Wastewater treatment and reuse in urban agriculture: exploring the food, energy, water, and health nexus in Hyderabad, India

Nutrients and water found in domestic treated wastewater are valuable and can be reutilized in urban agriculture as a potential strategy to provide communities with access to fresh produce. In this paper, this proposition is examined by conducting a field study in the rapidly developing city of Hyderabad, India. Urban agriculture trade-offs in water use, energy use and GHG emissions, nutrient uptake, and crop pathogen quality are evaluated, and irrigation waters of varying qualities (treated wastewater, versus untreated water and groundwater) are compared. The results are counter-intuitive, and illustrate potential synergies and key constraints relating to the food–energy–water–health (FEW–health) nexus in developing cities. First, when the impact of GHG emissions from untreated wastewater diluted in surface streams is compared with the life cycle assessment of wastewater treatment with reuse in agriculture, the treatment-plus-reuse case yields a 33% reduction in life cycle system-wide GHG emissions. Second, despite water cycling benefits in urban agriculture, only <1% of the nutrients are able to be captured in urban agriculture, limited by the small proportion of effluent divertible to urban agriculture due to land constraints. Thus, water treatment plus reuse in urban farms can enhance GHG mitigation and also directly save groundwater; however, very large amounts of land are needed to extract nutrients from dilute effluents. Third, although energy use for wastewater treatment results in pathogen indicator organism concentrations in irrigation water to be reduced by 99.9% (three orders of magnitude) compared to the untreated case, crop pathogen content was reduced by much less, largely due to environmental contamination and farmer behavior and harvesting practices. The study uncovers key physical, environmental, and behavioral factors that constrain benefits achievable at the FEW-health nexus in urban areas

Prioritization and Risk Ranking of Regulated and Unregulated Chemicals in US Drinking Water

Drinking water constituents were compared using more than six million measurements (USEPA data) to prioritize and risk-rank regulated and unregulated chemicals and classes of chemicals. Hazard indexes were utilized for hazard- and risk-based chemicals, along with observed (nondetects = 0) and censored (nondetects = method detection limit/2) data methods. Chemicals (n = 139) were risk-ranked based on population exposed, resulting in the highest rankings for inorganic compounds (IOCs) and disinfection byproducts (DBPs), followed by semivolatile organic compounds (SOCs), nonvolatile organic compounds (NVOCs), and volatile organic compounds (VOCs) for observed data. The top 50 risk-ranked chemicals included 15 that were unregulated, with at least one chemical from each chemical class (chromium-6 [#1, IOC], chlorate and NDMA [#11 and 12, DBP], 1,4-dioxane [#25, SOC], PFOS, PFOA, PFHxS [#42, 44, and 49, NVOC], and 1,2,3-trichloropropane [#48, VOC]). These results suggest that numerous unregulated chemicals are of higher exposure risk or hazard in US drinking water than many regulated chemicals. These methods could be applied following each Unregulated Contaminant Monitoring Rule (UCMR) data collection phase and compared to retrospective data that highlight what chemicals potentially pose the highest exposure risk or hazard among US drinking water, which could inform regulators, utilities, and researchers alike

Prioritization and Risk Ranking of Regulated and Unregulated Chemicals in US Drinking Water

Drinking water constituents were compared using more than six million measurements (USEPA data) to prioritize and risk-rank regulated and unregulated chemicals and classes of chemicals. Hazard indexes were utilized for hazard- and risk-based chemicals, along with observed (nondetects = 0) and censored (nondetects = method detection limit/2) data methods. Chemicals (n = 139) were risk-ranked based on population exposed, resulting in the highest rankings for inorganic compounds (IOCs) and disinfection byproducts (DBPs), followed by semivolatile organic compounds (SOCs), nonvolatile organic compounds (NVOCs), and volatile organic compounds (VOCs) for observed data. The top 50 risk-ranked chemicals included 15 that were unregulated, with at least one chemical from each chemical class (chromium-6 [#1, IOC], chlorate and NDMA [#11 and 12, DBP], 1,4-dioxane [#25, SOC], PFOS, PFOA, PFHxS [#42, 44, and 49, NVOC], and 1,2,3-trichloropropane [#48, VOC]). These results suggest that numerous unregulated chemicals are of higher exposure risk or hazard in US drinking water than many regulated chemicals. These methods could be applied following each Unregulated Contaminant Monitoring Rule (UCMR) data collection phase and compared to retrospective data that highlight what chemicals potentially pose the highest exposure risk or hazard among US drinking water, which could inform regulators, utilities, and researchers alike

May 2017 Meeting

Notes from the May Meetin

Data from: Estimating the reproducibility of psychological science

This record contains the underlying research data for the publication "Estimating the reproducibility of psychological science" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5257Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams