Sonocatalytic induced dye degradation and antibacterial performance of SrTiO3 nanoparticles embedded cotton fabric

Environmental Research, Journal Year: 2023, Volume and Issue: 240, P. 117541 - 117541

Published: Oct. 30, 2023

Language: Английский

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Hyperlocal environmental data with a mobile platform in urban environments

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Aug. 5, 2023

Abstract Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental sources limited due the lack of monitoring infrastructure, consistent quality, and availability public. This paper reports ( PM , NO 2 temperature, relative humidity) collected from 2020 2022 calibrated four deployments three global cities. Each collection campaign targeted specific problem related air such as tree diversity, community exposure disparities, excess fossil fuel usage. Firstly, we introduce mobile platform design its deployment Boston (US), NYC Beirut (Lebanon). Secondly, present cleaning validation process, for quality data. Lastly, explain format how datasets can be used standalone other assist evidence-based decision-making. Our sensing include cities varying scales, aiming address scarcity developing regions support policymaking.

Language: Английский

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Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1423 - 1423

Published: Feb. 26, 2025

Given the significant impact of air pollution on global health, continuous and precise monitoring quality in all populated environments is crucial. Unfortunately, even most developed economies, current networks are largely inadequate. The high cost stations has been identified as a key barrier to widespread coverage, making cost-effective devices potential game changer. However, accuracy measurements obtained from low-cost sensors affected by many factors, including gas cross-sensitivity, environmental conditions, production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships sensor responses thus enhance their readings accuracy. After gathering placed alongside reference station, data were used train such models. Assessments performance showed that tailored individual units greatly improved measurement accuracy, boosting correlation with reference-grade instruments up 10%. Nonetheless, this research also revealed inconsistencies similar prevent creation unified correction model for given type.

Language: Английский

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Calibration and validation-based assessment of low-cost air quality sensors

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 977, P. 179364 - 179364

Published: April 15, 2025

Air pollution poses a significant threat to public health. Low-cost air quality sensors (LCSs) can provide data foundation for monitoring, particularly supplementing the regulatory monitoring network and identifying local issues. However, performance varies considerably, questions remain regarding reliability accuracy of LCS data. We evaluated accuracy, stability precision six LCSs over three-month period collocation with reference instruments at two locations. A mathematical workflow including calibration validation was developed stability, incorporating combination environmental factors (e.g., temperature, relative humidity), linear nonlinear regression, followed by evaluation Bland-Altman plots. For particulate matter, from found be reliable after simple regression (R2 > 0.9 both validation). gas nitrogen dioxide, carbon monoxide, Ozone, methods that met requirements also performed well using models 0.7 validation), whereas machine learning models, such as random forest, could not pass validation, require cautious application. In non-laboratory environments, into function may lead subsequent instability. Regarding between LCSs, unstable measurement biases among devices have been observed. Linear method is recommended preferred onsite caution advised when due increased uncertainty. Furthermore, deploying it important consider their varying responses high or low pollutant concentrations.

Language: Английский

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Unmanned Aerial Vehicles and Low-Cost Sensors for Air Quality Monitoring: A Comprehensive Review of Applications Across Diverse Emission Sources

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106409 - 106409

Published: April 1, 2025

Language: Английский

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Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

As an emerging atmospheric monitoring technology, low-cost sensors for particulate matter of diameters below 2.5 μm (PM2.5LCSs) supplement traditional air quality instruments. Because their stability and accuracy are typically low, they require adequate calibration to meet operational requirements. Numerous studies have now been published on single-sensor PM2.5LCS models, research networks, designed measure pollutant concentration with high spatiotemporal resolution, is gradually starting. However, there no established standard procedure sensor calibration. Here we comprehensively reviewed evaluate the current status, identify major challenges, provide support applications networks. Regression machine learning were most common methods single PM2.5LCSs. Environmental factors duration period influenced model accuracy, especially (data-driven) algorithms. For included early evaluation homogeneous or colocated Method selection depended regional environmental conditions, concentration, presence absence reference Quality control crucial operation network, online drift detection management measures routine assurance control. In conclusion, use, intensive machine-learning-based must be conducted practical application large-scale

Language: Английский

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Performance and Applicability of Low-Cost PM Sensors to assess Global Pollution Variability through Machine Learning Techniques

Atmospheric Environment X, Journal Year: 2025, Volume and Issue: unknown, P. 100331 - 100331

Published: May 1, 2025

Language: Английский

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Coincidence Effect of a Low-Cost Particulate Matter Sensor: Observations from Environmental Chamber Tests at Diverse Particle Concentrations

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102581 - 102581

Published: May 1, 2025

Language: Английский

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Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors

Sensors, Journal Year: 2025, Volume and Issue: 25(10), P. 3183 - 3183

Published: May 19, 2025

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, sensitivity factors can lead measurement inaccuracies, necessitating effective calibration methods enhance reliability. In this study, an Internet of Things (IoT)-based system was developed tested using the most commonly preferred sensor types for measurement: fine particulate matter (PM2.5), carbon dioxide (CO2), temperature, humidity sensors. To improve accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), Stochastic Descent (SGD). Sensor performance evaluated by comparing measurements with a reference device, best-performing ML model determined each sensor. The results indicate that GB kNN achieved highest accuracy. For CO2 calibration, R2 = 0.970, RMSE 0.442, MAE 0.282, providing lowest error rates. PM2.5 sensor, delivered successful results, 2.123, 0.842. Additionally, temperature sensors, demonstrated accuracy values (R2 0.976, 2.284). These findings demonstrate that, identifying suitable methods, ML-based techniques significantly LCSs. Consequently, they offer viable cost-effective alternative traditional high-cost systems. Future studies should focus on long-term data collection, testing under diverse conditions, integrating additional further advance field.

Language: Английский

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Methodology for Occupant Head-Neck Injury Testing in Under-Body Blast Impact Based on Virtual-Real Fusion

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(11), P. 5796 - 5796

Published: May 22, 2025

The high-biofidelity dummy used to evaluate occupant protection under blast conditions is often costly and vulnerable. To address the limitations of low-cost, simplified head–neck structures, which exhibit significant differences in mechanical properties compared dummies, a virtual–real fusion-based test method for assessing injury under-body impacts proposed. A physical model, designed based on human biomechanical characteristics, constructed testing. mapping model 1D convolutional neural network (1D-CNN) developed as virtual counterpart process data, specifically head chest centroid accelerations, into acceleration upper neck axial compression force matching Hybrid III 50th numerical model. Pendulum collision tests are conducted simulate impacts, generating multiple sets data. Under identical loading conditions, computed. parameters then optimized using these simulated experimental datasets non-dominated sorting genetic algorithm II (NSGA-II). Validation through experiments demonstrates that proposed achieves high accuracy, with errors 10.9% Head Injury Criterion (HIC15) 2.4% maximum calculations. This approach effectively bridges gap between biofidelity cost-efficiency testing impact scenarios.

Language: Английский

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