This project analyzes NEON surface water sensor data to explore the relationship between nitrate (NO₃) and turbidity at the CUPE site in Puerto Rico. Using R, the workflow retrieves data directly from the NEON API, applies quality filtering, and aggregates measurements to daily and monthly time scales. The analysis includes exploratory visualizations, correlation matrices, and scatter plots to evaluate how nutrient concentrations relate to changes in water clarity and sediment dynamics. The goal is to provide an exploratory framework for understanding nutrient transport and water quality patterns in freshwater ecosystems.

This project analyzes high-frequency water quality sensor data from Boston (BOS) and Atlanta (ATL) to evaluate relationships between CDOM (Colored Dissolved Organic Matter) and other in situ environmental variables. Using R, I performed correlation analysis and developed multi-axis interactive time series visualizations to explore how CDOM covaries with conductivity, temperature, dissolved oxygen, pH, and optical backscatter. The workflow emphasizes reproducibility, structured data cleaning, and clear visualization using tidyverse, corrplot, and plotly. The result is an exploratory data analysis framework that supports rapid interpretation of sensor-based water quality dynamics across multiple urban systems.