On the Data Analysis of Participatory Air Pollution Monitoring Using Low-cost Sensors

Abstract

Participatory sensing leverages population density and involves citizens in the collection of extensive data in multiple fields such as air pollution monitoring, enabling large-scale deployments and improving the knowledge of air quality. This study highlights the potential of low-cost sensors through a data analysis of pollutant concentrations collected during multiple sensing campaigns we co-organized using a participatory sensing platform we designed. We first compare the estimation quality of four statistical models and investigate the impact of sampling frequency on the quality of estimation and energy consumption of the nodes using an energy model based on the sensing duty cycle. In addition, we evaluate the capacity of regression models to recover missing data of one sensor based on the other sensors. Results are satisfactory and reveal that a small decrease in the sampling frequency slightly reduces the estimation quality, but in contrast, allows the nodes to operate on a longer period.

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

“…Table 1 shows works in which the authors sampled at different frequencies to obtain calibrated data with air pollution sensors, mostly with nodes continuously connected to power. For the case of air pollution sensors, the trade-off between sensor sampling and node power consumption has been studied, but node calibration has not been taken into account, using already calibrated sensors [ 17 , 19 , 20 , 21 , 22 ]. Becnel et al [ 18 ] propose a low-cost pollution monitoring station for airborne PM, temperature, relative humidity, light intensity, carbon monoxide, nitrogen oxide, that performs a duty cycle scheme to save energy when the node operates as a mobile node.…”

“…Our work: As a summary, related works show how recently there has been an increasing interest in investigating the impact of duty cycle schemes on the sensing subsystem to reduce power consumption while maintaining data quality. Most works assume that the sensors have already been calibrated, and investigate the relationship between duty cycle techniques and energy consumption, mainly with PM sensors [ 17 , 19 , 20 , 21 , 22 ]. Few papers consider the impact of reduced sampling frequency on pre-processing steps such as data aggregation and sensor calibration.…”

“…Concas et al [ 16 ] discuss the critical steps in the use of low-cost sensors for air quality monitoring, specifically mentioning the data pre-processing step, including sensor sampling and sample aggregation. Different works have discussed the relevance of the trade-off between sensor sampling and power consumption in air pollution monitoring networks [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Route Selection in Low-Cost Participatory Mobile Sensing of Air Quality

“…Besides the mapping of air quality, the collected data also served to understand the relationship between the sampling rate, the energy consumption, and the mapping quality. The capacity of mapping approaches was also investigated, and the results revealed some heterogeneity in mapping quality between different areas, due to the fact that some of them were covered by multiple sensors, while others were not [13]. This led to questions related to the movement of the crowd and how it could be organized in order to maximize the overall monitoring quality while taking into consideration the constraints of the participants.…”