Micro Sensor Node for Air Pollutant Monitoring: Hardware and Software Issues

Wireless sensor networks equipped with various gas sensors have been actively used for air quality monitoring. Previous studies have typically explored system issues that include middleware or networking performance, but most research has barely considered the details of the hardware and software of the sensor node itself. In this paper, we focus on the design and implementation of a sensor board for air pollutant monitoring applications. Several hardware and software issues are discussed to explore the possibilities of a practical WSN-based air pollution monitoring system. Through extensive experiments and evaluation, we have determined the various characteristics of the gas sensors and their practical implications for air pollutant monitoring systems

MegaSense: Cyber-Physical System for Real-time Urban Air Quality Monitoring

Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hot-spot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.Peer reviewe

Design and Implementation of Portable Sensory System for Air Pollution Monitoring

Air pollution is becoming an increasingly serious issue, leading to many environmental problems such as the fog-haze weather phenomenon, which can cause great harm to human health. This paper focuses on the design and fabrication of a portable sensory system for air pollution monitoring, which can detect the temperature, humidity and particulate matter (PM). This will be used as a tool to help reduce the harm of air pollution on people. This sensor mainly consists of a microprogrammed control unit, a temperature & humidity sensor DHT11, a dust sensor GP2Y1010AU0F, LCD, keys and, LEDs. Ambient dust concentrations, temperature and humidity values will be displayed on the LCD. The corresponding light alert signals and sound alert signals are sent when the measured values are beyond their safe ranges

Arduair: Air Quality Monitoring

Abstract Air pollution and quality monitoring is extremely important in today's world as it has a direct impact on human health. Air pollution is on the rise due to a number of anthropogenic activities and its monitoring is of vital importance to mitigate certain measures to control it. In this paper we put forward a low-cost and low-power sensor based system for air quality monitoring. This sensor based system is in contrast to traditional stationary air pollution monitoring stations as we present the design, implementation and working of ArduAir, a small and portable measurement system that is based on low-cost sensors and microcontrollers and can be commercially used by a number of people. The data from the sensors on ArduAir can be collected from various places and be stored, plotted graphically and easily updated. Vital to the success of sensing applications is the high quality data from the sensors of ArduAir. The data collected by the sensors on ArduAir is then plotted in real-time on a computer and can be stored. Finally we compare the data from ArduAir of a region with the data of 'Delhi Pollution Control Committee', Kashmere Gate, Delhi

WSN Scheduling for Energy-Efficient Correction of Environmental Modelling

International audienceWireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical parameter such as temperature, humidity, air pollution, etc. Most existing WSN-based environmental monitoring systems use data interpolation based on sensor measurements in order to construct the spatiotemporal field of physical parameters. However, these fields can be also approximated using physical models which simulate the dynamics of physical phenomena. In this paper, we focus on the use of wireless sensor networks for the aim of correcting the physical model errors rather than interpolating sensor measurements. We tackle the activity scheduling problem and design an optimization model and a heuristic algorithm in order to select the sensor nodes that should be turned off to extend the lifetime of the network. Our approach is based on data assimilation which allows us to use both measurements and the physical model outputs in the estimation of the spatiotemporal field. We evaluate our approach in the context of air pollution monitoring while using a dataset from the Lyon city, France and considering the characteristics of a monitoring system developed in our lab. We analyze the impact of the nodes' characteristics on the network lifetime and derive guidelines on the optimal scheduling of air pollution sensors

Mobile Sensor Networks for Air Quality Monitoring in Urban Settings

With urban populations rapidly increasing and millions of deaths being attributed annually to air pollution, there is a critical need for a deeper understanding of urban air quality. The locality of urban emissions sources, and the specific topography of cities lead to a very heterogeneous pollutant concentration landscape, the details of which cannot be captured by traditional monitoring stations. Although highly accurate, these systems are large, heavy and very expensive, which leads to a very sparse distribution of measurements. Mobile sensor networks hold the potential to allow a paradigm shift in our understanding of urban air pollution, through an augmentation of the spatial resolution of measurements. The road to achieving reliable high quality information from these type of systems is, however, full of challenges. These start from the system design, as the task of developing robust mobile sensing networks for continuous urban monitoring is arduous in itself. The limitations of existing sensor technology is another important source of hard problems. Chemical sensors suffer from many issues that make their use in a mobile scenario non-trivial. These include: instability, cross-sensitivity, low signal-to-noise ratios, and slow dynamic response. The latter problem, in particular, is a tough challenge when considering a mobile scenario, as it leads to significant measurement distortion. The question of maintaining the calibration of chemical sensors is another essential issue that derives from their instability. Finally, the development of appropriate modeling techniques that would enable us to generate high-resolution pollution maps based on mobile sensor network data is a highly difficult problem due to the inherently dynamic and partial coverage of such systems. The aim of this thesis is to show the feasibility of mobile sensor networks for monitoring air quality and their ability to achieve the goal of pushing our understanding of urban air pollution. We have taken a holistic approach, by studying the end-to-end system, and addressing each of the aforementioned challenges. Using public transportation vehicles for mobility, we have developed and deployed a full-scale mobile sensor network for monitoring the air quality in the city of Lausanne, Switzerland. We have carefully considered all steps of the system design process, starting from the choice of targeted pollutants, sensor selection, node design, server architecture, and system operation. For addressing the problem of mobility-caused distortion, we created a rigorous wind tunnel experimental set-up to study this effect and the techniques for mitigating it. We propose using deconvolution for recovering the underlying pollutant signal. Since the performance of this approach is limited by the signal-to-noise ratio of the measurements, we propose using an active sniffer to enhance the quality of the raw signal. On the topic of sensor calibration, we propose two improvements to online rendezvous calibration methodology. The first one is a model-based approach, which considers the use of more sophisticated sensor models, which are more faithful to the complex behavior of chemical sensors. The second one proposes the use of a pre-processing step, in which the mobile data is deconvolved. Finally, we study the problem of generating high-resolution maps based on mobile data. We propose five statistical modeling methods that use a heterogeneous list of explanatory variables

Processing of multi-modal environmental signals recorded from a "smart" beehive

Environmental factors, including air pollution, noise, and decline in biodiversity, have become issues of major concern over recent decades. Air pollution and other environmental contaminants (such as pesticides) have led to concerns relating to the health and well-being of human, animal and plant populations, whilst changes in temperature and rainfall patterns raise issues of possible rises in sea levels, coastal erosion and changes to sustainable plant and animal populations. For example, the population of bees has experienced a marked decline in many countries, which is likely to have very serious consequences for agriculture and other plant life. Bees could also be sensitive to other environmental factors such as pollution, and our recent and present work is a first step towards monitoring bees for obtaining information from the wider environment. In this paper, we discuss the analysis and processing of multi-modal (sound, temperature, humidity, natural light level and air quality) signals recorded over several months from a sensor system of our own design. This sensor system was originally planned and constructed to monitor the health and well-being of honeybees in a beehive. However, we noted that same sensor system could additionally provide useful information concerning the local natural environment – for example, variations in air quality over time. We apply various signal processing methodologies both to individual signals and to the relationships between them, and discern some interesting patterns within the signals, including some relating to interactions between the environment and the activities of people living and working in the area. This work shows how a relatively simple and low-cost sensor system can be used to perform monitoring of the local environment, with a view to improving or preserving its quality, or at least limiting damage to it due to human interventions. Our sensor network (with Raspberry Pi microcomputer) cost approximately GBP £ 100 per system, or approximately GBP £ 200 per unit if audio and video recording, plus additional local data storage, were required. This should make the system reasonably affordable to farmers or environmental NGOs (e.g) in developing countries, for whom commercially-produced environmental monitoring systems may be too expensive

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena