Search Results (1,030)

Lowcost sensors are widely used for air quality monitoring due to their affordability, portability and easy maintenance. However, the performance of such sensors, such as PurpleAir Sensors (PAS), is often affected by changes in environmental (e.g., temperature and humidity) or emission conditions, and hence the resulting measurements require corrections to ensure accuracy and validity. Traditional correction methods, like those developed by the USEPA, have limitations, particularly for applications to geographically diverse settings and sensors with no collocated referenced monitoring stations available. This study introduces BaySurcls, a Bayesianoptimised surrogate model integrating deep learning (DL) algorithms to improve the PurpleAir sensor PM2.5 (PAS2.5) measurement accuracy. The framework incorporates environmental variables such as humidity and temperature alongside aerosol characteristics, to refine sensor readings. The BaySurcls model corrects the PAS2.5 data for both collocated and noncollocated monitoring scenarios. In a case study across multiple locations in New South Wales, Australia, BaySurcls demonstrated significant improvements over traditional correction methods, including the USEPA model. BaySurcls reduced root mean square error (RMSE) by an average of 20% in collocated scenarios, with reductions of up to 25% in highvariation sites. Additionally, BaySurcls achieved Nash–Sutcliffe Efficiency (NSE) scores as high as 0.88 in collocated cases, compared to scores below 0.4 for the USEPA method. In noncollocated scenarios, BaySurcls maintained NSE values between 0.60 and 0.78, outperforming standalone models. This improvement is evident across multiple locations in New South Wales, Australia, demonstrating the model’s adaptability. The findings confirm BaySurcls as a promising solution for improving the reliability of lowcost sensor data, thus facilitating its valid use in air quality research, impact assessment, and environmental management. Full article

Methane gas leakage can lead to pollution problems, such as rising ambient temperature. In this paper, the Vernier effect of a double D-shaped photonic crystal fiber (PCF) in a Sagnac interferometer (SI) is proposed for the accurate detection of mixed methane gas content in the gas. The optical fiber structure of the effective sensing in the sensing SI loop and the effective sensing in the reference SI loop are the same. Both of them adopt the polarization-maintaining photonic crystal fiber (PM-PCF) designed in this paper. The optical fiber structure of the effective sensing in the sensing SI loop deposited with the methane gas-sensitive film is polished to obtain a double-D structure. This operation makes it easier for methane gas to contact the sensitive film and realize the sensor’s repeated use. The sensing capability of the methane gas sensor was evaluated utilizing the finite element method (FEM). The numerical simulation results show that when the concentration of methane gas in the environment is 0~3.5%, the average sensitivity of two parallel Sagnac loops is 409.43 nm/%. Using Vernier effect cascade SI loops, the sensitivity of the sensor for detecting methane gas increased by four times. Without considering air and humidity, we provide a practical scheme for the development and design of high-sensitivity methane gas sensors. Full article

Background: Ambient air pollution remains a major risk factor for population health worldwide. The impact of PM_2.5 air pollution is underestimated in sub-Saharan Africa due to a lack of epidemiological studies. AirQ+ is proposed to reduce these inequalities in research. The aim of this study is to assess, by AirQ+, the impact of prolonged exposure to PM_2.5 on respiratory health in Kinshasa in 2019, and to estimate the health benefits of reducing this air pollution. Methods: Population and mortality data were obtained from the Institut National de la Statistique and the Institut de Métrologie et d’Évaluation en santé, respectively. PM_2.5 concentrations were measured using PurpleAir PA-II-SD sensors, and average annual concentration was 43.5 µg/m³ in 2019. AirQ+ was used to estimate the health effect attributable to PM_2.5 in adults aged over 25 in Kinshasa. Results: In 2019, the proportion of deaths attributable to PM_2.5 air pollution was 30.72% for ALRI, 26.55% for COPD and 24.32% for lung cancers. Each 10% reduction in current PM_2.5 levels would prevent 1093 deaths (from all causes) per year in Kinshasa. Life expectancy would increase by 4.7 years (CI 3.5–5.3) if the WHO threshold of 5 mg/m³ were respected. Conclusions: The results of this study highlight the major respiratory public health problem associated with air pollution by fine particles in Kinshasa. AirQ+ was used to assess the impact of prolonged exposure to PM_2.5 and respiratory deaths among adults in Kinshasa and revealed that this number of deaths could be avoided by improving air quality. Full article

As the wealth of evidence grows as to the negative impact of indoor air quality on human health, it has become increasingly urgent to investigate and characterise sources of air pollution within the home. Fine particulate matter with a diameter of 2.5 µm or less (PM_2.5) is a key cause for concern, and cooking is known to be one of the most significant sources of domestic PM_2.5. In this study, the aim was to demonstrate the efficacy of combining experimental techniques and cutting-edge High-Performance Computing (HPC) to characterise the dispersion of PM_2.5 during stir-frying within a kitchen laboratory. This was carried out using both experimental measurement with low-cost sensors and high-fidelity Computational Fluid Dynamics (CFD) modelling, in which Lagrangian Stochastic Methods were used to model particle dispersion. Experimental results showed considerable spatio-temporal variation across the kitchen, with PM_2.5 mass concentrations in some regions elevated over 1000 μg m−3 above the baseline. This demonstrated both the impact that even a short-term cooking event can have on indoor air quality and the need to factor in such strong spatio-temporal variations when assessing exposure risk in such settings. The computational results were promising, with a reasonable approximation of the experimental data shown at the majority of monitoring points, and future improvements to and applications of the model are suggested. Full article

Bisphenol A (BPA) is a commonly synthetic chemical mainly used in producing plastic items. It is an endocrine-disrupting compound that causes irreversible health and environmental damage. Developing a simple method for BPA effective quantitative monitoring is emergently necessary. Herein, a novel electrochemical sensor for BPA detection based on [(5,10,15,20-tetrakis(p-bromophenyl) porphyrinato] cadmium (II) [(CdTBrPP)] and gold nanoparticle (AuNPs)-modified screen-printed carbon electrode (SPCE) was elaborated. CdTBrPP was synthesized and then characterized with Ultraviolet–Visible Spectroscopy (UV/vis), Infrared Spectroscopy (IR), and Proton Nuclear Magnetic Resonance Spectroscopy (¹H NMR) to confirm its successful synthesis. After drop-coating AuNPs and CdTBrPP on the SPCE, the sensor performance was evaluated using square wave voltammetry (SWV), a linear response in a concentration range from 10⁻¹¹ M to 10⁻² M, with a low detection limit (LOD) of 9.5 pM. The CdTBrPP/AuNPs/SPCE sensor demonstrates a high selectivity and reproducibility, making it a promising candidate for developing a low-cost water-monitoring system for detecting BPA. Additionally, the proposed sensor effectively detected BPA in both tap and mineral water samples. Full article

The Phi29 DNA polymerase is renowned for its processivity in synthesizing single-stranded DNA amplicons by rolling around a circularized DNA template. However, DNA synthesis rolling circle amplification (RCA) is significantly hindered by the secondary structure in the circular template. To overcome this limitation, an engineered circular template without secondary structure could be utilized to improve the sensitivity of RCA-based assays without increasing its complexity. We herein proposed an entropy-driven special RCA technology for the detection of HPV16 E7 gene at room temperature. The strategy is composed of a molecular beacon containing a loop region for nucleic acid target recognition and a stem region to initiate RCA. With the target analyte, the stem region of the molecular beacon will be exposed and then hybridized with a special circular template to initiate the DNA amplification. We tested different designs of the molecular beacon sequence and optimized the assay’s working conditions. The assay achieved a sensitivity of 1 pM in 40 min at room temperature. The sensitivity of this assay, at 1 pm, is about a hundred-fold greater than that of conventional linear RCA performed in solution. Our proposed sensor can be easily reprogrammed for detecting various nucleic acid markers by altering the molecular beacon’s loop. Its simplicity, rapid assay time, and low cost make it superior to RCA sensors that utilize similar strategies. Full article

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m⁻³ value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution. Full article

This paper presents the results of a study on the effect of moisture content in polylactic acid (PLA) filaments on dust emissions during incremental manufacturing. The tests were conducted in a customised chamber using a standard 3D printer, and Plantower PMS3003 sensors were used to monitor air quality by measuring PM1, PM2.5 and PM10 concentrations. The filament humidity levels tested were 0.18%, 0.61% and 0.83%. The results show that a higher moisture content in the filament significantly increases dust emissions. For dry filaments (0.18% humidity), the average dust emissions ranged from 159 to 378 µg/m³. Slightly humid filaments (0.61%) produced higher emissions, with averages between 59 and 905 µg/m³, with one outlier reaching up to 1610 µg/m³. For very humid filaments (0.83%), the highest average emissions were observed, ranging from 57 to 325 µg/m³, along with greater variability (standard deviation up to 198). These findings highlight that increased filament humidity correlates with elevated dust emissions and greater instability in emission levels, raising potential health concerns during 3D printing. Full article

Toxic heavy metal ions, such as lead ions, significantly threaten human health and the environment. This work introduces a novel method for the simple and sensitive detection of lead ions based on biochar-loaded titanium dioxide nanoparticles (BC@TiO₂NPs) nanocomposites. Eco-friendly biochar samples were prepared from spent coffee grounds (500 °C, 1 h) that were chemically activated with TiO₂ nanoparticles (150 °C, 24 h) to improve their conductivity. Structural characterizations showed that BC@TiO₂NPs have a porous structure. The BC@TiO₂NPs material was evaluated for lead ion determination by assembling glassy carbon electrodes. Under optimal conditions, the sensor was immersed in a solution containing the analyte (0.1 M NaAc-HAc buffer, pH = 4.5) for the detection of lead ions via differential pulse voltammetry. A linear dynamic range from 1 pM to 10 μMwas achieved, with a detection limit of 0.6268 pM. Additionally, the analyte was determined in tap water samples, and a satisfactory recovery rate was achieved. Full article

This study intended to understand whether teachers’ perceptions of indoor air quality (IAQ) during classes aligned with the real levels of air pollutants and comfort parameters. For this purpose, an IAQ monitoring survey based on low-cost sensors using a multi-parameter approach was carried out in nine classrooms (a total of 171 monitored classes) in a Portuguese school. In each monitored class, the perception of IAQ reported by the teacher was assessed using a scale from 1 (very bad IAQ) to 10 (very good IAQ). Several exceedances regarding national legislation were found, with temperature being the parameter with a higher percentage of exceedance in all the studied classrooms (46%), followed by PM₁₀ (32%), and then CO₂ (27%). Temperature was found to be the only environmental parameter that was significantly associated with lower IAQ perception reported by the teachers, highlighting that typical pollutants such as CO₂ (which can be identified as stuffy air) did not contribute to the teachers’ perceptions. Full article

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO₂) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m³ daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as Bruma Seca). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country. Full article

The effects of air quality on health and cognition are well documented, but few studies have focused on its impact on emotions, leaving this area underexplored. This study investigates the influence of environmental factors—specifically particulate matter (PM₁, PM_2.5, and PM₁₀) and carbon dioxide (CO₂)—on students’ basic emotions in secondary school classrooms. For the collection of environmental data, we used low-cost sensors, which were carefully calibrated to ensure acceptable accuracy for monitoring air quality variables, despite inherent precision limitations compared to traditional sensors. Emotions were recorded via camera and analyzed using a custom-developed code. Based on these data, we found significant but modest correlations, such as the negative correlation between PM levels and happiness, and positive correlations of CO₂ concentrations with fear and disgust. The regression models explained between 36% and 62% of the variance in emotions like neutrality, sadness, fear, and happiness, highlighting nonlinear relationships in some cases. These findings underscore the need for improved classroom environmental management, including the implementation of real-time air quality monitoring systems. Such systems would enable schools to mitigate the negative emotional effects of poor air quality, contributing to healthier and more conducive learning environments. Future research should explore the combined effects of multiple environmental factors to further understand their impact on student well-being. Full article

We present a high-sensitivity curvature and strain Mach–Zehnder interferometer (MZI) fiber sensor based on a configuration of no-core fiber (NCF) and four-core fiber (FCF). We used an optical fiber fusion splicer to directly splice a segment of FCF between two segments of NCF, with both the FCF and NCF made of SiO₂, where the FCF exhibits multi-path interference characteristics that allow for higher sensitivity. The NCF, with its self-focusing property, excites higher-order modes, which split and transmit it into the four cores of the FCF. The experimental results show that within a curvature range of 0.0104 m⁻¹–0.1515 m⁻¹, the maximum sensitivity can reach −78.04 dB/m⁻¹ with a high linear value of ~0.99. Additionally, the strain response is also experimentally studied. In the range of 0–600 με, the maximum strain sensitivity is −6.49 pm/με. The sensor demonstrates high curvature and strain sensitivity, indicating its potential applications in sensing measurements. Full article

This study innovatively employs drones equipped with air quality sensors to collect three-dimensional air quality data in a campus microenvironment. Data are accurately corrected using a BP neural network, and a cubic model is constructed using three-dimensional interpolation. Combining photogrammetry technology, this study analyzes air quality patterns, finding significant differences from macro trends. Construction activities and large electronic experimental equipment significantly increase PM_2.5 levels in the air. In rainy weather, the respiration of vegetation is enhanced, leading to higher CO₂ concentrations, while water bodies exhibit higher temperatures in rainy weather due to their high specific heat capacity. This research not only provides a new perspective for microenvironment air quality monitoring but also offers a scientific basis for future air quality monitoring and management. Full article