How can indoor air problems be identified and prevented?

Indoor climate problems can appear in buildings in many forms. Typically the research of indoor climate problems in a building starts from the user’s perception, in which case the problem has existed for a long time. In the worst case, users’ health has deteriorated, and the building has been damaged by malfunctioning, so it reduces the life cycle of the building and leads to unexpected and high costs.


As a necessity of problem prevention and fast identification is data-driven indoor climate leadership, where the indoor climate is continuously measured with firm measuring devices and the measurement data are interpreted by an expert, who is able to make the proper insights from the measurement data as well as proposals for the measures to be taken for the maintenance of the property. To produce added value from measurement data, the reliability of the measurement data must be verified by validation of the data, the correlation of measurements within the technical operation of the building must be understood and deduce the right technical corrective measures in the identified problem situations.



What should be measured from the building and how to ensure the reliability of the measurement data?


When measuring the indoor climate of a building, usually it is settled to collect measurement data and make simple alerts to the property maintenance about exceeding the limit values. As a result of this, there are often too many alerts that there is no time to respond to them and the right cause of the problem remains uncorrected or the success of the corrective action may not be verified. Moreover, incorrect conclusions can be done from the measurement results if there is no certainty about the correct operation of the measurement technology when interpreting them, incorrect matters are measured or the effect of the building’s operation on the measurement results are not understood.


To understand the operation of the building and to reliably ensure the right operation of its heating system and ventilation, at least the temperature, carbon dioxide and pressure difference must be measured within the building. Temperature and carbon dioxide must be measured on a room-by-room basis and the pressure difference throughout the mantle from several measuring points per ventilation unit area. Also, it is important in terms of indoor climate leadership to ask building users for their opinion on the conditions and to communicate to them about healthy indoor climate.


Since measuring devices are not perpetual and their reliability of measurement data is affected by the data architecture, it is important to validate the reliability of measurement data with the help of artificial intelligence and experts. Validation can be done by reference measurements, on the basis of firm limit values, or by comparing the measured data with the assumed behavior of the area.



What type of insights can be made from the measurement data and how are deviations scalably identified from large building stocks?


Different levels of insights can be made from the measurement data of a building. The maintenance of a wide range of premises must be able to produce the highest possible level of insights, which allows the maintenance to effectively focus on rectifying the complications.


It has been noticed that in low-level insights the indoor climate conditions do not remain within certain limits, and they can be generated automatically based on firm limit values. Producing higher-level insights requires experts who produce high-quality indoor climate insights, explain where the deviations in the limit values are due, and give technical recommendations for corrective action. An expert is also needed to ensure the success of the corrective action. Different levels of insights can be classified in the following manner:


Level 1 insights occurs when a single measurement exceeds a limit value.


“The CO2 concentration in room A01 has risen above the limit value of 950 ppm.”



In Level 2 insight, the permissible limit values have repeatedly exceeded over a longer period of time.


“The CO2 concentration in room A01 has repeatedly risen above the limit value of 950 ppm.”



Level 3 insight produces targeted information about the deviation based on the technical information collected from the building.


“CO2 concentrations in the TK01 ventilation unit do not remain within the limits allowed by Indoor Climate Classification S2 on weekdays between 5 pm and 8 pm.”



In level 4 insight, by utilizing different measured quantities the cause of the technical failure can be deduced, and precise measure recommendations for action can be given.


“Carbon dioxide concentrations in the TK01 ventilation unit do not remain within the limits allowed by Indoor Climate Classification S2 on weekdays between 5 pm and 8 pm. Moreover, a strong underpressure is created at the same time in the area of influence of TK01. It is recommended to check the operation of the ventilation power dampers in the area of the ventilation unit TK01.”



Making Level 3 and 4 insights and giving recommendations for action requires expertise and knowledge of the building and its operation. To identify technical deviations scalably from large property stocks, engineers must be supported by advanced artificial intelligence, which is capable of automatically producing high-quality technical observations of buildings from millions of measuring points.



5 tips for preventing indoor air problems


  • Collect measurement data about the building as extensively as possible
  • Be sure the measurement data is reliable
  • Get an expert to interpret the data and make insights and recommendations for action
  • Make sure the actions taken are successful
  • Communicate to property users about good indoor climate and enhance the trust!



By Antti-Jaakko Alanko, Chief Technology Officer at IISY


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