Ortelium collects, visualises and analyses data holistically by streamlining data channels from various sources. These include human sensory observations, real-time and forecasted weather data, dynamic dispersion modelling, but also IoT-enabled sensor systems which are making use of sensors to detect and quantify, for instance, key substances of potential sources of environmental nuisance.
Understanding the environmental footprint of operational activities is often a complex task in which many factors play a role. We have, therefore, put an emphasis on providing visual information wherever useful for data interpretation.
This is achieved through flexibility in information design and the possibility of simultaneously laying multiple layers of information on top of each other from a spatial and temporal perspective to easily identify correlation and cause-effect relations across the data.
Select from our modules to tailor a solution for your use case. Perhaps you have your own data pool that could be combined with Ortelium to make your data even more meaningful? Talk to us today and we’ll connect both worlds.
No matter if you need to manage environmental complaints from local communities, report internal operational incidents, environmental surveys, or measurements, Ortelium offers a flexible and powerful solution to create custom web forms for streamlined data collection, analysis, and follow-up.
Depending on your specific needs, custom web forms can be configured and shared with different user groups. Users can access the forms to report their observations using webportal access via a web browser or our dedicated Ortelium app.
Observations collected are recorded centrally and can be assigned to user groups responsible for the management of these observations. In a standardized process, the observation tickets can be followed up for internal investigation, validation, statistical analysis, and reporting.
Observations managed in Ortelium can be displayed on the map and Ortelium timeline to understand when and where observations were made at a glance. In combination with displaying information from other complementary data channels, correlations between sensor readings, modelling results, and environmental observations can be identified easily to draw the right conclusions and make data-driven decisions.
For air quality, odour and dust observations, the Ortelium observation management module offers you the option of integrating backtracking capabilities to instantly analyse the pathway of these observations. Through advanced Hysplit Dispersion Modelling you can locate the potential source of, for instance, unpleasant odours by means of meteorological simulation and reverse trajectory modelling.
Reverse trajectories can be displayed on the map and animated through the timeline to understand complaint situations and the underlying meteorological and geographic influences better and faster, to prevent incidents related to air quality, odour and dust in the future. You can also provide evidence if your operations were not responsible for specific complaints, which is helpful especially in areas with a diversity of potential complaint sources. This allows you to use your resources only on justified complaints and to avoid misallocation of people, money and time.
Ortelium’s weather data module integrates data feeds from local meteorological stations, online weather services and other independent data pools. Depending on the feed, it can process and visualise historic, ‘nowcast’ and forecast data.
By adding meteorological data feeds to your atlas, Ortelium enables you to cross-check and validate other data channels against weather data, such as wind speed and wind direction but also cloud coverage, temperature, humidity, pressure, or precipitation data, if useful.
This gives you a greater understanding of the relations of impacts and the influence of certain meteorological conditions. We can also provide a selection of feeds of various meteorological data providers.
Ortelium offers tools for dynamic real time air quality & odour dispersion modelling and impact mapping that combine weather data feeds with the emission data from your plant’s sources. Calculating and dynamically visualizing the dispersion of your plant’s emissions on a digital map, Ortelium provides you with retrospective analysis, real-time monitoring, and forecasting capabilities to model and monitor the emission impact of your operations in past, current, and future situations.
This enables you not only to better understand and validate past odour incidents or air pollution episodes and their causes/origins. By forecasting possible impacts of your operations you can even take appropriate, proactive steps to avoid conflict situations, e.g. by finding the right window of opportunity to run operations at times in which impacts are minimized.
Even the best dispersion models will not be able to provide a reliable representation of the actual impact unless the emission data that is used as input for the calculations is accurately taken into account. Ortelium, therefore, provides full flexibility on emission source input data. Depending on the availability of the emission data, the models can be fed with static emission source data or dynamic emission source data. Dynamic emission source data can be provided through sensor data feeds, if available, scheduled processes or schemas. This flexibility allows for continuous and discontinuous emission sources to be considered for modelling, achieving the highest accuracy for your modelling results.
The overall impact can be filtered for individual source contributions providing users with valuable insights about the most relevant sources.
Ortelium can be configured to use steady-state and non-steady-state dispersion models to offer the best-suited approach for each individual project. Ortelium's Gaussian plume model can be fed with data provided by local weather stations or online feeds, while the Calpuff model is using high-resolution WRF data for the calculations.
If you are unsure about which model is the most appropriate model for your use case, our modelling experts will be happy to advise you on that decision.
In case your emission process is stable and controllable, Ortelium provides recommendations on how to adapt your process parameters (such as pumping speeds or ventilation rates) during emission-critical operation times, to reduce or even prevent alert situations, while keeping your operations running most efficiently. This is particularly useful for industries that are at risk of being panelized with temporary working restrictions, fines or in the worst case permit withdrawal.
For companies that need to operate within defined environmental compliance boundaries, it is essential to understand when you are operating within thresholds and when critical limits are exceeded.
Continuous monitoring of key parameters allows you to act in due time to remedy potential issues and to focus your resources on mitigation most effectively.
Do you need to create a dynamic noise map? Do you want to visualise and chart online-monitored PM2.5 or PM10 levels? Perhaps you later want to add NOx, SOx or other values? - Ortelium enables you to integrate sensor data feeds from any sensor system that can provide data online.
Ortelium is independent from data protocols and file structures and can adapt to your needs. It supports commonly used file formats for data exchange, such as XML, JSON and CSV. Whether you have an existing sensor network or intend to create a new one, Ortelium will help you make the most of your data. It can interface with existing sensor networks, either directly via a web interface or using M2M transmission from a centralised data pool.
If you plan to build a new sensor network, you can benefit from our sensor partner network. We can help you to identify the ideal sensors and suggest suitable sensor locations, based on local meteorological conditions, terrain, existing buildings and other parameters.
We will also work with you to suggest how best to visualise your sensor data, giving you rapid access to the most valuable information. By customizing sensor viewers to your specific needs, we ensure operators can understand the data at a glance, enabling you to take timely and appropriate action.
Ortelium allows you to combine, streamline and cross-reference different data channels, so that you can establish a more meaningful picture of the environmental situation, draw the right conclusions and make data-driven decisions.
Combining complaint data with local weather data, provides a structured complaint validation process, which is accompanied by an open communication process.
Benefit from the following features:
Combining these features in one system not only increases the efficiency of the processes it also provided a basis for internal or external reporting.
Companies which run odour intensive processes have an interest to self-monitor the emissions of their site and to understand if, when and where odours might be escaping from their facility boundaries.
Combining 24/7 monitoring capabilities of specifically trained and optimised fenceline odorant monitors (sensors), with local weather data and odour observations reported by internal staff, we can set up an odour monitoring programme designed just for you.
These sensor systems make use of gas sensors to detect and quantify industry-specific key odorants, such as H2S, CH4S, NOx, SO2 or PM. Even though there are still some methodical shortcomings and technical challenges to overcome in sensor-supported odour monitoring, industry-specific sensor signals can provide useful information for the odour management process, which, combined with human-sensory observations and meteorological data can help to better understand and evaluate the odour situation.
Combining forecasted weather data with dispersion modelling based on your plant’s process schedules and associated emission rates can help to better understand potential future impacts on residential areas and to provide a basis to proactively take actions.
Ortelium provides recommendations on how to adapt process parameters to reduce the forecasted odour impact, e.g. by increasing the amount of odour mitigating chemicals in wastewater treatment processes, reducing pumping speeds of pipes transporting odorous substances in the petrochemical industry or temporarily activating additional filter systems.
If the process conditions cannot be changed, a process might still be shifted to a different time slot, when meteorological conditions are more favourable to avoid impacts on the community.
In cases in which neither is possible, operators can still pro-actively and transparently inform communities about the likeliness of an odour impact, which helps to build trust and increases the acceptance of odour impacts when they actually occur.
Streamlining the registration of odour complaints in Ortelium further allows to use the generated odour dispersion results to retrospectively validate complaints received against the output of the modelling.