Essay Example Air Quality Studies: Gaussian Plume & Box Models

Introduction

When working with air quality studies it is relevant to investigate statistical data using mathematical and software models. the most common model is the Gaussian Plume model used for dispersion modeling commonly preferred by regulatory authorities in data analysis. Different models are suited for different atmospheric and structural options (Godish et al., 2014). the box model is best suited for the mass balance equation of pollutants while the CFD models preferred in the fluid pollutant flows in complex geometrical spaces. The Gaussian plume model is used in steady-state atmospheric conditions and the Gaussian puff model is best suited for variable and temporal atmospheric conditions. In reference to commercial software solutions, the box model is used in AURORA, CPB, PBM, and the gaussian plume model used in CALINE4, HIWAY2, CAR-FMI, and OSPM. The CFD model uses ARIA, MISKAM, ATMoS among other software solutions. It is critical to select software models based on the specific monitoring conditions including variable support for aerosol dynamics (Gurjar et al., 2010). Godish et Al (2014) presented the goal of modeling as the calculation of concentrations of a given pollutant for a known set of variables. Analysis of data can be approached in various ways; fuzzy logic, ranking, time series, and artificial neural network (Gurjar et al., 2010). The artificial neural network uses software solutions with known variables using regression analysis methods. The fuzzy logic method analyzes various air quality models to normalize excepted limits (Gurjar et al., 2010). Weighted variables are used in fuzzy interference method variants. This method enables s determination of upper and lower limits of two or more models used in the evaluation of data with high levels of uncertainty such as lack of background information on a new site. The ranking approach uses known variables to rank air quality models. This is through statistical analysis of the index scores of different software solutions used to analyze the data and then determining the most appropriate solution for the given scenario. Time-series analyses are used to accurately forecast pollutant levels in areas of high pollutant susceptibility relative to time (Gurjar t al., 2010). This enables high levels of statistical reliability effective in behavioral modification controls such as limitation of outdoor exposures during specific time points when the pollutant level is high.

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Scenario

Using the following steps, we will calculate contaminant analyses generated from the natural gas-fired heater for permit application using the units in our calculations for million British thermal units/hr (MMBtu/hr), thousand British thermal units/hr (MBtu/hr), and standard cubic feet/hr (SCF/hr). 1.0 lbs/MMscf would be 1.0 lbs of contaminant (pollutant) per one million standard cubic feet. The first calculation is the short term (hourly) emissions generated from the heater using the formulae; Lbs of air contaminants/hour = lbs air contaminant x 1.0 scf x 2.1 MMBtu MMscf 1,020Btu 1.0 hr. The natural-gas-fired cure heater has a firing rate of 2.1 MMBtu/hr, and firing liners in the curing process for a maximum of 2,500 hours/year. NOx contaminant has 100 lb/MMscf (lbs/million scf) mass equivalent. We multiply 100 lb NOx/MMscf by 1 scf/1,020 Btu by 2.1 MMBtu/hr to derive a value of 0.206 lbs of NOx/hr. CO contaminant has 84 lb/MMscf (lbs/million scf) mass equivalent. We multiply 84 lb CO/MMscf by 1 scf/1,020 Btu by 2.1 MMBtu/hr to derive a value of 0.173 lbs of CO/hr. PM contaminant has 7.6 lb/MMscf (lbs/million scf) mass equivalent. We multiply 7.6 lb PM/MMscf by 1 scf/1,020 Btu by 2.1 MMBtu/hr to derive a value of 0.016 lbs of PM/hr. VOC contaminant has 5.5 lb/MMscf (lbs/million scf) mass equivalent. We multiply 5.5 lb VOC/MMscf by 1 scf/1,020 Btu by 2.1 MMBtu/hr to derive a value of 0.011 lbs of VOC/hr. SO2 contaminant has 0.6 lb/MMscf (lbs/million scf) mass equivalent. We multiply 0.6 lb SO2/MMscf by 1 scf/1,020 Btu by 2.1 MMBtu/hr to derive a value of 0.001 lbs of SO2/hr.

The next step involves conversion into long term emissions by converting the hourly emissions to annual emissions (2,500 hours/yr.), and then to tons (2,000 lbs/ton). The following formula is used: Tons of air contaminant/hour = lbs air contaminant x 2,500 hr x 1.0-ton 1.0 hr 1.0 yr. 2,000 lb. The calculated hourly NOx is 0.206 lb/hr. We multiply the calculated lb of NOx/hr by 2,500 hrs/yr. by 1 ton/2,000 lb to derive 0.2575-ton NOx/yr. The calculated hourly CO is 0.173 lb/hr. Second, we multiply the calculated lb of CO/hr by 2,500 hrs/yr. by 1 ton/2,000 lb to derive 0.216-ton CO/yr. The calculated hourly PM is 0.016 lb/hr. Second, we multiply the calculated lb of PM/hr by 2,500 hrs/yr. by 1 ton/2,000 lb to derive 0.02-ton PM/yr. The calculated hourly SO2 is 0.001 lb/hr. Second, we multiply the calculated lb of SO2/hr by 2,500 hrs/yr. by 1 ton/2,000 lb to derive 0.00125-ton SO2/yr. The calculated hourly VOC is 0.011 lb/hr. Second, we multiply the calculated lb of VOC/hr by 2,500 hrs/yr. by 1 ton/2,000 lb to derive 0.01375-ton VOC/yr.