Prototype Development of an Early Warning System for Methane, NO₂, and SO₂ Exposure in Municipal Landfills
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1
Environmental Sciences Doctoral Program, Graduate School of Sebelas Maret University
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Department of Public Health, Faculty of Medicine, Universitas Sebelas Maret, Surakarta City, 57126, Indonesia
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Public Health Master Program, Graduate School, Universitas Sebelas Maret, Surakarta City, 57126
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ABSTRACT
Municipal solid waste landfills in developing countries often operate under poor environmental controls, leading to high emissions of hazardous gases such as methane (CH₄), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). These gases contribute to climate change, poor air quality, and respiratory health issues among nearby populations, particularly informal waste workers. Despite these risks, most landfill sites lack affordable, real-time monitoring systems. This study presents the design and development of a prototype early warning system for landfill gas exposure, integrating environmental health risk assessment with embedded sensor technology. The system uses MQ-series sensors to detect CH₄, NO₂, and SO₂, calibrated to convert output into µg/m³. Safe concentration thresholds were calculated based on inhalation rate, exposure duration, and reference doses (RfC), enabling the device to activate alarms when gas concentrations exceed health-based limits. Simulation tests were conducted in four scenarios: inorganic waste combustion, organic waste combustion, lighter gas exposure, and vehicle exhaust. Results showed strong detection performance for CH₄, particularly under lighter gas exposure (peak: 1.589 µg/m³), with rapid response time (<10 seconds) and high model accuracy (R² = 0.977). NO₂ and SO₂ remained at low or undetectable levels, indicating a need for improved sensitivity. The prototype offers a low-cost, portable, and replicable solution for high-risk landfill settings. It enables early hazard detection and supports risk-informed decision-making for landfill workers and local authorities. Future work should include sensor enhancement, digital integration, and field validation to strengthen the system's applicability in real-world landfill environments