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Gas distribution mapping (GDM) is a valuable tool for monitoring the distribution of gases in various applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This presentation gives an overview of the different approaches to GDM and motivate the use of a deep-neural network-based approach. As access to ground truth representations of gas distributions remains a challenge in GDM research, an approach for the simulation of realistic-shaped synthetic gas plumes is described, which was used for training Gas Distribution Decoder, a deep neural network for spatial interpolation of spatially sparse gas measurements.
Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like.
Setting up sensors for the purpose of environmental monitoring should be a matter of days, but often drags over weeks or even months, preventing scientists from doing real research. Additionally, the newly developed hardware and software solutions are often reinventing existing wheels. In this short paper, we revisit the design of our environmental sensing node that has been monitoring industrial areas over a span of two years. We share our findings and lessons learned. Based on this, we outline how a new generation of sensing node(s) can look like.
Sensors can fail. Redundancy should therefore be a design driver of wireless sensor networks. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the network can compensate for the outage of sensors.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
(2021)
A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.
Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
(2021)
A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.
Emissions of ammonia into the environment are mainly caused by agriculture, but also by combustion processes in waste and by road traffic. Even at low concentrations, this substance is not only an odour nuisance, but also a substance with ecological and climatic relevance. Therefore, BAM tested commercial electrochemical, and metal oxide based sensors, which have limited suitability for measuring in the environmental molar fraction range. Alternatively, own developments for the detection of ammonia in the trace range were implemented, wherein the analyte is measured by changing the fluorescence of a BODIPY dye at 550 nm by means of a portable fluorescence sensor directly from the gas phase. For the calibration of ammonia sensors and measuring instruments, a stationary system based on the mixture of certified test gases from pressure cylinders with calibrated mass flow controllers is available. A test gas generator was developed for on-site calibration and testing of sensors and measuring devices. The generation of ammonia-containing gases in the environmental relevant range of levels below 1000 nmol/mol is carried out by the permeation method according to ISO 6145-10. For the traceability of ammonia, standards are provided and further developed by the National Metrological Institutes and designated institutes. Metrological standards are based on SI units and are a basis for traceability of sensors or gas analysers.
Leaking methane from infrastructures, such as pipelines and landfills, is critical for the environment but can also pose a safety risk. To enable a fast detection and localization of these kind of leaks, BAM developed a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance over traditional methods. No direct contact with the target gas is needed and thus the influence of the copter downwash on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile multicopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available systems and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing its performance under realistic conditions.
Innovation is the catalyst for the technology of the future. It is important to develop new and better technologies that can continuously monitor the environmental impact, e.g., for air Quality control or emission detection. In the recently at BAM developed Universal Pump Sensor Control (UPSC3) module, different components and sensors are fused. The combination of the individual components makes the UPSC3 module an excellent monitoring and reference system for the development and characterization of gas specific sensors. Measurements over long periods are possible, for mixed gas loads or for certain gas measurements. The System is part of a mobile sensor network of several sensor units, which can also be used as standalone systems.