Exposure to airborne particulate matter during commuting using portable sensors: Effects of transport modes in a French metropolis study case

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 365, P. 121400 - 121400

Published: June 26, 2024

Outdoor exposure to particulate matter (PM2.5 and PM10) in urban areas can vary considerably depending on the mode of transport. This study aims quantify this difference during daily travel, by carrying out a micro-sensor measurement campaign. The pollutant was assessed simultaneously over predefined routes order allow comparison between different transport modes having same starting ending points. During six-week campaign, average reference values for PM background concentrations were 13.72 17.92μg/m3 PM2.5 PM10, respectively. results revealed that with highest adjusted concentration (PM2.5Norm) bus (1.65) followed metro (1.51), walking (1.33), tramway (1.31), car (1.09) finally bike (1.06). For PM10Norm, had (1.86), (1.68), (1.65), (1.61), (1.43) (1.39). level urbanization around route presence preferential lanes public transportation influenced which commuters exposed. active (bike walking), we observed frequent variations trip, characterized punctual peaks concentration, local characteristics road traffic morphology. Fluctuations inside vehicles partly explained opening closing doors stops, as well passenger flows, influencing re-suspension particles. one least exposed overall, lowest variability, although these greatly ventilation parameters used. These encourage measures move most users away from traffic, developing network entirely dedicated cycling walking, particularly densely populated areas, encouraging renewal motorized use less polluting fuels efficient systems.

Language: Английский

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Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia

Sensors, Journal Year: 2024, Volume and Issue: 24(13), P. 4052 - 4052

Published: June 21, 2024

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because the high cost establishing such systems, some are often completely left out regulatory networks. Recently, a new paradigm in has emerged that utilizes low-cost pollution sensors, thus making it possible reduce knowledge gap levels for not covered by and increase spatial resolution others. The benefits community almost self-evident since information about level can transmitted real time data analysed immediately wider area. However, accuracy reliability newly produced must also taken into account order able correctly interpret results. In this study, we analyse particulate matter from large network monitors was deployed placed outdoor spaces schools central western Serbia under Schools Better Air Quality UNICEF pilot initiative period April 2022 June 2023. consisted 129 devices 15 municipalities, with 11 municipalities having extensive real-time measurements concentration first time. analysis showed maximum concentrations PM2.5 PM10 were winter months (heating season), while during summer (non-heating several times lower. Also, values number daily exceedances (50 μg/m3) much higher than others because diversity differences sensor sampling sites. mass obtained sensors classified according European AQI (air index) applied data. This study confirmed useful providing warnings days episodes, particularly situations where there is lack local or national stations

Language: Английский

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Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 76 - 76

Published: Jan. 12, 2025

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with established standards set by US Environmental Protection Agency (US EPA). This study evaluates accuracy and reliability of a commonly PM2.5 monitoring device Thailand. A comparative analysis was conducted between DustBoy EPA’s Federal Reference Method (FRM) Equivalent (FEM). research involved both laboratory field testing, where DustBoy’s analyzed at various concentration levels environmental conditions. demonstrated that readings diverged from those standard monitors higher concentrations; however, positive correlation devices remained evident. Below 100 µg/m3, overestimated PM concentrations compared FRM but underestimated them FEM devices. At concentrations, showed significant overestimation, although trends aligned sensor also affected factors age model. Corrections were developed adjust match reference more closely, enhancing post-adjustment. These corrections will refine public reporting serve guidelines for other

Language: Английский

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Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration

Aerosol and Air Quality Research, Journal Year: 2025, Volume and Issue: 25(1-4)

Published: April 1, 2025

Abstract Background Low-cost sensors (LCS) are widely used for air quality monitoring, but their accuracy depends on proper calibration. This study compares linear regression (LR) and machine learning (ML) techniques, particularly random forest (RF), to determine optimal calibration strategies. Objectives aims compare the effectiveness of LR RF models in calibrating Plantower PMS 3003 sensor under different environmental conditions. It also explores ways streamline efforts while maintaining accuracy. Methods Sensor data were collected a controlled laboratory setting, with measurements compared against reference monitor. developed calibrate sensor, performance was evaluated based RMSE, R 2 , bias. Additionally, examined whether using fewer training could still produce reliable models. Results Both demonstrated strong performance. effective low moderate PM2.5 concentrations required computational resources, making them suitable large-scale monitoring limited resources. captured nonlinear relationships, showing superior at high PM conditions relative humidity. The findings suggest that trained smaller datasets can achieve practical accuracy, reducing need extensive individual Conclusions selection model should be guided by study-specific requirements, including resource availability. recommended studies constrained may offer advantages high-exposure environments due ability complex interactions. is first explore highlighting potential optimized strategies resource-limited settings. Future research validate these real-world deployments further refine LCS applications. Graphical

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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Lifecycle Analysis of an Air Quality Sensor

Acta Polytechnica Hungarica, Journal Year: 2024, Volume and Issue: 21(8), P. 67 - 85

Published: Jan. 1, 2024

Nowadays, it is becoming increasingly important to not only measure environmental impacts, in terms of energy efficiency, but also, the quality life.We have developed an outdoor, low-cost sensor that measures airborne dust concentrate and other air parameters.Our goal perform high-resolution measurements, based on hundreds metering devices, achieve full urban coverage.Using data measured collected by sensors, our second establish intelligent building management settlement systems services.As planned system will consist nodes, its impact durability cannot be neglected.Therefore, planning paramount importance during development.When developing advisable focus most factors ecological design.During life cycle analysis, combined nodes network itself, must examined.

Language: Английский

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Particulate matter 1µm (PM1) dataset collected by low-cost sensors in residential and industrial areas at the neighborhood level

Data in Brief, Journal Year: 2024, Volume and Issue: 54, P. 110411 - 110411

Published: April 15, 2024

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions particles (PM10, PM2.5, and PM1) allows the characterization concentration levels specific sources or events, including analysis ultrafine (PM1). Several studies have documented adverse effects on human health due to exposure PM1, such as morbidity mortality from respiratory, cardiovascular, and, some cases, carcinogenic diseases. Hence, studying that cause PM1 is imperative. LCS an alternative understanding contaminant by considering spatial temporal community dynamics critical zones. Furthermore, collecting managing large amounts data through automatic processing generates information support decision-making reduce risks people's health. dataset presents level (µg/m3) calculated size 0.03 µm, 0.05 1.0 µm recorded counted sensor a sample per minute 24 h seven continuous days. values meteorological factors relative humidity, temperature, heat index complement these attributes. comprises records collected (in same period) at four stations, which compose network supported Internet Things (IoT) technologies. collection points were located areas Reynosa, Mexico, strategic places environmental pollution, industrial parks, residential areas, avenues with high vehicular traffic transportation heavy cargo, airport.

Language: Английский

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Proposal for concentration calibration method for field evaluation of particulate matters monitors based on light scattering using decision tree techniques

Journal of Odor and Indoor Environment, Journal Year: 2023, Volume and Issue: 22(4), P. 314 - 327

Published: Dec. 30, 2023

Language: Английский

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Exposure to Airborne Particulate Matter During Commuting Using Portable Sensors: Effects of Transport Modes in a French Metropolis Study Case

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

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Surface PM₁₀ Air Pollution of Karachi, Pakistan: Spatial-Temporal Statistical Modeling Using Aerosol-Optical-Depth Remote Sensing

Environmental Engineering Science, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

Language: Английский

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