Dongzhen reservoir is one of the most important water resources in the Putian area and reservoir water quality is threatened by regional domestic sewage and agricultural non-point source pollution. Using monitoring data, a two-dimensional water quality model of Dongzhen reservoir was developed to simulate water quality and then predict water quality after implementation of environmental protection projects. Water quality parameters modeled include ammonia nitrogen, total nitrogen, total phosphorus and COD. Assuming the current pollutant discharges to Dongzhen reservoir did not change before implementing protection projects, the total discharge of ammonia nitrogen, total nitrogen, total phosphorus and COD were calculated for the planning year 2018. Without intervention, calculated concentrations of ammonia nitrogen, total nitrogen, total phosphorus and COD in Dongzhen reservoir would reach, respectively, 0.14-0.21mg/L, 3.3-3.9mg/L, 0.052-0.062mg/L and 1.1-1.6mg/L. Water quality would decline to Grade Ⅳ and fail to meet water use requirements. Measures for improving water quality were then simulated, including social and economic regulations, engineering controls to reduce pollutant emissions, ecological conservation and environmental supervision and management. Simulation results indicate that the measures would improve water quality, with simulated discharges of COD, ammonia nitrogen, total nitrogen and total phosphorus to Dongzhen reservoir of 1616.6, 163.7, 1182.4 and 155.5 t/a,respectively. The predicted concentrations of total nitrogen and total phosphorus were 0.4-0.8mg/L and 0.022-0.032mg/L, a decline of >50% and water quality would meet the Grade III surface water quality standard. The predicted concentrations of COD and ammonia nitrogen were 2.0-3.0mg/L, 0.11-0.18mg/L, meeting Grade II surface water quality standard. Implementation of these protection projects is critical for water quality improvement in Dongzhen reservoir and the project provides a model that can be used for environmental protection of similar areas. |