Odor Load Investigation for a Pharmaceutical Plant by Open Path Fourier Transform Infrared (OP-FTIR)/Environmental Protection Agency Regulatory Dispersion Model (AERMOD)

Abstract

In this article, a case study was conducted to correlate the odor index and possible pollutants from a pharmaceutical plant based on the odor threshold and Open Path Fourier Transform Infrared (OP-FTIR) technique to model the results using American Meteorological Society/Environmental Protection Agency Regulatory Dispersion Model (AERMOD). Although nine different pollutants were obtained from OP-FTIR, the contribution to the detected odor was various due to their different odor thresholds. Consequently, only pollutants with odor stimulation level (OSL) ≥ 1 were used to correlate with the odor index. OSL of pollutants is defined as the concentration of each pollutant divided by the odor threshold. Therefore, a linear regression equation between the odor index and the OSL of pollutants was derived with a correlation of determination (R ²) of 0.9994. Moreover, AERMOD was used to model the odorant dispersion route to identify the influenced area. The influenced contour for odor index and each pollutant was then obtained by AERMOD and a good correlation was observed between the AERMOD predictions and the actual observed values. This OP-FTIR/AERMOD approach can not only instantaneously detect the pollutant concentration, but also quickly identify odorant source and estimate the pollution profile for forensic application. The OP-FTIR data is a fingerprint for this particular plant, which, together with AERMOD, can predict the dispersion route of pollutants to confirm the area influenced by this plant.

Keywords:

• odor
• pharmaceutical plant
• OP-FTIR
• AERMOD
• odor stimulation level

Acknowledgment

The authors acknowledge the financial support from Taipei County Environmental Protection Bureau (Banciao, Taipei, Taiwan, ROC), and Open Path Fourier Transform Infrared (OP-FTIR) analysis from Industrial Technology Research Institute of Taiwan (Hsin Chu, Taiwan, ROC).

Notes

¹Highest detected concentration as ppb.

²Lowest detected concentration as ppb.

³Detected probability (%): Detected numbers divided by total monitoring numbers for certain pollutant.

¹Highest detected concentration as ppb.

²Lowest detected concentration as ppb.

³Detected probability (%): Detected numbers divided by total monitoring numbers for certain pollutant.

⁴Threshold probability (%): The numbers greater than odor threshold divided by detected numbers.