8 - 99.1 %. The electroanalysis of 4-NP in the presence of notable interferons shows that the proposed electrochemical sensor features outstanding selectivity toward 4-NP. Additionally, the results of the catalytic degradation of 4-NP at MoS2/SGCN show that the nanocatalyst catalyzed the transformation of 4-NP to 4-aminophenol (4-AP) with a first-order rate constant (k) estimated to be 4.2 ×10-2 s-1. The results of this study confirm that the MoS2/SGCN nanocatalyst is a useful implement for electroanalytical monitoring and catalytic degradation of the hazardous 4-NP in water samples.

Real-Time Detection of Microplastics Using an AI Camera.

Microplastics (MPs, size ≤ 5 mm) have emerged as a significant worldwide concern, threatening marine and freshwater ecosystems, and the lack of MP detection technologies is notable. The main goal of this research is the development of a camera sensor for the detection of MPs and measuring their size and velocity while in motion. This study introduces a novel methodology involving computer vision and artificial intelligence (AI) for the detection of MPs. Three different camera systems, including fixed-focus 2D and autofocus (2D and 3D), were implemented and compared. A YOLOv5-based object detection model was used to detect MPs in the captured image. DeepSORT was then implemented for tracking MPs through consecutive images. In real-time testing in a laboratory flume setting, the precision in MP counting was found to be 97%, and during field testing in a local river, the precision was 96%. This study provides foundational insights into utilizing AI for detecting MPs in different environmental settings, contributing to more effective efforts and strategies for managing and mitigating MP pollution.

Utilizing whole genome sequencing to characterize Listeria spp. persistence and transmission patterns in a farmstead dairy processing facility and its associated farm environment.

Farmstead dairy processing facilities may be particularly susceptible to Listeria spp. contamination due to the close physical proximity of their processing environments (PE) to associated dairy farm environments (FE). In this case study, we supported the implementation of interventions focused on improving (i) cleaning and sanitation efficacy, (ii) hygienic zoning, and (iii) sanitary equipment/facility design and maintenance in a farmstead dairy processing facility, and evaluated their impact on Listeria spp. detection in the farmstead's PE over 1 year. Detection of Listeria spp. in the farmstead's PE was numerically reduced from 50% to 7.5% after 1 year of intervention implementation, suggesting that these interventions were effective at improving Listeria spp. control. In addition, environmental samples were also collected from the farmstead's FE to evaluate the risk of the FE as a potential source of Listeria spp. in the PE. Overall, detection of Listeria spp. was higher in samples collected from the FE (75%, 27/36) compared with samples collected from the PE (24%, 29/120). Whole genome sequencing (WGS) performed on select isolates collected from the PE and FE supported the identification of 6 clusters (range of 3 to 15 isolates per cluster) that showed ≤ 50 high quality single nucleotide polymorphism (hqSNP) differences. Of these 6 clusters, 3 (i.e., clusters 2, 4, and 5) contained isolates that were collected from both the PE and FE, suggesting that transmission between these 2 environments was likely. Moreover, all cluster 2 isolates represented a clonal complex (CC) of L. monocytogenes commonly associated with dairy farm environmental reservoirs (i.e., CC666), which may support that the farmstead's FE represented an upstream source of the cluster 2 isolates that were found in the PE. Overall, our data underscore that, while the FE can represent a potential upstream source of Listeria spp. contamination in a farmstead dairy processing facility, implementation of targeted interventions can help effectively minimize Listeria spp. contamination in the PE.

Application of Environmental Monitoring Programs and Root Cause Analysis to Identify and Implement Interventions to Reduce or Eliminate Listeria Populations in Apple Packinghouses.

Controlling Listeria in produce packinghouses can be challenging due to the large number of potential contamination routes. For example, repeated isolation of the same Listeria subtype in a packinghouse could indicate persistence in the packinghouse or reintroduction of the same Listeria from an upstream source. To improve understanding of Listeria transmission patterns in packinghouses, we performed a longitudinal study in four apple packinghouses, including testing of 1,339 environmental sponges and whole genome sequencing (WGS)-based characterization of 280 isolates. Root cause analysis and subsequent intervention implementation were also performed and assessed for effectiveness. Listeria prevalence among environmental sponges collected from the four packinghouses was 20% (range of 5-31% for individual packinghouses). Sites that showed high Listeria prevalence included drains, forklift tires and forks, forklift stops, and waxing area equipment frames. A total of 240/280 WGS-characterized isolates were represented in 41 clusters, each containing two or more isolates that differed by ≤50 high-quality single nucleotide polymorphisms (hqSNPs); 21 clusters were isolated from one packinghouse over ≥2 samplings (suggesting persistence or possibly reintroduction), while 11 clusters included isolates from >2 packinghouses, suggesting common upstream sources. Some interventions successfully (i) reduced Listeria detection on forklift tires and forks (across packinghouses) and (ii) mitigated packinghouse-specific Listeria issues (e.g., in catch pans). However, interventions that lacked enhanced equipment disassembly when persistence was suspected typically appeared to be unsuccessful. Overall, while our data suggest a combination of intensive environmental sampling with subtyping and root cause analysis can help identify effective interventions, implementation of effective interventions continues to be a challenge in packinghouses.

Can remotely sensed catchment to lake area ratios predict mercury levels in subarctic fishes?

Mercury concentrations ([Hg]) in fish reflect a complex array of interacting biogeochemical and ecological variables. In northern regions where fish are a critical subsistence food, understanding and predicting fish [Hg] can be particularly difficult, largely due to a paucity of comprehensive data associated with the logistical challenges of field sampling. Building on previous work where we elucidated causal relationships between fish [Hg] and a variety of catchment, water quality, and ecological variables in subarctic lakes, we investigated whether using only ratios of catchment area to lake area (CA:LA) can predict [Hg] in northern freshwater fish species. As CA:LA can be sensed remotely, they may be more feasible and practical to obtain than field data in far northern regions. Our study included thirteen remote lakes that represent a CA:LA gradient of 6.2-423.5 within an ∼66,000 km2 subarctic region of Northwest Territories, Canada. We found that size-standardized [Hg] in three widespread fish species, including Lake Whitefish (Coregonus clupeaformis), Walleye (Sander vitreus), and Northern Pike (Esox lucius), were significantly and positively related to CA:LA (p < 0.007, r2 = 67-80%), indicating higher fish [Hg] in smaller lakes surrounded by relatively larger catchments. Our findings provide compelling evidence that remotely sensed CA:LA can be used to predict [Hg] in northern fishes and aid in prioritizing understudied and subsistence fishing lakes of the Canadian subarctic for [Hg] monitoring programs.

The concentrations and behavior of classic phthalates and emerging phthalate alternatives in different environmental matrices and their biological health risks.

Because of their excellent plasticity, phthalates or phthalic acid esters (PAEs) are widely used in plastic products. However, due to the recognized toxicity of PAEs and legislative requirements, the production and use of emerging PAE alternatives have rapidly grown, such as di-isononyl cyclohexane-1,2-dicarboxylate (DINCH) and di(2-ethylhexyl) terephthalate (DEHTP) which are the primary replacements for classic PAEs. Nowadays, PAEs and emerging PAE alternatives are frequently found in a variety of environmental media, including the atmosphere, sludge, rivers, and seawater/sediment. PAEs and emerging PAE alternatives are involved in endocrine-disrupting effects, and they affect the reproductive physiology of different species of fish and mammals. Therefore, their presence in the environment is of considerable concern due to their potential effects on ecosystem function and public health. Nevertheless, current research on the prevalence, destiny, and conduct of PAEs in the environment has primarily focused on classic PAEs, with little attention given to emerging PAE alternatives. The present article furnishes a synopsis of the physicochemical characteristics, occurrence, transport, fate, and adverse effects of both classic PAEs and emerging PAE alternatives on organisms in the ecosystem. Our analysis reveals that both classic PAEs and emerging PAE alternatives are widely distributed in all environmental media, with emerging PAE alternatives increasingly replacing classic PAEs. Various pathways can transform and degrade both classic PAEs and emerging PAE alternatives, and their own and related metabolites can have toxic effects on organisms. This research offers a more extensive comprehension of the health hazards associated with classic PAEs and emerging PAE alternatives.

ICP-MS As a Contributing Tool to Nontarget Screening (NTS) Analysis for Environmental Monitoring.

Due to the increasing number of chemicals released into the environment, nontarget screening (NTS) analysis is a necessary tool for providing comprehensive chemical analysis of environmental pollutants. However, NTS workflows encounter challenges in detecting both known and unknown pollutants with common chromatography high-resolution mass spectrometry (HRMS) methods. Identification of unknowns is hindered by limited elemental composition information, and quantification without identical reference standards is prone to errors. To address these issues, we propose the use of inductively coupled plasma mass spectrometry (ICP-MS) as an element-specific detector. ICP-MS can enhance the confidence of compound identification and improve quantification in NTS due to its element-specific response and unambiguous chemical composition information. Additionally, mass balance calculations for individual elements (F, Br, Cl, etc.) enable assessment of total recovery of those elements and evaluation of NTS workflows. Despite its benefits, implementing ICP-MS in NTS analysis and environmental regulation requires overcoming certain shortcomings and challenges, which are discussed herein.

Compost amendment in urban gardens: elemental and isotopic analysis of soils and vegetable tissues.

Urban horticulture poses a sustainable form of food production, fosters community engagement and mitigates the impacts of climate change on cities. Yet, it can also be tied to health challenges related to soil contamination. This work builds on a previous study conducted on eleven urban gardens in the city of Vienna, Austria. Following the findings of elevated Pb levels in some soil and plant samples within that project, the present study investigates the elemental composition of soil and plants from two affected gardens 1 year after compost amendment. Inductively coupled plasma mass spectrometry (ICP-MS) analysis of skin, pulp and seeds of tomato fruits revealed minor variations in elemental composition which are unlikely to have an impact on food safety. In turn, a tendency of contaminant accumulation in root tips and leaves of radishes was found. Washing of lettuce led to a significant reduction in the contents of potentially toxic elements such as Be, Al, V, Ni, Ga and Tl, underscoring the significance of washing garden products before consumption. Furthermore, compost amendments led to promising results, with reduced Zn, Cd and Pb levels in radish bulbs. Pb isotope ratios in soil and spinach leaf samples taken in the previous study were assessed by multi-collector (MC-) ICP-MS to trace Pb uptake from soils into food. A direct linkage between the Pb isotopic signatures in soil and those in spinach leaves was observed, underscoring their effectiveness as tracers of Pb sources in the environment.

Microbiome analysis in Asia's largest watershed reveals inconsistent biogeographic pattern and microbial assembly mechanisms in river and lake systems.

Microorganisms are critical to the stability of aquatic environments, and understanding the ecological mechanisms of microbial community is essential. However, the distinctions and linkages across biogeographic patterns, ecological processes, and formation mechanisms of microbes in rivers and lakes remain unknown. Accordingly, microbiome-centric analysis was conducted in rivers and lakes in the Yangtze River watershed. Results revealed significant differences in the structure and diversity of microbial communities between rivers and lakes, with rivers showing higher diversity. Lakes exhibited lower community stability, despite higher species interactions. Although deterministic processes dominated microbial community assembly both in rivers and lakes, higher stochastic processes of rare and abundant taxa exhibited in rivers. Spatial factors influenced river microbial community, while environmental factors drove differences in the lake bacterial community. This study deepened the understanding of microbial biogeography and formation mechanisms in large watershed rivers and lakes, highlighting distinct community aggregation patterns between river and lake microorganisms.

Evaluation of an Automated Ultrafiltration System for Concentrating a Range of Viruses from Saline Waters.

Pathogenic viruses in environmental water are usually present in levels too low for direct detection and thus, a concentration step is often required to increase the analytical sensitivity. The objective of this study was to evaluate an automated filtration device, the Innovaprep Concentrating Pipette Select (CP Select) for the rapid concentration of viruses in saline water samples, while considering duration of process and ease of use. Four bacteriophages (MS2, P22, Phi6, and PhiX174) and three animal viruses (adenovirus, coronavirus OC43, and canine distemper virus) were seeded in artificial seawater, aquarium water, and bay water samples, and processed using the CP Select. The recovery efficiencies of viruses were determined either using a plaque assay or droplet digital PCR (ddPCR). Using plaque assays, the average recovery efficiencies for bacteriophages ranged from 4.84 ± 3.8% to 82.73 ± 27.3%, with highest recovery for P22 phage. The average recovery efficiencies for the CP Select were 39.31 ± 26.6% for adenovirus, 19.04 ± 11.6% for coronavirus OC43, and 19.84 ± 13.6% for canine distemper virus, as determined by ddPCR. Overall, viral genome composition, not the size of the virus, affected the recovery efficiencies for the CP Select. The small sample volume size used for the ultrafilter pipette of the system hinders the use of this method as a primary concentration step for viruses in marine waters. However, the ease of use and rapid processing time of the CP Select are especially beneficial when rapid detection of viruses in highly contaminated water, such as wastewater or sewage-polluted surface water, is needed.

Sustainable Sensing with Paper Microfluidics: Applications in Health, Environment, and Food Safety.

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper's unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. This review discusses the fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability.

Decay of RNA and infectious SARS-CoV-2 and murine hepatitis virus in wastewater.

Wastewater-based epidemiology (WBE) has been an important tool for population surveillance during the COVID-19 pandemic and continues to play a key role in monitoring SARS-CoV-2 infection levels following reductions in national clinical testing schemes. Studies measuring decay profiles of SARS-CoV-2 in wastewater have underscored the value of WBE, however investigations have been hampered by high biosafety requirements for SARS-CoV-2 infection studies. Therefore, surrogate viruses with lower biosafety standards have been used for SARS-CoV-2 decay studies, such as murine hepatitis virus (MHV), but few studies have directly compared decay rates of both viruses. We compared the persistence of SARS-CoV-2 and MHV in wastewater, using 50 % tissue culture infectious dose (TCID50) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays to assess infectious virus titre and viral gene markers, respectively. Infectious SARS-CoV-2 and MHV indicate similar endpoints, however observed early decay characteristics differed, with infectious SARS-CoV-2 decaying more rapidly than MHV. We find that MHV is an appropriate infectious virus surrogate for viable SARS-CoV-2, however inconsistencies exist in viral RNA decay parameters, indicating MHV may not be a suitable nucleic acid surrogate across certain temperature regimes. This study highlights the importance of sample preparation and the potential for decay rate overestimation in wastewater surveillance for SARS-CoV-2 and other pathogens.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

OBJECTIVE: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. METHOD: Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. RESULTS: Indoor particles were measured in a range of 0.8-370.7 µg/m3 and 1.9-465.2 µg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 µg/m3 and 0.0-493.0 µg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 µg/m3 and 4.56 µg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 µg/m3 and 17.54 µg/m3, respectively), respectively. CONCLUSIONS: Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Non-Invasive Multi-Gas Detection Enabled by Cu-CuO/PEDOT Microneedle Sensor.

Metal-oxide-based gas sensors are extensively utilized across various domains due to their cost-effectiveness, facile fabrication, and compatibility with microelectronic technologies. The copper (Cu)-based multifunctional polymer-enhanced sensor (CuMPES) represents a notably tailored design for non-invasive environmental monitoring, particularly for detecting diverse gases with a low concentration. In this investigation, the Cu-CuO/PEDOT nanocomposite was synthesized via a straightforward chemical oxidation and vapor-phase polymerization. Comprehensive characterizations employing X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and micro Raman elucidated the composition, morphology, and crystal structure of this nanocomposite. Gas-sensing assessments of this CuMPES based on Cu-CuO/PEDOT revealed that the response current of the microneedle-type CuMPES surpassed that of the pure Cu microsensor by nearly threefold. The electrical conductivity and surface reactivity are enhanced by poly (3,4-ethylenedioxythiophene) (PEDOT) polymerized on the CuO-coated surface, resulting in an enhanced sensor performance with an ultra-fast response/recovery of 0.3/0.5 s.

Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis.

Solid-phase microextraction (SPME) has been widely proposed for the extraction, clean-up, and preconcentration of analytes of environmental concern. Enrichment capabilities, preconcentration efficiency, sample throughput, and selectivity in extracting target compounds greatly depend on the materials used as SPME coatings. Supramolecular materials have emerged as promising porous coatings to be used for the extraction of target compounds due to their unique selectivity, three-dimensional framework, flexible design, and possibility to promote the interaction between the analytes and the coating by means of multiple oriented functional groups. The present review will cover the state of the art of the last 5 years related to SPME coatings based on metal organic frameworks (MOFs), covalent organic frameworks (COFs), and supramolecular macrocycles used for environmental applications.