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Airify — A mobile solution for monitoring air quality in urban areas
In this paper, we propose a wireless sensor network based portable pollution monitoring system for monitoring the carbon monoxide (CO) concentration levels on the real time basis. Carbon Monoxide which is a critical and primary pollutant in air significantly affects the health of the people. With the rapid industrialisation and the exponential growth of automotive vehicles had led to the deterioration of air quality in the urban areas. Our design consists of Testbed of five nodes with calibrated carbon monoxide sensors for measuring CO concentration levels. By using the multi-hop mesh network, the CO sensors are integrated onto the Waspmote to communicate between the various nodes for the information exchange. The derived concentration levels of carbon monoxide from the different sensors on the board are made available on the internet through the platform which consists of Light Weight Middleware and Net Interface deployed on the server. Designed prototype had been implemented and tested in collecting the emission levels of CO in the Hyderabad city which had shown the consistent results under various circumstances.
Urban areas are generators of environmental emissions such as carbon dioxide (CO2), harmful air pollutants and noise, all with the potential to negatively impact the health and wellbeing of its human and non-human inhabitants. There is an urgent need to understand the characteristics of urban areas associated with variability in emissions and the potential for exposure to potential harmful environmental conditions. UrbanSense is a wireless sensor network (WSN) infrastructure designed to monitor environmental conditions at different temporal and spatial scales. The scalable infrastructure includes an extended range outdoor wireless sensing and data aggregation system, a web-based data management and visualization platform, and real-time event-based data stream integration. Sensors monitor changes in carbon dioxide (CO2), carbon monoxide (CO), noise (LAeq), as well as several meteorological conditions including wind speed and direction, temperature, relative humidity and precipitation. The implementation will provide opportunities for real-time data integration and an analysis system for environmental quality assessment, and may be realized on top of products arising from spatio-temporal (statistical) analyses and remotely-acquired data products such as satellite data. Sensor swapping and co-location with sensors from projects with different aims (traffic volume modelling and human tracking research) will add value for research in transportation planning, environmental regulation and policy and epidemiological studies focused on associations between environmental exposures and health outcomes.
This paper's findings suggest that an arbitrary Chinese policy that greatly increases total suspended particulates (TSPs) air pollution is causing the 500 million residents of Northern China to lose more than 2.5 billion life years of life expectancy. The quasi-experimental empirical approach is based on China's Huai River policy, which provided free winter heating via the provision of coal for boilers in cities north of the Huai River but denied heat to the south. Using a regression discontinuity design based on distance from the Huai River, we find that ambient concentrations of TSPs are about 184 μg/m(3) [95% confidence interval (CI): 61, 307] or 55% higher in the north. Further, the results indicate that life expectancies are about 5.5 y (95% CI: 0.8, 10.2) lower in the north owing to an increased incidence of cardiorespiratory mortality. More generally, the analysis suggests that long-term exposure to an additional 100 μg/m(3) of TSPs is associated with a reduction in life expectancy at birth of about 3.0 y (95% CI: 0.4, 5.6).
Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phenomenon are similar. Hence, when calibrating a sensor, we adjust its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sensors in the network, referred to as multi-hop calibration.
We are the first to study multi-hop calibration with respect to a reference signal (micro-calibration) in detail. We show that ordinary least squares regression---commonly used to calibrate noisy sensors---suffers from significant error accumulation over multiple hops. In this paper, we propose a novel multi-hop calibration algorithm using geometric mean regression, which (i) highly reduces error propagation in the network, (ii) distinctly outperforms ordinary least squares in the multi-hop scenario, and (iii) requires considerably fewer ground truth measurements compared to existing network calibration algorithms. The proposed algorithm is especially valuable when calibrating large networks of heterogeneous sensors with different noise characteristics. We provide theoretical justifications for our claims. Then, we conduct a detailed analysis with artificial data to study calibration accuracy under various settings and to identify different error sources. Finally, we use our algorithm to accurately calibrate 13 million temperature, ground ozone (O3), and carbon monoxide (CO) measurements gathered by our mobile air pollution monitoring network.
The air monitoring paradigm is rapidly changing due to advances in the development of portable, lower-cost air pollution sensors report high-time resolution data in near-real time along with supporting data and communication infrastructure. These changes are bringing forward opportunities to the traditional monitoring framework (supplementing ambient air monitoring and enhancing compliance monitoring) and also is expanding monitoring beyond this framework (personal exposure monitoring and community-based monitoring). Opportunities in each of these areas as well as corresponding challenges and potential solutions associated with development and implementation of air pollution sensors are discussed.
Hazardous chemicals escape to the environment by a number of natural and/or anthropogenic activities and may cause adverse effects on human health and the environment. Increased combustion of fossil fuels in the last century is responsible for the progressive change in the atmospheric composition. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. It ranges from minor upper respiratory irritation to chronic respiratory and heart disease, lung cancer, acute respiratory infections in children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or asthmatic attacks. In addition, short- and long-term exposures have also been linked with premature mortality and reduced life expectancy. These effects of air pollutants on human health and their mechanism of action are briefly discussed.
Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps
- D Hasenfratz
- O Saukh
- C Walser
- C Huegelin
- M Fierz
AIRWISE - An Airborne Wireless Sensor Network for Ambient Air Pollution Monitoring
- C Paper
Extending Urban Air Quality Maps Beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation
- A Marjovi
- A Arfire
- A Martinoli