The effi
cien
cy of heterogeneous photo
catalysis to degrade 17¦Á-ethynylestradiol (EE2), a syntheti
c estrogen hormone, in environmentally relevant samples was investigated. In most
cases, UV-A radiation at a photon flux of 2.81 ¡Á 10
?4 einstein/min was provided by a 9 W lamp and experiments were
condu
cted at various
con
centrations of Aeroxide P25 TiO
2 (50-1000 mg/L), EE2
con
centrations (50-900 ¦Ìg/L) and water matri
ces (from ultrapure water to se
condary treated wastewater). Some runs were performed at photon fluxes between 6.4 ¡Á 10
?7 and 3.7 ¡Á 10
?4 einstein/min to study the effe
ct of intensity on degradation. Changes in estrogen
con
centration were followed by high performan
ce liquid
chromatography. EE2 degradation, whi
ch follows first order kineti
cs, in
creases with (i) in
creasing
catalyst loading up to a threshold value beyond whi
ch it remains unaffe
cted; (ii) in
creasing photon flux and (iii) de
creasing matrix
complexity, i.e. the organi
c and inorgani
c constituents of wastewater retard degradation. This may be over
come
coupling photo
catalysis with ultrasound radiation at 80 kHz and 41 W/L power density; the
combined sonophoto
catalyti
c pro
cess a
cts synergisti
cally toward EE2 degradation. Several transformation produ
cts were identified by means of UPLC-MS/MS and a rea
ction network for the photo
catalyti
c degradation of EE2 is suggested.
An artificial neural network comprising five input variables (reaction time, TiO2 and EE2 concentration, organic content and conductivity of the water matrix), thirteen neurons and an output variable (EE2 conversion) was optimized, tested and validated for EE2 degradation. The network, based on tangent sigmoid and linear transfer functions for the hidden and input/output layers, respectively, and the Levenberg-Marquardt back propagation training algorithm, can successfully predict EE2 degradation.