SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors

The increasing attention paid to urban air quality modeling places higher requirements on urban air quality datasets. This article introduces a new urban air quality dataset—the SmartAQnet2020 dataset—which has a large span and high resolution in both time and space dimensions. The dataset contains 248,572,003 observations recorded by over 180 individual measurement devices, including ceilometers, Radio Acoustic Sounding System (RASS), mid- and low-cost stationary measuring equipment equipped with meteorological sensors and particle counters, and low-weight portable measuring equipment mounted on different platforms such as trolley, bike, and UAV

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

Improved Fine Particles Monitoring in Smart Cities by Means of Advanced Data Concentrator

Traffic reduction and air-quality improvement are among the main goals of several projects worldwide. This article presents a fine particle monitoring based on heterogeneous air quality mobile sensors and an advanced data concentrator (AdDC), so that the level of pollution in the urban area, where few accurate fixed measurement stations are present, can be assessed with better accuracy. Some urban buses are used to carry low-cost sensors, thus implementing a mobile sensor network and increasing the time and space resolution of air quality information. The data obtained by these low-cost sensors are significantly affected by uncertainties, also due to atmospheric factors, such as humidity. The proposed AdDC processes all the obtained measurements and exploits the information obtained by the accurate fixed stations to improve the accuracy of the low-cost mobile sensors. In particular, a new compensation methodology, specifically targeted to the fine particles monitoring, is proposed. The monitoring of relative humidity is added, with the relevant on-the-fly calibration, so that the measured values can be used to correct the effects of humidity on PM2.5 sensors. The validity of the proposed system is proven by means of simulations performed on an appropriate set up

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

Model-based Rendezvous Calibration of Mobile Sensor Networks for Monitoring Air Quality

Mobile Wireless Sensor Networks (WSNs) hold the potential to constitute a real game changer for our understanding of urban air pollution, through a significant augmentation of spatial resolution in measurement. However, temporal drift, crosssensitivity and effects caused by varying environmental conditions (e.g., temperature) in low-cost chemical sensors (typically used in mobile WSNs) pose a tough challenge for reliable calibration. Based on state-of-the-art rendezvous calibration methods, we propose a novel model-based method for automatically estimating the baseline and gain characteristics of low-cost chemical sensors taking temporal drift and temperature dependencies of the sensors into account. The performance of our algorithm is evaluated using data gathered by our long-term mobile sensor network deployment, developed within the Nano-Tera.ch OpenSense II project in Lausanne, Switzerland. We show that, in a realistic context of sparse and irregular rendezvous events, our method consistently improves rendezvous calibration performance for single-hop online calibration

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments