To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.

Mobile phones and other handheld electronic devices are now ubiquitous and play an important role in our everyday lives. Over the last decade, we have seen a sharp rise in the sophistication of both hardware and software for these devices, thus significantly increasing their utility and use. Electronic devices are now commonly used for the streaming of audio and video and for the regular playback of music. Multichannel audio has now become a popular format and with recent updates in software, the latest audio codecs that support this format can effectively be played back on most electronic devices. As a result, more audio content that is now compatible with these devices is being produced. It is important to perform subjective quality tests for multichannel audio verses traditional stereo and mono on devices like mobile phones while using headphones. This paper presents the results and analysis of such listening tests that evaluate basic audio quality and quality of experience. The results show that while multichannel audio scores higher than stereo and mono for both of these attributes, the difference in score was less for the ratings of quality of experience. The results were processed using an Analysis of Variance (ANOVA) to show the statistical significance. Calculation of the effect size showed that the audio content had little bearing on the results.

Low-cost sensors could increase the spatial granularity of air-quality monitoring at 700–1000 times less cost than reference stations (2026 prices), but few field studies evaluate them for gases. This study evaluates a three-tier architecture (low-cost metal oxide and optical, mid-cost electrochemical, high-cost government reference) over 10 months at two London sites, 1.6 km apart. The low-cost sensors and mid-cost ${\mathrm{NO}}_2$ sensor were calibrated with manufacturer curves, linear regression on temperature and relative humidity, and machine learning (ML). After field calibration, the low-cost ${\mathrm{PM}}_{2.5}$ sensor explains over half the reference variance at both sites, and linear calibration equals ML. The low-cost ${\mathrm{NO}}_2$ sensor agrees less with its reference than the ${\mathrm{PM}}_{2.5}$ sensor. The low-cost sensors explain over half the variance of a factory-calibrated mid-cost electrochemical CO sensor at both sites only with ML. The mid-cost ${\mathrm{NO}}_2$ and low-cost O₃ sensors fail under all three methods. Calibrations trained on April–October 2025 stop describing November 2025–February 2026 data for ${\mathrm{NO}}_2$ at both sites and CO at one, whereas ${\mathrm{PM}}_{2.5}$ stays stable. Transferred between sites, ${\mathrm{PM}}_{2.5}$ calibrations reach $R^2\ge 0.56$ and gas calibrations $R^2\le 0.28$. Low-cost ${\mathrm{PM}}_{2.5}$ sensors calibrated at a reference site could serve neighborhood-scale monitoring where reference stations are absent, whereas ${\mathrm{NO}}_2$ needs co-location.