Spectral analysis in determining water quality sampling intervals

Silva, Régis Leandro Lopes da; Silveira, André Luiz Lopes da; Silveira, Geraldo Lopes da

doi:10.1590/2318-0331.241920180077

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Scientific/Technical Article • RBRH 24 • 2019 • https://doi.org/10.1590/2318-0331.241920180077 copy

Spectral analysis in determining water quality sampling intervals

A análise espectral na determinação de intervalos de amostragem da qualidade da água

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

To make water quality series more representative, real-time monitoring techniques are developed. However, these techniques have obstacles in their use, such as high costs and difficulties in equipment installation, maintenance, and calibration. One alternative is near-real time water quality monitoring (NRTWQM), with sampling done less frequently than daily. The study objective was to evaluate, through spectral analysis, the water quality sampling frequency representativity for different catchments. For this purpose, a historical series of real time water quality monitoring stations were used in Brazil, Canada, and the USA. These series were submitted to spectral analysis to identify the denser frequencies and their representativeness across the series. To obtain the sampling intervals, the Nyquist-Shannon theorem was applied. Weekly intervals accounted for 65% of cumulative frequencies for the three verified parameters, and the sampling intervals obtained by means of the characteristic frequencies were shown to be executable in the NRTWQM models for up to the 90% of cumulative frequency. For cumulative frequency above 90%, the intervals approach the daily values.

Keywords:
Sampling frequency; Monitoring; Near real time

Country	N	HSMT	Minimum Area	Maximum Area
Brazil	4	9.2 years	240 km²	16,100 km²
Canada	5	8.4 years	64 km²	7,900 km²
USA	67	9.7 years	6 km²	2,916,000 km²

Accumulated Frequency	Sampling Intervals by Order (Days) - conductivity
Accumulated Frequency	S	MS	M	ML	L
5%	135	252	252	252	252
10%	62	120	120	120	120
15%	39	77	77	77	77
20%	27	55	55	55	55
25%	20	42	42	42	42
30%	15	32	32	32	32
35%	12	25	25	25	25
40%	9	20	20	20	20
45%	8	16	16	16	16
50%	6	12	12	12	12
55%	5	10	10	10	10
60%	5	8	8	8	8
65%	4	7	7	7	7
70%	3	5	5	5	5
75%	3	4	4	4	4
80%	2	3	3	4	4
85%	2	3	3	3	3
90%	1	2	2	2	3
95%	1	1	1	1	2

Accumulated Frequency	Sampling Intervals by Order (Days) - DO
Accumulated Frequency	S	MS	M	ML	L
5%	276	317	317	317	518
10%	134	152	152	152	152
15%	86	99	99	99	99
20%	62	71	71	71	71
25%	47	53	53	53	78
30%	36	42	42	42	63
35%	28	33	33	33	52
40%	22	26	26	26	44
45%	16	21	21	21	37
50%	11	16	16	16	31
55%	9	12	12	12	25
60%	7	9	9	9	18
65%	6	8	8	8	13
70%	5	6	6	6	10
75%	4	5	5	5	8
80%	3	4	4	4	6
85%	2	3	3	3	4
90%	2	2	2	2	3
95%	1	1	1	1	2

Accumulated Frequency	Sampling intervals by order (days) - pH
Accumulated Frequency	S	MS	M	ML	L
5%	247	247	247	247	247
10%	117	117	117	117	117
15%	75	75	75	75	75
20%	53	53	53	53	53
25%	40	40	40	40	40
30%	31	31	31	31	31
35%	18	27	27	27	27
40%	13	21	21	21	21
45%	10	17	17	17	17
50%	8	14	14	14	14
55%	7	11	11	11	11
60%	5	9	9	9	9
65%	4	7	7	7	7
70%	4	6	6	6	6
75%	3	5	5	5	5
80%	2	4	4	4	3
85%	2	3	3	3	2
90%	1	2	2	2	1
95%	1	1	1	1	1

Parameter	Accumulated Frequency	Catchment Size
Parameter	Accumulated Frequency	<100 km²	100<>100,000 km²	>100,000 km²
Electric Conductivity	35%	12	25	25
	50%	6	12	12
	65%	4	7	7
	80%	2	3	4
Dissolved Oxygen	35%	28	33	52
	50%	11	16	31
	65%	6	8	13
	80%	3	4	6
pH	35%	18	27	27
	50%	8	14	14
	65%	4	7	7
	80%	2	4	3

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[1] E-mails: regis.silva@ufsm.br (RLLS), andre@iph.ufrgs.br (ALLS), geraldo.silveira@unifal-mg.edu.br (GLS)