The management of pollutant emissions from industrial sites involves various crucial steps, including estimating emission quantities and assessing their impact on surrounding areas. While emissions from point sources, such as exhaust outlets, are relatively easier to manage, emissions from area sources, such as workshops and livestock facilities, are often challenging to measure due to various constraints. To address this issue, this study proposes a method for estimating emissions from area sources by utilizing data collected at site boundaries and applying a reverse modeling approach. Using data from actual livestock facilities, along with reverse modeling results, this study identified a strong correlation between the facility area and the number of livestock raised. Correlation analyses revealed positive relationships between the facility area and the average odor emission rate, as well as between the number of livestock and the average odor emission rate. In addition, the results of reverse modeling confirmed a significant correlation between odor emissions, the number of livestock, and the facility area. Based on these findings, this study developed an odor emission factor for livestock facilities using the number of livestock and the facility area as activity indicators. The odor emission factor is expressed in units of OU/s/pig/m², where “OU” represents odor units, “s” denotes seconds, “pig” corresponds to the number of livestock, and “m²” refers to the total facility area. By multiplying the number of livestock by the facility area, the total odor emission rate (OU/sec) can be calculated. Unlike traditional emission factors that rely solely on the number of livestock, this newly developed factor incorporates all facilities contributing to odor emissions within a livestock operation. This approach allows for the estimation of odor emissions using external measurement data and facility information, even in cases where direct measurements are impractical. The results of this study are expected to be effectively utilized for odor evaluation and management in livestock facilities.

In order to improve an accuracy of the real-time odor dispersion modeling system, a sensitivity of CALMET model with different input meteorological data was studied. The performance of CALMET model was tested by comparing the model predictions with the observations at the Daedeok Industrial Complex in Daejeon Metropolitan City. It is shown that the CALMET model with WRF (Weather Research and Forecasting) input data of GFS (Global Forecast System) depicts the measurements better than that of RDAPS (Regional Data Assimilation and Prediction System). The CALMET model could be further improved by selecting options of Divergence minimization, Froude number adjustment and Slope flows without choosing Kinematic Effects in the modeling procedure.

There are many odor complains in Ansan and Siheung industrial complexes. In order to solve the odor problem, it is necessary to identify the major odor emission sources and to understand odor dispersion mechanism in these areas by applying the real-time odor monitoring system. The proposed system mainly consists of the measuring network of odor causing materials and meteorological variables as well as the dispersion modeling system on real-time base. In this study, the effective ways is also proposed to apply the system to ameliorate the odor environments.