Biomedical subjects

James D Englehardt

Publications and source records attributed to James D Englehardt.

7 recordsLinked to original sources

PubMed2006

Treatment of landfill leachate by the Fenton process.

In recent years, studies of leachate treatment by conventional Fenton, photo-Fenton and electro-Fenton processes have indicated that these methods can effectively reduce concentrations of organic contaminants and color. In addition, the process can increase the biodegradable fraction of organic constituents in leachate, particularly in mature or biologically recalcitrant leachate. Oxidation and coagulation both play important roles in the removal of organics. Initial pH, dosages of Fenton reagents, aeration, final pH, reagent addition mode, temperature, and UV irradiation may influence final treatment efficiency. In this paper, current knowledge of performance and economics of Fenton processes for treatment of landfill leachate as reported for laboratory, pilot and full-scale studies is reviewed, with the conclusion that the Fenton process is an important and competitive technology for the treatment or pretreatment of landfill leachate.

Hydrogen Peroxide↗

PubMed2006

Electrochemical oxidation for landfill leachate treatment.

This paper aims at providing an overview of electrochemical oxidation processes used for treatment of landfill leachate. The typical characteristics of landfill leachate are briefly reviewed, and the reactor designs used for electro-oxidation of leachate are summarized. Electrochemical oxidation can significantly reduce concentrations of organic contaminants, ammonia, and color in leachate. Pretreatment methods, anode materials, pH, current density, chloride concentration, and other additional electrolytes can considerably influence performance. Although high energy consumption and potential chlorinated organics formation may limit its application, electrochemical oxidation is a promising and powerful technology for treatment of landfill leachate.

Chlorides↗

PubMed2006

Predictive Bayesian microbial dose-response assessment based on suggested self-organization in primary illness response: Cryptosporidium parvum.

The probability of illness caused by very low doses of pathogens cannot generally be tested due to the numbers of subjects that would be needed, though such assessments of illness dose response are needed to evaluate drinking water standards. A predictive Bayesian dose-response assessment method was proposed previously to assess the unconditional probability of illness from available information and avoid the inconsistencies of confidence-based approaches. However, the method uses knowledge of the conditional dose-response form, and this form is not well established for the illness endpoint. A conditional parametric dose-response function for gastroenteric illness is proposed here based on simple numerical models of self-organized host-pathogen systems and probabilistic arguments. In the models, illnesses terminate when the host evolves by processes of natural selection to a self-organized critical value of wellness. A generalized beta-Poisson illness dose-response form emerges for the population as a whole. Use of this form is demonstrated in a predictive Bayesian dose-response assessment for cryptosporidiosis. Results suggest that a maximum allowable dose of 5.0 x 10(-7) oocysts/exposure (e.g., 2.5 x 10(-7) oocysts/L water) would correspond with the original goals of the U.S. Environmental Protection Agency Surface Water Treatment Rule, considering only primary illnesses resulting from Poisson-distributed pathogen counts. This estimate should be revised to account for non-Poisson distributions of Cryptosporidium parvum in drinking water and total response, considering secondary illness propagation in the population.

Animals↗

PubMed2003

Analytical predictive Bayesian assessment of occupational injury risk: municipal solid waste collectors.

Unlike other waste streams, municipal solid waste (MSW) is collected manually, and MSW collection has recently been found to be among the highest-risk occupations in the United States. However, as for other occupational groups, actual total injury rates, including the great majority of injuries not compensated and those compensated by other insurance, are not known. In this article a predictive Bayesian method of assessing total injury rates from available information without computation is presented, and used to assess the actual numbers of musculoskeletal and dermal injuries requiring clinical care of MSW workers in Florida. Closed-form predictive Bayesian distributions that narrow progressively in response to information, representing both uncertainty and variability, are presented. Available information included workers' compensation (WC) data, worker population data, and safety records for one private and one public collection agency. Subjective input comprised epidemiological and medical judgment based on a review of 165 articles. The number of injuries was assessed at 3,146 annually in Florida, or 54 +/- 18 injuries per 100 workers per year with 95% confidence. Further, WC data indicate that the injury rate is 50% higher for garbage collectors specifically, indicating a rate of approximately 80 per 100 workers. Results, though subject to uncertainty in worker numbers and classification and reporting bias, agreed closely with a survey of 251 MSW collectors, of whom 75% reported being injured (and 70% reported illness) within the past 12 months. The approach is recommended for assessment of total injury rates and, where sufficient information exists, for the more difficult assessment of occupational disease rates.

Accidents, Occupational↗

PubMed2002

Scale invariance of incident size distributions in response to sizes of their causes.

Incidents can be defined as low-probability, high-consequence events and lesser events of the same type. Lack of data on extremely large incidents makes it difficult to determine distributions of incident size that reflect such disasters, even though they represent the great majority of total losses. If the form of the incident size distribution can be determined, then predictive Bayesian methods can be used to assess incident risks from limited available information. Moreover, incident size distributions have generally been observed to have scale invariant, or power law, distributions over broad ranges. Scale invariance in the distributions of sizes of outcomes of complex dynamical systems has been explained based on mechanistic models of natural and built systems, such as models of self-organized criticality. In this article, scale invariance is shown to result also as the maximum Shannon entropy distribution of incident sizes arising as the product of arbitrary functions of cause sizes. Entropy is shown by simulation and derivation to be maximized as a result of dependence, diversity, abundance, and entropy of multiplicative cause sizes. The result represents an information-theoretic explanation of invariance, parallel to those of mechanistic models. For example, distributions of incident size resulting from 30 partially dependent causes are shown to be scale invariant over several orders of magnitude. Empirical validation of power law distributions of incident size is reviewed, and the Pareto (power law) distribution is validated against oil spill, hurricane, and insurance data. The applicability of the Pareto distribution, in particular, for assessment of total losses over a planning period is discussed. Results justify the use of an analytical, predictive Bayesian version of the Pareto distribution, derived previously, to assess incident risk from available data.

Journal Article↗

PubMedDate not supplied

Predictive population dose-response assessment for Cryptosporidium parvum: infection endpoint.

Data validation of safe doses of pathogens in drinking water consistent with public health goals is not possible due to the number of subjects that would be needed at each dose. Because of this problem, together with the difficulty in extrapolating pathogenic response between species, and the ability of microbes to adapt rapidly, confidence-level-dependent assessments of Cryptosporidium parvum dose-response have been developed. However, these results, even on a relative basis, are dependent on confidence level, and the lack of scientific basis for this choice hampers efforts to set water quality standards. Therefore, a predictive Bayesian dose-response assessment method was proposed previously. In this article, a hierarchical predictive population dose-response Bayesian assessment for C. parvum is presented for the infection endpoint. Available data on the infectivity of three isolates of C. parvum, genotype C, were adjusted for sensitive and antibody-positive subpopulations not proportionately represented in the data, by bootstrap analysis. The diverse mean infectivities of the isolates were used to obtain a predictive distribution for population infectivity, used in turn to obtain the predictive population dose-response function. The predictive result is a distribution of unconditional probability of infection, based on available dose-response information. Information includes theoretical and empirical evidence for the conditional beta-Poisson parametric dose-response function. Results indicate that a dose of 6 x 10(-6) oocysts per exposure corresponds to 10(-4) infections per capita year. An allowable dose corresponding to goals of the SWTR should be increased over this value to reflect the illness endpoint, while potentially being reduced to account for secondary transmission among hosts if important for gastroenteritis in developed countries.

Animals↗

PubMedDate not supplied

Comparative assessment of municipal wastewater disposal methods in southeast Florida.

A comparative assessment of the risks of three effluent disposal alternatives currently available to wastewater utilities in Southeast Florida is presented in this paper. The alternatives are: deep well injection and ocean outfalls following secondary treatment, and surface water (canal) discharges following secondary wastewater treatment, filtration and nutrient removal. Water quality data, relative to disposal of wastewater treatment plant effluent were gathered, along with water quality data on the receiving waters, from utilities. Comparisons and conclusions regarding potential health concerns associated with the three disposal alternatives are presented. The results indicated that health risks associated with deep wells were generally lower than those of the other two alternatives. The proximity of injection wells to aquifer storage and recovery wells was a determining factor relative to injection well risk. Urban ecological risks were also indicated to be lower, though impacts of urban water use/reuse to the Everglades were not studied. Additional data collection and analysis were recommended to understand the effects of wastewater management on the cycling of water, nutrients and other constituents on southeast Florida. In particular, it was recommended that monitoring of effluents for nitrosamines and pharmaceutically active substances be implemented on a broad scale.

Bayes Theorem↗