Those measures sure had a positive effect on the situation, leading the International Olympic Committee chief to praise the efforts, saying China had done “everything humanly possible” to reduce PM2.5 levels (which measure particulate emissions of carbon, nitrogen, sulfur, and heavy metals).
But such high-impact provisions are not easy to implement and come with a huge economic cost.Despite claims to the contrary, they ended up being more one-shot measures than a real solution, and the ‘blue skies’ didn’t last: just a few years later, things seemed to be just as bad as before, forcing the local government, in December 2015, to issue its first ever ‘red alert’.
While the problem is still far from be solved, then, a glimmer of hope for residents might come from a ten-year collaboration between the Beijing’s Environmental Protection Bureau (EPB) and IBM IBM +1.60%, a joint effort which goes under the name of “Green Horizons“. The project, which started in 2014, is focused on using IoT and cognitive computing to improve air quality management and forecasting.
“Ground sensors actually are not enough to capture the whole picture of the pollution event, where does it come from, and where will it impact in the next few days. We use also data coming from the Chinese version of Twitter and Instagram to do some cross checking, and get a better understanding of the situation,” Zhang explains. A post on Sina Weibo or Tencent, a picture, a camera feed: everything is processed and analyzed.
Extra - Air Quality Forecasting in Delhi
Besides accuracy, the advantage of this cognitive, machine learning approach is that gives authorities some leverage to plan in advance which measures to take to reduce pollution and allows more targeted, tailored interventions than just shutting down more than 100 factories for weeks, like it happened in 2008.