Abstract

The combination of Light Detection and Ranging (LiDAR) data and artificial intelligence (AI) is a promising method for understanding and predicting dust levels around industrial sites, particularly in large operations such as mines, construction zones, or extraction sites. The dust generated by these activities can harm human health and have negatively affected surrounding ecosystems.

LiDAR uses beams of light to capture data on the environment in three dimensions. By projecting pulses of light and measuring the return time, LiDAR makes it possible to precisely map areas of terrain, buildings, and even particles in air, such as dust. This volumetric detection capability is essential for identifying the concentrations of fine particles and tracking their movements in the atmosphere around operation sites.

Therefore, LiDAR data can be combined with artificial intelligence algorithms to extract relevant information and develop predictive models for monitoring dust levels. AI models can analyze large quantities of data to identify complex patterns. By integrating LiDAR data, AI can determine correlations between the amount of dust in the air, environmental conditions, and site activity parameters (e.g., intensity of clearing operations and machine speed).

One of the main advantages of AI is its ability to automate data analysis in real time, enabling continuous monitoring and a rapid response to sudden increases in dust levels. LiDAR sensor networks, combined with machine learning models, can identify dust sources, predict their movement according to wind and other atmospheric conditions, and estimate their concentrations in the air at different points on a site.

AI predictive algorithms are particularly useful for predicting dust levels. By analyzing historical data and considering environmental and operational factors, these algorithms can predict high-risk areas and periods. For example, if a weather forecast indicates strong, dry winds, AI models can anticipate dust peaks and recommend mitigation measures such as watering floors or adjusting the use of certain machines.

Numerous industrial applications have demonstrated that these models can significantly improve the accuracy of suspended dust detection, while reducing the cost and effort associated with traditional monitoring methods.

In summary, the use of LiDAR data combined with artificial intelligence represents a major advancement in the management of dust levels around industrial operating sites. It enables proactive monitoring and predictive risk modelling, providing operators with an effective means of protecting human health and the environment while optimizing their processes.