News
EUROPE’S TRANSITION TO STRATEGIC NOISE MAPPING UNDER CNOSSOS-EU: FINDINGS FROM A DATA NEEDS ASSESSMENT REPORT—NOISE-ADAPT IRELAND
PUBLISHED: NOISE NEWS INTERNATIONAL (10 SEPTEMBER 2018)
By Jon Paul Faulkner
School of Architecture Planning and Environmental Policy
University College Dublin
By Jon Paul Faulkner
School of Architecture Planning and Environmental Policy
University College Dublin
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THE NOISE-ADAPT IRELAND PROJECT
In accordance with Art. 6.2 of the Environmental Noise Directive (END), the European Commission recently developed Common Noise assessment methods (CNOSSOS-EU) for road, railway, aircraft, and industrial noise, to be used by member states for the purpose of strategic noise mapping (SNM) as required by Article 7 of the END. The CNOSSOS-EU model will come into effect for the 2022 noise mapping round for all EU nations. The Noise-Adapt Ireland project aims to identify Ireland’s adaptation needs for transitioning to the CNOSSOS-EU standardized noise model and the standardized approach for population-exposure estimation. This article highlights some interesting findings from a data needs assessment report that aimed to identify Ireland’s key transitioning needs under CNOSSOS-EU. |
FINDINGS FROM THE REPORT
CNOSSOS-EU introduces five categories of vehicle for road-traffic noise, where formerly there were only two categories, for light and heavy vehicles (CRTN Methodology). The authority responsible for Ireland’s national road network is Transport Infrastructure Ireland (TII). This authority’s method of classification has limited accuracy in identifying certain classes of vehicle and currently suffers from cross-category overlap between existing TII classification and CNOSSOS-EU classification. If data gaps exist, a default decomposition of heavy vehicles into two categories can be implemented based on the type of road under analysis. Alternatively, if one of the two categories dominates the flow strongly, this category is recommended. However, it is possible that more accurate information may be garnered from regional statistics acquired through traffic counts by regional authorities (e.g., Dublin City Council) or estimated from commercial vehicle registration data collected by the Central Statistics Office (CSO).
The CNOSSOS-EU model recommends the calculation of average speed for each vehicle category. Ireland currently uses the legal speed limit on respective roads as a measure of average speed. Therefore, average vehicle speeds will need to be recorded using vehicle-speed monitors. In the context of agglomerations, Dublin City Council (DCC) has vehicle-speed monitors in place. The National Roads Authority (NRA) and DCC will need to translate its road categories to spectral α and β coefficient octave bands. Other specific data needs include road-surface gradient, roadside topography, and more detailed spatial information, particularly related to the positioning of traffic lights and stop signs.
In the context of railway noise, Irish Rail will need to gather data on the types of track composing the Irish rail network. It is imperative for practitioners to focus on track base and railhead roughness in the initial stages of CNOSSOS-EU implementation. Data must also be gathered in relation to track-base and sleeper-type as well as brake-type data. In relation to brake-type data, stock category assumptions will have to be made based on vehicle type and vintage and examination of publically accessible photographs (e.g., where brake discs are visible). In the scenario where vehicles have a combination of disc brakes and cast-iron tread brakes, only the latter is recommended for classification. Data sets for elevated structures such as bridges are required. In order to identify elevated structures, data sets may need to be manually generated through field surveys and analysis of aerial photography.
The CNOSSOS-EU model recommends the calculation of average speed for each vehicle category. Ireland currently uses the legal speed limit on respective roads as a measure of average speed. Therefore, average vehicle speeds will need to be recorded using vehicle-speed monitors. In the context of agglomerations, Dublin City Council (DCC) has vehicle-speed monitors in place. The National Roads Authority (NRA) and DCC will need to translate its road categories to spectral α and β coefficient octave bands. Other specific data needs include road-surface gradient, roadside topography, and more detailed spatial information, particularly related to the positioning of traffic lights and stop signs.
In the context of railway noise, Irish Rail will need to gather data on the types of track composing the Irish rail network. It is imperative for practitioners to focus on track base and railhead roughness in the initial stages of CNOSSOS-EU implementation. Data must also be gathered in relation to track-base and sleeper-type as well as brake-type data. In relation to brake-type data, stock category assumptions will have to be made based on vehicle type and vintage and examination of publically accessible photographs (e.g., where brake discs are visible). In the scenario where vehicles have a combination of disc brakes and cast-iron tread brakes, only the latter is recommended for classification. Data sets for elevated structures such as bridges are required. In order to identify elevated structures, data sets may need to be manually generated through field surveys and analysis of aerial photography.
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In the context of industrial noise, there has never been any SNM of industrial sites in Ireland. The first stage of SNM should begin by cataloging all applicable industrial sites within the agglomeration under analysis. The Environmental Protection Agency (EPA) of Ireland has a register of Integrated Pollution Prevention and Control (IPPC) licensed industries in Ireland. Since these plants have sound emission control conditions attached to their licences, this data can be extracted from reports submitted under the licensing requirements. This data is measured rather than modeled, however, it could potentially be used to carry out population assessments once the sound levels at the boundary of industrial sites are known. |
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In relation to sound propagation, geographic information systems (GIS) are proficient at generating accurate spatial dimensions for buildings along a horizontal plane but not along a vertical plane. In Ireland, precise building-height parameters need to be integrated into future SNM. DCC has a database on all building heights in DCC area, however, for other agglomerations in Ireland building height data will need to be retrieved from Ordinance Survey LiDAR data (a high resolution elevation data-set yielded from a Light Detection and Ranging technique of data collection. Accurate information regarding the position of noise barriers is also required, as even minor inaccuracies in height can result in major discrepancies in noise calculation. Noise barrier data may be acquired using Google Maps and Google Street-view.
CNOSSOS-EU LIMITATIONS
Road Vehicle Propulsion Noise
CNOSSOS-EU applies the same correction coefficient for respective road surfaces to both propulsion and rolling noise. This methodology is problematic because no distinction is made between variation in rolling noise caused by the roughness change associated with the road surface and the variation in rolling noise caused by absorption (Pallas and Dutilleux 2018). It is more relevant to apply a single measure of absorption effect to propulsion noise (Pallas and Dutilleux 2018).
Road Noise Emissions
At the present time, the current calculation of road noise emissions using the 2015/996 Directive is incorrect. This is because the CNOSSOS sound-power coefficients were drawn from the IMAGINE project using the Harmonoise propagation model. The majority of the frequency range (i.e., 63 Hz–8 kHz) predictions by Peeters and van Blokland (2018) calculate that CNOSSOS methodology underestimates noise levels of up to 3 dB due to transference in propagation models. Peeters and van Blokland found that after calculating the sound-emission level using sound-power coefficients presented in the Directive 2015/996 table F-1 and the CNOSSOS propagation model, outputs did not reflect the actual noise emission generated from roadside measurements.
Furthermore, Peeters and van Blokland highlight an inaccuracy in relation to road-surface reflection presented in Directive 2015/996. Directive 2015/996 states, “In this method, each vehicle (category 1, 2, 3, 4 and 5) is represented by one single point source radiating uniformly into the 2-π half space above the ground. The first reflection on the road surface is treated implicitly” (7). The directive assumes a “semi-free field” or “hemispherical” point source, and this assumption does not correspond with the IMAGINE/Harmonoise project. On the other hand, sound-power coefficients have been acquired for a free-field point source radiating into the 4-π space. Hence, “the first reflection on the road surface” (7) is not treated implicitly and should therefore not be treated as part of the propagation model. Directive 2015/996 relating to road surface reflection should be disregarded or modified to reflect a free-field (4-π) point source, whereby the first reflection on the road surface should be treated using the propagation model. Peeters and van Blokland (2018) modify the current coefficients in the Directive 2015/996 table F-1 (i.e., IMAGINE) to the CNOSSOS propagation model in order to calculate the correct sound-power coefficients for the CNOSSOS methodology.
There is also a discrepancy between the road-traffic emission and the propagation models within the CNOSSOS methodology. As such, the emission model applies a source in a free field, and the propagation model applies a source in a semifree field (i.e., above ground surface) (Salomons and Eisses 2018). This discrepancy can result in an error of 3 dB but may be resolved by a relatively elementary formula of correction (Salomons and Eisses 2018).
Railway Emission Calculation
In implementing the CNOSSOS-EU model in the context of Finland, Kokkonen (2018) expresses concern associated with the fact that railway emission calculation is fundamentally based on railway and wheel roughness. Finland has railway-roughness data for only a single point, and results indicate that there may be up to a 10 dB error when sound power is calculated using the CNOSSOS methodology. Kokkonen emphasizes the lack of guidance in relation to how national measurement of emission values should be considered within the CNOSSOS model, citing the example of absent guidance on how to adjust inaccurate speed correlation.
Aircraft-Noise Modelling
When default aircraft-noise data sets were reviewed, modified, and verified in relation to empirical data, Trow and Allmark (2018) found that noise outputs could vary by +/− 2 dB. More research is required in relation to aircraft-noise modelling, and models should move away from default datasets.
Sound Propagation
CNOSSOS does not provide a ground parameter value, G, for porous asphalt. In their investigation of the CNOSSOS propagation model in the context of the Netherlands, Salomons and Eisses (2018) use a ground parameter value of G = 0 for porous asphalt but suggest that G = 0.5 is possibly more applicable. Furthermore, in relation to the actual CNOSSOS propagation model, an error exists in how modified heights from equations VI-19 should be applied to VI-20 (Kephalopoulos, Paviotti, and Ledee 2012, 89). Hence, alternative unmodified heights should be used.
In the context of diffraction, the CNOSSOS description of path difference -λ / 20 between the ground model and the diffraction model applicable to flat ground/low barriers and high barriers, respectively, is ambiguous within the CNOSSOS methodology. Screening attenuation in the context of multiple diffraction is also highly problematic. Salomons and Eisses (2018) demonstrate that placing a second noise screen at 500 m from source (with the first screen 1020 m from source) results in a completely unrealistic increase of up to 20 dB in sound at high frequency. This problem derives from the CNOSSOS method of calculating acoustic path length differential under favorable conditions, whereby such an approach does not translate to more than a single diffraction point. Salomons and Eisses (2018) propose a solution for multiple diffraction under favorable conditions.
CNOSSOS-EU applies the same correction coefficient for respective road surfaces to both propulsion and rolling noise. This methodology is problematic because no distinction is made between variation in rolling noise caused by the roughness change associated with the road surface and the variation in rolling noise caused by absorption (Pallas and Dutilleux 2018). It is more relevant to apply a single measure of absorption effect to propulsion noise (Pallas and Dutilleux 2018).
Road Noise Emissions
At the present time, the current calculation of road noise emissions using the 2015/996 Directive is incorrect. This is because the CNOSSOS sound-power coefficients were drawn from the IMAGINE project using the Harmonoise propagation model. The majority of the frequency range (i.e., 63 Hz–8 kHz) predictions by Peeters and van Blokland (2018) calculate that CNOSSOS methodology underestimates noise levels of up to 3 dB due to transference in propagation models. Peeters and van Blokland found that after calculating the sound-emission level using sound-power coefficients presented in the Directive 2015/996 table F-1 and the CNOSSOS propagation model, outputs did not reflect the actual noise emission generated from roadside measurements.
Furthermore, Peeters and van Blokland highlight an inaccuracy in relation to road-surface reflection presented in Directive 2015/996. Directive 2015/996 states, “In this method, each vehicle (category 1, 2, 3, 4 and 5) is represented by one single point source radiating uniformly into the 2-π half space above the ground. The first reflection on the road surface is treated implicitly” (7). The directive assumes a “semi-free field” or “hemispherical” point source, and this assumption does not correspond with the IMAGINE/Harmonoise project. On the other hand, sound-power coefficients have been acquired for a free-field point source radiating into the 4-π space. Hence, “the first reflection on the road surface” (7) is not treated implicitly and should therefore not be treated as part of the propagation model. Directive 2015/996 relating to road surface reflection should be disregarded or modified to reflect a free-field (4-π) point source, whereby the first reflection on the road surface should be treated using the propagation model. Peeters and van Blokland (2018) modify the current coefficients in the Directive 2015/996 table F-1 (i.e., IMAGINE) to the CNOSSOS propagation model in order to calculate the correct sound-power coefficients for the CNOSSOS methodology.
There is also a discrepancy between the road-traffic emission and the propagation models within the CNOSSOS methodology. As such, the emission model applies a source in a free field, and the propagation model applies a source in a semifree field (i.e., above ground surface) (Salomons and Eisses 2018). This discrepancy can result in an error of 3 dB but may be resolved by a relatively elementary formula of correction (Salomons and Eisses 2018).
Railway Emission Calculation
In implementing the CNOSSOS-EU model in the context of Finland, Kokkonen (2018) expresses concern associated with the fact that railway emission calculation is fundamentally based on railway and wheel roughness. Finland has railway-roughness data for only a single point, and results indicate that there may be up to a 10 dB error when sound power is calculated using the CNOSSOS methodology. Kokkonen emphasizes the lack of guidance in relation to how national measurement of emission values should be considered within the CNOSSOS model, citing the example of absent guidance on how to adjust inaccurate speed correlation.
Aircraft-Noise Modelling
When default aircraft-noise data sets were reviewed, modified, and verified in relation to empirical data, Trow and Allmark (2018) found that noise outputs could vary by +/− 2 dB. More research is required in relation to aircraft-noise modelling, and models should move away from default datasets.
Sound Propagation
CNOSSOS does not provide a ground parameter value, G, for porous asphalt. In their investigation of the CNOSSOS propagation model in the context of the Netherlands, Salomons and Eisses (2018) use a ground parameter value of G = 0 for porous asphalt but suggest that G = 0.5 is possibly more applicable. Furthermore, in relation to the actual CNOSSOS propagation model, an error exists in how modified heights from equations VI-19 should be applied to VI-20 (Kephalopoulos, Paviotti, and Ledee 2012, 89). Hence, alternative unmodified heights should be used.
In the context of diffraction, the CNOSSOS description of path difference -λ / 20 between the ground model and the diffraction model applicable to flat ground/low barriers and high barriers, respectively, is ambiguous within the CNOSSOS methodology. Screening attenuation in the context of multiple diffraction is also highly problematic. Salomons and Eisses (2018) demonstrate that placing a second noise screen at 500 m from source (with the first screen 1020 m from source) results in a completely unrealistic increase of up to 20 dB in sound at high frequency. This problem derives from the CNOSSOS method of calculating acoustic path length differential under favorable conditions, whereby such an approach does not translate to more than a single diffraction point. Salomons and Eisses (2018) propose a solution for multiple diffraction under favorable conditions.
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CONCLUDING REMARKS
The CNOSSOS-EU process is an ongoing procedure. The Commission Directive (EU) 2015/996, on which CNOSSOS-EU is based, will be modified appropriately in accordance with any future technical and scientific developments required for the successful implementation of the CNOSSOS-EU model, and, similarly, will make any adjustments deemed necessary, based on the experience of Member States. As such, the report reflects this continuous procedure and will be amended appropriately in parallel with future phases required under the CNOSSOS process. The Noise-Adapt Project is being conducted by University College Dublin and Trinity College Dublin and is funded by the Environmental Protection Agency Ireland. |
.REFERENCES
- Commission Directive (EU) 2015/996; L 168
- Kokkonen, Jarno. 2018. “CNOSSOS-EU Noise Model Implementation in Finland and Experience of It in 3rd END Round”. Proceedings of Euronoise 2018.
- Pallas, Marie-Agnѐs, and Guillaume Dutilleux. 2018. “Experimental Confrontation of Medium–Heavy Vehicle Noise Emission to the CNOSSOS-EU Prediction Method”. Proceedings of Euronoise 2018.
- Peeters, Bert, and G. J. van Blokland. 2007. “The Noise Emission Model for European Road Traffic”. IMAGINE deliverable 11 (11).
- Salomons, Erik, and Eisses, Arno. 2018. "Investigations of the Cnossos sound propagation". Proceedings of Euronoise 2018.
- Trow, James, and Claire Allmark. 2018. “The Benefits of Validating Your Aircraft Noise Model”. Proceedings of Euronoise 2018.
New map to help city deal with noise
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A “noise map” has been developed by Shanghai Academy of Environmental Sciences which allows people to “see” noise. On this map, each street block and major road within the Outer Ring road is given a color. There are a total of 12 colors which represent 12 noise levels. For example, a purple color means noise detected in the certain area is between 30 and 35 decibels. Red color indicates 60-65 decibels and blue color is between 75 and 80 decibels, etc. |
Roads along busy elevated ways are often of red or blue color, while neighborhoods and parks are usually quieter, showing a color of green or purple. According to Zhou Yude, director of the academy’s noise control engineering research center, the project was kicked off about six years ago, and the map is still being tested and improved before being officially introduced to the public.
Noise detectors around the city help collect data to decide the color of certain areas. But for areas without detectors, researchers borrow indirect data from other city departments and “translate” them into decibels. “For roads that are not equipped with detectors, we collect traffic data from the transport department, including vehicle flowrate, car speed as well as car model,” said Zhou. “For example, If the flowrate of a road is 5,000 to 6,000 vehicles per hour with an average speed of about 50km, and the vehicles are mostly small-size cars, our calculation says the average noise level of this road is between 65 and 70 decibels during day time. “We use similar methods for roads along railways. We collect information like timetable and train speed, and calculate the noise in different times of a day. After years of improvement, on-site verification shows that the error rate of such calculation is no higher than 3 percent,” said Zhou.
Noise detectors around the city help collect data to decide the color of certain areas. But for areas without detectors, researchers borrow indirect data from other city departments and “translate” them into decibels. “For roads that are not equipped with detectors, we collect traffic data from the transport department, including vehicle flowrate, car speed as well as car model,” said Zhou. “For example, If the flowrate of a road is 5,000 to 6,000 vehicles per hour with an average speed of about 50km, and the vehicles are mostly small-size cars, our calculation says the average noise level of this road is between 65 and 70 decibels during day time. “We use similar methods for roads along railways. We collect information like timetable and train speed, and calculate the noise in different times of a day. After years of improvement, on-site verification shows that the error rate of such calculation is no higher than 3 percent,” said Zhou.
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According to Zhou, the map updates data every 20 minutes. If maps taken at different times are displayed together, it shows the effect similar to a meteorogram, giving researchers an idea how noise level changes with time in certain areas. Noise map is not a new invention, but quite new a technology for China. Back in 2007, European Union member countries have been required by the EU to make noise maps for cities with population over 250,000, so that the government can take measures against noise pollution. |
Some cities in Japan and South Korea also have noise maps, which are used for environmental protection purpose as well as urban planning. But most noise maps in European countries don’t get updated so frequently. Some get updated only on a yearly base.
“It doesn’t mean that we have better technology than those countries” said Zhou. “The updating rate of a noise map is decided by how we want to use it. In European countries, the map is usually used for long-term environment policy making and urban planning. But for a city like Shanghai which is still developing with fast speed, noise data changes quickly with on-going city construction. The maps of different months or seasons can be very different,” said Zhou.
The noise map can also help detect hidden noise sources. Zhou said in one particular case, the roof of a building which is next to a busy road showed blue color on the map. However, its color rarely changed and the noise level it indicated is often higher than the major road nearby. Local environmental protection authority’s investigation showed that there was a heat pump on the roof of the building which kept making noise. The authority then ordered the property management company of the building to take measures against the pump so as not to disturb nearby residents.
“It doesn’t mean that we have better technology than those countries” said Zhou. “The updating rate of a noise map is decided by how we want to use it. In European countries, the map is usually used for long-term environment policy making and urban planning. But for a city like Shanghai which is still developing with fast speed, noise data changes quickly with on-going city construction. The maps of different months or seasons can be very different,” said Zhou.
The noise map can also help detect hidden noise sources. Zhou said in one particular case, the roof of a building which is next to a busy road showed blue color on the map. However, its color rarely changed and the noise level it indicated is often higher than the major road nearby. Local environmental protection authority’s investigation showed that there was a heat pump on the roof of the building which kept making noise. The authority then ordered the property management company of the building to take measures against the pump so as not to disturb nearby residents.
Track dampers on Mumbai Metro to curb noise pollution
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In order to curb noise pollution, the Mumbai Metropolitan Region Development Authority (MMRDA) has planned to install dampeners, also known was track dampers, on the Metro tracks at several locations which have sharp curves. "There are several curves on the two under construction Metro corridor of Andheri East- Dahisar East Metro-7 and Dahisar- DN Nagar Metro-2A corridor. However, there are a total of three curves that require dampers on Metro-7 and Metro-2A near Dahisar that are steep," said Pravin Darade, Additional Metropolitan Commissioner, MMRDA. |
According to several studies, track dampers usage on tracks can achieve an approximately reduction of 1-3 decibels coming from tracks where curves are aligned compared to the tracks that do not have any fitting of such dampers."There are not too many steeps curves but we are going to install dampeners as a precautionary measure to have reduction in noise levels"
Currently, The MMRDA is in the process of having Metro coaches also known as rolling stocks. The bidding process for the same is ongoing and around 12 companies have shown interest. MMRDA has also proposed to have a first class compartment in the upcoming Metro corridors to attract car owners. Meanwhile, there have been past complaints filed by residents of Andheri against the curvature on the Versova-Andheri-Ghatkopar Metro-1 corridor. NK Goyle, a resident of JP Road, Andheri, said, "I have complained about the noise levels due to the curvature which causes immense inconvenience to around 100 people residing in my locality near Navrang Cinema."
COMPLAINTS
There have been past complaints against the curvature on the Versova-Andheri-Ghatkopar Metro-1 corridor, which makes a screeching sound every time a train passes from either side.
Currently, The MMRDA is in the process of having Metro coaches also known as rolling stocks. The bidding process for the same is ongoing and around 12 companies have shown interest. MMRDA has also proposed to have a first class compartment in the upcoming Metro corridors to attract car owners. Meanwhile, there have been past complaints filed by residents of Andheri against the curvature on the Versova-Andheri-Ghatkopar Metro-1 corridor. NK Goyle, a resident of JP Road, Andheri, said, "I have complained about the noise levels due to the curvature which causes immense inconvenience to around 100 people residing in my locality near Navrang Cinema."
COMPLAINTS
There have been past complaints against the curvature on the Versova-Andheri-Ghatkopar Metro-1 corridor, which makes a screeching sound every time a train passes from either side.
ENVIRONMENTHEALTHNoise pollution is one of the biggest health risks in city life24 May 2018
by Joanna Roberts
by Joanna Roberts
Noise is one of the biggest pollutants in modern cities but the risk is often overlooked despite being linked to an increased risk of early death, according to research conducted by scientists.
‘Noise produces a stimulus to the central nervous system and this stimulus releases some hormones,’ said Dr David Rojas from the Barcelona Institute for Global Health in Spain. ‘(This) increases the risk of hypertension, and hypertension has been related with many other cardiovascular (and) cerebrovascular diseases like infarction (heart attacks) and strokes.’
He was speaking in Brussels, Belgium, on 23 May at the annual Green Week conference, part of a Europe-wide event to help people swap best practices on environmental activities and policies. This year’s focus is how the EU is helping cities to become better places to live and work.
Dr Rojas, an environmental health researcher, says that despite the fact that noise pollution is a major public health problem in cities – and, in fact, beats air pollution as a risk factor in Barcelona – there is a tendency to overlook the problem because we can tune it out.
‘When we have a background noise, the brain has the capacity to adapt to this noise,’ he said. ‘And you don’t see it as an annoyance so much and you start to accept and adapt. But even if you are not conscious of the noise, this is still stimulating your organic system.’
Dr Rojas’ research, which was carried out under the HELIX and PASTA projects, gathered data on the multiplicity of pollutants that we encounter in cities. He hopes the findings can be used to shape policies that could help improve health in urban areas. For example, improving cycling infrastructure and encouraging parents to walk their children to school could not only cut noise and air pollution but would also improve levels of physical activity.
Multiple benefits
‘There are multiple benefits of good urban design,’ said Dr Rojas. ‘It’s not only air pollution or noise, it’s also physical activity, green spaces, (and) heat.’ As part of the HELIX project, Dr Rojas and his team measured the impact of encouraging all children who lived within 1 kilometre of a school to walk there each day. The result was a dramatic improvement in children’s health by reducing high blood pressure and obesity, as well as by avoiding a slew of minor road traffic injuries.
Their findings are also challenging the common misconceptions about whether the harm caused by air pollution counteracts the benefits of walking or cycling in cities. ‘The benefit is 70 times bigger than the risk,’ said Dr Rojas. He added that while pregnant women and young children were particularly vulnerable to urban pollutants, the problem affects everybody, regardless of life stage, resulting in a large public health burden.
‘In the case of air pollution, even if we feel that we are not vulnerable – we are adults and we are healthy – we are suffering from this exposure. We are losing quality of life and quantity of life because we are exposed to these pollutants.’ The conference also heard about some of the nature-based innovations, such as urban greening, that are being adopted throughout Europe in order to improve the quality of city life. Community gardens, living walls, and putting greenery in so-called grey spaces such as railway lines, are all examples of attempts to make urban areas more natural places to live and work, according to Dr Dora Almassy from the Central European University in Budapest, Hungary.
Nature-based solutions
She has helped to compile a database of nature-based solutions being used in towns and cities around Europe to produce an urban nature atlas as part of a project called Naturvation. ‘More than half of the projects (in the database) provided benefits for the health and wellbeing of the citizens,’ she said. This is because they create a healthier working environment and provide space for more leisure and recreational activities. Spending time in green spaces has also been linked to improved mental health, while such projects can also be used for food production and education. But not all of the projects achieve the same benefits for those who use them.
‘Certain types of nature-based solutions are better placed to improve the health of the citizens,’ said Dr Almassy. ‘These are community gardens, parks, or simple green grey infrastructures and … green roofs and green facades.’ One example is Alder Hey Children’s Hospital in Liverpool, UK, which is was designed to have green spaces inside and out, exposing the children being treated there to a more natural environment. The plants are also used to produce food, which is then supplied to the hospital canteen.
Dr Almassy said that many of these projects are reliant upon the public sector for funding and execution, so future research needs to look at business models that will encourage companies and individuals to contribute to a green urban environment. Dr Rojas added that another big challenge for the future will be to combine the data on different environmental factors to produce models that can give far more comprehensive predictions for policy decisions. ‘When you are in the city, we are not exposed to a single pollutant, we are exposed to a mix of things,’ he said. ‘We need to think of how we can approach in a better way this complexity of exposure. The urban environment is more than a single thing.’
The research in this article was funded by the EU. If you liked this article, please consider sharing it on social media.
Click here to go to link
‘Noise produces a stimulus to the central nervous system and this stimulus releases some hormones,’ said Dr David Rojas from the Barcelona Institute for Global Health in Spain. ‘(This) increases the risk of hypertension, and hypertension has been related with many other cardiovascular (and) cerebrovascular diseases like infarction (heart attacks) and strokes.’
He was speaking in Brussels, Belgium, on 23 May at the annual Green Week conference, part of a Europe-wide event to help people swap best practices on environmental activities and policies. This year’s focus is how the EU is helping cities to become better places to live and work.
Dr Rojas, an environmental health researcher, says that despite the fact that noise pollution is a major public health problem in cities – and, in fact, beats air pollution as a risk factor in Barcelona – there is a tendency to overlook the problem because we can tune it out.
‘When we have a background noise, the brain has the capacity to adapt to this noise,’ he said. ‘And you don’t see it as an annoyance so much and you start to accept and adapt. But even if you are not conscious of the noise, this is still stimulating your organic system.’
Dr Rojas’ research, which was carried out under the HELIX and PASTA projects, gathered data on the multiplicity of pollutants that we encounter in cities. He hopes the findings can be used to shape policies that could help improve health in urban areas. For example, improving cycling infrastructure and encouraging parents to walk their children to school could not only cut noise and air pollution but would also improve levels of physical activity.
Multiple benefits
‘There are multiple benefits of good urban design,’ said Dr Rojas. ‘It’s not only air pollution or noise, it’s also physical activity, green spaces, (and) heat.’ As part of the HELIX project, Dr Rojas and his team measured the impact of encouraging all children who lived within 1 kilometre of a school to walk there each day. The result was a dramatic improvement in children’s health by reducing high blood pressure and obesity, as well as by avoiding a slew of minor road traffic injuries.
Their findings are also challenging the common misconceptions about whether the harm caused by air pollution counteracts the benefits of walking or cycling in cities. ‘The benefit is 70 times bigger than the risk,’ said Dr Rojas. He added that while pregnant women and young children were particularly vulnerable to urban pollutants, the problem affects everybody, regardless of life stage, resulting in a large public health burden.
‘In the case of air pollution, even if we feel that we are not vulnerable – we are adults and we are healthy – we are suffering from this exposure. We are losing quality of life and quantity of life because we are exposed to these pollutants.’ The conference also heard about some of the nature-based innovations, such as urban greening, that are being adopted throughout Europe in order to improve the quality of city life. Community gardens, living walls, and putting greenery in so-called grey spaces such as railway lines, are all examples of attempts to make urban areas more natural places to live and work, according to Dr Dora Almassy from the Central European University in Budapest, Hungary.
Nature-based solutions
She has helped to compile a database of nature-based solutions being used in towns and cities around Europe to produce an urban nature atlas as part of a project called Naturvation. ‘More than half of the projects (in the database) provided benefits for the health and wellbeing of the citizens,’ she said. This is because they create a healthier working environment and provide space for more leisure and recreational activities. Spending time in green spaces has also been linked to improved mental health, while such projects can also be used for food production and education. But not all of the projects achieve the same benefits for those who use them.
‘Certain types of nature-based solutions are better placed to improve the health of the citizens,’ said Dr Almassy. ‘These are community gardens, parks, or simple green grey infrastructures and … green roofs and green facades.’ One example is Alder Hey Children’s Hospital in Liverpool, UK, which is was designed to have green spaces inside and out, exposing the children being treated there to a more natural environment. The plants are also used to produce food, which is then supplied to the hospital canteen.
Dr Almassy said that many of these projects are reliant upon the public sector for funding and execution, so future research needs to look at business models that will encourage companies and individuals to contribute to a green urban environment. Dr Rojas added that another big challenge for the future will be to combine the data on different environmental factors to produce models that can give far more comprehensive predictions for policy decisions. ‘When you are in the city, we are not exposed to a single pollutant, we are exposed to a mix of things,’ he said. ‘We need to think of how we can approach in a better way this complexity of exposure. The urban environment is more than a single thing.’
The research in this article was funded by the EU. If you liked this article, please consider sharing it on social media.
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