For 2 days so far, since 30 September, 8 cities in India had PM2.5 at least twice their usual for the time of year. The worst reading was Mandideep, Madhya Pradesh, India: 86.3 µg/m³ on 30 September, about 2.3× its usual 37.1.
Few fires were detected near these cities on those days. Spreads like this usually come from still weather trapping smoke from heating, traffic and industry, or from dust.
Cities reached8in 1 country
Days230 September to 1 October 2026
Worst day1 October7 cities counted
Furthest above usual2.8×Damoh
Day by day
Every measured city in the area, coloured by its PM2.5 that day; a dark ring marks a city at twice its usual or more. Bars: cities counted each day, coloured by their median level. Choose a day or press play; without JavaScript, the days link to that day's world ranking.
How it spread
Every counted city, in the order the episode reached them, 3 days before to 3 after. Each square is a day, coloured by that day's PM2.5; outlined: at twice the city's usual or more.
Daily means in µg/m³. Usual: the city's median for this time of year (its days within 21 days of the date, every year, leaving out the week around it). Above usual: its highest day against that.
Against other episodes in India
Number 50 of 54 episodes in India since our records began, by cities reached.
Bars: cities reached; colour: the level of the episode's highest daily reading. All 54.
Use this page
SmogRank, North-western India, September and October 2026: 8 cities with PM2.5 at twice their usual or more, 30 September to 1 October 2026, smogrank.com/episodes/2026-09-north-western-india-2 (accessed 3 October 2026).
You're welcome to reuse a single chart, image or figure with this citation and a link. Terms.
An episode is a run of days when many cities close to each other (four or more within 350 km on a day, 8 or more in all) each had a daily PM2.5 mean of at least 25 µg/m³ and at least 2 times their usual for that time of year. Found in our own measurements, not picked by hand. How we find episodes. Values are each city's daily mean of its own monitors; causes are the usual kinds for a pattern like this, not a confirmed source. Fire detections from NASA FIRMS; smoke maps from NOAA HMS.