621 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 A B S T R A C T This study aims to present the time behavior of wastewater flow parameters, organic matter, biogas flow, biogas composition, and its relations, measured through online sensors, in a municipal wastewater treatment plant (WWTP) operating full-scale upflow anaerobic sludge blanket (UASB) reactors, installed in the south of Brazil. WWTP has online measurement devices to evaluate some physicochemical variables of the sewage and the biogas. The COD analyzer (UV– Vis probe), ultrasonic flow meter, biogas flow meter, and biogas composition analyzer were the equipment used. The monitoring occurred for two time periods each of 72 h and one time period for 48 h in the year 2018. Data were checked with descriptive statistics, data independence was checked through the autocorrelation Box– Ljung test, normality behavior was checked with several tests (Shapiro– Wilk, Kolmogorov–Smirnov, Lilliefors, Anderson–Darling, D’Agostino K2, and Chen–Shapiro), and Spearman’s correlation coefficient was used to evaluate the correlations among the parameters. The mean sewage flow was 345 ± 120 L.s-1; removed organic load was, in average, 48%; biogas quality values were 82.32% ± 3.62% v/v (CH 4 ), 2.66% ± 1.19% v/v (CO 2 ), and 3453 ± 1268 ppm (H 2 S); and the production per capita R E S U M O O trabalho teve como objetivo a caracterização qualiquantitativa do biogás e suas relações com o comportamento temporal da carga orgânica em reatores anaeróbios de fluxo ascendente (UASB), em escala plena, no tratamento do esgoto sanitário, empregando equipamentos de medição online. O trabalho foi conduzido em uma estação de tratamento de esgotos (ETE) instalada no Sul do Brasil. A ETE possui dispositivos de medição online para avaliar algumas variáveis físico- químicas do esgoto e do biogás. Os equipamentos utilizados foram o analisador Demanda Química de Oxigênio (DQO) [sonda ultravioleta visível (UV–Vis)], medidor ultrassônico, medidor de biogás e analisador de composição de biogás. O monitoramento ocorreu por dois períodos de 72 horas cada e um período de 48 horas, no ano de 2018. Os dados foram analisados com estatística descritiva, a independência dos dados foi averiguada por meio do teste de correlação Box-Ljung, a normalidade foi verificada pelos testes Shapiro-Wilk, Kolmogorov- Smirnov, Lilliefors, Anderson-Darling, D’Agostino K2 e Chen-Shapiro, e foi usado o método de Spearman para avaliar as correlações entre os parâmetros. A vazão média de esgoto foi 345 ± 120 Ls-1. A carga orgânica removida foi, em média, 48%. Os valores de qualidade do Quali-quantitative characterization of biogas with the temporal behavior of organic load on wastewater treatment plant with upflow anaerobic sludge blanket reactors through measurement in full-scale systems Caracterização quali-quantitativa do biogás e suas relações com o comportamento temporal da carga orgânica em reatores anaeróbios de fluxo ascendente, em escala plena, no tratamento do esgoto sanitário, empregando equipamentos de medição online Orlando Antonio Duarte Hernandez1 , Ana Caroline Paula1 , Gustavo Rafael Collere Possetti2 , Mauricio Pereira Cantão1 , Miguel Mansur Aisse1 1Universidade Federal do Paraná – Curitiba, (PR), Brazil. 2Companhia de Saneamento do Paraná – Curitiba (PR), Brazil. Correspondence address: Orlando Antonio Duarte Hernandez – Graduate Program in Water Resources and Environmental Engineering, Department of Hydraulics and Sanitation, Universidade Federal do Paraná – PO Box 19011 – Jardim das Américas – CEP: 81531-990 – Curitiba (PR), Brazil. E-mail: oranduher1@gmail.com Conflicts of interest: the authors declare that there are no conflicts of interest. Funding: Brazil–Germany Project on Biogas (PROBIOGÁS), SANEPAR, UFPR, and INCT ETEs Sustentáveis (Brazilian National Science and Technology Institute for Sustainable WWTPs), Organization of American States (OAS), through cooperation with Brazilian Coordination for the Improvement of Higher Education Personnel – CAPES/UFPR. Received on: 02/21/2021. Accepted on: 08/27/2021. https://doi.org/10.5327/Z217694781059 Revista Brasileira de Ciências Ambientais Brazilian Journal of Environmental Sciences Revista Brasileira de Ciências Ambientais Brazilian Journal of Environmental Sciences ISSN 2176-9478 Volume 56, Number 1, March 2021 This is an open access article distributed under the terms of the Creative Commons license. https://orcid.org/0000-0003-4403-2517 https://orcid.org/0000-0002-5822-5996 https://orcid.org/0000-0001-8816-5632 https://orcid.org/0000-0002-9937-738X https://orcid.org/0000-0003-4620-559X mailto:oranduher1@gmail.com https://doi.org/10.5327/Z217694781059 Hernandez, O.A.D. et al. 622 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 Introduction The treatment of sewage in warm regions, such as South Amer- ica and Caribe, generally occurs via anaerobic technologies, such as upflow anaerobic sludge blanket (UASB) reactors. Von Sperling and Oliveira (2009), Noyola et  al. (2012), Chernicharo et  al. (2015), and Mainardis et al. (2020) recognized the great advantages of UASB, since it allows the reduction of the costs of implementation, operation, and maintenance of wastewater treatment plants (WWTP); besides, it re- quires a low initial investment. UASB reactors are well known for their efficiency on removal of organic matter and solids, low energy demand, and without adding chemicals. The structure of these reactors basically consists of a tank with a bottom layer of biological sludge and a settler and gas deflector on the top container. With the proper operation, a tendency of sep- aration of solid, liquid, and gas phases occurs (Lettinga et  al., 1983; Chernicharo et al., 1999). For these authors, among the main param- eters related to the design of UASB reactors, hydraulic volumetric rate (HVR), hydraulic retention time (HRT), volumetric organic loading rate (Lv), and upflow velocity should be accounted. Many studies have been conducted expressing or comparing the mean volumetric organic loading to the efficiencies of UASB reactors in the treatment of sewage. In this regard, volumetric organic loading is recommended to be between 2.5 and 3.5 kg COD.m3.d-1 (Chernicharo et  al., 1999; von Sperling and Chernicharo, 2005; Chernicharo et  al., 2015). Previous studies from Lettinga et al. (1983) reported lower loads, similar to Aisse et al. (2002), presenting values of 1.80 kg COD.m-3.d-1 for the hydraulic retention time of 8 h. Aisse et  al. (2002) mentioned the COD of (151 ± 64) mg.L-1 in the effluent of a UASB reactor treating urban wastewater. Considering the influent COD of (453 ± 147) mg.L-1, the authors obtained the COD efficiency removal of 67%. The gas phase, inherent to sewage treatment in UASB reactors, represents a great advantage, especially regarding biogas production with elevated methane content. Biogas in UASB reactors, treating mu- nicipal and domestic wastewater, presents its composition as follows: methane (70–80%), nitrogen (10–25%), carbon dioxide (5–10%), and H2S (1,000–2,000 ppm) (Noyola et al., 2006; Possetti et al., 2019). The proportion among these components depends on the type of bio- logical treatment applied and on the substrate, which could be urban solid residues, domestic and municipal wastewater, sludge from mu- nicipal wastewater treatment, animal waste, among others (Venkatesch and Elmi, 2013; Mainardis et al., 2020). Methane is associated with greenhouse gases, with CH4 global warming potential (GWP) being 28 times superior to CO2; thus, bio- gas combustion for energy production could avoid methane emissions and substitute fossil fuels, also reducing the CO2eq tons released to the atmosphere (IPCC, 2014). Methane has a lower calorific value of 9.9 kWh.Nm-3, and its concentration defines the potential of recover- ing energy from the biogas; electric power production from biogas is an alternative with great expansion potential in Brazil. Biogas produc- tion rates, verified by Lobato et al. (2012), from 9.8 to 17.1 NL.hab-1.d-1, and Cabral et al. (2017b), from 3 to 138 NL(CH4)/kg(CODremov), have been used by researchers and wastewater treatment plant managers. Possetti et  al. (2013), Waiss and Possetti (2015), and Cabral et  al. (2017b) observed a direct correlation between the influent sewage flow and rainfall, with the consequent lowering of HRT and the production of biogas. For Possetti et al. (2018), the rainwater results in sewage dilu- tion (increase of flow and lowering of COD concentration), significant- ly reducing the biogas production. Mota et al. (2019) studied the vari- ations in the concentration of methane (CH4), carbon dioxide (CO2), and oxygen (O2), during 24-h periods, in a sanitary landfill, located in the Northeast Region of Brazil, with a predominantly hot tropical and mild semi-arid climate. The research area showed no significant sea- sonal variation, only periods with more or less rainfall. There were few changes in the climate of the semi-arid region of Northeastern Brazil during the year. Pagliuso and Regattieri (2008) observed that the increasing mu- nicipal demand for electric power requires alternative sources, thus making it necessary a deep knowledge of the time behavior of biogas obtained was 4.51 ± 1.65 NL.hab-1.d-1. It was estimated an electric power generation of 3118.6 kWh.d-1, which is equivalent to an installed power of 130 KW. The behavior of removed organic load and biogas flow (Nm3.h-1), produced in the treatment plant, showed variable, periodic, and nonstationary time behavior. Keywords: biogas composition; biogas flow; chemical oxygen demand probe; sewage; ultrasonic flowmeter. biogás foram, para o metano (CH4), 82,32% ± 3,62% v/v (percentagem volume-volume), para o dióxido de carbono (CO2) 2,66% ± 1,19% v/v e para o sulfeto de hidrogênio (H2S) 3453 ± 1268 ppm. A produção de biogás per capita obtida foi 4,51 ± 1,65 NL.hab-1.d-1. Foi estimada uma produção de energia eléctrica de 3.118,6 kWh/d, o que é equivalente a uma potência instalada de 130 KW. O comportamento da carga orgânica removida e da vazão de biogás (Nm3.h−1) produzida na estação de tratamento, apresentaram um comportamento temporal variável, periódico e não estacionário. Palavras-chave: composição biogás; esgoto; medidor ultrassônico; sonda demanda química de oxigênio; medidor ultrassônico; vazão biogás. http://kWh.Nm Quali-quantitative characterization of biogas with the temporal behavior of organic load on wastewater treatment plant with upflow anaerobic sludge blanket reactors through measurement in full-scale systems 623 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 generated in anaerobic WWTP, which is still little used in Brazil. Elec- tricity generation and consumption in the WWTP itself are options used worldwide. Some guidelines on distributed electricity from biogas are available in Rosenfeldt et  al. (2015), Cabral et  al. (2017a), Gomes et al. (2017), and Possetti et al. (2019). New technologies rising in the market, especially those related to online and remote sensing, allow measurements in loco and in real time of biogas production in UASB reactors. Mota et  al. (2019) rec- ommended the development of further research, and estimating the potential biogas is particularly important to assess the feasibility of its exploitation for energy purposes. In this context, this study aims to present the time behavior of wastewater flow parameters, organic matter, biogas flow, and biogas composition, measured with online sensors, in a municipal wastewater treatment plant operating with UASB reactors, in full scale. Materials and Methods This study took place in a medium-size WWTP, installed in the south of Brazil, with a design flow of 420 L.s-1 of domestic sewage and serving approximately 180,000 inhabitants. The wastewater pre- treatment occurs with two mechanized screens and one grit cham- ber. The biological treatment is done in six UASB reactors (secondary treatment), and post-treatment of anaerobic effluent occurs in aerated followed by sedimentation ponds. The biogas generated by the UASB reactors at the plant is destroyed in an enclosed flare. The treatment plant has online measurement devices to evaluate the behavior of some physicochemical variables of the sewage and the biogas (Figure 1). The COD meter (probe) in the sewage, the sewage flowmeter, the biogas flowmeter, and the biogas quality analyzer were the equipment used in this research. Instrumentation The COD measurement system is composed of a spectrometer and a control unit; spectrometer probes work according to the principle of UV–Vis spectrometry. The system can determine concentrations be- tween 100 and 3,250 mg(COD).L-1. A detailed description of the probe can be found, e.g., in Langergraber et al. (2003) and Hernandez et al. (2018). The probe possesses an uncertainty of 1.8%, for a probability coverage of 95.45% (Hernandez, 2019). The treatment plant possesses an ultrasound flowmeter, with a resolution of ±0.2%, located over a Parshall flume in the inlet of the treatment plant. The equipment has an output with analog standard 4–20 mA, with an uncertainty of ±0.001%, for a probability coverage of 95.45% (Hernandez, 2019). The biogas flow was measured with a thermal dispersion transmit- ter, which is basically formed by two temperature probes (insert in the gas flow) and a heater. The energy required to maintain the sensor warm to a constant temperature is directly proportional to the gas velocity. Hence, correlations between energy and velocity are used to calculate the gas production. In this regard, the uncertainty of the equipment is 10.57% for a probability coverage of 95.45% (Hernandez, 2019). The gas analyzer is a measurement system composed of a static unity and a portable measurement device, which receives biogas sam- ples collected in the burning line. The biogas analyzer uses selective infrared probes to measure CH4 (0–100%) v/v and CO2 (0–100%) v/v, and electrochemical probes to measure O2 (0–25%) v/v and H2S (0– 5,000) ppm. Regarding the uncertainties, for the infrared probes, it is ± 1.5%, whereas for the electrochemical probes, it was assumed to be ± 0.03% for a probability coverage of 95.45%. Energy recovery from biogas The potential of energy generation via the use of the biogas pro- duced in the WWTP was estimated through the following Equation 1 (Cabral et al., 2017a): EP = QCH4 ⋅ EC ⋅ ηelectric (1) Where: EP = energy potential (kWh.d-1); QCH4 = methane flow rate (Nm 3.d-1); EC = energetic content of methane (9.9 kWh.Nm-3); ηelectric = electrical efficiency of a combined heat and power engine (36%). The power of the electric engine is calculated by dividing by 24 h, in case of continuous use. Statistical evaluation criteria Temperature and operational data collected in the treatment plant were used, and precipitation data were registered with a pluviometer Figure 1 – Flowsheet of WWTP liquid phase and measurement equipment location. (1) Sewage flowmeter; (2) COD concentration meter; (3) biogas flowmeter; (4) biogas quality analyzer; and (5) biogas enclosed flare. http://kWh.Nm Hernandez, O.A.D. et al. 624 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 also located in the plant. In addition, the obtained values were trans- mitted to a database and subsequently treated in electronic datasheets for the elaboration of the descriptive statistics. The monitoring period occurred hourly for three consecutive days (72 h), in August and in September (samplings 1 and 2); in October, the data were collected for 48 consecutive hours (sampling 3), all in the year 2018. The Spearman’s rank correlation coefficient (rs) was used to evaluate the monotonic correlations among the parameters for the significance level of 0.05. Rough data were checked with descriptive statistics and analyzed for outliers identification with the interquartile amplitude method. Data in- dependence was checked through the autocorrelation Box–Ljung test (Ljung and Box, 1978), and the normality behavior was verified with the following normality tests: the Shapiro–Wilk test of normality (Shapiro and Wilk, 1965) and the Kolmogorov–Smirnov, Lilliefors, Anderson– Darling, D’Agostino K2, and Chen–Shapiro tests (Adefisoye et al., 2016; Razali and Wah, 2011). If normal distribution and lack of autocorrela- tion are not to be rejected, for a 0.05 significance level, the p-values of the Shapiro–Wilk and Box–Ljung tests are higher than 0.05. Results and Discussion The climate of the South Region in Brazil, which is located below the Tropic of Capricorn in a temperate zone, is influenced by the sys- tem of disturbed circulation of the south, which produces the rains, mainly in the summer. In the evaluation period, the wastewater col- lection system was subjected to atmospheric precipitations of up to 38 mm/day. Regarding temperatures, the winter is cool and the sum- mer is hot. The annual medium temperatures range from 14 to 22°C, and in places with altitudes above 1,100 m, it drops to approximately 10°C. Some parts of the southern region also have an oceanic climate. Table 1 shows the meteorological data obtained at the treatment plant. Organic load In Figure 2, it is possible to observe the hourly behavior of the organ- ic load, calculated from the relation of the hourly measurements of the ultrasound meter (flow) and spectrometer probe (COD concentration). The  probe was used to measure COD in the influent and effluent of the reactor. The reported mean values for the three evaluated periods [sam- pling 1 (72 measurements), sampling 2 (72 measurements), and sampling 3 (48  measurements)] were 688 ± 243 mg.L-1 for the influent and 358 ± 116 mg.L-1 for the effluent. The mean sewage flow was 345 ± 120 L.s-1, inferi- or to the design flow. Therefore, the organic influent load in the reactors was 19,782 ± 9,949 kg.d-1 and the organic effluent load was 10,133 ± 4,566 kg.d-1. The UASB reactors presented the mean COD removal efficiency of (47.25% ± 12.03%), and the mean removed organic matter was 9,989 ± 5,980 kg(COD).d-1. Thus, the removal efficiencies were below the values reported by Aisse et al. (2002) and Oliveira and Von Sperling (2011). The re- moved organic matter was similar to the mean obtained by Bilotta and Ross (2016) for an equivalent treatment plant. The applied volumetric organic loading rate (Lv) was 1.70 ± 0.81 kg(COD).m3.d-1, which is in accordance with the values reported by Lettinga et al. (1983) and Aisse et al. (2002). The obtained HRT value of 9.58 ± 2.29 h is coherent with the val- ues reported by Oliveira and Von Sperling (2011), Chernicharo et  al. (2015), and Metcalf and Eddy (2016), between 6 and 10 h, in terms of the mean flow, respecting the recommendations of the Brazilian Regu- lation NBR 12209 (ABNT, 2011). Characterization and biogas production Figure 3 presents the behavior of removed organic load (kg.d-1) and biogas flow (Nm3.h-1) produced in the treatment plant. The curves present variable, periodic, and nonstationary time behavior, corrob- orating the biogas production values found by Possetti et  al. (2013), Cabral et al. (2017b), and Possetti et al. (2019). Figure 4A presents the behavior of the hourly biogas concentration (quality) and the histograms of these measurements. The collected values were 82.32% ± 3.62% v/v of methane (CH4), 2.66% ± 1.19% v/v of carbon dioxide (CO2), and 3,453 ± 1,268 ppm of hydrogen sulfide (H2S). In or- der to complete the 100% v/v in the biogas composition, the difference was attributed to nitrogen (N2) (~15%) v/v, dissolved in the raw sewage, and removed in the gas phase of the UASB reactor (Noyola et al., 2006). The presented results indicate that the control and the monitoring of the generated biogas characteristics should be performed continu- ously, since variation might occur. These variations could occur due to Table 1 – Meteorological data at the treatment plant. Day Temperature* (°C) Weather Pluviometry (mm) Day (-1)** Day (1) Day (2) Day (3) Average (mm) Sample Collection 1 (August) 17 Dry/cloudy 0 0 0 8 2 Sample Collection 2 (September) 20.1 Dry/rainy 0 2 2 4 2 Sample Collection 3 (October) 16 Rain 16 38 12 12 19.5 *The temperature means of the period evaluated; **the precipitation 1 day before starting the evaluation. Quali-quantitative characterization of biogas with the temporal behavior of organic load on wastewater treatment plant with upflow anaerobic sludge blanket reactors through measurement in full-scale systems 625 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 climate, characteristics of the basin, and population that contributes to the treatment plant or occurrence of disturbances in the process of anaerobic digestion (WEF, 1994, 1998; Brasil, 2017). Figure 4B shows the histograms of biogas hourly concentration. Re- garding H2S, it was possible to observe greater clusters between 1,700 and 3,700 ppm, highlighting the bimodal feature of the data. For sam- plings 1 and 2, values ranged mainly between 3,400 and 5,500 ppm, while for sampling 3, the values were located primarily in the interval between 500 and 3,000 ppm (see Figure 4A). The multimodality gener- ally occurs when the data are collected from more than one process or condition. It is believed that rainfall could be the explanation for such behavior. In the period of sampling 3, the mean rainfall was 19.5 mm.d-1, in comparison with samplings 1 and 2, with a mean rainfall of 2 mm.d-1. The gas emission did not show a significant difference between the end of the rainy period and the end of the dry period (Pinheiro et al. 2019). It is noteworthy that the minimum concentration of H2S was 130 ppm, and the maximum was 5,457 ppm (Figure 4B). The obtained data could be interesting to adequate, for example, the chemical dosage in the systems for controlling odor, in anaerobic treatment reactors, or to increase the dosage in periods where a greater concentration of H2S is expected. How- ever, for the use of biogas to generate energy, gas treatment is required. For example, motor-generator groups typically demand concentrations of H2S below 130 ppm for proper functioning (Soreanu et al., 2011). Carbon dioxide presented, as seen by Noyola et al. (2006), an asym- metry of the collected data distribution to the left, with the minimum concentrations of 0.7% and maximum concentrations of 6.2%  v/v. The histogram also indicates bimodal behavior. Methane was within the maximum of 94.5% and the minimum of 76.6%. It could be mentioned that in the greater data series, grouping is in the interval between 75 and 87.5%. Moreover, it is evident that the lowest values occurred during sampling 3, rainy period, which is coherent with meteorological conditions (see Table 1) and the data by Possetti et al. (2013) and Cabral et al. (2017b). The biogas flow showed a relative symmetric distribution, pre- senting higher frequency in the measurements when the equipment measured between 25 and 45 Nm3.d-1. Figure 4B shows a normal dis- tribution line for the biogas flow; visual inspection indicates possible normality for this parameter but not for the gas concentrations. Bio- gas flow and removed organic load were tested with the OriginPro© software-based normality tests: Shapiro–Wilk, Chen–Shapiro, An- derson–Darling, Kolmogorov–Smirnov, Lilliefors, and D’Agostino K2 (omnibus). Each collected sample and the ensemble of all samples were tested, and the results are shown in Table 2. Razali and Wah (2011) compared the power of the first four tests (Shapiro–Wilk, Anderson–Darling, Lilliefors, and Kolmogorov– Smirnov), verifying that they are in descending order (S–W being the most powerful and K–S the less one). Razali and Wah (2011) also showed that the maximum normality test power occurs for N > 200 for symmetric distributions and N > 50 for asymmetric distributions. Figure 2 – Organic load at UASB reactors [kg(COD).d-1]. Figure 3 – Removed organic load curve (kg.d-1) and biogas flow curve (Nm³.h-1) as function of time (h). Hernandez, O.A.D. et al. 626 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 Figure 4 – Behavior of biogas hourly concentration (quality) and the histograms of these measurements. (A) Data distribution of the H2S, CO2, CH4, and biogas flow data for all periods evaluated and (B) behavior of H2S, CO2, CH4, and biogas flow of each period evaluated. Table 2 – Normality test results for removed organic load and biogas flow. Normality tests Removed organic load Biogas flow Sample #1 Sample #2 Sample #3 All samples Sample #1 Sample #2 Sample #3 All samples Shapiro–Wilk Reject Reject Cannot reject Reject Cannot reject Reject Cannot reject Cannot reject Anderson–Darling Reject Reject Cannot reject Reject Cannot reject Reject Cannot reject Cannot reject Lilliefors Reject Reject Cannot reject Reject Cannot reject Reject Reject Cannot reject Kolmogorov–Smirnov Cannot reject Reject Cannot reject Cannot reject Cannot reject Cannot reject Cannot reject Cannot reject D’Agostino Omnibus Reject Reject Cannot reject Reject Cannot reject Reject Cannot reject Cannot reject Chen–Shapiro Reject Reject Cannot reject Reject Cannot reject Reject Cannot reject Cannot reject Table 2 demonstrates that the individual samples and the ensem- ble of all samples have different behavior. All the tests for the removed organic load sample #3 indicate possible normal distribution, while samples #1 and #2 clearly are not normal. Removed organic load all-samples ensemble reproduces the average behavior of major data. Only biogas flow sample #2 shows non-normal behavior. Most normality tests show coherent results, the exceptions being Kolmogorov–Smirnov and Lilliefors, which is a modification of the K–S test. Normality test results indicate a non-normality trend for re- moved organic load and normality trend for biogas flow. The biogas flow, along with the biogas quality, could be of great help, for example, in the operation of a sludge thermal drying system or the possible implementation of a gasometer, for the storage of biogas generated in the treatment plant. When comparing the removed organic matter with the flow pa- rameters, CH4 percentage, CO2 percentage, and concentration of H2S, Quali-quantitative characterization of biogas with the temporal behavior of organic load on wastewater treatment plant with upflow anaerobic sludge blanket reactors through measurement in full-scale systems 627 RBCIAMB | v.56 | n.4 | Dec 2021 | 621-629 - ISSN 2176-9478 it was observed that the organic load is positively correlated with the four parameters. The larger correlation coefficients were for re- moved organic matter versus biogas flow (rs = 0.44) and CH4 percent- age (rs  =  0.34), respectively. Additionally, there is no direct influence between the percentage of CH4 and H2S concentration, and a posi- tive correlation of 0.52 was evidenced between biogas flow and H2S. The  correlation coefficients obtained through the Spearman method are presented in Table 3. The correlation varies from negligible (|rs|~ 0) to moderate (|rs|~ 0.6). Specific biogas production and potential of energy generation Currently, the treatment plant attends a population of approxi- mately 180,000 inhabitants. Since the average biogas production, in the evaluation period, was 36.46 ± 12.35 Nm3.h-1, the biogas production rate per capita was calculated as 4.51 ± 1.65 NL.hab-1.d-1. The biogas production rate with the removal rate was 80.4 ± 29.68 NL.kg-1 (COD). The unitary relations obtained in the studied treatment plant were close to the inferior limit reported by Lobato et al. (2012). When sam- pling 3 is studied separately, its biogas production rate per capita presents a considerable reduction, with the mean of 2.72 ± 1.03  NL. hab-1.d-1. The periods of intense rain resulted in the lowering of biogas production. Power generation potential estimative based on the average biogas flow and methane content values found during the monitoring peri- od of WWTP was 3,118.6 kWh.d-1, which is equivalent to an installed power of 130 KW. According to Rosenfeldt et al. (2015), Cabral et al. (2017a), Gomes et al. (2017), and Possetti et al. (2019), the decision on the best way to use biogas energy depends on the size and operational conditions of each WWTP and on on-site specific requirements, in- cluding social and environmental aspects. Conclusions The presented results revealed the behavior of different sewage parameters, such as organic load in the influent/effluent and removed organic matter in a wastewater treatment plant implemented, with UASB reactors operating in full scale, including biogas production, and adopting the time behavior in a full-scale approach. Mean hourly values were reported in the evaluation period for COD in the influent sewage, COD in the effluent sewage, sewage flow, biogas flow, and bio- gas composition (82.32% of methane). Visual inspection indicates normality for biogas flow, but not for the gas concentrations. Most of the applied normality tests showed coherent re- sults, the exceptions being Kolmogorov–Smirnov and Lilliefors, which is a modification of the K–S test. Normality test results indicate a non-normal- ity trend for removed organic load and a normality trend for biogas flow. The organic load [kg(COD).d-1] was inferior to design parameters, and the removed organic matter efficiency was, in average, 48%. Both re- moved organic load and biogas flow (Nm3.h-1), produced in the treat- ment plant, showed variable, periodic, and nonstationary time behavior. The hourly removed organic matter has shown a positive moderate Spear- man’s rank correlation coefficient with biogas flow, CH4 percentage, CO2 percentage, and concentration of H2S. Also, it was verified that there are no direct correlations between biogas flow and the concentration of H2S. The mean biogas production per capita obtained was 4.51 ±  1.65  NL.hab-1.d-1, a value inferior to that reported in the literature. The  values of biogas composition (82.32% ± 3.62%) v/v (CH4) were in accordance with the values mentioned by Noyola et al. (2006), with H2S resulting in the superior limit reported in the literature (between 1,700 and 3,700 ppm). In the period of sampling 3, the mean rain- fall was 19.5 mm.d-1, resulting in the reduction of organic load and biogas production. It was estimated an electric power generation of 3,118.6 kWh.d-1, which is equivalent to an installed power of 130 KW. Removed organic load Biogas flow CH4 CO2 H2S Removed organic load 1 0.43 0.36 0.29 0.32 Biogas flow 1 0.15 0.30 0.52 CH4 1 0.57 -0.01 CO2 1 0.25 H2S 1 Table 3 – Matrix of Spearman’s correlation coefficient (rs) between analyzed parameters*. *0.05 significance level. 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