Low-Cost Air Quality System for Urban Area Monitoring

2015 ◽  
Author(s):  
Adrian-Cosmin Firculescu ◽  
Dan Stefan Tudose
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Low Cost ◽  
Quality System ◽  
Area Monitoring

scholarly journals URBAN DATA COLLECTION USING A BIKE MOBILE SYSTEM WITH A FOSS ARCHITECTURE

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 3-9 ◽  
Author(s):  
A. Abdul Jabbar ◽  
I. Aicardi ◽  
N. Grasso ◽  
M. Piras
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
3D Model ◽  
Time Synchronization ◽  
Low Cost ◽  
Laser Scanner ◽  
Digital Camera ◽  
Quality Parameter ◽  
Raspberry Pi ◽  
Safety Level

European community is working to improve the quality of the life in each European country, in particular to increase the quality air condition and safety in each city. The quality air is daily monitored, using several ground station, which do not consider the variation of the quality during the day, evaluating only the average level. In this case, it could be interesting to have a “smart” system to acquire distributed data in continuous, even involving the citizens. On the other hand, to improve the safety level in urban area along cycle lane, road and pedestrian path, exist a lot of algorithms for visibility and safety analysis; the crucial aspect is the 3D model considered as “input” in these algorithms, which always needs to be updated. <br><br> A bike has been instrumented with two digital camera as Raspberry PI-cam. Image acquisition has been realized with a dedicated python tool, which has been implemented in the Raspberry PI system. Images have been georeferenced using a u-blox 8T, connected to Raspberry system. GNSS data has been acquired using a specific tool developed in Python, which was based on RTKLIB library. Time synchronization has been obtained with GNSS receiver. Additionally, a portable laser scanner, an air quality system and a small Inertial platform have been installed and connected with the Raspberry system. <br><br> The system has been implemented and tested to acquire data (image and air quality parameter) in a district in Turin. Also a 3D model of the investigated site has been carried. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be described. different low cost sensors, in particular digital camera and laser scanner to collect easily geospatial data in urban area.

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.

Recent advancements in low-cost portable sensors for urban and indoor air quality monitoring

2021 ◽  
Author(s):  
A. Hernández-Gordillo ◽  
S. Ruiz-Correa ◽  
V. Robledo-Valero ◽  
C. Hernández-Rosales ◽  
S. Arriaga
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Portable Sensors

scholarly journals Low cost air quality monitoring

2020 ◽  
Author(s):  
Chekwube A. Okigbo ◽  
Amar Seeam ◽  
Shivanand P. Guness ◽  
Xavier Bellekens ◽  
Girish Bekaroo ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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