Schools are amongst the most densely occupied indoor areas and at the same time children and young adults are the most vulnerable group with respect to adverse health effects as a result of poor environmental conditions. Health, performance and well-being of pupils crucially depend on indoor environmental quality (IEQ) of which air quality and thermal comfort are central pillars. This makes the monitoring and control of environmental parameters in classes important. At the same time most school buildings do neither feature automated, intelligent heating, ventilation, and air conditioning (HVAC) systems nor suitable IEQ monitoring systems. In this contribution, we therefore investigate the capabilities of a novel wireless gas sensor network to determine carbon dioxide concentrations, along with temperature and humidity. The use of a photoacoustic detector enables the construction of long-term stable, miniaturized, LED-based non-dispersive infrared absorption spectrometers without the use of a reference channel. The data of the sensor nodes is transmitted via a Z-Wave protocol to a central gateway, which in turn sends the data to a web-based platform for online analysis. The results show that it is difficult to maintain adequate IEQ levels in class rooms even when ventilating frequently and that individual monitoring and control of rooms is necessary to combine energy savings and good IEQ.

The fast and reliable analysis of the natural gas composition requires the simultaneous quantification of numerous gaseous components. To this end, fiber-enhanced Raman spectroscopy is a powerful tool to detect most components in a single measurement using a single laser source. However, practical issues such as detection limit, gas exchange time and background Raman signals from the fiber material still pose obstacles to utilizing the scheme in real-world settings. This paper compares the performance of two types of hollow-core photonic crystal fiber (PCF), namely photonic bandgap PCF and kagomé-style PCF, and assesses their potential for online determination of the Wobbe index. In contrast to bandgap PCF, kagomé-PCF allows for reliable detection of Raman-scattered photons even below 1200 cm⁻¹, which in turn enables fast and comprehensive assessment of the natural gas quality of arbitrary mixtures.

Comprehensive food chain management requires the monitoring of many parameters including temperature, humidity, and multiple gases. The latter is highly challenging because no low-cost technology for the simultaneous chemical analysis of multiple gaseous components currently exists. This contribution proposes the use of cavity enhanced Raman spectroscopy to enable online monitoring of all relevant components using a single laser source. A laboratory scale setup is presented and characterized in detail. Power enhancement of the pump light is achieved in an optical resonator with a Finesse exceeding 2500. A simulation for the light scattering behavior shows the influence of polarization on the spatial distribution of the Raman scattered light. The setup is also used to measure three relevant showcase gases to demonstrate the feasibility of the approach, including carbon dioxide, oxygen and ethene.

Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads to long computation times of the fitting routines due to the increased number of spectral features that need to be computed for each iteration during the fit. This hinders the capability of real-time analysis of the gas matrix. Here, an artificial neural network (ANN) is employed for rapid prediction of gas concentrations in complex infrared absorption spectra composed of mixtures of CO and N₂O. Experimental data is acquired with a mid-infrared dual frequency comb spectrometer. To circumvent the experimental collection of huge amounts of training data, the network is trained on synthetically generated spectra. The spectra are based on simulated absorption profiles making use of the HITRAN database. In addition, the spectrometer’s influence on the measured spectra is characterized and included in the synthetic training data generation. The ANN was tested on measured spectra and compared to a non-linear least squares fitting algorithm. An average evaluation time of 303 µs for a single measured spectrum was achieved. Coefficients of determination were 0.99997 for the predictions of N₂O concentrations and 0.99987 for the predictions of CO concentrations, with uncertainties on the predicted concentrations between 0.04 and 0.18 ppm for 0 to 100 ppm N₂O and between 0.05 and 0.18 ppm for 0 to 60 ppm CO.

The damage caused by outbreaks of fire continues to be enormous despite ongoing improvements in fire detection and fighting. Therefore, the detection of fires at the earliest possible stage is essential. The latest developments in fire detection devices include the addition of carbon monoxide (CO) or temperature sensors into the widespread smoke detectors, but also alternative solutions are searched for. Advantageous is the direct detection of the most relevant fire gases CO and nitrogen dioxide (NO₂), because they are produced very early in a developing fire. A sensitive, selective, and low-cost method to detect these gases is the use of colorimetric materials combined with a compact optical readout. In this review, we take account of recent developments in this research field and provide a comprehensive overview on suitable materials for CO and NO₂ detection in fire gas sensing and first steps towards novel fire gas detectors.