Grantee Research Project Results

It was expected that this design would lead to a number of positive outputs/outcomes for both California Lutheran University and the community as a whole. Below is a list of four original questions and responses in terms of outputs and outcomes asked in the Phase I pilot study:

Environmental Sampling and Analysis: Our initial field-based studies centered on the design and development of a comprehensive sampling/storage protocol and the collection and tabulation of historical Ventura River watershed physicochemical data. Our investigative team, in collaboration with the Ventura County Public Works Agency (VCPWA), identified project goals, sampling locations, and instituted quality assurance procedures for proper time-dependent sampling of the river. Both sampling campaigns were performed at the Ojai Valley Sanitation District site identified by the VCPWA as a key location for study within the watershed and situated within the EPA-defined Level III Nutrient Ecoregion 6. Both water (filtered and nonfiltered) and surface sediment samples were taken in collaboration with field-based pH, dissolved oxygen and temperature measurements.

Figure 1. California Lutheran’s P3 team collecting water samples in the Ventura River.

 

River flow rates were obtained at the site from a United States Geological Survey (USGS) gauging point. Samples (including duplicates, field blanks and quality control samples) were transported back to the CLU laboratory for subsequent nutrient analysis using an automated flow injection analysis method. Total phosphorus (TP) concentrations ranged from 10 to 30 μM in sediments and 6 to 10 μM in the water column. The TP determined in the water column was at the lower end of the range (0.08 μM - 100 μM) defined by the EPA for our particular Nutrient Ecoregion. Dissolved phosphorus (the inorganic form readily available for biological use) concentrations were found to be at levels below 3 μM, values typically seen in such riverine systems. Total oxidized nitrogen (nitrate + nitrite) concentrations were in the range of 20 – 30 μM, values again within the range defined by the EPA (0.40 - 133 μM). For surface sediment samples, TP concentrations ranged from 20 – 40 μM. Total nitrogen (TN) determinations are currently being performed. We had limited success in analyzing samples for ammonia due to complications typical of such analyses—contamination from external sources and storage considerations. Efforts are underway to develop a more comprehensive preservation and storage protocol, with samples being prepared for trace metal analysis and pesticide determination. This will broaden our knowledge of the study area in both qualitative and quantitative terms.

 

Environmental Modeling and Assessment: The historical physicochemical data set obtained from VCPWA was valuable in our initial modeling efforts. As expected, this data set was dynamic in nature with a wide variety of parameters and concentration levels. Such complexity warrants the use of multivariate statistical approaches for meaningful data reduction and interpretation. Correlation analysis was initially used to detect the relationships among parameters with the measure of linear correlation assessed by coefficients (r). These correlations are presented on a scale from +1 to 0 to -1, with +1 being perfect positive linear correlation and - 1 indicative of perfect negative linear correlation. Correlation coefficients of r ≥ 0.40 were taken as meaningful at 99% confidence . For example, total mercury was strongly correlated with total dissolved solids (r = 0.94). Similarly, other metals followed this pattern. Total organic carbon was strongly correlated (r = 0.88) with biological oxygen demand (BOD). Flow rate, as expected, showed a high positive correlation with total suspended solids (r = 0.79).

Surface sediment correlation analysis is currently incomplete and will be addressed in Phase II of this project.

 

In order to gain a more reliable display method and a greater understanding of the relationships within the Ventura County watershed data set, we employed a technique termed principal component analysis (PCA). Principal components (PCs) are vehicles in which to map the structure of multivariate data sets as completely as possible using as few parameters as possible. Ostensibly, PCA reduces complex data sets to dimensionalities that are graphable for greater interpretation. Figure 2 presents the PC loading plot for the water column data. The first PC (Component 1) presents the linear combination of the original parameters that have the greatest variance.

 

Figure 2. Principal component loading plot for the Ventura River water column data.

 

Component 2 (uncorrelated with the first component) shows the second largest amount of variation. Component 1 had higher loadings for such parameters as total metals, total organic carbon, BOD, and total suspended solids. This suggests, for example, that such parameters either arise from a similar source and/or have similar transport and behavior characteristics. Component 1 is associated with pH, hardness as CaCO3, total dissolved solids, and conductivity. For surface sediments (data not shown), there was a major Component associated with metals and organic matter, which may represent heavy metal accumulation from similar sources within the watershed.

 

 

Additionally, a Component consisting of iron and manganese could be suggestive of manganese being included in stable iron sulfides. Although there were no noticeable anthropogenic inputs (e.g., sewage), we acknowledged that the potential for human influences within this region was strong. In summary, multivariate approaches such as those described proved valuable in examining and interpreting water column and surface sediment data. Initial results proved highly significant in our understanding of the physicochemical makeup of the Ventura River and will be a key driving force in Phase II of this project.

Evaluation of Risk and Disparities: Our study has shown that Ventura County is composed of a diverse population from various socio-economic and ethnic backgrounds. The table below presents Phase I selected census tract data and relevant information for a total of 40 subjects all in close proxmity to the watershed under study. For each census tract, five-point scale (from 1= Strongly Disagree to 5=Strongly Agree) survey questionaires assessing percieved risks and environmental exposure were adminstistered to local populations. Also included were questions on demographics, occupation and family history.

 

A strong correlation (r = 0.73) between communities with high minority populations and percent of families living under poverty level was found. Of particular notice was the city of Oxnard which had one of the highest percentage of families living below poverty (11.1%), as well as one of the lowest household incomes. Of the surveys conducted in the city, the majority (≈75%) felt that there were disparities among communities of low income and higher income.

 

 

Unexpectedly, a strong correlation (r = 0.51) between income and perceived risks was found; persons of lower income responded with less concern for potential health risks. We believe this is a result of a lack of formal education and training in regards to the hazards of environmental pollutants. In Phase II, more concentrated efforts in increasing our study population will be performed. In addition, a true understanding of the populations’ perception of risks and actual exposure cannot be obtained without extended environmental data sets, survey questionnaires and related environmental health information.

 

People, Prosperity, and the Planet: This student-led design has intially benefited local communities by providing a quantitative study of pollutants and a means to express their concerns in relation to potential exposure. Two different economic cross sections and varying ethnicities have interacted with our team on a personal and professional level. Students also benefited by studying real life issues facing society and aquired skills to prepare them for a career in the natural and social science professions. The short term costs to the team and the community were minimal with efficient use of EPA resources and investigator time. The design did not exhaust or degrade the local environment, provided intial steps for balancing the economic, social and environmental needs of the community, and will likely provide a model to study the long-term relationships between environmental exposure, social stability and potential health disparities.

Initial steps towards a more quantitative, methodological student-based design that defines and identifies potential pollutants in aquatic systems, adequately models their behavior and transport, and addresses environmental justice concerns to educate the community and public lawmakers is presented. We have identified key environmental parameters and concentrations within the Ventura River with progression towards a more comprehensive database. Initial modeling results have shed light on parameter associations and potential sources within the watershed. Survey questionnaires addressing perceived risks and disparities were distributed throughout the community, one which houses a diverse ethnic makeup with varied economic levels. In addition, we have actively involved undergraduate students in critical thought processes, improved their perception of science and community awareness and have begun instituting such inquiry-based learning in lower and upper division Chemistry and Environmental Science courses.

 

Phase II of this project will involve a more concentrated approach to characterizing the Ventura River watershed, identifying pollutant sources, and investigating the correlation between the chemical composition of aquatic systems and residents’ perceptions of risks and disparities in both low- and higher-income communities. Phase II EPA funding will provide the necessary resources to fulfill our objective of designing and developing a field-based monitoring system for higher temporal resolution aquatic measurements. This will provide a more extensive data set to refine our environmental modeling efforts and to further characterize the biogeochemical cycling, fate and transport of potential pollutants. Additional resources will also be valuable in meeting our objectives of increasing the number of residents participating in questionnaire surveys. The ability to reach a wider audience within the county and surrounding counties in the Los Angeles region will aid in assessing environmental exposures and the social processes that identify health disparities. Ultimately, results will be disseminated back to the community and local and regional authorities.