Why do cities need air pollution forecast models?
The growth of urban populations, increasing vehicle fleets, and climate change make the smog problem a key issue for megacities. Classic air monitoring methods record the state *post facto*, but do not provide an understanding of *when* and *why* pollution levels will sharply increase. Therefore, cities are transitioning to using neural network models capable of forecasting air quality in advance and helping to plan measures to reduce the harm from pollution.Modern machine learning models work with huge amounts of data: meteorology, traffic flows, chemical composition of air, street geography, population density, industrial zone data. Based on this, AI builds forecasts that allow cities to act preventively — from traffic management to optimizing public transport.
Why is smog forecasting with neural networks becoming a strategic tool?
The urban environment is a dynamic system where many factors interact simultaneously. Neural networks help to see the picture holistically and make decisions based on accurate forecasts, not assumptions. Using AI allows you to:- Identify pollution sources. Models detect patterns between transport, weather conditions, and industry.
- Forecast smog spikes. Algorithms take into account wind direction, temperature, pressure, humidity, and geographical features of areas.
- Assess the consequences of decisions. AI models simulate how introducing restrictions or changing traffic will affect air quality.
- Improve the accuracy of environmental programs. Instead of the same measures for all cities, it offers locally adapted strategies.
In practice, such systems are implemented in stages. To help businesses or municipalities assess future costs and choose a solution architecture, it is convenient to first understand the budget range — for example,
→ using the cost calculator for developing a neural network EasyByte.
And if you need concept development or model selection, you can
→ sign up for a free consultation with an EasyByte expert.
Technological foundation: what are forecasting models built on?
1. Time series analysis
Neural networks (LSTM, TCN, transformers) analyze the dynamics of air deterioration based on historical values and identify repeating patterns.
2. Spatiotemporal models
The application of GNN and CNN allows taking into account the form of the urban environment: building density, road layout, building height, the influence of “urban canyons”.
3. Multimodal analytics
Modern solutions combine data from weather stations, sensors, satellites, transport systems and model the environmental situation with high accuracy.
The combination of these approaches makes forecasts not only accurate but also interpretable, which is especially important for government bodies and environmental services.
Real-world cases of AI application in smog forecasting and planning measures
Case #1: Barcelona — Barcelona Supercomputing Center uses machine learning for air quality maps and forecast of exceeding norms
→ Barcelona Supercomputing Center has developed an AI method that creates air quality maps and forecasts the probability of exceeding permissible pollution levels in the urban environment. This gives the city a basis for preventive solutions: more accurate planning of restrictions, assessment of the effect of measures and focus on areas with the highest risk.
Case #2: London — city pollution forecasting and alerts system for residents
→ Greater London Authority describes an approach where London forecasts pollution levels using dispersion models to proactively assess how pollutant concentrations will change over time and across the territory. Such forecasts are used for early warnings and support managerial decisions — from communications with residents to adjusting urban measures during periods of increased risk.
How do cities use forecasts to plan environmental measures?
Having received AI forecasts, municipalities can proactively model and test environmental solutions. The most common measures include:
- Blocking or restricting traffic in areas where pollution exceeding is expected.
- Optimizing public transport schedules taking into account peak loads and wind direction.
- Postponing construction and road works to periods with minimal environmental risk.
- Timely notifications to residents about possible air deteriorations and recommended safety measures.
As a result, environmental strategies become not reactive, but proactive — and allow to significantly reduce the level of smog in cities with dense traffic and industrial areas.
📌FAQ: frequently asked questions about pollution forecasting and environmental neural networks
Question: How accurate are neural network models in predicting pollution levels?
Answer: Modern models achieve an accuracy of 75–90% depending on the quality of data, sensor density and weather conditions.
Question: What data is needed to build such a system?
Answer: Historical air monitoring data, meteorology, satellite observations, traffic flows, building and terrain data.
Question: Can the system be implemented in stages?
Answer: Yes, many cities start with models for individual districts and gradually expand coverage.
Question: Is this technology suitable for small cities?
Answer: Yes, especially if ready-made APIs or satellite analysis datasets are used.
Question: How to estimate the development cost in advance?
Answer: A preliminary estimate can be obtained
→ using the cost calculator for developing a neural network EasyByte, which helps to choose the optimal scale of the project.
Question: What security measures are required for data storage?
Answer: Encryption, access control and the use of certified cloud solutions are necessary.