AI Takes on Air Quality: A Smarter Approach to Pollution Predictions

Have you ever wondered about the unseeable aftermath of raging wildfires beyond the charred landscapes and immediate destruction?

Today, with the evident rise in fluctuating temperatures, droughts and gruesome wildfires have dramatically disrupted the environment and human health. In the year 2023, Canada reported its most sinister wildfire season, with fires emitting more than 29 million tons of carbon straight into the atmosphere. But we’re not just taking into account the wildfires in 2023; California encountered record-setting fire seasons in 2020 and 2021.

And, of course, how can we forget the great Amazon rainforest fires in 2022?

On 22nd August 22, about 3358 fires were detected in the Brazilian Amazon, according to the Brazilian space agency INPE. This was the most elevated number of fires recorded in 24 hours since 2007.

The world must do more than watch in horror as the world’s largest intact forest is being driven toward a climate tipping point.

The aftermath of these kinds of pollution goes from being a minor inconvenience to being the deadliest. Smoke emitted from Canadian fires reaches as far as Spain and Portugal, thus disrupting the air quality in some of the major cities in the US and Canada. This doesn’t only. Disrupting the air quality also ruins human health with symptoms such as stinging/ burning eyes, congestion/ stuffy nose, and heavy breathing for millions of individuals.

According to the National Institute of Healthcare, Air pollution is the reason for at least the death of 6.5 Million people annually and globally.

Marisa Hughes, Climate intelligence lead at the Johns Hopkins Applied Physics Laboratory (APL), Laurel, and assistant manager of the Human and Machine Intelligence program Maryland, mentions, “We know that dangerous air quality levels are a significant threat, but because exposure happens slowly, over time, it is more difficult to quantify.”

She also came up with a solution when she stated, “A more accurate, higher-resolution model can help protect populations by providing them with information about air quality over time so that they can better plan ahead.”

Birth of Intelligent Weather Forecasting

In order to comprehend the concept of how smoke pollutants travel within a specified time frame, Multiple researchers at APL and NOAA ( National Oceanic and Atmospheric Administration are leaning towards utilizing Artificial Intelligence to simulate Atmospheric Models.

These modules will help weather forecasters deliver news way before time with more accuracy and higher resolution of the directions and transitions in air quality hazards such as wildfire plumes and many more.

At the moment, the methodology utilized by weather forecasters depends on factors and data such as the temperature of the air, atmospheric composition and pressure. All these components are then calculated with the help of Complex equations that adhere to the laws of physics, chemistry and atmospheric transport, which in turn produce simulations of future weather events.

The time taken for each model to deliver the predictions is termed a Timestep. To foresee this further into the future for multiple timesteps, these models will require excessive computational power, data and time to analyze all parameters.

Principal Investigator Jennifer Sleeman, Senior AI Researcher at APL, stated that, In this case, the modules will monitor around 200 varieties of pollutants in the atmosphere for each timestep in a sequence, which is roughly 40% of their computation. Along with this, they also have to consider the interaction between these chemicals and how they decompose. So, the chemistry part here is around 30%. Thus, it is concluded that it takes a massive amount of energy to perform air quality forecasting with all the variables utilized.

When it comes to predicting forecasts with complete accuracy, one model isn’t enough. Many research studies use a method known as ensemble modelling. In this type of model, they can power a few variation models and go up powering 100 variation models to account for possible changes in varied conditions of cold spells or upcoming pressure systems and use the mean of all those variations for forecasting.

Role of AI

Here is where AI comes into the picture. With the help of AI-assisted methods, the concerns regarding speed and accuracy can be tackled smoothly, resulting in 100% accurate weather and pollution forecasts. This could potentially save a massive amount of computational power, thus cutting down timesteps, which is easier and faster to handle.

With the help of a deep-learning emulator, ensembles can be simulated and account for variations in weather data.

Benefits of AI in Pollution Prediction

• Swift and Accurate Predictions
• Optimized Computational Efficiency:
• Reduced Time Steps
• Enhanced Emulation with Deep Learning:
• Unparalleled Forecasting Precision
• Efficient Data Analysis
• Adaptability to Dynamic Conditions
• Early Warning Systems Development
• Decision Support for Policymakers
• Continuous Learning and Improvement

Are you ready for a future where AI plays a pivotal role in predicting and mitigating pollution risks associated with these disasters?

With advancements in AI-driven technologies, particularly in weather forecasting, companies like Mobiloitte are at the forefront of developing innovative solutions.

Are you prepared to embrace the power of AI in safeguarding our environment and communities from the unseen impacts of wildfires?

If yes, then it’s time to contact Mobiloitte and check out their AI Development services.

Read More:https://www.mobiloitte.com/artificial-intelligence-solution/