# Indoor Air Quality Assessment in Grocery Stores
## 2 Materials and Methods
### IAQ Monitoring Equipment  
Indoor levels of air pollutants and comfort parameters were monitored using air quality monitoring units (MBs) based on low-cost sensors. The MBs collected real-time data of CO2 PM2.5, PM10, VOCs, RH, and T, with a 5 min monitoring frequency. These MBs were already applied with success in previous studies [10]. Figure S1 (in the Supplementary Information section) provides a description of the layout of the different sensors and components in the MBs. The sensors used in the MBs were (i) an SCD30 for CO2 (with a measuring range of 400-10,000 ppm, and accuracy of \(\pm\)30 ppm), (ii) a MICS-VZ-89 TE for VOCs (with a measuring range of 0-1000 ppb in isobutyene), (iii) an HPMA115s0 for PM2.5 and PM10 (with a measuring range of 0-1000 ug.m\({}^{-3}\), and an accuracy of 15%), and (iv) an SHT31 for temperature (T) and relative humidity (RH) (precision: \(\pm\)2% RH and \(\pm\)0.3 \({}^{\circ}\)C). After being plugged in, the MBs sent data to an online platform instantaneously via wireless, which stored the data in a cloud. Simultaneously, the data were available visually online in real time. A total of three MBs were used in this study, with the following ID: AirQ1 (A), AirQ2 (B), and AirQ3 (C).  
The hardware platform that was used to manage all sensors was running by an Arduino bootloader on an ATSAMD21G18A Cortex-M0+ processor, which was responsible for measuring all the sensors, controlling airflow, and interacting with the GSM Modern (Quectel M66). Open software libraries were used to drive the used low-cost sensors, namely, for CO2, Sparkuns SCD30 (available online: [[https://github.com/sparkfun/SparkFun_SCD30_Arduino_Library](https://github.com/sparkfun/SparkFun_SCD30_Arduino_Library)]([https://github.com/sparkfun/SparkFun_SCD30_Arduino_Library](https://github.com/sparkfun/SparkFun_SCD30_Arduino_Library)), accessed on 2 November 2021) and for comfort parameters, Adafruits SHT31 (available online: [[https://github.com/adafruit/Adafruit_SHT31](https://github.com/adafruit/Adafruit_SHT31)]([https://github.com/adafruit/Adafruit_SHT31](https://github.com/adafruit/Adafruit_SHT31)),  
Figure 1: Locations of the 13 studied GSs in the Cascais municipality (delimitation defined by dark grey area), within the Lisbon metropolitan area.  
accessed on 2 November 2021). For PM\({}_{2.5}\) and PM\({}_{10}\) (sensor HPMA115s0) and VOCs (sensor MiCS-VZ-89 TE), the used libraries were developed in-house since no open-source libraries were available from mainstream hardware developers.  
The performance and data reliability (Table 1) were assessed by inter-comparison with reference real-time instruments (using a monitoring frequency of 5 min), namely, a DustTrak DRX monitor (8533 model, TSI, Shoreview, MN, USA) to assess PM\({}_{2.5}\) and PM\({}_{10}\), and a Graywolf (IQ-610 probe, WolfSense Solutions, Shelton, CT, USA) to assess CO\({}_{2}\), VOCs, T, and RH. Table 1 presents the intercomparison results, including the correction factors (m and b) applied to each MB.  
Very strong correlations were obtained (R\({}^{2}\) ranging from 0.80-1.00) for all the parameters, except for VOCs, which was considered moderate for AirQ1 (0.43) and strong for AirQ2 and AirQ3 (0.64 and 0.68, respectively).  
The raw monitoring data were downloaded from the online platform and, afterward, the correction factors of each sensor and each MB were applied to the raw data, by using Microsoft Excel.