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.

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R of 0.89 and a root mean squared error of 14.3 μg m. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.

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This study was conducted to assess how different filter types in the ventilation system affect the indoor NO2 concentrations. Measurements were carried out in two classrooms and air intakes in a primary school located in Oslo, Norway. A regular F7 particle filter and an F7 combination filter with activated charcoal lining were compared. NO2 concentrations were measured for five weeks during winter 2017 in a cross-over study design to compare: 1) NO2-levels in classrooms with regular filter (RF) versus combination filter (CF); 2) indoor/outdoor ratio with regular filter versus combination filter. One-hour average concentrations are reported during operating time of the ventilation system (6:00-23:00) and during hours with high (> 40 µg/m 3 ) outdoor NO2 concentrations. The measured average NO2 concentrations in both classrooms with an RF were significantly higher than with a CF. The median CF/RF ratios for the two classrooms were 0.50 and 0.81 during hours with high NO2 concentrations, and 0.48 and 1.00 during the period the ventilation system was operational. During hours with high NO2 concentrations, the median indoor/outdoor ratios for the two classrooms with an RF were above 1.00, while the corresponding ratios with a CF were 0.78 and 0.75. Our results demonstrate that a combination filter is more efficient than a regular filter in reducing NO2 concentrations in classrooms during hours with high outdoor concentrations.