The amount of data produced by businesses doubles roughly every two to four years and aviation is no exception. The sector produces petabytes of data that is regularly used to provide all sorts of safety, operational and commercial insights. But what about the environment? Can existing data provide any useful insight into sustainable airport operations? In this blog, Peter Straka shares insights from airport operators and his own experience delivering environmental performance monitoring schemes such as airline league tables.
Survey findings.
Before the pandemic, we ran a survey across more than fifty airport operators worldwide (with a focus on Europe) to understand what environmental data they collect, what metrics they monitor and how these insights influence their daily operations or strategic decision making. The results of the survey indicated that most airport operators only collect data necessary to meet regulatory reporting requirements.
- Airports operating on a single runway generally monitor between two and three noise metrics and one emission metric on average, whilst airports operating on two or more runways monitor four noise metrics and three emissions metrics on average.
- Ninety percent of respondents identified legal requirements as the main reason for monitoring airport noise performance. Noise exposure was monitored by more than eighty percent of respondents, and noise annoyance by more than seventy percent. Single event noise metrics or certification-based noise metrics were monitored by only roughly half of respondents - presumably due to the high cost of investing in monitoring stations for the single events, and a complicated data collection process for a certification-based approach.
- For three quarters of respondents, legal obligations remained the main driver for monitoring of fuel burn and emissions metrics. Eighty percent of respondents monitored concentration levels of at least one specific gas, with focus on NOx, COx and PM10 Less than half of respondents monitor a fuel burn related metric – this is due to a complicated methodology for accurate fuel burn calculations and uncertainties around several aircraft performance parameters for which the airport operator may not have reliable data.
Nearly all respondents agreed that the environmental data they collect influences their strategic decision making. Examples provided included environmental management activities, legal obligations, better targeting of mitigation activities addressing environmental impacts, adjustment of landing fees or planning of operational restrictions in next year’s scheduling process.
However, when it came to tactical decision making, the situation was different. Although half of respondents stated they make “tactical decisions on the day of operation” based on the observed performance in any of the environmental metrics they monitor, these stakeholders were either unable to provide relevant examples of such decision making, or the examples provided were more of a strategic nature. The prevailing concern for stakeholders involved in the operational chain revolved around safety and efficiency. Environment was not regularly considered when making tactical decisions as these decisions are primarily driven by safety and cost-efficiency and the decision makers often do not have the tools to support them in their actions.
Most importantly, the scope to influence environmental performance through tactical decision making is likely to be greater at airports with more runways. This provides opportunities for runway alternation schemes allowing for predictable noise respite or designation of preferential runways for noisier aircraft types.
So, it’s clear that airports do collect loads of data – primarily to prepare reports for the regulator. But once the data is collected and stored, why not use it to further improve environmental performance?
Improving environmental performance.
There are many activities airports could use their data for to better understand environmental impacts and support changing behaviors to reduce those impacts.
One example is to introduce airline league tables. These can encourage airlines to operate in the most efficient way, from both an operational and/or environmental perspective. Airlines are ranked based on their performance in various metrics which are designed to address selected airport objectives. For example, if the airport wants to reduce night noise, it may introduce a metric to measure airlines’ nighttime noise performance. A scoring or weighting system can also be introduced to reflect relative importance of selected metrics.
Communities sometimes confuse the environmental impact of airports and airlines. An airline league table can help communities understand that the airport is only as clean or as quiet as the airlines operating from it.
Real world results.
In Egis we have been supporting airports such as Heathrow, Gatwick, Toronto Pearson and Lisbon with their airline league table programmes for over a decade now. During that time, we have seen some real-world examples of these schemes providing tangible environmental improvements.
For example, one airline was struggling with its performance in the continuous descent metric. Thanks to the league table performance monitoring and benchmarking, it was discovered that the airline’s own CDO procedure was not operating in the way the airport had requested. After alignment of the airline procedures, this airline improved substantially in this metric, leading to additional reduction of noise around the airport and extra fuel saved for the airline.
In another example, it was discovered that early versions of one large passenger aircraft were deployed with a flight management system unable to properly follow pre-defined noise preferential routes. Again, this discovery was made because of a noise league table, and a simple software update was all that was needed to resolve the issue (and again, local communities benefitted from aircraft correctly flying within the prescribed areas).
Tailored metrics for available data.
During our work on these programmes, we learned that different airports have different operating circumstances and there is no “one-size-fits-all” metric that could be re-used globally. In fact, all the metrics for each of these programmes had to be developed from scratch, taking into account often contradicting priorities, local regulatory requirements and available data. The two key issues typically faced when establishing an airline league table programme are:
- Trade-off between accuracy of metric results and available data.
It is in the interest of all involved parties to come up with a metric that is as accurate as possible, to ensure fair and objective comparison of airline performance. However, any results will be only as accurate as the data used in their computation. We’ve seen instances where the airport started the data collection process only once the metric for the programme has been defined. This is certainly not ideal, as it does not allow up-front sensitivity testing of the whole programme for the inclusion of the intended metric. Additionally, there may be hidden bias in the metric results that will become obvious only once a critical amount of data has been collected and analysed (for example, long haul operators may be receiving better scores not because of their performance, but because of how the metric was set-up).
- Trade-off between accuracy of metric and its complexity.
In order to compare airlines’ performance as objectively as possible, any metric used should be designed in a way that the resulting score is not influenced by external factors. For example, when scoring airlines on their CDO performance, the metric should also consider the role of ATC and traffic complexity which both play important roles in a pilot’s ability to fly a CDO. As the airport operations chain is a complex and interlinked environment with many players, it is difficult to come up with objective metrics. Although this is possible (given data availability and computing power), such a metric is likely to be overly complicated. We shouldn't forget that the airline league table programme needs to be accessible to local communities with limited knowledge of aviation operations. So it makes sense to keep proposed metrics as simple as possible. Unfortunately, this goes against airline and airport priorities for the most objective scheme possible. Based on our previous experience, striking the right balance between fairness and simplicity of the metric is as much an art as it is a science.
Access and automation.
The exponential growth in aviation data, combined with the gradual deployment of artificial intelligence systems is likely to simplify some of the challenges described here. The data is out there – whether it is owned by an airport, airline or ATC - but gaining access to the data may not always be possible for confidentiality reasons. Scalable cloud-based solutions could be developed which would provide all the necessary answers to anyone, be it an ordinary person living next to the airport trying to figure out the flight number of the aircraft making excessive noise, the airport analyst preparing operational reports for the regulator, or the airport CEO using long-term trends to support his or her decision making.
But in the meantime, there are steps airports can take today to use the data they have for both strategic and tactical decision making that will improve environmental performance - ranging from league tables to dashboards, it is possible to automate processes and deliver insights that will make a difference.
