AQUAIR: A High-Resolution Indoor Environmental Quality Dataset for Smart Aquaculture Monitoring
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918150046482432 |
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| author | Sabiri, Youssef Houmaidi, Walid Maadi, Ouail El Chtouki, Yousra |
| author_facet | Sabiri, Youssef Houmaidi, Walid Maadi, Ouail El Chtouki, Yousra |
| contents | Smart aquaculture systems depend on rich environmental data streams to protect fish welfare, optimize feeding, and reduce energy use. Yet public datasets that describe the air surrounding indoor tanks remain scarce, limiting the development of forecasting and anomaly-detection tools that couple head-space conditions with water-quality dynamics. We therefore introduce AQUAIR, an open-access public dataset that logs six Indoor Environmental Quality (IEQ) variables--air temperature, relative humidity, carbon dioxide, total volatile organic compounds, PM2.5 and PM10--inside a fish aquaculture facility in Amghass, Azrou, Morocco. A single Awair HOME monitor sampled every five minutes from 14 October 2024 to 9 January 2025, producing more than 23,000 time-stamped observations that are fully quality-controlled and publicly archived on Figshare. We describe the sensor placement, ISO-compliant mounting height, calibration checks against reference instruments, and an open-source processing pipeline that normalizes timestamps, interpolates short gaps, and exports analysis-ready tables. Exploratory statistics show stable conditions (median CO2 = 758 ppm; PM2.5 = 12 micrograms/m3) with pronounced feeding-time peaks, offering rich structure for short-horizon forecasting, event detection, and sensor drift studies. AQUAIR thus fills a critical gap in smart aquaculture informatics and provides a reproducible benchmark for data-centric machine learning curricula and environmental sensing research focused on head-space dynamics in recirculating aquaculture systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_24069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AQUAIR: A High-Resolution Indoor Environmental Quality Dataset for Smart Aquaculture Monitoring Sabiri, Youssef Houmaidi, Walid Maadi, Ouail El Chtouki, Yousra Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Applications 62M10, 68T45, 62P35, 92C40, 65C20, 60G35, 92C42, 92C35, 93E10 I.2.6; C.2.4; H.3.4; I.2.4; H.3.5; C.2.4; C.3; I.4.8; I.5.1; J.3; K.6.1; H.2.8 Smart aquaculture systems depend on rich environmental data streams to protect fish welfare, optimize feeding, and reduce energy use. Yet public datasets that describe the air surrounding indoor tanks remain scarce, limiting the development of forecasting and anomaly-detection tools that couple head-space conditions with water-quality dynamics. We therefore introduce AQUAIR, an open-access public dataset that logs six Indoor Environmental Quality (IEQ) variables--air temperature, relative humidity, carbon dioxide, total volatile organic compounds, PM2.5 and PM10--inside a fish aquaculture facility in Amghass, Azrou, Morocco. A single Awair HOME monitor sampled every five minutes from 14 October 2024 to 9 January 2025, producing more than 23,000 time-stamped observations that are fully quality-controlled and publicly archived on Figshare. We describe the sensor placement, ISO-compliant mounting height, calibration checks against reference instruments, and an open-source processing pipeline that normalizes timestamps, interpolates short gaps, and exports analysis-ready tables. Exploratory statistics show stable conditions (median CO2 = 758 ppm; PM2.5 = 12 micrograms/m3) with pronounced feeding-time peaks, offering rich structure for short-horizon forecasting, event detection, and sensor drift studies. AQUAIR thus fills a critical gap in smart aquaculture informatics and provides a reproducible benchmark for data-centric machine learning curricula and environmental sensing research focused on head-space dynamics in recirculating aquaculture systems. |
| title | AQUAIR: A High-Resolution Indoor Environmental Quality Dataset for Smart Aquaculture Monitoring |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Applications 62M10, 68T45, 62P35, 92C40, 65C20, 60G35, 92C42, 92C35, 93E10 I.2.6; C.2.4; H.3.4; I.2.4; H.3.5; C.2.4; C.3; I.4.8; I.5.1; J.3; K.6.1; H.2.8 |
| url | https://arxiv.org/abs/2509.24069 |