IQA FAL A WATER QUALITY INDEX FOR LOTIC ENVIRONMENTS BASED ON FUZZY LOGIC

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1 IQA FAL A WATER QUALITY INDEX FOR LOTIC ENVIRONMENTS BASED ON FUZZY LOGIC Pessoa, M. A. R. (INEA - Rio de Janeiro Environmental Agency) Souza, F. J. (UERJ - Rio de Janeiro State University) Domingos, P. (INEA - Rio de Janeiro Environmental Agency) Azevedo, J.P.S. (Water Resources Division/COPPE/UFRJ )

2 Monitoring and evaluation of water quality aims: Knowing the conditions of aquatic environments.. Supporting the decision-making process of government actions to regulate the uses of water resources. Identify the current situation of the water body, its changes and trends in quality in time. Propose improvements and interventions.

3 The four steps of monitoring and evaluation of water quality 1 - Planning the Sampling 2 - Collecting the samples 3 Laboratory analysis of the samples 4 - Evaluating the Results

4 Etapa 1 Planejamento da Amostragem Elaboração do plano de coleta de amostras de água e sedimento em locais e datas prédeterminados.

5 Etapa 2 Coleta das Amostras Coletas das Amostras de Água e Sedimento nos Locais e Datas Pré-Determinados no Plano de Amostragem Doutorado: Recursos Hídricos e Saneamento - Professor: José Paulo Soares de Azevedo - Aluno: Marco Antonio Ribeiro Pessoa

6 Etapa 3 Análise das Amostras Amostras de água e sedimento são levadas ao laboratório onde são feitas as análises físicoquímicas e biológicas. Dados Brutos de Qualidade de Água i - Concentração de Substâncias Químicas na amostra de água e de sedimento Exemplo: Oxigênio Dissolvido; Demanda Bioquímica de Oxigênio; Fósforo Total; Fenois; etc. ii Dados físicos da amostra de água Exemplo: Temperatura; Condutividade; etc. iii Identificação e contagem dos organismos fitoplanctônicos ou zooplanctônicos na amostra de água Exemplo: Microcystis aeruginosa (Cianobactéria), Prorocentrum minimum (Dinoflagelado); etc.

7 Evaluation of the Results Analysis and interpretation of results using statistical techniques and mathematical models that aims Transform raw data of water quality into INFORMATION

8 Final Product Extensive reports with a detailed technical evaluation of the water and sediment quality in the water bodies. Diagrama de Agrupamento distância Euclidiana 2,5e9 2e9 1,5e9 1e9 5e8 RF00 - DISCO SECCHI - PERÍODO 2000/2002 3,5 Distância das Associações (RF-00) TRANSPARÊNCIA (m) RF ,0 2,5 2,0 1,5 1, /02/00 13/06/00 28/09/00 19/10/00 30/11/00 04/01/01 30/01/01 06/03/01 16/04/01 04/06/01 19/06/01 09/07/01 13/08/01 17/09/01 15/10/01 22/11/01 02/01/02 04/02/02 11/03/02 (%) ESP_06 ESP_19 ESP_11 ESP_23 ESP_05 ESP_26 ESP_10 ESP_20 ESP_03 ESP_02 ESP_13 ESP_12 ESP_24 ESP_17 ESP_25 ESP_21 ESP_09 ESP_08 ESP_22 ESP_16 ESP_15 ESP_28 ESP_14 ESP_07 ESP_27 ESP_04 ESP_18 ESP_01 0,5 0,0 Cianobactéria Diatomácea Clorofícea Criptofícea Flagelados F M A M J J A S O N D J F M A M J J A S O N D J F M A M MESES

9 There is an intrinsic difficulty of communication between those who produce and own knowledge about water quality and those who are not experts in water quality, but need this knowledge to support their management actions. How to translate a huge mass of raw data into a value or category that expresses the water quality in a particular location?

10 Water Quality Index Water quality index attempt to synthesize in a single value or category, the quality of water originally described by a complex set of environmental variables.

11 Horton (1965) developed general water quality indices, selecting and weighting several parameters. The U.S. National Sanitation Foundation (NSF, 2007) proposed the Water Quality Index- WQI by improving Horton methodology. WQI is the weighted average of some predefined parameters, normalized in a scale from0to100 Where: q i is thethe value of the i th normalized parameter w i is thethe relative weight of the i th parameter Ʃw i = 1

12 BrazilianWaterQualityIndex(IQA CETESB )*isanadaptationfromthensfindex. Nine variables are computed as the weighted product of the normalized values of these variables Where: q i is thethe value of the i th normalized parameter w i is thethe relative weight of the i th parameter The water quality parameters, which are part of the calculation of IQA CETESB, mainly reflect the contamination of water bodies caused by discharges of sewage. This index was developed to evaluate water quality, with the major determinant to be used for public supply, considering aspects related to treatment status. * Available in the São Paulo s State Water Quality Agency Reports

13 Utilização de índices de qualidade de água adequados ao uso do recurso hídrico A CETESB a partir de 2002 tem utilizado índices específicos para: águas destinadas para fins de abastecimento público IAP; águas destinadas para a proteção da vida aquática IVA; águas destinadas para o banho Classificação da Praia. O uso de um índice numérico global foi considerado inadequado, devido à possibilidade de perda de importantes informações, tendo sido proposta a representação conjunta dos três índices.

14 Índice Subjetivo de Qualidade de Água - CONESA O resultado da média ponderada dos valores normalizados dos parâmetros de qualidade de água é no final multiplicado por uma constante k cujos valores variam entre 0,25 e 1 em intervalos de 0,25. Essa constante é obtida a partir da observação visual da amostra de água coletada onde 0,25 corresponde a uma amostra com aspecto de muito poluída e 1 a uma amostra de água aparentemente limpa segundo equação 10.

15 Índice Preditivo para acidentes com a ictiofauna É um alerta preditivo à mortandade de peixes para a Lagoa Rodrigo de Freitas Considera as variáveis: oxigênio de superfície e de fundo da coluna d'água, temperatura de superfície, transparência (disco de Secchi) e percentual de dominância do fitoplâncton. Os parâmetros recebem ponderações diferenciadas em função da representação ecológica da variável frente ao risco de mortandade de peixes no ambiente estudado. A pós a ponderação dos dados obtêm-se as classes VIGILÂNCIA, ATENÇÃO, ALERTA e CRÍTICA.

16 The developing main steps in most methods for calculating water quality index are: Selection of water quality variables by water quality experts; Determination of quality curves that normalize water quality variables for the same scale; Aggregation of normalized water quality variables by a mathematical expression that is most often based on an arithmetic or geometric mean; The most commonly used water quality variables are Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Total Phosphorus, Nitrates and Thermotolerant Coliforms; Revision comparing different index of quality of the water. Fernandez(2004)

17 This work propose the IQAFAL A WATER QUALITY INDEX FOR LOTIC ENVIRONMENTS BASED ON FUZZY LOGIC This index was developed with the purpose of evaluating the quality of the aquatic environment as a whole, taking into account the different aspects of the environment including the quality of the biota. This index was developed with fuzzy logic and aims to represent the knowledge of experts in water quality of the Rio de Janeiro State Environmental Agency INEA. The IQAFAL has considered as reference the Brazilian law which determines the standards of water quality for secondary contact and Preservation of Fauna and Flora. IQAFAL Key Features: Specific index for lotic environment. Use of biological variables. Consider that critical levels of some variables, used in the index, should determine the outcome

18 Fuzzy Logic The fuzzy logic was originally conceived as a way to represent intrinsically vague or linguistic knowledge. It is based on the mathematics of fuzzy sets *. Fuzzy sets were defined in terms of a membership function that maps a domain of interest to the interval [0,1]. Fuzzy inference is the process that maps the input variables space into the output variable using fuzzy logic. Linguistic variables are the input or output variables of the system whose values are words or sentences from a natural language, instead of numerical values. A linguistic variable is generally decomposed into a set of linguistic terms. * Zadeh, 1965

19 Fuzzy Logic Linguistic Terms Degree of membership Low Median High Membership Functions Linguistic Variable Temperature Universe of Discourse

20 Fuzzy Logic The reasoning way of fuzzy logic is a inference methodology that uses tools and concepts of fuzzy logic to arrive at its conclusions. A set of rules formed by fuzzy implications in the form of propositions IF... THEN..., combined by fuzzy operators lead to infer fuzzy sets (OLIVEIRA, 1999). A fuzzy rule is a conditional expression in the form IF <fuzzy proposition> THEN <fuzzy proposition> where the fuzzy proposition is: the composition of a linguistic term associated with a linguistic variable, characterized by a membership function (Lermontov, 2009).

21 IQA FAL A WATER QUALITY INDEX FOR LOTIC ENVIRONMENTS BASED ON FUZZY LOGIC Variables Biological: i Shannon-Weaver Diversity Index ii Density of Cyanobacteria Nutrients: i Total Phosphorus ii Ammoniacal Nitrogen Oxygen: i DissolvedOxygen-DO ii BiochemicalOxygenDemand-BOD Bacteriológicas: i Escherichia Coli

22 IQA FAL -System Flow Bacteriological Index

23 IQA FAL FuzzySets for thelinguisticvariabledo Anoxic Bad Regular Good Supersaturation DO (mg/l)

24 (*)FAM - Fuzzy Associative Memory Fuzzy Inference Oxygen Index Rule Base FAM*

25 IQA FAL FuzzySets for thelinguisticvariableescherichiacoli Good Regular Bad Critical Escherichia Coli (NMP/100ml)

26 Fuzzy Inference Domestic Sewage Index Rule Base FAM

27 IQA FAL FuzzySets for theindex Very Bad Bad Regular Good Excelent IQA FAL

28 FuzzyInference IQA FAL RuleBase FAM

29 IQA FAL -Classes

30 IQA FAL -Results

31 IQA CETESB -Results

32 IQA FAL -System Flow Bacteriological Index

33 Trophic State Index- Results

34 IQA FAL -System Flow Bacteriological Index

35 Domestic Sewage Index- Results

36 IQA FAL -System Flow Bacteriological Index

37 Oxygen Index- Results

38 IQA FAL -System Flow Bacteriological Index

39 Escherichia Coli Index- Results

40 Resultados Coliformes Termotolerantes

41 IQA CETESB VariablesandWeight Escherichia Escherichia Coli Coli Despite the Escherichia Coli weight be the second biggest one and his normalized values at concentrations above 10 3 be less than 20 the IQA CETESB shows a smaller sensitivity to the high values of this variable for the data used in this work

42 IQAFAL CONCLUSIONS The application of the IQAFAL to the data from the time/historical series ( ) obtained by INEA at the sampling stations along a stretch of the Paraíba do Sul River and Guandu River shows that the indices results are in line with the perception of water quality experts from this institution regarding the quality of these aquatic environments, as they have revealed in their reports (FEEMA, 2002; INEA, 2008). This stretch of the river is affected, nevertheless, by the impact from the discharge of sanitary sewage, which is validated by the high concentrations of Escherichia Coli (FEEMA, 2002). For the IQAFAL, the separate analysis of the secondary indices showed that the Escherichia Coli variable is the one causing a higher frequency of the results for this index to occur in category "BAD" for almost all sampling stations. Due to fact that it is composed of secondary indices that include groups of water quality variable with similar and complementary meanings, the IQA FAL helped identify which water quality variables was determining the results.

43 IQA FAL Bacia Lagoa Rodrigo de Freitas -Rio Rainha LEGENDA EXCELENTE BOM REGULAR RUIM PÉSSIMO 38% 63% 37% 100% RN310 62% RN050 RN210 RN040 RN020 88% 12% 13% 12% RN010 38% 37% 12% Rio Rainha 88%

44 IQA CETESB Bacia Lagoa Rodrigo de Freitas -Rio Rainha LEGENDA EXCELENTE BOM REGULAR RUIM PÉSSIMO 13% 38% 62% RN310 25% 75% 87% RN050 RN210 RN040 RN020 50% 50% 13% RN010 37% 50% 37% Rio Rainha 63%

45 IQA FAL Bacia Lagoa Rodrigo de Freitas -Rio Cabeça

46 IQA CETESB Bacia Lagoa Rodrigo de Freitas -Rio Cabeça

47 IQA FAL Bacia Lagoa Rodrigo de Freitas -Rio Macacos

48 IQA CETESB Bacia Lagoa Rodrigo de Freitas -Rio Macacos

49 IQA FAL Versão sem fitoplâncton

50 IQA FAL/sem fitoplâncton Rio Piabanha

51 Sub-Índice Trófico -Rio Piabanha

52 Sub-Índice Despejos Domésticos - Rio Piabanha

53 Sub-Índice de Oxigênio -Rio Piabanha

54 Coliforme Fecal -Rio Piabanha

55 IQA FAL/sem fitoplâncton Bacía da Baía de Guanabara

56 IQA (Zoneamento Econômico Ecológico) Mediana resultados PARÂMETROS PESO RECALCULADO OD 0,25 DBO 0,14 Nitrogênio Total 0,14 Fósforo Total 0,14 ph 0,17 Temperatura 0,14

57 Resultados IQA FAL

58 Resultados IQA CETESB

59 IQA (Zoneamento Econômico Ecológico) Mediana resultados PARÂMETROS PESO RECALCULADO OD 0,25 DBO 0,14 Nitrogênio Total 0,14 Fósforo Total 0,14 ph 0,17 Temperatura 0,14

60 Índices de Qualidade de Água Vantagens 1 Facilidade de comunicação. 2 Combina em um único número e uma única unidade diferentes variáveis medidas em diferentes unidades. Desvantagens 1 Alguns parâmetros indexados podem influenciar demais injustificadamente. 2 Atenuação do impacto negativo de uma variável frente ao comportamento estável das demais (efeito eclipse).

61 IQA FAL SecondaryIndex Biological Index - BIO Nutrients Index - NUT IQA FAL Oxygen Index - OXY Bacteriological Index - BAC

62 BIO NUT IQA FAL SecondaryIndex OXY BAC BIO NUT OXY BAC

63 IQA (ZEE) x IQA FAL