pH Precursors as a Factor for Assessing Rainwater Quality in Roofing Sheets: A Case Study of Rivers State, Nigeria

Abstract

Rainwater harvesting is vital for water management in water-scarce regions. This study in Rivers State, Nigeria, assesses rainwater quality from different roofing sheets, emphasizing critical pH precursors. Results show a pH range from 4.50 to 7.90, shifting toward alkalinity with increased rainfall. Temperature rises steadily, while conductivity, turbidity, total dissolved solids, and suspended solids decrease during the wet season. Anionic composition showed that Rumuodomaya/Rumuodome had a high level of 4.77 mg/L nitrate, 1.32 mg/L nitrite, and 1.15 mg/L phosphate, while Chokocho has a high level of 11.51 mg/L chloride, 6.48 mg/L sulfate and 3.44 mg/L hydrogen carbonate compared to Ogale and Diobu for zinc roof. Light metal composition indicates concentrations of sodium, calcium, potassium, ammonium, and aluminum. The neutralization factor analysis highlights NH₄⁺ importance, with Ca²⁺, Na⁺, and Mg²⁺ play significant roles. Hierarchical and factor analysis showed that influences from industrial emissions, agriculture, biomass burning, road construction, limestone mining, soil resuspension, and metabolic processes impact rainwater’s ionic composition. pH emerges as critical, reflecting anthropogenic influences. pH is critical in all aspects of ionic influence from anthropogenic sources that can impact the rainwater quality over a long period. So, rainwater must be treated before consumption or usage for domestic purposes.

1. Introduction

Access to clean and safe drinking water is fundamental to human health and well-being from an ethical viewpoint [1]. In many parts of the world, including Nigeria, inadequate access to potable water sources poses significant challenges, as a result, alternative water sources, such as rainwater harvesting, have gained considerable attention as a sustainable solution to address water scarcity issues [2]. Rainwater harvesting involves the process of collecting and storing rainwater for various domestic and non-potable uses as it offers several advantages such as its availability and ease of collection (surface or underground), including reduced strain on existing water resources and the potential for cost savings [3, 4, 5]. However, rainwater quality is influenced by multiple factors, which are contamination due to microbial load, and physiochemical, and its suitability for consumption depends on the presence of contaminants [6].

Roofing sheets serve as the primary collection surface for rainwater harvesting systems, and play a crucial role in determining the quality of harvested rainwater via the age of the roofing sheet, type of the roofing materials, deposition time and its corresponding intensity [7] in addition to the composition of roofing materials that can introduce various contaminants, including heavy metals, organic compounds, and microbial agents, into the collected rainwater [8, 9, 10]. Among these factors, pH precursors have been recognized as an important indicator of water quality, as they can affect the corrosivity and taste of harvested rainwater [11, 12]. The pH of rainwater can be influenced by several factors, such as atmospheric pollutants, airborne particles, and physiochemical interactions with roofing materials [13, 14]. Roofing sheets made of different materials, such as metal, concrete, or asbestos, can leach substances that alter the pH of rainwater [15, 16, 17]. These pH precursors can have potential implications for human health, as they may affect the taste, usability, and suitability of rainwater for various applications, including drinking, cooking, and sanitation [18, 19].

Rivers State, located in southern Nigeria (Figure 1), experiences significant rainfall throughout the year, making rainwater harvesting a viable option for meeting water demands. However, several research works such as Omokpariola and Omokpariola, [18], Nduka et al. [20] and Omokpariola et al. [21, 22] conducted studies on the quality of rainwater from different roofing materials in the same region without particular focus on the role of pH precursors. Therefore, this research aims to address this knowledge gap by conducting a comprehensive investigation of the impact of pH precursors on rainwater quality in roofing sheets within the context of Rivers State. By evaluating the pH precursors present in rainwater collected from various roofing materials commonly used in Rivers State, we can gain valuable insights into the quality and safety of harvested rainwater. The investigation aims to shed light on the potential implications of pH precursors for human health and environmental sustainability, ultimately contributing to the development of guidelines and best practices for safe and reliable rainwater harvesting systems in the region. The findings of this study will contribute to enhancing our understanding of the factors affecting rainwater quality and provide valuable information for policymakers, researchers, and individuals involved in rainwater harvesting practices.

Figure 1.

Sample locations in Rivers state, Nigeria.

2. Materials and methods

2.1 Sample collection and storage

Rainwater samples were collected from different roofing surfaces (zinc, aluminum, asbestos and stone-coated) and ambient (open-air) between the period of April to November 2019 as described by Omokpariola et al. [1]. The sampling containers (500 ml) were prewashed, and rinsed severally with rainwater from the sampling location, thereafter, the rainwater samples were placed on support for collection above 1 m using a precleaned bowl across different sampling surfaces respectively. The pH was determined in-situ using a pH meter to give an on-the-spot assessment of rainwater, subsequently, the rainwater was transferred into different sampling containers, labeled accordingly, and packaged in a black cellophane bag for storage and transfer to the laboratory.

2.2 Determination of physiochemical parameters

The methodology used in the study included the determination of the physical and chemical parameters of rainwater samples. The physical parameters measured were pH, temperature, turbidity, conductivity, total dissolved solids, and total suspended solids [23, 24]. For pH determination, a pH meter was calibrated using reference buffer solutions [4, 7, 10], and the pH of rainwater samples was measured in situ. Temperature was determined using a mercury-in-glass thermometer by immersing it in the rainwater sample and recording the reading after equilibrium. Turbidity was measured using a turbidity meter, and conductivity was measured using a conductivity meter that was standardized with a KCl solution. Total dissolved solids (TDS) were determined by filtering a 50 mL rainwater sample, evaporating the filtrate, and weighing the residue. Total suspended solids (TSS) were determined by evaporating the rainwater sample and weighing the total solids. The difference between total solids (TS) and total dissolved solids (TDS) gave the total suspended solids. The chemical parameters analyzed in the rainwater samples included chloride, sulfate, phosphate, hydrogen carbonate, ammonium ion, nitrate, nitrite, and various metallic ions [24, 25]. The chloride concentration was determined using Mohr’s argentometric method. Sulphate was measured using the SulfaVer 4 method, and phosphate was measured using UV spectrophotometry. Hydrogen carbonate was determined by titrating the rainwater sample with hydrochloric acid and observing the color change. Ammonium, nitrate, and nitrite were determined using Nessler, Cadmium Reduction, and Diazotization methods, respectively. The metallic ions: sodium, potassium, magnesium, calcium, and aluminum were determined using an Agilent inductive coupled plasma optical emission spectrophotometer (ICP-OES) equipped with an autosampler, simultaneous optical viewing and multi-elemental determination of metallic ions as described by Omokpariola and Omokpariola [18, 26], where pre-digestion was done using concentrated nitric acid, calibration was determined using sample standard at absorbance values of sodium (Na) at 589.6 nm, potassium (K) at 766.5 nm, magnesium (Mg) at 285.2 nm, calcium (Ca) at 315.9 nm, aluminum (Al) at 308.2 nm with regression between 0.9969 and 1.0000 respectively.

2.3 Statistical analysis

Statistical analysis was carried out using Microsoft Excel 2019, XRealStats Excel Add-ins and Past: paleontological statistics software to determine the mean, standard deviation, range, coefficient of variance, and graphical plots (clustering) of the parameters obtained over three sampling periods for the various rainwater sampled were presented [27, 28, 29, 30].

2.4 Neutralization factor

The neutralization factor (NF) is used to indicate the degree of neutralization of cations by specific anion species in atmospheric precipitation [30].

$$\mathrm{NF}\left(\mathrm{x}\right)=\frac{{\mathrm{C}}_{\mathrm{x}}}{{\mathrm{C}}_{{\mathrm{Cl}}^{-}}+{\mathrm{C}}_{\mathrm{S}{\mathrm{O}}_4^{2-}}+{\mathrm{C}}_{\mathrm{N}{\mathrm{O}}_3^{-}}+{\mathrm{C}}_{\mathrm{N}{\mathrm{O}}_2^{-}}+{\mathrm{C}}_{\mathrm{P}{\mathrm{O}}_4^{3-}}+{\mathrm{C}}_{\mathrm{HC}{\mathrm{O}}_3^{-}}}$$E1

Where $C_x$ is the concentration of cation ($x$) of interest.

The role of Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺, Al³⁺ plays an important role in neutralization with anion ($x$) of interest.

2.5 Factor analysis

Factor analysis (Principal component analysis) has been widely used in different studies such as [30]. The principal component analysis is based on the mathematical model used to analyze multidimensional data to correlate variables thereby extracting salient information. Factor analyses were done using the principal component method using Excel, 2019 – XRealStats Add-in Package for Microsoft Windows. It was done to determine the factor underlying the inter-correlation between measured species. According to Kovacs et al., [31], absolute values of factor loading higher than 0.71 are indicated as possible indicators. The physiochemical parameters (pH; Temperature; Conductivity; Turbidity; TDS; TSS; Cl⁻, SO₄²⁻, NO₃⁻, NO₂⁻, PO₄³⁻, HCO₃⁻, Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺, and Al³⁺) were taken into considerations in the factor analysis. Initial factors were extracted for sampling surfaces from different study area in Rivers state, Nigeria. Factor with eigenvalue greater than 1 were considered for varimax rotation to obtain the final matrix for possible interpretation of possible sources.

3. Result

The study assessed the physiochemical and metallic concentration in Ogale, Eleme local government area, Rumuodomaya/Rumuodome, Obio-Akpor local government area, Diobu, Port-Harcourt local government area, Chokocho, Etche local government area of Rivers State for the different monthly rain period.

3.1 Physical parameters of rainwater samples

Table 1 shows mean and standard deviation (SD) of physical parameters from rainwater sampling surfaces (ambient, zinc roofing, aluminum roofing, asbestos roofing, and stone-coated roofing) different locations respectively. The values of pH obtained ranged from 4.50 to 7.90 for different sampling surfaces in all locations (Ogale, Rumuodomaya/Rumuodome, Diobu and Chokocho) respectively, which showed sharp increase from acidity to alkalinity. In relation to sampling surfaces, the sum concentration of pH for Ogale and Diobu reveals that asbestos roofing sheet was highest, while zinc roofing was lowest. Rumuodomaya/Rumuodome and Chokocho reveals that Stone-coated roofing sheet was highest compared to aluminum roofing sheet that was lowest. The values of temperature obtained ranged from 18.70 to 25.90°C for different sampling surfaces in all locations. Temperature increased from early rain (April) to late rain (November) regiments as mean temperature for all location was zinc roof gave highest, while ambient was lowest except for Rumuodomaya/Rumuodome where stone-coated roof was higher. Electrical conductivity values ranged from 8.71 to 714.20 μS/cm for different sampling surfaces. There was a sharp decrease in conductivity as reading as rainfall events takes place from early to late rain across all sampling surfaces. The mean concentration of electrical conductivity for location of study reveals that for Ogale, aluminum roof was highest compared to ambient surface; for Rumuodomaya/Rumuodome assessed, asbestos roof was highest, while ambient was least; for Diobu, zinc roof was highest, while ambient was least and Chokocho, zinc roof was highest compared to stone-coated roof.

Sampling surface type	Type of parameter	Ogale, Eleme LGA	Rumuodomaya/Rumuodome, Obio-Akpor LGA	Diobu, Port-Harcourt LGA	Chokocho, Etche LGA
N: Number of Samplings		Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Ambient; N: 6; N: 11; N: 7; N: 8	Temp. (°C)	23.81 ± 1.44	24.03 ± 1.61	24.04 ± 1.11	24.19 ± 0.96
	pH	6.41 ± 0.91	6.83 ± 0.72	6.72 ± 0.55	6.67 ± 0.69
	Cond. (μS/cm)	106.43 ± 173.75	45.13 ± 33.28	66.85 ± 90.65	126.51 ± 143.64
	Turbidity (NTU)	1.54 ± 1.18	2.89 ± 1.81	1.84 ± 1.18	0.97 ± 0.34
	TDS (mg/L)	10.14 ± 5.76	19.36 ± 11.00	13.61 ± 9.65	12.93 ± 10.39
	TSS (mg/L)	2.88 ± 2.70	2.81 ± 1.71	2.51 ± 1.54	3.66 ± 3.11
	Cl^− (mg/L)	2.58 ± 5.01	3.02 ± 5.12	6.75 ± 7.18	11.79 ± 12.09
	SO4^2− (mg/L)	1.57 ± 1.66	1.97 ± 1.03	3.14 ± 3.60	5.09 ± 4.63
	NO3^− (mg/L)	3.08 ± 2.62	2.87 ± 2.38	2.25 ± 1.73	3.39 ± 3.12
	NO2^− (mg/L)	0.98 ± 1.04	0.64 ± 0.39	0.35 ± 0.22	0.72 ± 0.46
	PO4^3− (mg/L)	0.44 ± 0.29	0.82 ± 0.43	0.51 ± 0.39	0.49 ± 0.41
	HCO3^− (mg/L)	1.89 ± 1.66	1.31 ± 0.50	0.64 ± 0.36	0.63 ± 0.59
	Na^+ (mg/L)	1.09 ± 1.42	0.81 ± 0.96	0.75 ± 1.00	0.97 ± 2.04
	K^+ (mg/L)	0.26 ± 0.31	0.17 ± 0.28	0.15 ± 0.16	0.18 ± 0.37
	Mg^2+ (mg/L)	0.33 ± 0.30	0.26 ± 0.35	0.07 ± 0.05	0.09 ± 0.04
	Ca^2+ (mg/L)	0.67 ± 0.60	0.37 ± 0.54	0.18 ± 0.09	0.23 ± 0.10
	Al^3+ (mg/L)	0.04 ± 0.06	0.07 ± 0.07	0.02 ± 0.03	0.01 ± 0.01
	NH4^+ (mg/L)	4.83 ± 3.68	5.38 ± 3.94	3.97 ± 2.50	5.55 ± 3.77
Zinc Roof; N: 2; N: 3; N: 3; N: 2	Temp. (°C)	24.06 ± 1.31	23.90 ± 2.10	24.14 ± 1.14	24.40 ± 0.90
	pH	6.20 ± 1.03	6.70 ± 0.51	6.51 ± 0.53	6.09 ± 1.05
	Cond. (μS/cm)	115.43 ± 216.76	79.09 ± 62.83	144.91 ± 213.04	194.11 ± 244.71
	Turbidity (NTU)	1.60 ± 1.43	3.45 ± 1.78	2.70 ± 1.32	1.67 ± 0.67
	TDS (mg/L)	18.89 ± 19.58	27.64 ± 16.24	20.01 ± 19.03	19.01 ± 17.48
	TSS (mg/L)	6.36 ± 7.73	4.79 ± 4.01	6.07 ± 9.66	7.63 ± 9.98
	Cl^− (mg/L)	3.36 ± 6.29	5.40 ± 10.00	12.27 ± 16.41	13.94 ± 17.08
	SO4^2− (mg/L)	3.06 ± 2.94	3.66 ± 3.00	3.71 ± 4.02	7.61 ± 8.12
	NO3^− (mg/L)	4.75 ± 4.01	4.76 ± 5.67	1.86 ± 1.21	5.10 ± 5.88
	NO2^− (mg/L)	1.29 ± 1.10	0.83 ± 0.50	0.55 ± 0.27	0.99 ± 0.62
	PO4^3− (mg/L)	0.81 ± 0.42	1.74 ± 1.74	3.00 ± 4.20	4.04 ± 5.25
	HCO3^− (mg/L)	2.71 ± 2.00	1.47 ± 1.09	1.93 ± 2.57	2.36 ± 1.96
	Na^+ (mg/L)	1.44 ± 2.05	1.13 ± 1.58	0.89 ± 1.03	2.29 ± 5.08
	K^+ (mg/L)	0.34 ± 0.44	0.56 ± 0.72	0.26 ± 0.32	0.17 ± 0.28
	Mg^2+ (mg/L)	0.51 ± 0.42	0.33 ± 0.35	0.12 ± 0.04	0.22 ± 0.23
	Ca^2+ (mg/L)	1.07 ± 0.93	0.51 ± 0.63	0.46 ± 0.23	0.29 ± 0.15
	Al^3+ (mg/L)	0.08 ± 0.10	0.21 ± 0.32	0.04 ± 0.03	0.04 ± 0.03
	NH4^+ (mg/L)	6.89 ± 4.86	7.35 ± 6.40	5.79 ± 5.85	5.82 ± 3.31
Aluminum Roof; N: 2; N: 3; N: 3; N: 2	Temp. (°C)	23.80 ± 1.55	23.91 ± 2.22	24.01 ± 1.04	24.37 ± 0.70
	pH	6.24 ± 0.97	6.49 ± 0.71	6.46 ± 0.63	6.30 ± 0.85
	Cond. (μS/cm)	139.75 ± 255.48	70.02 ± 87.89	123.61 ± 169.53	116.74 ± 122.84
	Turbidity (NTU)	2.02 ± 1.47	3.43 ± 2.87	2.21 ± 1.15	1.80 ± 0.70
	TDS (mg/L)	23.68 ± 29.91	27.08 ± 14.37	12.91 ± 8.20	24.01 ± 33.73
	TSS (mg/L)	7.21 ± 11.48	4.24 ± 3.13	4.54 ± 6.71	8.46 ± 14.69
	Cl^− (mg/L)	5.23 ± 10.65	3.15 ± 4.79	12.29 ± 17.45	9.98 ± 8.93
	SO4^2− (mg/L)	3.55 ± 4.16	2.81 ± 1.33	4.53 ± 4.44	4.90 ± 5.18
	NO3^− (mg/L)	5.42 ± 5.94	3.65 ± 3.91	1.86 ± 1.35	5.74 ± 7.60
	NO2^− (mg/L)	1.30 ± 1.29	0.64 ± 0.32	0.54 ± 0.45	1.06 ± 0.91
	PO4^3− (mg/L)	0.75 ± 0.41	1.23 ± 0.41	0.81 ± 0.53	0.45 ± 0.30
	HCO3^− (mg/L)	2.79 ± 2.07	1.78 ± 1.22	3.25 ± 5.83	4.31 ± 7.74
	Na^+ (mg/L)	1.55 ± 2.16	0.84 ± 1.13	0.95 ± 0.99	2.30 ± 4.98
	K^+ (mg/L)	0.50 ± 0.81	0.37 ± 0.42	0.17 ± 0.08	0.30 ± 0.50
	Mg^2+ (mg/L)	0.46 ± 0.51	0.40 ± 0.53	0.18 ± 0.11	0.43 ± 0.35
	Ca^2+ (mg/L)	1.05 ± 0.93	1.11 ± 1.19	0.47 ± 0.34	0.58 ± 0.52
	Al^3+ (mg/L)	0.13 ± 0.17	0.28 ± 0.35	0.08 ± 0.05	0.07 ± 0.04
	NH4^+ (mg/L)	7.71 ± 6.97	6.08 ± 4.95	4.95 ± 4.38	6.28 ± 3.01
Asbestos Roof; N: 2; N: 3; N: 2; N: 2	Temp. (°C)	23.86 ± 1.38	24.10 ± 1.59	24.13 ± 1.12	24.36 ± 0.91
	pH	6.47 ± 1.00	6.80 ± 0.52	6.60 ± 0.58	6.40 ± 1.12
	Cond. (μS/cm)	121.58 ± 215.52	127.51 ± 172.43	134.45 ± 195.06	156.56 ± 175.67
	Turbidity (NTU)	1.52 ± 1.38	3.93 ± 2.00	2.20 ± 1.16	1.70 ± 0.70
	TDS (mg/L)	19.87 ± 22.91	31.31 ± 19.67	14.56 ± 7.09	21.51 ± 21.98
	TSS (mg/L)	4.76 ± 7.02	4.80 ± 4.44	3.71 ± 2.90	9.39 ± 16.55
	Cl^− (mg/L)	3.55 ± 6.81	4.46 ± 7.68	10.32 ± 13.74	12.92 ± 14.92
	SO4^2− (mg/L)	3.01 ± 3.06	3.22 ± 2.33	4.02 ± 3.48	6.62 ± 6.54
	NO3^− (mg/L)	3.51 ± 4.30	3.64 ± 4.49	2.71 ± 2.01	4.76 ± 6.60
	NO2^− (mg/L)	1.04 ± 1.23	0.85 ± 0.55	0.57 ± 0.40	0.86 ± 0.49
	PO4^3− (mg/L)	0.85 ± 0.45	1.43 ± 0.40	1.12 ± 0.92	0.56 ± 0.60
	HCO3^− (mg/L)	2.32 ± 1.84	3.25 ± 5.31	3.71 ± 6.56	4.87 ± 7.33
	Na^+ (mg/L)	1.50 ± 2.24	1.29 ± 2.14	1.09 ± 1.32	2.52 ± 5.44
	K^+ (mg/L)	0.42 ± 0.65	0.60 ± 0.78	0.38 ± 0.36	0.41 ± 0.67
	Mg^2+ (mg/L)	0.44 ± 0.45	0.48 ± 0.40	0.36 ± 0.43	0.30 ± 0.34
	Ca^2+ (mg/L)	1.13 ± 1.62	0.94 ± 1.40	0.57 ± 0.49	0.49 ± 0.31
	Al^3+ (mg/L)	0.19 ± 0.36	0.27 ± 0.38	0.05 ± 0.02	0.06 ± 0.06
	NH4^+ (mg/L)	6.20 ± 5.80	6.30 ± 4.20	4.85 ± 3.57	4.56 ± 2.52
Stone-Coated Roof; N: 0; N: 2; N: 0; N: 2	Temp. (°C)		23.91 ± 2.43		24.36 ± 0.98
	pH		6.99 ± 0.25		6.68 ± 0.58
	Cond. (μS/cm)		52.21 ± 27.65		42.86 ± 33.49
	Turbidity (NTU)		3.04 ± 1.44		1.33 ± 0.91
	TDS (mg/L)		21.97 ± 11.13		17.46 ± 19.48
	TSS (mg/L)		4.17 ± 3.39		4.57 ± 5.36
	Cl^− (mg/L)		3.19 ± 5.67		10.30 ± 12.31
	SO4^2− (mg/L)		3.14 ± 1.88		2.63 ± 1.14
	NO3^− (mg/L)		3.04 ± 2.69		5.53 ± 6.26
	NO2^− (mg/L)		0.65 ± 0.43		0.93 ± 0.63
	PO4^3− (mg/L)		0.90 ± 0.45		0.34 ± 0.30
	HCO3^− (mg/L)		1.63 ± 0.86		2.56 ± 2.24
	Na^+ (mg/L)		0.68 ± 1.31		1.24 ± 2.89
	K^+ (mg/L)		0.28 ± 0.39		0.22 ± 0.41
	Mg^2+ (mg/L)		0.54 ± 1.01		0.24 ± 0.13
	Ca^2+ (mg/L)		1.16 ± 2.20		0.49 ± 0.50
	Al^3+ (mg/L)		0.23 ± 0.29		0.04 ± 0.03
	NH4^+ (mg/L)		4.91 ± 3.07		6.10 ± 3.67

Table 1.

Physiochemical composition of rainwater samples from various locations and sampling surfaces.

N: Number of samplings across different sampling locations.

The turbidity value ranged from 0.41 to 5.25 NTU, for different sampling surfaces. The mean turbidity for all locations reveals that for Ogale, aluminum roof was highest compared to asbestos roof, which was the least, for Rumuodomaya/Rumuodome, Diobu and Chokocho, asbestos, zinc and aluminum roofs was highest, while ambient was least. The values of TDS obtained ranged from 3.45 to 98.12 mg/L for different sampling surfaces. The mean TDS assessed for all locations revealed that ambient was least, while aluminum roof was higher for Ogale and Chokocho, Rumuodomaya/Rumuodome and Diobu produced higher TDS values for asbestos and zinc roof. The values of TSS obtained ranged from 0.51 to 40.61 mg/L, as the mean TSS assessed in terms of sampling surfaces revealed that zinc roof was highest in TSS concentration compared to ambient for Ogale, Rumuodomaya/Rumuodome and Diobu, while Chokocho was highest for asbestos roof compared to ambient that was least.

3.2 Anionic composition of rainwater

Table 1 shows the mean and standard deviation (SD) of anionic composition from rainwater samples from different locations respectively. Chloride values ranged from 0.11 to 50.80 mg/L for different sampling surfaces in all locations. The mean chloride concentration revealed that zinc roof was highest for Rumuodomaya/Rumuodome, Diobu and Chokocho, while for Ogale - aluminum roof was highest as ambient was least for all locations assessed. The values of sulfate ranged from 0.40 to 23.97 mg/L for different sampling surfaces and locations respectively. The mean sulfate concentration agreed with chloride assessment for all locations and sampling surfaces. Nitrate values ranged from 0.12 to 22.69 mg/L for different sampling surfaces respectively. The mean nitrate concentration reveals that the aluminum roof was highest for Ogale and Chokocho, zinc and asbestos roof was highest for Rumuodomaya/Rumuodome and Diobu as ambient was least for all locations.

Nitrite values ranged from 0.08 to 5.68 mg/L for different sampling surfaces which was agreed with nitrate assessment for all locations and sampling surfaces. Phosphate values obtained ranged from 0.01 to 2.73 mg/L as the mean concentration of phosphate disclose that asbestos roof was highest, while ambient was least for Ogale, Rumuodomaya/Rumuodome and Diobu, for Chokocho, zinc, and stone-coated roof was highest and least accordingly. Hydrogen carbonate values obtained ranged from 0.15 to 21.83 mg/L, hydrogen carbonate discloses that asbestos roof was highest for Rumuodomaya/Rumuodome, Diobu and Chokocho – aluminum roof was highest for Ogale, while ambient was least for all locations.

3.3 Cationic composition of rainwater

Table 1 shows the mean and standard deviation (SD) of light metals composition from rainwater samples from different locations respectively. The sodium values obtained ranged from 0.004 to 13.76 mg/L for different sampling surfaces respectively. The mean concentration of sodium reveals that asbestos roof was highest for Rumuodomaya/Rumuodome, Diobu and Chokocho, aluminum roof was highest for Ogale, ambient was least for all locations. The potassium values obtained ranged from 0.002 to 2.30 mg/L for different sampling surfaces respectively. The mean potassium concentration agreed with the sodium assessment for all locations and sampling surfaces. The magnesium values obtained ranged from 0.002 to 2.82 mg/L for different sampling surfaces, while the mean concentration of magnesium reveals that zinc, stone-coated, asbestos, and aluminum roofs were highest for Ogale, Rumuodomaya/Rumuodome, Diobu and Chokocho as ambient was least.

The calcium values obtained ranged from 0.001 to 4.76 mg/L for different sampling surfaces in Ogale, Rumuodomaya/Rumuodome, Diobu, and Chokocho respectively, as the mean calcium concentration showed asbestos roof was highest for Ogale and Diobu; stone-coated and aluminum roof was highest for Rumuodomaya/Rumuodome and Chokocho as ambient was least. Ammonium values obtained ranged from 0.009 to 20.22 mg/L, as the aluminum roof was highest for Ogale and Chokocho, zinc roof was highest for Rumuodomaya/Rumuodome and Diobu, while ambient was least for all locations. Aluminum values obtained ranged from 0.0005 to 1.06 mg/L, this showed that asbestos roof was highest for Ogale and Chokocho, as aluminum roof was highest for Rumuodomaya/Rumuodome and Diobu as ambient was least across all locations.

3.4 Neutralization factor

The neutralization factor refers to the ability of certain ions (anions and cations) to influence the pH of a solution when they undergo chemical reactions that affect the concentration of hydrogen ions (H+) or hydroxide ions (OH-) in the solution. Figure 2 shows the impacts of selected metals to neutralize anions from rainwater samples across different locations determined from Eq. (1). Across the different sampling surfaces, NF shows that NH₄⁺ played major role due to release by fertilizers industry and other pollutants from industrial emissions like aerosols or secondary pollutants like sanitary landfills, while Ca²⁺, Na⁺ and Mg²⁺ contribute to NF by releases from limestone mines and natural soil. K⁺ and Al³⁺ played minor roles in ionic species. This draws to conclude that high concentration of NH₄⁺ with contribution from Ca²⁺, Na⁺ and Mg²⁺ on neutralization factor, thus raising the pH values in rainwater.

Figure 2.

Neutralization factor of cationic to anionic interactions.

3.5 Hierarchical cluster analysis

Hierarchical clustering involves the process of merging similar data points or clusters into larger clusters until all data points are grouped into a single cluster (agglomerative) or until each data point is its own cluster (divisive), as the data can be visualized as a dendrogram (tree-like structure) [32, 33]. Figure 3 shows the hierarchical clustering analysis using the correlation coefficient for the set of physiochemical data available for different rainwater surfaces and locations. As shown in Figure 3a–e, the data set showed a heat map where conductivity was highest across all rainwater surfaces (ambient, zinc sheets, aluminum sheets, asbestos sheets, stone-coated sheets) respectively, while locations matrix depict that ambient and aluminum roofing sheet data formed similar forming clusters for Ogale, Eleme LGA and Chokocho, Etche LGA and vice versa for zinc and asbestos sheet for Chokocho, Etche LGA and Diobu, Port Harcourt LGA showing similar correlation with Rumuodomaya/Rumuodome Obio-Akpor LGA. The dataset for all rainwater surfaces showed that shorter branches represent a higher similarity between clusters, while longer branches represent a lower similarity, while the heights of the branches indicate levels of dissimilarity or no form of relationship with each merged cluster. Each merged cluster shows a strong or close relationship among themselves.

Figure 3.

Hierarchical clustering of rainwater from different sampling surfaces and locations: (a) ambient rainwater, (b) Zinc roofing sheets (c) Aluminium roofing sheet (d) Asbestos roofing sheet, (e) Stone coated roofing sheet.

3.6 Factor analysis

Factor analysis is a statistical technique used to identify underlying factors or dimensions that explain the patterns of correlation among a set of variables, as conducting factor analysis with a rotated varimax rotation, we aim to simplify the data by identifying latent factors that explain the interrelationships among these physiochemical parameters. Table 2 shows the factor analysis conducted for different roofing sheets in the aforementioned sampled locations shown below with significance levels of p < 0.05 and p < 0.01 with the threshold for determining the statistical significance of the factor loadings, as Table 2 gives a total of five (5) varimax factors (F1-F5) with cumulative variance at 73.77%, as F1 is associated with elemental ions and conductivity that suggest the dissolution of minerals and salts from soil and rocks leading to increased total solids (as dissolved solids (TDS) and suspended solids) as shown that contribute to higher conductivity and presence of ions in rainwater. F2 suggests turbiditic influences that are from industrial releases that contribute to particulates in that atmosphere and eventually into rainwater. F3 showed a negative correlation matrix with Ca²⁺, Mg²⁺, and Al³⁺ that are due to different polluting or interacting sources from mining operations or marine sprays that affect acidity in rainwater [34]. F4 shows the influence of nitrogen oxides (NOx) from industrial and fossil fuel combustion resulting in acid rain in tandem with ammonia from organic decomposition from waste dumps released into the atmosphere turning to ammonium (NH⁴⁺) in the presence of rainwater molecules [22]. F5 represents the combination of pH and temperature that are positively correlated which are due to the potential impact of seasonal variation affecting rainwater composition. Table 3 gives the factor analysis (rotated varimax) of zinc roofing sheets in which the cumulative variance was 61.36% with four (4) factors components, as F1 gave the most variance at 25% with high positive loadings for variance like Conductivity, Total Suspended Solids), Cl⁻ (Chloride), SO₄²⁻ (Sulfate), NO₃⁻ (Nitrate), PO₄³⁻ (Phosphate), HCO₃⁻ (Bicarbonate), Na⁺ (Sodium), K⁺ (Potassium), and NH₄⁺ (Ammonium), which are due to atmospheric deposition of pollutants, urban runoff, and weathering that impacts water quality and ionic conditions, potentially indicating pollution or specific processes affecting zinc roofing sheets. F2 accounts for 15% with positive loading with turbidity and aluminum (Al³⁺) and negative loading for temperature. F3 and F4 show 10.41% and 10.96% respectively. Table 4 shows the rotated varimax with 62.43% cumulative variance for aluminum roofing sheets, as F1 gives 21.62% with contributions from both dissolved and suspended solids, ions like Cl⁻, SO₄²⁻, NO₃⁻, HCO₃⁻ and several cations (Na⁺, K⁺, Mg²⁺, and NH₄⁺) that shows strong correction with conductivity at 0.74% with probable source from industrial discharges, agricultural runoff, and atmospheric deposition of pollutants. F2 produced a variance of 13.93% with strong correction with Mg²⁺, Ca²⁺, and Al³⁺ which are associated with construction, mining, and soil resuspension [1] that can lead to increasing turbidity and dissolved solids. F3 shows influence from particulates from natural and anthropogenic releases. F4 was influenced by nitrite-related pollutants. Table 5 shows the factor analysis with the influence of rainwater on asbestos roofing sheets with a cumulative variance of 61.30%. F1 shows 24.47% for a strong correlation with cations and anions from industrial discharges, agricultural runoff, and atmospheric deposition of pollutants affecting asbestos roofing sheets. F2 accounts for 12.63% with a strong relationship for TDS, turbidity, phosphate, and potassium which relate to the presence of specific ions and pollutants, possibly from industrial sources or weathering of roofing materials. F3 and F4 showed a negative correlation due to different biotransformation and migration rate. Table 6 shows the factor analysis of rainwater from stone-coated roofing sheets as cumulative variance was at 70.77%. F1 shows significant loading for variance like TDS (Total Dissolved Solids), TSS (Total Suspended Solids), Cl⁻ (Chloride), NO₃⁻ (Nitrate), HCO₃⁻ (Bicarbonate), Na⁺ (Sodium), K⁺ (Potassium), and NH₄⁺ (Ammonium) at 25.08% suggesting influences from various anionic and cationic influences leading to increased total solids (dissolved and suspended) and conductivity at 0.60 leading to salt formation and taste change. F2 shows the influence of weathering, agricultural and industrial activities from phosphate (PO₄³⁻), sulfate (SO₄²⁻) and aluminum (Al³⁺). F3 and F4 account for 19.36% and 10.47% of the variance with a negative correlation.

Table 2.

Factor analysis of ambient rainwater.

Table 3.

Factor analysis of rainwater from zinc sheet.

Table 4.

Factor analysis of rainwater from aluminum sheet.

Table 5.

Factor analysis of rainwater from Asbestos sheet.

Table 6.

Factor analysis of rainwater from stone-coated sheet.

4. Discussion

Having assessed rainwater from varying locations and sampling surfaces, pH steadily increased from acidity to alkalinity by dilution potential over more rainfall events throughout the sampling months, pH is usually a quick indication of water quality as it shows the impact of external contaminants in rainwater. The presence of dissolvable anions such as oxides of sulfur, nitrogen, chloride and phosphates and hydrogen carbonate impacts on pH of rainwater, which agrees with Okpoebo et al. [35]; Gav et al. [36]; Al-Amoush et al. [37] physiochemical assessment, which they attributed to releases from industrial, automobile, and gas flares, which leads to degradation of metallic surfaces over long period. Low pH values influence high reactivity, and dissolution of metallic surfaces, as metallic ions leach out into rainwater influencing taste and color [1]. This constant degradation of metallic surfaces is due to different chemical reactions such as redox, precipitation, hydrolysis, and neutralization, therefore producing metallic ions that are released into rainwater. Although other external factors such as transboundary pollutants, marine contribution, burning of sanitary fields, mining activities and agricultural activities can further initiate reaction and leaching of metallic surfaces thus influencing the esthetic quality of rainwater.

Temperature is an esthetic measure of drinking water, although it conforms to the WHO recommended range of 20–30°C thereby posing no risk to human health [38]. Temperature is due to convectional cooling and heating of the earth’s surface, absorbed by different surfaces and dissipated as infrared from anthropogenic inputs such as industrial cooling towers, power plants and gas flares around varying sampling locations in Rivers states. The relatively low temperature is attributed to the time of sampling, which was done in the morning and evening after rainfall events. Elevated temperatures lead to an increase in toxicity and bioaccumulation in organisms [39]. Temperature has a strong influence on the solubility and conductivity of ions, dissolved oxygen and contents and biological activities in rainwater, consequently leading to mineralization (chemical reactions) over a period [40]. Mineralization is usually from cationic and anionic interactions, which form precipitate or dissolved particles. This mineralization of particles forms a rationale for turbidity and total solids (a combination of dissolved solids and suspended solids), which increases ionic mobility, temperature, and pathogen growth from anthropogenic inputs [38, 39, 40]. Turbidity and total solids impact the taste, odor and color of rainwater, as consumption of contaminated rainwater has the potential to cause diseases from microorganisms such as diarrhea, dysentery, stomach upset, and purging in humans [38, 41]. The cationic and anionic composition of rainwater influences pH, temperature, conductivity, turbidity, total solids in rainwater quality over a period due to chemical reactions taking place as rainfall deposition takes place with inputs from collection surfaces for domestic and other uses. Chloride ions combine with varying cations such as calcium, magnesium, sodium, potassium, aluminum, and ammonium ions forming salt-based rainwater that impacts the taste of rainwater, which can be attributed to marine and sea-sprays contribution, soil resuspension as seen in neutralization factor (NF) assessment presented in Figure 3, One can iterate that pH was influenced from releases of these contaminants in rainwater [42]. Across the different sampling surfaces, NF shows that NH₄⁺ played major role due to release by fertilizers industry and other pollutants from industrial emissions like aerosols or secondary pollutants like sanitary landfills, while Ca²⁺, Na⁺ and Mg²⁺ contribute to NF by releases from limestone mines and natural soil. K⁺ and Al³⁺ played minor roles to ionic species. This draws to conclude that high concentration of NH₄⁺ with contribution from Ca²⁺, Na⁺ and Mg²⁺ on neutralization factor, thus raising the pH values in rainwater [20].

Sulphate, nitrate, nitrite, and phosphates in rainwater gives the eutrophication potential of water quality as it led to microbial growth and releases of obnoxious odor over a period, which was evident in the factor analysis [35]. The oxides of these anions (SOx, NOx, COx, POx) cause climate change, photochemical smog and acid rain from industrial emissions and gas flaring, waste combustions and automobile emission that causes eye irritation, skin reaction with burning sensation, also high concentration of sulfate has laxative and dehydration effect with presence of cations such as magnesium or calcium [42]. Nitrate and nitrites cause methemoglobinemia in bottle-fed babies [38, 41]. Marine contribution is probable with sodium, which therefore shows that sulfate, chloride, nitrate, and nitrite has negative impacts on humans from consumption of rainwater without treatment [43]. Hydrogen carbonate produced correlation with chloride, sulfate and nitrate is attributed to metrological and anthropogenic influences, which increases sediment, turbidity, and taste of rainwater [44].

The wet or dry deposition of anions influences metallic leaching on metallic surfaces over a long period, which thus affects rainwater quality. Sodium, potassium, magnesium, calcium, aluminum, and ammonium plays an important role as it influences, pH, conductivity, turbidity, and total solid potential of water with the influence of anions. They also influence taste, water hardness, dissolved oxygen, and color of water [45, 46]. Magnesium and calcium are an indication of water hardness due to the presence of carbonate, sulfate, and chloride as it was evident in factor analysis conducted, which showed high correlation ranging between 0.63 and 0.99 for all roofing sheet, attributed to influence of soil or dust resuspension from weathering, road construction limestone mining [30]. The correlation of sodium and potassium reveal strong correlation with chlorine, sulfate, nitrate, nitrite, and hydrogen carbonate as seen in all F1 (Table 2) of all rainwater surfaces suggesting marine contribution from the Atlantic Ocean, mining activities, soil resuspension, burning activities of biomass, agricultural residues, utilization of charcoal for cooking fuels and industrial emission from gas flares, power plants and heat exchangers that impact pH of rainwater [9, 10, 26].

Ammonium showed strong correlation to sulfate, nitrate, and nitrite, which suggest that in rainwater ammonium sulfate, ammonium nitrate and ammonium nitrite that is due to dissolution of aerosols and secondary pollutant, agriculture, and fertilizer emissions [47]. Ammonia is produced from microbial degradation of organic components in water and atmospheric releases from sanitary landfills, sewage, and industrial effluents, which impacts taste and odor, at lower pH, it combines with rainwater forming ammonium and hydroxide ions [38]. Nitrogen composition in the atmosphere is 78%, which is evident in the high correlation with other cations and anions in the environment. Nitrogen content is divided into organic (Kjeldahl nitrogen, ammonium) and inorganic (nitrate, nitrite, and ammonia) forms that is released by natural and anthropogenic sources, which impacts on the composition of nitrogen. Inorganic nitrogen (NOx) across different sampling surfaces were dominant to interact than organic nitrogen (NH₄⁺), due to loss of ammonia gas from sample bottles thereby having lower total nitrogen content. Presence of submicron particles of minerals could have increased pH during transport of samples which led to loss of ammonia (NH₃); although, other anomalies from underestimation from spectroscopic method could have led to lower value of total nitrogen content [43]. Inorganic form of nitrogen is via biomass burning, fossil fuel combustion, soil releases, lightening, aviation, volcanic eruptions etc. Organic forms of nitrogen are from microbial actions of dead matter and industrial effluent waste, which forms ammonia as by-product. Nitrogen oxides combines with water vapor in the atmosphere to form acids which when precipitation takes place, it led to lower pH as seen from the Table 1 with high concentration of NO₃⁻, NO₂⁻ and NH₄⁺ respectively. Due to presence of aerosols from interactions with water vapor and other particulates, there is potential for nitrogen groups to combine forming (NH₄)HSO₄, (NH₄)₂SO₄, NH₄NO₃ and NH₄NO₂ that can dissolve into cloud droplets forming smog with sulfate groups which has adverse effects on health issues such as asthma attack, difficulty to breath well [48]. According to Ohara, [49], presence of free radicals or reactive ions of anionic oxide can react with ozone causing decrease in ozone content by influence of ultraviolet radiation from the sun.

4.1 Impact of sampling surface on rainwater quality and leaching matrix

Rainwater collected from diverse sampling surfaces (zinc roof, aluminum roof, asbestos roof, and stone-coated roof), it used to calculate its influence on ambient rainwater samples assessed using the formula:

$$\text{Influence of ambient to sampling surfaces}\left(\mathrm{A}-\mathrm{SS}\right)=\frac{{{\mathrm{C}}_{\mathrm{x}}}_{\text{ambient}}}{{{\mathrm{C}}_{\mathrm{x}}}_{\text{roof of interest}}}\times 100\%$$E2

$$\text{Infuence of external contaminant}=100-\left(\mathrm{A}-\mathrm{SS}\right)$$E3

Where C_x = Concentration of parameters of interest

The concentration of anions and cations with microbial input has great influence on physical parameters of rainwater quality in comparison to different sampling surfaces and location assessed. The presence of particles in the atmosphere released from anthropogenic sources reacts in the atmosphere forming chemical products that are transboundary before wet deposition (rainfall) or dry deposition (soil resuspension and particulate matter) takes place [50, 51]. Several factors impact the physicochemical concentration across sampling surfaces for rainwater samples such as: the number of particles deposited on different surfaces, age of metallic surfaces, rate of deterioration (leaching), rainfall intensity and duration of wet deposition [52]. In addition, one cannot conclude that metal leaching is from respectively surfaces (roofing sheets) as external contaminant from anthropogenic releases has immersed impact on rainwater quality [53, 54].

A preliminary assessment showed that as anions concentration increased, it leads to high degradation (metal leaching) of the diverse sampling surfaces due to its chemical composition as given in Table 1, which was prevalent in quantitative assessment of cations in the study. For zinc roof, dissolved zinc metal in relation to ambient rainwater ranged from 41.85 to 84.95% for all locations, which is lower than Heijrick et al. [55] who reported between 96 and 99.9%; other metals cumulatively ranged between 0 and 96.67% for all locations. For aluminum roof, dissolved aluminum metal in relation to ambient rainwater samples ranged between 29.10 and 36.19% for all locations, which was lower than chemical composition of aluminum (Table 7) between 99.0 and 99.60%, as cumulative metals ranged between 0 and 93.32% respectively. For asbestos roofs, dissolved magnesium metal ranged from 21.99 to 71.12%, which was lower than chemical composition of asbestos that contains about 99.95% of hydrous magnesium silicate, other metals ranged from 0 to 100%. For stone-coated roof, dissolved aluminum for Rumuodomaya/Rumuodome and Chokocho are 33.95% and 36.59%, which is lower than 55% aluminum as stated in Table 1, dissolved zinc metal is 87.57% and 93.02% that is higher than 43% zinc composition of stone-coated roof, other metals ranged between 1.44 and 93.02% respectively [60].

Table 7.

Summary of chemical composition of roofing materials.

The cumulative impact of different sampling surfaces (zinc, aluminum, asbestos and stone-coated) with ambient (open-air) were conducted for various parameters are shown in Table 8. Several external issues could influence the results from instrumental error, contaminants deposited before rainfall events, amount of anthropogenic contaminant releases in terms of transboundary movement and atmospheric physiochemical reaction [61, 62, 63]. Having assessed these, aluminum and zinc roofs did not leach higher metal concentrations compared to asbestos and stone-coated roofs, and so rainwater must be filtered and treated prior to domestic use. For asbestos, has been categorized by the United States Environmental Protection Agency, USEPA [25] and World Health Organization, WHO [64] as being carcinogenic to human health: as such, rainwater collection from it should be discontinued to prevent possible illness and death for both adults and children.

	Correlation to Ambient rainwater			Influence of External contaminants
Sampling surface	OG	R/R	DB	CK	OG	R/R	DB	CK
Zinc	68.58	64.48	63.39	71.78	31.42	35.52	36.62	28.23
Aluminum	59.63	76.24	64.58	76.13	40.37	24.19	35.42	23.87
Asbestos	74.62	61.5	60.64	75.35	25.38	38.47	39.36	24.65
Stone-coated	—	78.93	—	77.36	—	21.07	—	25.81

Table 8.

Average of physicochemical parameters.

OG: Ogale, R/R: Rumuodomaya/Rumuodome, DB: Diobu, CK: Chokocho.

5. Conclusion

The study analyzed the impact of pH precursors in rainwater quality, which are predominantly from roofing surfaces and physiochemical influence in rainwater matrix that interacts across a range of acidity and alkalinity as seen with the pH range of 4.70–7.90. It is critical to also note that as pH decreases (greater acidity), metal solubility increases and the metal concentrations in runoff also increase and vice-versa that was predominant from early rain period to late rain that is alkaline rainwater, as the physicochemical parameters in compliance to study aims provide the following findings:

1. Most of the physicochemical parameters analyzed from diverse sampling surfaces (ambient, zinc roof, aluminum roof, asbestos roof, and stone-coated roof) showed varying concentrations of rainwater quality.

2. The rainwater quality of the sampling surfaces decreased from early rain to late rain due to more rainfall dilution taking place except for temperature (18.70–25.90°C) that increased in progressive medium, as rainwater collected contained contaminants, which implies rainwater must be treated before portable and convenient use.

3. The chemical composition and age of roofing materials have a tremendous impact on rainwater quality as anions (Cl⁻: 0.12–50.80 mg/L; SO₄²⁻: 0.40–23.97 mg/L; NO₃⁻: 0.08–5.68 mg/L) were deposited in increasing progression or vice versa via wet or dry deposition, it initiates cation (Na⁺: 0.004–13.76 mg/L; Mg²⁺: 0.002–2.82 mg/L; Ca²⁺: 0.001–4.76 mg/L) leaching on the roofing sheets as it ages.

4. In terms of study locations, as particulate contaminants are deposited on different sampling surfaces from natural and/or anthropogenic releases via geographical and transboundary movement, it increases total solids (TDS: 3.45–98.12 mg/L; TSS: 0.51–40.61 mg/L), temperature (18.70–25.90°C), and turbidity (0.41–9.77 NTU) and decreases the pH of rainwater quality.

5. Aluminum and zinc roofs are recommended for rainwater utilization but must be filtered and pre-treated before use, stone-coated and asbestos roofs are not the best surfaces for rainwater harvesting, and as such, rainwater from asbestos roofs should not be utilized due to their negative health implications on humans.

Rainwater is pure but due to the negative impact from contaminant release into the atmosphere, it interacts with rainwater thereby influencing the quality of rainwater portability. Rainwater collected from diverse sampling surfaces shows the satisfactory concentration in terms of the physicochemical properties of rainwater as dilution takes place from early rain to late rain, hence, further purification and treatment must be done to safeguard the health and well-being of humans. The negative impacts from anthropogenic releases into the atmosphere, which interacts with rainwater cause adverse health-related issues for man and the environment.

Acknowledgments

We would like to express our sincere gratitude to all those who have contributed to the completion of this research work, as their support, guidance, and encouragement have been invaluable in making this research possible. Also, we acknowledge anonymous reviewers and editors of this book series who were involved in the entire process.

Conflict of interest

All the authors declare no conflict of interest regarding this manuscript.

Notes/thanks/other declarations

We are thankful for the invitation to contribute to Rainfall - Observations and Modeling and other anonymous reviewers for their assistance. This research work was self-funded, as the authors declare no conflict of interest.