Remediation of phenanthrene & cadmium co-contaminated soil by using a combined process including soil washing and electrocoagulation

In the present work, the application of a combined process including soil washing by using the non-ionic surfactant Tween 80 and EDTA and electrocoagulation in remediation of phenanthrene (PHE) and cadmium (Cd) contaminated soils was investigated. In order to examine the effect of operational parameters on the efficacy of the process, Response Surface Methodology under Box–Behnken design was applied in both stages. Tween 80 solution and EDTA with concentrations of 1000–3000 and 1000–2000 mg L−1 respectively, at liquid/soil (L/S) ratio of 10, 20, and 30 v/w in a time interval of 2–24 h, were applied to remove PHE and Cd simultaneously from the co-contaminated soil. PHE and Cd extraction efficiency were mostly influenced by Tween 80 and EDTA concentration, respectively (PvaluePvalue≅ 3000 mg L-1, EDTA concentration ≅ 2000 mg L-1, L/S ratio ≅ 30 v/w and washing time = 2 h resulted in the removal efficiency of 59.284 ± 4.347% and 74.35 ± 3.632% for PHE and Cd, respectively. Electrocoagulation of the adjusted synthetic effluent based on the optimal operational conditions of soil washing phase was carried out at pH values of 3–11, with a voltage of 10–30 v, and in reaction time of 45–90 min. The results demonstrated that the removal efficiency of both contaminants was mostly enhanced by increasing pH (PvaluePvalue< 0.0001). The presented optimum conditions for electrocoagulation including pH of 11, a voltage of 30 v, and reaction time of 45 min provided a removal efficiency of 94.85 ± 1.715 and 100% for PHE and Cd respectively.</p

Humans with humans science: leveraging the power of big data analytics for a better world

Conference abstract

Acid-based deep eutectic solvents followed by GFAAS for the speciation of As(III), As(V), total inorganic arsenic and total arsenic in rice samples

In the present study, an efficacious, safe, inexpensive and eco-friendly microextraction was provided by deep eutectic solvents based on dispersive liquid–liquid microextraction (DLLME − DES) followed by GFAAS. A series of DESs were synthesised using l-menthol as hydrogen bond acceptor (HBA) and carboxylic acids with 4, 6, 8 and 10 carbon atoms as hydrogen bond donors (HBD). The synthesised DESs were used as extractants of arsenic ions. Under optimised conditions, good linearity with coefficient of determination (r2) 0.992 and an acceptable linear range of 0.3–100 µg kg−1 was obtained. The limit of detection was 0.1 µg kg−1 (S/N = 3) for arsenite (As(III)) ions, and a high enrichment factor (EF = 200) was obtained. The enhancement factor and extraction recovery (ER%) of the method were 340 and 60%, respectively. RSDs including inter- and intra-day ranged from 3.2% to 5.8% in three examined concentrations. After a specific digestion, the capability of the synthesised DES in the extraction of As(III) from rice was tested. Total inorganic arsenic was separated similarly after reduction of arsenate (As(V)) to As(III), and As(V) concentration was calculated by difference. Using a second digestion method, total arsenic concentration (sum of organic and inorganic arsenic) in the samples was determined.</p

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0.71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50.2% exceed this threshold for suitability in at least one 5×5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify

Mapping subnational HIV mortality in six Latin American countries with incomplete vital registration systems

Background: Human immunodeficiency virus (HIV) remains a public health priority in Latin America. While the burden of HIV is historically concentrated in urban areas and high-risk groups, subnational estimates that cover multiple countries and years are missing. This paucity is partially due to incomplete vital registration (VR) systems and statistical challenges related to estimating mortality rates in areas with low numbers of HIV deaths. In this analysis, we address this gap and provide novel estimates of the HIV mortality rate and the number of HIV deaths by age group, sex, and municipality in Brazil, Colombia, Costa Rica, Ecuador, Guatemala, and Mexico. Methods: We performed an ecological study using VR data ranging from 2000 to 2017, dependent on individual country data availability. We modeled HIV mortality using a Bayesian spatially explicit mixed-effects regression model that incorporates prior information on VR completeness. We calibrated our results to the Global Burden of Disease Study 2017. Results: All countries displayed over a 40-fold difference in HIV mortality between municipalities with the highest and lowest age-standardized HIV mortality rate in the last year of study for men, and over a 20-fold difference for women. Despite decreases in national HIV mortality in all countries—apart from Ecuador—across the period of study, we found broad variation in relative changes in HIV mortality at the municipality level and increasing relative inequality over time in all countries. In all six countries included in this analysis, 50% or more HIV deaths were concentrated in fewer than 10% of municipalities in the latest year of study. In addition, national age patterns reflected shifts in mortality to older age groups—the median age group among decedents ranged from 30 to 45 years of age at the municipality level in Brazil, Colombia, and Mexico in 2017. Conclusions: Our subnational estimates of HIV mortality revealed significant spatial variation and diverging local trends in HIV mortality over time and by age. This analysis provides a framework for incorporating data and uncertainty from incomplete VR systems and can help guide more geographically precise public health intervention to support HIV-related care and reduce HIV-related deaths.</p

Additional file 3 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 3: Supplemental figures.Figure S1. Prevalence of male circumcision. Figure S2. Prevalence of signs and symptoms of sexually transmitted infections. Figure S3. Prevalence of marriage or living as married. Figure S4. Prevalence of partner living elsewhere among females. Figure S5. Prevalence of condom use during most recent sexual encounter. Figure S6. Prevalence of sexual activity among young females. Figure S7. Prevalence of multiple partners among males in the past year. Figure S8. Prevalence of multiple partners among females in the past year. Figure S9. HIV prevalence predictions from the boosted regression tree model. Figure S10. HIV prevalence predictions from the generalized additive model. Figure S11. HIV prevalence predictions from the lasso regression model. Figure S12. Modeling regions. Figure S13. Age- and sex-specific vs. adult prevalence modeling. Figure S14. Data sensitivity. Figure S15. Model specification validation. Figure S16. Modeled and re-aggregated adult prevalence comparison. Figure S17. HIV prevalence raking factors for males. Figure S18. HIV prevalence raking factors for females. Figure S19. Age-specific HIV prevalence in males, 2000. Figure S20. Age-specific HIV prevalence in females, 2000. Figure S21. Age-specific HIV prevalence in males, 2005. Figure S22. Age-specific HIV prevalence in females, 2005. Figure S23. Age-specific HIV prevalence in males, 2010. Figure S24. Age-specific HIV prevalence in females, 2010. Figure S25. Age-specific HIV prevalence in males, 2018. Figure S26. Age-specific HIV prevalence in females, 2018. Figure S27. Age-specific uncertainty interval range estimates in males, 2000. Figure S28. Age-specific uncertainty interval range estimates in females, 2000. Figure S29. Age-specific uncertainty interval range estimates in males, 2005. Figure S30. Age-specific uncertainty interval range estimates in females, 2005. Figure S31. Age-specific uncertainty interval range estimates in males, 2010. Figure S32. Age-specific uncertainty interval range estimates in females, 2010. Figure S33. Age-specific uncertainty interval range estimates in males, 2018. Figure S34. Age-specific uncertainty interval range estimates in females, 2018. Figure S35. Change in HIV prevalence in males, 2000-2005. Figure S36. Change in HIV prevalence in females, 2000-2005. Figure S37. Change in HIV prevalence in males, 2005-2010. Figure S38. Change in HIV prevalence in females, 2005-2010. Figure S39. Change in HIV prevalence in males, 2010-2018. Figure S40. Change in HIV prevalence in females, 2010-2018. Figure S41. Space mesh for geostatistical models

Additional file 1 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 1: Supplemental information.1. Compliance with the Guidlines for Accurate and Transparent Health Estimates Reporting (GATHER). 2. HIV data sources and data processing. 3. Covariate and auxiliary data. 4. Statistical model. 5. References

Additional file 4 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 4: Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges

Additional file 2 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 2: Supplemental tables.Table S1. HIV seroprevalence survey data. Table S2. ANC sentinel surveillance data. Table S3. HIV and covariates surveys excluded from this analysis. Table S4. Sources for pre-existing covariates. Table S5. HIV covariate survey data. Table S6. Fitted model parameters