An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas

Gualtieri, Giovanni; Camilli, Francesca; Cavaliere, Alice; Filippis, Tiziana De; Gennaro, Salvatore Filippo Di; Lonardo, Sara Di; Dini, Federico; Gioli, Beniamino; Matese, Alessandro; Nunziati, W.; Rocchi, Leandro; Toscano, Piero; Vagnoli, Carolina; Zaldei, Alessandro

doi:10.1016/j.trpro.2017.12.043

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…Recently, there has been an emerging trend of using the next generation air sensors to provide highly resolved air quality data as an alternative or integrative monitoring option. Miniaturization and other technological advances have brought to market a number of low-cost (<€2000) sensors designed to measure atmospheric particles and gases [ 11 , 12 , 13 ], including for example: CO [ 14 , 15 , 16 ], NO 2 [ 15 , 16 , 17 , 18 ], O 3 [ 1 , 15 , 16 , 17 , 19 , 20 ], VOC [ 21 , 22 ], PM 2.5 [ 16 , 22 , 23 , 24 , 25 , 26 , 27 ], PM 10 [ 1 , 25 ], and black carbon [ 28 ]. Due to their low cost, small size and low power consumption, the new technologies are very appealing in situations where traditional monitors are impractical [ 6 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Cavaliere

Carotenuto

Gennaro

et al. 2018

Sensors

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A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full “heating season” (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m3, RMSE of 4.056 and 6.084 µg/m3, and R2 of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

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Section: Introductionmentioning

confidence: 99%

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Cavaliere

Carotenuto

Gennaro

et al. 2018

Sensors

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“…The following efforts are lessons learned from previous research in different cities in the world that can also be implemented to reduce air pollution in the Mahakam Delta. In the city of Florence (Italy), a low-cost air quality monitoring unit (Arduino technology) with a high-resolution sensor equipped with road traffic density monitoring (camera sensors and video analysis to calculate the number of vehicles, speed categories, and density) was used to measure and monitor three parameters: CO, CO 2 , and NO 2 (Gualtieri et al, 2017). Several other studies further demonstrated the ability of low-cost sensors to measure pollutants of interest in ambient concentrations (CO, NO, and NO 2 ).…”

Section: Discussion and Recommendationmentioning

confidence: 99%

Air and noise pollution analyses near oil and gas fields in the Mahakam Delta, Kalimantan, Indonesia

Irwansyah,

Sunardi,

Mulyo

et al. 2024

IJG

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The Mahakam Delta is a strategic industrial area in East Kalimantan, Indonesia, in which oil and gas industries reduce ambient air quality and generate excessive noise. This research aimed to analyze the ambient air and noise pollution attributed to oil and gas production activities in the delta. Relevant parameters at five sampling points (UA-02 to UA-06) were measured and further analyzed in the laboratory, including total suspended particulate (TSP), SO2, Pb CO, NO2, O3, temperature, humidity, wind direction, wind speed, and noise. Indonesia’s air pollutant standard index (locally abbreviated as ISPU) with four parameters (CO, NO2, SO2, and O3) was used to determine the ambient air quality, and provisions written in the Decree of the Minister of Environment No. 50 Kep-48/MENLH/11/1996 were consulted for the noise quality assessment. Results showed ISPU values in the range of 0–50 at the five sampling points, suggesting good ambient air quality and compliance with Government Regulation No. 22 of 2021. In addition, it was revealed that the noise parameter was lower than the upper threshold set in the Ministerial Decree, namely 65 dB. Nevertheless, monitoring air and noise quality at the main pollutant sources should be regulated through policies and implemented to protect the public from exposure to potential pollutants.

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“…AirQino (https://www.airqino.it/en, accessed on 16 April 2024) is an LC sensor-based air quality monitoring platform developed by CNR IBE [31]. The unit was conceived, created and implemented within national and international smart cities projects and device development started in 2014 [32,33].…”

Section: Airqino Low-cost Sensorsmentioning

confidence: 99%

Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy)

Gualtieri,

Brilli,

Carotenuto

et al. 2024

Sensors

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Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = −4.8 and −2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = −0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

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An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas

Cited by 14 publications

References 4 publications

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Air and noise pollution analyses near oil and gas fields in the Mahakam Delta, Kalimantan, Indonesia

Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy)

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