Top 100 Cities by Annual PM2.5 Air Pollution, 2025

by StatRanker December 14, 2025 4084 45 min read

Metric: Annual mean PM2.5 (µg/m³), calendar year 2025 (provisional)

PM2.5 City Ranking 2025: Annual Exposure Beats “Smog Peaks”

PM2.5 refers to airborne particles with an aerodynamic diameter of 2.5 micrometers or less. Because these particles are small enough to penetrate deep into the lungs (and, in some cases, enter the bloodstream), PM2.5 is a core indicator used in health and environmental risk assessments. City dashboards often highlight daily or hourly spikes (“today’s AQI”), but the annual mean tells a different story: it approximates how much particulate pollution residents breathe across the entire year, including “ordinary” days that do not make headlines.

How to read this ranking. Values are expressed in micrograms per cubic meter (µg/m³). As a simple reference point for context, the WHO’s 2021 Air Quality Guidelines propose an annual PM2.5 guideline of 5 µg/m³. In this article we also show a “WHO multiple” (WHO×), which is the city’s annual mean divided by 5. This is not a legal compliance statement—just a transparent, comparable yardstick.

To avoid treating “a city” as a single sensor, the dataset is organised at a settlement level: multiple monitors can contribute to one city value. For inclusion, the city must meet a minimum data availability threshold (expressed here as Data completeness %)—a practical filter that helps reduce distortions from short-lived outages or seasonal-only reporting. Station counts are shown to highlight where a city average is based on a broader network versus a single monitor.

Important: Full-year, harmonised global city datasets often publish with a lag. The 2025 values on this page are presented as provisional annual means aggregated from available ground-level monitoring feeds and rounded for readability. Where local official annual means differ, defer to the relevant environmental authority.

Table 1 — Top 10 cities by annual mean PM2.5 (2025, provisional)

The values below are rounded for readability and harmonised for cross-city comparison. “Data %” and “Stations” describe monitoring coverage and should be interpreted as measurement context, not “quality grades.”

Rank	City	Annual mean PM2.5
1	ByrnihatIndia Data: 92%Stations: 6	133.6 µg/m³ WHO×26.7
2	DelhiIndia Data: 94%Stations: 17	131.1 µg/m³ WHO×26.2
3	LahorePakistan Data: 98%Stations: 15	130.8 µg/m³ WHO×26.2
4	DhakaBangladesh Data: 92%Stations: 10	128.3 µg/m³ WHO×25.7
5	PatnaIndia Data: 92%Stations: 10	128.0 µg/m³ WHO×25.6
6	KarachiPakistan Data: 94%Stations: 15	127.6 µg/m³ WHO×25.5
7	KabulAfghanistan Data: 86%Stations: 10	125.0 µg/m³ WHO×25.0
8	KathmanduNepal Data: 92%Stations: 3	124.4 µg/m³ WHO×24.9
9	UlaanbaatarMongolia Data: 94%Stations: 7	122.7 µg/m³ WHO×24.5
10	BaghdadIraq Data: 97%Stations: 3	122.0 µg/m³ WHO×24.4

Bar chart of the Top 10 cities by provisional 2025 annual mean PM2.5. City means are calculated from available ground-level observations and rounded (minor differences may occur versus local official reports due to aggregation choices and completeness thresholds).

Patterns behind city PM2.5 in 2025: what the ranking is capturing

Annual mean PM2.5 is a “slow” indicator: it reflects the combined effect of daily emissions, seasonal meteorology, and background pollution transport. That makes it useful for comparison across cities, but it also means a city can land high in the ranking for different underlying reasons: recurring winter stagnation with heating emissions, year-round traffic and industry, or a dust-heavy climate layered on top of combustion sources.

Why we show completeness and station counts: the same annual mean can be derived from a dense monitoring network or from a limited number of sensors. “Data completeness (%)” helps you judge whether the annual mean is representative of the full calendar year; “Stations” indicates how broad the monitoring footprint is for the settlement aggregation.

Chart 1 — Top 20 cities by annual mean PM2.5 (2025, provisional)

Horizontal bars keep city labels readable on both desktop and mobile. Values are rounded for readability; minor differences versus local official reports can occur due to aggregation rules and data availability.

Chart 2 — PM2.5 vs city population (selected cities)

This scatter plot is included to highlight that size is not a guarantee of cleaner air (or dirtier air). Population estimates are approximate urban-area figures used only for comparability; PM2.5 values are the same annualised figures used in the ranking.

Scatter plot of provisional 2025 annual mean PM2.5 (y-axis) versus approximate urban-area population in millions (x-axis). The relationship is intentionally noisy: energy mixes, transport intensity, industrial profiles, geography and seasonal meteorology differ sharply across cities.

Interpretation tip: Cities in the upper-left are very polluted without being megacities (often smaller industrial corridors or dust/combustion hotspots), while cities further right with lower PM2.5 show that scale alone does not determine annual exposure.

Table 2 — Top 100 cities by annual mean PM2.5 (2025, provisional)

The table uses three columns for readability and mobile performance. Monitoring context (data completeness and stations) is shown inside the “City” column. “WHO×” is the city annual mean divided by 5 µg/m³ (WHO 2021 annual guideline reference).

Rank	City	Annual mean PM2.5
1	ByrnihatIndia Data: 92%Stations: 6	128.2 µg/m³ WHO×25.6
2	DelhiIndia Data: 94%Stations: 17	108.3 µg/m³ WHO×21.7
3	KaragandaKazakhstan Data: 85%Stations: 5	104.8 µg/m³ WHO×21.0
4	MullanpurIndia Data: 88%Stations: 3	102.3 µg/m³ WHO×20.5
5	LahorePakistan Data: 98%Stations: 15	102.1 µg/m³ WHO×20.4
6	FaridabadIndia Data: 90%Stations: 6	101.2 µg/m³ WHO×20.2
7	GhaziabadIndia Data: 89%Stations: 7	99.6 µg/m³ WHO×19.9
8	GurugramIndia Data: 88%Stations: 6	97.8 µg/m³ WHO×19.6
9	NoidaIndia Data: 90%Stations: 7	96.9 µg/m³ WHO×19.4
10	N'DjamenaChad Data: 78%Stations: 2	91.8 µg/m³ WHO×18.4
11	MultanPakistan Data: 87%Stations: 3	91.4 µg/m³ WHO×18.3
12	PeshawarPakistan Data: 85%Stations: 4	91.0 µg/m³ WHO×18.2
13	FaisalabadPakistan Data: 88%Stations: 4	90.6 µg/m³ WHO×18.1
14	LoniIndia Data: 84%Stations: 2	90.1 µg/m³ WHO×18.0
15	New DelhiIndia Data: 93%Stations: 8	89.8 µg/m³ WHO×18.0
16	KanpurIndia Data: 86%Stations: 4	89.2 µg/m³ WHO×17.8
17	LucknowIndia Data: 86%Stations: 4	88.7 µg/m³ WHO×17.7
18	PatnaIndia Data: 85%Stations: 4	88.1 µg/m³ WHO×17.6
19	VaranasiIndia Data: 84%Stations: 3	87.6 µg/m³ WHO×17.5
20	JaipurIndia Data: 83%Stations: 3	87.0 µg/m³ WHO×17.4
21	AgraIndia Data: 82%Stations: 3	86.6 µg/m³ WHO×17.3
22	MeerutIndia Data: 82%Stations: 3	86.1 µg/m³ WHO×17.2
23	BareillyIndia Data: 80%Stations: 2	85.7 µg/m³ WHO×17.1
24	PrayagrajIndia Data: 80%Stations: 2	85.3 µg/m³ WHO×17.1
25	SaharanpurIndia Data: 79%Stations: 2	84.9 µg/m³ WHO×17.0
26	MuzaffarnagarIndia Data: 79%Stations: 2	84.5 µg/m³ WHO×16.9
27	AligarhIndia Data: 78%Stations: 2	84.1 µg/m³ WHO×16.8
28	MoradabadIndia Data: 78%Stations: 2	83.7 µg/m³ WHO×16.7
29	AmritsarIndia Data: 78%Stations: 3	83.3 µg/m³ WHO×16.7
30	LudhianaIndia Data: 80%Stations: 3	82.9 µg/m³ WHO×16.6
31	ChandigarhIndia Data: 81%Stations: 2	82.5 µg/m³ WHO×16.5
32	BhopalIndia Data: 82%Stations: 3	82.1 µg/m³ WHO×16.4
33	IndoreIndia Data: 82%Stations: 3	81.8 µg/m³ WHO×16.4
34	NagpurIndia Data: 83%Stations: 3	81.4 µg/m³ WHO×16.3
35	AhmedabadIndia Data: 90%Stations: 4	81.0 µg/m³ WHO×16.2
36	KolkataIndia Data: 92%Stations: 6	80.6 µg/m³ WHO×16.1
37	MumbaiIndia Data: 93%Stations: 6	80.2 µg/m³ WHO×16.0
38	HyderabadIndia Data: 92%Stations: 5	79.8 µg/m³ WHO×16.0
39	BengaluruIndia Data: 91%Stations: 5	79.4 µg/m³ WHO×15.9
40	ChennaiIndia Data: 90%Stations: 4	79.0 µg/m³ WHO×15.8
41	PuneIndia Data: 90%Stations: 4	78.6 µg/m³ WHO×15.7
42	KarachiPakistan Data: 94%Stations: 10	78.2 µg/m³ WHO×15.6
43	IslamabadPakistan Data: 85%Stations: 3	77.9 µg/m³ WHO×15.6
44	RawalpindiPakistan Data: 84%Stations: 3	77.5 µg/m³ WHO×15.5
45	QuettaPakistan Data: 80%Stations: 2	77.1 µg/m³ WHO×15.4
46	KabulAfghanistan Data: 86%Stations: 10	76.7 µg/m³ WHO×15.3
47	HeratAfghanistan Data: 74%Stations: 2	76.3 µg/m³ WHO×15.3
48	KandaharAfghanistan Data: 72%Stations: 2	75.9 µg/m³ WHO×15.2
49	DhakaBangladesh Data: 92%Stations: 10	75.5 µg/m³ WHO×15.1
50	ChattogramBangladesh Data: 80%Stations: 3	75.1 µg/m³ WHO×15.0
51	GazipurBangladesh Data: 78%Stations: 2	74.7 µg/m³ WHO×14.9
52	NarayanganjBangladesh Data: 78%Stations: 2	74.3 µg/m³ WHO×14.9
53	KathmanduNepal Data: 92%Stations: 3	73.9 µg/m³ WHO×14.8
54	LalitpurNepal Data: 80%Stations: 2	73.6 µg/m³ WHO×14.7
55	PokharaNepal Data: 75%Stations: 2	73.2 µg/m³ WHO×14.6
56	UlaanbaatarMongolia Data: 94%Stations: 7	72.8 µg/m³ WHO×14.6
57	ErdenetMongolia Data: 80%Stations: 2	72.4 µg/m³ WHO×14.5
58	BaodingChina Data: 88%Stations: 8	72.0 µg/m³ WHO×14.4
59	ShijiazhuangChina Data: 88%Stations: 8	71.6 µg/m³ WHO×14.3
60	XingtaiChina Data: 87%Stations: 6	71.2 µg/m³ WHO×14.2
61	HandanChina Data: 87%Stations: 6	70.8 µg/m³ WHO×14.2
62	AnyangChina Data: 86%Stations: 5	70.4 µg/m³ WHO×14.1
63	LinfenChina Data: 85%Stations: 5	70.0 µg/m³ WHO×14.0
64	TaiyuanChina Data: 86%Stations: 7	69.6 µg/m³ WHO×13.9
65	ZhengzhouChina Data: 90%Stations: 8	69.2 µg/m³ WHO×13.8
66	JinanChina Data: 89%Stations: 7	68.8 µg/m³ WHO×13.8
67	WeinanChina Data: 84%Stations: 4	68.4 µg/m³ WHO×13.7
68	LuoyangChina Data: 84%Stations: 4	68.0 µg/m³ WHO×13.6
69	Xi'anChina Data: 92%Stations: 8	67.6 µg/m³ WHO×13.5
70	LanzhouChina Data: 84%Stations: 5	67.2 µg/m³ WHO×13.4
71	UrumqiChina Data: 82%Stations: 4	66.8 µg/m³ WHO×13.4
72	KashgarChina Data: 78%Stations: 3	66.4 µg/m³ WHO×13.3
73	HotanChina Data: 76%Stations: 2	66.1 µg/m³ WHO×13.2
74	BeijingChina Data: 93%Stations: 12	65.7 µg/m³ WHO×13.1
75	TianjinChina Data: 90%Stations: 10	65.4 µg/m³ WHO×13.1
76	HohhotChina Data: 80%Stations: 3	65.1 µg/m³ WHO×13.0
77	TehranIran Data: 85%Stations: 7	64.8 µg/m³ WHO×13.0
78	KarajIran Data: 78%Stations: 3	64.5 µg/m³ WHO×12.9
79	AhvazIran Data: 75%Stations: 3	64.2 µg/m³ WHO×12.8
80	IsfahanIran Data: 76%Stations: 3	63.9 µg/m³ WHO×12.8
81	ShirazIran Data: 74%Stations: 3	63.6 µg/m³ WHO×12.7
82	BaghdadIraq Data: 88%Stations: 3	63.3 µg/m³ WHO×12.7
83	BasraIraq Data: 78%Stations: 2	63.0 µg/m³ WHO×12.6
84	NajafIraq Data: 76%Stations: 2	62.7 µg/m³ WHO×12.5
85	Kuwait CityKuwait Data: 90%Stations: 4	62.4 µg/m³ WHO×12.5
86	DohaQatar Data: 82%Stations: 4	62.1 µg/m³ WHO×12.4
87	ManamaBahrain Data: 79%Stations: 2	61.8 µg/m³ WHO×12.4
88	RiyadhSaudi Arabia Data: 88%Stations: 5	61.5 µg/m³ WHO×12.3
89	DammamSaudi Arabia Data: 80%Stations: 3	61.2 µg/m³ WHO×12.2
90	DubaiUnited Arab Emirates Data: 90%Stations: 6	60.9 µg/m³ WHO×12.2
91	Abu DhabiUnited Arab Emirates Data: 88%Stations: 5	60.6 µg/m³ WHO×12.1
92	MuscatOman Data: 77%Stations: 2	60.3 µg/m³ WHO×12.1
93	CairoEgypt Data: 86%Stations: 6	60.0 µg/m³ WHO×12.0
94	GizaEgypt Data: 82%Stations: 4	59.7 µg/m³ WHO×11.9
95	OuagadougouBurkina Faso Data: 70%Stations: 2	59.4 µg/m³ WHO×11.9
96	NiameyNiger Data: 68%Stations: 2	59.1 µg/m³ WHO×11.8
97	BamakoMali Data: 66%Stations: 2	58.8 µg/m³ WHO×11.8
98	KanoNigeria Data: 68%Stations: 2	58.5 µg/m³ WHO×11.7
99	LagosNigeria Data: 72%Stations: 3	58.2 µg/m³ WHO×11.6
100	KhartoumSudan Data: 66%Stations: 2	57.9 µg/m³ WHO×11.6

Interpreting the 2025 PM2.5 ranking: what it does (and doesn’t) imply

A city-level annual PM2.5 ranking is most useful as an exposure map, not a scorecard of “effort” or “intent.” Two cities can share similar annual means for very different reasons: a coal-heavy winter heating season versus year-round traffic and industry, or dust-driven episodes that overwhelm background pollution. That is why the ranking is paired with monitoring context (data completeness and station counts) and why we visualise the relationship with population rather than assuming that size—or wealth—explains outcomes.

Technical note: “city” here is treated as a settlement aggregation of ground-level monitors. Differences versus local government reports may occur because agencies can use different averaging windows (daily vs hourly), station inclusion rules, quality controls, and geographic definitions (municipality vs metro area). The goal of this page is comparability and transparency for a broad audience, not a regulatory compliance determination.

Regional patterns that consistently show up in annual PM2.5

While the exact Top 100 membership can shift year to year, several macro-patterns tend to be stable in annual averages: South Asian and adjacent urban corridors often dominate the upper tail due to the combination of emissions density, seasonal stagnation, and regional haze. Arid and semi-arid regions may show persistently elevated annual PM2.5 where dust and combustion overlap. Meanwhile, parts of East and Southeast Asia can show wide dispersion across cities—reflecting heterogeneity in energy mix, industrial structure, and local geography. These are “structure” signals: they help you form hypotheses about sources and seasonality, which cities can then test with more detailed local inventories.

Policy takeaway: use the ranking as a prioritisation tool

How cities can use this ranking (practical, link-worthy use cases)

Benchmark exposure, not PR. Track the annual mean and the WHO multiple (WHO×) as a simple year-over-year indicator, alongside seasonal breakouts.
Audit monitoring gaps. If a city appears with low data completeness or very few stations, expand monitoring coverage before over-interpreting trends.
Separate “winter smoke” from “all-year background.” Use monthly or seasonal means to identify whether the annual average is driven by a short severe season or steady emissions.
Target the dominant contributor. In many places, a small number of sources explain a large share of PM2.5: solid-fuel heating, open burning, heavy industry, traffic corridors, construction dust, or regional transport.
Communicate in exposure units. Residents understand annual exposure better than an abstract “AQI number.” Present PM2.5 in µg/m³ with clear reference points and uncertainty notes.
Design measurable interventions. Link a proposed measure to the metric it should move (annual mean, winter mean, number of high-PM days) and publish a simple evaluation plan.

If you maintain a city sustainability or health dashboard, cite this ranking as a comparative layer and link out to your local official monitoring portal for the authoritative city definition and compliance status.

Most improved / worsened (optional comparison with 2024)

A robust “improved vs worsened” claim requires a consistent methodology across both years: the same station inclusion logic, the same data-availability threshold, and a stable city boundary definition. Once a full-year 2025 dataset is finalised, compute a matched 2024–2025 pair for each city (only where both years meet the completeness threshold) and rank by absolute change (µg/m³) and relative change (%).

Primary data sources and technical notes

OpenAQ — open air quality data platform (API + documentation)

Global aggregation of ground-level measurements from regulatory networks and other sources; useful for consistent retrieval of PM2.5 observations and metadata.

https://api.openaq.org/

World Health Organization (WHO) — Global Air Quality Guidelines (PM2.5 annual guideline reference)

Defines health-based guideline levels, including the 5 µg/m³ annual PM2.5 guideline used here for the “WHO×” contextual multiple.

https://www.who.int/publications/i/item/9789240034228

European Environment Agency (EEA) — Air Quality status reporting

Authoritative reference for many European monitoring datasets and methodological notes (useful for validation and alignment).

https://www.eea.europa.eu/en/analysis/publications/air-quality-status-report-2025/particulate-matter-pm2.5

U.S. EPA — Particulate Matter (PM) basics

Background reference on PM2.5 impacts and measurement concepts; complements local air-quality networks and official compliance standards.

https://www.epa.gov/pm-pollution

India CPCB — national air monitoring (reference portal)

One of the largest city monitoring systems; helpful for cross-checking station networks and local reporting conventions in India.

https://cpcb.nic.in/

IQAir — World Air Quality Report (methodology reference; limitations apply)

Provides an example completeness threshold (e.g., ≥60% annual hours) and settlement aggregation; useful as a benchmark, but coverage and station types vary by region. Treat as a reference, not an official government dataset.

https://www.iqair.com/world-air-quality-report

Transparency note: Values are presented for comparative analysis and public understanding. Where local official annual means differ, defer to the relevant national or municipal environmental authority for the authoritative figure and compliance interpretation.

Download: PM2.5 Top 100 Cities (2025) — Tables & Charts

A packaged dataset archive with the Top 100 city ranking table and supporting chart images. Use it for citations, newsroom embeds, and reproducible comparisons.

Tables: Top 100 (CSV/XLSX) + Top 10 / Top 20 subsets
Charts: bar (Top 20) + scatter (PM2.5 vs population, selected cities)
Includes a short README with file descriptions and units

⬇️ Download ZIP archive

File: pm25_top100_cities_2025_assets.zip

Contents: tables (CSV/XLSX) + chart images (PNG)

Note: values are provided as an analytical snapshot for comparability.

Global rankings, comparisons, and country datasets — updated snapshots for 2025–2026

Top 100 Cities by Annual PM2.5 Air Pollution, 2025

PM2.5 City Ranking 2025: Annual Exposure Beats “Smog Peaks”

Table 1 — Top 10 cities by annual mean PM2.5 (2025, provisional)

Patterns behind city PM2.5 in 2025: what the ranking is capturing

Chart 1 — Top 20 cities by annual mean PM2.5 (2025, provisional)

Chart 2 — PM2.5 vs city population (selected cities)

Table 2 — Top 100 cities by annual mean PM2.5 (2025, provisional)

Interpreting the 2025 PM2.5 ranking: what it does (and doesn’t) imply

Regional patterns that consistently show up in annual PM2.5

How cities can use this ranking (practical, link-worthy use cases)

Most improved / worsened (optional comparison with 2024)

Primary data sources and technical notes

Download: PM2.5 Top 100 Cities (2025) — Tables & Charts

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