Machine Learning’s Expanding Role in Environmental Health Research

By Ankit SinghReviewed by Bethan DaviesMar 26 2025

When we think about what affects our health, it's easy to picture things like diet, exercise, or sleep. But the environment plays a massive role too—air quality, water safety, pollution, and even the loss of biodiversity can all influence our well-being.

Image Credit: Sansoen Saengsakaorat/Shutterstock.com

The problem is that these environmental issues aren’t simple or easy to track. They overlap, change quickly, and don’t always follow clear patterns. And the tools we’ve used in the past—like fixed monitoring stations or manual data collection—can be too slow or limited to keep up. That’s where machine learning can help. It’s not about replacing people or making things overly technical—it’s about using smarter tools to spot problems earlier, connect the dots faster, and support better decisions when it really counts.

Rather than relying solely on predefined statistical models, ML enables researchers to uncover nonlinear, high-dimensional patterns in environmental data—patterns that often evade traditional epidemiological approaches.

In environmental health, ML is being applied to model complex exposure-response relationships, disentangle confounding variables, and integrate disparate datasets such as satellite imagery, wearable sensor data, and electronic health records. This has facilitated more precise source attribution for pollutants, early detection of environmentally linked disease clusters, and dynamic modeling of how climate change impacts population health across time and geography.¹

So, let’s walk through how ML is already being used across the environmental health space and why it might be one of the most important tools we have for making sense of our changing world.

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Getting Personal: ML and the Exposome

To understand how the environment affects health, we need to know what people are actually exposed to over time. That’s the idea behind the exposome—a concept that includes all the environmental exposures an individual experiences, from childhood to adulthood.

Tracking that sounds almost impossible, but ML is making it more manageable. Researchers are combining data from personal air monitors, wearable sensors, biological samples, and environmental records to get a detailed picture of exposure over time. ML models help piece these data together, identifying patterns that would be too complex for traditional methods to catch.

Once we can map exposure more accurately, the next step is understanding what it means for health.²

From Exposure to Risk: Predicting Health Outcomes

With a clearer sense of who’s being exposed to what, ML can help predict what might happen next. By analyzing massive datasets that include environmental, genetic, and clinical information, ML algorithms are being used to forecast health outcomes and identify people at higher risk.^1,2

This is especially useful in early-life studies. For example, researchers are developing exposome risk scores that combine everything from pollution levels to clinical records to predict the likelihood of childhood asthma or developmental issues. These models allow for earlier intervention and better public health planning.^1,2

And when the risk is environmental and widespread, timing is everything.

Smarter Air Quality Monitoring

Air pollution is a major public health concern, but tracking it effectively is no easy task. Traditional systems rely on a limited number of fixed sensors that can’t always provide a full picture in real time.

ML helps by merging satellite data, weather models, and ground-level sensors to estimate pollution levels with far greater coverage and accuracy. Algorithms like neural networks and random forests are used to predict concentrations of harmful particles like PM2.5 and ozone. Cities can use this information to issue health advisories, adjust traffic flow, or enforce pollution controls more quickly.³

One model, known as WLSTME (a weighted long short-term memory extended model), has been found to take this a step further by enhancing the accuracy of forecasts for PM2.5 air pollution. 

This model takes into account spatiotemporal correlations, site density, and wind conditions. By utilizing data from nearby monitoring stations and applying multilayer perceptron (MLP) and LSTM architectures, WLSTME effectively captures the dynamic patterns of pollutant transport. When tested on meteorological data, the model outperformed existing methods, offering some of the most accurate pollution forecasts yet.³ But the need for clean air is only part of the story.

Protecting Water Quality

Access to clean water is essential—but threats like lead contamination, harmful algal blooms, and industrial runoff make it harder to guarantee. Traditional lab testing is time-consuming and often reactive.

ML accelerates this process. It can analyze data from in-situ sensors, remote sensing technologies, and historical contamination records to flag risks early. ML models can even be trained on spectral data from satellites to detect harmful algal blooms in lakes, which produce toxins detrimental to both humans and aquatic life.⁴

In one such case, researchers built a dynamic electrochemical that accurately predicted lead leaching in copper water pipes by combining electrical field simulations with ML forecasting. This model integrates electric potential fields and mass transport processes to accurately forecast the dissolution of lead and copper across various pipe types and stagnation times. The predictions from the model closely matched experimental data, providing a practical tool for assessing water safety.⁵

Whether it's used to detect pollution in lakes or predict pipe corrosion, the goal is always the same: respond before people get sick.

Scaling Up: Climate Change and Public Health

Zooming out, many local environmental issues are tied to larger forces—none bigger than climate change. Rising temperatures, shifting weather patterns, and extreme events are already affecting public health through heat-related illness, changing disease vectors, and food system disruptions.

ML is helping here too. It can improve the accuracy of carbon emission maps using satellite data, assess the effectiveness of climate policies, and even model future risks. This information assists policymakers in evaluating the effectiveness of initiatives like reforestation projects and carbon taxes.⁶

Renewable energy systems also benefit from ML. Wind and solar farms use predictive models to forecast energy generation based on weather patterns, which helps ensure grid stability and reduces reliance on fossil fuels. ML models can also be used to simulate climate scenarios to guide adaptation efforts, such as designing flood-resistant infrastructure or identifying areas at risk of desertification.⁶

All in all, this isn’t just about tech for tech’s sake. It’s about giving policymakers the information they need to be able to prepare and adapt to any scenario.

Watching Over Ecosystems

Climate change has also been found to accelerate biodiversity loss—and that has ripple effects on human health. Disrupted ecosystems can lead to the spread of zoonotic diseases and reduce access to essential resources like food and clean water.

ML is being used to track these changes.

By analyzing data from remote sensing technologies and ecological surveys to identify areas at risk of biodiversity loss, ML aids in developing conservation strategies. For example, ML algorithms have been used to map habitats and predict how environmental changes will impact species distribution, thereby supporting efforts to preserve ecosystems that are crucial for maintaining environmental health.⁷

By analyzing satellite imagery and ecological survey data, ML models can identify habitats under threat, predict how species might shift due to environmental pressures, and support conservation efforts.⁷ In other words, it helps us act before ecosystems reach a breaking point.

Rethinking Waste

Waste management might not be the first thing that comes to mind in a conversation about environmental health, but it matters more than we think. Mismanaged waste leads to pollution, contamination, and health hazards.

ML is making waste systems smarter. Smart waste bins equipped with computer vision classify trash into recyclables, organics, and landfill items, reducing human error. Additionally, ML optimizes waste collection routes by predicting the fill levels of bins, thereby cutting fuel use and emissions. In some cities, drone imagery is also being analyzed with ML to help identify illegal dumping sites quickly, enabling authorities to clean up issues before they become a bigger problem.⁸

From infrastructure to enforcement, better data means cleaner communities.

Challenges and Ethical Considerations

None of this is without complications. ML models are only as good as the data they’re trained on—and biased or incomplete data can lead to inaccurate or unfair outcomes. Transparency is also an issue: many advanced models operate like black boxes, making it hard for decision-makers to trust or act on their predictions.

Privacy is another concern, especially when tracking individual exposures through wearables or personal data. Striking a balance between insight and individual rights is an ongoing conversation.⁹

If ML is going to play a meaningful role in environmental health, it needs to be ethical, inclusive, and collaborative.

Final Thoughts

Machine learning isn’t a silver bullet for environmental health—but it is one of the most promising tools we have right now. From understanding personal exposure to mapping global climate impacts, ML helps us connect the dots faster and respond more effectively.

The key is not just in building smarter models but in using them to support smarter decisions. As long as we keep people, equity, and transparency at the center, ML can be a powerful partner in building a healthier future.

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Want to keep exploring?

Here are a few topics worth digging into next:

• Machine Learning (ML) Algorithms | A Guide
• Identifying and Mitigating Bias in Machine Learning Models
• Sustainable AI: Balancing Tech and Ethics
• The Relationship Between Artificial Intelligence and Machine Learning

References and Further Reading

1. Alotaibi, E., Nassif, N. (2024). Artificial intelligence in environmental monitoring: in-depth analysis. Discover Artificial Intelligence 4, 84. DOI:10.1007/s44163-024-00198-1. https://link.springer.com/article/10.1007/s44163-024-00198-1
2. Guimbaud, J. et al. (2024). Machine learning-based health environmental-clinical risk scores in European children. Communications Medicine, 4(1), 1-14. DOI:10.1038/s43856-024-00513-y. https://www.nature.com/articles/s43856-024-00513-y
3. Xiao, F. et al. (2020). An improved deep learning model for predicting daily PM2.5 concentration. Scientific Reports, 10(1), 1-11. DOI:10.1038/s41598-020-77757-w. https://www.nature.com/articles/s41598-020-77757-w
4. Zhu, M. et al. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107-116. DOI:10.1016/j.eehl.2022.06.001. https://www.sciencedirect.com/science/article/pii/S2772985022000163
5. Chang, L. et al. (2022). Prediction of lead leaching from galvanic corrosion of lead-containing components in copper pipe drinking water supply systems. Journal of Hazardous Materials, 436, 129169. DOI:10.1016/j.jhazmat.2022.129169. https://www.sciencedirect.com/science/article/pii/S0304389422009591
6. Rolnick, D. et al. (2023). Tackling Climate Change with Machine Learning. ACM Computing Surveys, 55(2), 1–96. DOI:10.1145/3485128. https://dl.acm.org/doi/10.1145/3485128
7. Konya, A., & Nematzadeh, P. (2023). Recent applications of AI to environmental disciplines: A review. Science of The Total Environment, 906, 167705. DOI:10.1016/j.scitotenv.2023.167705. https://www.sciencedirect.com/science/article/pii/S0048969723063325
8. Chen, X. (2022). Machine learning approach for a circular economy with waste recycling in smart cities. Energy Reports, 8, 3127-3140. DOI:10.1016/j.egyr.2022.01.193. https://www.sciencedirect.com/science/article/pii/S2352484722001937
9. Barbierato, E., & Gatti, A. (2023). The Challenges of Machine Learning: A Critical Review. Electronics, 13(2), 416. DOI:10.3390/electronics13020416. https://www.mdpi.com/2079-9292/13/2/416

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