Land –Based Sources of Pollutants to Coastal Waters of Southern Belize – Comparison of Predictive Model with Empirical Data
Degree Granting Department
Environmental Science and Policy
Joseph Dorsey, Ph.D.
Kathleen Carvalho-Knighton, Ph.D.
Ambe Njoh, Ph.D.
pesticides, metals, coast, coral reef, monitoring
A Level III fugacity-type model was applied to southern Belize (Stann Creek and Toledo Districts) to predict which of the pesticides most heavily used in banana and citrus farms are most likely to end up in streams and coastal waters via surface runoff. Concentrations of all target pesticides in coastal waters of southern Belize were then measured during two sampling campaigns (dry season and rainy season) in 2008. Several pesticides were measured in significant levels, including some as far out as waters overlying coral reefs. The presence of these pesticides in the coastal waters indicates that agricultural activities in southern Belize may have a potential impact on coral reefs offshore. Results of the predictive model were compared with the empirical data to determine how well the model works in a tropical region such as southern Belize. Overall, there is considerable agreement between the two, indicating that the model employed herein can be applied to other tropical areas. Concentrations of mercury and lead were also measured in the same study area. Mercury levels were uniform and low, suggesting natural sources. Lead levels varied and are most likely explained by the presence of unregulated and illegal waste dumps near streams in the region. An analysis was carried out to examine government policy on pesticide use. Findings indicate a lack of a coherent governmental policy on the sale and use of pesticides, most likely because of too many agencies/ministries being involved and the lack of national standards for these pesticides in the environment.
Scholar Commons Citation
Alegria, Victor Eduardo, "Land –Based Sources of Pollutants to Coastal Waters of Southern Belize – Comparison of Predictive Model with Empirical Data" (2009). USF Tampa Graduate Theses and Dissertations.