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.

scholarly journals Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

2021 ◽  
Vol 17 (2) ◽  
pp. 1-44
Author(s):  
Francesco Concas ◽  
Julien Mineraud ◽  
Eemil Lagerspetz ◽  
Samu Varjonen ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Environmental Monitoring ◽  
Low Cost ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Great Promise ◽  
Air Quality Monitoring ◽  
Monitoring Stations

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

scholarly journals Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Wisam Mohammed ◽  
Nicole Shantz ◽  
Lucas Neil ◽  
Tom Townend ◽  
Adrian Adamescu ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Long Distance ◽  
Air Quality Measurements ◽  
Custom Made ◽  
The Impact ◽  
Quality Measurements ◽  
Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.

scholarly journals Wireless Sensors Network Monitoring of Saharan Dust Events in Bari, Italy

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 898 ◽  
Author(s):  
Michele Penza ◽  
Domenico Suriano ◽  
Valerio Pfister ◽  
Mario Prato ◽  
Gennaro Cassano
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Cost Effective ◽  
Saharan Dust ◽  
Quality Monitoring ◽  
Sensor Data ◽  
Air Quality Monitoring ◽  
Dust Events ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

A sensors network based on 8 stationary nodes distributed in Bari (Southern Italy) hasbeen deployed for urban air quality monitoring during advection events of Saharan dust in theperiod 2015–2017. The low-cost sensor-systems have been installed in specific sites (buildings,offices, schools, streets, airport) to assess the PM10 concentration at high spatial and temporalresolution in order to supplement the expensive official air monitoring stations for citizen sciencepurposes. Continuous measurements were performed by a cost-effective optical particle counter(PM10), including temperature and relative humidity sensors. They are operated to assess theperformance during a long-term campaign (July 2015–December 2017) of 30 months for smart citiesapplications. The sensor data quality has been evaluated by comparison to the reference data of the9 Air Quality Monitoring Stations (AQMS), managed by local environmental agency (ARPA-Puglia)in the Bari city.

scholarly journals The applicability of low-cost PM10 sensors for atmospheric air quality monitoring

2018 ◽  
Vol 57 ◽  
pp. 02013
Author(s):  
Bartosz Szulczyński ◽  
Jacek Gębicki
Keyword(s):  
Air Quality ◽  
Measurement Uncertainty ◽  
Low Cost ◽  
Ambient Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Methods ◽  
Atmospheric Air ◽  
Reference Methods ◽  
The Impact

Described in this work are the results of field tests carried out in the Tricity Agglomeration between 01 April 2018 and 30 June 2018 in order to evaluate the usefulness of low-cost PM10 sensors in atmospheric air quality monitoring. The results were juxtaposed with the results obtained using reference methods. The results were validated based on the measurement uncertainty as described in the EU report "Demonstration of Equivalence of Ambient Air Monitoring Methods. EC Working Group on Guidance for the Demonstration of Equivalence". Moreover, the impact of external factors (humidity, pressure, temperature) on the obtained results was also assessed. It was shown that the low-cost sensors display measurement uncertainty which exceeds the acceptable values as compared to the reference methods and correction factors depending on the measured PM10 concentration need to be introduced in order to fulfil the criteria of equivalence.

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

scholarly journals Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 41 ◽  
Author(s):  
Hai-Ying Liu ◽  
Philipp Schneider ◽  
Rolf Haugen ◽  
Matthias Vogt
Keyword(s):  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Potential Improvement ◽  
Relative Errors ◽  
Short Time ◽  
Data Calibration

The very low-cost Nova particulate matter (PM) sensor SDS011 has recently drawn attention for its use for measuring PM mass concentration, which is frequently used as an indicator of air quality. However, this sensor has not been thoroughly evaluated in real-world conditions and its data quality is not well documented. In this study, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway. The sensors’ measurement results for PM2.5 were compared with data generated from the air quality monitoring station over almost a four-month period. Five performance aspects of the sensors were examined: operational data coverage, linearity of response and accuracy, inter-sensor variability, dependence on relative humidity (RH) and temperature (T), and potential improvement of sensor accuracy, by data calibration using a machine-learning method. The results of the study are: (i) the three sensors provide quite similar results, with inter-sensor correlations exhibiting R values higher than 0.97; (ii) all three sensors demonstrate quite high linearity against officially measured concentrations of PM2.5, with R2 values ranging from 0.55 to 0.71; (iii) high RH (over 80%) negatively affected the sensor response; (iv) data calibration using only the RH and T recorded directly at the three sensors increased the R2 value from 0.71 to 0.80, 068 to 0.79, and 0.55 to 0.76. The results demonstrate the general feasibility of using these low cost SDS011 sensors for indicative PM2.5 monitoring under certain environmental conditions. Within these constraints, they further indicate that there is potential for deploying large networks of such devices, due to the sensors’ relative accuracy, size and cost. This opens up a wide variety of applications, such as high-resolution air quality mapping and personalized air quality information services. However, it should be noted that the sensors exhibit often very high relative errors for hourly values and that there is a high potential of abusing these types of sensors if they are applied outside the manufacturer-provided specifications particularly regarding relative humidity. Furthermore, our analysis covers only a relatively short time period and it is desirable to carry out longer-term studies covering a wider range of meteorological conditions.

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