Abstract
Since the introduction of environmental legislation and
directives in Europe, the impact of combined sewer overflows (CSO) on receiving water
bodies has become a priority concern in water and wastewater treatment industry. Timeconsuming
and expensive local sampling and monitoring campaigns have been carried out
to estimate the characteristic flow and pollutant concentrations of CSO water. This study
focused on estimating the frequency and duration of wet-weather events and their impacts
on influent flow and wastewater characteristics of the largest Italian water resource recovery
facility (WRRF) in Castiglione Torinese. Eight years (viz. 2009-2016) of routinely collected
influent data in addition to the arithmetic mean daily precipitation rates (PI) of the plant
catchment area, were elaborated. Relationships between PI and volumetric influent flow rate
(Qin), chemical oxygen demand (COD), ammonium concentration (N-NH4) and total
suspended solids (TSS) are investigated. Time series data mining (TSDM) method is
implemented for segmentation of time series by use of sliding window algorithm to partition
the available records associated with wet and dry weather events based on the daily
variation of PI time series. Appling the methodology in conjunction with results obtained from
data reduction techniques, a wet-weather definition is proposed for the plant. The results
confirm that applied methodology on routinely collected plant data can be considered as a
good substitute for time-consuming and expensive sampling campaigns and plant
monitoring programs usually conducted for accurate emergency response and long-term
preparedness for extreme climate conditions.
directives in Europe, the impact of combined sewer overflows (CSO) on receiving water
bodies has become a priority concern in water and wastewater treatment industry. Timeconsuming
and expensive local sampling and monitoring campaigns have been carried out
to estimate the characteristic flow and pollutant concentrations of CSO water. This study
focused on estimating the frequency and duration of wet-weather events and their impacts
on influent flow and wastewater characteristics of the largest Italian water resource recovery
facility (WRRF) in Castiglione Torinese. Eight years (viz. 2009-2016) of routinely collected
influent data in addition to the arithmetic mean daily precipitation rates (PI) of the plant
catchment area, were elaborated. Relationships between PI and volumetric influent flow rate
(Qin), chemical oxygen demand (COD), ammonium concentration (N-NH4) and total
suspended solids (TSS) are investigated. Time series data mining (TSDM) method is
implemented for segmentation of time series by use of sliding window algorithm to partition
the available records associated with wet and dry weather events based on the daily
variation of PI time series. Appling the methodology in conjunction with results obtained from
data reduction techniques, a wet-weather definition is proposed for the plant. The results
confirm that applied methodology on routinely collected plant data can be considered as a
good substitute for time-consuming and expensive sampling campaigns and plant
monitoring programs usually conducted for accurate emergency response and long-term
preparedness for extreme climate conditions.
| Original language | English |
|---|---|
| Title of host publication | New Trends in Urban Drainage Modelling |
| Publisher | Springer |
| Pages | 706-711 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-3-319-99867-1 |
| ISBN (Print) | 978-3-319-99866-4 |
| DOIs | |
| Publication status | E-pub ahead of print - 1 Sept 2018 |
| Event | 11th International Conference on Urban Drainage Modelling - Palermo, Italy Duration: 23 Sept 2018 → 26 Sept 2018 |
Publication series
| Name | Green Energy and Technology |
|---|---|
| ISSN (Print) | 1865-3529 |
Conference
| Conference | 11th International Conference on Urban Drainage Modelling |
|---|---|
| Abbreviated title | UDM 2018 |
| Country/Territory | Italy |
| City | Palermo |
| Period | 23/09/18 → 26/09/18 |
