Air pollution has emerged as a prominent factor in the worldwide disease burden on the environment. According to the World Health Organization (WHO), 92% of the global population is subjected to pollutants exceeding the air quality thresholds considered detrimental to health (WHO, 2017). 

Extensive research from China indicates that prolonged exposure to inadequate air quality significantly increases the susceptibility of individuals to heart attacks, lung cancer, strokes, and other severe life-threatening ailments. Consequently, Canadian researchers state that there is a significant need for the scientific community to offer dependable and precise remedies to address the impact of air pollution on human health. 

AI has become the dominant technology for managing and preventing the harmful effects of various air pollutants. It has garnered significant attention in the fields of atmospheric and medical sciences. Multiple researchers have employed AI methods as clinical decision support systems to diagnose, manage, and treat diseases caused by air pollution. 

The following are some of the contributions from the researchers worldwide:

• The Netherlands researchers developed a deep learning model to forecast lung cancer initiation using CT-scan datasets from 10,368 individuals. The authors stated that the classification model effectively detected benign lung tumours. They also highlighted the importance of consistent screening techniques to enhance the model's accuracy. 
• Researchers from Vietnam, Iraq and Saudi Arabia introduced a new approach for identifying cardiac illnesses by combining Fuzzy logic and Support vector machine in an ensemble model known as FSVM. The suggested FSVM model used a historical heart disease database consisting of 573 observations as inputs. The results indicate that the ensemble model demonstrated high accuracy in classifying cardiac illness and significantly reduced the computational time required for disease diagnosis.
• The researchers from Canada and California stated that AI is essential for local environmental protection agencies to make intelligent choices about reducing air pollution and protecting the public from harm. 
• The researchers from Spain stated that AI can deal with the complicated and non-linear connections between air quality factors, which helps them make better predictions about pollution episodes. It's a great way to keep track of the current pollution level and a quick and accurate way to find places where pollution is terrible. This method also creates analytical algorithms for various variables from air quality and meteorological data. These help us understand and predict smog, haze, visibility, and other weather changes and better control air quality. 

Air pollution prediction

Recent years have seen a rise in interest in AI-based air pollution forecasting methods for predicting pollution levels in the air. With fast technological progress in big data analytics, like better computing platforms, scalable storage systems, and high-speed parallel processing machines, AI has caught the attention of researchers who want to make air pollution forecasting systems that are more advanced and accurate. Researchers have already examined how AI-based methods relate to other methods for predicting air pollution. 

• In 2002, the Canadian researcher Mc Kendry looked at ANN and Multiple Linear Regression (MLR), a statistical method that uses several explanatory factors to guess the values of a response variable for simulating PM10 and PM2.5 particles. 
• In 2021, the Indian researchers Dutta and Jinsart looked at how well ANN and decision tree models worked at estimating PM10 levels. 
• In the same way, the researchers from Spain looked at how well the Back-propagation neural network (BPNN) and the Autoregressive integrated moving average (ARIMA), another statistical time-series forecasting tool, could predict levels of CO, SPM (suspended particulate matter) and SO2 in an industrialized area. 
• Chinese researchers have developed a new way to predict hourly PM2.5 concentrations using random forest and ANN methods. 

These studies show that the AI-based approach is the best way to predict air pollution because it has many benefits over traditional forecasting methods, such as handling data quickly, accurately, and with little to no error. Because AI is constantly improving and can be used to predict air pollution, especially when figuring out how much certain pollutants there are, all the research on the most popular data-driven methods needs to be reviewed carefully.

Conclusion

AI can help the fight against pollution by providing data in real-time, streamlining processes, and allowing for strategic strategies to lower emissions. But it's important to remember that AI is only one tool in the fight against air pollution. This complicated problem needs a comprehensive plan to solve it.

AI-based techniques are regarded as the most revolutionary technology for forecasting air pollution. It is because they possess unique characteristics such as organic learning, high precision, superior generalization, excellent fault tolerance, and better specificity. Furthermore, their utilization has yielded fresh perspectives on the environmental variables that substantially influence air pollution levels.

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