Urban slums in Kenya face significant environmental challenges due to inadequate waste management systems and poor water quality, necessitating innovative solutions for sustainable urban development. A replication study involving the deployment of low-cost sensors to monitor environmental parameters. The study used statistical models to assess sensor performance and reliability under varying conditions. The deployed IoT sensors demonstrated a precision accuracy rate of 95% in monitoring air pollution levels, with a confidence interval indicating a robust standard error range of ±3% for the measurements. The replicated IoT solutions proved effective in urban slums, offering a viable and cost-effective alternative to traditional environmental monitoring methods. Further research should focus on integrating predictive analytics into the sensor data collection process to enhance real-time decision-making capabilities. IoT, Urban Slums, Environmental Monitoring, Precision Accuracy, Statistical Models 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.