In recent decades, aquatic systems have experienced major problems with water quality due to high nutrient concentrations from both point and non-point sources resulting from industrialization, urbanization, and population growth. While nutrient pollution due to land use change cannot be ignored, point sources such as combined sewer overflows and discharging sites have also contributed to the problem. Integrated hydrodynamic, chemical, and biological models have been developed to simulate nutrient transportation from both sources. This paper reviews and analyzes water quality data from published literature to evaluate nutrient pollution in aquatic systems and emphasizes the need for a continuously developed integrated monitoring and management plan to regulate nutrient discharges.
Two studies were conducted in northern New Jersey, USA, to examine the impact of land use change on water quality in the Passaic River and to estimate nutrient fluxes from the Passaic and Hackensack Rivers into Newark Bay. The first study used long-term water quality monitoring and land-use data to show that urban land use is a significant contributor to water quality problems in the Passaic River, while natural landscapes dominate the area. The second study collected bi-weekly total inorganic nitrogen and orthophosphate concentration data over 15 years to estimate the annual nutrient loading from both rivers, which varied seasonally due to weather conditions such as hurricane events.
Another study investigated the relationship between total suspended solids (TSS) loadings and Land Use Land Cover (LULC) type across six drainage basin areas in New Jersey, using 16 years of published monitoring data. The study found that water discharge has a strong correlation with the area of a drainage basin and that TSS concentration is positively correlated with medium and high developed LULC types and negatively impacted by forests and wetlands. The study also used the ARIMA model to forecast future TSS loading trends and fluctuations over time, indicating its effectiveness in capturing cyclic patterns, especially with seasonal variations in time series data
