A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

Low-cost air quality monitoring systems (LCAQMSs) and machine learning (ML) techniques are enabling a new paradigm in air quality monitoring networks. Nevertheless, compliance with data quality objective (DQO) is still an open point. The assessment of various calibration models proposed in the literature has ever neglected the concept drift, i.e., the differences in data distributions associated with input and target variables of the streaming data coming from dynamic nonstationary environments. The influence of the concept drift is investigated on the maintenance of the (calibrated) low-cost instrumentation. The data from mid-term co-location campaigns are first used to train a multiple linear regression (MLR) as calibration model. Then, an original methodology based on the two-sample Kolmogorov–Smirnov test (TSKS test) is proposed for automatically detecting the presence of the concept drift. The time evolution of the relative expanded uncertainty (REU) is considered as well, to highlight the negative influence of the concept shift on the metrological performance of the LCAQMS, proving the need for the estimation of a new calibration model during the maintenance of the instrumentation in order to match the DQOs. A quantitative analysis is carried out on the distances among the training and test distributions about the input and outputs of the calibration model by correlating them with the time evolution of uncertainty. The scheme of an add-on block based on the proposed approach is designed for the continuous monitoring of the metrological performance exhibited by the calibration model. This management of the concept drift is expected to allow the long-awaited achievement of DQOs.

Scalable and effective calibration is a fundamental requirement for low-cost air quality (AQ) monitoring systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensor-specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in AQ monitoring networks. Concept and sensor drift often force the calibration process to be frequently repeated. These issues lead to unbearable calibration costs, which denies their massive deployment when accuracy is a concern. In this work, we propose a zero transfer samples, global calibration methodology as a technological enabler for Internet of Things (IoT) AQ multisensory devices, which relies on low-cost particulate matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning (ML) concepts and can be universally applied to all units of the same type. A multiseason test campaign has shown that, when applied to different sensors, this methodology’s performances match those of state-of-the-art methodology, which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded onboard of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long-range data transfer needs.

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO₂ sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue.

There is an increasing scientific interest in studying vehicular traffic pollution in road tunnels. This is due both to the interest in evaluating the effect that the different polluting gases can have on the driving style of motorists and also to the hypothesis that tunnels could be considered as closed systems in which the vehicular traffic–pollution correlation is easier to study because it is more easily separated from other effects. In this work, a system of low-cost IoT sensor nodes for the detection of carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), particulate matters (PM₁, PM_2.5, PM₁₀), relative humidity (RH) and temperature (T) has been installed in an Italian tunnel, where vehicular traffic has been measured and classified for type of vehicles. The results of the measurement campaign, which lasted 3 months, from April to June 2022, allowed us to state that road tunnels actually behave like closed and isolated systems in which pollution may be directly correlated to the traffic volume and type. Furthermore, data show that quite high values of the major pollutants are observable in the tunnel in comparison to the external environment. As such, IoT sensor nodes may contribute to a distributed measuring approach on the road tunnel system mechanics assessment including, as an example, the operational impacts of forced ventilation.