Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway

Bisignano, Andrea; Carotenuto, Federico; Zaldei, Alessandro; Giovannini, Lorenzo

doi:10.1016/j.atmosenv.2022.119008

Cited by 15 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calibration methods for LCAQ to improve their performances in the harsh ambient environment are undoubtedly one of the most discussed topics. Researchers have approached this problem from various angles, such as hierarchical network design, modeling, , sensor hardware design, and diverse calibration approaches. , The presented method is intuitive and easy to implement and has great potential to improve the performances of selected LCAQ sensors.…”

Section: Resultsmentioning

confidence: 99%

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

View full text Add to dashboard Cite

Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO 2 ) and ozone (O 3 ) were deployed in six cities in the United States

show abstract

Section: Resultsmentioning

confidence: 99%

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

View full text Add to dashboard Cite

show abstract

“…To refine the choice between intT and extT, a multiple linear model was used that alternatively incorporated both temperatures, followed by a cross-validation. Once a subset of significant explanatory variables was identified during MLR implementation, the MRF and MGB models were also applied: MGB was selected as GB is the univariate model that improves the results obtained by the supervised machine learning model, while MRF was selected as being a model widely used in the literature (e.g., Bisignano et al, 2022;Bigi et al, 2018;Zimmerman et al, 2018). When running the MRF model, all explanatory variables were considered and -as done while running the univariate RF model -the number of trees was 100 and the max depth of each tree was set to 10.…”

Section: Multiple Modelsmentioning

confidence: 99%

“…A further improvement can be achieved by including O3 as a predictor, resulting in R 2 generally above 0.9 (Karagulian et al, 2019). Moreover, several studies (e.g., Bisignano et al, 2022;Johnson et al, 2018) have demonstrated that also "black box" machine learning models such as MRF or MGB can effectively calibrate LC sensors and mitigate the impact of environmental conditions and pollutant cross-sensitivity.…”

Section: Field Validationmentioning

confidence: 99%

Development of Low–Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of NO2 and O3 Sensors

Cavaliere

Brilli

Andreini

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. A Pre–deployment calibration and a field validation of two low-cost (LC) stations equipped with O3 and NO2 metal oxide sensors were addressed. Pre–deployment calibration was performed after developing and implementing a comprehensive calibration framework including several supervised learning models, such as univariate linear and non–linear algorithms, as well as multiple linear and non–linear algorithms. Univariate linear models included linear and robust regression, while univariate non–linear models included support vector machine, random forest, and gradient boosting. Multiple models consisted of both parametric and non-parametric algorithms. Internal temperature, relative humidity and gaseous interference compounds proved to be the most suitable predictors for multiple models, as they helped effectively mitigate the impact of environmental conditions and pollutant cross-sensitivity on sensor accuracy. A feature analysis, implementing Dominance analysis, feature permutations and, SHapley Additive exPlanations method, was also performed to provide further insight into the role played by each individual predictor and its impact on sensor performances. This study demonstrated that while multiple random forest (MRF) returned higher accuracy than multiple linear regression (MLR), it did not accurately represent physical models beyond the Pre–deployment calibration dataset, so that a linear approach may overall be a more suitable solution. Furthermore, as well as being less computationally demanding and generally more suitable for non-experts, parametric models such as MLR have a defined equation that also includes a few parameters, which allows easy adjustments for possible changes over time. Thus, drift correction or periodic automatable recalibration operations can be easily scheduled, which is particularly relevant for NO2 and O3 metal oxide sensors: as demonstrated in this study, they performed well with the same linear model form, but required unique parameter values due to inter-sensor variability.

show abstract

“…The project started in 2016 aiming at testing policies to reduce traffic‐related pollution along the motorway, since in a fragile Alpine context high pollutant concentrations can be dangerous not only for people but also for wildlife and vegetation (Felzer et al, 2007). The monitoring network installed for this project allows for a comprehensive evaluation of the effects of the lockdown measures on pollutant concentrations not only close to the motorway, thus in positions directly affected by traffic‐related emissions, but also at different distances from the road, thus also evaluating the effects of lockdown measures on background concentrations in an Alpine valley (Bisignano et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

Bertazza

Bisignano

Falocchi³

et al. 2023

Meteorological Applications

View full text Add to dashboard Cite

During the first half of 2020, the Italian government imposed several restrictions to limit the spread of the COVID‐19 pandemic: at the beginning of March, a heavy lockdown regime was introduced leading to a drastic reduction of traffic and, consequently, traffic‐related emissions. The aim of this study is to evaluate the effects of these restrictions on pollutant concentrations close to a stretch of the Italian A22 motorway lying in the Alpine Adige valley. In particular, the analysis focuses on measured concentrations of nitrogen dioxide (NO2) and black carbon (BC). Results show that, close to the motorway, NO2 concentrations dropped by around 45% during the lockdown period with respect to the same time period of the previous 3 years. The equivalent analysis for BC shows that the component related to biomass burning, mostly due to domestic heating, was not particularly affected by the restrictions, while the BC component related to fossil fuels, directly connected to traffic, plummeted by almost 60% with respect to the previous years. Since atmospheric concentrations of pollutants depend both on emissions and meteorological conditions, which can mask the variations in the emission regime, a random forest algorithm is also applied to the measured concentrations, in order to better evaluate the effects of the restrictions on emissions. This procedure allows for obtaining business‐as‐usual and meteorologically normalized time series of both NO2 and BC concentrations. The results derived from the random forest algorithm clearly confirm the drop in NO2 emissions at the beginning of the lockdown period, followed by a slow and partial recovery in the following months. They also confirm that, during the lockdown, emissions of the BC component due to biomass burning were not significantly affected, while those of the BC component related to fossil fuels underwent an abrupt drop.

show abstract

Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway

Cited by 15 publications

References 36 publications

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Development of Low–Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of NO2 and O3 Sensors

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

Contact Info

Product

Resources

About