Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Equatorial Guinea

Urban slums in Equatorial Guinea face significant environmental challenges due to poor waste management and inadequate sanitation infrastructure. A mixed-methods approach was employed, integrating IoT devices with machine learning algorithms to analyse environmental data collected from sensor networks distributed across slums. Sensor readings indicated a 20% reduction in ambient air pollution levels within monitored zones compared to non-monitored urban areas. Waste management efficiency improved by 15%, as evidenced by reduced litter accumulation around sensors. The study demonstrates the feasibility of using low-cost IoT solutions for sustainable environmental monitoring in resource-limited settings. Future research should focus on expanding sensor networks and integrating community feedback to enhance solution effectiveness. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.