Data on CO2, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic

In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020–2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors. One of the most commonly used techniques to check for good ventilation has been CO2 monitoring. This work provides a set of 80,000 CO2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations.


a b s t r a c t
In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020-2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors. One of the most commonly used techniques to check for good ventilation has been CO 2 monitoring. This work provides a set of 80,0 0 0 CO 2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations.

Value of the Data
• The dataset presented in this paper can be used for experiments to analyse spatiotemporal variation and air quality dynamics in indoor scenarios. • Creation of geostatistical models to analyse the relationship of carbon dioxide levels with other environmental or meteorological covariates such as temperature or humidity. • Development of spatiotemporal risk maps of carbon dioxide concentrations in indoor public places. • The near-real-time monitoring of carbon dioxide concentration data can help the administration and researchers plan strategies and decide on the adequate ventilation of indoor public places. It can help the school administration plan breaks (and their duration) between regular class schedules to improve students' classroom performance.

Data Description
The dataset has been published online in the Zenodo data repository [1] . The data presented were collected between 1 May 2021 and 23 June 2021 using a low-cost CO 2 sensor called SCD30 ( https://bit.ly/3dDWXu1 ). The same sensor can also observe other kinds of phenomena such as temperature and air humidity. Battery level values are also recorded. To transfer the measurements captured, the sensor is coupled to a microcontroller with 3G connectivity. The node consists of open hardware and other elements such as 3D-printed pieces to join all the components. Six nodes were built and deployed in six classrooms in two different schools in two different periods. In the first school, located in Vilafamés (Castellón, Spain), a total of 38,891 observations were carried out. Altogether 34,570 measurements were captured in the second school located in Vall d'Alba (Castellón, Spain). Each node transmitted all the measurements to the main server in real time every 5 minutes. The resulting dataset has been published as raw data, which means that invalid or missing values may appear. These could have been caused by the low-quality sensors themselves, the lack of 3G coverage or other node deployment failures [2] . The raw data  ( Tables 1 and 2 ) examine summary statistics for each phenomenon and report the number of measurements, mean, standard deviation, median, minimum, maximum and range. Moreover, the tables also show some detailed summary statistics components such as trimmed (truncated) mean, median absolute deviation (Mad) and standard error (se). Skewness (skew) and kurtosis are also reported as a measure of symmetry or asymmetry of the data distribution. To distinguish which node has generated the observations, a sensor identifier has been used.
The dataset depicted in the present study was used to explore spatiotemporal variations in indoor air quality in secondary schools in urban areas of Spain [3] . We analysed spatial and temporal variations of CO 2 in classrooms using other atmospheric covariates such as temperature and humidity. Moreover, the study was conducted to identify the variation in CO 2 concentrations based on the number of students, class hours, schedule and duration of breaks between classes, and the size and location of classrooms. The study can provide strategic support for administration and researchers on adequate ventilation of indoor public places.
There has been extensive evidence showing that prolonged exposure to high levels of CO 2 concentration is detrimental to the performance of schoolchildren [ 4 , 5 ]. Hence, a second study was conducted to quantitatively address similar previously suspected problems based on some sparse dataset. The study was designed to test how CO 2 concentration in classrooms influences the level of student attention and reduces mental performance. Observations of CO 2 concentrations could be extended in different classrooms throughout the entire academic year to improve the study. Further development of the study can be performed in other schools with varying types of ventilation during the same time frame.

Hardware components and materials
The published data were collected using a set of nodes created by the authors. These nodes were based on the nodes presented in [ 6 , 7 ] and were built using open-hardware components ( Fig. 1 ). The cited structure defined four different groups Core, Sensing/Acting, Power Supply and Communication. In the following, each category is defined by summarising each component used to build a node: • Core . The core is the main part and is responsible for managing all the behaviour of the IoT node. The microcontroller used is the Particle Boron and follows an open-source design. It includes the Nordic nRF52840 (ARM Cortex-M4F 32-bit processor @ 64MHz and 1MB flash, 256KB RAM) and u-blox SARA U201 (2G/3G), with built-in battery charging circuitry, which makes it easier to connect a Li-Po battery, and 20 mixed-signal GPIOs to interface with sensors, actuators and other electronics. • Sensing/Acting . A shield circuit compatible with the Particle Boron microcontroller is used.
This component can connect and disconnect the sensors and actuators easily. Only one sensor is included, the SCD30 from the Sensirion company. It can measure CO 2 , temperature and humidity. The sensor supports CMOSens technology for IR detection that enables highly accurate carbon dioxide measurement at a competitive price. • Power supply . The node is connected using a USB cable to provide power. An 800 mAh Li-po battery is included in the node to offer an autonomous solution in case of any energy supply issues. • Communication . As previously mentioned, this node offers 3G communication using a SIM card.

Deployment
Six units of the node were built and all of them were placed in different classrooms in two different schools during two different periods ( Fig. 2 ). The first school (CEIP Sant Miquel) is located in Vilafamés (40.1162389, -0.0467876). The total population of schoolchildren in CEIP Sant Miquel is 154, with 101 pupils in primary education and 53 in preschool.
The second school is located close to Vilafamés, in Vall d'Alba (40.1763202, -0.0387236). This school is called CEIP L'Albea, and the total population of schoolchildren is 272, with 186 attending primary school and 86 in preschool.
In the Vilafamés school, the sensors collected observations during the period 1 May 2020 to 28 May 2020, and in the CEIP L'Albea during the period 1 June 2021 to 23 June 2021. Fig. 3 shows the plans of CEIP Sant Miquel, the classes where each sensor was installed being indicated in orange. In addition, each of them has been labelled with the sensor identifier. Table 3 shows the characteristics of each of the classrooms where a sensor was installed, indicating the level of education, the number of pupils, the square metres of each space and the number of windows and doors. Table 4 shows the timetables when each classroom was in use by the students. Table 5 shows the different activities carried out outside the classroom by each class during the monitoring period.   Below, Fig. 4 and Tables 6-8 show the same information for the Vall d'Alba school.

Particle Sketches (code)
The source code developed to collect and send measurements to a main server using 3G connectivity is available in the following repository [8] .   Table 7 Summary of student attendance schedules in each of the classrooms during the monitoring period at the Vall d'Alba school.