Smart Cities

Better Air, Better Health in the U.S. and Canada with AI + IoT

Trusted data exchange is essential for faster, data-driven decision-making

Sanjeevan Srikrishnan

This week marks the 14th annual Air Quality Awareness Week in the U.S.[i] The theme for this year is “Better Air, Better Health!”, which is especially pertinent now. Air pollution is a major cause of death and disease affecting the lungs and heart, killing an estimated seven million people worldwide every year according to the World Health Organization (WHO). The data shows that 90% of people breathe polluted air, the majority of which are in urban areas.[ii] Moreover, recent research shows that areas with higher levels of air pollution may experience more severe impact from pandemics such as COVID-19.[iii] The good news is that lockdowns and remote working are having a massive impact on clearing the air as the before/after photos of Los Angeles, California below illustrate.

Source: Business Insider[iv]

However, that is not expected to last when cities begin to reopen, so imagine if your phone could alert you when the local air quality was unsafe so you could decide whether to stay home or wear a mask to go out. Or, if you are driving, your car could automatically detect air pollution and switch on the air filter while suggesting alternate routes that were less polluted. On the flip side, if governments and businesses are informed when the air quality in their community reaches hazardous levels, they could make better decisions about shutting down or moving to virtual operations until it improves. Getting to this level of insight will require trusted data exchange between multiple parties with governance models that address data privacy concerns. By directly interconnecting multiple parties, a trusted data exchange can generate deeper insights and shared value while keeping data private and safe in transit.

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Air quality monitoring in North America

Most governments around the world already have air quality monitoring systems in place. In the U.S. and Canada, the national/federal government captures air quality data from sensors and other monitoring devices, which is publicly shared on their websites.[v] And while these agencies don’t typically push real-time alerts or action recommendations, there are a number of companies developing innovative approaches for delivering intelligent air quality insights to agencies, citizens and businesses in North America. For example: [vi]

  • The AIR app by Plume Labs goes beyond the public air quality data provided by the government. It also integrates land-based measurements with satellite imagery and machine learning (ML) models to deliver air quality reports and pollution forecasts. The company also sells a personal air quality sensor that can provide real-time localized air quality information on the app, and this data is fed back into the ML algorithms to improve the accuracy over time.
  • The BreezoMeter app provides real-time, location-based air quality data in 90+ countries by collecting data from multiple nearby pollution stations, weather, wind direction/speed, time of day and other factors, combined with ML and artificial intelligence. The app delivers real-time localized air quality information, daily pollen counts, fire alerts and personalized health recommendations based on the insights. BreezoMeter also provides API access for businesses that want to tap into their data and algorithms for smart homes/buildings/cities, connected vehicles, medical devices and wearables and more.
  • Air control agencies in several regional districts in Southern California use Envirosuite to monitor air quality and detect pollution sources in real-time. Envirosuite’s platform combines multiple sources of data on air quality, weather, emission rates, altitude and more with atmospheric dispersion ML models to provide precise air quality measurements and pollution forecasts. This enables agencies to respond more quickly with actions and plans to reduce air pollution.

Getting smarter while minding data privacy

Connecting data dots to detect high levels of air pollution in real-time and provide appropriate recommendations and actions is not difficult to solve from a technology point of view. However, it can be more challenging to solve from a regulatory and data privacy perspective. Digital surveillance is not generally accepted in North America except when it is for protecting public safety and even then it can be tricky. For example, in an effort to help track the spread of COVID-19, Apple and Google recently announced a rare joint collaboration to enable Bluetooth-based contact tracing features. And although the smartphone system is opt-in only, collects no location data from users and collects no data at all from anyone without a positive COVID-19 diagnosis, the announcement has still been met with some data privacy pushback.[vii] Alphabet’s Sidewalk Labs encountered similar pushback for its Toronto smart city project and initially scaled back its plans, but has since cancelled the initiative due to added market uncertainty from COVID-19.[viii]

Despite these challenges, data sharing is essential to deliver hyper-local/personalized insights. Notifying a vulnerable population with preexisting lung conditions about air pollution levels requires data to be exchanged between various smart devices across healthcare providers, governments, and the public/private sector.  Privacy concerns can be alleviated by through the use of data trusts to establish the right balance  between data value and data protection, while private interconnection solutions like those on Platform Equinix® help ensure that any data and insights shared between multiple entities are exchanged safely and securely.

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