Low-Cost Environmental Monitoring Station to Acquire Health Quality Factors ^†

Abstract

With the exponential development of MEMS (Micro-Electromechanical Systems) in the last decade, emphasis has been placed on the construction of IoT devices in conjunction with an appropriate information system to assist citizens in various fields (transportation, trade, etc.). More specifically, in the health sector,, there are specific IoT devices which can monitor a patient’s health condition or provide environmental data for the area, information which affects health quality conditions. In densely populated areas and especially in large cities, in terms of environmental pollution, as well as the known issue of air pollution, citizens are also exposed to solar radiation (ultraviolet UVA UVB radiation), as well as to noise pollution in areas where people live and work. Ultraviolet radiation, especially during the summer months, is responsible for skin cancer and various eye diseases, while noise pollution can create mental disorders in humans, especially in children. In this article, a low-cost solar radiation and noise pollution monitoring station is presented. The parts that compose the station and its implementation are a microcontroller (TTGO-OLED32) with an integrated LoRa device, an ultraviolet radiation sensor and sound sensors. In addition, a mini ups device is used in case of power failure and a GPS device is utilized for the location point. The measurements are obtained by the sensors every ten minutes and are transmitted via the LoRa network to an application server in which the user has direct access to the environmental data of a specific area. In conclusion, the data obtained from such IoT devices help in the study of cities to optimize factors in people’s lives.

1. Introduction

Issues regarding environmental pollution and health quality, specifically in developed large cities, have been the subject of various studies in recent years [1,2,3,4]. In general, the term health is linked to terms like the wellness of the body and the effects on human life [5,6,7,8]; however, mental health also proves to be equally significant [9,10,11]. In the everyday environment where someone lives and works, there is a set of pollutants, such as atmospheric pollutants including increased greenhouse gases, but also other forms of pollution such as noise and light. Regarding atmospheric pollution, the air quality in densely populated areas has been shown to have an impact on human health, both from gaseous pollutants and from particulate matter [12,13,14], which are the result of human activities such as the development of industry, residential heating systems, traffic, etc. In addition, there are other forms of environmental pollution, like the acoustic noise of an area originating from industrial or residential activities, leading to the degradation of life quality, as well as the incidence of mental disorders. It greatly contributes to human mental disturbance [15], and although it has not been proven to be connected with mental diseases, a correlation between residences near airports and the prevalence of strong symptoms of depression is evident [16]. Subjective health symptoms, such as fatigue and headaches, are consistently reported more often by children who live near an airport facility or go to school in noisy areas [17]. There are issues reported where the light pollution of an area with ultraviolet radiation can be harmful to humans. Research has shown the harmful effects of human exposure to ultraviolet radiation from the sun, demonstrating that it causes skin cancer in a very large percentage [18]. UV radiation can also affect vision as it carries higher energy than visible light and high dose exposure to UV radiation causes direct cell damage, which plays an important role in cancer development [19]. In the last decade, the rapid development and construction of electronic circuits has resulted in the creation of reliable microcontrollers and low-cost sensors. This aspect offers feasibility to build affordable environmental monitoring systems, both in terms of air quality monitoring [20] and environmental conditions monitoring such as acoustic noise and light pollution of an area, giving citizens invaluable information for their residential area. As a communication carrier, a LoRaWAN [21] network can be used to ensure the precise linkage of monitoring stations without fees in a spatial coverage of 3 km. This paper presents the study and implementation of a low-cost environmental monitoring station which includes both noise and light pollution sensors. The retrieved values of the noise level are measured at dB for noise pollution, while the level of ultraviolet radiation is measured as a UV index of light pollution. Due to rapid technological developments, there are many reliable low-cost systems that meet low power consumption and excellent processing standards such as the TTGO@ESP32 microprocessor [22]. These systems offer sufficient local data analysis with low power consumption and LoRa wireless connectivity. This article is organized into the following sections: Section 2 describes the materials and methods, Section 3 comprises the results and discussion, and Section 4 offers the conclusions.

2. Materials and Methods

Three sections compose the IoT architecture [23], perception, network, and application, as seen in Figure 1.

In the case of this work, the low-cost environmental monitoring station constitutes the perception part, including the microcontroller and sensors. The microprocessor unit (CPU) is the TTGO@ESP32, and as sensors, a GYML8511 UV [24] sensor for light pollution and a sound sensor module [25] for sound pollution were used. The network section, also referred to as the transparent segment, aims to connect the perception and application sections. This module is responsible for transmitting data from the low-cost stations to a central station (server) that contains the application section. In this work, we utilize the built-in LoRa network function contained in TTGO@ESP32. Data transfer is conducted using “The Things Network (TTN)” LoRaWAN network. The application section is the final stage of the IoT system, which provides the services to the end user. This section supports many applications for the development of IoT (Internet of Things) systems. In this work, the application section is supported by the Cayenne application server web portal [26]. The components of the low-cost environmental conditions monitoring station are the microprocessor and the sensors. In addition, an expansion board has been constructed for the interconnection between the CPU and sensors.

• TTGO@ESP32

The main CPU is the TTGO@ESP32 [22] (Figure 2a), which is an open-source Arduino-based firmware for IoT implementations. It is an ideal module for IoT devices as it remains highly affordable with a plethora of features, such as processing capabilities with a fast time response. It can be programmed using the open-source Arduino Software (IDE), providing convenience in coding and uploading to the board while being a familiar and user-friendly software. This processor satisfies the requirements of communication for the sensors array (25 I/O ports, UART, I2C, SPI interfaces). The CPU data processing speed further satisfies this implementation with low power consumption. The microprocessor integrates the data transfer communication which can be implemented over a long-range (LoRa) network or a wireless network (Wi-Fi). In addition, a battery (type 18650) is connected to the integrated charger of the main board for uninterrupted operation in the event of a power failure and voltage stability.

• Sensors

For the environmental conditions, light and noise pollution sensors were utilized; the GYML8511 [24] ultraviolet sensor (Figure 2b) outputs an analog signal in relation to the amount of the UV light, as the sensor is capable of detecting wavelengths of 280–390 nm light with high precision. For noise pollution validation, a sound sensor module [25] (Figure 2c) of a dynamic microphone (electromagnetic microphone) and amplifier circuit with analog output was exploited. In addition, a GPS module (Figure 2d) was used for the station location.

• Station implementation

The diagram of implementation and the final construction of the environmental conditions monitoring station are shown in Figure 3a and Figure 3b, respectively. The whole device is integrated in a waterproof box type ΙP66 (with dimensions of 115 mm × 150 mm), which is rather small. The total cost of the station is approximately EUR 80. Every ten minutes, a measurement is conducted and transferred over the LoRa network via the Internet to the application server.

3. Results and Discussion

The evaluation and accuracy of the measurements from the low-cost environmental conditions monitoring station are presented in this section. This custom-made monitoring layout has been installed on the roof of a building, directly in the sun, at a height of 1.5 m, located in eastern Attica in Greece, specifically, in the municipality of Agia Paraskevi. In the evaluation of the UV low-cost sensor, the common known measurement of the UV pollution is the UV index, the calculation of the output of the low-cost UV sensor is given in miliWatt/square centimeter, and the investigation of the transformation of the output sensor to the UV index has taken place. According to the datasheet of the sensor [24], specifically, the graph of the output voltage, UV intensity characteristics can extract the slope of the sensor response. The proposed UV index (Equation (1)) is the result, as the output of the sensor is multiplied by the slope and the result is divided by a correction factor (A).

$$UVI=\frac{\left(calculateoutput\left(\frac{\mathrm{m}\mathrm{W}}{{\mathrm{c}\mathrm{m}}^2}\right)·slope\left(\frac{{\mathrm{c}\mathrm{m}}^2}{\mathrm{m}\mathrm{W}}\right)\right)}{A}$$

(1)

The reference data are received by the official monitoring station in cooperation with the National Observatory of Athens [27]. The distance between the low-cost station and the reference station is 3 km. The following Figure 4 shows the time-series measurements and the correlation of the UV index of the low-cost monitoring station and the reference station on the last day of June, July and August of 2023.

Regarding noise pollution, the results of the measurements are shown indicatively as there are no reference measurements. The calibration of the microphone module was performed in a laboratory where the noise level decibel (dB) was detected by official instruments. Figure 5 shows the time-series measurements of noise pollution for the low-cost monitoring station on the last day of June, July and August of 2023.

4. Conclusions

In the environment, alongside atmospheric pollution, there are other forms of pollution, such as light and noise, which affect human health and wellness. In this article, a low-cost UV radiation and noise pollution monitoring station has been presented. Data transfer has been conducted through the LoRa network, while visualization has been supported by Cayenne, which is an IoT application server on the Internet. The measurements have been retrieved in vivo and the measured values have been corrected using a proposed equation to extract the UV index from the UV sensor and the sound output in decibels (dB) from the sound sensor. The results are encouraging as the low-cost UV sensor shows a correlation coefficient R² greater than 90% with respect to the reference data, while the noise value accuracy shows satisfactory results, as the station was installed in a quiet neighborhood of northeast Attica. The use of low-cost sensors and their utilization with microcontrollers of new technology can satisfy a wide range of detection for environmental conditions. The aim is to attract more and more people to actively participate in actions such as public health monitoring which is a common good for all. However, it is a good and affordable solution to inform the general public about the environmental conditions where they live and work.