These integrate monitoring approaches based on human sensory observations, real-time and forecasted weather data, dynamic dispersion modelling, but also sensor systems which are making use of sensors to detect and quantify, for instance, key substances of potential sources of environmental nuisance.
Most solutions available in the market only allow an isolated consideration of such heterogeneous data sources. Ortelium is different! Our platform allows you to combine, streamline and cross-reference different data channels, so that a more meaningful picture of the environmental situation can be established.
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.
Ortelium’s observation management module offers streamlined data collection via a dedicated Smartphone app or directly via the Ortelium web platform. The module helps you get the most from event-based observations – such as complaints, incidents, in-house inspections or health & safety reports – while improving efficiency in both data management and internal or external reporting.
Configurable viewer options on the map and on the timeline let you understand your data in one view. Animating your data allows you to see the development of complex incident situations over time at a glance, to identify trends, and visualise historic development of such situations. You’ll also be pleased to know that there’s no need for spreadsheets, as all data can be processed statistically and reported in a standardised format with just a few clicks.
Ortelium’s observation management module can be used as a communication tool to interact with particular communities. Its flexible data security and access framework means you can share the right level of information with different stakeholder groups.
Incoming observations will be handled as ticket which can be assigned to responsible staff within your organization for further investigation. That way you can easily track and follow-up on incoming observations, and, if desired, actively communicate the status and actions taken back to the observer.
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.
The Ortelium module for dispersion modelling & impact analysis combines weather data feeds with the odour emission data from your plant’s odour 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 odour impact of your operations in past, current and future situations.
This enables you not only to better understand and validate past incidents 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 odorous operations at times in which environmental impacts are minimized.
In the module you can manage multiple odour sources and their processed substances by defining key parameters such as substance concentrations (OU/m³), source height and heat capacity, volume flows of your installations, as well as terrain roughness. You can also add information on your installation’s continuous and discontinuous processes by entering process schedules and process-specific parameters, for instance production capacities, start (and end) times.
By highlighting points or areas that are of special interest for your data monitoring (e.g.neighbouring residential areas, or other receptor points and areas) on the map, the module will instantly warn you if, and tell you how your site’s sources might impact these areas under the current and forthcoming weather conditions. In addition, the Ortelium timeline colour-coding provides instant information on odour-critical operation times, allowing you to proactively initiate appropriate actions to avoid potential incidents.
The Ortelium module for dispersion modelling and impact analysis comes with a conventional GAUSSIAN dispersion model or, if required, more advanced dispersion models, such as, but not limited to CALPUFF, HYSPLIT and WRF. If you are unsure about which model is the most appropriate model for your usecase, 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 these odour-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.
Do you want 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? - No problem. Ortelium’s sensor integration module lets you integrate any kind of sensors for continuous environmental monitoring and data visualisation.
The module 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, this module 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. For example, 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, and enabling you to take timely and appropriate action.
Ortelium allows you to combine, streamline and cross-reference different data channels, so that a more meaningful picture of the environmental situation can be established.
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.