Study of the dynamics of surface runoff composition of the urban lands

Summary

The qualitative composition of surface runoff of urban lands is affected by significant fluctuations. Studying its dynamics, determining possible regularities and causes of these fluctuations can be conveniently carried out with the use of the mathematical apparatus of the time series analysis. The procedure of seasonal decomposition was applied to the time series of monthly dynamics with the annual periodicity of seasonal fluctuations with the help of the multiplicative model. The results of the quantitative chemical analysis of surface runoff at the «XXII Party Congress» outfall of the Samara municipality for 2004–2016 were used as benchmark data. As a result of the performed analysis the occurrence of the seasonal regularities of the surface runoff composition changes was determined. The seasonal indices of 15 effluent quality parameters were determined: BOD_full, suspended solids, mineralization, chlorides, sulfates, ammonium ion, nitrite ion, nitrate anion, phosphates (as phosphorus), total iron, copper, zinc, aluminium, oil products, and detergents (anionic). Based on the given seasonal decomposition of the time series the qualitative assessment of the effect of the trend, seasonal and random components on the unsteadiness of the surface runoff quality parameters was performed.

Key words

surface runoff , wastewater composition , outfall to the water body , multiplicative model , time series , seasonal decomposition , seasonal index

Simulation of the temporal variations in water color of a water source

Summary

The time series of water color at the surface water intake were processed for a 28-year period (1994–2021). The attained results were compared with the results of a previous study for 18 years (1997–2014). New patterns of water color variations (28 years) were identified that differ from those of the earlier studies (18 years). It was determined that the annual seasonal changes in water color noted earlier (18 years) existed also over the 28-year period; however, the values of this indicator for the same months differed. It was found that the average annual values of the indicator changed stochastically; periods of the water color reduction were noted (1998–2009, 2016–2021) alongside with the periods of the indicator value increase (1995–1998, 2009–2015). It was revealed that the downward trend in the indicator values continued for another period of 28 years; however, more slowly compared to the 18-year period. Apparently, the upward trend in water color values in the period 2009–2015 had certain effect on the trend equation for the entire period, as well as on the changes in the contribution of the time series components compared to the 18-year period. The analysis of the time series (additive modelling with the average annual smoothing method) revealed that for the 28-year period, the contribution of the seasonal value to the water color values decreased and amounted more than 49.6%. A fairly significant increase in the contribution of the random variable (from 27.8 to 46.2%) was also noted. The analysis and comparison of different periods (1998–2009, 2009–2015 and 2016–2021) shows that the quality of water in the source by color is affected by different factors. An increase in the contributions of the seasonal and random components leads to an increase or decrease in the indicator values. Differentiation of the annual cycle of the water source into the periods characterized by similar trends in water color variations revealed differences in the time frames compared to the 18-year period. This may be evidence that, in addition to natural factors, water color may also be affected by the urban agglomeration.

Key words

water quality monitoring , time series , additive model , water color , surface water intake , seasonal component , random component , average annual smoothing method , trend-cyclic component