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Water managers have increasingly shown that demand management solutions are more important than searching for alternative sources to resolve the challenges and shortages of water supply services. This study identifies the impact of climate variables on residential water demand in three geographically and spatially dispersed towns (Arba Minch, Ziway, and Debre Birhan) in Ethiopia. Monthly mean temperature, relative humidity, and precipitation are analyzed using multivariate regression models to identify and evaluate the impacts of the parameters on water consumption. Principal component analysis (PCA) is also used to determine the dominant independent variable affecting the rate of water consumption. Mean temperature is shown to be the dominant variable causing the changes in water consumption in Arba Minch. The water consumption at Debre Birhan is slightly affected by relative humidity. Analyzed climate variables do not affect the water consumption changes at Ziway. The main findings of this paper show that geographical distribution and other determinants are more important determinants of residential water demand. It is concluded that the analyzed climate variables are not the dominant determinants which impact drinking water consumption at the study sites. Thus, it is recommended to include relevant information about the climate variables alongside other determinants in order to enhance the water management system in evaluating and auditing water usage.

Additional file 1: Table S1 Origin of the 15 investigated sewage sludges and sludge treatment at respective wastewater treatment plant (WWTP). Table S2 Characterization of the 15 investigated sewage sludges and HNO3 addition (for samples with more than 500 mg C on 3,000 mg according to EN 16174), contents related to dry matter. Table S3 Phosphorus recovery of certified reference materials (sewage sludges, Sigma-Aldrich, Laramie, USA). Averages (n=2) of respective measurement day related to fresh matter (FM) and dry matter (DM). Table S4 Recovery rates of spiked and diluted (1:5) samples by digestion (DG). Elemental P standards (10,000 mg/l for ICP-OES; 1,000 mg/l for ICP-MS diluted 1:10) of MERCK were used for spiking. Table S5 Phosphorus contents by method combination in 15 sewage sludges in g/kg. Table S6 Standard deviations of the phosphorus contents by method combination in 15 sewage sludges in g/kg. Table S7 Phosphorus recovery rate of 15 investigated sewage sludges after normalization to the grand mean. Table S8 Phosphorus recovery rate of 15 investigated sewage sludges after normalization to digestion mean. Table S9 Two-way analysis of variance for the normalized (grand mean) 15 sewage sludges. Table S10 XRF results in g/kg for homogeneity study of the sludge 15 for interlaboratory comparison. Powder analysis of 10 out of 64 bottles (regular intervals) from the line of bottling, three samples (x-1 to x-3) per bottle. After analysis sample shaking and remeasurement (x-4 to x-6). Table S11 One-way analysis of variance of homogeneity study (XRF results for phosphorus content). Table S12 ILC results by laboratory and method class (MC) and outliers (Grubbs or Cochran) according to ISO 5725-2 (total summarized values main text, Table 3). Table S13 Interlaboratory comparison, instrumental parameters by laboratory sorted by analytical method. Table S14 Interlaboratory comparison, deviations of lab from the sample digestion prescribed in the sample instruction letter. Table S15 Interlaboratory comparison, deviations by of lab of from the analytical method prescribed in the sample cover instruction letter. Figure S1 Exemplary calibration curves for ICP-OES (a), ICP-MS (b), and photometric phosphorus determination with ammonium molybdate (c). Figure S2 Phosphorus recovery rate from reference materials CRM029-50G (029) and CRM031-40G (031) by digestion and analysis. Figure S3: Phosphorus recovery from 15 sewage sludges as determined by method classes A–F* normalized to the grand mean (a) and normalized to the mean value by digestion (b). The error bars (a) represent the standard deviation of the five replicates (sludge 15 no. reflux digestions: three replicates) per sludge. For an explanation of the method class abbreviations see main text Figure 1. Figure S4 Schematic representation of the cross-riffling to generate bottles for interlaboratory comparison (ILC). Figure S5 XRF homogeneity results of 10 bottles (six replicates). Measurement of three samples per bottle, shaking and new measurements from measurement number 42. Figure S7 Cumulative curve of the moisture content analyzed by the labs during the interlaboratory comparison. The error bars mark the standard deviation (n=2 to 6). Figure S6 Interlaboratory comparison results sorted by analytical method (the bars represent laboratory means and standard deviations). The method class lab code is shown on the x-axis and represents the lab number and the letter of the method class. Outliers have been eliminated for the calculations. Figure S8 Interlaboratory comparison, distribution obtained with a kernel density estimator. Figure S9 Significant differences by method combination and method class (main text, Figure 1) from the interlaboratory comparison and investigation of 15 sewage sludges t-test.

Additional file 1: Figure S1. Phosphorus content variations (dry matter) in municipal sewage sludge at 9 WWTPs. Figure S2. Range of monthly phosphorus and iron content by annual mean during the year. Figure S3. Ranges of relative standard deviation (RSD) of matrix elements and organic content for the 9 WWTPs over the whole year. Table S1. Certified reference material sewage sludges used for analysis and calibration limits. Table S2. Total data for WWTP a, all analyzed values per sample. Table S3. Relative standard deviation data of the replicates for analyzed values of WWTP a. Table S4. Total data for WWTP b, all analyzed values per sample. Table S5. Relative standard deviation data of the replicates for analyzed values of WWTP b. Table S6. Total data for WWTP c, all analyzed values per sample. Table S7. Relative standard deviation data of the replicates for analyzed values of WWTP c. Table S8. Total data for WWTP d, all analyzed values per sample. Table S9. Relative standard deviation data of the replicates for analyzed values of WWTP d. Table S10. Total data for WWTP e. all analyzed values per sample. Table S11. Relative standard deviation data of the replicates for analyzed values of WWTP e. Table S12. Total data for WWTP f. all analyzed values per sample. Table S13. Relative standard deviation data of the replicates for analyzed values of WWTP f. Table S14. Total data for WWTP g. all analyzed values per sample. Table S15. Relative standard deviation data of the replicates for analyzed values of WWTP g. Table S16. Total data for WWTP h. all analyzed values per sample. Table S17. Relative standard deviation data of the replicates for analyzed values of WWTP h. Table S18. Total data for WWTP i. all analyzed values per sample. Table S19. Relative standard deviation data of the replicates for analyzed values of WWTP i. Table S20. Share of in neutral ammonia citrate (NAC) extractable phosphorus (PNAC) in several sewage sludge samples and molar ratios of P and potential precipitation agents Fe and Al.

Hydrogen sulphide (H2S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety of sewer workers, sewers are commonly monitored using H2S gas sensors. In this work, three commercial H2S gas sensors for air quality monitoring were compared at two different sites in Berlin, Germany. Two of the sensors provide online access to data, while the other one is a data logger. Moreover, based on statistical measures (RMSE, MAE, MB, and a graphical analysis), we evaluated whether a rotation/exchange between data logger (reference) and online sensors is possible without significant differences in the gas measurements. Experimental evaluation revealed that measurement differences are dependent on the H2S concentration range. The deviation between sensors increases as the H2S concentration rises. Therefore, the interchange between reference and online sensors depends on the application site and the H2S levels. At lower ranges (0–10 ppm) there were no observed problems. Finally, to support practitioners on-site, a management procedure in the form of a decision-making tool is proposed for assessing whether gas sensors should be exchanged/rotated.

Hydrogen sulfide (H2S) related to wastewater in sewer systems is known for causing significant problems of corrosion and odor nuisance. Sewer systems severely affected by H2S typically rely on online H2S gas sensors for monitoring and control. However, these H2S gas sensors only provide information about the H2S emission potential at the point being monitored, which is sometimes inadequate to design control measures. In this study, a comparison of three market-ready online sensors capable of liquid-phase H2S detection in sewer systems was assessed and compared. Two of the three sensors are based on UV/Vis spectrophotometry, while the other adapted the design and principles of a Clark-type electrochemical microsensor. The H2S measurements of the sensors were statistically compared to a standard laboratory method at first. Following that, the performance of the online sensors was evaluated under realistic sewer conditions using the Berlin Water Company (BWB) research sewer pilot plant. Test applications representing scenarios of typical H2S concentrations found in sulfide-affected sewers and during control measures were simulated. The UV/Vis spectrometers showed that the performance of the sensors was highly dependent on the calibration type and measurements used for deriving the calibration function. The electrochemical sensor showed high sensitivity by responding to alternating anaerobic/anoxic conditions simulated during nitrate dosing. All sensors were prone to measurement disturbances due to high amounts of sanitary solids in wastewater at the study site and required continuous maintenance for reliable measurements. Finally, a summary of the key attributes and limitations of the sensors compared for liquid phase H2S detection is outlined.