Current Status of Drinking Water Quality in a Latin American Megalopolis

Abstract

(1) Background: Treated water distributed in a Latin American megapolis has been the target of criticism in recent years. During the summers of 2020 and 2021, treated water had a taste and smell of mud in the metropolitan area of Rio de Janeiro city, affecting ~10 million people. (2) Methods: To evaluate the SRJ water quality, a comprehensive dataset was analyzed. In total, 194,821 samples were analyzed between 2018 and 2021, corresponding to three parameters (chlorine concentration, n = 67,723; turbidity, n = 55,186; and coliform abundance, n = 71,912). The 92 SRJ cities were analyzed using two approaches according to the relevant law: a quantitative and qualitative approach. (3) Results: In the qualitative analyses, four groups of cities were found (Group A: three parameters analyzed; 49 to 51 cities; Group B, two parameters analyzed, 9 to 14 cities; Group C, one parameter analyzed, 17 cities; Group D, no parameters analyzed, 12 to 16 cities). (4) Conclusions: In 2021, the top ten cities in Group A showed 100% performance in both the qualitative and quantitative rankings. However, several large cities, such as Duque de Caxias and São Gonçalo, did not have the minimum required number of samples or analyses, which poses serious risks for water quality control and public health.

1. Introduction

Drinking water is a universal right highlighted in Sustainable Development Goal No. 6 of the United Nations (https://sdgs.un.org/goals (accessed on 20 November 2020)). Nordic countries such as Finland and Norway have strict water quality monitoring systems, and their water is treated within the levels required in their legislation as a result of control mechanisms offered to the population [1]. Approximately 35 million Brazilians do not have access to safe drinking water. The National Commission on Social Determinants of Health presents an unequivocal overview of the social causes of inequalities in health in Brazil [2]. Water quality is a matter of concern to public health in both developing and developed countries. Recent studies in Africa and South America have revealed that drinking water was contaminated with Escherichia coli, Enterococcus, and other types of bacteria [3,4,5,6]. Sewerage water (fecal) becoming mixed with drinking water was a dominant contaminant due to the poor infrastructure for sanitation [7,8,9,10]. Outbreaks of waterborne diseases may also occur in locations with deficient water systems in the Northern Hemisphere (e.g., Ohio, Canadian Arctic, and Massachusetts) [11,12,13]. A link between ulcerative colitis, Crohn’s disease, and the quality of drinking water in public, rural, and private supplies was found in Croatia [14]. Enteroviral diseases may also be related to drinking water quality [15]. In the summers of 2020 and 2021, the metropolitan region of Rio de Janeiro went through a crisis in the quality of treated water, the so-called geosmin crisis, which affected ~10 million people [16]. The treated water distributed had a taste and smell of mud, high turbidity, and bacterial growth. In this context, the objective of this study was to carry out an analysis of the drinking water quality profile of the SRJ cities. Statistical analysis, deep learning techniques, and multiple criteria approaches have been developed for water quality monitoring [17,18]. In this work, we use simpler data science tools. A ranking of SRJ cities was established based on the arithmetic mean indicator using three parameters (chlorine, turbidity, and coliforms). This new indicator may help to access the status of water quality across vast areas of the SRJ. The cities were also ranked according to the required number of samples analyzed per year so as to determine possible successful cases in water management that could serve as benchmarks for the development of treated water management policies in the SRJ. Finally, we propose a matrix of responsibilities that embraces all stakeholders across the treated water chain process.

2. Materials and Methods

Data referring to the monitoring of water quality in the State of Rio de Janeiro (SRJ) for the years 2018 to 2021 were obtained from the Water Quality Surveillance Information System for Human Consumption (SISAGUA) database (https://dados.gov.br/dataset/sisagua-controle-mensal-quantitativos-de-analises (accessed on 10 December 2020)). This database is connected to the Water Quality Surveillance Program that acts continuously to ensure that (1) water consumed by the population meets the standards and norms established in current legislation and (2) to assess the risks that drinking water poses to human health [6]. SISAGUA was created in 2000 with the objective of collecting data related to the water consumed by the population [19,20]. Most water quality samples were taken from conventional water supply systems.

Treated water quality monitoring is commonly based on 3 to 72 parameters [21,22,23]. According to the National Water Quality Sampling Plan Guideline for Human Consumption, the basic parameters of water analysis are turbidity, free residual chlorine (or other active residual compounds if the disinfecting agent used is not chlorine), total coliforms/Escherichia coli, and fluoride. These parameters were defined due to their importance as basic indicators of the microbiological quality of water for human consumption [24]. Thus, we selected the parameters of free residual chlorine, turbidity, and total coliforms by which to evaluate the status of the water quality of the 92 SRJ cities. The limits for chlorine (0.2–5 mg/L), turbidity (<5 NTU), and coliforms (absent in 100 mL) should be reached according to MS 2914/2011 and MS 888/2021 [25].

2.1. Quantity Ranking

The number of samples, frequency, and geographic distribution of collection points is essential for a successful sampling plan [25,26]. In addition, a minimum number of analyzed samples is required throughout the year, according to the number of inhabitants in each municipality. For the city of Rio de Janeiro, for example, at least 2028 samples should be analyzed for the three parameters (chlorine, turbidity, and coliforms). The number of samples (N) is divided by the minimum defined by the relevant law (N′), and, in this way, the rate of compliance according to the standard can be determined. For the city of Rio de Janeiro, for example, the quantity of chlorine samples was 162% in 2018, 104% for coliforms, and 83% for turbidity, with an average of 117% in the year. The same reasoning was applied to the other three years (2019, 2020, and 2021) with quantitative indexes (QTI) of 128%, 164%, and 174%, respectively.

$$\mathrm{QTI}=\raisebox{1ex}{$\left(\frac{\mathrm{Ncl} + \mathrm{Ntu} + \mathrm{Nco}}{{\mathrm{N}}^{\prime }} \right)$}\!\left/ \!\raisebox{-1ex}{$3$}\right.$$

2.2. Quality Ranking

From the number of samples within the levels established by legislation [25], a rate of compliance was determined. Hence, the compliance for these parameters is the number of adequate samples (N″) over the total number of samples (N). For a given year, the three compliances are combined using an average, and the quality indicator (QLI) for a municipality is obtained. For example, for the city of Rio de Janeiro, chlorine had a QLI of 89% for turbidity and 79% for coliforms in 2018, with an average of 86%. The same reasoning was applied to the other years, and the averages of the parameters (which make up the table) were 85%, 89%, and 90% for 2019, 2020, and 2021, respectively. Cities that did not have any type of monitoring in the last four years (2018 to 2021) were also listed.

$$\mathrm{QLI}=\raisebox{1ex}{$\left(\frac{{\mathrm{N}}^{″}\mathrm{cl}}{\mathrm{Ncl}}+\frac{{\mathrm{N}}^{″}\mathrm{tu}}{\mathrm{Ntu}}+\frac{{\mathrm{N}}^{″}\mathrm{co}}{\mathrm{Nco}}\right)$}\!\left/ \!\raisebox{-1ex}{$3$}\right.$$

2.3. Remarks on the Quality Indicator

An arithmetic mean indicator (chlorine, turbidity, coliforms) was used for the analysis in the present study as previously applied in the USA and in Europe [27,28,29,30,31,32]. In Brazil, there are basically two types of raw water monitoring. For lotic waters, the National Water Agency (ANA) and the State Environmental Institute (INEA) use the Water Quality Index (IQA) to analyze raw water (inea.rj.gov.br; pnqa.ana.gov.br accessed on 1 December 2020), which is an index adapted from the American IQA-NSF by Cetesb [33] of São Paulo in 1975. This index takes into account 10 water parameters with weighted and geometric means. Another unweighted index, also adapted by Cetesb from the Canadian IQA-CCME, is used for lentic waters and includes 8 parameters. However, an indicator that takes into account treated water is not available. We opted for an arithmetic indicator because methods that assign or derive weights are complex and subjective; therefore, aggregation models such as the arithmetic mean are sometimes preferred because they treat all sub-indexes equally in the aggregation [23].

3. Results and Discussion

There was no significant decrease in the quantity of samples taken between 2018 and 2021 (≈90%; p-value = 0.16), nor in the qualitative average, which remained constant at around 88% (p-value = 0.29). Therefore, the period between 2018 and 2021 may be considered stable. Only seven cities had complete quantitative sampling monitoring for the parameters selected in all the years in this study (N ≥ N′).

3.1. Four Groups of Cities in the SRJ

The cities were classified into Group A (three analyzed parameters), Group B (two analyzed parameters), Group C (only one analyzed parameter), and Group D (no data available) (Figure 1 and

Table 1. Cities in Group A for the years of 2018 to 2021. Each year has four columns: the Quantitative Index (QTI), an arithmetic mean of the number of samples taken (N) according to that required by law (N′) for chlorine, turbidity, and coliforms; the Qualitative Index (QLI) samples complying with the water quality standard. The other two columns indicate the relative ranking of the SRJ cities for both aspects. Color code (light green, Group A; dark green, Group B; yellow, Group C). Cities in Group D (red) are without basic monitoring samples.

		2018				2019				2020				2021
Cities	N′	QTI	QLI	QT Rank	QL Rank	QTI	QLI	QT Rank	QL Rank	QTI	QLI	QT Rank	QL Rank	QTI	QLI	QT Rank	QL Rank
Angra dos Reis	360	170.9	81.7	4	42	204.3	79.0	4	46	114.4	69.3	8	47	129.2	80.0	8	42
Aperibé	120	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Araruama	276	416.1	97.7	1	7	398.3	98.0	1	6	153.3	90.7	5	32	136.6	93.3	7	29
Areal	120	131.4	91.0	10	27	111.1	91.3	13	29	116.9	96.3	7	15	25.8	96.8	49	17
Armação dos Búzios	144	65.5	89.7	41	28	111.1	91.7	12	27	76.6	90.0	38	36	22.2	30.2	11	7
Arraial do Cabo	144	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	1.2	6.7	17	17
Barra do Piraí	240	115.8	79.3	20	45	161.3	85.0	7	44	134.9	71.3	6	46	151.0	67.2	4	48
Barra Mansa	336	118.4	92.0	17	24	110.0	95.3	15	19	107.2	95.7	14	20	85.7	95.0	19	24
Belford Roxo	540	23.1	29.3	10	5.0	27.5	27.7	10	10	26.8	29.3	10	11	19.8	33.2	12	1
Bom Jardim	144	18.5	31.3	14	3.0	41.4	89.3	48	36	38.4	90.7	46	34	39.6	61.3	5	7
Bom Jesus do Itabapoana	156	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	16.9	66.7	12	2
Cabo Frio	372	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Cachoeiras de Macacú	192	66.7	85.7	40	35	27.4	47.3	10	12	43.1	61.3	5	4	27.1	60.3	9	8
Cambuci	120	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Campos dos Goytacazes	540	102.6	84.0	25	39	101.2	86.7	25	42	101.1	92.7	22	28	79.3	95.7	24	22
Cantagalo	132	110.4	92.0	23	25	86.1	93.3	38	24	102.3	92.7	20	27	90.9	96.4	15	19
Carapebus	120	28.1	25.7	5	9	30.6	28.7	5	9	36.7	30.7	3	7	30.6	29.7	3	9
Cardoso Moreira	120	91.1	82.3	28	41	96.4	78.0	32	47	88.6	89.3	29	38	55.8	94.5	43	26
Carmo	132	26.8	48.0	10	9	50.5	89.0	47	38	104.5	80.0	16	44	84.1	85.9	21	40
Casimiro de Abreu	168	40.5	65.3	7	2	28.2	31.3	9	4	36.5	32.0	4	4	27.8	33.1	4	3
Com. Levy Gasparian	108	69.8	77.7	38	47	81.5	90.7	41	32	52.5	91.0	44	31	70.1	95.8	34	21
Conceição de Macabú	132	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Cordeiro	132	139.1	97.0	8	10	208.3	96.3	3	17	249.0	95.3	1	21	233.6	93.8	1	28
Duas Barras	120	62.2	89.3	42	29	73.6	92.7	43	25	83.9	88.7	32	40	65.8	93.2	39	30
Duque de Caxias	636	25.6	28.3	9	7	32.2	27.0	3	11	33.5	27.7	7	12	17.8	29.0	14	11
Eng. Paulo de Frontin	120	22.8	15.3	11	17	32.8	26.3	1	13	2.8	30.0	16	9	19.2	30.0	13	8
Guapimirim	192	69.6	60.0	4	4	107.5	92.3	16	26	94.1	98.0	25	9	68.9	98.5	36	5
Iguaba Grande	144	28.0	29.3	6	4	32.6	56.0	8	9	68.1	49.7	4	8	77.8	73.2	27	45
Itaboraí	384	26.7	18.3	8	16	32.5	21.0	2	17	39.2	21.7	2	16	32.1	19.4	2	15
Itaguaí	276	0.0	0.0	-	-	3.6	30.0	17	6	65.0	30.7	1	6	116.5	87.0	9	39
Italva	120	28.6	31.3	4	2	30.6	30.3	4	5	0.0	0.0	-	-	30.8	58.5	7	9
Itaocara	132	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Itaperuna	240	103.1	94.3	24	17	101.7	97.0	23	9	52.1	100.0	45	1	27.5	33.2	5	2
Itatiaia	144	54.4	70.0	43	50	78.9	63.3	42	50	73.6	56.0	41	49	68.1	54.4	38	49
Japeri	240	22.1	25.7	12	10	31.7	57.7	9	6	23.8	25.7	12	13	13.3	26.7	15	14
Laje do Muriaé	108	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Macaé	396	85.0	92.7	29	21	116.2	94.7	11	21	66.6	96.3	43	17	51.6	64.4	2	6
Macuco	108	99.1	96.0	27	14	3.7	66.7	13	2	0.0	0.0	-	-	88.3	94.4	17	27
Magé	384	148.3	47.3	1	10	104.9	56.0	2	7	111.5	57.3	1	7	101.0	90.0	11	35
Mangaratiba	168	82.7	88.0	33	31	43.3	46.0	5	13	5.6	0.0	-	-	107.9	76.7	10	44
Maricá	312	3.7	24.7	17	11	10.1	25.7	15	15	15.3	21.0	14	17	0.0	0.0	-	-
Mendes	120	47.8	84.7	46	38	105.8	77.3	17	49	74.4	69.0	39	48	36.9	69.6	47	47
Mesquita	336	35.1	28.3	2	6	30.1	29.7	7	7	36.3	29.3	5	10	24.1	28.1	10	12
Miguel Pereira	144	11.8	98.7	51	5	18.5	29.3	13	8	0.0	0.0	-	-	24.8	64.9	10	5
Miracema	132	62.9	59.3	5	6	40.4	66.3	6	3	11.6	32.7	15	3	24.2	32.6	9	4
Natividade	120	37.5	31.7	1	1	88.1	97.0	36	11	83.1	97.3	34	13	68.3	98.4	37	6
Nilópolis	312	27.8	24.3	7	12	24.9	26.0	11	14	30.9	24.0	9	15	26.4	18.3	7	16
Niterói	540	42.8	100.0	47	1	15.7	100.0	50	1	7.5	100.0	10	2	13.8	66.7	13	3
Nova Friburgo	348	79.0	99.3	35	3	103.1	98.7	20	3	102.3	98.3	19	7	74.8	98.6	31	4
Nova Iguaçu	612	122.1	83.7	13	40	102.0	87.7	22	40	86.7	93.0	30	25	79.2	97.1	25	14
Paracambi	180	76.5	81.3	36	44	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
Paraíba do Sul	168	56.2	62.7	6	3	100.4	88.0	27	39	104.0	96.3	17	18	61.3	90.4	42	34
Parati	168	235.5	75.0	2	48	118.1	51.7	1	10	31.0	66.0	7	2	32.5	31.7	1	5
Paty do Alferes	144	49.8	91.7	45	26	100.0	94.0	28	22	101.4	92.3	21	29	86.1	92.7	18	31
Petrópolis	420	230.7	85.7	3	33	189.8	82.3	5	45	90.9	88.0	26	41	99.6	89.8	12	36
Pinheiral	132	23.2	100.0	49	2	103.0	96.7	21	14	81.1	97.7	35	10	41.7	99.4	45	2
Piraí	144	120.8	92.3	15	23	90.0	93.7	35	23	104.6	99.0	15	5	65.3	95.6	40	23
Porciúncula	120	123.3	95.7	11	15	101.1	96.7	26	13	78.3	99.7	37	3	69.7	98.1	35	8
Porto Real	132	69.4	86.0	39	32	54.8	62.3	3	4	72.7	57.7	3	6	72.2	78.7	32	43
Quatis	120	74.7	70.0	37	49	28.3	24.3	8	16	31.9	25.3	8	14	25.0	29.6	8	10
Queimados	300	99.2	88.3	26	30	93.1	90.0	33	33	80.3	88.0	36	42	96.2	91.6	13	33
Quissamã	132	118.4	96.0	16	13	136.1	96.7	8	12	114.1	95.3	10	22	90.4	98.7	16	3
Resende	276	122.7	97.3	12	8	110.3	95.3	14	18	114.3	97.0	9	14	83.2	97.4	23	10
Rio Bonito	192	41.5	97.0	48	12	83.3	91.0	39	31	101.0	93.0	23	24	75.3	94.9	29	25
Rio Claro	120	137.5	94.0	9	18	99.7	89.3	29	35	85.0	93.0	31	26	84.4	91.8	20	32
Rio das Flores	108	82.1	85.0	34	36	104.6	91.7	19	28	96.0	89.3	24	37	75.3	83.4	30	41
Rio das Ostras	300	0.0	0.0	-	-	10.2	31.7	14	3	35.0	33.0	6	2	39.4	65.6	6	4
Rio de Janeiro	2028	116.6	85.7	18	34	127.9	85.3	9	43	164.6	88.7	4	39	174.1	89.8	2	37
Santa Maria Madalena	108	50.9	94.0	44	19	34.6	61.0	7	5	82.1	63.3	2	3	77.8	66.7	1	1
Santo Antônio de Pádua	168	13.7	85.0	50	37	0.0	0.0	-	-	0.2	33.3	17	1	0.0	0.0	-	-
São Fidélis	156	17.9	24.3	15	13	19.0	32.0	12	1	22.9	32.0	13	5	11.5	31.5	16	6
São Fran. de Itabapoana	168	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-
São Gonçalo	684	31.6	27.7	3	8	30.3	27.0	6	12	41.4	48.3	6	9	42.1	50.8	4	13
São João da Barra	156	32.3	49.0	9	8	14.1	49.3	11	11	26.1	54.7	8	50	8.3	53.5	14	12
São João de Meriti	528	114.6	98.0	21	6	38.6	96.3	49	16	74.1	94.3	40	23	45.5	97.1	44	13
São José de Ubá	108	71.3	66.7	3	1	101.5	98.7	24	4	89.5	99.3	28	4	71.0	99.7	33	1
São J. do Val. do R Preto	132	11.4	20.7	16	14	55.1	87.0	45	41	22.2	98.3	9	8	31.8	98.0	48	9
São Pedro da Aldeia	240	83.5	81.7	31	43	98.2	89.3	30	37	108.2	77.0	13	45	91.9	73.1	14	46
São Sebastião do Alto	108	150.6	95.3	7	16	91.7	97.0	34	10	164.8	97.3	3	12	145.7	98.1	6	7
Sapucaia	120	0.0	0.0	-	-	0.0	0.0	-	-	0.0	0.0	-	-	21.4	37.2	11	14
Saquarema	228	19.7	20.3	13	15	60.5	99.7	44	2	109.1	98.3	11	6	174.1	97.3	3	11
Seropédica	228	115.9	99.0	19	4	86.8	96.7	37	15	103.9	96.3	18	16	78.1	96.8	26	18
Silva Jardim	132	121.7	59.0	14	51.0	105.1	90.0	18	34	108.3	91.7	12	30	83.8	97.0	22	15
Sumidouro	120	83.3	93.0	32	20	54.4	98.3	46	5	71.7	97.7	42	11	61.9	96.9	41	16
Tanguá	156	0.0	0.0	-	-	6.0	32.0	16	2	0.0	0.0	-	-	27.4	26.8	6	13
Teresópolis	336	121.9	50.3	2	7	243.7	91.0	2	30	22.8	59.0	8	5	28.8	55.5	8	11
Trajano de Moraes	108	155.9	92.3	6	22	124.7	95.0	10	20	83.6	90.7	33	33	37.0	97.3	46	12
Três Rios	216	84.4	97.3	30	9	83.0	97.3	40	8	23.8	30.7	11	8	47.1	56.7	3	10
Valença	216	158.8	77.7	5	46	166.7	77.3	6	48	168.2	87.3	2	43	150.0	88.4	5	38
Varresai	108	0.0	0.0	-	-	10.2	66.7	12	1	12.3	66.7	9	1	0.0	0.0	-	-
Vassouras	156	33.8	59.7	8	5	45.5	56.0	4	8	35.5	90.3	47	35	0.0	0.0	-	-
Volta Redonda	408	111.4	97.0	22	11	97.3	98.0	31	7	90.5	96.0	27	19	77.0	96.2	28	20

). The dataset covers four years of monitoring (2018–2021) with four relevant pieces of information for each of the 92 municipalities evaluated: QLI value, QTI value, QLI ranking, and QTI ranking. The QTI and QLI values varied from 0 to 233,6%, and 99,7% to 0, respectively. Cordeiro (Group A), Santa Maria Madalena (Group B), and Parati (Group C) had the highest QTI ranking. Additionally, Sao Jose do Uba (Group A), Santa Maria Madalena (Group B), and Belford Roxo (Group C) had the highest QLI ranking (Table 1). The cities in Groups B and C did not have the minimum amount of sampling between 2018 and 2021. Therefore, the cities in Groups B and C require a significant improvement in their water quality monitoring. The top ten cities in Group A were São José de Ubá, Pinheiral, Quissamã, Nova Friburgo, Guapimirim, Natividade, São Sebastião do Alto, Porciúncula, São José do Val. Do Rio Preto, and Resende between in 2021 for the QLI (>97,4%; Table 1). On the other hand, in the quantitative ranking of Group A, the top ten cities were Cordeiro, Rio de Janeiro, Saquarema, Barra do Piraí, Valença, São Sebastião do Alto, Araruama, Angra dos Reis, Itaguaí, and Mangaratiba in the last year studied (>107.9%; Table 1). In general, only 36 cities in the SRJ were in Group A in all the years between 2018 and 2021. These 36 cities demonstrated a consistent water quality monitoring program throughout this period; however, further improvements are desirable. For instance, the City of Rio de Janeiro showed 87.4 ± 2.2% compliance (QLI) regarding the quality of drinking water for 2018–2021. Itatiaia showed only 60.9 ± 7.2% in the samples analyzed (Table 1). This result demonstrates the need for improved monitoring, even in Group A.

Cities with high population density, such as Belford Roxo, Duque de Caxias, Itaboraí, Maricá, Nilópolis, and São Gonçalo, carry out partial monitoring (Group B and C; Table 1). Comparing the quantity and quality rankings revealed that Niterói ranked first in Group A between 2018 and 2019 and was the only one with 100% compliance in monitoring drinking water quality according to the three parameters (Table 1). This municipality may be considered the gold standard in the control of drinking water quality in the SRJ, at least until 2019. The source water supply of Niteroi is a key factor in the excellent results obtained.

3.2. Cities outside the Water Quality Monitoring Program

Twenty-two cities in the SRJ were placed in Group D for some of the years studied because there was no monitoring of the three parameters (turbidity, chlorine, and coliforms) of the quality of drinking water supplied to the population (Figure 1). These cities cover different parts of the SRJ: i. the coastal lowlands (Arraial do Cabo, Cabo Frio, and Rio das Ostras); ii. the center-south (Sapucaia, Miguel Pereira, and Vassouras); iii. the northwest (Aperibé, Itaocara, Italva, Laje do Muriaé, S. Antônio de Pádua, and Varre-sai); iv. the north (Cambuci, Conceição de Macabú, S. Francisco de Itabapoana, and Bom Jesus de Itabapoana); v. the metropolitan region (Itaguaí, Mangaratiba, Maricá, Paracambi, and Tanguá); vi. the mountain region (Macuco). Although these cities are medium or small, their total population is approximately 1400 K inhabitants. Therefore, it is evident that monitoring measures need to be incorporated by these cities so that the values of chlorine, coliforms, and turbidity can be known. Furthermore, it is important to highlight the evolution or behavior of the number of cities within Group D throughout the period 2018–2021. There were 14 in 2018, 12 in 2019, 16 in 2020, and 12 in 2021. Seven cities did not have any available data for the whole four-year period, which can be considered a warning sign for the need for state surveillance (Aperibé, S. Francisco de Itabapoana, Cabo Frio, Cambuci, Conceição de Macabú, Itaocara, and Laje do Muriaé.).

The absence of data from several cities in the SRJ could be a consequence of: (i) a lack of resources or excessive change of managers (secretaries and coordinators), for example, there are cities that change their managers three to six times a year and have ahigh turnover of human resources in general; (ii) a lack of equipment (field analysis equipment, vehicles, computers, and the internet); (iii) the philosophy of the manager who chooses to prioritize other health sectors, such as primary care, or even within environmental surveillance when the manager privileges other programs such as dengue monitoring, perhaps because such programs have more resources and visibility; (iv) a lack of mechanisms by which to enforce water quality monitoring programs; (v) a limited distribution of resources; (vi) economic difficulties of smaller cities, and (vii) the visibility of the programs by control agencies and society. Despite these problems, it is worth noting the important advances in recent years compared to the last few years. In 2019 and 2020, 88% and 80% of the cities in the SRJ, respectively, performed the monitoring of total coliforms, against 53% in 2015 and 73% in 2016. However, a significant portion of the cities still do not have the number of samples recommended in the water potability ordinance. Measures are needed to improve water quality with the involvement of different entities and spheres of the SRJ.

3.3. Proposal for Water Quality Monitoring

Water quality monitoring depends on several stakeholders, including those who are responsible, the relevant authorities, and those who are consulted and informed. Responsibility may be divided among the participating entities, with well-defined functions, e.g., water quality in the supply network is the water supply company’s responsibility; the management part with a more general view of monitoring is the responsibility of state surveillance, while the implementation and monitoring practice is the responsibility of municipal bodies. Any matter regarding epidemiology is the responsibility of specific government agencies. Feeding and managing the data system and disseminating information is the responsibility of the federal and state levels. The orientation and formation of guidelines for analysis techniques are undertaken by the central laboratory of the state, the Noel Nutels Central Laboratory (LACEN). To leverage greater participation of government bodies controlling public administration, such as the Public Prosecution Service, a designated work group may collect results (the Environment Specialized Action Group of Public Prosecution Service, GAEMA/MP). The Rio de Janeiro State Regulatory Agency for Energy and Basic Sanitation (AGENERSA), the State Institute for the Environment (INEA), and the bodies responsible for sanitation and the environment should be informed at the relevant points with increased participation in gradual steps. None of the aforementioned agents exercises authority and only one should be consulted in monitoring activities: the water supply company. In addition, an alert system also appears to be necessary for cities outside the monitoring system or for those cities that show monitoring results of low quality. Cities in Groups B and C with incomplete monitoring must also be part of this alert system. Crafting guidelines, regulations, tools and resources, public health support, and context-specific evidence for water safety plans has enhanced drinking water quality in other countries [34,35,36]. Iceland has a national framework for safe drinking water that assigns responsibilities to actors at different levels, with strong and independent municipal authorities [37]. The US drinking water regulations align fairly well with its water safety plans [WSPs]; while those of Iceland rely on setting national standards for contaminant levels, those of the US prevent the distribution of contaminated water with its national drinking water quality standards, but in practice, they fail sometimes, e.g., the case of Flint, Michigan and lead [38,39]. The SRJ follows the national regulations and has a disordinate national water resources plan that does not connect with the state level.

3.4. Drinking Water Quality and Public Health

The impact of water quality on public health cannot be denied. Improved environmental conditions [40] may improve raw water quality and prevent the presence of opportunistic pathogens in the treated water [41]. The loss of biodiversity and pollution may be associated with inflammatory bowel disease (IBD) in Brazil; the SRJ has a significant IBD prevalence [42]. In addition, the cities of Rio de Janeiro, Duque de Caxias, São Gonçalo, Niterói, Campos dos Goytacazes, Nova Iguaçu, and Belford Roxo reported a significant number of deaths due to waterborne infectious disease and parasites (amoebiase, diarrhea, leptospirosis, hepatitis, schistosomosis, filariosis, and ascariosis), and there is a possible link to inadequate environmental sanitation according to FUNASA of the Ministry of Health [43]. Cholera outbreaks are becoming a global issue, with at least 30 countries currently suffering from this disease (https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON419 (accessed on 20 July 2022)). Given the several cities outside the water quality monitoring program (n = 22), or those with only partial monitoring (n = 34 cities), including large cities such as São Gonçalo, Belford Roxo, Duque de Caxias, and Nilópolis (total population of almost 3 million people), we propose a more cohesive monitoring system with the engagement of several stakeholders (government, society, industry, and academia). Non-conformities (>10% of the analyzed samples) in cities that monitored the three parameters (Group A), such as Rio de Janeiro and Iguaba Grande, indicate potential contamination of treated water distributed to the population in this situation, with risks to public health. These deficiencies in the quality of treated water can be explained by the poor quality of the water treatment services provided, even in situations where the raw water quality is reasonable, but the water treatment is not adequate. The situation of the municipality of Niterói is highlighted; it uses the water of the Imunana Channel (which itself receives water from the Guapi-Açu and Macacu rivers) as its water source, which is of good quality compared to that of the Guandu river. This situation alerts us to the necessity for governments at the municipal, state, and federal levels to prioritize investment in the sanitation of sewage in the watershed of the SRJ water sources, especially Guandu, as well as in reforestation and the improvement of the environmental health of these regions. In addition, improvements in the efficiency of environmental licensing and the inspection of projects that could have potential impacts on the hydrographic basins of these rivers are necessary in order to ensure environmental sustainability of the water supply of the population of Rio de Janeiro and to avoid a future water crisis [16].

4. Conclusions

Problems due to the absence of data can be summarized as resulting from poor infrastructure and lack of public resources, mainly in smaller municipalities. A comprehensive water quality monitoring program needs to be implemented in the SRJ. Ideally, this program will bring together the various stakeholders involved in monitoring the quality of water for human consumption. This program will also help to address new challenges concerning emergent contaminants in the water, such as rare elements, hormones, pesticides, and emergent pathogens. Municipalities recording cases of deaths due to waterborne diseases will require further attention. The newly proposed indexes (and rankings) contribute to increased accessibility of data regarding public health conditions across vast geographic areas of the SRJ.