NASA Reveals How Much Air Pollution Declined During Coronavirus Lockdowns | Science | news



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The coronavirus pandemic has led to lockdowns around the world, with experts immediately noticing a drop in carbon emissions around the world. Fewer planes were in the sky and fewer cars were on the roads, leading to a welcome disruption of carbon emissions across the planet. Air pollution has been touted by the Air Quality Life Index (AQLI) to contribute to billions of health problems around the planet and lead to shorter lives for humans.

Particle pollution can trigger asthma attacks, heart attacks and strokes, and cause lung cancer.

However, according to NASA computer simulations, there was a 20% drop in the global reduction in emissions of nitrogen dioxide – a major pollutant – due to blockages.

The team created the image by running a simulation that detailed what 2020 would be like without the pandemic.

With many countries promising to reduce their nitrogen dioxide emissions, NASA wanted to see how much of the decline could be attributed to blockages.

What the findings reveal is that humans are still contributing massively to nitrogen dioxide in the atmosphere.

Project leader and NASA researcher Christoph Keller said, “We all knew the blocks would have an impact on air quality.

“In a way, I was surprised at how much he fell.

“Many countries have already done a great job of lowering their nitrogen dioxide concentrations over the past few decades due to clean air regulations, but what our results clearly show is that there is still a significant contribution driven by the human behaviour”.

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“People were probably reducing transit because there was talk of the COVID-19 threat even before we were actually told to shut down.”

Once the blockages eased, the nitrogen dioxide decreases eased, but they were still below the “business as usual” pattern.

Keller said: “We would expect them to be somewhat related because nitrogen dioxide is so closely linked to economic activities, such as people traveling and factories running.

“It looks like our data captures it very well.”



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