Receptor Model Evaluation of the Southeast Michigan Ozone Study Ambient NMOC Measurements

Abstract

Large-scale studies like the Southeast Michigan Ozone Study (SEMOS) have focused attention on quantifying and spedating inventories for volatile organic compounds (VOCs). One approach for evaluating the accuracy of a VOC emission inventory is the development of a chemical mass balance (CMB) receptor model for ambient non-methane organic compound (NMOC) measurements. CMB evaluations of ambient hydrocarbon data provide a sample-specific allocation of emissions to individual source categories. This study summarizes the results of an application of the CMB model to the NMOC data from the SEMOS study. Comparison of CMB results with emission inventory values for the Detroit area show that vehicle emissions are well represented by the inventory, as are architectural coatings and coke ovens. Estimated emissions from petroleum refineries and graphic arts industries are much lower in the inventory than determined from the receptor allocation. Under-reporting of fugitive VOC emissions from petroleum refineries is an ongoing problem. Emissions from graphic arts industries are underestimated in the inventory partly because of the broad characterization of the emission factor (i.e., mass emitted/capita), which may be less useful when specific locations and days are under consideration. This study also demonstrates the effectiveness of the CMB approach when used prospectively to track the implementation of emission control strategies. While vehicle emission concentrations were unchanged from 1988 to 1993, measurement-based CMB results suggest a decrease in evaporative emissions during this time period resulting from Reid vapor pressure (RVP) reductions (from 11.0 psi in 1988 to 8.6 psi in 1993) and fleet turnover. Changes in emissions from coke plants and petroleum refineries were also seen in the CMB allocations for these sources.     

Nonlinear regression adjustments of multiple continuous monitoring methods produce effective characterization of short-term fine particulate matter

This study comprehensively characterizes hourly fine particulate matter (PM(2.5)) concentrations measured via a tapered element oscillating microbalance (TEOM), beta-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 beta-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.     

Adjusting tapered element oscillating microbalance data for comparison with Federal Reference Method PM2.5 measurements in region 5

Continuous monitoring of particulate matter (PM) with a diameter less than 2.5 microm (PM2.5) is quickly gaining acceptance as an alternative means of measuring fine PM in the United States. For this project, data were taken from all monitoring sites within Region 5 that used the tapered element oscillating microbalance (TEOM) for PM2.5 and had a collocated Federal Reference Method (FRM) monitor. Scatter plots of TEOM versus FRM show that for a significant fraction of the observations, an independent factor causes the TEOM to underestimate the FRM value. This underestimation appears to increase as temperature decreases. For this analysis, a linear relationship was fit to the TEOM versus FRM data, allowing a break or knot in the relationship, modeled as a change of slope, at a site-specific temperature. To test whether the models are adequate for adjusting future measurements, models were also developed using the first year of data only, and the remaining observations were used to test the durability of the relationships. For all but one monitor in Minnesota, the models developed for each site had consistently high R2s, were predictive of future measurements, and could be used to derive "FRM-like" results from the TEOM measurements. The temperature knots fitted by the model for individual sites ranged from 12.9 to 20.6 degrees C. Data from all six sites in the state of Michigan were also combined to determine if a single model could be developed for the entire state. While the single model for the state of Michigan worked reasonably well, some of the predicted concentrations at individual sites were systematically underestimating the observed concentrations on more polluted days. The same conclusion was drawn for a Region 5-wide model. This approach was also found to work very well for six individual TEOM monitors in New York State.     

Source Fingerprints for Volatile Non-Methane Hydrocarbons

Non-methane hydrocarbon (NMHC) source profiles consisting of 35 hydrocarbon species were measured for vehicle and petroleum refinery emissions. Refueling emissions were found to be sensitive to the grade and volatility class of fuel and to be composed mainly of saturated hydrocarbons such as n-butane and 2-methy I butane. Unsaturated and aromatic hydrocarbons, which are released from the tailpipe of vehicles as products of combustion and unburned fuel, were more prevalent in roadway emissions comprising approximately 34 percent of the total NMHCs. Cold-start emissions were nearly indistinguishable from the roadway emission profile. The only significant differences were in toluene, ethylene and acetylene, which may be related to the efficiency of combustion when the vehicle is initially started. Saturated hydrocarbon distributions of the hot-soak profiles were found to be similar to refueling emissions. The only significant difference in the profiles was in the aromatic content, which may be related to the grade of the gasoline and the effectiveness of evaporative emission control devices. The temporal variation in refinery emissions was significant and may be related to variations in refinery activities such as the production and blending of feed stocks to produce different fuels.     

Defining the Photochemical Contribution to Particulate Matter in Urban Areas Using Time-Series Analysis

Abstract

The objective of this project is to demonstrate how the ambient air measurement record can be used to define the relationship between O₃ (as a surrogate for photochemistry) and secondary particulate matter (PM) in urban air. The approach used is to develop a time-series transfer-function model describing the daily PM₁₀ (PM with less than 10 μm aerodynamic diameter) concentration as a function of lagged PM and current and lagged O₃, NO or NO₂, CO, and SO₂. Approximately 3 years of daily average PM₁₀, daily maximum 8-hr average O₃ and CO, daily 24-hr average SO₂ and NO₂, and daily 6:00 a.m.-9:00 a.m. average NO from the Aerometric Information Retrieval System (AIRS) air quality subsystem are used for this analysis. Urban areas modeled are Chicago, IL; Los Angeles, CA; Phoenix, AZ; Philadelphia, PA; Sacramento, CA; and Detroit, MI. Time-series analysis identified significant autocorrelation in the O₃, PM₁₀, NO, NO₂,CO, and SO₂ series. Cross correlations between PM₁₀ (dependent variable) and gaseous pollutants (independent variables) show that all of the gases are significantly correlated with PM₁₀ and that O₃ is also significantly correlated lagged up to two previous days. Once a transfer-function model of current PM₁₀ is defined for an urban location, the effect of an O₃-control strategy on PM concentrations is estimated by calculating daily PM₁₀ concentrations with reduced O₃ concentrations. Forecasted summertime PM₁₀ reductions resulting from a 5 percent decrease in ambient O₃ range from 1.2 μg/m³ (3.03%) in Chicago to 3.9 μg/m³ (7.65%) in Phoenix.     

A comparison of rapid and conventional measures of indicator bacteria as predictors of waterborne protozoan pathogen presence and density

E. coli and enterococci in recreational waters are monitored as indicators of fecal contamination, pathogen presence, and health risk. Quantitative polymerase chain reaction (qPCR) tests for fecal indicator bacteria can provide beach managers with same-day information about water quality, unlike culture methods which provide that information the following day. The abilities of qPCR measurements of indicator bacteria, as compared to culture measurements of indicator bacteria, as predictors of pathogen presence or density in surface waters are not well understood. The purpose of this study was to make such comparisons between water samples collected from Chicago area surface waters, including rivers, inland lakes, Lake Michigan, and the Chicago Area Waterways System, which is dominated by wastewater effluent. A total of 294 twenty-litre samples were collected and analyzed for Giardia and Cryptosporidium. qPCR and membrane filtration methods were used to quantify E. coli and enterococci. Correlation, logistic regression, and zero-inflated Poisson modeling were utilized to evaluate associations between indicators and parasites. qPCR and culture measures of the indicator bacteria were similar in their ability to predict parasite presence and density. Correlations between parasites and indicators were generally stronger at waters not dominated by effluent. Associations between indicator density and Giarida presence were observed more consistently than between indicator density and Cryptosporidium presence. Associations between enterococci and parasites were generally stronger than associations between E. coli and parasites. The use of qPCR monitoring in our setting would generate more timely results without compromising the ability to predict parasite presence or density.     

Defining the photochemical contribution to particulate matter in urban areas using time-series analysis

The objective of this project is to demonstrate how the ambient air measurement record can be used to define the relationship between O3 (as a surrogate for photochemistry) and secondary particulate matter (PM) in urban air. The approach used is to develop a time-series transfer-function model describing the daily PM10 (PM with less than 10 microm aerodynamic diameter) concentration as a function of lagged PM and current and lagged O3, NO or NO2, CO, and SO2. Approximately 3 years of daily average PM10, daily maximum 8-hr average O3 and CO, daily 24-hr average SO2 and NO2, and daily 6:00 a.m.-9:00 a.m. average NO from the Aerometric Information Retrieval System (AIRS) air quality subsystem are used for this analysis. Urban areas modeled are Chicago, IL; Los Angeles, CA; Phoenix, AZ; Philadelphia, PA; Sacramento, CA; and Detroit, MI. Time-series analysis identified significant autocorrelation in the O3, PM10, NO, NO2, CO, and SO2 series. Cross correlations between PM10 (dependent variable) and gaseous pollutants (independent variables) show that all of the gases are significantly correlated with PM10 and that O3 is also significantly correlated lagged up to two previous days. Once a transfer-function model of current PM10 is defined for an urban location, the effect of an O3-control strategy on PM concentrations is estimated by calculating daily PM10 concentrations with reduced O3 concentrations. Forecasted summertime PM10 reductions resulting from a 5 percent decrease in ambient O3 range from 1.2 microg/m3 (3.03%) in Chicago to 3.9 microg/m3 (7.65%) in Phoenix.     

印染行业清洁生产技术

论述了目前印染行业水污染的现状及原因，指出调整产业结构、应用新工艺、大力提倡节约用水、清沽生产将是印染行业实现可持续发展的必由之路。     

Determination of VOC Emission Rates and Compositions for Offset Printing

Abstract

The release rates of volatile organic compounds (VOC) as fugitive emissions from offset printing are difficult to quantify, and the compositions are usually not known. Tests were conducted at three offset printing shops that varied in size and by process. In each case, the building shell served as the test “enclosure,” and air flow and concentration measurements were made at each air entry and exit point. Emission rates and VOC composition were determined during production for (1) a small shop containing three sheetfed presses and two spirit duplicators (36,700 sheets, 47,240 envelopes and letterheads), (2) a medium-size industrial in-house shop with two webfed and three sheetfed presses, and one spirit duplicator (315,130 total sheets), and (3) one print room of a large commercial concern containing three webfed, heatset operations (1.16 x 106 ft) served by catalytic air pollution control devices. Each test consisted of 12 one-hour periods over two days. Air samples were collected simultaneously during each period at 7-14 specified locations within each space. The samples were analyzed by gas chromatography (GC) for total VOC and for 13-19 individual organics. Samples of solvents used at each shop were also analyzed by GC. Average VOC emission rates were 4.7-6.1 kg/day for the small sheetfed printing shop, 0.4-0.9 kg/day for the industrial shop, and 79-82 kg/day for the commercial print room. Emission compositions were similar and included benzene, toluene, xylenes, ethylbenzene, and hexane. Comparison of the emission rates with mass balance estimates based on solvent usage and composition were quite consistent. The average compositions of emissions from these tests were much more detailed than has been previously available, but they agreed well with previous chemical fingerprint values for toluene and o-xylene, which have been successfully applied to source-receptor modeling for graphic arts.     

Source Fingerprints for Receptor Modeling of Volatile Organics

The development of receptor models for the determination of the sources of an ambient air pollutant requires that the composition of the pollutant at the point of emission be known. For this study, composition information for 10 sources of volatile organic compounds (VOC) were evaluated and source fingerprints developed. The source categories include motor vehicles, gasoline vapor, petroleum refineries, architectural coatings, graphic arts, waste-water treatment, vapor degreasing, drycleaning, automobile assembly (including body painting), and polyethylene production. The fingerprints are presented for a group of 23 compounds. These compounds were selected for a variety of reasons including ease of measurement in the ambient environment, compound toxicity, reactivity, and usefulness in previous receptor modeling applications. In general, the data for sources of VOC are remarkably consistent from study to study. Because the profiles for many of the sources of VOC are controlled by physical and chemical processes (e.g. combustion) and not raw material composition, the fingerprints have general applicability.