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The air we breathe is becoming increasingly polluted, to the point that air pollution is considered the world’s most significant environmental health threat. It is estimated that nine out of 10 people are subjected to polluted air, and globally, toxic pollutants exceed average annual values recommended by the World Health Organization’s air quality guidelines.
There are several air pollutants, many of which result from combustion and vehicle traffic, including nitrogen dioxide, sulfur dioxide, and ozone (which are produced by burning fossil fuels), particulates from solid and liquid matter (created during fuel combustion), and carbon monoxide.
Smart Cities and the IoT
Reducing air pollution is necessary for the future, and cities around the world are using technology and Smart City Initiatives to take on the problem. Smart cities are traditionally based on the Internet of Things – which allows the interconnectivity of physical devices and everyday objects using the internet – big data and advanced computing.
Projects are likely to include AI-assisted monitoring platforms that can access big data feeds and allow smarter analysis using machine learning. These platforms could be used to examine whether or traffic commuter data and help predict areas of poor air quality. This information can then be utilized by local governments to reduce traffic and output from factories, thereby decreasing pollution.
Mobile phone operators are also aiding smart city governments using predictive IoT, where sensors are employed to provide updates on air pollution in real-time. Predictive IoT can also be used in disease control, weather and natural disaster management, and traffic optimization in smart cities.
Case study: Glasgow
The Scottish city of Glasgow, as part of their Sensing the City pilot project, has been using the IoT to monitor air quality and reduce emissions. CENSIS, the center of excellence for Sensor and Imaging Systems technologies, and the University of Strathclyde employed Libelium IoT sensor nodes to decrease pollution using mobile technology.
The portable system is built on wireless sensor networks that monitor air quality: the node consists of several different sensors, which measure dust, carbon monoxide, temperature, humidity, pressure, nitrogen oxide and dioxides, and ozone. The system was sent across the city on a fleet of vans to provide indicative data in areas of low or no coverage, and to aid the identification of sources of pollution.
In this example, data is sent back to the sensor hub and then the Cloud via 3G. It is visualized in CitySense, a web-based user interface that allows data visualization based on the Microsoft Azure IoT platform. The hope is that the platform can be extended to other smart city applications, including road conditions monitoring, traffic management, and energy conservation of buildings via thermal imaging.
Case study: California
Likewise, West Oakland and parts of East Oaklands in California saw two Google Street View cars embedded with sensors travel the area to map three urban air pollutants - black carbon particles, nitric oxide, and nitrogen dioxide - block-by-block. Three Oakland neighborhoods were repeatedly mapped during daytime hours for over a year, visiting different neighborhoods on different days, and collecting more than 3 million data points.
The research found pollution varied between and within neighborhoods, even one block apart. People near industrial operations or high traffic areas were subject to more toxins than neighboring blocks.
These are just a few examples of how sensors can be used to monitor air pollution. By keeping an eye on conditions, it is possible to reduce the number of toxins in the air, by maintaining traffic flow and restricting industrial output in hotspot areas, to limit and even prevent air pollution.
Sources and Further reading