Bangladesh · Rajshahi Division · 11 cities
Air quality in Rajshahi Division today The AQI right now, city by city
Right now, Sirajganj has the cleanest air of the 11 cities we track in Rajshahi Division (AQI 101, Unhealthy for sensitive groups), and Mohammadpur the worst (AQI 192, Unhealthy). Nawābganj has the most tree cover, at 64.3% — #7 of 39 cities measured in Bangladesh.
Every city is measured the same way, over the same 2 km around its center: the air quality right now, from ground stations where there are any, and its tree cover.
AQI 148
Average right now
Unhealthy for sensitive groups
- cities tracked
- 11
- average tree cover
- 54.2%
- Bangladesh: 52.9%
- worst air now: Mohammadpur
- 192
- cleanest air now: Sirajganj
- 101
Lower is better on the US EPA index: 0–50 is Good, above 300 is Hazardous. Measured by Airleaf, refreshed hourly.
Rajshahi Division · average AQI 148
Air quality today across Rajshahi Division, city by city
| Rajshahi | 173Unhealthy | 39.8% | 565 | #33 of 39 |
|---|---|---|---|---|
| Mohammadpur | 192Unhealthy | 49.8% | — | — |
| Shibganj | 178Unhealthy | 40.8% | — | — |
| Natore | 172Unhealthy | 53.7% | — | #19 of 39 |
| Bogra | 156Unhealthy | 49.2% | — | #24 of 39 |
| Pār Naogaon | 161Unhealthy | 56.6% | — | #16 of 39 |
| Pābna | 116Unhealthy for sensitive groups | 47.3% | — | #26 of 39 |
| Puthia | 130Unhealthy for sensitive groups | 82.3% | — | — |
| Nawābganj | 139Unhealthy for sensitive groups | 64.3% | — | #7 of 39 |
| Sirajganj | 101Unhealthy for sensitive groups | 64.1% | — | #8 of 39 |
| Shāhzādpur | 107Unhealthy for sensitive groups | 48.3% | — | #25 of 39 |
Air quality is the US AQI from ground monitoring stations via OpenAQ where available, otherwise the Open-Meteo / CAMS model, refreshed hourly. Tree cover is measured within 2 km of each city center from Sentinel-2 imagery; the rank compares every city measured in Bangladesh.
Deforestation · 2001–2025
How much forest has Rajshahi Division lost?
Since 2001, Rajshahi Division has lost about 331 hectares of forest (about 460 football pitches): 4.1% of the forest it had in 2000. The biggest cause was storms, floods and other natural causes (28%), after which forest can grow back. About 89 hectares (27%) was deforestation: forest cleared for good for farms, mines or towns.
- Forest lost since 2001
- 331 haabout 460 football pitches
- Share of its forest
- 4.1%of what there was in 2000
- Cleared for good
- 89 hadeforestation: turned into farms, mines or towns
Hectares of forest lost each year
Show the figures as a table
| Year | Cleared for good | Can grow back | Total |
|---|---|---|---|
| 2001 | 9 ha | 37 ha | 46 ha |
| 2002 | 7 ha | 23 ha | 31 ha |
| 2003 | 2 ha | 6 ha | 8 ha |
| 2004 | 7 ha | 27 ha | 34 ha |
| 2005 | 6 ha | 15 ha | 21 ha |
| 2006 | 8 ha | 21 ha | 29 ha |
| 2007 | 19 ha | 37 ha | 57 ha |
| 2008 | 13 ha | 26 ha | 39 ha |
| 2009 | 3 ha | 9 ha | 11 ha |
| 2010 | 6 ha | 25 ha | 30 ha |
| 2011 | 3 ha | 11 ha | 13 ha |
| 2012 | 2 ha | 3 ha | 5 ha |
| 2013 | under 1 ha | under 1 ha | under 1 ha |
| 2014 | under 1 ha | under 1 ha | under 1 ha |
| 2015 | under 1 ha | under 1 ha | under 1 ha |
| 2016 | under 1 ha | under 1 ha | under 1 ha |
| 2017 | under 1 ha | under 1 ha | under 1 ha |
| 2018 | 2 ha | under 1 ha | 2 ha |
| 2019 | 2 ha | under 1 ha | 2 ha |
| 2020 | under 1 ha | under 1 ha | under 1 ha |
| 2021 | under 1 ha | under 1 ha | under 1 ha |
| 2022 | under 1 ha | under 1 ha | under 1 ha |
| 2023 | under 1 ha | under 1 ha | under 1 ha |
| 2024 | under 1 ha | under 1 ha | 1 ha |
| 2025 | under 1 ha | under 1 ha | under 1 ha |
The worst year was 2007, when about 57 hectares of forest were lost.
What is causing deforestation in Rajshahi Division?
- Storms, floods and other natural causescan grow back28% · 94 ha
- Farms, pasture and plantationscleared for good20% · 68 ha
- Towns and roadscleared for good6.6% · 22 ha
Also: logging, unknown causes.
Causes for 2025 are estimates, based on what caused forest loss in the same places up to 2024.
Source: Hansen/UMD/Google/USGS/NASA, Global Forest Change (GFC-2025-v1.13); causes: WRI/Google DeepMind drivers of forest loss; both CC BY 4.0, summed by Airleaf for each area. How it's measured
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