Issues
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TRL Level
SRL Level
Resiliants: WATER aims to create a predictive dashboard for avocado...
Aim 1 To determine air pollution source from either vessels, port operation or external source outside boundary of port authority.
Aim 2 To identify more specifically estimates of emissions from vessels and impact on air quality ambient measurement for locations of the port authority based on specific amount of vessels and meteorological scenarios.
Aim 1 To determine air pollution source from either vessels, port operation or external source outside boundary of port authority.
Method: Emission Modelling from vessels, air quality sensors and machine learning to categories patterns of air quality measurement categorising these to further determine emission sources. Benefit: Often when port authorities are produce excess air pollution it is residents nearby that are suffering, forming into campaign groups, measuring air pollution using air quality measurements and asking questions of the port authority. Being able to determine whatis being caused by vessels or port authorities along with details explanation of mitigation strategies can allow residents to measure the difference of impact. This can be measured through their own air quality measurement sensors so validating mitigation strategies further and gaining support of residents.
Benefit: It can provide a method to determine when vessels or port operation or external source air pollution is largest so that these can be balanced more easily so only one has a high emission amount. This can allow collaboration with local authority which is often difficult because port authorities are large emitters yet large economic benefit.
Aim 2 To identify more specifically estimates of emissions from vessels and impact on air quality ambient measurement for locations of the port authority based on specific amount of vessels and meteorological scenarios.
Method: Integration of modelling of air pollution dispersion in port authority in different meteorological scenarios.
Benefit: It shall allow to be able to balance category of vessels in the port authority in a timeframe based on different meteorological scenarios and cost scenarios.
Benefit: To be able to dynamically adjust the balance of category of vessels selected based on change to meteorological scenarios. i.e. wait vessels outside of port authority when meteorological scenarios would cause air pollution are estimated to be above threshold on site in port authority. This can be done when meteorological prediction become available often 7 days before.
Aim 3 To determine difference of impact on air pollution at specific designated protected zones in local residencies or in port authorities by comparing different routes of vessels and balance of amounts of different categories of vessels in selected zones.
Method: To dynamically assign zones in port authority that impact air pollution in protected zone most.
Benefit: Often a large source of air pollution is when vessels use their engines for energy on the vessels while in the port authority. This would determine how to balance usage of shore power to vessel power to reduce air pollution at these designated protected
See more information about this level and the TRL and SRL levels.
The BRIGAID Business Development Programme has been successfully completed. A MAF+ assessment has been conducted and its results have been enriched and incorporated into a business plan document.
The market assessment of this innovation has been self-declared by the provider and has not been independently verified. For more details, please contact the innovator directly.
Early Warning System, which involves the implementation of advanced sensors capable of real-time data collection for climate-related parameters, including temperature, humidity, wind intensity, fog and air quality and effectively monitor and analyze climate-related events
AirNode’s SaaS was first constructed to evaluate air pollution mitigation by a consultancy at Scottish Government and Scottish Environmental Protection Agency after wining a tender for this. The Scottish Government provided financing to scale this to a commercial product. The commercialisation provided specifying to commercial use case which was done for port authorities. AirNode gained further clients where further functionality was constructed and IP was retained with European Centre for Medium Range Weather forecasting. Which resulted in machine learning to classify emission sources from air quality measurements and other available big datasets. Further clients of the Northern Ireland Government and Glasgow city council provided further validation of AirNode SaaS, expertises and dashboard. AirNode further specified its AirNode SaaS and dashboard to port authorities by being accepted to the Liverpool city region programme for maritime innovation called Port city innovation setup by University of Liverpool, Peel Ports of Liverpool and Wirral Council. This provided a consultancy at Peel port of Liverpool to determine how to optimise air quality monitoring and selection of air pollution mitigation for the Peel Port of Liverpool. This included scoping of the consortium, integrating air quality sensors from Libelium in the Peel Port of Liverpool. This included scoping usage of green hydrogen for generation of shower power and cost benefit analysis of this. Further research project where constructed at Napier Edinburgh University, University of Edinburgh and University of Surrey. This has provide a defined product in AirNode SaaS and dashboard that is integrated to Libelium air quality sensors.
The limitations are most the amount and standard levels of inputs. The information about vessels determines the accuracy of the emission modelling. The precision of the meteorological scenarios determines the accuracy and precision of the dynamic zone classification. The model prediction have a probability factor which is in high 90% so have a margin of error. There are multitple predictions so incorrect predictions can cause larger impact on other predictions.
Aim 1 To determine air pollution source from either vessels, port operation or external source outside boundary of port authority.
Aim 2 To identify more specifically estimates of emissions from vessels and impact on air quality ambient measurement for locations of the port authority based on specific amount of vessels and meteorological scenarios.
Aim 1 To determine air pollution source from either vessels, port operation or external source outside boundary of port authority.
Method: Emission Modelling from vessels, air quality sensors and machine learning to categories patterns of air quality measurement categorising these to further determine emission sources. Benefit: Often when port authorities are produce excess air pollution it is residents nearby that are suffering, forming into campaign groups, measuring air pollution using air quality measurements and asking questions of the port authority. Being able to determine whatis being caused by vessels or port authorities along with details explanation of mitigation strategies can allow residents to measure the difference of impact. This can be measured through their own air quality measurement sensors so validating mitigation strategies further and gaining support of residents.
Benefit: It can provide a method to determine when vessels or port operation or external source air pollution is largest so that these can be balanced more easily so only one has a high emission amount. This can allow collaboration with local authority which is often difficult because port authorities are large emitters yet large economic benefit.
Aim 2 To identify more specifically estimates of emissions from vessels and impact on air quality ambient measurement for locations of the port authority based on specific amount of vessels and meteorological scenarios.
Method: Integration of modelling of air pollution dispersion in port authority in different meteorological scenarios.
Benefit: It shall allow to be able to balance category of vessels in the port authority in a timeframe based on different meteorological scenarios and cost scenarios.
Benefit: To be able to dynamically adjust the balance of category of vessels selected based on change to meteorological scenarios. i.e. wait vessels outside of port authority when meteorological scenarios would cause air pollution are estimated to be above threshold on site in port authority. This can be done when meteorological prediction become available often 7 days before.
Aim 3 To determine difference of impact on air pollution at specific designated protected zones in local residencies or in port authorities by comparing different routes of vessels and balance of amounts of different categories of vessels in selected zones.
Method: To dynamically assign zones in port authority that impact air pollution in protected zone most.
Benefit: Often a large source of air pollution is when vessels use their engines for energy on the vessels while in the port authority. This would determine how to balance usage of shore power to vessel power to reduce air pollution at these designated protected
The BRIGAID Business Development Programme has been successfully completed. A MAF+ assessment has been conducted and its results have been enriched and incorporated into a business plan document.
The business plan for this innovation has been evaluated by The Funding Company and it is considered to be ready for investment.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
Early Warning System, which involves the implementation of advanced sensors capable of real-time data collection for climate-related parameters, including temperature, humidity, wind intensity, fog and air quality and effectively monitor and analyze climate-related events
AirNode’s SaaS was first constructed to evaluate air pollution mitigation by a consultancy at Scottish Government and Scottish Environmental Protection Agency after wining a tender for this. The Scottish Government provided financing to scale this to a commercial product. The commercialisation provided specifying to commercial use case which was done for port authorities. AirNode gained further clients where further functionality was constructed and IP was retained with European Centre for Medium Range Weather forecasting. Which resulted in machine learning to classify emission sources from air quality measurements and other available big datasets. Further clients of the Northern Ireland Government and Glasgow city council provided further validation of AirNode SaaS, expertises and dashboard. AirNode further specified its AirNode SaaS and dashboard to port authorities by being accepted to the Liverpool city region programme for maritime innovation called Port city innovation setup by University of Liverpool, Peel Ports of Liverpool and Wirral Council. This provided a consultancy at Peel port of Liverpool to determine how to optimise air quality monitoring and selection of air pollution mitigation for the Peel Port of Liverpool. This included scoping of the consortium, integrating air quality sensors from Libelium in the Peel Port of Liverpool. This included scoping usage of green hydrogen for generation of shower power and cost benefit analysis of this. Further research project where constructed at Napier Edinburgh University, University of Edinburgh and University of Surrey. This has provide a defined product in AirNode SaaS and dashboard that is integrated to Libelium air quality sensors.
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