uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness

*Innocent Rangeti and Bloodless (Rimuka) Dzwairo*

## **Abstract**

One of the main challenges facing the potable water production industry is deterioration of the quality of raw water. Drinking water that does not meet quality standards is unfit for consumption. Yet, this quality is a function of various factors, key among them being quality of the raw water from which it is processed. This is because costs related to potable water treatment are related to the nature of raw water pollutants and the degree of pollution. Additionally, survival of aquatic species depends on self-purification of the water bodies through attenuation of pollutants, therefore, if this process is not efficient it might result in dwindling of the aquatic life. Hence, this chapter presents spatial and temporal water quality trends along uMngeni Basin, a critical raw water source for KwaZulu-Natal Province, in South Africa. As at 2014 the basin served about 3.8 million people with potable water. Results from this study are discussed in relation to uMngeni River's health status and fitness for production of potable water treatment. Time-series and box plots of 11 water quality variables that were monitored at six stations over a period of eight years (2005 to 2012), were drawn and analysed. The Mann Kendall Trend Test and the Sen's Slope Estimator were employed to test and quantify the magnitude of the quality trends, respectively. Findings showed that raw water (untreated) along uMngeni River was unfit for drinking purposes mainly because of high levels of *Escherichia coli.* However, the observed monthly average dissolved oxygen of 7 mg/L, that was observed on all stations, suggests that the raw water still met acceptable guidelines for freshwater ecosystems. It was noted that algae and turbidity levels peaked during the wet season (November to April), and these values directly relate to chlorine and polymer dosages during potable water treatment.

**Keywords:** water quality trend, uMngeni, river health, treatability, spatio temporal, water quality, monitoring

## **1. Introduction**

Water is an integral natural component of any ecosystem as well as a social and economic good [1, 2]. However, utilisation of this resource is highly influenced by its quantity and quality. For example, due to limited capacity and the prohibitive cost of treatment chemicals, water treatment utilities and municipalities, especially in developing countries, struggle to provide water for domestic use. This is particularly severe in rapidly growing informal settlements and peri-urban areas. A key factor affecting the production of adequate potable water has been deterioration of

the quality of raw water sources [3–5]. This has mainly been attributed to anthropogenic factors [6] such as rapid urbanisation, industrial waste [7], population growth, and climate change among others. The protection of drinking water sources is thus a critical step in ensuring the production of drinking water at a reasonable price. This is also made possible through the participation of various stakeholders with varying knowledge on water science. The research sector has lately been playing a pivotal role in ensuring that water treatment utilities are updated regarding the state of river basins. Hence, failure to incorporate water quality data in water resource management and infrastructure planning challenges the ability of decision makers to respond to the increasing economic and social demands of associated use and consumption of poor-quality water.

The management of water quality is a complex process because of the diversity of polluting parameters as well as their interaction and resultant characteristics within a polluted environment [2]. An important aspect of water quality assessments for drinking water source protection is identifying changes in water quality trends and specific pollution sources, which are an integral process during appraisals of decisions related to environmental protection [8, 9]. To assist with these appraisals, long-term water quality data are employed as they are useful for detecting the spatial and temporal changes in the state of aquatic environments, when interpreting effectiveness of measures aimed at [10]. Water quality monitoring can thus assist to reveal the history of a water body and further give a reflection of the current status. Water quality trends analysis present a simple method of determining the long-term fitness of a catchment for a given use. The chosen technique is expected to assist with identifying how the concentration of a given water parameter changed over a period of time. With this method, one can determine the long-term fitness of a water body for specific uses such as potable production and protection of aquatic biodiversity. The success of a trend analysis depends on the main principles (1) acquisition of water quality data from a properly-designed monitoring program, (2) application of appropriate statistical methods for trend detection, and (3) a good understanding of relevant water quality parameter correlations [11].

It is known that rivers are often the ultimate sinks of effluent and other forms of pollution [12, 13]. Various processes, for example, soil erosion, disposal of waste from urban-industrial and domestic activities including fertiliser run off from agricultural activities, cause the deterioration of water quality [2]. Specifically, polluted water is a threat to flora and fauna and is unsuitable for any human need [14]. Thus, consumption of water that is polluted results in ill health which, in extreme cases, can cause death [4]. Pathogens present in drinking water including many bacterial, viral and protozoan agents have been reported in various research articles to cause illness and death to various degrees and in different year timelines [15–17]. Some specific health and water-related problems have statistically was grouped by Khan, Tareen [18] as follows: hepatitis A (32%–38%), gastroenteritis (40%–50%), hepatitis B (16%–19%), hepatitis C (6-7%), dysentery (28–35%), and diarrhea (47%–59%).

Each water quality variable (parameter) also has an effect, either beneficial or detrimental on aquatic organisms. The effect depends on whether they act synergistically or antagonistically [12]. To aquatic organisms, this effect is influenced by the tolerance limit of that organism. However, the fundamental principle of pollution prevention is to stop or minimise anthropogenic activities, which result in the contamination of water systems. In determining pollution prevention strategies, it is important to firstly identify the key pollutants of concern [19]. South Africa is not exceptional to the current worldwide deterioration of the quality of river waters. It is perceived as one of the major impediments to South Africa's capability to provide sufficient water of appropriate quality to meet its current needs and to ensure sustainable water provision for the future [20]. The quality of water directly affects its

#### *uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

use, such that poor quality water is less available for various uses. An understanding of the temporal and spatial changes in the water quality of rivers is critical for water resource protection and management.

During the production of potable water, several chemicals, for example, disinfectants, coagulants and oxidants, are added at various stages of the process [21], the objective being to produce water that is biologically, chemically and aesthetically acceptable. The cost of chemicals is a major component of operating a potable water production plant [22, 23] although the types and quantities vary depending on a range of factors such as incoming raw water quality and the treatment technology [21]. Because water treatment costs determine the pricing of potable water, it means that ultimately cost presents a barrier regarding access to safe and adequate water for drinking and sanitation purposes, especially for poor communities [5, 24]. Additionally, the measured water quality variables, for example, turbidity and pH, have either a direct or indirect effect on the dosage and performance of the treatment chemicals. One condition is when there is high pH water, which will require a comparably lower coagulant dosage since coagulant optimisation is at high pH values. On the other hand, an elevated turbidity level increases coagulant dosage. Coagulant is a chemical that conglomerates dissolved pollutants into flocs that can then be removed using filtration and sedimentation [25]. Thus, the cost of potable water treatment due to diminished water quality represents an important component of the societal costs of water pollution [24, 26]. Therefore, finding timely, cost-effective, and appropriate solutions that address water pollution and its resulting challenges is critical.

Hence, the main objective of this chapter was to highlight water quality trends in uMngeni Basin for the period 2005 to 2012. The focus was on (1) how water quality variability impacted chemical dosage during potable water production at Durban Heights and Wiggins Water Treatment Plant and (2) determining how the quality variability affected the overall freshwater ecosystem health within the basin, using specific parameters. The following sections describe uMngeni Basin, provide the methods employed during analysis as well as results and conclusions drawn.

## **2. Study area**

uMngeni River (232 km) and its basin (4432 km2 ) are the primary source of raw water that is then treated to potable use for a population of almost 6.7 million people [27] within and around Durban Metro as well as Pietermaritzburg City, in KwaZulu-Natal (KZN) Province, South Africa (**Figure 1**) uMgeni Water, a State-Owned Entity, is the biggest 'user' of raw water in the uMngeni Basin as it produces 472 million cubic meters of potable water per year for commercial, industrial and domestic use along the basin and its adjacent coastal areas [27]. The catchment receives an annual precipitation of 410–1450 mm, mean annual runoff which varies from 72 mm to 680 mm and a mean annual evaporation of 1360–2040 mm (CSIR., 2011).

uMngeni Basin supports a diverse range of livelihood activities which contribute approximately 16% to the gross national product of South Africa [28]. Although the province covers a small portion of the country's land area, the province contributes almost 30% to the national agricultural output and, therefore, significantly bridges food security in South Africa. A consequence of this development has been high levels of pollutants entering the water resources system and getting flushed out to sea.

The basin's main river rises close to the uMgeni Vlei, near Fort Nottingham at an altitude of 2071 m (**Figure 2**). The land surrounding the headwaters is mainly used for forestry plantation and agriculture [29]. Most of the farmers practice dairy, piggery and maize production [29]. As a result of many economic activities, damage to the

**Figure 1.** *South Africa, KwaZulu-Natal Province and uMngeni Basin plus four dams.*

#### **Figure 2.**

*uMngeni River and dams along its course, showing dam inflows and outflows.*

headwater wetlands has been reported [30]. The removal of riparian vegetation and invasion by exotic flora has also contributed to erosion of the riverbank throughout the upper uMngeni River. Four major dams namely; Midmar, Albert Falls, Nagle and

#### *uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

Inanda are located along the river course as it flows towards the Indian Ocean. Except for Albert Falls, raw water is abstracted from the other three dams by Umgeni Water Amanzi, for the purpose of producing potable water. Because Umgeni Water Amanzi's primary business depends on the quality of raw water, the treatment utility works in conjunction with the Department of Water and Sanitation (DWS) to monitor and manage the dams. The monitoring program has generating a vast amount of data that could be used to improve management of the basin, overall. This chapter is based on the analysis of historical data, which was obtained from uMgeni Water (2013).

*Station 1: Midmar Dam Inflow (MDI):* The station is located at the mouth of Midmar Dam. It measures the quality of inflow that helps to reflect the polluting activities upstream the river. Literature has cited agricultural activities runoffs and sewage bursts from low-cost settlements such as Mpophomeni, as significant polluting activities in the upstream of this dam [31]. Intensive livestock production and its related activities like cattle grazing, poorly managed dairies and piggeries, are the dominant sources of faecal pollution in Midmar and Albert falls dam catchments.

*Station 2: Midmar Dam Outflow (MDO):* The station is located where the dam re-joins river. It measures the quality of water before being discharged into uMngeni River. This helps reflect the natural purification efficiency of the dam. Since treatment plants generally abstract water of the best quality, this station should indicate the quality expected at Midmar Water treatment plant.

*Station 3: Nagle Dam Inflow (NDI):* The station is located downstream of Albert Falls Dam, which acts as reservoir for the former. Since it is located at the mouth of Nagle Dam, it measures the inflow from the river and thus helps reflect the pollution activities and river health status between Albert Falls and Nagle dams.

*Station 4: Nagle Dam Outflow (NDO):* The station is located where Nagle Dam re-joins uMngeni River. The quality of water at this station is expected to give an overview of the raw water being abstracted for treatment at Durban Heights.

*Station 5: Inanda Dam Inflow (IDI):* The station is located at the mouth of Inanda. It basically reflects the pollution activities from Nagle Dam to Inanda Dam. It gives information on the pollution activities arising due to activities by the rural community in the Valley of Thousand hills. Inanda Dam has been reported to experience some periods of elevated algal counts.

*Station 6: Inanda Dam Outflow (IDO):* The station is located at the outflow of Inanda into uMngeni River. When compared with its subsequent inflow station, it helps reflect the capacity of the dam to naturally purify water due to retention. Since treatment plants generally abstract from the best quality point, this station is expected to show the treatability of water at Wiggins treatment plant.

Catchment water quality monitoring involves the analysis of various parameters such as biological pathogens (e.g. *E. coli*), physical parameters (e.g. conductivity)*,* metals, organic pollutants, turbidity, total suspended solids and pH [32]. These parameters should fall within specified guideline values for the raw water to be deemed safe for use. Studies have reported that statistic analysis of water quality data, is mainly affected by the nature of the data-sets, which are often non-monotonic trends, non-linearity, non-normally distributed, exhibit seasonal variations and have time spacing that is uneven [9, 33, 34]. This approach informed on the methods and materials for this study.

## **3. Material and methods**

Eight-year data-sets (2005 to 2012) for the six stations as indicated in **Figure 2**, were acquired from the then uMgeni Water (2013) and analysed. Eleven (11) parameters namely alkalinity, pH, electrical conductivity (EC), nitrate, phosphorus, temperature,

turbidity, total algae count, *E. coli*, dissolved oxygen and ammonia were finally considered for spatial and temporal trend analysis. These parameters were selected for trend analysis in this study based on the following three criteria; (1) literature citing the parameter as significant to aquatic biodiversity health, (2) the availability of data and (3) the parameter being of interest mainly due to pollution activities in the study area. In addition to raw water quality, potable water quality data-sets for Durban Heights and Wiggins treatment plant for the period 2005 to 2012 were also obtained for comparative analysis. Both graphical (time-series and box-plot) and statistical SK test methods were employed.

### **3.1 Time-Series Plot**

A time series is a set of observations obtained by measuring a single variable regularly over a given period. The main characteristics of a time series analysis are that the data are not independently sampled, their dispersion varies in time, and they are often governed by a trend and cyclic components. In this study, time-series line plots were produced for all parameters in order to depict the variable patterns. Even though they graphically provided an easy and quick method of assessing the pollution patterns, their inability to display the crucial data characteristics, specifically mean, mode and median, was a major drawback. While considering the importance of these characteristics for describing water quality of the basin, box-plots were also done to augment the graphical trending [35]. Since effective trend analysis requires a fairly long sequence of data for a given sampling site, it was considered to use a five-year monthly data-set at a minimum, for monotonic trend analysis. However, for a step-trend, at least two years monthly data before and after treatment would be required [36]. These time frames are only guidelines as the longer the periods of record, the greater would be the sensitivity to detect smaller changes, based on the minimum guideline for monotonic and step.

#### **3.2 Box-plot**

One of the main advantages of a box-plot, also known as box and whisker, is its ability to summarise the distribution of data-sets in a way that allows for spatial comparison. This technique gives a visual summary of; (1) the centre of the data (the median = the centre line of the box), (2) the variation or spread (inter-quartile range = the box height), (3) the skewness (quartile skew = the relative size of box halves) and (4) presence or absence of unusual values 'outliers' [35, 37]. In this study the box plot was used to visually capture spatial variation among the study area stations. By allowing a comparison of data without making any statistical assumption, a box-plot presented a greater advantage compared to other observational techniques. However, the main limitation of observational methods was inability to quantify the magnitude of the observed trend [35, 37]. While considering the importance of determining the magnitude of trend especially when forecasting, statistical methods were also employed in this study.

When a study depends on secondary data, it is reasonable to consider a statistical method that accommodates outliers, missing values and censored-values which are common in water quality data-sets. The Seasonal Kendall test (SK test), which is a special case of the Mann Kendall Trend Test, is such a non-parametric test that can handle non-normal distributed data even with outliers, missing values or censoredvalues [35]. It is used to test for a monotonic trend of a parameter of interest when the data collected over time are expected to change in the same direction (up or down) for one or more seasons, e.g., months. The following assumptions underlie the SK test: (1) When no trend is present the observations are not serially correlated

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

over time, (2) the observations obtained over time are representative of the true conditions at sampling times, (3) the sample collection, handling, and measurement methods provide unbiased and representative observations of the underlying populations over time, (4) any monotonic trends present are all in the same direction (up or down). If the trend is up in some seasons and down in other seasons, the SK test will be misleading, (5) the standard normal distribution may be used to evaluate if the computed SK test statistic indicates the existence of a monotonic trend over time [33].

A positive SK value indicates an increasing trend whilst a negative value shows a decrease. A trend is considered statistically significant if the calculated P - value is less than 0.05 [36]. In this study, for situations where a significant trend was obtained, the Sen's Slope test was then employed to determine the magnitude of the trend. More detailed descriptions of the SK are found in Hirsch, Slack [38], Darken [39] and Helsel and Hirsch [35]. Both the SK and Sen's slope were calculated using XLSTAT 2014 software. The software runs in Excel and does not require prior programming knowledge, making it simple and user-friendly.

## **4. Results and discussion**

#### **4.1 Turbidity**

The time series graph depicted in **Figure 3** shows that turbidity exhibited seasonal variation with regular peaks in the wet season (October to March). The most plausible explanation for the peaks is the increased soil and other particulates runoff due to rain. The mean and median values (**Table 1**) for all stations studied shows that turbidity was above the South Africa expected limit for drinking water, which is 0–1 NTU [40]. The SK test presented in **Table 1** shows a decreasing turbidity trend from Nagle Dam inflow station to Inanda Dam outflow. The most plausible explanations for the improvement of quality especially at Inanda Outflow station could be the retention effect which could have occurred as the flow decreased. Midmar Inflow (306 NTU) recorded the highest turbidity level, which could be attributed to intensive farming activities upstream this station.

Chlorination of highly turbid waters tends to increase the level of trihalomethane (THM) which has been reported as carcinogenic [41, 42]. Excessive turbidity in drinking water is aesthetically unappealing and is of health concern as the suspended particles can provide food and shelter for pathogens [43]. If not removed, turbidity can promote regrowth of pathogens in the distribution system and this could lead to waterborne disease outbreaks. The South African guideline requires

**Figure 3.** *Turbidity time series and box -plot.*

#### **Table 1.**

*Descriptive statistics for turbidity.*

that turbidity level for drinking water be below 5 NTU [21, 44]. Kuutondokwa [45] reported of viable coliform counts in water with turbidity higher than 3 NTU, even in the presence of free residual chlorine. Dearmont, McCarl [26] reported that a 1% increase in turbidity tends to increase chemical costs by one fourth of a percent. Pernitsky and Edzwald [46] further reported that coagulant doses were generally higher when raw water turbidity was high, although the relationship was not linear. To aquatic life, higher than normal turbidity decreases the percentage of light transmitted, which results in decreased photosynthesis [45]. Additionally, excessive turbidity tends to demand an increase in the dosage of disinfectant in order to cater for envisaged pathogens that could be harbouring on the surfaces of the particles. Further, treating water with elevated levels of turbidity naturally results in excess sludge [45], which is undesirable for the environment.

On the other hand, a relatively low turbidity level observed at Inanda Dam Outflow station was desirable for the Wiggins Potable Water Treatment Plant since it meant using minimal quantities of chlorine and polymer. Dennison and Lyne [23], however, while studying factors causing high treatment cost at DV Harris Water Treatment Plant, observed that in contrary, low turbidity levels at Midmar Dam resulted in high treatment cost. The cost was attributed to use of bentonite, a coagulation enhancer that tends to facilitate the coagulation process for rather difficult to 'clean' (less turbid) raw water. The cost of bentonite tends to counteract the reduction of polymer dosage, making it rather expensive to treat water of with turbidity lower than optimal for efficient treatment to potable water quality.

#### **4.2 Algae**

For quantifying algae, only dam outflow station data-sets were used. Results for the three stations show that algae is a major problem along uMngeni Basin. As expected, algae exhibited peaks during the wet seasons [47] (**Figure 4**). The mean range of 1900 to 7055 cell/mL indicates that algae were dominant along the basin (**Table 2**). By comparing the mean observations of the three stations it is noted that Nagle Outflow recorded the highest mean concentration among all the three stations.

However, a comparison of the median values (**Table 2**) clearly shows that Inanda is the most deteriorated among the three stations. The latter results conform to earlier studies [29, 48]. During potable water treatment algae is known to have a direct relationship to polymer and chlorine dosage. The high level observed using water from Nagle Outflow suggests that more polymer and chlorine is required to treat water from Nagle compared to that from Inanda Outflow.

#### *uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

#### **Figure 4.**

*Algae time series and the box -plot.*

#### **Table 2.**

*Descriptive statistics for Algae.*

### **4.3 pH**

The time-series plot (**Figure 5**) illustrates that pH variation is not a major problem along uMngeni Basin. Except for Inanda Inflow, the results for the SK test (**Table 3**) reveal no significant temporal variation of pH among the six stations. The observed minimum to maximum ranges were within the general national and international limits for no human risk (6.0 to 9.0 pH) and the survival of fresh water biodiversity [40, 49]. Growth and reproduction of freshwater aquatic species such as fish are found to be ideal within a pH range of 6.5 to 8.5 while pH below 4 or above 10 tends to be lethal to most aquatic animals [21, 50]. The pH values observed would not ideally promote toxicity of dissolved metal ions and protonated species, resulting in aesthetically pleasing water. Additionally, consumption of the water would not cause significant adverse health effects.

The pH of an aquatic system determines the concentration, accumulation and bioavailability of various species, which could be advantageous if required but a disadvantage if this process results in release of pollutants [51]. For example, a

**Figure 5.** *pH time series and the box-plot.*

**Table 3.**

*Descriptive statistics for pH.*

decrease in pH increases the corrosivity of water, and increasing the pH increases the tendency to precipitate mineral scales such as calcium carbonate (CaCO3). Another example relates to ammonia, which is more toxic in alkaline water than in acidic because free (NH3) at high pH values (pH >8.5) is more toxic to aquatic biota than when in its oxidised form, NH4 + . Additionally, reduction of pH enhances solubility and speciation of some metals, which elevates their toxicity [52]. On the other hand, alkaline conditions promote precipitation of some cations when they complex with other dissolved ligands [53]. Thus, overall, mobilisation of the species might be beneficial or undesirable, depending on the receiving environment and the concentrations resulting [51].

#### **4.4 Total alkalinity**

Alkalinity ranged from a minimum value of 14.6 mg/L at Inanda Inflow to a maximum value of 83.5 mg/L at Inanda Outflow (**Figure 6**) This indicates that uMngeni Basin still had the ability to resist pH change to some degree. Regarding river health, the observed high mean range (27.974 mg/L - 62.842 mg/L) (**Table 4**) suggests that uMngeni River still had the potential to resist pH, resulting in protection of the aquatic animals. This is in agreement with Dallas and Day [54], who reported that South Africa's rivers are naturally alkaline. The SK test results presented in (**Table 4**) show that alkalinity was generally stable during the period 2005 to 2012. A comparison of the mean and median values illustrates that both the inflow and outflow stations showed a moderate spatial increase towards downstream of the uMngeni River. Inanda Inflow station recorded a monthly mean value of 55.840 mg/L which was approximately twice that of Midmar Inflow (28.11 mg/L) (**Table 4**).

**Figure 6.** *Total alkalinity time series and the box-plot.*

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

**Table 4.** *Descriptive statistics for Total alkalinity.*

Regarding potable water production, the observed high alkalinity level at Inanda Dam stations reflects its ability to resist pH change as a result of coagulation. This could explain the low lime dosage used at Wiggins Treatment Plant compared to Durban Heights Treatment Plant.

#### **4.5 Temperature**

As expected with South African seasons [47], temperature showed seasonal variation with fluctuations (7.2–12°C) in winter (May – August) and high temperatures (24.7–29.9°C) in summer (September to March) (**Figure 7**) The small variation at the stations at each given time of the year could be explained by the differences in sampling time. The SK and Sen's slope results in **Table 5** show that water temperature decreased slightly towards downstream along uMngeni Basin except at Midmar Outflow.

High temperatures observed in summer coincided with algae bloom. Graham [48] used a data range from 1990 to 1999 to explain that algae bloom was a significant driver for potable water treatment costs along uMngeni Basin. Low temperatures have been reported to result in the formation of small flocs, which are vulnerable to disintegration due to fluid shear force [55]. On the other hand, Veenstra and Schnoor [56] explained that high chemical dosages for potable water treatment during summer were meant to compensate for losses caused by the sun's radiation, making it more expensive to treat water in summer. Even then, since very little can be done operationally to remedy temperature variation, a treatment plant should have strategies in place to counter the effect of low temperature in winter, especially for coagulation and flocculation processes.

Additionally, temperature tends to influence the metabolic rates of aquatic organisms, which influence the quality of the raw water. In warm waters respiration rate tends to increase, leading to increased oxygen consumption and decomposition of organic matter. The concentration of oxygen in water is also affected by temperature,

**Figure 7.** *Temperature time series and the box-plot.*

#### **Table 5.**

*Descriptive statistics for Temperature.*

with increasing temperature encouraging escape of the gas from water. The rate of biodegradation of organic compounds increases with increased temperature and this further adds to the reduction of dissolved oxygen as well as nutrient accumulation [57]. Metals and compounds behave differently in low and high temperature environments. For example, CaCO3 precipitates out in hot water to form scaling, which can cause clogging in hot water pipes [58]. Therefore, overall, temperature affects the quality of raw water that feeds into a potable water treatment plant.

#### **4.6 Dissolved oxygen**

Dissolved oxygen levels varied between 4.2 mg/L and 11.80 mg/L as depicted in **Figure 8**. Except for Inanda Outflow, the SK test results depicted in the table show that dissolved oxygen remained fairly stable among the stations studied between 2005 and 2012. The high mean (7.926 mg/L - 9.385 mg/L) and median (8.100 mg/L - 9.500 mg/L) (**Table 6**) range shows that dissolved oxygen along uMngeni Basin was within the 80–100% assuming 8.0 mg/L as saturation level [59]. Dissolved oxygen concentrations in unpolluted water normally range between 8 and 10 mg/L [60].

The minimum values observed at Midmar Dam, which are below 5 mg/L (**Table 6**) is worrisome when considering fitness for the survival of aquatic biodiversity. Low dissolved oxygen concentrations are known to adversely affect

**Figure 8.**

*Dissolved oxygen time series and the box-plot.*

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

**Table 6.**

*Descriptive statistics for dissolved oxygen.*

the performance and survival of aerobic organisms while levels below 2 mg/L may be fatal to fish. Regarding treatment, the causal relationship between dissolved oxygen and chemicals used during treatment is not well understood. Some reports have, however, highlighted that oxygen tends to attach to the floc particles making them lighter and relatively impossible to settle for easy flocculation.

#### **4.7 Nitrate**

The time-series plot (**Figure 9**) shows that nitrate exhibited an irregular pattern at the six stations studied. This could be attributed to many factors key among them being the irregular discharge of poor-quality sewage effluent along the basin. High concentrations observed in winter tended to be a response of reduced flows, which affected the dilution capacity. Peaks observed during the wet season can be explained by the increased surface run-off, which would encourage movement of fertilisers, animal feedlots effluent and increased sewage effluent discharge. Even though the results indicated that nitrate levels (**Table 7**) were within the regulatory limit for potable use (<11 mg/L), it is important to note that the parameter is the cause of eutrophication especially if levels exceed the recommended limits for no risk, i.e., 0 to 0.5 mg/L as N [44].

Under eutrophic conditions, dissolved oxygen greatly increases during the day, but is greatly reduced after dark by the respiring algae and microorganisms that feed on the elevated mass of dead algae. Oxygen is required by all aerobically respiring plants and animals and it is replenished during daylight by photosynthesising plants and algae. When dissolved oxygen levels decline to hypoxic (inadequate) levels, fish and other aquatic organisms that require oxygen suffocate. These challenges could cause odour and taste problems as well as death of aquatic animals. Additionally, algal blooms limit sunlight penetration to bottom-dwelling organisms and cause wide swings in the amount of dissolved oxygen in the water.

**Figure 9.** *Nitrate time series and the box-plot.*

**Table 7.**

*Descriptive statistics for nitrate.*

Nitrate (NO3 − ) as depicted in (**Figure 9**) could account for algae blooms in the wet season, which would ultimately cause increased chlorine and coagulant dosages during potable water treatment. However, the nitrogen levels along uMngeni Basin are not considered to pose a problem to communities when the receiving water bodies are used for domestic and recreational purposes. It is important to maintain the levels low as studies have reported that (NO3 − ) concentration above the permissible limits could be lethal to infants by causing the "blue baby" syndrome in bottle-fed babies [15, 61, 62].

#### **4.8 Total phosphate**

Just like nitrate, total phosphate showed irregular variation with peaks throughout the year (**Figure 10**). This suggests that the two variables could be emanating from the same sources. The most plausible sources could be fertilisers and sewage effluent pollutants from anthropogenic activities along the river. Furthermore, while considering that the soils of uMngeni Catchment are generally phosphorus deficient as highlighted by Furness and Richard [63], it is reasonable to suggest that human activities could be responsible for the peaks. The SK test reveals that there were no significant trends in total phosphate values at any of the stations during the study period (**Table 8**). Descriptive statistic results (**Table 8**) illustrate that Inanda Inflow station had comparably twice (130.078 μg/L) the total phosphate concentration as that of Nagle Inflow station (57.798 μg/L) which is upstream station. Darvill Wastewater Treatment Works (WTW) treats domestic and industrial effluent from Pietermaritzburg, contributing a significant nutrient load (total phosphorus is 15% and soluble phosphorus 50%) and high oxygen demand to Inanda Dam.

Regarding potable water treatment, the effect of total phosphate is indicative of the resultant algae bloom. Thus, in order to reduce the cost associated with the production of potable water along uMngeni River catchment, attention needs to be focused on the reduction of the nutrient load into the river system.

**Figure 10.** *Total Phosphate time series and the box–plot.*

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

#### **Table 8.**

*Descriptive statistics for total phosphate.*

#### **4.9** *E. coli*

The time-series plots in **Figure 11** depict seasonal fluctuation and peaks of *E. coli* levels. The peaks that are pronounced in the wet season could be as a result of increased surface runoff from burst sewer pipes and slurry from intensive livestock farming operations that flow into the river course [64, 65]. Storm wash-off containing accumulated human, animal and domestic waste material and overflowing pit-latrines also cause contamination during high flows [64, 65]. The mean range of 97 to 1319 CFU/100 mL (**Table 9**) shows that water quality along uMngeni Basin is not fit for direct drinking purposes without disinfecting. South Africa's guidelines stipulate that there should be zero *E. coli* in 100 mL of the test water [40]. The observed high range could be due to many factors chief among them being the discharge of poor-quality sewage effluent.

With regard to human health, the observed high *E. coli* levels recorded at Inanda Inflow are worrisome when considering that there are nearby rural communities of The Valley of a Thousand Hills, which might use the river water for domestic purposes. Serious faecal contamination problems have been reported, for example, in Pietermaritzburg and Durban, as well as in the settlements in The Valley of a Thousand Hills and Vulindlela (Henley), where Karar [66] reported that annual deaths were associated with water borne diseases. The major sources of pollution were noted to be leakage and blocked sewers in formally serviced residential areas (e.g. Pietermaritzburg and Durban) and illegal disposal of waste into stormwater drains as well as general drainage.

Regarding potable water treatment costing, the elevated levels of *E. coli* observed towards downstream at Inanda added pressure to the Wiggins Potable Water

**Figure 11.** *E. coli time series and box-plot.*

#### **Table 9.**

*Descriptive statistics for* E. coli*.*

Treatment Plant. It is, therefore, reasonable to argue that more chlorine might be needed at Wiggins in order to thoroughly disinfect the water. Poor treatment of water with *E. coli* could cause the spread of waterborne diseases.

#### **4.10 Ammonia**

Ammonia concentration among the six stations studied showed irregular cyclic pattern with peaks (**Figure 12**). Of concern is the high monthly mean recorded at Midmar Dam outflow station of 3.640 mg/L (**Table 10**) that exceeded guidelines. Such an observation is worrisome when considering the toxicity of ammonia to aquatic animals, which is related to pH and temperature. Concentration 0.06 mg/L could damage fish gills while those above 0.3 mg/L be lethal to fish [67]. The presence of ammonia at higher than geogenic levels is an important indicator of faecal pollution [68].

Except for Nagle Dam station, the other dam inflow stations generally exhibited ammonia levels comparable to the corresponding outflow stations. Regarding treatment, ammonia is known to react with chlorine during treatment to form chloramine [21]. Chloramine is, however, a relatively weak oxidant [21] but with a long-lasting residual effect. Taste and odour problems as well as decreased disinfection efficiency are expected if drinking water containing more than 0.2 mg of ammonia per litre is chlorinated [68]. This is because up to 68% of the chlorine may react with the ammonia and become unavailable for disinfection [68]. Regarding the observed levels of ammonia in the data analysed, it can be argued that chlorine dosage at Wiggins Water Treatment Plant is more influenced by ammonia compared to Durban Heights Plant. The presence of elevated ammonia levels in raw water is also reported to interfere with the operation of manganese-removal filters as it may increase oxygen requirement due to nitrification and this may result in mouldy,

**Figure 12.** *Ammonia time series and box-plot.*

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

**Table 10.**

*Descriptive statistics for Ammonia.*

earthy-tasting water [68]. Furthermore, the presence of the ammonium cation in raw water may result in drinking-water containing nitrite as the result of catalytic action or the accidental colonisation of filters by ammonium-oxidising bacteria [69].

### **4.11 Electrical conductivity**

Electrical Conductivity (EC) is the measure of dissolved ions or inorganic materials including calcium, bicarbonate, nitrogen, phosphorus, iron and sulphur. This parameter, which was used in Dzwairo, Otieno [5] as a surrogate for pollution, showed irregular variation with peaks and fluctuations (**Figure 13**). The distinct low level that was more pronounced in summer could be explained by an increased river flow and discharge, which tended to dilute the downstream watercourse pollution. However, all values observed (5.280 mS/m - 52.95 mS/m) were within the acceptable level for drinking water purposes, hence, are no cause for concern (**Table 11**).

**Figure 13.** *Conductivity time series and the box-plot.*

#### **Table 11.**

*Descriptive statistics for Conductivity.*

Previous studies have also reported uMngeni River as a low conductivity river. Graham [48], using data from 1990 to 1999, also noted that Nagle Dam had a daily average of 8.9 mS/m, which is slightly lower than the 10.361 mS/m observed in this study. This then implies that the concentration of dissolved ions at Nagle Dam has increased over time. As presumed, the box and whisker plot show that conductivity increased towards downstream of the uMngeni Basin. Regarding treatment, the relatively high conductivity observed at Inanda Outflow suggests that conductivity could be driving coagulant dosage more at Wiggins compared to Durban Heights. In an ecosystem, the combination of high EC coupled with high temperatures increases the toxicity of metals. At low temperatures EC tends to be low, which reduces the mobilisation of metals bound in sediments [70].

## **5. Conclusion**

In a semi-arid country like South Africa, it is pertinent that the country's water resources be developed and managed for the benefit of the whole. Effective management of a basin such as uMngeni requires a sound understanding of the sources and status of water pollution as well as early detection of water quality changes. This will assist in strategising effective methods for combating pollution, making the water fit for various purposes including potable water production.

From the foregoing observations, it can be concluded that for the period under consideration, the quality of water along uMngeni River showed characteristics that favoured eutrophication with respect to turbidity, chloride, algae, conductivity and nitrate levels. This is more pronounced when comparing among the three inflow stations. Drinking water should be free from colour, turbidity, odour, and microbes in order to make it safe and aesthetically pleasant. Regarding the high *E. coli* levels that were observed at all stations, it could be concluded that the water was not fit for drinking purposes without at least disinfection. The high turbidity recorded at all stations suggests that the raw water was aesthetically unpleasant for drinking purposes.

The spatial analysis shows a downstream deterioration of river water quality for mineral salts namely chloride and conductivity. However, the quality of water was still acceptable for other uses such as irrigation, freshwater ecosystem habitat, and industrial use, which require less stringent guidelines than the standards for potable use. While, the quality of uMngeni River water was acceptable for agricultural purpose, it is important for the management and communities to implement effective measures for controlling agricultural practices that may significantly affect water quality such as stream bank cultivation and intensive fertiliser, which may be washed into the river. This is also the case with putting measures to control sewage bursts and open defecation, which have made the water unfit for human consumption. Future studies need to look at in-depth analysis of the ecological effectiveness of the temporal and spatial variation of the water quality in the basin.

### **Acknowledgements**

The authors gratefully acknowledge Durban University of Technology for hosting and funding the main author during his Master's degree study.

*uMngeni Basin Water Quality Trend Analysis for River Health and Treatability Fitness DOI: http://dx.doi.org/10.5772/intechopen.94844*

## **Author details**

Innocent Rangeti1 and Bloodless (Rimuka) Dzwairo2 \*

1 Faculty of Health Sciences, Department of Community Health Studies, Durban University of Technology, Durban, South Africa

2 Durban University of Technology, Civil Engineering Midlands, Imbali, South Africa

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 5**


**3. Data Processing**

There are many sources of vibration in a vehicle. Different vibration sources have certain differences in frequency domain characteristics. In this study, a quarter of the vehicle model and the abnormal road surface model are established to analyze the excitation effect of the abnormal road on the overall vehicle system. In order to obtain the vibration frequency range of the vehicle body, a frequency spectrum analysis of the vibration acceleration of the vehicle body is implemented, and a filter based on this analysis result is designed.

#### *3.1. Vehicle Dynamics Analysis*

In order to study the interaction between the vehicle and the road, a quarter-vehicle model is utilized to analyze the vibration of the vehicle in the vertical direction. The simplified vehicle dynamics model is shown in Figure 2.

**Figure 2.** The quarter vehicle model.

mu is the unsprung mass. ms is the sprung mass. *KS* is the stiffness of the spring. *CS* is the damping coefficient of the shock absorber. KT is the stiffness of the tire. *xs* is the vertical displacement of the vehicle body. *xu* is the vertical displacement of the wheel. *xg* is the road surface excitation. The differential equation of motion of the vehicle is described as Equation (1).

$$\begin{cases} \mathbf{m}\_{\sf s} \ddot{\mathbf{x}}\_{\sf s} + \mathbb{C}\_{\sf S} (\dot{\mathbf{x}}\_{\sf s} - \dot{\mathbf{x}}\_{\sf u}) + \mathbf{K}\_{\sf S} (\mathbf{x}\_{\sf s} - \mathbf{x}\_{\sf u}) = \mathbf{0} \\\ \mathbf{m}\_{\sf u} \ddot{\mathbf{x}}\_{\sf u} - \mathbb{C}\_{\sf S} (\dot{\mathbf{x}}\_{\sf s} - \dot{\mathbf{x}}\_{\sf u}) - \mathbf{K}\_{\sf S} (\mathbf{x}\_{\sf s} - \mathbf{x}\_{\sf u}) + \mathbf{K}\_{\sf T} (\mathbf{x}\_{\sf u} - \mathbf{x}\_{\sf S}) = \mathbf{0} \end{cases} \tag{1}$$

This paper analyzes the dynamic response of the vehicle when the vehicle passes through the road surface at different speeds [26]. An abnormal pavement model is shown in Figure 3. The length of the abnormal road surface is L, and the height is h. *v* is the speed of the vehicle. t1 and t2 are the starting moment and the ending moment of the road surface excitation, respectively. The mathematical model is described as Equation (2) [27].

$$\mathbf{x}\_{\mathcal{S}}(t) = \begin{cases} 0.5 \mathbf{h} \big( 1 - \cos \left( \frac{2\pi \mathbf{u}}{L} t \right) \big) \mathbf{t}\_1 \le t \le \mathbf{t}\_2\\ 0, \mathbf{t}\_1 < t \text{ or } t > \mathbf{t}\_2 \end{cases} \tag{2}$$

**Figure 3.** Abnormal road surface excitation at different speeds.

When the vehicle passes through an abnormal road at different speeds, the vertical acceleration response of the vehicle body is shown in Figure 4. The speed of the vehicle has a significant effect on the vertical acceleration of the vehicle. The frequency–domain analysis of the acceleration signal is performed to obtain a spectrogram of the vehicle vibration acceleration signal, as shown in Figure 5. As the vehicle speed increases, the frequency of body vibration increases, but its main component is still in the low-frequency range (30 Hz).

## *3.2. Butterworth Filter*

When the data is collected, the position of the smartphone is not flat, and the slope of the test road surface may interfere with the recognition of the abnormal road surface. Therefore, the data needs to be filtered before using to recognize the abnormal road surface. According to the simulation data analysis results, the vibration caused by the abnormal road surface excitation is mainly distributed in the low-frequency range.

In this paper, the Butterworth filter is used to filter out the through component and the high-frequency noise with the frequency greater than 30 Hz. There are several reasons for choosing the Butterworth filter. Firstly, the Butterworth filter does not generate a ripple in the passband. Secondly, it has been successfully implemented in many commercial tools. Figure 6 shows the frequency response of the fifth-order Butterworth's numerical low-pass filters with a cut-off frequency of 30 Hz.

**Figure 6.** The frequency response of the Butterworth filter.

In addition, the filter delay has a negative impact on the positioning accuracy of abnormal roads surface. The filter delay will shift the positioning of the abnormal road surface towards the vehicle driving direction for a distance. However, because the filtering delay is very small (about 0.1 s), the positioning error caused by the filtering delay is not large, and the positioning accuracy requirements for abnormal road recognition are not high. Therefore, the Butterworth filter can be applied to the recognition of abnormal roads.

#### **4. Abnormal Road Surface Recognition**

#### *4.1. Overview of Abnormal Road Surface Recognition Algorithm*

The framework of the abnormal road recognition algorithm is shown in Figure 7. In order to collect vehicle speed, acceleration, and position information, the smartphone's built-in accelerometer and global positioning navigation system are used. First, the raw data is preprocessed using a Butterworth filter. Secondly, the Gaussian background model is improved by using fuzzy logic control. The improved Gaussian model is combined with the acceleration threshold condition to extract the characteristic acceleration value caused by the abnormal pavement. Finally, the kNN algorithm is used to classify the abnormal road surface.

**Figure 7.** Road surface recognition algorithm framework.

#### *4.2. Gaussian Background Model*

When the vehicle is traveling on a flat road surface, the vibration acceleration in the vertical direction of the vehicle is Gaussian-distributed. In order to verify the conclusion, we used both Kolmogorov–Smirnov and Lilliefors test methods to test whether the acceleration generated by the vehicle on a flat road conforms to the Gaussian distribution. The results show that the assumption is valid. By measuring the vibration acceleration of the vehicle, the vehicle's vertical acceleration will be abrupt when the vehicle passes through an abnormal road surface compared to traveling on a flat road [28]. The Gaussian model is described as Equation (3).

$$\eta(z|\mu, \sigma^2) = \frac{1}{\sigma\sqrt{2\pi}}\mathbf{e}^{\left(\frac{-(z-\mu)^2}{2\sigma^2}\right)}\tag{3}$$

μ is a mathematical expectation. σ is a standard deviation, and *z* is a body vibration acceleration value. If the vehicle passes over an abnormal road, the body acceleration at this time will no longer conform to the Gaussian distribution. Therefore, if *z* is the vertical acceleration of the vehicle caused by abnormal road excitation, the absolute value of the difference between *z* and μ is greater than the product of the threshold TG and the standard deviation σ. The equation can be described as Equation (4).

$$\left| (z - \mu) \right| > \mathcal{T}\_{\mathcal{G}} \* \sigma \tag{4}$$

If Equation (4) is not satisfied, the vehicle is considered to be traveling on a flat road. At this time, the background of the Gaussian model will change, and the model parameters will be updated. The Gaussian model parameter update equation is described as Equation (5).

$$\begin{cases} \mu\_{t+1} = (1 - a)\mu\_t + \alpha(z - \mu\_t) \\ \sigma\_{t+1}^2 = (1 - a)\sigma\_t^2 + \alpha(z - \mu\_t)^2 \end{cases} \tag{5}$$

where α is the learning rate, and the size of the learning rate indicates the speed of the update. μ*t*+1 and σ*t*+1 are the updated mean and standard deviation.

#### *4.3. Improved Gaussian Background Model*

The vibration acceleration of the vehicle is a ffected by the traveling speed. When the vehicle passes over abnormal roads at a di fferent speed, the amplitude of the vibration acceleration is di fferent. In order to avoid false alarms at high speed and false negatives at low speed, the Gaussian model described as Equation (3) needs to be improved. After the improvement, if *z* is the vertical vibration acceleration caused by the abnormal road surface, it can be described as Equation (6).

$$\left| (z - \mu) \right| > \left( \frac{\upsilon}{\mathrm{T\_V}} \right) \* \mathrm{T\_G} \* \sigma \tag{6}$$

In addition, when the vehicle passes an abnormal road surface, the vertical acceleration of the vehicle is large. Therefore, if *z* is the acceleration caused by passing through the abnormal road, *z* will satisfy the following Equation (7).

$$z > \left(\frac{v}{T\_V}\right) T\_Z \tag{7}$$

Similar to the Gaussian background model, the improved Gaussian background model will also update the model parameters. When the acceleration *z* does not satisfy Equations (6) and (7), the data is considered as background data and will be used to update the background parameters. The parameter update formula is shown in Equation (5).

Where *v* is the current vehicle speed, and TV is the speed threshold. Vehicle suspension parameters are di fferent for di fferent types of vehicles. The vibration response of the vehicles body will be significantly di fferent when di fferent types of vehicles pass through the same road surface. The original Gaussian background model has a fixed threshold *TZ*, which is obviously not applicable to di fferent types of vehicles. Therefore, this paper proposes designing a fuzzy logic controller to optimize the Gaussian background model.

Due to the complexity of the vehicle system, it is di fficult to establish accurate mathematical models to describe the relationship between di fferent vehicles and abnormal road surfaces. Fuzzy logic control is based on artificial experience and does not require an accurate mathematical model of the controlled object. Using fuzzy logic, the basic idea of optimizing the abnormal road surface recognition algorithm is: Firstly, find the fuzzy relationship between the road surface recognition algorithm parameter *TZ* and the vehicle suspension parameters. Second, in the process of road surface recognition, according to the di fference of *KS* and *CS* parameters of di fferent vehicles, fuzzy logic is used to modify *TZ*, so that the abnormal road surface recognition algorithm can adapt to di fferent types of vehicles.

In this section, to make the abnormal road recognition algorithm applicable to di fferent types of vehicles, a fuzzy logic inference machine is used to calculate an appropriate *TZ*. The input quantities of the fuzzy logic inference machine are defined as the vehicle suspension sti ffness *KS* and damping *CS*. The basic domain of *KS* is [0,200], the basic domain of *CS* is [0,4], and the basic domain of *TZ* is [0.5,1].

As shown in Figure 8, the membership function is gaussmf type. The input 1 ( *KS*) is qualitative into five sets, denoted as NB, NS, ZO, PS, and PB. The input 2 ( *CS*) is qualitative into four sets, denoted as NB, NS, PS, and PB. The output ( *TZ*) is qualitative into three sets, denoted as NB, ZO, and PB, where NB is negative big, and PB is positive big.

**Figure 8.** Membership function. (**a**) Membership function of input 1 (*KS*). (**b**) Membership function of input 2 (*CS*). (**c**) Membership function of the output (*TZ*).

The establishment of the rule bases of the fuzzy logic inference machine is based on the experiments and CarSim simulation. The rule bases of the fuzzy logic inference machine are shown in Table 1, and Figure 9 shows the interface of the output.

**Table 1.** Rule bases of *TZ*.

**Figure 9.** The surface of fuzzy logic controller's output.

The improved Gaussian background model is described in Algorithm 1.

#### **Algorithm 1:** Abnormal road surface recognition method

**Input:** *z*, the Z-axis acceleration; *v*, the vehicle speed; *KS*, the spring stiffness; *CS*, the damping coefficient. **Output:** *event\_z*, Acceleration due to abnormal road surface.

1. **Algorithm begin:** *2.* μ = 0 *%* μ is a mathematical expectation 3. σ = 0 *%* σ is a standard deviation 4. TG = 2 %TG is the Gaussian matching threshold 5. TV = 20 % TV is the speed threshold 6. if (*v* > TV) 7. *z\_match* ←abs(*z* − μ)/σ 8. % calculating thresholds *TZ* using fuzzy logic inference machines 9. *TZ* ← fuzzy\_control (*KS*, *CS*) 10. if (*z\_match* > TG\**v*/TV) && (abs(*z*) > (*TZ* \* *v*/TV)) 11. *event\_z* ← *z* 12. else % update μ and σ, as described in Equation (5) 13. μ ← (1 − α) \* μ + α\**z* 14. σ ← SQRT ((1 − α) \* σˆ2 + α \* (*z* − μ)ˆ2) % SQRT means calculate square root 15. end if 16. end if 17. return *event\_z* 18. **Algorithm end**

#### **5. kNN Algorithm Abnormal Road Surface Classification**

The kNN classifier is a sample-based machine learning algorithm. Due to the simple and effective characteristics of kNN, kNN has been widely used in engineering applications [29]. First, the abnormal road surface acceleration signal extracted by the abnormal road surface recognition algorithm is used to identify the k nearest neighbor values in the training data set. Then, the abnormal pavement labels corresponding to these nearest neighbor values are counted, and the number of neighboring samples belonging to each possible type is calculated. The most common type of pavement that belongs to most of the k nearest neighbors is the type of pavement being measured.

#### *5.1. Training and Testing Sample Data Sets*

Both the training samples and the test samples are collected by the acceleration sensor of a smartphone. In this study, multiple sets of acceleration data caused by abnormal road surfaces are collected as training data sets. In addition, in order to maintain the independence between training data and test samples, the training samples and test samples are collected on two different roads. The number of training and test samples obtained in this paper is shown in Table 2.

**Table 2.** Number of training and testing sample.

From the perspective of the classification process, kNN most directly establishes a relationship between training samples and test samples, which can effectively avoid the negative impact caused by the improper selection of category features. Another widely used classification algorithm support vector machine (SVM) is utilized to compare with kNN on classifying the road surface. The same training and testing samples are used and the results are shown in Figure 10. The results show that kNN has an advantage over SVM in the classification of abnormal roads.

**Figure 10.** The performance of kNN and SVM.

#### *5.2. Classification Algorithms and Tuning Parameters*

The kNN classification algorithm classifies objects based on the attributes of k neighbors. The value of k is a key parameter of the kNN algorithm. This paper tests the classification effect when k takes different values in the range of 1–19 (odd numbers). Figure 11 shows the classification accuracy when k takes different values. It can be seen from the figure that as the value of k increases, the classification accuracy decreases, and the effect is best when k = 3. Therefore, k is set as 3 in this work.

**Figure 11.** The relationship between classification accuracy (y-axis) and k value (x-axis).

#### **6. Test and Analysis**

## *6.1. Test Conditions*

To validate the performance of the proposed algorithm, an A-class vehicle (Cavalier) and SUV (Qoros 5) are used to perform the test. An app working on a smartphone (Redmi Note 8 Pro) is developed to collect the acceleration, speed, and position. The sampling frequency is 400 Hz. According to the sampling theorem, if the frequency information of a signal is to be saved, the sampling frequency must be twice as much as the frequency of the measured object. If the amplitude information of a signal is to be saved, the sampling frequency is preferably ten times as much as the frequency of the measured object. The sampling frequency of vehicle speed and position is 1 Hz. This is because the GPS module data update frequency in the smartphone used in the experiment is 1 Hz. The smartphone is fixed on the handrail of the driver's seat during the test, as shown in Figure 12.

**Figure 12.** Smartphone installation location.

The vehicle travels at a different speed on an abnormal road and a flat road and performs multiple tests. There are many abnormal road surfaces on the test road, as shown in Figure 13. The actual measurement shows that the area of the pothole on the road is about 1 square meter and the maximum depression depth is 30 mm. The diameter of the raised manhole cover is 700 mm, and the height is 50 mm. The width of the speed bump is 350 mm, and the height is about 30 mm.

**Figure 13.** Abnormal road surfaces on the test road.

#### *6.2. Vehicle Dynamic Response under Di*ff*erent Road Excitations*

When the vehicle is travelling on a flat road surface, its vibration acceleration is relatively stable. When the vehicle passes through an abnormal road surface such as a road pit or a road surface bump, the acceleration of the vehicle in the vertical direction changes significantly. Figure 14 shows the vibration acceleration of the vehicle when the vehicle passes through different roads at a speed of 30 km per hour. Figure 14a shows the vehicle traveling on a flat road surface. In Figure 14b, the vehicle is excited by the pothole road surface. In Figure 14c, the vehicle is excited by the bump road surface. Under the influence of gravity acceleration, the Z-axis acceleration fluctuates around 9.8.

After the original acceleration data is processed by the Butterworth filter, the filtered Z-axis vibration acceleration shown in Figure 15 is obtained. It can be clearly observed that the valid data collected by the smartphone is retained, and the through component and the high-frequency noise are eliminated.

#### *6.3. Test Results and Analysis*

Figure 16 depicts the vertical acceleration values of the vehicle body when the experimental vehicle passes through the same abnormal road surface at different vehicle speeds. The blue triangle in Figure 16 represents the experimental data of the A-class vehicle, and the red rectangle represents the experimental data of the SUV.

It can be observed from Figure 16 that as the vehicle speed increases, the vertical vibration acceleration of the vehicle body also increases. In addition, when the different types of vehicles pass the same abnormal road surface, the vertical vibration acceleration of the vehicle body also has a significant difference.

Comparing the field measurement results with the algorithm identification results, as shown in Table 3, the results show that the proposed method can effectively identify the road surface potholes and bumps. In the 68 sets of road surface pothole data, 64 groups are successfully identified and classified, and the accuracy rate is 94.12%. Among the 151 sets of road surface bumps data, 145 groups are successfully identified and classified, and the accuracy rate is 96.03%. Through onsite investigation of the reported road surface, it is found that the abnormal road surface size of the false alarm is small, or the multiple abnormal road surfaces are close to each other, which leads to erroneous recognition results.

**Table 3.** Abnormal road surface identification result.

**Figure 14.**

**Figure 15.**

**Figure 16.** Vehicle speed and body vibration acceleration.