#BeatAirPollution – How AI and IoT could help people combat air pollution issues

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Technology can be an enabler to help address air pollution prevention at source, helping organizations optimize their operations and reduce their impact.

When we think of air pollution our attention is often drawn to cities such as Mumbai or Beijing, yet the underground rail networks of London, Paris, and Toronto also struggle with similar issues. Research in those locations shows that concentration levels of particulate matter (PM) are often at a level consistent with the average day in pollution-choked Beijing.[1] Indeed, the World Health Organization reports that nine out of ten people around the world breathe polluted air.[2] Therefore, it is with little surprise that the UN’s 2019 World Environment Day Is a call to action to #beatairpollution.

As a sector, we influence air quality in terms of the energy used to drive our electronics, data centers and, indeed, through business travel. With a large-scale industry presence in Asia, home to some of the most polluted cities in the world, we need to do what we can to minimize these impacts.

But technology can also be part of the solution. Last year, Capgemini announced a new global ambition to leverage technology to help organizations with their sustainability challenges, recognizing that this is the biggest impact we can make. Technology can be an enabler to help address prevention at source, helping organizations optimize their operations and reduce their impact.

Mumbai on October 28, 2018        

But with 4.2 million deaths every year as a result of exposure to ambient outdoor air pollution, how can we also leverage technology to monitor, inform, and ultimately change the behaviors of those most affected as they head into our many cities?

Technology can allow us to reach people directly, combining personal health indicators with external environmental factors

Currently air quality is communicated to the general public via useful platforms which use a combination of measured and modelled data. Assuming we, as the general public, are aware of this, it would allow us to take behavioral actions to minimize our personal exposure. However, it is not something that we may think of or even be aware of, as we go about our day-to-day activities.

The advances in technology give us the opportunity to reach people directly and build a more sophisticated monitoring and communication network. We could leverage both artificial intelligence (AI) and the internet of things (IOT) with the capabilities from an increasing range of personal devices whether it be the 2.5 billion smart phones or the estimated 278 million smart watches in the world.[3] Indeed, the wearable health and fitness technology sector is set to grow 10–20% in the next five years, with an expanding set of capabilities. These devices measure elements such as heart rate, blood pressure, and breathing rate, which are indicators of overall health and are also measurables that change with exposure to air pollutants such as PM, nitrogen oxide and sulfur oxides. Yet they also monitor spatial and GPS data, which if combined could demonstrate the impact of the external environment on health factors, and better inform people of the issues.

Data from different sources and AI technology could allow us to drill down on very local issues

If we overlay current air quality monitoring data sources onto an individual, it would allow us to give a very precise prediction of local air quality issues. We could then integrate AI, to both refine and include a wider range of factors such as weather conditions and traffic levels. Added to this, if automatic number plate recognition (ANPR) is integrated, we could discern the proportion of vehicle fuel types being used in specific locations. This is important because diesel vehicles emit 90% of particulate matter.

Data analytics over time would allow people to understand impacts on their health – and change behavior

Over time, as an individual’s health and diagnostics data are inputted into a data analytics model alongside their own spatial data and air pollution exposure data, they could receive an analysis of how air pollution is impacting their physiology. Based on this, they could receive tailored suggested actions to take as well. The ability to overlay a Google Map of your walk to school or work to the air quality data around you could, instead of highlighting traffic congestion, show air quality issues and provide the options to re-route to avoid, or offer alternative options for time to start a journey.

Being able to inform, engage and predict the air quality issues around us is potentially very useful to those susceptible to exposure air pollution whether it be asthma, lung, or cardiovascular disease alongside others such cyclists, people walking or running, many of whom use wearable technology.

Over time, data could be used to understand trends which could create opportunities for prevention

Air quality is a significant issue of today, yet people seem apathetic to deliver rapid change. Indeed, we are all affected in our daily lives with air quality issues, some significantly more than others. However, if we use technology, the data will allow people to understand the impacts on their health and that of others to first make their own changes in behavior. Through real-time monitoring and data collection, it would be also be possible to collate trends and analysis which in turn creates the opportunities for prevention. Then, with the resources at our finger tips, we can start to make a positive change and in turn focus our attentions on those that are the source of the issue.

[1] https://www.railway-technology.com/features/featureis-the-air-on-london-underground-fit-to-breathe-5918758/

[2] World Health Organization – https://www.who.int/airpollution/en/

[3] https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/

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