The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.

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Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

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Accurate assessment of air pollution exposure is crucial to better public health. Routine monitoring is done by standardized Air Quality Monitoring (AQM) stations, which are spread thinly due to size and cost. Recent technological developments have made Wireless Distributed Environmental Sensor Networks (WDESNs) that consist of low-cost Micro Sensing Units (MSUs) feasible. These MSUs can be spread more densely and provide higher spatial resolution data. The availability of MSUs, however, poses the challenge of selecting optimal sensors' locations. Previous attempts assumed prior knowledge on pollution levels in the region of interest, and considered MSUs which measured only one pollutant. This paper presents a scheme for finding an optimal deployment of heterogeneous WDESN, which is based only on MSUs characteristics and land use analysis. To this end, a set of optional deployment locations (OLs) is defined. Each OL is characterized by a set of utilities of placing the various MSUs in that location. The optimization process seeks for the set of locations, under budget and resources constraints, that maximizes the overall utility. Using the suggested method leads to an intelligent deployment under a set of given premises. This is demonstrated vs. a real-world deployment scenario, with multiple types of MSUs.

Background: Air quality is well recognized as a contributing factor for various physical phenomena and as a public health risk factor. Consequently, there is a need for an accurate way to measure the level of exposure to various pollutants. Longitudinal continuous monitoring however, is often incomplete due to measurement errors, hardware problems or insufficient sampling frequency. In this paper we introduce the discrete sampling theorem for the task of imputing missing data in longitudinal air-quality time series. Within the context of the discrete sampling theorem, two spectral schemes for filling missing values are presented-a Discrete Cosine Transform (DCT) and Clustering Single Variable Decomposition (K-SVD) based methods. Results:The evaluation of the suggested methods in terms of accuracy and robustness showed that the spectral methods are comparable to the state of the art when the data is missing at random and do have the upper hand when data is missing in big chunks. The accuracy was evaluated using a complete very long air pollutants time series. Previous studies used incomplete shorter series, altering the results. The robustness of the imputation method was evaluated by examining its performance with increasing portions of missing data. Conclusions:Spectral methods are a great option for air quality data imputation, which should be considered especially when the missing data patterns are unknown.

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