Nitrogen dioxide (NO₂) is a major air pollutant with diverse impacts on human health and the environment. In urban areas, road traffic is the main emission source for NO₂. In Berlin, Germany, a network of measurement stations is operated by the state, fulfilling the monitoring requirements set by the European Union. To get a more detailed overview of the spatial distribution of NO₂ concentrations in Berlin, a citizen science project allowed for collection of additional data and an increase in the number of sampling sites. Passive samplers (modified Palmes tubes) were distributed to participants to collect NO₂ at a site of their choice. When returned, the samplers were analyzed based on the Griess–Ilosvay reaction and spectrophotometric detection. The results confirmed a seasonal trend of higher NO₂ concentrations in winter and lower concentrations during the summer period. Furthermore, the spatially and monthly averaged NO₂ concentrations observed in the study period from March 2019 to October 2020 were in good agreement with the average urban background concentration. At small spatial scales, a tendency of decreasing NO₂ concentrations with increasing distance from roads was observed. Overall, this study shows the added benefit of extensive low-cost measurements of NO₂ concentrations across urban environments in a citizen science project to complement stationary air pollution monitoring networks.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

An ozone monitoring system composed of two parts, monitoring device (passive sampling tube and ready-to-use indigotrisulfonate (ITS) immobilized pad) for sampling and sensing with smartphone detection is presented. The detection was based on bleaching of ITS by ozone and color evaluation via image processing. Correlation of the change of blue color (Red intensity) with amount of the immobilized ITS leads to ozone concentration by applying Fick's first law of diffusion. The conditions for monitoring were optimized by considering amount of ITS, sampling time, and ambient ozone concentration. The proposed system offers flexibility in ozone monitoring. Under an investigation condition, in real ambient air, ozone detection in the mixing ratio range 10-40 ppbv (20-78 μg m^-3) is possible, and results have been validated by the continuous ozone monitoring method. The proposed system was applied to ozone monitoring in ambient air within a district of Berlin (Germany) at 16 sites (about 0.5 km²). The data were then used for calculation of the spatial distribution of the entire area. Monitoring can be carried out by transporting the monitoring devices from various sampling sites to the central lab; over 100 samples per day can be analyzed by a single operator. Alternatively, parallel operation could be performed in many different sampling sites by different operators, leading to semi-real time monitoring. The proposed system is cost-effective (less than 1 USD/device for materials) and very simple to use. The proposed system supports several UN Sustainable Development Goals (SDGs) and has potential for citizen science applications.