A Simple Methodology for the Determination of Back Trajectories

Abstract

This study comprehensively characterizes hourly fine particulate matter (PM_2.5) concentrations measured via a tapered element oscillating microbalance (TEOM), β-gauge, and nephelometer from four different monitoring sites in U.S. Environment Protection Agency (EPA) Region 5 (in U.S. states Illinois, Michigan, and Wisconsin) and compares them to the Federal Reference Method (FRM). Hourly characterization uses time series and autocorrelation. Hourly data are compared with FRM by averaging across 24-hr sampling periods and modeling against respective daily FRM concentrations. Modeling uses traditional two-variable linear least-squares regression as well as innovative nonlinear regression involving additional meteorological variables such as temperature and humidity. The TEOM shows a relationship with season and temperature, linear correlation as low as 0.7924 and nonlinear model correlation as high as 0.9370 when modeled with temperature. The β-gauge shows no relationship with season or meteorological variables. It exhibits a linear correlation as low as 0.8505 with the FRM and a nonlinear model correlation as high as 0.9339 when modeled with humidity. The nephelometer shows no relationship with season or temperature but a strong relationship with humidity is observed. A linear correlation as low as 0.3050 and a nonlinear model correlation as high as 0.9508 is observed when modeled with humidity. Nonlinear models have higher correlation than linear models applied to the same dataset. This correlation difference is not always substantial, which may introduce a tradeoff between simplicity of model and degree of statistical association. This project shows that continuous monitor technology produces valid PM_2.5 characterization, with at least partial accounting for variations in concentration from gravimetric reference monitors once appropriate nonlinear adjustments are applied. Although only one regression technically meets new EPA National Ambient Air Quality Standards (NAAQS) Federal Equivalent Method (FEM) correlation coefficient criteria, several others are extremely close, showing optimistic potential for use of this nonlinear adjustment model in garnering EPA NAAQS FEM approval for continuous PM_2.5 sampling methods.     

Automated Economic Analysis Model For Hazardous Waste Minimization

Abstract

The U.S. Army has established a policy of achieving a 50 percent reduction in hazardous waste generation by the end of 1992. To assist the Army in reaching this goal, the Environmental Division of the U.S. Army Construction Engineering Research Laboratory (USACERL) designed the Economic Analysis Model for Hazardous Waste Minimization (EAHWM). The EAHWM was designed to allow the user to evaluate the life cycle costs for various techniques used in hazardous waste minimization and to compare them to the life cycle costs of current operating practices. The program was developed in C language on an IBM compatible PC and is consistent with other pertinent models for performing economic analyses. The potential hierarchical minimization categories used in EAHWM Include source reduction, recovery and/or reuse, and treatment. Although treatment is no longer an acceptable minimization option, its use is widespread and has therefore been addressed in the model. The model allows for economic analysis for minimization of the Army’s six most important hazardous waste streams. These include, solvents, paint stripping wastes, metal plating wastes, industrial waste-sludges, used oils, and batteries and battery electrolytes. The EAHWM also includes a general application which can be used to calculate and compare the life cycle costs for minimization alternatives of any waste stream, hazardous or non-hazardous. The EAHWM has been fully tested and implemented in more than 60 Army installations in the United States.     

A Profile and Management of the US Army's underground storage tanks

The US Army owns more than 10,000 underground storage tanks (USTs), many of which are old and may be leaking. The Resource Conservation and Recovery Act of 1976 required tank owners to collect and report data on them by May 1986. In order to manage the large amounts of information on its USTs, the Army developed a microcomputer-based data base system. The data base system is user friendly and allows the user to store, organize, and manipulate the UST data. A leak potential index (LPI) was also developed and calculated for each of the Army's USTs. The LPI is used to prioritize USTs so that those with higher LPIs can be monitored closely. A characteristic profile of Army USTs according to construction material, capacity, age, content, and LPI is presented in this paper.The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the US Department of the Army, nor does the mention of trade names or commercial products constitute endorsement or recommendation for use.