Sep 21, 2021

How Real-Time Environmental Monitoring Systems are Improving our Relationship with the Planet

Try HeavyIQ Conversational Analytics on 400 million tweets

Download HEAVY.AI Free, a full-featured version available for use at no cost.


Environmental monitoring is more critical than ever to the protection of our health and the health of the environment. As the human population continues to increase and industrial development continues to expand, energy use is surging and pollution is posing a major threat to the environment. Both world and community leaders and scientists are increasingly turning to advanced technology and data science to help quantify and  mitigate the impact of human activity on the environment. 

Despite advances in pollution control in recent years, the Intergovernmental Panel on Climate Change’s 2021 climate change report served as a stark warning to leaders that policy changes must be enforced expeditiously to mitigate the unprecedented environmental changes already set in motion. As each new year ushers in a slew of record-breaking floods, wildfires, hurricanes, and global temperatures, it is all the more urgent to employ environmental monitoring methods and data science to gain full visibility of our activities on the environment.

Innovations in technology, in combination with data science, have introduced real-time environmental monitoring systems, which incorporate modern real-time sensor networks, the Sensor Observation Service (SOS), Geographic Information Systems (GIS), telemetry systems, machine learning techniques, the Internet of Things, predictive analytics, and other technologies to gather and statistically analyze environmental data in real-time. This real-time data is used to make predictions and inform time-critical decisions related to environmental conditions. 

Real-time environmental monitoring and assessment technologies are more sophisticated than ever

The technologies used in real-time environmental monitoring systems include a wide variety of hardware, software, and methodologies. Some of the most common technologies include:

Real-Time Sensor Networks

A dispersed group of environmental sensors monitor and record the conditions of an environment, and stream data in real-time over the Internet via their own API or bespoke connection. Once data is transmitted to a data center, it is subject to anomaly detection, which identifies potentially inaccurate data produced from compromised monitoring devices. Typically a stream-based importer is used to ingest data into a database. 

Analytics Platforms

Provides a centralized location where massive GIS datasets can be ingested, queried, visualized on a granular level, and analyzed at scale in real-time. An accelerated analytics platform features interactive data visualizations, and allows analysts to cross-filter billions of geospatial data records and polygons alongside other features in milliseconds.

Sensor Observation Service

A web service to query real-time sensor network data and sensor data time series, and applicable to use cases in which real-time sensor data streams need to be managed in an interoperable way. SOS allows querying observations, real-time sensor metadata, as well as representations of observed features. Standards are defined by the Open Geospatial Consortium.

Geographic Information Systems

GIS are computer and software tools for gathering and analyzing data connected to geographic locations and their relation to human or natural activity on Earth. GIS mapping software uses spatial data to create maps and 3D models out of layers of visual information, revealing patterns and relationships in the GIS data. Remote sensing data is pulled from sources like USGS Earth Explorer and the Socioeconomic Data and Applications Center.

Telemetry Systems

Telemetry is the automatic in situ recording of measurements or other data at remote points and their transmission to receiving IT equipment at a different location. Common options include cellular, radio, or satellite. Telemetry platforms are appropriate for a variety of remote, real-time monitoring applications.

The Internet of Things

IoT based environmental monitoring enable us to visualize, monitor, and control crucial phenomena in the environment with the use of devices that can sense, process, and wirelessly transmit data to remote storage such as the cloud, where data can be stored, analyzed, and presented in a valuable way.

Machine Learning

Data history is recorded with data science tools like Pandas inside a Notebook environment. Machine Learning models can then be implemented as a way of infilling or back painting missing data. Deep learning algorithms have revolutionized the ability to detect complex objects in imagery, such as wisps of clouds. A human element is still necessary, a role filled by an environmental monitoring technician.

The benefits and advantages of environmental monitoring in real-time are enormous

The objectives of environmental monitoring are simple: minimize the impact an our activities have on an environment. Real-time environmental monitoring systems are helping humans develop a proactive relationship with the Earth, and advancing Earth analytics can even help mitigate catastrophic events. Continuous environmental monitoring with real-time technologies helps provide data that is used to identify trends, make predictions, and establish parameters and trigger levels, which is essential for early warning strategies. 

A major benefit of the proliferation of real-time environmental monitoring is accountability. Our ability to conduct environmental monitoring and impact assessments in some of the most remote corners of the world ensures corporate compliance and adherence to government regulations. Real-time monitoring helps agencies and enterprises set environmental performance goals and emission reduction targets, and create environmental monitoring reports to track their progress. 

Another major benefit is the improvement of disaster response and preparedness. Disaster management data collection and emergency management software facilitate innovative emergency management endeavors such as crisis mapping, social media mining, and event simulations, which use enormous volumes of real-time and historical data to assist emergency management directors and their teams in developing proactive, protective strategies, such as flood warning systems. 

Real-time environmental monitoring applications are all around us every day

The practical applications of real-time environmental monitoring are vast and varied. Wildfires, landslides, flooding, biodiversity, and waste and pollution are all issues of increasing concern as the climate crisis is predicted to worsen in coming years. Wildfire monitoring systems have been particularly useful in 2020, which saw a record-setting number of wildfires ravage California.

Real-time sensor data combined with data science and advanced analytics tools facilitate high visibility and granular analysis of environmental phenomena. The most common environmental monitoring types include air, soil, and water:

Air Monitoring

Wireless sensor networks for real time air pollution monitoring is performed with the use of specialized observation tools, such as sensor networks and Geographic Information System (GIS) models, from multiple different environmental networks and institutes is integrated into air dispersion models, which combine emissions, meteorological, and topographic data to detect and predict concentration of air pollutants and measure temperature humidity. 

Soil Contamination Monitoring

Grab sampling (individual samples) and composite sampling (multiple samples) are used to monitor soil, set baselines, and detect threats such as acidification, biodiversity loss, compaction, contamination, erosion, organic material loss, salinization, water leak detection, and slope instability. Salinity monitoring, contamination monitoring, and erosion monitoring help identify imbalances, toxins, and behavior that can impact everything from crop yield to diseases. 

Water Quality Monitoring

Water quality monitoring and sampling equipment and techniques include judgmental, simple random, stratified, systematic and grid, adaptive cluster, grab, and passive; semi-continuous and continuous environmental condition monitoring; remote sensing, and biosensors for water quality monitoring, are used to measure and monitor ranges for biological, chemical, radiological, microbiological, and population parameters.

Environmental monitoring solutions feature tools and capabilities that are tremendously useful to data analysts

Whether you’re measuring air quality or analyzing soil samples, most real-time environmental monitoring solutions tend to feature the same general capabilities and features:

  • IoT visualization
  • Automated data collection tools for quantitative and qualitative data
  • Built-in notifications for tracking environmental samples
  • Validation and auditing tools
  • Regulatory exports and reports
  • Multi-tiered security 
  • Local Language Support
  • Built-in GIS mapping
  • Mobile integrations
  • Advanced query tools
  • Pre-built calculations for analytical data

Data science is quickly becoming one of the most valuable tools for protecting the environment

Data science and Artificial Intelligence are helping drive technological innovations for some major environmental initiatives and projects. Some compelling examples include:

Simulation Modeling of the Earth

Scientists are creating a digital twin of our planet that will capture continuous, real-time environmental data to help improve climate forecasts. Scientists and policymakers will be able to evaluate different scenarios that would support sustainable development and influence environmental policies.

Accelerating Research at NASA

NASA Center for Climate Simulation (NCCS) is combining AI and machine learning with high-performance computing to manage and gain insight from its increasingly massive data stores. Projects include landslide detection, surface water monitoring, and habitat suitability prediction.

Environmental Protection Using AI

Microsoft's “AI for Earth” initiative provides scientists and researchers access to AI and machine learning technology, awarding grants to support projects related to agriculture, biodiversity, climate and water. One such project is Ocean Cleanup, which uses machine learning to identify plastic pollution in rivers and simulate how it moves in the ocean.

There are still major challenges in implementing real-time environmental monitoring systems

As is the case with many advanced technologies, the greatest barrier of entry for real-time environmental monitoring systems is upfront cost. While real-time monitoring systems are not cheap, billions of dollars are spent every year on asset management, and a significant percentage of those funds are wasted on inefficiencies. Real-time monitoring systems combine preventive risk analysis and logistics to help teams make smarter, data-driven decisions. 

Managing the sheer volume of existing environmental assets is growing impractical without advanced environmental monitoring tools, so while the upfront cost may cause sticker shock, the long-term benefits and savings from real-time monitoring outweighs the initial cost. 

The HEAVY.AI Difference

As the pioneer in accelerated analytics, the HEAVY.AI data science platform is used to find real-time data insights beyond the limits of mainstream analytics tools. While the enormous volumes of data collected by modern environmental monitoring sensors and IoT devices easily overwhelm legacy GIS tools, HEAVY.AI's accelerated analytics platform allows analysts to cross-filter billions of location data records and polygons alongside other features in milliseconds. Learn more about how HEAVY.AI supports government data analytics for the public sector as well as across a wide variety of use cases.


HEAVY.AI (formerly OmniSci) is the pioneer in GPU-accelerated analytics, redefining speed and scale in big data querying and visualization. The HEAVY.AI platform is used to find insights in data beyond the limits of mainstream analytics tools. Originating from research at MIT, HEAVY.AI is a technology breakthrough, harnessing the massive parallel computing of GPUs for data analytics.