{ "background": "Artisanal and small-scale mining (ASM) in West Africa poses significant environmental risks, including water contamination and soil degradation. Current monitoring solutions are often prohibitively expensive and technically complex for widespread deployment in remote regions, leading to a critical data gap.", "purpose and objectives": "This study aimed to design, fabricate, and field-test a novel, low-cost Internet of Things (IoT) sensor network for real-time, in-situ monitoring of key environmental parameters at ASM sites.", "methodology": "A modular sensor node was developed using low-cost, locally available components to measure turbidity, pH, and particulate matter. A long-range wide-area network (LoRaWAN) gateway transmitted data to a custom dashboard. Network performance and sensor accuracy were validated over an extended deployment at three active mining sites. Data analysis employed a linear mixed-effects model: $y{ij} = \\beta0 + \\beta1 x{ij} + ui + \\epsilon{ij}$, where $ui \\sim N(0, \\sigma^2u)$, to account for site-specific random effects.", "findings": "The system achieved a mean data transmission success rate of 94.2% (95% CI: 91.5, 96.3) under field conditions. A key finding was that particulate matter concentrations measured downstream from ore-crushing activities consistently exceeded upstream baselines by a factor of 8 to 12 during operational hours.", "conclusion": "The developed IoT network provides a technically viable and cost-effective solution for continuous environmental diagnostics, demonstrating robust performance in a challenging operational setting.", "recommendations": "Future work should integrate additional sensors for heavy metals and explore solar-powered node designs for enhanced autonomy. Scaling requires partnerships with local regulatory bodies for maintenance and data governance.", "key words": "Internet of Things, environmental monitoring, sensor network, artisanal mining, low-cost technology, water quality", "contribution statement": "This paper presents