Community Sanitation Risk Assessment of Tanjung Raja Village: A Rural Slum Study

Background: Areas with high sanitation risks have the potential to transmit infectious diseases. Meanwhile, Tanjung Raja Village is an area with a high level of slums and frequent flooding, so it has the potential to have sanitation risks. This study aimed to assess sanitation risk in Tanjung Raja village. Method: This was a quantitative study using the Environmental Sanitation Risk Assessment method. The study sample was all households in Neighborhood III of Tanjung Raja Village as many as 115 respondents using Simple Random Sampling. Results: The sanitation risk assessment of Tanjung Raja Village had a scoring category in RT 5 with high-risk results (score 3) and in RT 6 with fewer risk results (score 1) and Environmental health risks obtained related to sanitation included clean water, ownership of latrines, ownership of household waste bins, and wastewater disposal facilities. Conclusion: Tanjung Raja village has the potential to have a high sanitation risk with densely populated areas and flooded areas

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations

International audienceA reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (> 10 5) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10 12. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment