By using pollution source survey data, sewage treatment plant data, sediment monitoring results and SWMM, this study estimated the non-point source and overflow load of the basin. By analyzing the spatial and temporal distribution of major pollutants, the following results were obtained. 1) The chemical oxygen demand (COD), ammonia nitrogen (NH3-N) and total phosphorous (TP) loads in Shenzhen River basin in 2015 were 36760 t/a, 5715.65 t/a and 494.36 t/a, respectively. The COD, NH3-N and TP loads of the point source were 26300 t/a, 5496.9 t/a, and 463.55 t/a. The point source accounts for 72% of COD, 96% of NH3-N, and 94% of TP of the whole year. The COD, NH3-N and TP loads of the non-point source were 8608 t/a, 99.8 t/a and 18 t/a. The COD, NH3-N and TP loads of the overflow in rainy season (April to September) were 1894.05 t, 118.95 t and 12.81 t. 2) The COD, NH3-N and TP loads in Shenzhen Bay basin in 2015 were 116.5 t/d, 15.75 t/d and 1.412 t/d; the sewage outlets and the leakage sewage to tributary were the largest proportion of all source during the dry season. The COD, NH3-N and TP loads of the point source were 71.94 t/d, 15.06 t/d, and 1.27 t/d during the dry season. During the rainy season, the non-point source COD accounted for the largest proportion (34.21%), followed by and the leakage sewage to tributary and the sewage outlets, which were 28.73% and 22.3%. 3) Due to a large amount of pollutant load were transported into the waterbody, the pollutant load from non-point source and overflow considerably effected the water quality during the rainy season that could not be ignored, especially in the rainy days, and it took a long time to return to normal water quality.
Taking Dalang River Basin, Shenzhen city of Guangdong Province as an example, HSPF model was used to simulate hydrological effects of rainfall runoff under different scenarios. The results showed that runoff rate of the efficacy maximization, economical and moderate scenario were decreased by 34.9%, 14.2% and 28.5% than that in background scenario. The peak value of these three scenarios were lower 40.5%, 19.8% and 33.0% than that in background scenario. Base flow of these three scenarios were higher 88.9%, 11.1% and 44.4% than that in background scenario. The economical scenario didn’t reach good effect. The effect of moderate scenario was better than economical scenario and inferior to efficacy maximization scenario.
The purpose of the study is flush characteristics of urban runoff pollutant on different underlying surface. Select 5 typical hardened surfaces in Changzhou City and monitor the change process of rainfall runoff pollutant from March to August in 2015. The results showed that event mean concentrations (EMC) of pollutants in road runoff were higher than that of roof runoff. For road runoff, SS concentration was higher than the water quality standards by 1.34 times; COD concentration was higher than the water quality standards by 2.59 times. For the roof runoff, COD concentration was higher than the water quality standards by 1.8 times; and TN concentration was higher than the water quality standards by 2.6 times. For the roof runoff, the dissolved-bound fraction was 72.78% for COD, 57.99% for TN. For road runoff, the dissolved-bound fraction was 61.59% for TN. The pollutant concentrations were commonly higher at the initial stage, while decreased with prolonging of the rainfall time and gradually became stable at the later stage. The initial concentrations of pollutants from the underlying surface were as follows: concrete ground, asphalt ground, paved ground, flat roof and slope roof. During the rain flush, the concentration of pollutants on the underlying surface increases with the increase of the intensity of the rain which was fluctuated. The intensity of the first flush intensity varied by surface and was most intense for the flat roof, followed by the slope roof, then the asphalt road and finally the concrete road. Rainfall in pre-period was intensive and pollutant concentration was exponentially attenuated. When rainfall changes smoothly, the concentration of pollutants was stable firstly and then attenuation. When the rainfall was sparse in pre-period and intensive in the late-period, the curves of pollutant change type was multi-peak type. The index flush model had good effect to pollutant runoff simulation, the flush coefficients of COD on the slope roof, flat roof, and concrete road were 0.871, 0.765, and 0.025 mm−1, the roof flush intensity was much larger than the ground. The flush coefficient of dissolved-bound of COD and granular-bound of COD was similar on the slope roof. The flush coefficient of granular-bound of COD was greater than the dissolved-bound of COD on the flat roof and the concrete road.
This study takes Moyang River basin, which is lack of hydrologic data, as the research object to simulate the temporal and spatial distribution of water flow through HSPF (hydrological simulation program-Fortran) model, and calculates the temporal and spatial distribution of chemical oxygen demand (COD) and ammonia nitrogen water environmental capacity using one-dimensional steady-state water quality mathematical model. Results show that 1) HSPF model’s yearly and monthly hydrological errors are below 15%, Nash-Sutcliffe coefficient is over 0.9; the relative error of the water quality model is around 10%, Nash-Sutcliffe coefficient over 0.8. 2) Under 90%, 50% and 10% assurance rate, COD capacity of Moyang River basin is 164500 t, 218400 t and 249700 t respectively, and ammonia nitrogen is 5100 t, 8800 t and 11400 t respectively. Affected by seasonal runoff fluctuations, difference of water environment capacity between dry season and wet season is obvious. The variation of water environment capacity in January is minimum and June the maximum. 3) Bearing capacity of main stream of Moyang River is larger than that of the primary and secondary tributaries. Therefore, in some basins where hydrological data is lack, HSPF model can be applied to simulate the hydrology, analysis time and space distribution of water environmental capacity, and provide guidance for the establishment of total amount control scheme of water environmental capacity.
