Environmental Monitoring in Oil-Producing Regions: Integrating IoT, Machine Learning, and Community Participation for Sustainable Development in the Niger Delta

Abstract

Technology alone cannot resolve the environmental crisis in oil-producing regions such as the Niger Delta, where oil spills, gas flaring, and industrial discharge have degraded ecosystems, endangered public health, and eroded community trust. This article presents a meta-analytic synthesis of over seventy studies (2020–2025) on IoT, machine learning, and participatory approaches to environmental monitoring, synthesising technological and participatory literatures into a single, integrated framework for sustainable development. It documents the scale of degradation from oil spills, gas flaring, and water contamination; catalogues structural limitations, namely delayed reporting, weak coordination, and limited community involvement; and synthesises the performance of IoT- and ML-enabled monitoring architectures reported across the corpus. It makes the case for community-based monitoring as a necessary complement to sensing, arguing that citizen participation, transparency, and environmental justice are structural requirements for legitimacy. It concludes with an integrated, multi-stakeholder framework and policy recommendations for government, oil companies, researchers, and communities.