Low-Cost Water Quality Sensors for IoT: History
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Contributor:
Edson Tavares de Camargo
,
Fabio Alexandre Spanhol
,
Juliano Scholz Slongo
,
Marcos Vinicius Rocha da Silva
,
Jaqueline Pazinato
,
Adriana Vechai de Lima Lobo
,
Fábio Rizental Coutinho
,
Felipe Walter Dafico Pfrimer
,
Cleber Antonio Lindino
,
Marcio Seiji Oyamada
,
Leila Droprinchinski Martins

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things (IoT) can enable real-time environmental monitoring networks, providing valuable water quality information to the public.

  • low-cost sensor
  • water quality
  • Internet of Things
  • remote sensor
  • environmental monitoring
  • environmental measurements
  • remote water quality monitoring

1. Introduction

The concept of Internet of Things (IoT) is widely used in various sectors of society due to the proliferation and advancement of sensing and communication technologies [1][2]. Specialized electronic devices with minimal processing capabilities, also known as smart objects, are used in homes, industries, cities, large farms, and even small rural producers. These devices can measure and send monitored data to the Internet in real time, usually through a wireless communication network. Once the data is stored in the cloud, it opens up possibilities for data analysis, optimization, and real-time decision-making. The data collected is diverse and can include variables such as temperature, humidity, geographic location, heart rate, images of open or closed environments, and others. In the context of water quality monitoring, the data collected includes parameters such as potential of Hydrogen, Dissolved Oxygen (DO), turbidity, Oxidation-Reduction Potential (ORP), and others [3][4][5].
Continuous, remote, and reliable monitoring of water quality can improve the management and control of water quality. This, combined with the use of low-cost sensing devices, can increase public access to high-quality water. Worldwide, at least three billion people do not know the water quality they depend on because there is no monitoring [6]. Providing access to high-quality freshwater for the population and improving water quality in the surrounding area are ambitious goals set out in the United Nations 2030 Development Sustainable Agenda. Therefore, comprehensive and up-to-date water quality monitoring data is essential for decision-makers to ensure the availability and sustainable management of water resources for both human use and healthy aquatic ecosystems [7].

2. What Sensors Were Used?

Identifying low-cost sensors for water quality monitoring is one of the central issues herein. Based on this, it is possible to create an initial database including the major sensor vendors, the technologies used to measure the physicochemical variables of interest, the cost/performance factor, etc. The identification of sensors will also allow the development of new works focused on analyzing the robustness, reliability, and durability of these devices when installed in different environments.
Table 1 lists the low-cost sensors used to monitor water quality, as identified in the selected articles. The majority of these sensors (46%46%) were manufactured by DFRobot, followed by Atlas Scientific (8%8%) and Vernier (2%2%). Some papers mention other manufacturers such as Thermo Fisher Scientific, Hach Company, Istek, Mettler Toledo, Asmik, Wilsen, Adafruit Industries, Daejin Instrument, Sensirion, HiLetGo, eKoPro, BHZY, Maxim Integrated, and Bosch Sensortec. However, it is worth noting that several reviewed papers do not mention the sensors used for their measurements; they only describe the measured parameters.
Table 1. Low-cost sensors used to monitor water quality parameters.
Supplier Sensor Articles
DFRobot DO 7
electrical conductivity 7
ORP 3
pH 26
TDS 8
temperature 47
turbidity 30
water depth/level 1
Atlas Scientific DO 8
electrical conductivity 5
ORP 2
pH 5
temperature 3
Vernier DO 1
ORP 1
pH 1
electrical conductivity 1
salinity 1
water flow 1
Hach Company DO 1
  BOD 1
Thermo Scientific suspended solids 1
Other manufacturers – 44
Not identified – 77
As reported by the manufacturer, Table 2 presents the cost, range, precision, and accuracy of sensors from the three most cited manufacturers (DFRobot, Atlas Scientific, and Vernier). The DS18B20 temperature sensor and the SEN0205 level sensor are the least expensive among the listed sensors, priced at US$6.90. Conversely, the ENV-50-DO sensor, which measures Dissolved Oxygen (DO), is the most expensive at US$ 353.99.
Table 2. Low-cost sensors from three main manufacturers. FS stands for Full-scale reading.
Manufacturer Parameter Sensor Model Cost (US$) Range Precision/Accuracy
DFRobot DO SEN0237 169.00 0∼20 mg/L –
EC DFR0300 69.90 0∼20 ms/cm ±5% FS
ORP SEN0165 89.00 −2 V∼2 V ±10 mv (25 ∘∘C)
pH SEN0161 29.50 0∼14 pH ±0.1 pH (25 ∘∘C)
pH SEN0169 56.90 0∼14 pH ±0.1 pH (25 ∘∘C)
TDS SEN0244 11.80 0∼1000 ppm ±10 % FS (25 ∘∘C)
temperature DS18B20 6.90 −55∼125 ∘∘C ±0.5 ∘∘C (−10∼85 ∘∘C)
turbidity SEN0189 9.90 – –
water depth/level SEN0205 6.90 – ±0.5 mm
Atlas Scientific DO ENV-20-DOX 134.99 0∼50 mg/L ±0.2 mg/L
DO ENV-40-DOX 214.99 0∼100 mg/L ±0.05 mg/L
DO ENV-50-DO 353.99 0∼100 mg/L ±0.05 mg/L
EC ENV-20-EC-K1.0 123.99 5∼200,000 μS/cm ±2 %
EC ENV-40-EC-K1.0 157.99 5∼200,000 μS/cm ±2 %
EC ENV-50-EC-K1.0 274.99 5∼200,000 μS/cm ±2 %
ORP ENV-10-ORP 243.99 −2∼2 V ±1 mV
ORP ENV-20-ORP 83.99 −2∼2 V ±1 mV
ORP ENV-30-ORP 58.99 −1.1∼1.1 V ±1.1 mV
ORP ENV-40-ORP 128.99 −2∼2 V ±1 mV
ORP ENV-50-ORP 237.99 −2∼2 V ±1 mV
pH ENV-10-pH 237.99 0∼14 pH ±0.002 pH
pH ENV-20-pH 60.99 0∼14 pH ±0.002 pH
pH ENV-30-pH 48.99 2∼13 pH ±0.1 pH
pH ENV-40-pH 85.99 0∼14 pH ±0.002 pH
pH ENV-45-pH 139.99 0∼14 pH ±0.002 pH
pH ENV-50-pH 234.99 0∼14 pH ±0.002 pH
temperature ENV-10-TMP 64.99 −200∼200 ∘∘C ±(0.15 + 0.002 ×T)
temperature ENV-50-TMP 70.99 −55∼220 ∘∘C ±(0.15 + 0.002 ×T)
Vernier DO DO-BTA – 0∼15 mg/L ±0.2 mg/L
DO ODO-BTA – 0∼20 mg/L ±0.2 mg/L
DO GDX-ODO – 0∼20 mg/L ±0.2 mg/L
EC GDX-CONPT – 0∼20,000 μS/cm ±10 μS/cm
EC GDX-CON – 0∼20,000 μS/cm ±1 % FS
ORP GDX-ORP – −1∼1 V ±20 mV
ORP ORP-BTA – −450∼1100 mV –
pH GDX-PH – 0∼14 pH ±0.2 pH
pH PH-BTA – 0∼14 pH ±0.2 pH
salinity SAL-BTA – 0∼50,000 ppm ±1 % FS
flow velocity FLO-BTA – 0∼4.0 m/s ±1 % FS
Among the cited manufacturers of the sensor, DFRobot stands out as the most frequently cited, as can be seen in Figure 1. The DFRobot sensors used in the reviewed articles include the following models: DO SEN0237, Electrical Conductivity (EC) DFR0300, Oxidation-Reduction Potential (ORP) SEN0165, potential of Hydrogen (pH) SEN0161 and SEN0169, Total Dissolved Solids (TDS) SEN0244, temperature sensor DS18B20, turbidity sensor SEN0189, and level/depth sensor SEN0205. These models are particularly noteworthy, given their frequency of use in the reviewed literature.
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Figure 1. Low-cost sensors from the two most frequently cited manufacturers.
Although some works use only DFRobot sensors, as in the case of Concepcion et al. [8], Billah et al. [9], Ang et al. [10], others, such as Billah et al. [11], Abbasi et al. [12], opt for their association with sensors from other manufacturers. Following this strategy, Islam et al. [13] use DFRobot sensors to measure pH, TDS, and temperature, in conjunction with DO and BOD sensors from Hach Company and suspended solids sensors from Thermo Scientific. The association of DFRobot sensors with Atlas Scientific sensors is presented by Fonseca-Campos et al. [14]. Atlas Scientific manufactures environmental and electrochemical sensors used in environmental monitoring. The company offers a dedicated product line for IoT, which comprises three main models: env-20, env-40, and env-50. The env-20-DO is the smallest model. This sensor has a measurement range of 0–50 mg/L, a price of around US$135, and a life expectancy of 2.5 years. The env-40 model offers a higher measurement range of 0–100 mg/L for DO and has a life expectancy of approximately 4 years. It is priced at US$244 and is suitable for more demanding environmental monitoring applications. The env-50 is the industrial version of the DO sensor and is designed for heavy-duty industrial applications. It has a higher price point of around US$354, but it offers better performance and durability. To facilitate the integration of their sensors with microcontrollers and single-board computers, Atlas Scientific offers two boards, Gravity and EZO, which use analog, UART, and I2C communication protocols to interface with the sensors.
Madeo et al. [15] and Garuglieri et al. [16] use Vernier sensors and report accepted performance in measuring pH, ORP, DO, salinity, and flow in water quality monitoring in rivers, lakes, and coastal waters. The sensor models mentioned are PH-BTA, ORP-BTA, SALT-BTA, DO-BTA, FLO-BTA, and LJ-A. The cost of such sensors starts at US$127.00 for the pH sensor and increases to US$502.00 for the DO sensor. Arunplod [17], in turn, uses only Atlas Scientific sensors to measure temperature, pH, DO, and EC in rivers and lakes in the Philippines.
Furthermore, researchers highlight the use of some sensors whose technology is not owned by the previous manufacturers. This is the case with the DHT11, DHT12, DHT22, PT100, and LM35 temperature sensors that have been reported in works such as Abbasi et al. [12], Jayalakshmi and Hemalatha [18], Trevathan et al. [19]. Additionally, there are some sensors used in the literature whose manufacturers could not be identified, such as pH sensors E201-C, PH-4502C, DO D-6800, AR8210, and DOS-600, as well as turbidity sensors TSW-20M, BL5419, and ST100. These sensors were utilized in studies such as Simitha and Raj [20], Huan et al. [21], and AlMetwally et al. [22].
As for the processes required for reliable measurement results, all manufacturers establish specific calibration protocols for each sensor based on the technology used and the parameter measured. For instance, DFRobot offers calibration scripts based on the use of standard solutions and linearization processes, while Atlas Scientific offers scripts for calibrating its sensors using linearization processes with one to three points, depending on the parameter being measured.
The manufacturer Vernier also provides calibration protocols for its sensors, with each probe having its specific procedure. However, Vernier ties these protocols to the use of data acquisition equipment and proprietary applications provided by the manufacturer itself, such as LabQuest Mini, LabQuest 2 and 3, LoggerPro, and Graphical Analysis. This requirement can complicate the calibration process if such tools are not available.
In addition, the manufacturers recommend regular maintenance of the sensors depending on the characteristics of the water bodies under study. They point out that the sensors must be kept free of dirt deposits, as these can affect the functioning of the sensors and lead to measurement errors. It is also advisable to check the sealing systems to prevent water from entering the dry areas of the sensors, thus, ensuring their proper functioning. Section 4 will address whether the use of the low-cost sensors are considered adequate, from the point of view of the authors of each work, for their respective monitoring objectives. The various fields of application are comprehensively presented in Section 5, with a particular emphasis on monitoring water bodies, including rivers, lakes, seas, and springs, as well as aquaculture and fish farming applications.

3. What Are the Water Quality Parameters Monitored?

As described previously, there are several parameters associated with water quality monitoring, the combination of which allows us to determine whether the water of a particular water body is suitable for certain critical uses, such as human consumption, animal consumption, agriculture, aquaculture, and so on.
Figure 2 shows the main parameters measured in the articles evaluated herein. Several articles measured more than one parameter, and the figure summarizes the total number of articles by parameter. It can be observed that the parameter measured in a larger number of articles was pH (90%), followed by temperature (80%), turbidity (59%), DO (38%), and EC (36%). Of these parameters, only EC is considered a complementary parameter in the definition of water quality, as it is closely related to salinity and allows conclusions to be drawn about the content of dissolved salts in the water. Information on salinity and EC is crucial in classifying water as salty, brackish, or fresh, which helps define its use in regions with fresh and/or drinking water shortages.
Figure 2. Water quality parameters measured.
Among the parameters shown in Figure 2, pH stands out clearly. This can be partly explained by the fact that several papers on aquaculture/hydroponics [11][12][21][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] and the pH is closely related to the metabolism of various aquatic species, so its monitoring and control is essential in this activity. Moreover, together with temperature, this is the sensor with more options of brands/models and technological maturity to do measurements in the aqueous medium. On the other hand, the DO content in water, a crucial parameter for maintaining aquatic life in a given water body, is less measured, which could be related to the application field (most on aquaculture/hydroponics) and the difficulty of performing reliable DO measurements in real conditions. Clean water generally has higher dissolved oxygen concentrations, about 5 mg/L, due to photosynthesis by algae and physical processes associated with water movement [40].
Finally, other parameters related to water quality were measured and mentioned less frequently in the articles reviewed: TDS, ORP, BOD, Fluorescent Dissolved Organic Matter (fDOM), suspended solids, total nitrogen, dissolved organic matter concentration, ammonia, nitrate, nitrite, iron, and magnesium, among others. In addition, about 13% of the studies mentioned parameters not directly related to water quality but to hydrological characteristics (water resource morphology). These include water level/depth, pressure, and flow.

4. Did the Sensors Prove Adequate Considering Their Fields of Application?

Most studies (111) considered that low-cost sensors were appropriate. Only 21 reported inadequate results, and 10 reported inconclusive results. However, most studies reported that they did not compare results with reference devices. See the next section for more details.
Among the reasons the authors gave for the unsatisfactory performance, those related to the need for maintenance and conservation stand out. In their work, Trevathan et al. [19] report damage to sensors due to water penetration into their dry parts. Wong et al. [41] report sensor malfunctions and measurement errors caused by the deposition of debris or biological encrustations on the sensors. Tholen et al. [42] reports on the degradation of electrical components by marine air. Technical problems are also mentioned, such as the lack of precision and accuracy of the sensors or their rapid degradation due to exposure to the environment in which they are installed [19][43][44][45].
In one recent paper, Xavier et al. [3] evaluated the DFRobot turbidity sensor SEN0189 compared to a reference device. The sensor withstood immersion and gave results close to those of the reference sensor during the 8-h test period. After this period, the difference between the response curves of the SEN0189 and the reference device increased continuously. At the time when the water change takes place (18 h), the error increases significantly. The sealing and the mechanical structure of the SEN0189 sensor are not prepared to be submerged for a long period of time.

5. Were the Results Obtained through Low-Cost Sensors Compared with the Results of Reference Equipment?

Although calibration with standard solutions is an important step to improve the reliability of low-cost sensors, it is not sufficient to guarantee their accuracy. It is essential to compare the results obtained from low-cost sensors with those of reference devices, such as multiparameter probes, to ensure their validity. This not only demonstrates the low-cost sensors’ proper operation but also helps to establish the accuracy and reliability of the obtained results. This is the focus of RQ4. In this regard, only 18 studies performed the expected comparison. On the other hand, 124 studies did not include reference instruments or did not discuss validation. In general, this lack of validation makes the results obtained with low-cost sensors less reliable.
Table 3 presents the works that bring a comparison of low-cost sensors with reference equipment. It is worth noting that the test periods in the reported studies were relatively short. Only one study [46] lasted for six months, while most studies were limited to a period of a few weeks or even a few hours. Among the papers comparing measurements of low-cost sensors with reference probes, the papers by Wu and Khan [47], Kinar and Brinkmann [48], Méndez-Barroso et al. [49], Bórquez López et al. [50] in which multiparametric probes from Xylem (YSI EXO, YSI 551, YSI-556) and Hanna Instruments (HI98128) are used as reference when testing pH, turbidity, TDS, DO, and temperature sensors. Probes from Eutech Instruments (CON 450) and Horiba are used as references by Weerasinghe et al. [51] and Demetillo et al. [52], respectively. In turn, other authors present the use of laboratory equipment or other sensors already installed in the test areas as reference sensors [14][21][29][53][54][55][56].
Table 3. Comparison of low-cost sensors with reference equipment. NSS stands for Non-identified Standard Sensors.
Work Standard Equipment Testing Period Statistical Analysis of Data
Adriman et al. [25] Refractometer Atago and PCS Tester 35 Not informed Relative error
Bórquez López et al. [50] HANNA HI98128 and YSI 551 multiparameter probes 5 days R2, variance, mean, standard deviation, etc.
Demetillo et al. [52] Horiba Water Checker Not informed R2 and absolute error
Goparaju et al. [57] HANNA HI 99300 multiparameter probe Not informed R2 and root mean squared error (RMSE)
Hawari and Hazwan [29] NSS 13 h Standard deviation and absolute error
Huan et al. [21] HASH COMPANY MS5—Hydrolab Not informed relative error
Kinar and Brinkmann [48] YSI EXO2 multiparameter probe Not informed None
Malissovas et al. [46] NSS 6 months Relative error and absolute error
Martínez et al. [58] NSS 1 month R2, Relative error and standard deviation
Méndez-Barroso et al. [49] YSI EXO3 multiparameter probe 3 months R2, RMSE, standard deviation, Pearson correlation coefficient and bias
Nandakumar et al. [54] NSS Not informed Relative error
Rezwan et al. [53] NSS 1 day None
Singh et al. [56] Systronics 802 pH meter Not informed None
Tsai et al. [59] NSS 20 days None
Wannee and Samanchuen [55] NSS 30 min Relative error
Weerasinghe et al. [51] Thermo Scientific Eutech CON 450 and other NSS Not informed RMSE
Wu and Khan [47] YSI EXO multiparameter probe and Vernier sensors Not informed None
Xu et al. [60] CHI660E electrochemical workstation Not informed R2 and relative error
Wu and Khan [47] describe a novel seawater monitoring system that uses an unmanned floating surface vehicle in the form of a catamaran. The system is equipped with LoRaWAN transmitters, enabling remote data transmission and real-time monitoring of water quality parameters in seawater. The USV carries a suite of sensors from DFRobot to measure pH (SEN0169), turbidity (SEN0189), and temperature (DS18B20), as well as a DO sensor from Atlas Scientific, model not identified. These sensors were calibrated in the laboratory using the Labquest 2 instrument and Vernier sensors. In the field, the results were validated by comparison with measurements made with the multiparametric probe YSI EXO. Comparison data between the probe and the sensors are not described, nor is the evaluation time of the sensors.
To assess the water quality in coastal regions, estuaries, and tidal channels, Méndez-Barroso et al. [49] developed a monitoring system capable of measuring several parameters including water level, temperature, salinity, pH, EC, TDS, and DO in the water. The ENV40 sensors from Atlas Scientific were calibrated in the laboratory using standard solutions, and the results were compared with those obtained from the multiparameter Ysi Exo 3 probe. Field tests were conducted for 45 days. The results were validated with the Ysi Exo 3 probe and statistically evaluated using measures such as standard deviation, coefficient of determination (R2), Root Mean Squared Error (RMSE), Pearson correlation coefficient, and statistical bias. From the analyses carried out, the authors report good performances of the low-cost sensors, emphasizing the accuracy and durability comparable to first-class equipment.
Moreover, for monitoring water quality in lakes, Huan et al. [21] propose an additional assessment of salinity by measuring the electrical conductivity of the water. In addition, the authors implement a correction of the pH, DO, and EC values measured with DFRobot sensors as a function of the temperature value [21]. Calibration laboratory procedures are performed before and after the field tests to evaluate the reliability of the results in terms of absolute error and mean square error over the 20 days of testing. The instruments used as a reference for calibration are an RTD sensor for temperature, and a FOPTOD ODO sensor for dissolved oxygen, while the pH and EC sensors were calibrated with standard solutions.
Standard pH and DO solutions were also used in the calibration procedures for Atlas Scientific sensors presented in Demethyllo et al. [52]. The results of the two-week tests, conducted in two streams, were validated by comparing the measured pH and DO values with those of a multiparametric probe from Horiba. The authors claim that the sensors perform well and evaluate the mean absolute error and coefficient of determination R2 with values of 0.97920.9792, 0.97310.9731, and 0.97460.9746 for DO, pH and temperature, respectively [52].
Bórquez López et al. [50] present a system for monitoring water quality in the context of precision aquaculture. In a laboratory setting, they implement and evaluate the performance of a low-cost open-source platform for measuring pH, DO, and temperature using Atlas Scientific sensors. Validation of the measured parameters was performed using Hanna HI98128 and ISY 155 multiparametric probes, obtaining coefficients of determination R2 of 0.810.81, 0.720.72, and 0.970.97 for DO, pH and temperature, respectively [50]. The authors also evaluated the continuity of operation, reproducibility, and reliability. The continuity of the operation was evaluated by continuous monitoring of parameters for 30 days, and the results show proper system functioning. Reproducibility was confirmed by evaluating three similar systems and comparing the results obtained. Reliability, on the other hand, was confirmed by the survey or statistical analysis of the sensitivity, resolution, precision, and accuracy of the measurements.
In their study, Malissovas et al. [46] present a monitoring system for temperature, pH, and salinity (measured through EC) that is applied to rivers and water channels. The pH sensor’s raw data is corrected for instantaneous temperature, while the EC values are referenced to a temperature of 25 ∘C using a linear compensation method. After temperature corrections and compensations, an algorithm based on phase angle measurement of impedance and voltage levels is used to analyze anomalous events, such as biofouling and possible sensor failures, to identify maintenance needs, repositioning, or replacement of the sensors. The authors evaluate the sensors’ performance for six months under adverse environmental conditions and without any maintenance interventions. Using unidentified reference sensors, they evaluate the low-cost sensors’ performance in terms of absolute and relative error, reporting correlations of 80% and 95% for pH and EC, respectively.

6. The Sensors Analyzed What Environments?

Sensors have been evaluated in a broad range of environments. In some cases, sensors are evaluated in strictly controlled laboratory settings (e.g., [14][61][62]), where environmental conditions are tightly regulated to provide consistent and reliable test conditions. In other papers, sensors are tested in more complex and dynamic environments, such as aquaculture tanks (e.g., [12][21][23][27][28][29][50][63][64][65]), rivers and lakes (e.g., [10][13][15][16][43][46][52][66][67][68][69][70][71]). These natural environments can present challenges that are not present in controlled laboratory environments, such as temperature variations, water currents, and the presence of contaminants. The intrinsic characteristics of the environments where sensors are evaluated can have a significant impact on their performance. For example, in natural aquatic environments, sensors may need to be designed to withstand biofouling, which is the accumulation of biological organisms on the sensor surface that can interfere with its functionality. Similarly, in industrial environments, sensors may need to be able to withstand exposure to harsh chemicals and extreme temperatures.
Figure 3 provides an overview of the number of papers that analyze sensor data in different environmental settings. The majority of the papers (56) focus on natural water bodies, with a specific emphasis on rivers (23), lakes and ponds (18), oceans, estuaries, and tidal channels (7), as well as aquifers and sources (8). Aquafarming applications (38) are the second most common area of focus. It should be noted that 16 articles did not provide information about the environmental context in which the sensors were tested.
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Figure 3. Number of papers considering the environment analyzed by sensors.

This entry is adapted from the peer-reviewed paper 10.3390/s23094424

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