Siemens has developed intelligent software that relies on artificial neural networks to accurately predict the degree of air pollution in large cities several days in advance. The software can give cities and their residents the information needed to minimize pollution peaks before they are likely to happen, thus improving the quality of life.
Cities have always been considered engines of industrial growth, as they offer their residents opportunities for employment and prosperity. This fact has become particularly pronounced in modern times. Indeed as of 2009, for the first time in the history of humanity, more than half of the world’s population lived in urban centers. Furthermore, by 2050, 70 percent of the world’s people will live in cities, almost as many people as are alive today.
But the downside of urbanization is easy to see. The explosive growth in the number of city dwellers is posing a huge challenge for urban infrastructures, which are reaching their limits in many places. For example, today more than 50 percent of the world’s population has settled on less than two percent of the earth’s surface area. As a result, urban centers with their traffic, industry, and energy needs already account for up to 70 percent of global greenhouse gas emissions.
Cities literally generate stuffy air. And that air is increasingly unwholesome for residents. According to an analysis published by the World Health Organization (WHO) in May 2015, almost 90 percent of the world’s urban population breathes in air with pollutant levels that are much higher than the recommended thresholds.
Seven Million Deaths
The consequences are chilling. According to WHO data, approximately seven million people die each year from the effects of air pollution. Thus, one out of every eight deaths worldwide is a result of polluted air.
But the WHO strikes an optimistic note as well. It says that cities have the ability to greatly improve their air quality through local measures – whether it’s by means of modern and efficient solutions for smart infrastructures or through simple measures that can be implemented on short notice, such as traffic regulations and attractive incentives for pedestrians and bicyclists. Ideally, this would be done right at the places where air pollution is worst. That, however, requires knowledge of how pollutant levels change over time in specific locations.
Precise Forecasts of Air Pollution
This challenge has been taken up by Dr. Ralph Grothmann from Siemens Corporate Technology (CT). Grothmann has developed air pollution forecasting models that are based on neural networks. These models can accurately predict the degree of pollution in large cities several days in advance. “Neural networks are computer models that operate like the human brain. Through training, they learn to recognize relationships and to make predictions,” says Grothmann. His models are deep neural networks, which use considerably more layers of artificial neurons than in the past. Each level is devoted to a different plane of abstraction. Because a large number of levels are interlinked, the findings are much more detailed than was the case with earlier neural networks. It sounds a bit like science fiction, but neural networks have been a proven technology at Siemens for many years and in multiple sectors. For example, they have been used to predict levels of economic activity, raw materials prices, and even the expected electricity yield from renewable energies.