Ecology

Mapping kelp persistenceThe study area for this analysis encompasses the region where Macrocystis pyrifera is the dominant canopy kelp species in the Northeast Pacific Ocean. The region extends from Año Nuevo Island in the north (latitude ~37.1°), California, USA, to Punta Prieta in the south (latitude ~27°), Baja California Sur, Mexico. We mapped the distribution of giant kelp canopy and characterized persistence using a 30-m resolution satellite-based time series covering our entire study area27. These data provide quarterly estimates of kelp canopy area across the study region from 1984 to 2018. We estimated giant kelp canopy from three Landsat sensors: Landsat 5 Thematic Mapper (1984–2011), Landsat 7 Enhanced Thematic Mapper+ (1999–present), and Landsat 8 Operational Land Imager (2013–present). We downloaded all imagery as atmospherically corrected Landsat Collection 1 Level-2 products. Each Landsat sensor has a pixel resolution of 30 × 30 m and a repeat time of 16 days (8 days when two Landsat sensors were operational). Since Landsat imagery can be obscured by cloud cover, we obtained a clear estimate of kelp areas ~16 times per year from 1984 to 2018 (mean = 16.2, std = 4.1). The repeated observations across the time series avoid missing kelp canopy due to physical processes such as tides and currents. Multiple Landsat passes over seasonal timescales are successful at mitigating the effect of tide and tidal currents on Landsat kelp canopy detection27.While the pixel resolution of Landsat sensors is 30 × 30 m, we were able to observe the presence and density of kelp canopy on subpixel scales using a fully automation procedure. We first masked all land areas using a global 30 m resolution digital elevation model (asterweb.jpl.nasa. gov/gdem.asp) and classified the remaining pixels as seawater, cloud, or kelp canopy using a binary decision tree classifier trained on a diverse array of pixels within the study region27. We then used Multiple Endmember Spectral Mixture Analysis39 to model each pixel as the linear combination of seawater and kelp canopy. This method can accurately obtain kelp canopy presence as long as kelp canopy covers ~13% of a 30 m pixel. These methods were validated using 15 years of monthly kelp canopy surveys by the Santa Barbara Coastal Long Term Ecological Research project at two sites in Southern California. We filtered errors of commission (such as free-floating kelp paddies) by removing any pixels classified as kelp canopy in More

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Database constructionCollection of metagenomic dataBetween March 2012 and May 2016, seawater samples were collected from five different locations around Sendai Bay, Ofunato Bay, and A-line. Samples (N = 142) were collected from the surface and the subsurface chlorophyll-a maximum (SCM) layers (Fig. 1). DNA was extracted from microorganisms trapped in filters (pore sizes, 0.2, 0.8, 5, 20, and 100 μm). Shotgun metagenomic sequence data (N = 454) were acquired (Supplementary Information 1), comprising 3.57 × 109 reads and 3.56 × 1011 bases in total (Supplementary Information 2). 16S rRNA gene sequences in the 0.2-μm fraction were subjected to PCR using universal primers to obtain 111 amplicon metagenomic sequences (6.92 × 106 reads, 1.69 × 109 bases) (Supplementary Information 2).Figure 1Location, changes in water temperature, and changes in chlorophyll-a (Chl-a) concentrations at the sampling points. (a) Sampling points along the Pacific coast of northeastern Japan (Sendai Bay, Ofunato Bay, and A-line). (b) Sampling points C5 and C12 in Sendai Bay. The map was generated using Ocean Data View (https://odv.awi.de) with data imported from the NOAA server (accessed on 22 February 2021). (c) Changes in water temperature and chlorophyll concentrations at Sendai Bay and A-line sampling points. Red circles indicate the depth of the sampled water. X-axis: dates from 2012 to 2014. Y-axis: water depth from the surface.Full size imageTime-series analysis of microbial species compositionWe performed microbial taxonomic assignments through analyses of amplicon and shotgun metagenomic sequence data using the SILVA and NCBI NT databases. For C5 and C12 samples from Sendai Bay and A4 and A21 samples from A-line, sufficient time-series data were available to plot changes in microbiota over time (Fig. 2). By clicking the displayed taxon at the website of Ocean Monitoring Database, the microbiota composition of lower taxa is revealed. For example, Fig. 2 shows that the cyanobacteria community increased in abundance during the summer.Figure 2Time-series analysis of microbial communities along the Pacific coast of northeastern Japan. Each sampling point shows the number of ribosomal sequences normalized to 1000 (excluding no hits). Clicking on the graph at the website of Ocean Monitoring Database exhibits the next taxonomic levels. This figure shows an example of the change in Cyanobacteria communities over time from April 2012 to May 2014. SUF: surface layer (1 m), SCM: the subsurface chlorophyll-a maximum layer.Full size imageWe acquired substantial 16S rRNA gene amplicon sequencing data at the C5 fixed point at Sendai Bay between 2012 and 2014. We generated a 3D graph to simultaneously display the date, water depth, and species composition at this site (Fig. 3). By clicking on the taxon shown in the graph at the website of Ocean Monitoring Database, the composition of the microbiota within a lower taxon is displayed. The 3D display is suitable for presenting a bird’s-eye view of the metagenomic data, which is extremely useful for visualizing and understanding the relationships among microbial communities among sampling points. This innovative function was incorporated into the metagenomic database. Figure 3 shows a contour map of chlorophyll concentrations on the x-axis and the proportion of microbial communities on the z-axis. The proportion of flavobacteria may increase following an increase in chlorophyll concentration during a spring bloom. For example, Buchan et al. reported that the proportion of flavobacteria increase late in a spring bloom16; our results show similar patterns (Fig. 3).Figure 3Three-dimensional (3D) display of microbial communities. 3D display of bacterial communities identified using 16S rRNA gene amplicon analysis of the Sendai Bay C5 samples from 2012 to 2014. The x-axis indicates the date, the y-axis indicates the water depth, and the z-axis indicates the percentage abundance of bacterial genera. The contour plot on the xy plane indicates the chlorophyll concentration. The composition of Flavobacteriaceae is shown as an example.Full size imageDigital DNA chip (DDC) databaseA DDC analysis (DDCA) system is useful for visualizing the characteristics of shotgun metagenomic data as a microarray17 of, for example, filter size, water sampling point, water sampling time, temperature, salinity, and nitrate and phosphate concentrations (Fig. 4a). By mapping sequence data against the probe sets described above, which are associated with environmental factors, we predicted that sequence data would be more enriched and inclusive of environmental information. Figure 4b displays the DDCA shotgun metagenomic data of the 0.2–0.8-μm fraction of the C5 sample collected from Sendai Bay on December 1, 2013. The sample contains a bacterial-fraction DNA marker with filter sizes of 0.2–0.8 µm and a specific DNA marker for December in Sendai Bay. Even if there is only NGS data and no environmental information, just by looking at the digital DNA chip, we can assume this sample is extracted from 0.2 to 0.8 µm fraction and is from Sendai Bay (Fig. 4b).Figure 4Visualization of metagenomics data using digital DNA chips. (a) Overview of in silico probes associated with the environmental factors on a digital DNA chip (See Supplementary Information 8 for details). (b) Digital DNA chip of shotgun metagenomics data of a 0.2–0.8-μm fraction of December 1, 2013, Sendai Bay C5. There are 748 probes, and spots that are positive for digital hybridization are shown in red. Negative spots are black. The hybridization positive probes are an indicator of environmental information of the sequence data.Full size imageDevelopment of a shotgun metagenomic databaseWe assembled the shotgun metagenomic sequence data using Megahit version 1.0.218. There were 57.95 M contigs, with an N50 of 995 bp, a maximum length of 307,212 bp, and a total of 12.39 Gbp (Supplementary Information 3). We calculated the abundance pattern of each contig. Those contigs whose appearance pattern matched with a Pearson correlation coefficient of ≥ 0.95 were clustered into a MAG. We next added the annotation of assembled contigs to the results of the BLAST search of the NCBI NT database and using classification by clustering with Pfam (CCP). This novel annotation method is described below. We developed the database showing the abundance pattern of homologous contigs against a queried sequence by BLAST for each sampling point and filter size (Fig. 5). For example, we found novel PolD families using this database.Figure 5Search for homologous contigs to a query sequence and display of temporal variation patterns. Using nucleotide and amino acid sequences as queries, contigs homologous to the query sequence are identified using BLAST, and the temporal variation patterns and taxonomy information of the hit contigs are displayed.Full size imageDevelopment of a new annotation method for metagenome contigsWe annotated the assembled contigs using BLAST to analyze the NCBI NT database. However, we were unable to annotate  > 50% of the contigs (Fig. 6). Therefore, we developed a novel method, i.e., CCP, to annotate contigs according to their species names. Analysis using a single contig generally does not provide sufficient information for assigning an annotation. However, CCP assigns the appropriate annotation to the sequence because it aggregates the Pfam information of all contigs in a MAG. CCP annotates a MAG by comparing the similarity to the reference genome. Comparing the nucleotide sequences of a MAG directly using blastn to analyze the NCBI NT database shows relatively low homology to de novo virus sequences.Figure 6Comparison between BLAST and CCP annotation results at the super-kingdom level. Comparison of classification results using BLAST to annotate contigs and classification by clustering with Pfam (CCP); the percentage of unknowns was 57% for BLAST and 8% for CCP.Full size imageHowever, a Pfam domain search using HMMER, which employs a different principle19 than BLAST, often detects more informative sequences, even those of viruses. For example, phylogenetic trees constructed according to the type and number of Pfam domains of individual bacterial genomes and those of higher eukaryotes such as humans closely approximate those generated using existing phylogenetic trees20. Thus, genomes with a similar Pfam domain may represent phylogenetically closely related species. We, therefore, searched the Pfam domains for reference genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq (as of August 31, 2015). We next calculated the number of domains for each species and constructed a CCP database. The types and numbers of Pfam domains contained in the contig obtained from the metagenome were summarized in MAG units. We compared the results using the CCP database and annotated the known genomes with the closest correlation coefficient (Fig. 7).Figure 7Overview of CCP. Flowchart of the search of the Pfam domain against known genomes of viruses, bacteria, archaea, and eukaryotes included in RefSeq to create a Pfam hit database. The Pfam domains were searched in metagenome-assembled genome (MAG) units and the known genomes whose type and number of Pfam domains are closest to the MAG.Full size imageBy annotating the top 10,000 contigs with the highest abundance in our database using CCP,  > 90% of the contigs were explained (Fig. 6). In contrast, the BLAST species search (the existing method) returned  15%, indicating that CCP is a robust method, particularly when applied to the identification of virus annotation. We next compared the agreement between CCP and BLAST annotations using contigs annotated using both CCP and BLAST (Supplementary Information 4). The virus-level agreement between CCP and BLAST was 89.6%, and the kingdom-level agreement was 76.9%. It was difficult to determine whether the contig represented a virus using BLAST; however, CCP showed higher accuracy.Shotgun metagenomic analysisPeriodicity of metagenomic dataFor the bacterial fraction (0.2–0.8 µm) of the shotgun metagenomic data from Sendai Bay, we generated a multidimensional scaling (MDS) plot according to the pattern of the abundance of the assembled contigs (Fig. 8). The MDS plot shows similarities among the samples collected during the same month during different years. Furthermore, the plot reveals that the shotgun metagenomic data exhibit an annual seasonal cycle like the 18S rRNA amplicon data10. However, we did not observe the same annual cycle among all contigs. We, therefore, extracted contigs included in the top 20 highly abundant MAGs from the bacterial fractions of Sendai Bay samples collected from March 2012 to April 2014 and plotted the fluctuation patterns. Only one such contig showed a complete 2-year cycle (Fig. 9a), and four contigs showed an incomplete 2-year cycle with peaks in March 2012 and 2013 but not in 2014 (Fig. 9b). Furthermore, 13 MAGs showed a transient pattern (Fig. 9c), and two MAGs showed peaks with irregular patterns (Fig. 9d). These results suggest that marine microbial communities generally undergo an annual cycle.Figure 8Multidimensional scaling (MDS) plot as a function of the abundance of contigs. MDS plots of bacterial fractions (0.2–0.8 µm) of shotgun metagenomic data from 2012 to 2015 acquired from Sendai Bay according to the pattern of abundance of assembled contigs.Full size imageFigure 9Variation patterns of contigs in the top 20 most abundant metagenome-assembled genomes (MAGs).The top 20 MAGs in the bacterial fractions of Sendai Bay C5 and C12 from March 13, 2012, to April 2, 2014, were classified as follows: (a) complete 1-year cycle for 2.5 years, (b) Incomplete 1-year cycle for 2.5 years, (c) transient peaks, and (d) irregular peaks. A peak within 1 month of ≥ 25% relative to the previous year’s peak was considered cyclical.Full size imageIdentification of repeat sequences in the metagenomesDuring the collection and analysis of the DNA sequencing data, we identified a number of repeat sequences in the metagenomes as follows: (TAG)n, (TGA)n, (GAA)n, and (ACA)n microsatellites. We then determined the frequencies and highest numbers of (TAG)n repeats as a function of filter size. We found that the (TAG)n repeats included up to 7.5% of the 5–20-μm fraction (Supplementary Information 5a,b). To investigate whether this was a characteristic feature of the northeastern coastal region of Japan, we analyzed the shotgun metagenomic sequence data of Tara Oceans21,22. As shown in Supplementary Information 5c,d, Tara Oceans data contained up to 1.9% of TAG repeats.To determine whether these (TAG)n repeats represented artifacts of the NGS method, we performed Southern blot and dot-blot hybridization analyses of the DNA samples extracted from seawater (Fig. 10). The dot-blot hybridization experiment analyzed 13 different samples with various content rates (Fig. 10a). We detected signals from the eight samples containing the (TAG)n that were repeated in  > 0.9% of the labeled d54-mer with the (TAG)18 repeat. In contrast, six samples with a low content ( > 0.2%) were negative (Fig. 10b). To determine whether these repeated sequences originated from a single locus or multiple loci, we performed Southern blot analysis (Fig. 10c, Supplementary Information 6) using two samples with high contents of (TAG)n repeats. A (TAG)n representing a single locus is detectable as a discrete band versus the diffuse bands exhibited by two samples with a high content of (TAG)n repeats. The data (Fig. 10c) suggest that the (TAG)n repeats were derived from multiple loci of distinct genomes. Samples with low numbers of (TAG)n repeats were negative.Figure 10Detection of TAG repeats using Southern blot and dot-blot hybridization analyses. (a) Contents of the TAG repeats of the samples according to next-generation sequencing analysis. (b) Dot-blot analyses. The sample numbers and their amounts, (right side) correspond to the signals of each dot in the left panels. The intact pTV119N plasmid without an insert indicates pTV(0). The calculated contents of TAG repeats (%) are indicated in parentheses. (c) Southern blot analysis of EcoRI-digested samples subjected to 0.8% agarose gel electrophoresis. The plasmid pTV (TAG) (0.35 ng and 1 ng) served as a positive control. E. coli genomic DNA served as a negative control. The calculated contents of TAG repeats (%) are indicated on the bottom of each graph. The length (nt) of the TAG-repeated fragment excised from pTV (TAG) is shown on the right.Full size imageThese results reveal for the first time that such repeat sequences are abundant in the genomes of marine microorganisms. However, their species of origin and functional roles were not identified here. The repeat sequences found in Escherichia coli23, subsequently called CRISPR, led to fundamental discoveries that are essential in the field of genetic engineering24. Thus, understanding the biological significance of trinucleotide repeats in marine microorganisms is of particular importance and may reveal a new research frontier. More

Calculation of the indicatorsFigure 1 shows the distribution of the urban greening benefit indicators computed at European scale under the current scenario, while Fig. 2 shows the cumulative distribution of impervious urban areas by increasing value of each indicator, under the current and future scenarios. It should be stressed that, while the indicators of Eqs. (1–4) are computed for every grid cell, the curves of Fig. 2 reflect also the spatial distribution of impervious urban surfaces, and hence they give more prominence to the values of the indicators in the most densely urbanized areas of the continent.Figure 1Maps of benefits per m2 across Europe for ΔTs (a), ΔT (b), RR/P (c) and CB (d), in the present scenario.Full size imageFigure 2Cumulative curves of urban surfaces versus the indicator ΔTs (a), ΔT (b), RR/P (c) and CB (d). The black line represents present conditions, while lines in color stand each for one climatic scenario. The y-axis is the cumulative surface area of the present European urban areas.Full size imageThe reduction of surface temperature ΔTs (Fig. 1a) is highest in the warmer and not excessively dry climates of Central and Southern Europe, reflecting the patterns of actual evapotranspiration. Most European urban areas would achieve temperature reductions of about 3–3.5 °C (Fig. 2a), slightly increasing with the severity of climate heating under the various scenarios, causing a reduction of sensible heat to the atmosphere, a driver of urban heat island effects, between 20 and 40% (see Appendix 1, Supplementary Material for further details). The highest temperature reduction at the roof surface, ΔT, is mostly perceived in the South of Europe (Fig. 1b), consistent with the pattern of potential evapotranspiration, similarly to the production of dry biomass CB (Fig. 1d). The reduction of temperature at the roof is predicted between 15 and 17 °C for most of Europe under the current scenario, and may increase of about 2 °C under the most severe climate scenario (Fig. 2b). Runoff reduction is significantly higher in areas with moderate precipitation, particularly in the plains, compared to rainier areas such as the Atlantic edge of the continent and high mountain ranges (Fig. 1c).The maximum storage volume, Vmax , calculated by Eq. 6, would allow to reuse 92% of the annual runoff, while Vmin and Vavg would allow to store 77% and 86% of the runoff, respectively, as resulting from a daily balance of the storage volume calculated over the 14 year time series. As the storage volume normalized to the annual runoff Rc is 0.24, 0.36 and 0.51 for Vmin, Vavg and Vmax, respectively (Figure 4b), choosing a storage volume equal to Vmin appears to be the most cost-effective solution. Vmin is mapped as shown in Fig. 3a for the case of constant demand, under the current scenario, while in Fig. 3b the volumes are plotted versus the cumulated areas.Figure 3Storage volume Vmin required to store the runoff in the case of constant demand.Full size imageFigure 4Runoff that could be harvested, and normalized storage volume Vmin versus the annual average runoff (Rc) for the case of constant withdrawal, calculated throughout the 14 year time series.Full size imagePhysical and environmental implicationsThese potential effects of green surfaces at European scale correspond to potential benefits. The total benefits extrapolated for the EU are summarized in Table 1. Results are referred to the impervious surfaces corresponding to building roofs, that are assumed to amount to a total of 26,450 km2 as per Bódis et al.40 . This represents 35% of the European impervious surface. Although it is highly unlikely that the majority of the roofs may support a uniform soil cover of 30 cm, they could still bear patches of that thickness over a part of their surface. Moreover, additional surfaces such as sealed ground could be greened. Overall, having in mind these considerations, we pragmatically regard this 35% of impervious urban areas as a maximum extent that could be greened in Europe. All benefits calculated below would obviously scale proportionally for any reduction of the percentage of area subjected to greening. The quantification of Table 1 is explained below.Table 1 Climatic descriptors and quantification of annual benefits at the European scale in the present and future climatic scenarios, assuming to green all roof surfaces, or 35% of the European impervious surfaces.Full size tableThe reduction of land surface temperatures, ΔTs, reduces the thermal irradiation and convective heat flux from urban surfaces (see Appendix 1 of Supplementary Material), which are the drivers of the heat island effect44. As a first order approximation, the reduction of air temperature at 2 m from the surfaces can be expected to be about a half of ΔTs45 as an average value in summer. The reduction of air temperature would generate economic benefits, like the life cycle extension of electronic material and cars, benefits in the health and transport sectors, reduction of social stress and morbidity, and reduction of damages to trees and animals46,47,48.The reduction of the surface temperature ΔT potentially reduces the cooling demand in summer (Eq. 5) by 92 TWh year−1. This energy saving corresponds to 29.9 Mtons of CO2 for the present scenario, considering emissions of 0.325 kg CO2 equivalent kWh−1 for European electricity39. Our estimate is arguably an upper limit of cooling energy savings. In many cases, underroof spaces of buildings are not cooled and effectively work already as an insulation, hence the reduction in the heat transferred from the roofs to underlying inhabited spaces may be lower than we estimate.The yearly produced biomass CB is a benefit in itself whenever the biomass may be used (e.g. crops from urban agriculture). However, more importantly, it may be appraised in terms of carbon and carbon dioxide sequestration. The carbon dioxide sequestered from the atmosphere through biomass growth is 25.9 Mtons year−1 in the present scenario. This must be summed to the reduction of carbon emission following the expected decrease in cooling energy use for a total of 55.8 Mtons, or about 1.2% of the 4500 Mtons CO2 produced in the EU every year37.It should be stressed that carbon dioxide sequestration by the biomass in green roofs is effective only if residues are not significantly degraded. This may be achieved by removing the biomass periodically before it undergoes respiration and mineralization. One could alternatively employ woody plants with a higher carbon accumulation capacity instead of herbaceous vegetation. Although our calculations are referred to a herbaceous annual crop, the results in terms of dry biomass would not be radically different had we considered a tree or shrub crop, as the dry matter potentially produced per unit surface is relatively independent of the plant49. On the other hand, trees and shrubs may be expected to have higher evapotranspiration, thus enhancing the benefits quantified here for a herbaceous crop.If greening is implemented on about 35% of the impervious urban areas, we expect a reduction of runoff in the order of 17.5% compared to the total. Considering that pollutant loads associated to runoff are estimated in the order of about 30 million population equivalents (PE) in terms of biochemical oxygen demand (BOD), about 18 million PE in terms of total nitrogen and about 6 million PE in terms of total phosphorus 6,35, this can be a sizable contribution to the treatment of pollution from European urban areas. Besides the reduction of runoff volume, greened surfaces may also help reduce the frequency of combined sewer overflows because they buffer runoff and release it more slowly than impervious surfaces. This effect is arguably more important for smaller storm events, and tends to disappear as events cause the saturation of green roof storage.It should be stressed that the above analysis considers a soil thickness of 30 cm on greened surfaces. Using the meta-models proposed in30 for the thickness of 10 cm we obtain a ratio between the indicators for thickness of 10 and 30 cm ranging between 80–97% for the reduction of surface temperatures, 55–57% for roof temperatures, 47–57% for biomass, and 84–86% for runoff. Soil thickness affects in particular the roof temperature, due to the associated thermal insulation effect, and the biomass, because a thicker soil can store a larger amount of water and allows a higher evapotranspiration for vegetation growth, while not impeding root growth. A comparison of different climate scenarios sheds light on the sensitivity of our results to the input climatic predictors (P and ET0). From Table 1, it can be calculated that the range (difference between the maximum and minimum value) of precipitation and potential evapotranspiration, as a percentage of the average value, is 20.3%, and 21.4% respectively. The corresponding ranges are 7% of the average for the cooling reduction, 3.7% for the reduced carbon dioxide emission, and 34% for the runoff reduction. The curves in Fig. 2 visualize the relatively small sensitivity of results to the climatic scenario.Economic implicationsMost of the benefits of green roofs are collective. Only a few (e.g. energy saving in summer, and gardening) have an apparent private nature. The costs of greening roofs, on the contrary, are primarily borne by the private owners50. It has been observed that, in the absence of specific incentives, green roof implementation can be economically convenient only for specific commercial and multifamily buildings25. Therefore, private investments should be encouraged through appropriate fiscal and funding policies if the objective is to facilitate a mainstream uptake of this solution. In this section, an indicative cost-benefit analysis is carried out in order to shed light on the possible financing needs at stake, and considering to green the impervious surfaces covered by roofs.The two main benefits that can be easily monetized are the avoided cost of cooling in summer (based on energy prices) and the reduction of carbon dioxide emissions (based on greenhouse gas emissions market prices). By summing the results of Eq. (5) for all gridcells in Europe where the greened surface is assumed to be 35% of the impervious urban area in the gridcell, cooling savings can reach 18.4 billion Є each year for the current scenario. For comparison, the current expenditure for residential cooling in summer can be assumed to be 78 billion Є year−1, based on an electricity use of 391 TWh51. Therefore, the cooling energy saving is 23.5% (18.4 billion Є/78 billion Є), in agreement with the results of Manso et al.15 for the value of 15% estimated for the hot-summer Mediterranean climate.At the present carbon market price of 22.5 Є tons−1 (Ruf and Mazzoni43), the annual benefit related to the estimated reduction of greenhouse gas emissions corresponds to about 1.26 billion Є. It should be stressed how this is apparently an upper limit of this benefit, because not all greened surfaces may correspond to roofs of cooled building volumes, and because the biomass is likely to undergo at least a partial mineralization if not timely removed from the green surfaces. The benefit associated to the reduction of the heat island effect can also be quantified to some extent on the basis of existing literature studies, although their estimation is very complex and would require ad hoc studies. For example, for the city of Phoenix, this benefit was quantified in 80 € for 1 °C decrease per working resident, considering costs of electronic devices, maintenance of cars and performance of cooling47. In another analysis for the Melbourne area, the annual cost was quantified in 18 € per inhabitant, including health, transport, social distress, electric grid faults and damages to animal and trees48. In Malaysia, the annual cost of hazes, related to the urban heat island, was quantified in 12 € per habitant in 1997, including cost of illness, productivity loss, flight cancellation, tourism reduction, decline in fish landings, fire-fighting, cloud seeding and masks46. Therefore, costs can vary significantly among different contexts. Assuming conservatively a yearly benefit of 20 € for each of the ca. 559.5 million European urban inhabitants living in urban areas (75% of the total52), the Net Present Value (NPV) of this benefit over 40 years would be 221 billion € using a discount rate of 4%.The cost of greening the roofs or other impervious surfaces is more difficult to quantify as it depends on several design details and site-specific conditions. For example, in Finland the cost ranges between 70 and 80 Є m−2, in Germany between 13 and 41 Є m−2, while in Switzerland around 20 Є m−253. Assuming an average unit cost of 50 Є m−2, the costs to turn 26,450 km2 of impervious urban areas in Europe into green surfaces amounts to 1323 billion Є. This corresponds to an annual cost (discount rate 4%, 40 years life) of 63 billion euro. This means a cost of 6.3 € m−3 of annual runoff saved (assuming an average annual runoff saving of 10 km3), which is reasonably in line with an estimate of 9.2 € m−3 for the U.S. context, where the annual runoff volume reduction was 12%54 compared to our estimate of 17.5%.Assuming a lifespan of 40 years55 and a discount rate of 4%50, the NPV of the cost saving of summer cooling over 40 years (18.4 billion Є year−1 in Table 1), that is the main private benefit of a green roof installed in a private building, is 364 billion Є (using a discount rate of 4%). The benefits of CO2 reduction, monetized in an emission trading system, would lead to a NPV of 24.85 ≈ 25 billion Є over 40 years (55.8 Mtons year−1). The NPV of the heat island benefit over 40 years would be 221 billion €. Deducting the sum of these benefits (totalling 610 billion €) from the estimated investment of 1323 billion €, yields a net gap of 713 billion Є, corresponding to an annual cost of about 60 € for each of the 559.5 million European citizens living in urban areas. This estimated annual cost is apparently affected by the uncertainty on green roof costs: it could reduce to 4 Є/year per urban citizen if the cost of the green roof is 25 Є m−2, and 129 Є/year per urban citizen if the cost is 80 Є m−2. An annual cost of 60 Є/year per urban citizen may be in many cases compensated by the additional benefits not quantified here. For example, the average increase of property value (rental prices) was estimated to be 8%15. Other benefits can be associated e.g. to leisure and recreation, socialization, amenity of the urban environment, and the creation of habitat or ecological connections in urban areas, besides the abovementioned positive effects in terms of water pollution and floods. Table 2 summarizes the economic results. Table 2 Summary of benefits and costs of urban greening considered in this study for the European context.Full size tableThe harvesting of runoff is a potential additional benefit, but it also entails costs. These can be quantified as a first approximation considering a cost of the storage volume Cs = 50 € m−3, a lifetime of the storage of 100 years, a discount rate of 4% and annual operation and maintenance costs of 3% of the investment. For a unit greened surface, the runoff potentially harvested equals P-RR and can be computed from Eq. 3, while the required storage volume to harvest it is given by Eq. 6. The cost of harvesting one m3 of runoff (marginal harvesting costs) follows from the abovementioned costing parameters. Figure 5 depicts the cumulate value of runoff as a function of the marginal harvesting cost. It can be seen that about 75% of the runoff can be harvested with marginal costs below 0.7 € m−3, a value compatible with urban water prices usually applied in Europe. Cs may be lower than 50 € m−3 , but often it may also be higher. Hence our calculation can be only regarded as a first indication and is accurate not more than within one order of magnitude. The quality of water from green surface runoff harvesting is arguably adequate for non-potable domestic use, but depends on the type of green roof and vegetation13. Figure 5Cumulate runoff versus the cost of storage per unit of runoff, for a storage cost of 50 € m−3. Different climatic scenarios are shown.Full size image More

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