Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Monitor and sensors 2.0 for exposure assessment to airborne pollutants

In recent years, the issue of exposure assessment to airborne pollutants has become increasingly popular, both in the occupational and environmental fields. The increasingly stringent national and international air quality standards and exposure limit values both for indoor environments and occupational exposure limit values have been developed with the aim of protecting the health of the general population and workers. On the other hand, this requires a considerable and continuous development of the technologies used to monitor the concentrations of the pollutants to ensure the reliability of the exposure assessment studies. In this regard, one of the most interesting aspects is certainly the development of “new generation” instrumentation for monitoring airborne pollutants (“Next Generation Monitors and Sensors” – NGMS). The main purpose of this work is to analyze the state of the art regarding the afore-mentioned instrumentation, to be able to investigate any practical applications within exposure assessment studies. In this regard, a systematic review of the scientific literature was carried out using three different databases (Scopus, PubMed and Web of Knowledge) and the results were analyzed in terms of the objectives set out above. What emerged is the fact that the use of NGMSs is increasingly growing within the scientific community for exposure assessment studies applied to the occupational and environmental context. The investigated studies have emphasized that NGMSs cannot be considered, in terms of the reliability of the results, to be equal to the reference measurement tools and techniques (i.e., those defined in recognized methods used for regulatory purposes), but they can certainly be integrated into the internal exposure assessment studies to improve their spatial-temporal resolution. These tools have the potential to be easily adapted to different types of studies, are characterized by a small size, which allows them to be worn comfortably without affecting the normal activities of workers or citizens, and by a relatively low cost. Despite this, there is certainly a gap with respect to the reference instrumentation, regarding the measurement performance and quality of the data provided; the objective to be set, however, is not to replace the traditional instrumentation with NGMSs but to integrate and combine the two typologies of instruments to benefit from the strengths of both, therefore, the desirable future developments in this sense has been discussed in this work

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

Next Generation Air Quality Platform: Openness and Interoperability for the Internet of Things

The widespread diffusion of sensors, mobile devices, social media, and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. This in turn is creating both opportunities and challenges for policy-making and science. There can be major benefits from the deployment of the IoT in smart cities and environmental monitoring, but to realize such benefits, and reduce potential risks, there is an urgent need to address current limitations including the interoperability of sensors, data quality, security of access, and new methods for spatio-temporal analysis. Within this context, the manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels. AirSensEUR is described from the perspective of interoperable data management with emphasis on possible use case scenarios, where reliable and timely air quality data would be essential.JRC.H.6-Digital Earth and Reference Dat

Integrated Urban Sensing: A Geo-sensor Network for Public Health Monitoring and Beyond

Pervasive environmental monitoring implies a wide range of technical, but also socio-political challenges, and this applies especially to the sensitive context of the city. In this paper, we elucidate issues for bringing out pervasive urban sensor networks and associated concerns relating to fine-grained information provision. We present the Common Scents project, which is based on the Live Geography approach, and show how it can overcome these challenges. As opposed to hitherto sensing networks, which are mostly built up in monolithic and closed systems, the Common Scents approach aims to establish an open, standards based and modular infrastructure. This ensures interoperability, portability and flexibility, which are crucial prerequisites for pervasive urban sensing. The implementation – a real-time data integration and analysis system for air quality assessment – has been realised on top of the CitySense sensor network in the City of Cambridge, MA US together with the city’s Public Health Department responding to concrete needs of the city and its inhabitants. The second pilot using mobile sensors mounted on bicycles has been deployed in Copenhagen, Denmark. Preliminary results show highly fine-grained variability of pollutant dispersion in urban environments.Singapore-MIT Alliance. Center for Environmental Sensing and MonitoringSingapore-MIT Alliance for Research and Technology CenterEuropean Commission (FP7 GENESIS project)Bundesministerium für Wissenschaft und ForschungResearch Studio iSPAC

Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl