Odor emission rate estimation of indoor industrial sources using a modified inverse modeling method.

Odor emission rates are commonly measured in the laboratory or occasionally estimated with inverse modeling techniques. A modified inverse modeling approach is used to estimate source emission rates inside of a postdigestion centrifuge building of a water reclamation plant. Conventionally, inverse modeling methods divide an indoor environment in zones on the basis of structural design and estimate source emission rates using models that assume homogeneous distribution of agent concentrations within a zone and experimentally determined link functions to simulate airflows among zones. The modified approach segregates zones as a function of agent distribution rather than building design and identifies near and far fields. Near-field agent concentrations do not satisfy the assumption of homogeneous odor concentrations; far-field concentrations satisfy this assumption and are the only ones used to estimate emission rates. The predictive ability of the modified inverse modeling approach was validated with measured emission rate values; the difference between corresponding estimated and measured odor emission rates is not statistically significant. Similarly, the difference between measured and estimated hydrogen sulfide emission rates is also not statistically significant. The modified inverse modeling approach is easy to perform because it uses odor and odorant field measurements instead of complex chamber emission rate measurements.

Comparison of two dynamic measurement methods of odor and odorant emission rates from freshly dewatered biosolids.

Odor and odorant emission rates from freshly dewatered biosolids in a dewatering building of a Water Reclamation Plant (WRP) are measured using the EPA flux chamber and wind tunnel methods. Experimental results are compared statistically to test whether the two methods result in similar emission rates when experiments are performed under field conditions. To the best of our knowledge the literature is void of studies comparing the two methods indoors. In this paper the two methods are compared indoors where the wind velocity and air exchange rate are pertinent field conditions and can be measured. The difference between emission rates of odor and hydrogen sulfide measured with the two methods is not statistically significant (P values: 0.505 for odor, 0.130 for H(2)S). It is concluded that both methods can be used to estimate source emissions but selection of the most effective or efficient method depends on prevailing environmental conditions. The wind tunnel is appropriate for outdoor environments where wind effects on source emissions are more pronounced than indoors. The EPA flux chamber depends on the air exchange rate of the chamber, which simulates corresponding conditions of the indoor environment under investigation and is recommended for estimation of indoor pollution sources.

The indoor environmental index and its relationship with symptoms of office building occupants.

An index for indoor environmental quality, the Indoor Environmental Index (IEI), was developed. This study aggregates the Indoor Air Pollution Index, an index found in the literature, and a new index: the Indoor Discomfort Index. The average of these two indices is the IEI, which is calculated using concentrations of eight pollutants and two comfort variables measured in 100 office buildings in the United States. The database used was developed for the U.S. Environmental Protection Agency Building Assessment Survey Evaluation study. A symptom index also is developed to denote persistent occupant symptoms. The IEI and the symptom index are used to investigate the relationship between indoor environmental quality and symptoms. Two simple linear regression models were formulated; these models explain 67 and 79% of the variation in the average symptom index, with the variation of the average IEI depending on the method of averaging used in the construction of the models. In addition, a conceptual explanation is provided for the empirical or regression models formulated. The IEI and the associated models relating indoor environmental quality with the office occupant symptom index may be used as management tools, as illustrated with an example.

Chapter three: methodology of exposure modeling.

In this chapter, the concept of exposure assessment and its evolution is introduced, and evaluated by critically appraising the pertinent literature as it applies to exposures to Particulate Matter (PM). Exposure measurement or estimation methodologies and models are reviewed. Three exposure/measurement methodologies are assessed. Estimation methods focus on source evaluation and attribution, sources include those outdoors and indoors as well as in occupational and in-transit environments. Fate and transport models and their inputs are addressed to estimate concentrations outdoors and indoors; source attribution techniques help focus on the contributing sources. Activity pattern techniques are also reviewed and their use in exposure models to estimate inhalation exposure to PM is presented. Deterministic, regression and other stochastic models of exposure to PM are reviewed and evaluated. Strengths, limitations, assumptions and affirmations of the use of exposure assessment as an integral component of risk assessment and risk management are discussed in the conclusions and discussions section of this work.

Modeling exposure to particulate matter.

Exposure assessment, a component of risk assessment, links sources of pollution with health effects. Exposure models are scientific tools used to gain insights into the processes affecting exposure assessment. The purpose of this paper is to review the process and methodology of estimating inhalation exposure to particulate matter (PM) using various types of models. Three types of models are discussed in the paper. Indirect type of models are physical models that employ inventories of outdoor and indoor sources and their emission rates to identify major sources contributing to exposure to PM, and use fate and transport and indoor air quality models to estimate PM concentrations at receptor sites. PM concentrations and time spent by a subject at each receptor site are input variables to the conventional exposure model that estimates the desired exposure levels. Direct type models use measured exposure or exposure concentrations in conjunction with information obtained from questionnaires to formulate exposure regression models. Stochastic models use exposure measurements, estimates can also be used, to formulate exposure population distributions and investigate associated uncertainty and variability. Since models developed using databases from western countries are not necessarily applicable in developing countries, the difference in requirements among western and developing countries is highlighted in the paper. Employment of exposure modeling methods in developing countries requires development of local information. Such information includes local outdoor and indoor source inventories, local or regional meteorological conditions, adjustment of indoor models to reflect local building construction conditions, and use of questionnaires to obtain local time budget and activity patterns of the subject population.