Chromophoric dissolved organic compounds in urban watershed and conventional water treatment process: evidence from fluorescence spectroscopy and PARAFAC.

This study aimed to investigate the origin, quantity, and composition of chromophoric dissolved organic matter (CDOM) from two urbanized watersheds (Cikapundung and Cimahi River), examine how CDOM compounds and absorbances change along the process of two different conventional WTPs (WTP Dago and Cimahi) using PARAFAC, and identify absorbance as potential surrogate parameters for CDOM compounds. Samples were collected from intake, secondary treatment, and filter outlets. PARAFAC was conducted based on two data scenarios: (1) from rainy and dry seasons in Cikapundung river and WTP Dago and (2) from the two rivers and two WTPs during rainy season. Tryptophan-like (C1A) and humic-like (C2A) compounds were identified based on scenario-1 analysis. For scenario-2, humic-like (C1B), peak-M (C2B), and tryptophan-like (C3B) were the main compounds. CDOM compound quantity is consistent with the fluorescence index (FI) and biological index (BIX) which confirmed sewage and animal manure pollution in both watersheds. The best overall removal of CDOM compound occurred in WTP Dago in rainy season. The high concentration of tryptophan-like in Cikapundung River in dry season and in Cimahi River in rainy season has worsen the WTP capability to reduce CDOM. Scenario-1 has shown that in WTP Dago, the potential surrogate parameter for C1A was A240 in rainy season (r = 0.60; p < 0.01) and A410 in dry season (r = - 0.43, p < 0.05). Based on scenario-2, for the WTP Dago in rainy season, C1B strongly correlated with A254 (r = 0.86; p < 0.01), C2B has the strongest correlation with A298 (r = 0.93; p < 0.01), and C3B correlated well with A240 (r = 0.59; p < 0.01). In WTP Cimahi, during rainy season, all compounds correlated well with all measured absorbances, with the strongest correlation with A298.

Turing Patterns of Non-linear S-I Model on Random and Real-Structure Networks with Diarrhea Data.

Most developed models for solving problems in epidemiology use deterministic approach. To cover the lack of spatial sense in the method, one uses statistical modeling, reaction-diffusion in continuous medium, or multi-patch model to depict epidemic activities in several connected locations. Here, we show that an epidemic model that is set as an organized system on networks can yield Turing patterns and other interesting behaviors that are sensitive to the initial conditions. The formed patterns can be used to determine the epidemic arrival time, its first peak occurrence and the peak duration. These epidemic quantities are beneficial to identify contribution of a disease source node to the others. Using a real structure network, the system also exhibits a comparable disease spread pattern of Diarrhea in Jakarta.