Magora Systems Blog Leveraging AI for Environmental Monitoring and Waste Management

Table of contents The Importance of Environmental Monitoring in Urban Areas AI in Sustainability: Air Quality Monitoring AI in Sustainability: Water Quality Monitoring AI in Sustainability: Waste Management Benefits and Challenges of Implementing AI in Cities AI in Sustainability: Final Thoughts With the rapid boom of Artificial Intelligence (AI), it’s now evident that this new technology will dramatically impact every aspect of life. And while a lot has been said about the ways it could influence sectors like healthcare, education, real estate and hospitality, there’s less discussion around the intersection of AI and sustainability.  Will AI play an important role in the fight against climate change? That’s quite likely, according to a recent study by PwC UK. According to their research, AI applications in sectors such as agriculture, water, energy, and transport could add up to $5.2 trillion USD to the global economy by 2030, a 4.4% increase compared to current projections.  The study also found that AI could help cut global greenhouse gas emissions by 4%, or 2.4 Gt CO2e. To put that in context, this is the equivalent to the combined annual 2030 emissions of Australia, Canada, and Japan.  The Importance of Environmental Monitoring in Urban Areas  One key aspect where AI can significantly contribute to sustainability is environmental monitoring. Environmental monitoring uses sensors, wireless connectivity, edge computing, and visualization tools to track environmental health and detect potential issues.  A broad term encompassing areas such as air and water quality monitoring, and waste management, the effectiveness of environmental monitoring greatly impacts urban regions. Here’s why robust environmental monitoring is crucial:   Public Health Environmental monitoring, and air quality monitoring in particular, is crucial for public health. According to the World Health Organization (WHO), nearly 7 million premature deaths happen every year due to air pollution.  Climate Change Mitigation  Urban areas are responsible for more than 70% of global greenhouse gas emissions. That’s why, in order to fight climate change—which is one of today’s biggest threats—environmental monitoring is a must.  Urban Heat Islands  Perhaps you’ve heard of the term Urban Heat Islands (UHI). It means that the temperature is often higher in the cities than in rural areas. That’s because human-made structures like buildings and roads absorb and reemit heat more than natural landscapes. The UHI effect is bad for both public health and the environment – it is linked to heat-associated health risks and higher energy consumption.  Disaster Preparedness Environmental monitoring tracks natural hazards like floods, landslides, and extreme weather events. Thanks to the data it produces, people living in cities can be better prepared to address such events.  Biodiversity Protection Monitoring urban biodiversity helps in maintaining and restoring natural habitats within cities. This is important because it supports the conservation of local species and ensures ecological balance. Let’s take a closer look at how AI supports sustainability in the different areas of environmental monitoring.  Created with Dall-E. 1. AI in Sustainability: Air Quality Monitoring Air quality monitoring refers to the measurement of different air pollutants, also known as the “criteria level pollutants” (particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and lead (Pb)).  There are three key technologies used for air quality monitoring, and they all take significant advantage of AI: air quality sensors, satellite data and remote sensing, and predictive analytics. Air Quality Sensors Air quality sensors use optical or electrochemical sensors to detect pollutants.  Optical sensors emit light into the air and measure the scattering or absorption caused by particles. Electrochemical sensors, on the other hand, detect gases based on chemical reactions. How does AI help?  AI helps to assess and manage air quality in real time. In this way, governments and scientists can continuously monitor pollutants with unprecedented accuracy. Real-life example: Thanks to AI, scientists now accurately measure PM2.5 levels in New York City. Previously, the researchers had to rely on complex models that needed extensive data inputs, which was making them hard to use.  Engineers at Cornell University came up with a simplified model that is easier to use. They leverage AI to gain a detailed view of street air pollution, and PM2.5 levels in particular. Coming from traffic exhaust, this type of pollution is detrimental to human health. By combining traffic data, topology and meteorology, the AI algorithm simulates and predicts traffic-related air pollution concentrations. Satellite Data and Remote Sensing Satellite-based remote sensing uses complex tools like spectrometers and radiometers to measure the atmospheric composition from space. These instruments capture data across various wavelengths of light, which allows them to detect and quantify pollutants, aerosols, and greenhouse gases. How does AI help? AI algorithms process the massive volumes of spectral data that satellites collect. They also analyze the data to identify patterns, trends, and correlations in air quality metrics much more efficiently than traditional methods. In this way, scientists can rapidly assess and manage air quality on a global scale. Created with Dall-E. Real-life example:  The European Space Agency's (ESA) Copernicus project launched a new satellite called "Sentinel-5P," which can measure various pollutants and share the data publicly.  A scientific paper explores a method to combine satellite data with pollution measurements, showing that simpler models can work just as well as advanced neural networks. A new dataset was created with pollution measurements from stations across Europe, including factors like altitude, population density, environmental classification, and satellite data from the ESA project.  This dataset trains the Air Quality Network (AQNet) model, which uses these different data sources to predict pollutants.The machine learning model successfully predicts three pollutants: NO2, O3, and PM10. These predictions are combined into an "air quality index" to compare air quality across regions. AQNet performs better than a model using only satellite images. According to the paper, testing the AI model with data from the UK and Ireland gave satisfactory results, though pollution levels were generally overestimated by about 20%. Using Historical Data for Predictive Analytics AI algorithms can use extensive historical datasets, including air pollutant levels, meteorological conditions, traffic patterns, and industrial activities. By analyzing this data, AI can recognize patterns and correlations and in this way predict air quality variations over time. For example, AI models can predict seasonal spikes in particulate matter (PM2.5) levels based on historical trends and weather conditions. Real-Life Example:  UNEP's World Environment Situation Room (WESR), which was established in 2022, is a digital platform that uses AI's capabilities to analyze complex datasets. Using earth observation and sensor data, WESR supports near real-time analysis and predictions on a number of factors, including the concentration of carbon dioxide in the atmosphere.  2. AI in Sustainability: Water Quality Monitoring Water quality monitoring involves regularly testing water sources, such as rivers and lakes, to assess their condition and identify any contaminants.  This process is crucial because clean water is essential for human health, agriculture, industry, and the environment. By monitoring water quality, cities can detect pollutants, track changes over time, and identify potential hazards before they become serious problems. This information helps in making informed decisions about water management, protecting ecosystems, ensuring safe drinking water, and preventing waterborne diseases.  How does AI help? AI enhances water quality monitoring by analyzing large datasets from various sources such as sensors, satellites, and historical data. AI algorithms can detect changes in water quality parameters (e.g. pH levels) and the presence of contaminants such as heavy metals and harmful bacteria. With this real-time analysis, scientists can detect pollution events early and take proactive measures to protect water resources. Real-life examples: In practice, AI has been successfully implemented in various case studies: AI-powered Autonomous Underwater Vehicles (AUVs): These vehicles are equipped with sensors and AI algorithms to monitor water quality parameters in lakes, rivers, and oceans. A team of U.S. National Science Foundation-funded researchers at the Minnesota Interactive Robotics and Vision Laboratory is developing advanced AUVs to collect data, provide insights into species distribution, and create habitat maps. These innovations, tested on the MeCO platform with multiple sensors, aim to identify and track invasive species in Minnesota lakes. This project aims to make underwater robots more effective for conservation, enhancing the protection of underwater environments. Aquabyte's AI-powered Fish Health Monitoring: Startup Aquabyte uses AI and computer vision to monitor fish health in aquaculture farms. By analyzing images of fish, AI can detect diseases early, optimize feeding practices, and reduce environmental impacts associated with fish farming. 3. AI in Sustainability: Waste Management Waste management refers to the collection, transportation, disposal, and recyc