Substantive audit testing of sewer systems using Brazilian open database: stat methods for compliance screening

Abstract

The monitoring of data and the management of information are important tools for the decision-making and guidance of investments, which is a crucial procedure for the effective capital opening of the sanitation sector. The research aims at analyzing the correlation of urban population growth with the variables of importance for evaluating the overall performance of sanitary sewage systems. The study was developed based on raw data and indicators, in the long term period (1999 to 2017), with the information provided by the service providers themselves to the SNIS (national system of information on sanitation). The methodology consists of an application of a substantive test, following the guidelines of ISO 19.011 :2018, NBC TI 01 and, NBC PI 01: 2012, where the structure is presented in the format of the analytical review. For the validation of the test, statistical routines were used. Scores were elaborated to determine the probable inconsistencies found during the statistical routine, where the results, denominated evidence by the audit, were divided into conformities and non-conformities, being indicated 19 conformities and seven non-conformities. It is concluded that the methodology used can help evaluate public domain data and generate subsidies for sector managers and investors.

Study of the Impact of sanitary decisions over water quality using Bayesian Belief Networks in Upper Pantanal Wetland Basin – Brazil

“…The model was constructed based on previous studies (Silva, et al, 2020) when was demonstrated the effect of seasonality (rain and dry seasons) on the water quality variables. The effect of urbanization and sanitation systems at the river basin scale on the water quality was discussed in another previous study (Garcia, et al 2020).…”

“…Complementary hydraulic data (river discharge) was obtained by the National Water Authority (Agencia Nacional de Águas -ANA). The period selected to study was between 2006 and 2017 due to previous database consistency study (Garcia, et al 2020). A total of 1778 points of dataset over 14 variables were select to study.…”