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Hybrid air quality monitoring network for urban pollution

Home » Sectors » Hybrid air quality monitoring network for urban pollution

Urban pollution is not uniform: it varies between streets, intersections, neighborhoods, and even between different times of day. Reference stations are often insufficient to provide a representative and accurate picture of air quality in cities, and their cost and maintenance are a hindrance to tighter municipal budgets.

At Envira, we have the solution: the deployment of hybrid monitoring networks with portable stations based on IoT sensors that complement current reference stations, densifying data collection and obtaining a real, hyperlocal pollution map at a fraction of the cost of traditional infrastructure.

What is a hybrid air quality monitoring network?

 

A hybrid urban air monitoring network is an integrated system designed for cities that combines two complementary types of monitoring instruments: certified reference stations that measure with maximum accuracy, and low-cost IoT sensors that expand spatial coverage.

This combination represents an evolution in how we measure and improve urban air quality, which traditionally relied exclusively on one or two reference stations that offered high accuracy but limited spatial coverage. Thanks to wireless environmental monitoring stations, this spatial coverage can be expanded exponentially and much more economically.

Hybrid air quality monitoring network for urban pollution infography

 

Components of the hybrid urban air quality monitoring network:

  • Fixed monitoring stations: regulatory-grade instruments designed to meet regulatory requirements for reporting to the EU. They also establish the scientific basis for the entire network, providing data for remote validation and calibration of nearby sensors.
  • Environmental monitoring sensors: compact, low-cost electrochemical devices that can be easily installed on streetlights, traffic lights, or even integrated into buses and transport fleets. This allows for the creation of a dense network for hyperlocal monitoring and surveillance of urban air quality to create dynamic, real-time pollution maps.
  • Data platform: the communication infrastructure based on the MQTT protocol allows data to be collected and centralized on a visualization platform accessible from any device with an internet connection and integrated into municipal Smart City platforms.

Total coverage and monitoring of air quality in cities with low-cost sensors

The use of  low-cost sensors to monitor urban air quality is becoming increasingly widespread, as their advantages are clear:

  • They allow hyperlocal data (neighborhood by neighborhood) to be collected to create extensive networks for monitoring and controlling air and noise pollution.
  • They detect pollution hotspots, identifying accumulations of NO2 or PM2.5 at specific intersections or during periods of heavy traffic.
  • Their compact design with 4G, WiFi, or LoRaWAN connectivity options makes them Plug & Play devices that can be installed and set up in minutes.

Nanoenvi: low-cost, wireless stations for urban pollution monitoring networks.

  • Nanoenvi EQ: easily monitors key air pollution and noise parameters in cities. These compact devices with IoT connectivity are designed to be deployed in minutes with minimal maintenance.
  • Nanoenvi dB: continuously monitor noise and obtain legally valid data with this devices that integrates a Class I sound level meter.

 

Remote calibration system versus reference stations

The biggest challenge in monitoring with electrochemical sensors is correcting drift, that is to say, the loss of accuracy that occurs over time and is inherent to this type of technology.

Nanoenvi devices can be calibrated remotely against a nearby reference station, applying proprietary algorithms that compare the readings of these sensors against the fixed station and apply corrections for any deviations.

This provides indicative measurements with data quality close to that of the reference instrumentation.

Real-time urban air pollution and noise data platform

 

The hybrid urban air quality monitoring network includes a real-time data visualization and management platform accessible from any device with an internet connection.

This represents a paradigm shift in environmental governance: while older systems provided daily or monthly averages—a “snapshot” of what had happened with urban air quality—our solution offers a platform that allows for continuous data collection and real-time monitoring of air pollutants, providing a constant and dynamic flow of information.

Hybrid air quality platform

 

Monitoring noise and environmental pollution on roads and railways

 

Urban centers and residential areas close to road infrastructure such as roads, highways, motorways, and railways are simultaneously exposed to higher levels of noise and air pollution. This combination significantly increases the health risk to the populations of these residential areas.

In addition, controlling air and noise pollution in this type of linear infrastructure requires covering a large number of kilometers, so the use of low-cost monitoring sensors allows this task to be carried out without incurring a huge investment.

What parameters and pollutants need to be monitored?

  • Air quality: mainly nitrogen dioxide (NO2), nitrogen oxides (NOx), CO, and PM particles are monitored, which are atmospheric pollutants associated with road traffic.
  • Noise levels: on the one hand, major roads must regularly update their noise maps and, on the other hand, although railways and electric trains are key to decarbonization, it is necessary to monitor and discriminate between noise events near urban environments in order to assess their impact and verify the effectiveness of mitigation measures.

Advantages of implementing a hybrid network for urban air quality control

Profitability and efficiency

Profitability and efficiency

When combined with certified reference instrumentation, a network of wireless stations allows coverage to be multiplied without increasing costs, offering a high-precision solution at a lower cost than traditional infrastructure.

Compliance with LEZ requirements

Compliance with LEZ requirements

A hybrid network is the ultimate tool for designing, implementing, and legally justifying Low Emission Zones by providing the objective data required by European and national regulations.

Dynamic pollution map

Dynamic pollution map

This monitoring solution based on wireless stations with IoT sensors is the only one that can detect pollution “hot spots” and provide a complete map of the city's actual pollution levels.

Transparency and health

Transparency and health

The ability to provide real-time data to citizens through dashboards or apps improves the perception of municipal management and positions the project within the Smart City ecosystem.

Frequently Asked Questions (FAQ) about hybrid networks for monitoring and controlling air pollution in cities

Does this network replace the the official reference stations operated by regional or national authorities?

No, it supports them. Legislation requires reference stations for official reports to the EU. The Hybrid Network is used for daily management, monitoring of LEZs, and for measuring those areas where the official station does not reach.

What maintenance do low-cost sensors require?

Maintenance is minimal. As they are modular, only the electrochemical gas cartridges need to be replaced when they reach the end of their useful life, without having to uninstall the entire unit. Calibration is automatic and remote.

Is the data owned by the provider or the City Council?

The data is 100% owned by the City Council. The system offers open and integrable communication via MQTT on any Smart City platform in the municipality or on its own dashboards.

What exactly is being monitored?

The system is configurable, but typically monitors: Nitrogen Dioxide NO₂, Ozone O₃, Particulate Matter PM₁, PM₂.₅, PM₁₀, Carbon Monoxide CO, Sulfur Dioxide (SO₂), as well as environmental noise and meteorological parameters.

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