Air pollution in northern Thailand remained critical today (March 12) after it emerged that more than 250,000 people have received hospital treatment due to smog.
The amount of harmful PM 2.5 particles in the atmosphere in Chiang Mai province have been higher than safe levels for six consecutive days – making it the worst in the world each morning.
The level of PM 2.5 dust particles in the province recorded at 9am on March 11 was 260 microgrammes per cubic metre, according to ‘AirVisual’. The safe level is 50.
Dr Aphinan Tantiwut at Lanna Hospital said PM 2.5 dust particles could damage respiratory and cardiac systems, and they could also cause skin irritation and allergy.
He said that more than 30,000 people reportedly sought treatment at the hospital in recent weeks. It is more than 250,000 across the northern region.
Suwanchai Wattanayingcharoenchai, director-general of the Department of Health, said the medical complaints were cause by PM 2.5 dust particles in the atmosphere.
The smog has been caused by wildfires spreading in the mountainous region and agricultural burning, combined with still atmospheric conditions with a lack of wind and rain. Visible Infrared Imaging Radiometer Suite (VIIRS) satellites showed 610 hotspots which signified the locations of wildfires in 17 provinces of the northern region.
The Thai government has been criticised for its handling of the country’s air pollution crisis. Measures introduced in 2019 to reduce pollution included spraying water into the air to increase humidity without success.
Prime Minister Prayut Chan-o-cha had a meeting with governors from 17 provinces affected by the hazardous haze when it escalated in September 2020.
The Thai leader promised the government would tackle the problem by eradicating the roots of wildfires but the agricultural practice – known as ‘slash and burn’- used by farmers to clear waste crops has continued.
Scientists believe is caused by a combination of still atmospheric conditions, a lack of rain and wind, agricultural burning and soaring numbers of cars and construction projects.