Energy
13 columns
200 rows
8 formats

Real-Time Environmental Sensor Readings

This dataset provides granular, real-time environmental sensor readings—including temperature, humidity, and particulate matter—across diverse geographic locations. It enables environmental monitoring, anomaly detection, and supports advanced analytics for air quality assessment and public health research.

last updated 21 Aug 2025
by GoMask

The brief that made it

Real-time air quality monitoring and reporting

Sample rows

preview · 8 of 200 rows · all 13 columns

reading_idstring	sensor_statusstring	humidity_percentfloat	anomaly_detectedboolean	sensor_idstring	location_idstring	location_namestring	latitudefloat	longitudefloat	reading_timestampdatetime	temperature_celsiusfloat	pm2_5_ugm3float	pm10_ugm3float
RDG-00001	active	50.3	false	SNS-101A	LOC-01	East Office Level 2	40.7128	-74.006	2024-01-08T09:14:25Z	21.4	12.5	24.1
RDG-00002	active	49.7	false	SNS-101A	LOC-01	East Office Level 2	40.7128	-74.006	2024-01-08T09:19:31Z	22	11.9	22.8
RDG-00003	active	56	false	SNS-102B	LOC-02	Main Lab South Wing	40.7131	-74.0055	2024-01-14T10:01:17Z	19.8	9.7	21.3
RDG-00004	active	38.5	false	SNS-103C	LOC-03	Server Room West	40.7136	-74.0048	2024-01-19T11:36:08Z	24.2	18.1	35.4
RDG-00005	active	46.7	false	SNS-104D	LOC-04	Warehouse North	40.7142	-74.0039	2024-02-03T13:45:27Z	17.5	13.6	28.3
RDG-00006	active	89.2	false	SNS-201E	LOC-05	Parking Lot Outdoor	40.7151	-74.0025	2024-02-07T15:28:11Z	2.8	45.6	81.1
RDG-00007	error	100	true	SNS-202F	LOC-06	Open Field North Edge	40.7163	-74.0013	2024-02-11T22:16:39Z	-15.7	120.7	305
RDG-00008	active	97.3	false	SNS-202F	LOC-06	Open Field North Edge	40.7163	-74.0013	2024-02-11T22:19:33Z	-14.9	47.2	82.5
RDG-00009	active	32.2	false	SNS-301G	LOC-07	Cell Tower Base	40.7175	-74.0001	2024-02-25T03:07:54Z	16.2	130.5	98.9
RDG-00010	active	34.7	false	SNS-301G	LOC-07	Cell Tower Base	40.7175	-74.0001	2024-02-25T03:09:45Z	15.8	127.1	96.2
RDG-00011	error	0	true	SNS-302H	LOC-08	Mountain Station 3	42.3611	-71.057	2024-03-10T05:41:18Z	-25.2	0	0
RDG-00012	error	14.2	true	SNS-303J	LOC-09	Airport Runway West	33.9416	-118.4085	2024-03-13T16:25:44Z	33.9	150	305
RDG-00013	active	89.4	false	SNS-401K	LOC-10	Greenhouse A	44.9778	-93.265	2024-03-18T13:07:55Z	28.1	27.8	56.2
RDG-00014	maintenance	90.2	false	SNS-401K	LOC-10	Greenhouse A	44.9778	-93.265	2024-03-18T13:12:46Z	27.6	29.2	58.7
RDG-00015	active	78.5	false	SNS-402L	LOC-11	Tunnel North Portal	41.8781	-87.6298	2024-03-22T10:04:12Z	15.2	31.8	61.4
RDG-00016	error	100	true	SNS-501M	LOC-12	Experimental Station Equator	0	39	2024-03-25T22:56:03Z	70	5000	9000
RDG-00017	error	0	true	SNS-502N	LOC-13	blank	-66	140	2024-04-02T03:27:12Z	-50	0	0
RDG-00018	maintenance	0	true	SNS-502N	LOC-13	blank	-66	140	2024-04-02T03:31:16Z	-50	0	0
RDG-00019	active	51.2	false	SNS-601P	LOC-14	Office South Wing	40.7123	-74.0057	2024-04-09T15:23:41Z	19.6	13.9	26.1
RDG-00020	active	51.4	false	SNS-601P	LOC-14	Office South Wing	40.7123	-74.0057	2024-04-09T15:27:38Z	19.8	14.1	26.3

What the 200 rows show

from the 200-row sample

Error (sensor status) stands out: 37 of its 37 rows have anomaly_detected = true, against 13 of 163 for the rest.

25%anomaly_detected = true
50.0median humidity_percent
44locations
20.5median temperature_celsius
18.2median pm2_5_ugm3
35.5median pm10_ugm3

Anomaly detected rate by sensor_statusanomaly_detected = true

humidity_percent200 rows, in bands of 10

Median 50.0, from 0.0 to 100.0.

13 columns by typefrom the column list below

string 5
float 6
datetime 1
boolean 1

Columns

13 columns in four groups

blueprint · 13 columns
column	type	description	example
Text 5 columns
`reading_id`	string	Unique identifier for each sensor reading recordunique	RDG-00001
`sensor_id`	string	Unique identifier for the sensor device reporting the reading	SNS-101A
`location_id`	string	Unique identifier for the location where the sensor is installed	LOC-01
`location_name`	string	Human-readable name or description of the sensor locationoptional	East Office Level 2
`sensor_status`	string	Operational status of the sensor at the time of readingactive · inactive · maintenance · error · optional	active
Numbers 6 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	-74.006
`temperature_celsius`	float	Measured ambient temperature in degrees Celsius-50 to 70	21.4
`humidity_percent`	float	Measured relative humidity as a percentage (0-100%)0 to 100	50.3
`pm2_5_ugm3`	float	Measured particulate matter (PM2.5) concentration in micrograms per cubic meter (μg/m³)0 or more	12.5
`pm10_ugm3`	float	Measured particulate matter (PM10) concentration in micrograms per cubic meter (μg/m³)0 or more	24.1
Dates and times 1 column
`reading_timestamp`	datetime	Date and time when the sensor reading was recorded (UTC)	2024-01-08T09:14:25Z
True or false 1 column
`anomaly_detected`	boolean	Indicates whether the reading was flagged as anomalous by the monitoring systemoptional	false

Use it for

An energy dashboard
The anomaly_detected rate and humidity_percent by sensor_status. Excel, Power BI or Tableau.
Why do 50 of 200 rows have anomaly_detected = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Readings200
RDG-0000150.3active
RDG-00007100error
RDG-000110error
A software demo
Believable readings with sensor_id, location_id and location_name to fill a screen in front of a buyer.

This dataset200 rows13 columns

Yours1,000 rows13 columnslocation_id: UK only

blueprint · real-time-environmental-sensor-readings

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 13 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

Sensor ID and location coordinates included
Timestamp and environmental metrics logged
Calibration status flagged
Sensor type and manufacturer listed
Event anomalies marked

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: real-time-environmental-sensor-readings

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

An energy dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Energy

What should your data show?