In this paper, we study whether odors from industrial sources impair the general well-being of residents in often-low-income surrounding communities. The ordinal logistic regression we used shows that participant who were employed, female participants, and those who reported the air is very fresh have higher levels of well-being in general. The three measures of satisfaction (a participant is satisfied with life nowadays, a participant is satisfied with how life turned out to be, and a participant is satisfied with standards of living) are consistent and represent the general well-being very well. Recent enjoyment and happiness did not show an association with employment, but they were associated with gender and air freshness. On the other hand, recent negative measures (depression, surrendering and sadness), may not be good representatives of general well-being. Our model shows employed individuals tend to have higher levels of well-being based on satisfaction. Several studies have shown that unemployment has strong negative impact on well-being. According to "How's life? Measuring well-being" and Theodossiou psychological well-being is affected by gender. Specifically, males tend to have a lower psychological well-being compared to females. This is consistent with the findings of our study. Based on our model, a male tends to have a lower level or recent enjoyment, happiness and satisfaction with how life turned out to be. A male also tends to have higher levels of recent depression. Interestingly, our ordinal model shows air freshness is associated with well-being. In other words, individuals who have reported air is very fresh tend to have higher levels of well-being. We plan to test more demographic independent variables that are known to be associated with well-being: income, race, number of children, marital status, and education. Two odor-related independent variables are to be tested as well. These two variables are odor intensity and effect of odors on physical activities. A spatial analysis on the reporting tools data will be conducted to better understand the relationship between location, wind direction and type of odor. This allows us to identify the odor sources with high accuracy.
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