Government
21 columns
64 rows
8 formats

Smart City Air Quality Events

This dataset provides granular air quality sensor readings and event logs across smart city zones, including pollutant concentrations, event types, incident triggers, and compliance indicators. It enables municipal teams and analysts to monitor environmental risks, detect anomalies, and optimize rapid incident response, supporting data-driven urban innovation and regulatory compliance.

last updated 29 Dec 2025
by GoMask

The brief that made it

Real-time environmental risk monitoring and anomaly detection

Sample rows

preview · 8 of 64 rows · all 21 columns

event_idstring	event_typestring	humidity_percentfloat	incident_reportedboolean	zone_idstring	sensor_idstring	zone_namestring	latitudefloat	longitudefloat	timestampdatetime	pm25float	pm10float	no2float	o3float	cofloat	so2float	temperature_cfloat	event_descriptionstring	incident_idstring	compliance_flagboolean	data_quality_flagboolean
EVT-20240601-0001	normal	52.1	false	Z01	SNS-001-A	Downtown Central	40.7128	-74.0059	2024-06-01T07:43:12	12.4	22.1	17.2	38.9	0.5	7.9	18.3	Routine hourly sensor reading.	blank	true	true
EVT-20240601-0002	threshold_exceeded	68.4	true	Z02	SNS-002-B	Harbor District	40.7043	-74.0138	2024-06-01T08:05:15	37.7	41.2	40.3	44.1	0.8	15.1	19	PM2.5 exceeds city threshold.	INC-20240601-101	false	true
EVT-20240601-0003	anomaly	47.8	true	Z03	SNS-003-C	Tech Park	40.7291	-73.9946	2024-06-01T08:11:45	blank	12.5	27	60.2	0.4	8.2	21.1	Unusual NO2 spike detected.	INC-20240601-102	false	true
EVT-20240601-0004	normal	56.9	false	Z04	SNS-004-D	Uptown Green	40.7712	-73.9745	2024-06-01T09:04:33	10.1	17.4	10.4	blank	0.3	4.3	20	Routine air quality reading.	blank	true	true
EVT-20240601-0005	threshold_exceeded	40.2	true	Z05	SNS-005-E	Old Town	40.7211	-73.991	2024-06-01T09:19:54	56.2	61.5	50.9	70.1	1.1	28.4	22.8	Multiple pollutants exceed threshold.	INC-20240601-103	false	true
EVT-20240601-0006	normal	61.3	false	Z02	SNS-006-F	Harbor District	40.7059	-74.0165	2024-06-01T10:00:10	13.3	blank	22.7	50.8	0.6	11	18.7	Scheduled sensor reading.	blank	true	true
EVT-20240601-0007	normal	53.7	false	Z06	SNS-007-G	Riverfront	40.7517	-73.9723	2024-06-01T10:16:08	8.8	14.6	12.1	42.3	blank	5.5	20.9	Routine sensor check.	blank	true	true
EVT-20240601-0008	normal	49.1	false	Z01	SNS-008-H	Downtown Central	40.7132	-74.0065	2024-06-01T10:37:51	9	18.2	blank	35.4	0.3	6.8	19.7	Routine air quality reading.	blank	true	true
EVT-20240601-0009	threshold_exceeded	35	true	Z07	SNS-009-I	Industrial Park	40.7304	-73.9843	2024-06-01T11:15:28	72.3	90.5	67.9	80.2	2	39	25.1	Industrial emissions detected.	INC-20240601-104	false	true
EVT-20240601-0010	normal	57.4	false	Z08	SNS-010-J	University District	40.7295	-73.9977	2024-06-01T11:33:19	11.7	20.1	28.3	37.7	0.6	9.1	21.8	Routine university zone reading.	blank	true	true
EVT-20240601-0011	normal	64.2	false	Z09	SNS-011-K	Market Square	40.7426	-73.9899	2024-06-01T12:09:25	blank	15.8	13.9	33.5	0.4	3.1	20.5	Routine sensor check.	blank	true	true
EVT-20240601-0012	normal	48.6	false	Z04	SNS-012-L	Uptown Green	40.7704	-73.9761	2024-06-01T12:42:08	15.2	19.4	20.7	39.2	0.5	9.7	22.6	Standard air quality reading.	blank	true	true
EVT-20240601-0013	anomaly	39.5	true	Z10	SNS-013-M	East Village	40.7282	-73.9846	2024-06-01T13:20:42	53	45.2	60.9	71	1.5	24.3	24.2	Sudden spike in NO2 and SO2.	INC-20240601-105	false	true
EVT-20240601-0014	normal	50.5	false	Z08	SNS-014-N	University District	40.7297	-73.9988	2024-06-01T13:44:04	12.6	19	14.5	32.6	0.5	6.3	19.2	Hourly sensor reading.	blank	true	true
EVT-20240601-0015	threshold_exceeded	42.9	true	Z05	SNS-015-O	Old Town	40.7208	-73.9922	2024-06-01T14:08:35	44.7	48.6	42.2	65.5	1	blank	23.4	High PM10 detected.	INC-20240601-106	false	true
EVT-20240601-0016	normal	54.8	false	Z06	SNS-016-P	Riverfront	40.7523	-73.9731	2024-06-01T14:31:19	10.8	14.2	11.2	40.7	0.4	5.2	20.3	Routine sensor check.	blank	true	true
EVT-20240601-0017	threshold_exceeded	34.3	true	Z07	SNS-017-Q	Industrial Park	40.7312	-73.9851	2024-06-01T15:02:47	61.5	79.4	58.8	75.3	1.7	35.2	26	Multiple pollutants exceed limits.	INC-20240601-107	false	true
EVT-20240601-0018	normal	49.6	false	Z03	SNS-018-R	Tech Park	40.7278	-73.9952	2024-06-01T15:18:34	14.3	16.7	18.1	36.2	0.4	6	22.2	Routine sensor reading.	blank	true	true
EVT-20240601-0019	normal	55.4	false	Z10	SNS-019-S	East Village	40.7294	-73.9839	2024-06-01T15:51:50	blank	22.3	25.7	45.1	0.6	8.5	20.9	Routine sensor check.	blank	true	true
EVT-20240601-0020	normal	51.2	false	Z01	SNS-020-T	Downtown Central	40.7139	-74.0081	2024-06-01T16:15:07	13.8	19.2	12.9	34.8	0.5	6.7	19.4	Routine reading.	blank	true	true

What the 64 rows show

from the 64-row sample

Threshold_exceeded (event type) stands out: 18 of its 18 rows have incident_reported = true, against 3 of 46 for the rest.

33%incident_reported = true
58.1median humidity_percent
17zone names
15.1median pm25
21.1median pm10
18.4median no2

Incident reported rate by event_typeincident_reported = true

humidity_percent64 rows, in bands of 10

Median 58.1, from 31.8 to 80.8.

zone_id64 rows · top 10 of 19 values

Z014
Z024
ZN-0014
ZN-0054
ZN-0064
ZN-0084
ZN-0094
Z033
Z043
Z053

21 columns by typefrom the column list below

string 7
float 10
datetime 1
boolean 3

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 7 columns
`event_id`	string	Unique identifier for each air quality event or sensor reading.unique	EVT-20240601-0001
`sensor_id`	string	Unique identifier for the air quality sensor reporting the data.	SNS-001-A
`zone_id`	string	Identifier for the smart city zone where the sensor is located.	Z01
`zone_name`	string	Human-readable name of the smart city zone.optional	Downtown Central
`event_type`	string	Type of event detected (e.g., 'normal', 'anomaly', 'threshold_exceeded', 'sensor_error').5 values	normal
`event_description`	string	Detailed description of the event or anomaly detected.optional	Routine sensor check.
`incident_id`	string	Reference to an incident response record if applicable.optional	INC-20240601-101
Numbers 10 columns
`latitude`	float	Latitude coordinate of the sensor location.-90 to 90	40.7128
`longitude`	float	Longitude coordinate of the sensor location.-180 to 180	-74.0059
`pm25`	float	Particulate matter (PM2.5) concentration in micrograms per cubic meter (µg/m³).0 or more · optional	12.4
`pm10`	float	Particulate matter (PM10) concentration in micrograms per cubic meter (µg/m³).0 or more · optional	22.1
`no2`	float	Nitrogen dioxide (NO2) concentration in parts per billion (ppb).0 or more · optional	17.2
`o3`	float	Ozone (O3) concentration in parts per billion (ppb).0 or more · optional	38.9
`co`	float	Carbon monoxide (CO) concentration in parts per million (ppm).0 or more · optional	0.5
`so2`	float	Sulfur dioxide (SO2) concentration in parts per billion (ppb).0 or more · optional	7.9
`temperature_c`	float	Ambient temperature in degrees Celsius at the time of reading.optional	18.3
`humidity_percent`	float	Relative humidity percentage at the time of reading.0 to 100 · optional	52.1
Dates and times 1 column
`timestamp`	datetime	Date and time when the sensor reading or event was recorded.	2024-06-01T07:43:12
True or false 3 columns
`incident_reported`	boolean	Indicates if the event triggered an incident report for response.	false
`compliance_flag`	boolean	Indicates if the reading is compliant with municipal air quality standards.optional	true
`data_quality_flag`	boolean	Indicates if the sensor reading passed data quality checks.optional	true

Use it for

A government dashboard
The incident_reported rate, humidity_percent by event_type and a breakdown of zone_id. Excel, Power BI or Tableau.
Why do 21 of 64 rows have incident_reported = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Events64
EVT-20240601-000152.1normal
EVT-20240601-000268.4threshol…
EVT-20240601-000347.8anomaly
A software demo
Believable events with sensor_id, zone_id and zone_name to fill a screen in front of a buyer.

This dataset64 rows21 columns

Yours1,000 rows21 columns

blueprint · smart-city-air-quality-events

Behind this dataset

Same schema. As many rows as you need.

These 64 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each row represents a unique air quality incident detected by IoT sensors
Includes timestamp, sensor ID, zone, pollutant levels, and incident severity
Severity must be categorized as Low, Moderate, or Critical based on pollutant thresholds
Response action status required (Pending, In Progress, Resolved)
Sensor readings must be validated against city environmental standards

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: smart-city-air-quality-events

What should your data show?

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Sample rows

What the 64 rows show

Columns

Use it for

A government dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

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What should your data show?