Air pollution is a major issue across the world. The rising population and expanding industrialization have led to a significant increase in air pollution, resulting in various health problems for human beings. Breathing polluted air can lead to respiratory diseases, heart problems, and even premature death. According to the World Health Organization (WHO), air pollution accounts for approximately seven million premature deaths worldwide each year. However, there is hope. Artificial intelligence (AI) is providing a promising avenue for curbing air pollution and improving air quality. In this article, we will explore how AI can be used to improve air quality, its effectiveness, and challenges that may come with it.
**How AI can be used to improve air quality**
AI is a versatile technology that can be used in various applications, including improving air quality. Two common approaches utilize its capabilities which are:
– Monitoring air quality
– Predicting future air quality
**Monitoring air quality**
Efficient data collection and analysis are crucial to achieve effective strategies for controlling air pollution. Traditional air quality monitoring systems can be costly, lengthy, and may not capture accurate data in real-time. However, AI-based air quality monitoring systems are changing that. The data collected from sensors, satellites, and other sources can be analyzed in real-time, providing up-to-date information beneficial in mitigating pollution.
Through AI, data analytics can deduce patterns and relationships that may not be apparent from the raw data. This information then can help in targeted interventions. For example, identifying specific pollutant sources in real-time to react immediately by deploying personnel to mitigate the effects. Or inform policymakers on the emissions reduction potential of alternative fuels such as electric cars.
**Predicting future air quality**
Apart from tracking pollutants in real-time, AI can predict air quality and pollution levels at specific periods in the future, thereby developing an early warning system to aid in making proactive interventions.
AI-powered models can predict future pollution levels, using real-time pollution data fed into machine learning algorithms that learn to identify patterns in this data. The model can provide predictions on which regions or areas are more susceptible to high pollution and the expectations on the severity of pollutants at a specific time. Such predictions aid in providing early warnings before pollutants reach dangerous levels and the necessary measures put in place.
**Effectiveness of AI-based air quality interventions**
Machine learning algorithms need vast amounts of quality data to learn and make predictions. AI-based air quality interventions rely on data such as meteorological data, wind speed and direction, and pollutant levels to achieve efficient performance and accuracy. These data are usually generated from sensors installed on buildings and satellites. The European Space Agency (ESA) has collocated data from satellite Earth Observation images with in-situ data to estimate air quality, especially for regional and continental scales. Such data often are collected and maintained by various governmental agencies and institutions such as NASA and the US Environmental Protection Agency EPA.
AI-based interventions can enhance pollution reduction programs such as emissions tracking and control. One example is Beijing, China, which has identified a significant decrease in particulate matter pollution using such interventions from 2017 to 2019. Beijing’s government provided citizens with a smartphone application that used AI algorithms to record and analyze the city’s air quality data. Citizens use the app to monitor air quality in real-time, enabling the identification of pollution hotspots. This application has led to a reduction in PM2.5 concentrations by up to 20%, according to the China News Service.
**Challenges in AI-based air quality interventions**
AI-powered air quality interventions have great potential in controlling pollution. However, there are still some challenges that AI must face when used for air quality interventions. These challenges include:
– Limited data availability
– Data standardization
– Non-linear correlation between different pollutants
– The high cost and maintenance of sensor data collection systems
In Africa, particularly in sub-Saharan regions, air quality data is scarce. This shortage limits the effectiveness of AI-based solutions in supporting policies and mitigating pollution. There are also challenges in the integration of data from different sources, typically resulting in poor data standardization, and data bias. Poor data input or lack of information on pollutants can reduce the efficacy of machine learning algorithms.
Another challenge is the lack of a clear correlation between different air pollutants. There are often non-linear relationships between different pollutant concentrations, which can make it difficult to achieve correct predictions through AI algorithms.
Finally, sensor installation and maintenance costs are still a significant challenge. The cost of investing in AI-based air quality monitoring is still relatively high, especially in developing countries. However, current trends towards low-cost, portable devices may provide a solution in the future.
**Conclusion**
AI-based air quality interventions are a vital tool in the fight against air pollution. With its smart data analysis capabilities and predictive models, it has the potential to transform air quality management policies and reduce the adverse health effects of air pollution. Although there are some present challenges, as technology improves, it is expected that the cost and maintenance of sensor data collection systems will reduce. It is, therefore, vital for governments and private organizations to invest in these technologies to aid in the fight against air pollution and improve air quality for the betterment of human health.