1. Introduction
Institutions and researchers around the world report rises in the Earth’s surface temperature year by year [
1,
2,
3]. Global warming concerns governments that signed the Paris Agreement in 2015 to keep the temperature rise under 2 °C. The effects of global warming are well known as extreme weather events, polar ice melting, and increases in average temperatures. Increasing the average temperature in populated areas can cause heat stress over the population at specific zones and times. That is relevant because it directly affects people’s health [
4,
5] and has a significant economic impact [
6].
Heat Stress (HS) is a known phenomenon that can be parametrized, and its monitoring can help to improve the citizens’ quality of life. HS occurs when a body cannot expel excess heat. When that happens, people feel dizzy, have a high heart rate, have muscle cramps, and have an increased risk of heatstroke. HS is usually monitored to guarantee safe indoor and outdoor working conditions [
7]. Still, it is also essential to study healthy living conditions in populated areas [
8]. This is particularly critical in cities, as urban microclimate conditions can cause higher heat stress outdoors, as well as having a direct influence on thermal conditions inside the buildings [
9,
10].
Modern cities are evolving to the Smart City concept [
11,
12], making relevant information available to citizens and governors. In that direction, many researchers are developing sensor networks to gather details about city performance [
12,
13,
14,
15]. As stated by Bacco et al. [
16], environmental monitoring is a key issue in Smart Cities as it can effectively monitor air quality [
17] and thermal comfort.
However, many wireless sensors are needed to monitor a city thoroughly, so cost-effective solutions must be developed [
18]. In this sense, recent research explores the cost reduction in Smart City monitoring applications. For example, Almalki et al. [
19] presented a sensor architecture for Smart Cities to reduce the energy consumption of each node in the network. Another approach to lowering the cost of measuring data is using non-dedicated sensing systems (such as mobile phones) to increase the number of nodes [
20] and merge this information with traditional sensing nodes. Nevertheless, some measures, such as humidity, wind speed, and wind direction (among others), are complicated to measure from non-dedicated nodes. A low-cost sensor for meteorological purposes is presented in Nan et al. [
21]. The sensor can be deployed as a node in the measurement network. The variables it measures are temperature, humidity, and pressure, but the authors report low operating time and weaknesses against extreme weather conditions. Another approach oriented to low-cost sensors is presented by Hashmy et al. [
22]. In this case, the authors do not show a device but rather a calibration method for low-cost sensors that increase the reliability of the measures obtained from low-cost devices.
ISO 7726 [
23], ISO 7933 [
24], and ISO 7243 [
25] regulations standardize the methods for defining and measuring a heat stress index. The most used parameter to measure heat stress is the wet-bulb globe temperature (WBGT) [
26,
27]. Other commonly used indices are the Predicted Heat Strain (PHS) [
28], the Universal Thermal Climate Index (UTCI) [
29], the Tropical Summer Index (TSI), and the Thermal Working Limit (TWL). Other parameters such as the mean radiant temperature (MRT) [
30] can also be helpful. In any case, to state the current HS, environmental variables (temperature, humidity, etc.) must be continuously measured. When studying HS in cities, a net of reliable measuring nodes will be needed.
Generally, commercial devices compute WBGT to characterize HS, although most modern HS meters give other indicators such as TWL. To calculate WBGT, they measure several magnitudes: air temperature, relative humidity, black globe temperature, radiant sunlight, or air pressure. They usually use chargers or batteries as the power source. In the standalone power case, the device autonomy can reach 16 days at the best chance. Regarding data logging, not all devices allow this feature. Those allowing data storage use custom applications to show data and do not allow real-time connection with external systems. Customers can find HS meters in the market, ranging from EUR 250 to EUR 800. Moreover, high-precision HS meters are available at prices reaching EUR 5000, turning these options too expensive if a large network with multiple nodes is needed or not suitable for outdoor urban environments. Some low-cost dedicated solutions to measure heat stress have already been developed, such as the one presented in Sulzer et al. [
31]. In that case, a very cost-effective solution was obtained, but it required on-grid power supply and W-Fi connection, which are not always available.
Heat stress numerical modeling represents a different but complementary approach to measurement systems for evaluating and managing heat stress. Measurement systems directly assess physiological responses, such as core body temperature and heart rate, providing real-time and accurate data on individuals’ conditions. This approach allows for immediate interventions, catering to individual variability and offering concrete validation of heat stress in specific environments. On the contrary, heat stress modeling relies on computer simulations to predict heat stress levels based on environmental factors, enabling proactive planning and cost-effective assessments across diverse scenarios. While measurement systems excel in precision and real-time action, modeling offers predictive capabilities and cost efficiency, making the choice between them dependent on the specific goals and context of the application.
Currently, several numerical models are employed to simulate the heat stress index. PALM [
32] (Parallelized Large-Eddy Simulation Model) was crafted for simulating atmospheric and environmental flows. It has gained widespread usage in investigating heat stress in urban areas [
33], taking into account factors such as radiation [
34,
35], convection, and the thermal properties of surfaces. Its capability to simulate complex urban structures adds depth to its applications. Some authors have developed works using PALM [
36,
37]. In contrast, ENVI-met [
38] (Environmental Meteorology) is a microscale meteorological model primarily focusing on simulating urban microclimates. This model considers intricate interactions between buildings, vegetation, and the atmosphere, enabling the modeling of temperature, humidity, and other meteorological parameters at high spatial and temporal resolutions. ENVI-met proves valuable in assessing the impact of urban planning and design on heat stress, offering insights that help optimize green spaces and ventilation to create more comfortable urban environments. Works carried out using ENVI-met have been developed by [
39]. Another tool is MITRAS [
40] (Microscale Transport and Stream model), a comprehensive urban climate model developed to analyze the effects of different urban structures and materials on local climate conditions. MITRAS considers factors such as radiation, heat conduction, and airflow to simulate the thermal behavior of urban surfaces. It plays a pivotal role in evaluating the thermal performance of building materials and urban layouts, aiding in the design of energy-efficient and heat-resilient urban areas. Salim et al. [
41] describes the developing theory and underlying processes of the microscale obstacle-resolving model MITRAS. Finally, RayMan [
42], developed by the Meteorological Institute at the University of Freiburg, Germany, is designed for assessing solar radiation and thermal comfort. RayMan calculates human-biometeorological parameters, including the physiological equivalent temperature (PET), to measure thermal comfort. The RayMan model considers variables such as solar radiation, wind speed, and humidity to estimate the thermal sensation and potential heat stress experienced by individuals in outdoor environments. Human Thermal Comfort and heat stress models in urban areas using RayMan are analyzed in [
43,
44]. While these models significantly contribute to developing effective strategies for mitigating heat stress, it is essential to underscore the necessity for real measurements. Integrating these models with on-the-ground observations is crucial for comprehensively understanding and implementing strategies to combat heat stress in diverse environments and comprehensive means to assess such parameters.
This paper shows a cost-effective proposal for heat stress index measuring in outdoor environments. The Heat Stress Monitoring (HSM) system prototype can be deployed as a sensor network, thus covering broad areas, and it is battery-powered, featuring interesting off-grid and wireless capabilities.
4. Results and Discussion
4.1. Application Case: Benicalap (Valencia)
The system was tested in Valencia (Spain). Twelve transmitters and a receiver were installed in various locations within the Benicalap-Ciutat Fallera district. They were left to operate for more than one year, and the data was collected for analysis. The devices’ locations are shown in
Table 3 and
Figure 7.
As noted, in some cases, several devices are positioned in the same locations. The reason is to collect data from different environmental conditions within the same site, each serving a different purpose. For instance, in the senior center, HSM11 is located indoors. Meanwhile, HSM10 and HSM12 are installed outdoors on different roof sections. HSM12 will measure the effects of a planned green roof project, while HSM10 serves as a control. The same situation applies at the school with HSM8 outdoors and HSM9 indoors and with both devices installed on both sides of the same wall. The rest of the units are installed in various points of interest within the neighborhood, with different insolation levels or urban layout.
This application case is part of the Grow Green project, a European Union initiative. Its main objective is to tackle heat stress in urban environments through the use of nature-based solutions. Thus, several different actions have been planned in the Benicalap-Ciutat Fallera district, such as green roofs and walls, or to expand the green areas throughout the district. In this scenario, the HSM prototype plays a key role in the scientific research around the experimental validation of the solutions proposed.
4.2. Testing Results
4.2.1. Wireless Data Transmission Test
Analyzing a month of data registered in the database, given that measures should be taken every 15 min, we can determine how many radio transmissions failed. Due to the slow dynamics of the studied parameters, such as temperature, it is not critical to lose some inputs. For that, we calculated in which time intervals the measures were taken to determine when a significant amount of data was lost. As an example, a histogram of elapsed time between successful measures for HSM1 is shown in
Figure 8.
Comparing these results with devices in other locations,
Table 4 shows data from three devices and two additional test units installed in Universitat Politècnica de València (HSMUPV) that cover short, medium, and long-range measures, in both urban and line-of-sight settings. Data shows at least 97.5% of the radio transmissions were successful at most every 35 min (that is, every 15 or 30 min). We can therefore approve the radio system and consider it valid. Furthermore, if this data is contrasted with the distance of every device to the receiver, it can be determined that there is no significant change in the efficiency. However, there is a mean reduction of 31% in transmission errors when the transmitter is in line of sight. With that, we can establish a maximum valid tested range for the radio system of 345 m for urban environments and 700 m in line of sight.
4.2.2. Power Supply and Autonomy Test
The INA219 sensor measures the power consumption of the transmitter through its input current and voltage when the transmitter is switched on (about 20 s). The rest of the time, the TPL5110 timer switches off the transmitter to save energy. Two tests were carried out to obtain the transmitter power consumption and autonomy.
In the first test, a transmitter was left to operate in a laboratory, taking measures every minute until the battery was drained. As a result, it was obtained that the average power consumption of the transmitter while sending data was 0.641 W (33.33% of the time during the test) and 0.31 W in standby mode (66.66% of the time). The energy consumed to discharge the battery during the test (21.67 h) was 9 Wh. Thus, we obtain an estimated prototype battery life of around nine days per battery.
A second test was carried out to verify the device’s performance and energy consumption. It used data from a device installed in its final location under actual operating conditions, taking measures every 15 min with its PV panel disconnected. The test results revealed that using all the sensors, the device could operate autonomously for up to 36 days using four batteries without recharging (
Figure 9).
This supports the results in
Figure 10 which shows that, when the PV panel is connected, the battery state of charge is always above 95%, as long as the daily average solar irradiation is over 10 W·m
−2. Even under long periods of solar irradiation under 10 W·m
−2, it is able to maintain the charge level above 70%. This ensures an unlimited battery life for the transmitter, working with no interruptions or energy shortages even on cloudy days, in low light regions, under dense foliage, or in the event of a faulty PV system.
4.2.3. Device Robustness
Several transmitters have been thoroughly tested in an urban environment for over a year or more. They have endured harsh weather conditions, including temperatures from −5 °C to 45 °C, strong winds, heavy rain, and long periods of intense solar irradiation.
Previous prototypes of transmitters had several flaws that required continuous updates. The most critical problems were water-related issues. Although 3D-printed PLA parts are easy to print, they are not water-tight because of the nature of fused deposition modelling. Also, PLA is not UV resistant; the material degrades after prolonged sunlight exposure [
52]. Some boxes even broke when trying to open them. Thus, water filtrations during light and heavy rain were common, which led to corrosion and electrical problems. Another frequent problem was insects entering the box through the sensor radiation shield and creating dirt in the electronic circuit.
The proposed modifications included redesigning the box to minimize potential water infiltration paths. All 3D-printed components were coated with exterior acrylic paint and transparent varnish to protect them against direct sunlight exposure and enhance the seal. Critical joints were sealed using PTFE tape and silicone. An insect screen was installed within the radiation shield to deter insect entry. Subsequently, rigorous laboratory testing involving a continuous flow of water falling on the box for five minutes confirmed the absence of water infiltration issues, with no further reported problems in this regard.
It is important to note the transmitter’s limitations concerning sensor robustness. Sensors were selected for accuracy, prioritizing cost-effectiveness. However, certain sensors, such as the DHT22, AM2320, and BME280 relative humidity sensors, exhibited problems after prolonged exposure to high humidity. Fortunately, these sensors are affordable and readily replaceable. Another concern is the solar irradiation sensor (Cebek C-0121), which experiences degradation over time, manifesting as yellowing and dirt accumulating on its surface, affecting measurement accuracy. To mitigate this issue, the proposed solution involves enclosing the sensor within a methacrylate dome.
No breakdowns were reported in the most recent transmitter iterations due to weather conditions (after one year of testing). However, regular sensor checks during annual maintenance and timely necessary replacements are advisable.
4.3. Sensor Data Collected
Data extracted directly from the database allows us to plot the main parameters under study.
Figure 11 shows air temperature, black globe temperature, and relative humidity data, and
Figure 12 shows wind speed and solar radiation data collected from HSM1 from 10 January to 12 January of 2021.
We can also plot the annual temperature variations by gathering data from various months.
Figure 13 shows the monthly maximum, minimum, and average air temperature calculated from the daily averages from HSM3 from January 2020 to November 2021.
The system successfully operated for nearly two years, diligently capturing regular daily and seasonal variations in air temperature patterns, humidity levels, black globe temperature, and solar radiation. To validate its accuracy, temperature and humidity values were meticulously compared with the data from the nearest reference weather station (Viveros, AEMet ref. 8416, N 39°28′50″ W 0°21′59″). The outcomes revealed a mean absolute error of approximately 1.15 °C for temperature and 8.01% for humidity. However, it is fundamental to acknowledge that the reference station, situated around 3 km from the HSM sensor, operates under distinct conditions. Furthermore, the reference station provides hourly data, while the HSM delivers information at a higher temporal resolution every 5 min.
Consequently, the comparison’s relevance is somewhat limited due to the temporal mismatch and differing environmental contexts. Despite these considerations, a cross-verification of temperature and humidity sensors was conducted. The BME280 and AM2320 relative humidity sensors, along with the DS18B20 and AM2320 temperature sensors integrated into the same device, were scrutinized (refer to
Table 1). The analysis yielded an average absolute error of 0.25 °C and 5.96% for temperature and humidity, respectively, well within the nominal accuracy range. This underscores the HSM’s capabilities in ensuring measurement reliability, particularly through sensor redundancy. The redundancy mechanism significantly enhances the system’s robustness, contributing to its overall effectiveness in providing accurate and dependable environmental data.
4.4. Heat Stress Indices Calculation
Using the data collected by the HSM system, we can estimate the heat stress indices described in the Methodology (see
Section 2.1).
Figure 14 shows the results of calculating the WBGT, MRT, and TSI index for HSM1 from 10 January to 12 January 2019.
4.5. Total Cost of the Measurement System
Table 5 describes a detailed list of the cost of materials for an HSM system. This includes the individual cost of every transmitter and receiver and its components plus an extra 10% added for tooling use and production losses. Manufacturing costs as well as time and resources invested in design, development, and testing are not included.
The total cost per device of the whole HSM system is reduced as more transmitters are included, as they can communicate with the same receiver. This is especially convenient for study locations like urban environments where many measuring points are needed in a reduced area. Finally, it is worth noting that the final cost of the system can be significantly reduced depending on the application. The list of sensors can be reduced to the most strictly necessary ones, the power needs may be covered with only two batteries, and the 4G modem and data plan may not be needed at all if the receiver is installed in a site with Wi-Fi connection.
As
Table 6 shows, the HSM system’s final cost per node is significantly lower than other commercial solutions [
53,
54], and it has some additional useful features that mean an advantage over its competitors.
Finally, if we calculate the total cost of the HSM system described in the application case (see
Section 4.1), with a 12-node network of transmitters and one receiver, we obtain a total of EUR 2429.54. This allows us to cover large and dense areas while maintaining a very competitive price.
5. Conclusions
This study aimed to design, build, commission, and test a low-cost Heat Stress Monitoring (HSM) system capable of measuring in an urban environment. The devices have been tested for more than one year. The HSM system consists of two parts: a series of transmitter devices that measure the environmental parameters through sensors and send the data through radio transmission, and a receiver device that receives the data from the transmitter and sends it to a cloud-based database.
The key environmental parameters the HSM system measures include air temperature, relative humidity, wind speed, atmospheric pressure, solar irradiation, and black globe temperature. The transmitter devices can be powered off-grid by a PV panel and Li-ion batteries or connected to the grid through an AC charger. They include a timer that puts the device in standby mode between measures to save energy. This gives an autonomy of up to 36 days per charge, although the PV panel can provide unlimited autonomy.
The HSM system successfully provided the parameters required to estimate heat stress indices such as WBGT, MRT, and TSI using the data collected by the devices. The HSM system can operate indoors and outdoors, and is designed to be replicable, open-source, and low-cost. The material cost for a 12-point network is around EUR 190 per measuring point, significantly lower than other commercial solutions.
The devices have demonstrated resilience in challenging weather conditions but are not without limitations. Previous prototypes were prone to water-related issues, with water infiltration during rain leading to corrosion and electrical problems. Insects entering through the sensor radiation shield caused dirt buildup in the electronic circuit. While design improvements have addressed these problems, the transmitters still have limitations related to sensor robustness. Some sensors, like relative humidity and solar irradiation, may degrade over time, particularly in high-humidity environments or due to yellowing and dirt accumulation. Replacing these sensors during periodic maintenance is recommended to maintain accuracy and functionality.
Further developments of the HSM system will focus on improving long-term durability by adopting more robust sensors, scaling up manufacturing to bring down costs even further, and exploring the possibility of giving 4G capabilities to the transmitters, eliminating the need for a receiver and making the system even more versatile.
The HSM system developed in this study is an innovative and cost-effective solution for monitoring heat stress in urban environments. The system’s ability to measure key environmental parameters, estimate heat stress indices, and operate in extreme weather conditions makes it an attractive option for researchers, city planners, and public health officials concerned with mitigating the effects of heat stress.