###### Abstract
The case study revealed that the spatial decay of PM\({}_{1.0}\) concentration was slower than that of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations, i.e., the air pollution of PM\({}_{1.0}\) was not easily dispersed. [PERSON] and [PERSON] [8] presented an environment-monitoring IoT system for prediction, which featured a low power consumption of 25.67 mW and the prediction of PM\({}_{2.5}\) by machine learning. [PERSON] et al. [9] further investigated the heavy metals Cd, Pb, Mn, Zn, and Cu bound in PM\({}_{2.5}\) air pollutants in both indoor and outdoor campus environments. The results pointed out that approximately 52% of Cd, 40-43% of Pb, 40-42% of Mn, 34-49% of Zn, and 26-29% of Cu indoors originated from outdoors. That is to say, the heavy metals Cd, Pb, Mn, Zn, and Cu bound in indoor PM\({}_{2.5}\) air pollutants primarily came from surrounding outdoor emissions. Recently, monitoring workplace environment quality has attracted much attention in smart agriculture, smart manufacturing, and smart medicine, health, and care. In 2020, a design concept of a real-time industrial environment-monitoring system for metal-processing operation was addressed by [PERSON] [10] for the surveillance of the ambient environment, air quality (AQ), and water quality conditions. MQ-2 CO/SO\({}_{2}\) and NOx gas sensors, as well as PM PM\({}_{1.0/2.5/10}\) sensors, were used to monitor the environmental quality of the industrial workplace of a metal factory. Furthermore, [PERSON] et al. [11] illustrated an industrial IoT (IIoT) case study with smart helmet personal protective equipment (PPE) by integrating a light sensor, a 6-DoF motion-tracking MPU6050 sensor, and a BME680 multi-sensor module with ESP-32 Wi-Fi module for monitoring environmental conditions of a workplace and fall detection. The observation data were further analyzed using a deep convolutional neural network (ConvNet/CNN) to predict the possible occupational risks. The BME680 device in the proposed Smart Helmet 5.0 platform was used to provide an air quality index (AQI) in compliance with ISO16000-29 [12]--indoor air--Part 29: test methods for VOC detectors. In 2021, [PERSON] et al. integrated both MQ-2 and MQ-135 gas sensors with ESP8266 modules using WSN technologies with the message-queuing telemetry transport (MQTT) protocol for a model of working environment monitoring in a smart manufacturing system of paper haberdashery [13]. On the other hand, both fixed and mobile environment-monitoring systems for monitoring the microclimate in urban areas of the city of Bolzano, Italy, were presented by [PERSON] and [PERSON] in the year 2022 [14]. From the viewpoint of cost effectiveness, a Raspberry Pi (RPi) platform was used to aggregate the sensing devices and forward the sensing data through a wireless LoRa network to the environmental data platform (EDP) of Eurac Research. For an environment-monitoring system, the authors used the same data as the data from the city of Bolzano, Italy, and the data from the city of Bolzano, Italy, and the data from the city of Bolzano, Italy. The authors used the same data from the city of Bolzano, Italy, and the data from the city of Bolzano, Italy, and the data from the city of Bolzano, Italy. The authors used the same data from the city of Bolz  
applications on a campus, [PERSON] et al. [15] adopted a commercially available indoor AQ (IAQ) monitoring BME680 sensor to monitor the ambient temperature, humidity, and IAQ level for volatile organic compound (VOC) detection such as NO\({}_{2}\) and CO, and it facilitated the indication of the IAQ level of the Rovira i Virgili University campus through LoRa network and The Things Network (TTN) platform. Furthermore, [PERSON] et al. adopted both SGP30 and SGP40 gas sensors to measures total VOCs (TVOCs) and indoor air quality (IAQ) index to identify the best and most flexible solution for air quality threshold selection of hazardous/toxic gas detection for IAQ alarm systems [16]. In addition, an SHTC3 was used to measure ambient temperature and humidity.