8–9) (Kohl and Medlar, 2006; Brandhuber et al., 2013). Knocke et al. (1990a) reported that dosages 4 times greater than the stoichiometric requirements for free chlorine and a minimum contact time of 3 hours were needed to lower an initial Mn(II) concentration of 1.0 mg/L to 0.7 mg/L at pH 7.0 and a temperature of 25°C. When pH was increased to 9.0, Mn(II) was oxidized within one hour to below 0.05 mg/L. Lower temperatures (14°C) and the presence of DOC also increased reaction times significantly. An additional consideration with chlorine is that elevated dissolved Mn(II) concentrations leaving a treatment plant can be oxidized by the free chlorine residual in the distribution system due to longer contact times resulting in the creation of MnOx(s). This can lead to consumer complaints due to water discolouration (Kohl and Medlar, 2006; Brandhuber et al., 2013). As discussed previously, although chlorine is not effective for direct oxidation of dissolved Mn(II) under typical treatment plant operations, it is an integral component of the effective operation of certain adsorption/oxidation processes discussed is section 7.2.3 (Brandhuber et al., 2013)./p> 1.0 mg/L), initial Mn(II) concentrations (0.03 to 0.3 mg/L) and hydraulic loading rates (up to 24 gpm/ft2)./p> 4.0), and so require substantially higher hydraulic loading rates during backwashing operations in order to fluidize the media bed. /p> 99% removal of manganese was achieved (< 0.03 mg/L in the effluent) in these plants. The pH of the source waters ranged from 6.46 to 7.63 and temperature was 9 to 10°C./p>9.5–10) above the solubility of various manganese hydroxide and carbonate solid phases. Likewise, the elevated pH present in lime or lime/soda ash softening will greatly increase the rate at which dissolved Mn(II) is oxidized in the presence of DO. Where DO is present, formation of oxidized MnOx(s) solids will occur. Raising the pH of the source water to achieve dissolved Mn(II) removal is not a cost-effective treatment approach by itself; rather, this treatment method is typically used only if softening of the source water is also required. Many lime or soda ash softening facilities achieve highly effective manganese removal (and iron removal) as a by-product of the chemical softening reactions. Furthermore, the plant personnel often do not proactively operate the treatment system for achieving manganese removal. In some cases, manganese removal occurs in softening treatment plants without the operator's knowledge, particularly when manganese is not being routinely monitored in source and treated water. A full-scale lime softening treatment plant reported lowering the average manganese concentration in the source water from 0.520 mg/L down to an average treated water concentration of 0.001 mg/L (Kohl and Medlar, 2006)./p> 0.1 µm) entering the distribution system, levels of organic matter was found to be six times less than in the conventional distribution system where particles were present. Vreeburg (2010) suggested that the particles could offer increased surface area for biological regrowth./p> 1 mg/L). In a more detailed report prepared using the same data, Brodeur and Barbeau (2015) reported that POU reverse osmosis systems were capable of achieving treated water concentrations below 1.0 µg/L when manganese in the well water ranged between 141 and 3.9 µg/L (86–100% removal). Controlled laboratory testing has also been conducted on POU reverse osmosis systems to assess their ability to lower manganese concentrations at the tap (Health Canada, 2015). Three different POU reverse osmosis systems certified under NSF/ANSI Standard 58 for removal of other metals (i.e., arsenic, barium, chromium, copper, lead) were tested as part of this study. Testing was conducted according to the requirements outlined for inorganic chemical reduction claims under NSF/ANSI Standard 58. Two rounds of testing were conducted with different influent manganese concentrations. In the first round, the median influent manganese concentration to each RO system was 100 µg/L (representative of average well water concentrations). Two of the systems demonstrated median removals greater than 95%, achieving treated water concentrations below 4 µg/L. The third system, which was tested with the storage tank removed, had a median removal of 88% to achieve treated water concentrations below 24 µg/L. In the second round of testing, influent concentrations to the RO systems ranged between 1.5 and 3.5 mg/L (representative of maximum concentrations of manganese in well water). Results were similar to those achieved in the first round of testing with median removals for two of the systems of 95–97% to achieve median treated water concentrations between 63 and 100 µg/L. The system without the storage tank achieved a lower median removal of 91% and a median treated water concentration of 140 µg/L. Overall, under most conditions POU RO systems are capable of removing manganese to below the MAC. Well owners with high manganese concentrations (>2 mg/L) may need to combine two treatment technologies such as POE ion exchange followed by POU RO in order to achieve treated water concentrations below 100 µg/L./p> 11.2 μg/L) and high hair (> 747 ng/g) manganese concentrations were significantly associated with lower full-scale IQ scores, compared with the control groups (blood: 8.2–11.2 μg/L; hair: 207–747 ng/g), after adjusting for confounders (the most important being creatinine, blood lead, community, gender, and parent's IQ and education). The authors only looked at the association between the predictors and the outcomes, and did not interpret their results in terms of risk. The measured associations can be biased since the information on participants and exposure was poorly detailed. For example, the authors excluded participants with missing data for any model covariates and those with higher manganese levels, which could bias the observed relationship (e.g., if participants with missing data had low manganese and low IQ, or high manganese and high IQ, then excluding these would bias the results away from the null in both scenarios, artificially inflating the effects). The characteristics of the excluded participants were not presented, so it is unknown how well the included subjects represent the population. Exposure was subjected to misclassification, since previous exposures and variations over time were poorly assessed (i.e., hair and blood are limited bioindicators; there were no estimate of the manganese total intake or the contribution from drinking water; only one measurement of the manganese bioindicators and of the other covariates levels was taken). There was also a risk of confounding, since the baseline characteristics were not stratified according to exposure groups (cannot assess differences other than manganese at baseline) and the statistical models were not built in a way to ensure inclusion of all important confounders (changes in the outcome variable should be the basis for covariates selection, and not in the exposure variable). Moreover, manganese was correlated with other included covariates, lowering the confidence in the model. Also, participants could be classified in different quartiles of exposure, depending on the bioindicator chosen as the main predictor variable (hair or blood Mn). There was also a risk of chance finding, and it would have been useful to provide the significance (p-values) of the multivariable models since the results were imprecise (large 95% CI almost including the null)./p> 400 µg/L and decreased mathematics test scores was observed after adjustment for confounders (i.e., arsenic, school grade, maternal education, paternal education, head circumference, and within-teacher correlations in rating the children). In a previous study by the same authors and with a similar protocol, increasing MnW was associated with classroom negative behaviors after adjustment for confounders (i.e., arsenic, sex, BMI, maternal education, and arm circumference) (Khan et al., 2011). The conclusions of these two studies may be qualified by the lack of account of other possible exposure to other neurotoxic substances (e.g., incomplete arsenic and lead adjustments for exposure), the lack of a thorough characterization of the exposure (and consideration of other sources of manganese such as air and dust), the high variability of the results (wide 95% CI), and the risk of teacher's bias./p> 300 µg/L = 41). Blood manganese levels were not statistically different between the two groups. The cross-sectional study by Hernandez-Bonilla et al. (2011) found no significant difference in neuromotor outcomes between children 7 and 11 years old residing in rural communities with high or low manganese biomarkers of exposure (blood: 9.7 vs 8.2 µg/L; hair: 12 vs 0.57 µg/g), after adjusting for covariates (blood Pb, Hg, sex, age, maternal education), except for a weak association between finger tapping tests and manganese in blood only. The results generally showed a lack of association and were not interpreted in terms of risk by the authors. The marginal association with finger tapping ability is not conclusive since the authors did not adjust for other covariates (smoking by the parents, differences in nutrition, and presence of 13 overweight children in the low exposure group compared with one in the high exposure group, possibly indicative of other socio-economical factors); also, past exposures was not assessed. Conclusions based on these three studies showing no effects are limited because of their cross-sectional design, the possibility that only healthy individuals stayed in areas with higher concentration of the element (sick survivor effect), the possibility of other sources of exposure or unaccounted variations in the past, and the absence of adjustment for major covariates (e.g., nutrition status)./p>
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