Leveraging AI For enhancing working conditions in Punjab’s smog-afflicted factories

Industrial emissions and agricultural residue burning have contributed to dangerous smog in Punjab, specifically, leading to poor air quality and working conditions in many factories.

This is something that must be addressed because it’s important for employee health and productivity. With artificial intelligence (AI), we have a transformative opportunity to mitigate the negative consequences of smog in the workplace, as well as on the air quality and actual operational practices.

Many AI solutions can do real-time data analysis, predictive maintenance, and great frameworks for effective decisions in an industrial environment facing environmental challenges.

The deteriorating quality of air in smog-affected factories is of critical concern to smog-affected factories, as poor air quality results in poor respiratory health and reduces worker efficiency.

Other research shows that industrial workers exposed to continuous levels of PM2.5 and PM10 have a greatly increased risk of developing COPD (Jin, et. al. 2020).

But sensors and Internet of Things (IoT) devices powered by AI may have a crucial part in the real tracking of air quality at the time. For instance, an advanced AI model can survey sensor data to identify an imminent toxic gas leak, or increasing particulate matter; immediately calling for mitigation actions (Gupta et al., 2021). Beyond that, it safeguards workers' health and avoids the disruptions in factory operations caused by health-based absenteeism.

Wearable technology applications of AI further bolster worker protection. AI algorithms are used to equip smart wearables for monitoring physiological parameters like heart rate, oxygen saturation, and levels of stress that can issue early warnings about possible health risks (Ahmed et al., 2022).

Because these devices are especially useful to people in smog-affected areas, where there is a greater risk of hypoxia and associated conditions. With AI-driven wearables we ensure timely medical intervention that results in fewer incidences of health-related issues and thus a safer work environment.

Additionally, these technologies can be integrated, along with factory-wide data systems, for the purpose of individual personalized health management that incorporates safety protocols that are customized for the individual.

AI does not just deal with the two initially highlighted aspects, of health monitoring and air quality assessment, but can also tackle broader systemic problems which are causing workers to work in unsafe conditions.

The poor maintenance of ventilation systems further aggravates smog's impact in factory premises where used air is circulated back due to lack of ventilation.

With predictive maintenance driven by AI, HVAC (heating, ventilation, air conditioning) system failures can be predicted before they happen to keep systems running optimally, and provide cleaner indoor air (Smith & Zhao, 2020).

Historic records from the HVAC equipment are fed to machine learning algorithms which predict breakdown patterns to reduce the failure of the system and associated health hazards.

Not only does this increase air quality and improve efficiency, but it also cuts operational costs to the tune of dual benefits for factory management.

One other major contribution of AI is freeing workers from performing tedious, risky tasks where they are exposed directly to potentially dangerous pollutants.

More and more, automated robotic systems equipped with AI are used for such tasks as welding, painting, and material handling, which produce large amounts of air pollutants (Chen et al., 2019).

The manufacturing process can be performed without the direct involvement of humans in such activities, to minimise the risk of respiratory diseases and to guarantee compliance with the occupational safety standards.

However, additionally, AI-based robots are able to undertake these tasks with better precision and more efficiency and in turn help in raising the overall productivity.

In smog-affected regions, the predictability that characterizes AI also applies to environmental risk management. AI models can forecast pollution with advanced analytics, using meteorological data, industrial activity, and seasonal varia issues (Park et al., 2021).

By making these predictions, factory managers can take adaptive measures to improve such as rescheduling outdoor tasks during peak pollution hours, or improving indoor air filtration systems.

AI-driven forecasting coupled with factory operations is integrated such that environmental conditions are being met dynamically to reduce workers’ exposure to harmful pollutants.

Additionally, AI has some policy implications regarding its implementation in smog-afflicted factories. AI systems generate accurate and comprehensive data on air quality and worker health and hence can provide insights for regulatory compliance and the formulation of policy.

AI real-time air quality monitoring can help factories meet national and international environmental standards, this will help them avoid paying penalties as well as help improve their public image (Kumar et al., 2023).

Furthermore, the data of AI systems may inform policymakers about the effects of the existing regulations to improve environmental governance.

However, integrating AI in Punjab’s factories comes with challenges. However, barriers to implementation include the high initial cost of AI technologies and lack of technical expertise.

Furthermore, workers' trust in AI and the ethical use of AI-generated health data must be kept in mind to maintain compliance (Mishra & Singh, 2022). Policymakers and industry stakeholders need to work together in developing AI adoption frameworks that facilitate AI adoption without eviscerating the rights of the workers.

The barriers to the widespread use of AI in solving environmental challenges may be overcome in great measure by the use of public-private partnerships and government incentives.

Finally, the use of AI technology to increase working conditions in Punjab's smog-infested factories is a multi-pronged approach to air quality and workers' health.

The adverse effects of smog are reduced by AI's capabilities to monitor and predict, automate tasks, minimize environmental risks, and more.

As yet, implementation remains a challenge, but strategic investments and policy support can unleash the power of AI for a safer, more sustainable industrial environment.

Punjab’s factories can not only protect their workforce but can also establish an example for environmentally conscious industrial practices by accepting AI-driven solutions.