Nitrite (Not Free Nitrous Acid) Is the Main Inhibitor of the Anammox Process at Common

NITRITE (NOT FREE NITROUS ACID) IS THE MAIN INHIBITOR OF THE ANAMMOX PROCESS AT

COMMON PH CONDITIONS.

D. Puyol, J.M. Carvajal-Arroyo, R. Sierra-Alvarez, J.A. Field.

Department of Chemical and Environmental Engineering, The University of Arizona, Tucson, 85721-0011, AZ,

USA. [email protected]; [email protected]; [email protected]; [email protected].

Corresponding author (D. Puyol): [email protected]; Phone +1(520)548-8310; Fax +1(520)621-6048

SUPPLEMENTARY INFORMATION

Study the pH effect on the anammox reaction

Materials and methods.

Batch experiments (100 mL) were conducted where around 80 mg volatile suspended solids (VSS) L-1 were incubated in 160 mL anaerobic serum bottles at 30 ± 1 C in rotating shakers. The same experimental conditions described in the main manuscript were used with the exception of the pH control, which was achieved by using

HEPES buffer system (25 mM) instead of the more conventional bicarbonate/CO2 buffer, and flushing the liquid and the headspace with He only instead of He/CO2 80:20 v/v. pH was adjusted in all cases by adding a 30% NaOH

-1 solution directly to the medium before adding the biomass. In this case, 50 mg NaHCO3 L was also incorporated as a carbon source. Two protocols were carried out depending on the pH range of the experiments. Since free nitrous acid (FNA) is pH dependent, the inhibitory effect of FNA was avoided by using very low total nitrite (TNi) concentrations at low pH values. So, protocol 1 was used in pH values from 6.69 to 7.01, where four consecutive spikes of 16 mg TNi-N L-1 were added. Protocol 2 was used in a pH range from 7.01 to 7.56, where 160 mg TNi -N

L-1 was added in a sole spike. In both cases, ammonium was firstly incorporated at 50 mg N L-1. In the most negative case, FNA concentration was never beyond 0.04 mg N L-1 and maximum ionized nitrite (INi) concentration was

159.99 mg N L-1, so that both FNA and nitrite inhibitory effects can be considered as negligible. The anammox

1 activity was monitored through N2 measurement following the same procedure described in the main manuscript.

Normalized anammox activities (NAAs) were therefore used as the dependent variable.

Statistical treatment: analysis of variance.

The effect of pH on the NAA values was studied by one-factor analysis of variance (ANOVA) procedure at a confident level of 0.05. Origin 8.5 software (OriginLab, Northampton, MA, USA) was used for this purpose.

Results.

Figure S1 shows the effect of pH on the NAA values. As can be seen, there is apparently no effect of pH on the anammox activity. In order to analyze statistically this finding, an ANOVA study was conducted where pH was used as the sole factor affecting the NAA values. Table S1 shows the main results from this analysis. Based on the statistics obtained, it can be concluded that pH is not a relevant factor of the anammox activity for pH values ranged from 6.69 and 7.56, so that it can be discarded as an inhibiting factor in the present study.

Figure S1: Effect of pH on the normalized anammox activity at successive nitrite spikes.

Table S1: One-factor ANOVA tests for mean values (at 0.05 level) of normalized anammox activities at different pH values.

Degrees of Mean Protocol Spike Data Sum of Squares F Value Prob>F* Freedom Square Model 3 107.7 35.9 4.30 0.0964 1st Error 4 33.46 8.4 spike Total 7 141.1 Model 3 195.8 65.3 3.83 0.114 2nd Error 4 68.2 17.1 spike Total 7 264.1 1 Model 3 81.5 27.2 0.63 0.631 3rd Error 4 171.1 42.8 spike Total 7 252.6 Model 3 160.4 53.5 2.75 0.176 4th Error 4 77.7 19.4 spike Total 7 238.1 2 Model 3 107.9 36.0 2.59 0.190

2 - Error 4 55.6 13.9 Total 7 163.6 * At the 0.05 level, all the population means are not significantly different.

Modeling.

Derivation of the Equation 1.

The anammox process can be described by double Monod kinetics, as follows:

-1 -1 Where µmax is the specific maximum growth rate (d ), Y is the biomass yield factor (mg VSS mg N2-N), X is the -1 -1 biomass concentration (mg VSS L ), and the KS are the saturation constants (mg N L ). Considering ionized nitrite (INi) as inhibitor by substrate (or auto-inhibitor), Eq. [S1] is modified as follows:

-1 Where Ki-INi is the inhibition constant for the substrate inhibition process (mg N L ) and nINi is the inhibition factor (dimensionless). Incorporating the inhibitory contribution of FNA as a non-competitive inhibitor, Eq. [S2] can be rewritten as:

-1 Where Ki-FNA is the inhibition constant for the non-competitive inhibition process by FNA (mg N L ) and nFNA is the inhibition order (dimensionless). Since the anammox process is a known very low-growing process, the variation of biomass can be considered as negligible with respect to the substrate addition and the initial biomass concentration used in these experiments. Thus, the parameters µmax, Y and X can be lumped in a sole parameter, maximum -1 -1 anammox rate (Vmax, mg N L d ). Dividing both terms by the biomass concentration, anammox specific activities are obtained (SAA, mg N mg VSS d-1). So, Eq. [S3] is transformed into:

-1 Where SAAmax is the maximum specific anammox activity (mg N mg VSS d ). Normalizing the SAA values, Eq. [S4] becomes:

Where NAAmax is the maximum normalized anammox activity (%). In the maximum rate, the concentrations of the + substrates are much higher than the saturation constants, so that Ks-NH4+ is negligible with respect to NH4 concentration, and then Eq. [S5] can be converted into:

The Eq. [S6] was used for analyzing the inhibitory impact of both INi and FNA on the anammox process simultaneously (Eq. [1] on the manuscript).

Data handling and statistical treatment.

3 All the experiments were conducted by duplicate. The NAA values extracted from all the duplicates were used for the fitting analyses in order to minimize the contribution of the degrees of freedom and to optimize the fitting procedure.

The surface fitting of the experimental data to the Eq. [1] was conducted by a non-linear least squares minimization of the error by using the Levemberg-Mardquardt algorithm. The goodness of fitting was analyzed by the coefficient of correlation. All the kinetics analyses were conducted by using the software Origin 8.5 (OriginLab, Northampton, MA, USA).