Supplementary Tables S1 (nutrients, NH4+, pH, COD and TSS) and S2 (metal elements) summarize the results of the physicochemical parameters for the wastewater samples. Regarding metals, As, Cd and Hg were always below the quantification limit of the method throughout the year, whereas all the others (Fe, Pb, Cr, Cu, Mn and Ni) occurred in a frequency that varied between 27.3 (Cr) and 100% (Fe and Mn) in the influent samples, and between 17.3 (Cr) and 100% (Fe and Mn) in the effluent samples. The H9c2(2 − 1) cell-based SRB results showed that all the assays were accepted (validated), with the CV of negative controls never exceeding 4.4% (Table 1). Moreover, the positive controls (2% DMSO) always significantly decreased cell mass (P < 0.05). The 24-h cell toxicity results were also presented in Table 1. In 9.6% of the cases the effluent EC50,24hdetermination, despite reportable, was not valid because of the low number of more extreme assay concentrations on the lower plateau, i.e., the sample presented low toxicity and, thus, a larger sample volume should have been lyophilised. Therefore, in those cases, a valid concentration-response curve was only possible after constraining the bottom of the curve to zero.
Table 1
Validity data and concentration-response relationship results of H9c2(2 − 1) SRB-based assays from municipal wastewater samples.
Date
|
Influent
|
Effluent
|
CV Max
(%)
|
FE
(%)
|
Goodness of fit
(r2)
|
EC50,24h (95% CI)
(%)
|
CV Max
(%)
|
FE
(%)
|
Goodness of fit
(r2)
|
EC50,24h (95% CI)
(%)
|
Jan 4
|
-
|
-
|
-
|
-
|
1.8
|
4.7
|
0.975
|
2493 (2263–2748)*
|
Jan 11
|
-
|
-
|
-
|
-
|
1.6
|
7.4
|
0.987
|
1514 (1299–1766)
|
Jan 19
|
-
|
-
|
-
|
-
|
4.1
|
4.9
|
0.958
|
2777 (2506–3078)*
|
Jan 26
|
-
|
-
|
-
|
-
|
1.4
|
23
|
0.913
|
1842 (1139–2978)
|
Feb 2 or 4
|
1.0
|
4.7
|
0.984
|
365 (330.9-403.3)
|
1.8
|
6.3
|
0.942
|
2912 (2555–3320)*
|
Feb 9
|
-
|
-
|
-
|
-
|
2.4
|
22
|
0.941
|
1172 (733.7–1871)
|
Feb 16
|
-
|
-
|
-
|
-
|
2.8
|
11
|
0.942
|
1888 (1503–2373)
|
Feb 23
|
-
|
-
|
-
|
-
|
2.6
|
22
|
0.887
|
1810 (1145–2861)
|
March 1
|
2.4
|
4.9
|
0.986
|
630 (569.5-697.4)
|
1.9
|
18
|
0.910
|
1057 (731.6–1527)
|
March 8
|
-
|
-
|
-
|
-
|
2.2
|
12
|
0.937
|
941 (739.3–1198)
|
March 14
|
-
|
-
|
-
|
-
|
2.6
|
7.3
|
0.959
|
2161 (1858–2514)*
|
March 22
|
-
|
-
|
-
|
-
|
3.2
|
4.2
|
0.983
|
597 (547.8-651.5)
|
March 29
|
-
|
-
|
-
|
-
|
1.5
|
12
|
0.962
|
968 (747–1255)
|
April 6
|
2.0
|
22
|
0.921
|
775 (486.0-1235)
|
2.3
|
5.4
|
0.977
|
1279 (1142–1433)
|
April 13
|
-
|
-
|
-
|
-
|
2.9
|
8.1
|
0.967
|
760 (641.7-899.3)
|
April 19
|
-
|
-
|
-
|
-
|
2.7
|
10
|
0.926
|
919 (741.3–1138)
|
April 27
|
-
|
-
|
-
|
-
|
2.2
|
3.4
|
0.990
|
528 (491.6–567.0)
|
May 4
|
3.5
|
7.2
|
0.967
|
400 (344.4-464.2)
|
3.3
|
9.9
|
0.962
|
914 (743.8–1123)
|
May 11
|
-
|
-
|
-
|
-
|
1.5
|
10
|
0.926
|
644 (521.1-794.7)
|
May 18
|
-
|
-
|
-
|
-
|
1.4
|
11
|
0.929
|
480 (383.7-599.2)
|
May 25
|
-
|
-
|
-
|
-
|
1.6
|
7.3
|
0.971
|
493 (423.6-573.9)
|
May 30
|
-
|
-
|
-
|
-
|
1.7
|
11
|
0.973
|
801 (639.2–1003)
|
June 8
|
1.1
|
6.3
|
0.975
|
476 (417.5-542.5)
|
2.1
|
12
|
0.921
|
451 (351.7-578.1)
|
June 15
|
-
|
-
|
-
|
-
|
2.1
|
9.5
|
0.970
|
995 (816.7–1212)
|
June 22
|
-
|
-
|
-
|
-
|
4.4
|
11
|
0.937
|
996 (785.8–1263)
|
June 29
|
-
|
-
|
-
|
-
|
3.6
|
8.6
|
0.966
|
1116 (932.3–1335)
|
July 6
|
1.4
|
6.4
|
0.971
|
707 (619.1-807.3)
|
3.7
|
14
|
0.941
|
1098 (825.8–1460)
|
July 13
|
-
|
-
|
-
|
-
|
2.7
|
5.2
|
0.939
|
3681 (3305–4101)*
|
July 20
|
-
|
-
|
-
|
-
|
1.9
|
8.6
|
0.983
|
1021 (852.7–1221)
|
July 27
|
-
|
-
|
-
|
-
|
2.7
|
7.8
|
0.964
|
1910 (1623–2248)
|
August 3
|
0.9
|
11
|
0.952
|
519 (411.3-655.5)
|
0.83
|
32
|
0.946
|
1178 (602.5–2303)
|
August 8
|
-
|
-
|
-
|
-
|
2.8
|
15
|
0.893
|
851 (621.9–1165)
|
August 17
|
-
|
-
|
-
|
-
|
2.2
|
27
|
0.953
|
1364 (771.0-2412)
|
August 24
|
-
|
-
|
-
|
-
|
1.7
|
12
|
0.970
|
1055 (823.6–1351)
|
August 31
|
-
|
-
|
-
|
-
|
1.0
|
23
|
0.981
|
1422 (872.3–2320)
|
September 5
|
3.6
|
14
|
0.886
|
419 (314.5-558.5)
|
2.7
|
6.5
|
0.982
|
638 (556.8-731.3)
|
September 14
|
-
|
-
|
-
|
-
|
2.8
|
15
|
0.971
|
818 (594.4–1124)
|
September 21
|
-
|
-
|
-
|
-
|
1.9
|
9.4
|
0.931
|
1677 (1379–2039)
|
September 28
|
-
|
-
|
-
|
-
|
1.0
|
13
|
0.956
|
1030 (786.4–1349)
|
October 3
|
2.5
|
11
|
0.954
|
341 (268.2-433.1)
|
3.9
|
12
|
0.981
|
908 (702.4–1174)
|
October 12
|
-
|
-
|
-
|
-
|
1.6
|
4.2
|
0.986
|
962 (880.8–1050)
|
October 19
|
-
|
-
|
-
|
-
|
1.3
|
5.1
|
0.984
|
901 (809.8–1001)
|
October 26
|
-
|
-
|
-
|
-
|
1.2
|
16
|
0.910
|
1456 (1046–2027)
|
November 2
|
2.1
|
5.5
|
0.978
|
354 (315.4-396.6)
|
2.2
|
10
|
0.956
|
932 (753.8–1151)
|
November 9
|
-
|
-
|
-
|
-
|
1.9
|
8.3
|
0.980
|
832 (699.8-987.9)
|
November 16
|
-
|
-
|
-
|
-
|
1.9
|
6.0
|
0.972
|
652 (575.3-738.1)
|
November 23
|
-
|
-
|
-
|
-
|
1.8
|
9.4
|
0.983
|
800 (657.8-972.5)
|
November 30
|
-
|
-
|
-
|
-
|
0.8
|
5.6
|
0.983
|
479 (426.7-538.3)
|
December 7
|
2.2
|
9.9
|
0.943
|
1668 (1356–2053)
|
1.8
|
11
|
0.955
|
813 (640.4–1032)
|
December 14
|
-
|
-
|
-
|
-
|
1.9
|
7.9
|
0.973
|
979 (829.2–1155)
|
December 21
|
-
|
-
|
-
|
-
|
1.6
|
10
|
0.971
|
717 (585.9-876.8)
|
December 26
|
-
|
-
|
-
|
-
|
3.2
|
8.2
|
0.967
|
498 (419.3-591.4)
|
CV, coefficient of variation of the mean; FE, fitting error; CI, confidence interval *after constraining the bottom of the curve to zero |
In order of characterise the ability of the H9c2(2 − 1) cell-based SRB assay to assess the temporal variability of municipal wastewater, confirming whether cell-based results reveal wastewater composition, correlation coefficients between H9c2(2 − 1) EC50,24h data and PO43−, NO3−, Si, NH4+, COD, TSS, Fe and Mn were calculated. In line with the precautionary approach, correlations were only possible for COD (for influence data), as the missing value of November was completed with the maximum value determined during the year, as well as for Pb and Ni (for both influent and effluent data-sets) as gaps (when values were below MQL) were completed with a value immediately lower of MQLs: 0.24 µg L− 1 for Pb and 2.4 µg L− 1 for Ni. Seeing that there is a mixture of domestic and rain water in the collector of the old town covered by the selected wastewater treatment plant (technical information provided by the wastewater treatment plant), daily rainfall data (mm) provided by the Portuguese Institute for Sea and Atmosphere was also used to assess whether this meteorological event disrupts the wastewater treatment system, while Covid-19 data was used to assess whether the presence of SARS-CoV-2 viral particles would affect H9c2(2 − 1) cells. The freeze-drying technique, used in the present study to concentrate wastewater samples prior to cell-based assays, has the potential to preserve virus infectivity for several years (Adams, 2007). It is also the preferred method for stabilizing live attenuated virus vaccines for long-term preservation and worldwide distribution (reviewed by Hansen et al. (2015)), and to preserve viruses’ collections (Baronti et al., 2021). Possibly due to the low number of data pairs (N = 11), correlations revealed that for influent data no significant relationships were observed between H9c2(2 − 1) results (EC50,24h) and the selected variables (Fig. S1, supplementary material), whereas H9c2(2 − 1) results covariated negatively with four variables (PO43−, COD, TSS and Covid-19) and positively with one variable (Mn) for effluent data (Table 2, or see the complete Spearman correlation matrix on Fig. S2 of supplementary material). This means that toxicity EC50,24h values decrease (indicating higher toxicity of the wastewater) whenever PO43−, COD and TSS values increase, as well as whenever Covid-19 positive case reports increase; and that toxicity EC50,24h values decrease when Mn decreases. Note that the contrary is also true. The correlation results suggest that in aquatic environments PO43− is a major contributor to biological impairment, and should, therefore, be included in the list of variables (as COD and TSS are) with a specific discharge limit for environmental protection against wastewater disposal in the water environment. Moreover, H9c2(2 − 1) cells appear to be a promising platform for modelling Covid-19 outbreaks using effluent samples, and possibly for biochemical studies of SARS-CoV-2 replication. The cellular receptor of SARS coronaviruses, the angiotensin converting enzyme 2 (ACE-2) (Li et al., 2003), is known to be expressed on cardiomyocytes (Gallagher et al., 2008), and thus, in addition to respiratory illness, SARS-CoV-2 might initiate a cascade of deleterious events associated with cardiac injury, arrhythmia and cardiac arrest (reviewed by e.g., Arévalos et al., 2021, Bugert and Kwiat et al., 2021). Regarding Mn, the determined levels were in line with its normal range in drinking water (recommended safety limit is 50 µg L− 1, Decree-law 152, 2017), and thus a positive significant relationship was observed as it is known that this transition metal is an important cofactor nutrient and a structural component of many proteins, playing a vital role in the cellular metabolism. The same result would be expected for Fe, Cr, Cu and Ni, as they all serve important cellular roles (Andreini et al., 2008). However, the failure of a significant correlation with Fe is possibly because this metal element presented values above the Portuguese discharge limit in 29% of the sampled weeks (Table S2, supplementary material). For Cr and Cu no correlation analysis was performed due to their low occurrence frequency. The very low concentrations determined for Ni: maximum concentration of 7.4 µg L− 1, which is even lower than the recommended safety limit of 20 µg L− 1 for drinking water (Decree-law 152, 2017), could be a reason for the failure of a significant correlation. Correlation analysis also reveals that the effluent levels of NO3−, Si and NH4+ throughout the year do not impact the biological cell model selected for the present study, and neither does precipitation. The H9c2(2 − 1) cell-based SRB assay provided thus an estimate of the overall toxic burden of a mixture of pollutants present in municipal effluents over time. Gathered results corroborate previous findings that demonstrate the high sensitivity of H9c2(2 − 1) cells to environmental pollutants as pesticides (Rodrigues et al., 2015, 2019), pharmaceuticals (Bains et al., 2013; Rodrigues et al., 2020b), industrial chemicals (Han et al., 2017), charged polymers and heavy metals (Mohammad and Arfin, 2013), as well as toxins (Neves et al., 2020; Varela et al., 2020).
Table 2
Summary of the Spearman correlation results applied to H9c2(2 − 1)-based SRB results (EC50,24h) and abiotic variables of effluent samples collected weekly throughout 2020. Bold indicates significance (significance level at 0.0042).
H9c2(2 − 1)-based SRB results
vs
|
|
PO43−
|
NO3−
|
Si
|
NH4+
|
COD
|
TSS
|
Fe
|
Pb
|
Mn
|
Ni
|
pp
|
Covid-19
|
r
|
-0.526
|
-0.143
|
-0.165
|
-0.066
|
-0.610
|
-0.428
|
-0.034
|
0.179
|
0.485
|
0.071
|
-0.097
|
-0.591
|
P
|
< 0.0001
|
0.3121
|
0.2440
|
0.6388
|
< 0.0001
|
< 0.0042
|
0.8105
|
0.2053
|
< 0.0042
|
0.6154
|
0.4943
|
< 0.0001
|
N
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
52
|
PO43−, phosphates; NO3−, nitrates; Si, silicates; NH4+, ammonium; COD, chemical oxygen demand; TSS, total suspended solids; Fe, iron; Pb, lead; Mn, manganese; Ni, nickel; pp, precipitation.
The impact of effluent disposal on water quality was also studied, and according to the results, several effluent data were non-compliant with the Portuguese standards, namely NO3− (in 26.9% of the samples), NH4+ (88.5%), COD (9.6%), TSS (3.9%) and Fe (28.9%) (Tables S1 and S2, supplementary material). The high values of Fe are probably due to the fact that this wastewater treatment plant uses ferric chloride as orthophosphate precipitation agent (technical information provided by the wastewater treatment plant). Since effluent NH4+ presented high levels during almost the whole year (non-compliance frequency of 88.5%), the biological nitrification process implemented in the wastewater treatment plant seems to require some kind of upgrade (e.g., properly sized lagoon aeration system), or a better control process (e.g., better monitoring of dissolved oxygen, biochemical oxygen demand, pH or temperature levels). To a smaller extent, denitrification (that converts NO3− in nitrogen gas) seems to also need improvement.
The H9c2(2 − 1) cell-based SRB assay was effective to discriminate influent and effluent toxic characteristics, as a significant difference was obtained by testing the two EC50,24h data sets (P < 0.01), with effluents being 83.1% (mean value) less toxic to H9c2(2 − 1) cells than influents, thus demonstrating the success of this assay to evaluate the toxicity reduction of the wastewater treatment process. By comparing mean values of influent and effluent data, the wastewater treatment process allowed the expected increase of NO3−due to nitrification in a percentage increase of 227.4%, and the effective reduction of PO43−, NH4+, COD and TSS in a percentage decrease of 77.1, 54.5, 48.4 and 84.5%, respectively (all P values < 0,001). Both Si and pH remained unchanged (Si P value = 0,828, and pH P value = 0.316). Except for Fe and Mn, with an occurrence frequency of 100%, and for Ni which increased frequency, the occurrence frequency between influent and effluent decreased for Pb, Cr and Cu (see Table S2 of the supplementary material). When correspondent influent and effluent samples were compared, which was only possible when concentrations were above the MQL in both determinations, the results showed that Fe, Cu, Pb and Mn presented a significant difference (all P values ≤0.05), with an effective decrease of Cu (68% decrease) and Pb (63% decrease) levels, and an increase of Fe (408% increase) and Mn (40% increase) levels, while Ni remained unchanged (P value = 0.965). Statistical analysis was not possible for Cr as only one correspondence was found. The H9c2(2 − 1) cell-based SRB assay is thus a suitable bioanalytical tool for detection of non-specific toxicity, and might be routinely applied for water quality monitoring and for surveillance of the efficacy of treatment processes.