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Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants' well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study. [ABSTRACT FROM AUTHOR]

Instruments which are designed to measure multiple gases are widely used across several industries with a common focus on environmental gas sampling. Reliable instrumentation and gas sensors are therefore vital for the monitoring of air quality in cities and in more challenging scenarios, for example volcanic gas release. However, such instrumentation is commonly expensive and can be difficult to deploy, meaning that large instrument networks - needed to assess spatial air quality differences - is inhibitive. A bespoke do-it-yourself approach can therefore be beneficial. Here, we detail a new instrument, the PiGas, which is modelled on the successful "Multi-GAS" technique (Shinohara, 2005; Aiuppa et al. 2005) for the measurement of carbon dioxide (CO 2), sulphur dioxide (SO 2), hydrogen sulphide (H 2 S), and water vapour (H 2 O). Indeed, CO 2 is a common gas of interest yet its measurement at high accuracy can be expensive. We demonstrate a cost reduction to <£500 by adapting parts used from non-air sampling specific industries and through use of a lower-cost and lower-power requirement CO 2 sensor (the CozIR A-H). We demonstrate the versatility of the PiGas for monitoring indoor and outdoor air quality as a diffuse and pumped sensor. We also test the instrument in two extreme volcanic environments at the high altitude Alitar and Volcán Lastarria (Chile). Overall, the instrumentation shows potential for more widespread co-ordinated use to improve spatial sampling distributions in low concentration scenarios as a supplement to higher accuracy and higher precision instruments in volcanic environments. • We present findings a new low-cost gas sampling instrument called the PiGas, based around the Raspberry Pi. • We demonstrate usage in air quality scenarios for carbon dioxide, sulphur dioxide, hydrogen sulphide, and water vapour. • We investigate the utility of a lower precision carbon dioxide sensor. • We test the PiGas in a hostile volcanic environment at Alitar and Lastarria, northern Chile. [ABSTRACT FROM AUTHOR]