A NALYSIS OF E XISTING S EPTIC S YSTEM D ENSITY

Because septic systems are significant contributors of nitrate in ground water, an analysis of the existing septic system density within the Highlands Region was performed. Septic system density as determined from 1990 census data, the last year that septic system information was reported in the United States census, is shown in the figure Septic System Density in HUC14 Basins, from 1990 Census

Highlands Water Resources Technical Report Volume 1: Watersheds and Water Quality

130

Septic System Density in HUC14 Basins, from 1990 Census Data of the New Jersey Highlands

This figure shows that by 1990, the great majority of the Highlands Region had a density of less than one to one septic system per acre.

There were some areas with as few as 0.1 septic systems per acre, mostly in undeveloped areas, but this also occurred in areas of higher density development where it is likely that much of the census block was already served by public sewer. The highest septic system densities are in the central, eastern and northeastern areas that were more urbanized, yet had significant areas that had not yet been provided with sewer service in 1990. One exaggeration to this general pattern is the area surrounding Lake Hopatcong, where septic system systems were installed on small lots as the norm during the process of local residential development. Due to the deleterious impacts of this historic practice on lake water quality, this area is currently in the process of being sewered. Septic system density for non-sewered residential areas in the Highlands Region is shown in the figure. The 1990 census information was updated using 2000 census data and dasymetric mapping techniques that allow for the finer resolution in determining where septic systems may be in use, based on remote land use data indicating where residential land use occurs and information developed regarding the location of non-sewered areas. The assumption is that a house without sewer service indicates the

Highlands Water Resources Technical Report Volume 1: Watersheds and Water Quality

131

location of a septic system to accommodate the need for wastewater treatment. This combination of data was used to refine the 1990 census block-based septic system density mapping.

Septic system density as determined using this method is shown in the figure Septic System Density in

Non-Sewered Residential Areas of the Highlands Region, from 2000 Census Data Dasymetric Mapping of the New Jersey Highlands The related figure Septic System Density in HUC14 Basins, from 2000 Census Data Dasymetric Mapping of the New Jersey Highlands, also generated from U.S. Census 2000 Data, shows the

number of septic systems per HUC14, but only accounts for the non-sewered areas and expresses that density as if it applied to the entire HUC14, which is not actually the case. The density shown in this figure does not normalize the data over the entire HUC14. It simply illustrates the data for the non-sewered areas. While this allows for analysis of the use of individual systems on a subwatershed scale, the difference between the dasymetric-derived data, which can be considered spatially “concentrated”, versus expressing the data as an overall value for the entire HUC 14 must be clearly understood. The septic system density shown is not assumed to be evenly distributed over the entire land area of the subwatershed.

Septic System Density in Non-Sewered Residential Areas of the Highlands Region,from 2000 Census Data Dasymetric Mapping of the New Jersey Highlands

Highlands Water Resources Technical Report Volume 1: Watersheds and Water Quality

132

Septic System Density in HUC14 Basins, from 2000 Census Data Dasymetric Mapping of the New Jersey Highlands

Normalization of the data is an additional, necessary step for data analysis and modeling. Normalization techniques allow one to "compare apples and oranges" and adjust map values to express the data in a way that is useful for the analysis in question. While the descriptive statistics are different, the numeric and spatial relationships in the data are preserved during normalization. Looking at the figure generated from the dasymetric mapping, it appears that in 2000, most of the Highlands Region had an increased septic system density compared to 1990, with values ranging from less than one to four septic systems per acre. This increase likely reflects a few factors, beyond the more accurate distribution allowed for by the dasymetric mapping and normalization issue discussed above. Among them are an overall increase in development across the Region and residences being built in less urbanized areas that are likely to be non-sewered. This pattern is a consequence of where available land was located and homebuyers’ preferences. The net result is that while there was a demonstrated increase in septic system density, it is not likely to be as great in reality as a comparison of the data for 1990 and 2000 would initially indicate.

While these discrepancies due to the lag time in acquiring data and analytical techniques are acknowledged, the Highlands Council used the most recent and reliable data available to perform theses analyses. The Council also intends to refine the information as better information becomes available or can be developed, and to develop more refined logistic regression models.

Highlands Water Resources Technical Report Volume 1: Watersheds and Water Quality

133

DETERMINING SUB‐WATERSHED NITRATE CONCENTRATIONS IN GROUND WATER WITH  LOGISTIC REGRESSION MODELING 

The statistical approach of calculating the median nitrate concentration from measured well data is an appropriate method for characterizing water quality at a regional scale. However, the median value calculated with the water quality data is biased by the well locations, which are disproportionately located in more developed areas subject to higher nitrate concentrations. Furthermore, this type of analysis, while important, is limited for estimating median nitrate concentrations at the smaller subwatershed scale, or for quantifying and understanding how concentrations change with different land use conditions.

In order to overcome these limitations, an empirical-based logistic regression modeling approach was used to estimate median nitrate concentrations at the subwatershed scale based upon measurable land use characteristics and conditions. In addition, the models were used to estimate the median nitrate concentration for the Highlands Region as a whole, as well as pristine conditions prior to land development. The models also helped identify land use variables that influence and/or are correlated with nitrate concentrations in ground water.