Jennifer Jay, Ph.D.


Research Topics


Professor Jay’s Research Team Website

Near real-time detection of Fecal Indicator Bacteria and human-associated Bacteroides

A significant challenge facing coastal water quality is the ability to effectively identify and track sources of contamination throughout a watershed. The beach team is currently working to develop and optimize methodologies that more effectively and more rapidly identify the source of fecal contamination. The beach team has developed two IMS/ATP assays that allow for rapid (results within one hour) tracking of fecal contamination. This work focuses on the development and application potential of two IMS/ATP assays for rapid enumeration of Enterococcus and Bacteroides thetaiotaomicron in recreational waters. Application of IMS/ATP assays for assessment of water quality would allow for rapid, field measurement of water quality to be made.

Sediment survival of fecal indicator bacteria, pathogens, and antibiotic resistance genes

Molecular quantitative PCR is used to dentify sources contributing to poor water quality. These methods enable more successful source tracking by allowing for same-day water quality monitoring results and information regarding the source of the fecal contamination.  Knowledge of degradation rates of source-associated markers in sediments is important for data interpretation. However, at this point this topic is not well-understood.  We are working to better understand decay of pathogens, source associated markers, FIB, and antibiotic resistance genes (ARGs) in sediment. Sediment can act as a reservoir for FIB, pathogens, and ARGs; thus we are interested in examining the role of sediment and sediment characteristics in the fate and transport of these microbes and genes in the environment.

Arsenic-contaminated groundwater: Using experimental data and modeling approaches to develop remediation schemes

We are conducting batch and column studies to better understand chemical controls on the adsorption of As on natural sediments from our field site.  We are incorporating our field and experimental data into geochemical equilibrium models of the site to better design and predict performance of remediation options.

Arsenic (As) mobilization and contamination of groundwater affects millions of people worldwide. Progress in developing effective in-situ remediation schemes requires the incorporation of data from laboratory experiments and field samples into calibrated geochemical models.

At a site where leaching of high pH industrial waste from unlined surface impoundments led to mobilization of naturally occurring As up to 2 mg/L, sequential extractions of solid phase As as well as batch sediment microcosm experiments were conducted to understand As partitioning and solid-phase sorptive and buffering capacity. These data were combined with field data to create a series of geochemical models of the system with modeling software PHREEQC and FITEQL. Different surface complexation modeling approaches were compared, including component additivity, generalized composite, and a hybrid method. Developed models were fitted to data from batch acidification experiments to simulate potential remediation scenarios. Several parameters strongly influence the concentration of dissolved As including pH, presence of competing ions (particularly phosphate) and the number of available sorption sites on the aquifer solids. Lowering the pH of groundwater was found to have a variable, but limited impact on decreasing the concentration of dissolved As. The models indicate that in addition to lowering pH, decreasing the concentration of dissolved phosphorous and/or increasing the number of available sorption sites could significantly decrease the As solubility. The hybrid modeling results fit the experimental data well with reasonable effort and can be implemented in further studies for validation.

Adsorption and Desorption of Arsenate on Sandy Sediments from Contaminated and Uncontaminated Saturated Zones: Kinetic and Equilibrium Modeling

Application of empirical models to adsorption of contaminants on natural heterogeneous sorbents can be challenging due to the uncertainty associated with fitting the experimental data and determination of adjustable parameters. Sediment samples from contaminated and uncontaminated portions of a study site in Maine, USA were collected and investigated for adsorption of arsenate [As(V)]. Both pseudo-first order and pseudo-second order kinetic models were used to describe the results of single solute batch adsorption experiments. Piecewise linear regression of data resulted in a cutoff time point of 14 to19 h delineating the biphasic behavior of solute adsorption. During the rapid adsorption phase an average of 60-80% of total adsorption took place. Both Langmuir and Freundlich isotherms provided reasonable fits to the adsorption data at equilibrium. Langmuir-derived maximum adsorption capacity (St) of the studied sediments ranged between 29 and 97 mg/kg increasing from contaminated to uncontaminated sites. Results showed that the Langmuir model is very sensitive to the highest concentration used in the experiments and this parameter controls the derived adsorption capacity. Solid phase content of As in the sediments ranged from 3.8 to 10 mg/kg. Sequential extractions showed that while the As/Fe ratios were highest in the amorphous phase, the majority of phosphorous adsorbed on solid phase existed in this fraction, outcompeting As. High-pH desorption experiments conducted on sediments before and after adsorption experiments showed that a greater percentage of  adsorbed As was released back into solution from experimentally-loaded sediments than from original samples suggesting that As(V) adsorption takes place on different reversible and irreversible surface sites.

Antibiotic resistance genes in air, water, and soil in California

Increased microbial resistance to antibiotics is a major public health problem. The role of environmental compartments in the proliferation and dissemination of antibiotic resistance genes (ARGs) is just beginning to be explored. Animal agricultural use of antibiotics is known to foster development of antibiotic resistance in bacteria, and livestock workers have been shown to have higher resistance to antibiotics. However, people living in agricultural communities have not been assessed for exposure to ARGs through environmental routes.

Last year we conducted an assessment of ARG levels in ambient air, drinking water, and soils from 27 public parks across California. Six parks were chosen from each of four cities: Bakersfield, Fresno, Los Angeles, and San Diego. An effort was made to have a wide range in proximity to agriculture in the parks. In addition, three rural parks were analyzed for soils. Notably, for drinking water, air and soil, we saw city-to-city differences that were statistically significant and distinct for the two ARGs analyzed thus far. For example, for drinking water, levels for one of the genes analyzed per bacterium in the sample were dramatically higher in Bakersfield compared to the other cities while levels of the other gene analyzed much higher in Bakersfield, Fresno, and Los Angeles than they were in San Diego. For air, levels of one of the genes were much higher in Fresno and Bakersfield than Los Angeles or San Diego.

Antibiotic resistance of Escherichia coli isolated from Conventional, No Antibiotics, and Pristine Organic chicken meat

The use of antibiotics for therapeutic and non-therapeutic purposes in livestock farms can promote the development of antibiotic resistant bacteria. In this study, we cultured Escherichia coli from retail poultry falling into three categories of farming practices: Conventional, No Antibiotics, and Pristine Organic. We then examined the antibiotic resistance of the E. coli isolates (n = 263) by exposing them to six common antibiotics via disk diffusion and a high-throughput, liquid culture-based method: doxycycline, cefoperazone, gentamicin, ampicillin, oxytetracycline, and erythromycin.