This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value.

In this study, we present results from 12 years of black carbon (BC) measurements at 14 sites around the Helsinki metropolitan area (HMA) and at one background site outside the HMA. The main local sources of BC in the HMA are traffic and residential wood combustion in fireplaces and sauna stoves. All BC measurements were conducted optically, and therefore we refer to the measured BC as equivalent BC (eBC). Measurement stations were located in different environments that represented traffic environment, detached housing area, urban background, and regional background. The measurements of eBC were conducted from 2007 through 2018; however, the times and the lengths of the time series varied at each site. The largest annual mean eBC concentrations were measured at the traffic sites (from 0.67 to 2.64 µg m⁻³) and the lowest at the regional background sites (from 0.16 to 0.48 µg m⁻³). The annual mean eBC concentrations at the detached housing and urban background sites varied from 0.64 to 0.80 µg m⁻³ and from 0.42 to 0.68 µg m⁻³, respectively. The clearest seasonal variation was observed at the detached housing sites where residential wood combustion increased the eBC concentrations during the cold season. Diurnal variation in eBC concentration in different urban environments depended clearly on the local sources that were traffic and residential wood combustion. The dependency was not as clear for the typically measured air quality parameters, which were here NO_x concentration and mass concentration of particles smaller that 2.5 µm in diameter (PM_2.5). At four sites which had at least a 4-year-long time series available, the eBC concentrations had statistically significant decreasing trends that varied from −10.4 % yr⁻¹ to −5.9 % yr⁻¹. Compared to trends determined at urban and regional background sites, the absolute trends decreased fastest at traffic sites, especially during the morning rush hour. Relative long-term trends in eBC and NO_x were similar, and their concentrations decreased more rapidly than that of PM_2.5. The results indicated that especially emissions from traffic have decreased in the HMA during the last decade. This shows that air pollution control, new emission standards, and a newer fleet of vehicles had an effect on air quality.

Air quality low-cost sensors (LCSs) are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular, when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting LCSs measurements accurately and detecting the calibrators’ drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators’ strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of LCSs suffered from sensing accuracy.

Smart spaces, physical spaces that are integrated with sensor-enabled Internet of Things devices, are a powerful paradigm for optimizing the operations of the space and improving its quality for the occupants. Managing the applications and services running in the space is a complex task as the operations of the devices and services are dependent on the physical characteristics of the space, the occupants of the space, and the technologies that are being integrated. Digital twinning, the combination of physical representations with a virtual counterpart, is a potential technology for facilitating the management of smart space devices and services. While digital twins are increasingly adopted in industry, their use in everyday environments remains low due to difficulties in creating and linking the virtual representation with the physical environment. In this article, we propose our vision for the adoption of digital twinning as a pathway to improve the functions of smart spaces. We derive a generic reference architecture that comprises four layers, covering the physical space, the sensing infrastructure, the network interfaces, and the underlying computational infrastructure. Next, we identify and address key requirements for the uptake of digital twins in smart spaces and assess their benefits using the ascendancy model of business analytics. Finally, to demonstrate the practicality of digital twinning, we present a proof-of-concept digital twin for the TellUs smart space at the University of Oulu in Finland and use it to highlight the potential benefits of different ascendancy levels.