# An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
## 3 Industry 4.0 Safety and Exposure Assessment Tools
### Tools for Exposure Assessment and Control  
Real-time data analytics and IoT can provide significant improvements in the case of airborne hazards and mitigation of risks from occupational exposure. The use of smart sensors in the workplace and their installation in areas of high concern can provide live feedback on the operation control room and engineering equipment for emergency shutdown and the operators, as well as the employees via information exchange with their smart devices and wearables [66]. These areas are thermal treatment and processing facilities with high risks of releasing particulate matter or VOC. Additionally, the use of descriptive statistics for trend analyses between hazardous events and unfortunate or catastrophic outcomes provide guidance for health and safety professionals to conduct a risk assessment and design and implement adequate control systems [51; 67]. Predictive AI algorithms have been found to correlate chemical and physical data from installed sensors and provide information on the occupation environment in terms of airborne particles and bioaerosol material [68]. More specifically, a real-time fluorescence-based aerosol cytometer was used to identify bioaerosols based on the light-scattering and fluorescence spectra of airborne particles, classifying them as either bacteria-, fungi- or pollen-like by analysing their fluorescence and optical size. The physical and chemical properties of indoor air (temperature; relative humidity; and the concentrations of carbon dioxide, TVOC, PM2.5, and PM10 particulate matter) were monitored using a commercial-grade indoor air quality (IAQ) sensor. AI models were developed to predict real-time or near-future concentrations of five target features: bacteria-, fungi-, and pollen-like particles (in a number of concentrations), as well as PM2.5 and PM10 (in mass concentrations). To assess the predictive accuracy of each model, the disparities between the measured values and the predictions generated by each model were evaluated. This evaluation was conducted using metrics such as mean squared error (MSE), root-mean-square error (RMSE), and/or a revised version of Willmott's index (WI) for prediction errors between models and actual values. Of course, such predictive algorithms are susceptible to human mobility, temporal variations, and activities in the workplace. These parameters could hinder data quality, highlighting the importance of sensor validation and the dataset provided for the AI training [68; 69]. On-site measurements are limited by the spatiotemporal distribution of hazardous substances in the workplace. The use of virtual simulations and real-time information exchange from wearable devices on the operators' safety gear, could support decision making for movement and--in the worst case--emergency evacuation when the concentrations of chemicals exceed the permitted exposure limits. Digital twins have the ability to simulate the airflow patterns in the workplace and the concentration of particulate matter and chemical species released during manufacturing [69]. These simulations allow the optimisation of the hazard control systems (ventilation, PPE, etc.) [49; 70]. Simulation accuracy and model validation should be taken into careful consideration, comparing the simulated values against real-time data from sensors, ensuring the predictability of models [70]. VR systems (and to a lesser extent, AR devices) have been investigated for employee training during risk assessment and safety procedures in the workplace, taking into account the emissions of airborne hazardous substances [71; 72]. The drawbacks of this technology is the stress induced in the employees by the continuous information reception, discomfort, and visual fatigue [12]. Finally, wearable sensors--such as wristband devices--have been showcasedto continuously monitor air quality and can provide real-time information exchange with control equipment via the cloud network [73; 74].