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Real-scale comparison between simple and composite raw sewage sampling

  • Paulo Sergio Scalize EMAIL logo and Juliana Moraes Frazão
Published/Copyright: June 7, 2018
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Open Engineering
From the journal Open Engineering Volume 8 Issue 1

Abstract

The present study performed a qualitative and quantitative characterization of the raw sewage collected at the entrance of the sewage treatment station of the city of Itumbiara, state of Goiás. Samples were collected every two hours over a period of seven consecutive days. Characterization of both point samples and composite samples was performed. The parameters analyzed were: temperature, pH, alkalinity, chemical oxygen demand, oil and grease, electric conductivity, total phosphorus, settleable solids, ammoniacal nitrogen, total suspended solids, volatile suspended solids, fixed suspended solids and turbidity. These results allowed us to verify that it is possible to perform the collection and analysis of a point sample, instead of a composite sample, as a way of monitoring the efficiency of a sewage treatment plant.

Keywords: water quality; wastewater; flow variation; organic load

1 Introduction

The characterization of raw and treated sewage is of paramount importance for verifying the efficiency of the treatment used. However, there are several potentially influential variables from collection to the analytical procedures employed.

According to Leitão (2004), domestic sewage usually varies widely in flow due to the number of inhabitants and residences connected to the sewage network, the specific characteristics of the collection network (type, material, length, maintenance, infiltration and the use of lift stations), as well as climate, topography, domestic and industrial contributions and time. Francisqueto (2007) adds to this group; family income, cultural characteristics of the population and periods of festivals and vacations.

Sperling (1996), considered that between 60% and 100% of the water consumed returns in the form of sewage, and admits a usual coefficient of return of 80%, since part of the water consumed can be incorporated clandestinely into the rainwater network, used in green areas, or infiltrated, among other situations. Metcalf & Eddy (2003) affirm the existence of variation in hourly flow, with the maximum occurring between 7 a.m. and 3 p.m., and the minimum after midnight, inferring that in some cities mean flow values can oscillate between 50% and 200%. Tsutiya (2005) mentions that the volume of sewage produced can range from 50 L.person−1.day−1 to 600 L.person−1.day−1 and is directly related to the volume of water consumed by the population.

Facing this reality, Borges (2005) discusses variation in the organic and hydraulic loads of raw sewage that are sent to Upflow Anaerobic Sludge Blanket (UASB) reactors, because they are perturbation factors that result in the reduction of reactor performance or even in structural failures. Thus, in order to analyze raw sewage, care must be taken to consider the factors responsible for variation in the characteristics of this material, especially when performing composite sampling to represent a whole, further highlighting the use of automatic samplers (PETRIE et al., 2017). Baker & Kasprzyk-Hordern (2011) also point out that for some parameters this type of sampling with this type of sampler, is inadequate, and can influence the results.

The collection and analysis of a composite sample makes it possible to reduce the quantity of samples to be analyzed, since only one sample is needed instead of several simple/point samples collected throughout the period of monitoring. APHA, AWWA & WEF (2005) explain that composite samples should provide a repesentative sample of a group with greater heterogeneity in which the concentration of the analytes of interest can vary over short periods of time. Otherwise, composite camples can be obtained by combining multiple samples or by using automatic sampling devices. According to CETESB (2011), composite samples can not be used for assessing variables that change during aliquot manipulation, citing dissolved oxygen, pH, free carbon dioxide, microorganisms, dissolved metals, volatile compounds and oils and grease, which can be altered (transfer among bottles, volatilization, oxidation and reduction, loss of viability, etc.) during the composition process or during the time period required between collection and analysis. In their study, Hillebrand et al. (2013) concluded that if immediate analysis is not possible, storage time should be minimized. For these situations, simple/point samples can be used, but is of interest to know at what time of day the collection and analysis of simple/point samples would represent the result of the composite sample. According to Ort et al. (2010), a representative sample is a prerequisite for delivering significant analytical results and cannot be compensated for by a large number of samples, accurate chemical analysis or sophisticated statistical evaluation. Thus, the present study aims to characterize raw sewage over a period of seven consecutive days, determine the composite sample and discuss the best time of day for performing simple/punctual sampling aimed at the replacement of the composite sample.

2 Material and methods

The study took place in the municipality of Itumbiara, Goiás, Brazil, and according to data from the local operator, has 19,884 sewer connections, which reach the Estação de Tratamento de Esgoto [Sewage Treatment Station (STS)] - Itumbiara with an average daily flow of 287 L.s−1. The system is composed of three sewage lift stations and a sewage treatment plant with four UASB reactors and five maturation ponds.

According to the characteristics of the system, the study was carried out at the sewage lift station SLS III (Point 2 in Figure 1), where practically all sewage from the city arrives by gravity.

Figure 1 Location of the municipality of Itumbiara. Source: Adapted from www.itumbiara.go.gov.br
Figure 1

Location of the municipality of Itumbiara. Source: Adapted from www.itumbiara.go.gov.br

In order to characterize the raw sewage affluent going to STS - Itumbiara, samples were collected over seven consecutive days at intervals of two hours, generating 84 samples from which the following parameters were analyzed: temperature, pH, alkalinity, chemical oxygen demand (COD), oils and greases (OG), electrical conductivity (EC), total phosphorus (Ptotal), settleable solids (SetS), ammoniacal nitrogen (Nammoniacal), total suspended solids (TSS), volatile suspended solids (VSS), fixed suspended solids (FSS) and turbidity, according to Standard Methods (APHA; AWWA; WEF, 2005), with the exception of the parameter oils and greases (EPA, 1993). The parameter for biochemical oxygen demand (BOD) was not included in the parameters due to the difficulty in carrying out the laboratory analysis within 24 hours. However, this parameter can be estimated as a function of COD as reported in some studies (ORSSATTO et al., 2009; SILVA E MENDONÇA, 2004 & SCALIZE et al., 2003).

Samples were acquired with a 3.5 L container, conditioned in two vials, one of plastic material and the other of amber glass with sulfuric acid (for preservation of the sample), and then packed in a thermal box for transportation to the Laboratório de Saneamento (Laboratory of Sanitation) of UFG, where the physico-chemical analyses were performed (Nammoniacal, Ptotal, OG, COD, TSS, VSS and FSS). The parameters: temperature, alkalinity, SetS, pH, turbidity and EC were analyzed within 90-minutes of collection.

The collection site was at the outlet of the Parshall gutter installed in SLS III, thus the raw sewage was collected almost entirely by gravity, with the exception being that collectionat SLS Dionária Rochaarrived by pumping. At the moment of collection, the flow was recorded using an ultrasonic meter installed in the Parshall gutter in order to determine the composition of the organic load.

The data were collected at intervals of 2 hours (0 to 24 h), and a composite of the values was obtained to acquire a mean for each time interval using Equation 1, where, Q = flow rate (m3.h−1); C = concentration (g.m−3) and M = mean mass of the analyzed parameter (kg.h−1).

Mmedia(0−2h)=(Q0h×C0h+Q2h×C2h)/(2.1000)(1)

This calculation was performed for the measurements made during the monitoring, from which 12 results were obtained daily for a total of 84 values at the end of the seventh day.

For each time interval (0-2 h; 2-4 h; 4-6 h; …; 22-24 h) mean, minimum and maximum values were obtained analyzing the seven days of the week. The standard deviation and coefficient of variation were then calculated. With these data, graphs were constructed showing the variation over the days of the week and the hours during the day.

3 Results and Discussion

Figure 2 shows that the mean flow rate for the study period was 891.7 m3.h−1 (247.7 L.s−1), with the days of lowest and highest contribution being Monday and Thursday. Figure 2 also shows that the lowest recorded values occurred from 4 h to 6 h and the highest flow from 14 h to 16 h. The sewage flow ranged from 523.8 m3.h−1 to 1,117.1 m3.h−1 and the total volume recorded over the seven days of the study was 149,810 m3.

Figure 2 Variation in sewage flow rate during the study period of seven consecutive days, highlighting the average weekly flow and the average flow during the collection intervals (a), and variation of the sewage flow rate as a function of the monitoring schedule, showing the lowest and highest flow detected (b).
Figure 2

Variation in sewage flow rate during the study period of seven consecutive days, highlighting the average weekly flow and the average flow during the collection intervals (a), and variation of the sewage flow rate as a function of the monitoring schedule, showing the lowest and highest flow detected (b).

The pH values of the 84 simple/point samples varied between 7.16 and 7.67, so that at any time of day the pH value varied little, as was also observed by Souza et al. (2015) in raw sewage from a refectory and toilets sent to a pilot STS.

Considering that the working hours for the majority of the employees of a sanitation company is between 8 h and 16 h, this would be the most suitable time for a possible simple/point sample collection instead of a composite sample. Tables 1 and 2 show the coefficient of variation (CV) for the parameters Nammoniacal, COD, Ptotal, alkalinity, EC, temperature and SetS, which were lower in the range of 8 h to 12 h, being below 10% (with the exception of SetS, which was 14.5%). We can also include in this group of parameters turbidity, which presented a CV of 8.6% within this time interval (8 h to 10 h and 10 h to 12 h). The parameters TSS, VSS, FSS, OG and turbidity had lower CV values between 12 h and 16 h, but the CV values of these parameters, with the exception of turbidity, were higher than 10%, reaching 28.2% in this time interval. For the latter parameters, CV values varied from 6.3% to 70.6%.

Table 1

Values of mean mass, standard deviation and coeflcient of variation for the physico-chemical parameters investigated in raw sewage during the study period, highlighting the lower CV values between 8 h and 15 h.

Time intervalParameter
NammmoniacalCODPtotalAlkalnityECTemprature
Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)
0-227.95.319.1244.034.314.10.35.326.71302.3110.08.4389.929.77.620.91.36.0
2-417.13.621.2129926.920.70.23.620.0958.2124.513.0273.238.714.217.12.011.5
4-638.94.912.6143.534.223.80.24.922.41057.2216.620.5292.948.616.617.50.73.8
6-882.69.711.8321.4139.343.40.59.718.31869.7250.613.4535.488.516.522.52.19.5
8-1092.59.09.7620.4124.820.10.89.017.22508.0210.08.4755.363.48.426.91.66.0
10-1281.211.614.3867.380.69.30.911.67.02498.3177.67.1774.126.33.429.21.03.5
12-1469.511.216.1912.497.010.60.811.27.92304.0175.97.6717.241.85.830.21.55.1
14-1655.57.713.8759.2149.919.70.67.716.81988.0191.19.6626.452.38.328.72.17.3
16-1857.49.216.1636.2117.818.50.69.219.81793.5129.87.2576.129.55.127.42.28.2
18-2061.98.513.7631.966.310.50.68.511.91822.759.53.3591.613.62.327.71.34.7
20-2257.73.56.1587.4142.424.20.63.519.81725.2165.79.6556.035.16.326.51.24.7
22-2445.64.18.9434.397.822.50.44.126.01553.2147.29.5486.729.26.024.01.14.7

  1. CV – coefficient of variation: σ - standard deviation.

Table 2

Values of mean mass, standard deviation and coeflcient of variation for the physico-chemical parameters investigated in raw sewage during the study period, highlighting the lower CV values during the interval of 8 h to 15 h.

Time intervalParameter
SetSTSSVSSFSSOils and greasesTurbidity
Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)Mean mass (kg.h−1)σCV (%)
0-23.00.516.160.615.425.453.014.727.87.62.330.110.30.66.378.07.39.4
2-41.60.530.429.04.616.023.73.816.15.32.852.46.51.319.452.07.113.6
4-61.90.736.360.19.215.351.08.416.59.13.538.912.57.458.846.410.723.0
6-84.70.715.8156.244.928.7133.742.031.422.59.642.732.47.824.296.817.317.8
8-107.61.114.8241.672.830.1206.761.229.635.015.644.652.714.928.3150.518.212.1
10-128.01.214.5270.762.523.1227.156.424.943.714.733.566.817.826.6172.214.78.6
12-148.11.620.2295.754.618.5245.353.321.750.415.029.866.112.619.1173.413.17.6
14-167.81.418.3281.5100.835.8237.791.038.343.812.428.252.58.516.1159.316.210.2
16-185.81.220.6204.286.842.5171.672.142.033.219.558.646.511.825.4143.215.210.6
18-205.31.120.3179.652.529.2154.045.829.726.310.640.242.816.739.1141.911.07.7
20-225.51.629.9178.963.035.2160.057.535.918.910.756.531.36.520.7128.4IS.914.7
22-244.41.227.7124.150.640.8111.343.639.112.79.070.618.53.016.197.714.614.9

  1. CV – coefficient of variation: σ - standard deviation.

Tables 4 to 6 show the calculated values of the physico-chemical parameters studied. Mass values are also included for the time intervals during the seven consecutive days of monitoring. The standard deviation of the means obtained each day Values of CV, calculated by taking into account the weekly average of each parameter, varied between 3.5% and 26.2%. Thus, even if we collect? composite samples for 24 hours, we will have variation among the days of the week.

Table 3

Comparison of the coeflcients of variation obtained in the analyses of the physico-chemical parameters considering the time interval and days of the week.

ParameterCoefficient of variation obtained from the hourly analysis (from Tables 1 and 2)Coefficient of variation obtained from the daily analysis (from Tables 4. 5 and 6)
Ammoniacal nitrogen9.79.1
Chemical oxygen demand9.310.2
Total phosphorus7.07.1
Alkalinity7.13.5
Electrical conductivity3.43.5
Temperature3.53.5
Settleable solids14.59.1
Total suspended solids18.523.8
Volatile suspended solids21.724.9
Fixed suspended solids28.226.2
Oils and greases16.18.9
Turbidity7.67.1

Table 4

Mass of ammoniacal nitrogen, chemical oxygen demand, total phosphorus and alkalinity for the time intervals as a function of the days of the week, with the mean, minimum and maximum mass, plus the standard deviation and CV for the study period.

ParameterTime intervalmass (kg.h−1)
SunMonTueWedThuFriSathourly averagehourly minimumhourly maximum
Ammoniacal nitrogen weekly mean = 57.3kg.h−1; σ=5.2; CV = 9.1%0-233.224.821.521.032.532.529.833.224.821.5
2-420.018.411.012.919.718.818.820.018.411.0
4-631.637.840.044.244.440.633.731.637.840.0
6-874.770.684.599.489.677.482.274.770.684.5
8-1099.976.990.0104.589.489.896.899.976.990.0
10-1298.666.072.288.371.386.086.398.666.072.2
12-1487.654.659.875.664.568.476.187.654.659.8
14-1668.443.853.461.154.552.254.968.443.853.4
16-1865.543.755.272.256.756.851.765.543.755.2
18-2067.047.461.274.963.661.657.467.047.461.2
20-2262.251.356.959.758.855.659.762.251.356.9
22-2451.443.141.140.547.347.748.151.443.141.1
Daily mean63.448.253.962.957.757.358---
Chemical oxygen demand weekly mean = 524 kg.h−1; σ=53.6; CV = 10.2%0-2206235261208302265231244206302
2-48615113111415811315513086158
4-6108136215143139145119143108215
6-8418326486469161214176321161486
8-10736613712778554482468620468778
10-1280080787993010158028398678001015
12-14857765100898010298498999127651029
14-16811623941912825561642759561941
16-18738606684756706443522636443756
18-20658713632584714573550632550714
20-22602865593454640486471587454865
22-24475599404312498406346434312599
Daily mean541537579553562445451---
Total phosphorus weekly mean = 0.53kg.h−1; σ=0.04; CV = 7.1%0-20.230.310.280.200.380.180.320.270.180.38
2-40.130.210.170.130.220.180.190.180.130.22
4-60.160.270.250.150.270.230.220.220.150.27
6-80.310.530.530.580.430.470.500.480.310.58
8-100.600.830.831.000.630.800.840.790.601.00
10-120.830.850.990.940.820.880.930.890.820.99
12-140.820.730.730.890.830.820.880.810.730.89
14-160.600.610.520.830.670.530.680.640.520.83
16-180.620.500.520.630.530.370.700.550.370.70
18-200.470.660.630.580.550.590.670.590.470.67
20-220.450.680.640.430.430.630.590.550.430.68
22-240.530.470.400.240.380.400.580.430.240.58
Daily mean0.480.560.540.550.510.510.59---
Alkalinity weekly mean = 1782 kg.h−1; σ=63.2; CV = 3.5%0-21414130812631131145412271319130211311454
2-4968981836792110090711239587921123
4-674212361321991968886125510577421321
6-81776182222742008179014591958187014592274
8-102684227125382812244922312570250822312812
10-122587232823492714229125232697249822912714
12-142556202022622347216023592423230420202556
14-162285168620961955193620881869198816862285
16-181965163617721746177719731686179316361973
18-201820189418291707187318251810182317071894
20-221782182818771454184115281766172514541877
22-241707158916481304165914031561155313041707
Daily mean1857171718391747177517011836---

  1. CV – coefficient of variation: σ - standard deviation.

Table 5

Mass of electrical conductivity, settleable solids, total suspended solids and volatile suspended solids for the time intervals as a function of the days of the week, with the mean, minimum and maximum mass, plus the standard deviation and CV for the study period.

ParameterTime intervalmass (kg.h−1)
SunMonTueWedThuFriSathourly averagehourly minimumhourly maximum
Electrical conductivity weekly mean = 548 kg.h−1; σ=19.3; CV = 3.5%0-2401387380344441374401390344441
2-4262298234224321258315273224321
4-6225337369278303255284293225369
6-8522562677596529414448535414677
8-10787759815827748648703755648827
10-12772749776823747761791774747823
12-14750638727764692733715717638764
14-16702540660642620634585626540702
16-18616537581593575593537576537616
18-20602608588603592576573592573608
20-22594584566527572493555556493594
22-24514486503445522453485487445522
Daily mean562541573556555516533---
Seatleable solids weekly mean = 5.3kg.h−1; σ=0.5; CV = 9.1%0-23.62.92.22.83.43.22.73.02.23.6
2-42.20.91.21.52.31.61.71.60.92.3
4-61.81.43.32.01.81.11.81.91.13.3
6-84.74.S5.75.54.33.64.24.73.65.7
8-108.48.87.28.97.56.16.57.66.18.9
10-127.68.89.68.17.55.98.58.05.99.6
12-147.28.010.96.18.66.79.28.16.110.9
14-167.76.310.68.67.57.07.07.86.310.6
16-186.03.87.37.35.35.65.65.83.87.3
18-205.15.65.23.47.15.35.65.33.47.1
20-225.38.25.23.57.24.34.85.53.58.2
22-244.96.83.83.45.13.53.64.43.46.8
Daily mean5.45.56.05.15.64.55.1---
Total suspended solids weekly mean = 174 kg.h−1; σ=41.3; CV = 23.8%0-240555886765752614086
2-427272333372530292337
4-652536876645355605276
6-8133145179246136146109156109246
8-10222223252397205223170242170397
10-12254223227402269287233271223402
12-14261227341372336283249296227372
14-16254195400437289209186282186437
16-18219139242367204158100204100367
18-20217132251219181151106180106251
20-22189232280182153110106179106280
22-2499206160143126666912466206
Daily mean164155207247173147122---
Volatile suspended solids weekly mean = 148 kg.h−1; σ=36.9; CV = 24.9%0-230515176664947533076
2-420222223322324242032
4-639465865504949513965
6-81011321472171101389113491217
8-10177205207338170199152207152338
10-12205195189352208231211227189352
12-14212184284340258231210245184340
14-16207158355374235179156238156374
16-181691162273021621349117291302
18-201731112291861491329815498229
20-22165192264164139999716097264
22-2485175149133113596611159175
Daily mean132132182214141127108---

  1. CV – coefficient of variation: σ - standard deviation.

Table 6

Mass of fixed suspended solids, oils and greases, temperature and turbidity for the time intervals as a function of the days of the week, with the mean, minimum and maximum mass, plus the standard deviation and CV for the study period.

ParameterTime intervalmass (kg.h−1)
SunMonTueWedThuFriSathourly averagehourly minimumhourly maximum
Fixed suspended solids weekly mean = 26 kg.h−1; σ=6.7; CV = 26.2%0-21057109748410
2-48529527529
4-613691014479414
6-8321332292681823832
8-1045184559342419351859
10-1249283851625622442262
12-1450435832785339503278
14-1647384663543030443063
16-1850231465422314331465
18-2044212233331912261244
20-22244016181411819840
22-2414311110137313331
Daily mean32232532322115---
Oils and greases weekly mean = 36.6kg.h−1; σ=3.2; CV = 8.9%0-210.611.310.010.39.410.79.710.39.411.3
2-46.34.65.77.26.08.47.66.54.68.4
4-63.95.614.823.87.819.412.312.53.923.8
6-825.333.246.138.523.928.131.632.423.946 1
8-1059.564.572.039.644.230.558.552.730.572.0
10-1284.773.776.563.645.740.183.166.840.184.7
12-1482.065.571.358.748.855.780.566.148.882.0
14-1659.148.458.536.056.550.058.952.536.059.1
16-1842.749.436.455.664.028.649.046.528.664.0
18-2025.445.124.159.868.033.343.642.824.168.0
20-2222.729.725.829.842.435.333.431.322.742.4
22-2420.621.019.612.616.620.418.518.512.621.0
Daily mean36.937.738.436.336.130.040.6---
Tempetature weekly mean = 24.9kg.h−1; σ=0.9; CV = 3.5%0-221.320.620.119.222.920.021.920.919.222.9
2-417.717.414.814.319.017.119.517.114.319.5
4-616.917.118.216.918.617.417.317.516.918.6
6-822.020.325.624.823.320.021.822.520.025.6
8-1027.424.828.028.827.324.527.426.924.528.8
10-1229.727.929.631.128.529.028.829.227.931.1
12-1431.127.631.331.728.931.329.430.227.631.7
14-1630.025.130.331.128.628.927.028.725.131.1
16-1827.924.427.731.328.127.225.327.424.431.3
18-2027.426.128 129.928.627.326.527.726.129.9
20-2227.426.127.926.027.624.625.526.524.627.9
22-2425.223.724.922.825.322.723.324.022.725.3
Daily mean25.323.425.525.725.624.224.5---
Turbidity weekly mean = 120 kg.h−1; σ=8.5; CV = 7.1%0-283.480.272.366.088.179.876.278.066.088.1
2-455.355.754.436.657.353.551.552.036.657.3
4-632.940.165.847.052.139.447.446.432.965.8
6-890.088.8116.2111.9110.667.492.496.867.4116.2
8-10151.4142.3155.1181.6158.9122.1142.5150.5122.1181.6
10-12165.3156.8169.5202.1179.2166.2166.4172.2156.8202.1
12-14172.4150.1178.5183.3190.4174.2164.7173.4150.1190.4
14-16162.8138.5172.2173.5172.6160.1135.1159.3135.1173.5
16-18152.4132.6148.6155.3154.7145.0113.6143.2113.6155.3
18-20148.6143.7147.0139.2156.8135.9122.3141.9122.3156.8
20-22139.5148.5134.1113.8149.3104.8108.8128.4104.8149.3
22-24109.8110.395.283.7117.282.385.597.782.3117.2
Daily mean122.0115.6125.8124.5132.3110.9108.91---

  1. CV – coefficient of variation: σ - standard deviation.

By analyzing the CV values obtained in the hourly and daily analyses (Table 3), it is evident that the variation is very small; that is, the error that may occur by collecting a composite sample on different days of the week is practically the same as that of collecting and analyzing in a determined interval of time.

4 Conclusions

The conclusion of the present study is that the analysis of a composite sample should be performed within a period of at least seven consecutive days, since there is qualitative and quantitative variation among days of the week. In addition, it is possible to collect simple/point samples in substitution for a composite sample for determining the parameters of Nammoniacal, COD, Ptotal, alkalinity, EC, turbidity and temperature with a CV lower than 10%. For the parameters of SetS, TSS, VSS, FSS and OG, the CV was higher than 14.5%, reaching almost 30%. The CV calculated for the different days of the week as a function of the composite samples collected at two-hour intervals is close to the CV obtained from the simple/point samples collected in the pre-established time intervals. The results obtained in this study support the collection and analysis of a simple/point sample in substitution for a composite sample for assessing the studied parameters.

References

APHA, AWWA, WEF, 2005. Standard Methods for the Examination of Water and Wastewater. 21st Ed., Washington, USA.Search in Google Scholar

Baker, D.R.; Kasprzyk-Horden, B. Critical evaluation of methodology commonly used in sample collection, storage and preparation for the analysis of pharmaceuticals and illicit drugs in surface water and wastewater by solid phase extraction and liquid chromatography-mass spectrometry. Journal of Chromatography A, v. 1218, n. 44, pp. 8036–8059, 2011. http://dx.doi.org/10.1016/j.chroma.2011.09.01210.1016/j.chroma.2011.09.012Search in Google Scholar PubMed

Borges, R. M. Desenvolvimiento e aplição de um sistema de diagnóstico fuzzy baseado em modelos para reatores UASB tratando esgoto sanitário. Tese. Universidade Federal do Espírito Santo, Centro Tecnológico, 2005.Search in Google Scholar

Cetesb. Guia nacional de coleta e preservação de amostras: água, sedimento, comunidades aquáticas e efluentes líquidos. Companhia Ambiental do Estado de São Paulo. Organizadores: Carlos Jesus Brandão … [et al.]. São Paulo: CETESB; Brasília: ANA, 326p., 2011.Search in Google Scholar

Environmental Agency Protection. Domestic Septage Regulatory Guidance: United States Environmental Protection Agency. 1993.Search in Google Scholar

Francisqueto, L. O. S. Comportamento de reatores UASB frente a varições horárias de vazão de esgoto sanitário. Dissertação. Universidade Federal do Espírito Santo, Centro Tecnológico, 2007.178 p.Search in Google Scholar

Hillebrand, O.; Musallan, S.; Scherer, L.; Nödler, K.; Licha, T. The challenge of sample-stabilisation in the era of multiresidue analytical methods: a practical guideline for the stabilisation of 46 organic micropollutants in aqueous samples. Science of the total environment, v. 454-455, pp. 289-298, 2013. http://dx.doi.org/10.1016/j.scitotenv.2013.03.02810.1016/j.scitotenv.2013.03.028Search in Google Scholar PubMed

Leitão R. C. Robustness of UASB reactors treating sewage under tropical conditions. Thesis Wageningen University. 2004.Search in Google Scholar

Metcalf & Eddy. Wasterwater Engineering: treatment and reuse, 4 ed. New York: Tata McGraw – Hill, 2003.1334 p.Search in Google Scholar

Orssatto, F. et al. Correlção entre DQO E DBO5 e monitoramento de uma estação de tratamento de esgoto através de técnicas estatísticas de controle de processos. Engenharia Ambiental: Pesquisa e Tecnologia, v. 6, n. 3, p. 155-167, 2009.Search in Google Scholar

Ort, C.; Lawrence, M. G.; Reungoat, J.; Mueller, J.F. Sampling for PPCPs in wastewater systems: comparison of different sampling modes and optimization strategies. Environmental Science Technology, v. 44, n. 16, p. 6289–6296, 2010. http://dx.doi.org/10.1021/es100778d10.1021/es100778dSearch in Google Scholar PubMed

Petrie, B. et al. Critical evaluation of monitoring strategy for the multi-residue determination of 90 chiral and achiral micropollutants in effluent wastewater. Science of the total environment, v. 9, p. 569-578, 2017. http://dx.doi.org/10.1016/j.scitotenv.2016.11.05910.1016/j.scitotenv.2016.11.059Search in Google Scholar PubMed

Scalize, P. S. et al. Correlção entre os valores de DBO e DQO no afluente e efluente de duas ETES da cidade de Araraquara. In: Exposição de Experiências Municipais em Saneamento (VIII), Caxias do Sul - RS. 34a. Assembleia Nacional da ASSEMAE, 13 p. 2004.Search in Google Scholar

Silva, S. R.; Mendonça, A. S. F. Correlção entre DBO e DQO em esgotos domésticos para a região da grande Vitória-ES. Engenharia Sanitária Ambiental, Rio de Janeiro, v. 8, n. 4, p. 213-20, 2003.Search in Google Scholar

Souza, C. F. et al. Eficiência de estação de tratamento de esgoto doméstico visando reuso agrícola. Revista Ambiente & Água, Taubaté, v. 10, n. 3, p. 587-597, 2015. http://dx.doi.org/10.4136/ambi-agua.154910.4136/ambi-agua.1549Search in Google Scholar

Sperling, M. Princípios básicos do tratamento de esgotos. Departamento de Engenharia Sanitária e Ambiental, UFMG, Belo Horizonte, Brasil– 1996, 211p.Search in Google Scholar

Tsutiya, M.T. Abastecimento de Água. 2. Ed. São Paulo. Departamento de Engenharia Hidráulica e Sanitária da escola Politécnica da Universidade de São Paulo, 2005.Search in Google Scholar

Received: 2017-11-12
Accepted: 2018-04-10
Published Online: 2018-06-07

© 2018 Paulo Sergio Scalize and Juliana Moraes Frazão

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/eng-2018-0019
Keywords for this article
water quality; wastewater; flow variation; organic load
Creative Commons
BY-NC-ND 4.0

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