Over the next decade, the Internet of Things (IoT) is expected to offer even more services beyond connectivity. These include platforms, apps and managed services.
Poor air quality is a significant public health problem since breathing polluted air increases the risk of debilitating and deadly diseases such as lung cancer, stroke, heart disease and chronic bronchitis. Air pollution is now the world’s fourth-leading fatal health risk, reported as causing one in ten deaths in 2013.
However, air quality remains a global challenge for governments, regulators, city administrators and citizens. Dystopic scenes of smog-choked cities like Beijing, New Delhi and London where air pollution levels exceed legal and World Health Organization (WHO) limits often permeate our screen, forcing local governments to invest in policies and solutions to improve air quality. IoT data solutions will likely be the technology we need to tackle these challenges.
Today, air quality monitoring is performed by large, expensive scientific instruments permanently installed and professionally maintained, at a relatively small number of fixed locations. For example, London has around 100 monitoring stations. This makes data collection challenging to collect and communicate, leaving citizens bewildered about the levels of pollution they experience (with monitoring data not being available in realtime).
Advances in sensors, IoT platforms and mobile communications technologies have led to the emergence of smaller, portable, low cost. These mobile-enabled sensors can measure and report air quality in near realtime. “Big Data” capabilities, such as analytics and machine learning, can then be applied to this data and related data sets, such as weather and traffic, to understand the causes and fluctuations in air pollution.
IoT Sensors Free Movement:
According to Aruna Srinivasan GSMA: “These portable IoT sensors can travel freely anywhere […] including across parks, along footpaths, pedestrian routes, major roads and side roads. With so much data being generated, conventional analytical techniques become difficult to apply to the data, and this is where the ‘Big Data’ approach, including ‘Machine Learning’, becomes vitally important. As they assess air quality in ‘realtime’ we expect they will provide much more granular data on the air quality in different locations throughout the day.”
Local governments that are worried about public health hazards due to inadequate air quality control will benefit from procuring these technologies, allowing them to improve their understanding of the causes and patterns underlying poor air quality. If you work for your local government and you’re interested in these solutions, please do get in touch with us. We would be happy to collaborate with you to serve our local communities.
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