System for monitoring air quality in urban environments applyng low-cost solutions

2020 ◽  
Author(s):  
Jose Bazurto ◽  
Willian Zamora ◽  
Johnny Larrea ◽  
Dolores Munoz ◽  
Dahiana Alvia
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Urban Environments

scholarly journals Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

2018 ◽  
Author(s):  
Ashley Collier-Oxandale ◽  
Michael P. Hannigan ◽  
Joanna Gordon Casey ◽  
Ricardo Piedrahita ◽  
John Ortega ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Low Cost ◽  
Complex Mixture ◽  
Urban Environments ◽  
Monitoring Networks ◽  
Linear Calibration ◽  
Spatial And Temporal Scales

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first, a one-month deployment along the Colorado Front Range includes sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment in an urban Los Angeles neighborhood, an subject to complex mixture of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

scholarly journals City Wide Participatory Sensing of Air Quality

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrew Rebeiro-Hargrave ◽  
Pak Lun Fung ◽  
Samu Varjonen ◽  
Andres Huertas ◽  
Salla Sillanpää ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Age Groups ◽  
Personal Exposure ◽  
Urban Environments ◽  
Sensor Data ◽  
Automated System ◽  
Participatory Sensing ◽  
Good Correspondence

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

scholarly journals Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 926 ◽  
Author(s):  
Iasonas Stavroulas ◽  
Georgios Grivas ◽  
Panagiotis Michalopoulos ◽  
Eleni Liakakou ◽  
Aikaterini Bougiatioti ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Forest ◽  
Low Cost ◽  
Field Evaluation ◽  
Urban Environments ◽  
Spatiotemporal Variability ◽  
Monitoring Networks ◽  
Fire Event ◽  
Ambient Relative Humidity ◽  
Reference Measurements

Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

scholarly journals Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

2018 ◽  
Vol 11 (6) ◽  
pp. 3569-3594 ◽  
Author(s):  
Ashley Collier-Oxandale ◽  
Joanna Gordon Casey ◽  
Ricardo Piedrahita ◽  
John Ortega ◽  
Hannah Halliday ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Low Cost ◽  
Urban Environments ◽  
Monitoring Networks ◽  
Linear Calibration ◽  
Spatial And Temporal Scales ◽  
Rural And Urban

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a 1-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesburg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing and explore cross sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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