A low-cost IoT-based multi-gas monitoring platform for assessing air quality dynamics in laying hens receiving dietary Chlorella supplementation

Dietary microalgae supplementation may influence the gaseous environment of poultry housing by altering nutrient utilization and manure characteristics, but continuous, treatment-level monitoring of in-pen gases remains challenging. This study combined a low-cost Internet of Things (IoT)-based multi-gas monitoring platform with a nutritional intervention to investigate treatment-associated changes in the gaseous environment of laying-hen pens receiving graded levels of dietary Chlorella supplementation. Following six weeks of dietary supplementation, three pens receiving 0% (Control), 0.25%, or 0.50% Chlorella were continuously monitored for 8 days using dedicated sensing units equipped with MQ137, MQ4, and MG811 sensors for ammonia ( NH 3 ), methane ( CH 4 ), and carbon dioxide ( CO 2 ), respectively. Each unit was interfaced with a Raspberry Pi 5 and ADS1115 analog-to-digital converter, with measurements transmitted to ThingSpeak for remote logging. Before deployment, the sensing units were co-located to establish unit-specific clean-air baselines and identify hardware faults. Sensor outputs were subsequently calibrated, quality controlled, smoothed, and aggregated into 30-min observations for analysis. Compared with the Control pen, both Chlorella -supplemented pens generally exhibited lower sensor-derived CO 2 , NH 3 , and CH 4 concentrations during the later monitoring periods. The 0.25% treatment showed the most consistent reduction in CO 2 , whereas 0.50% Chlorella generally showed the greatest reduction in CH 4 . NH 3 concentrations were also lower in the supplemented pens after the first 48-h period. Continuous monitoring additionally revealed within-day variation in NH 3 and CO 2 that was not evident from period-level summaries. The findings demonstrate the potential of low-cost edge-based sensing systems to continuously characterize poultry-house air quality and detect temporal environmental patterns associated with nutritional interventions. Such systems may support precision livestock farming by enabling scalable real-time environmental surveillance. Because only one pen was instrumented per dietary treatment and gas concentrations were estimated from low-cost sensors, the findings should be interpreted as exploratory treatment-associated patterns rather than definitive treatment effects. Replicated monitoring studies incorporating reference-grade instrumentation and environmental covariates are warranted.