Monitoring air pollution exposure for 1.8 billion children globally.
Long-term exposure to fine particulate matter (especially PM2.5, i.e. particles with a diameter less than 2.5 μm) are estimated to cause 8 million excess deaths annually. The impact of air pollution is felt more acutely by the young, with 1 in every 4 deaths under 5 years related to environmental risks. Globally, 93% of children live in places where air pollution levels exceed World Health Organization (WHO) guidelines, defined as PM2.5 values above an annual mean of 5 𝜇g/m³ or a 24-hour mean of 15 𝜇g/m³. Our air pollution problem costs 1% of global GDP - that is $2.6 trillion annually. Unfortunately, due to COVID-19, shifting patterns of travel behavior have meant there is a lack of an accurate way to monitor fluctuating air quality levels globally, leading to uncertainty among public health professionals on children’s exposure to air pollutants. This makes it difficult for humanitarian organizations, and local partners to monitor air pollution accurately, resulting in insufficient evidence to justify investing in respiratory health improvement projects.
There is a need for public health professionals, humanitarian organizations, governments and health insurance providers to monitor air pollution accurately and assess its health impact on populations. At AQAI, we believe every child has a right to know what they’re breathing. To do this, we develop a machine learning solution to accurately assess the global children populations’ exposure to high levels of air pollution. We generate global predictions of the PM2.5 concentrations (particles with a diameter less than 2.5 micrometers), to augment accurate but limited air pollution data collected using ground sensors with machine learning predictions. Our API, SDK and geovisualization engine enables humanitarian organizations, governments and health insurance providers to monitor air pollution accurately and assess the health impact of air pollution on child populations.
Looking forward to developers and testers to supercharge the current development pipeline. Visit our Project Repository and find our projects. On the end to end pipeline for PM2.5 prediction we are currently focussed on geospatial analytics. Find out more about that at OpenAQ-Engine Repository and read the Documentation. Contact us following the Contribution Guidelines to get started.