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Temperate forests are particularly threatened by urbanization and fragmentation, with over 20% (120lJ000 km2) of recently urbanized land in the U.S. subsuming natural forests. We leveraged a unique, well-characterized urban-to-rural and forest edge-to-interior gradient to identify the combined impact of these two land use changes - urbanization and forest fragmentation - on soil microbial community in native, remnant forests. We found evidence of mutualism breakdown between trees and their fungal root mutualists (ectomycorrhizal (ECM) fungi) with urbanization, where ECM fungi colonized fewer tree roots and had less connectivity in soil microbiome networks in urban forests compared to rural forests. However, urbanization did not reduce the relative abundance of ECM fungi in forest soils; instead, forest fragmentation alone led to strong reductions in ECM fungal abundance. At forest edges, ECM fungi were replaced by plant and animal pathogens, as well as copiotrophic, xenobiotics-degrading, and nitrogen-cycling bacteria, including nitrifiers and denitrifiers. Urbanization and fragmentation interacted to generate “suites” of microbes, with urban interior forests harboring highly homogenized microbiomes, while edge forests microbiomes were more heterogeneous and less stable, showing increased vulnerability to low soil moisture. When scaled to the regional level, we found that forest soils are projected to harbor high abundances of fungal pathogens and denitrifying bacteria, even in rural areas, due to extreme, widespread forest fragmentation. Our results highlight the potential for soil microbiome dysfunction - including increased greenhouse gas production - in temperate forest regions that are subsumed by urban expansion, both now and in the future.Significance StatementUrbanization and forest fragmentation are increasingly altering Earth’s ecosystems, yet the effects on soil microbiomes, crucial for plant health and climate regulation, remain unclear. Our data indicate that, in forested land, these two combined, compounding stressors reshape the soil microbiome in ways that could lead to more pathogen infections of plants and animals, higher rates of N loss due to denitrification, and the possibility of tree symbiont extinctions. By identifying the specific environmental stressors that lead to these microbiome shifts, our analysis can be used to inform urban development and forest management plans to mitigate impacts on the soil microbiome to sustain environmental quality and the ecosystem services that remnant native forests provide to society in the coming decades.ClassificationBiological Sciences/Ecology

As urbanization and forest fragmentation increase around the globe, it is critical to understand how rates of respiration and carbon losses from soil carbon pools are affected by these processes. This study characterizes soils in fragmented forests along an urban to rural gradient, evaluating the sensitivity of soil respiration to changes in soil temperature and moisture near the forest edge. While previous studies found elevated rates of soil respiration at temperate forest edges in rural areas compared to the forest interior, we find that soil respiration is suppressed at the forest edge in urban areas. At urban sites, respiration rates are 25% lower at the forest edge relative to the interior, likely due to high temperature and aridity conditions near urban edges. While rural soils continue to respire with increasing temperatures, urban soil respiration rates asymptote as temperatures climb and soils dry. Soil temperature- and moisture-sensitivity modeling shows that respiration rates in urban soils are less sensitive to rising temperatures than those in rural soils. Scaling these results to Massachusetts (MA), which encompasses 0.25 Mha of the urban forest, we find that failure to account for decreases in soil respiration rates near urban forest edges leads to an overestimate of growing-season soil carbon fluxes of350,000 Mg C. This difference is almost 2.5 times that for rural soils in the analogous comparison (underestimate of143,000 Mg C), even though rural forest area is more than four times greater than urban forest area in MA. While a changing climate may stimulate carbon losses from rural forest edge soils, urban forests may experience enhanced soil carbon sequestration near the forest edge. These findings highlight the need to capture the effects of forest fragmentation and land use context when making projections about soil behavior and carbon cycling in a warming and increasingly urbanized world.

Mercury (Hg) is an environmental toxicant dangerous to human health and the environment. Its anthropogenic emissions are regulated by global, regional, and local policies. Here, we investigate Hg sources in the coastal city of Boston, the third largest metropolitan area in the Northeastern United States. With a median of 1.37 ng m-3, atmospheric Hg concentrations measured from August 2017 to April 2019 were at the low end of the range reported in the Northern Hemisphere and in the range reported at North American rural sites. Despite relatively low ambient Hg concentrations, we estimate anthropogenic emissions to be 3-7 times higher than in current emission inventories using a measurement-model framework, suggesting an underestimation of small point and/or nonpoint emissions. We also test the hypothesis that a legacy Hg source from the ocean contributes to atmospheric Hg concentrations in the study area; legacy emissions (recycling of previously deposited Hg) account for ∼60% of Hg emitted annually worldwide (and much of this recycling takes place through the oceans). We find that elevated concentrations observed during easterly oceanic winds can be fully explained by low wind speeds and recirculating air allowing for accumulation of land-based emissions. This study suggests that the influence of nonpoint land-based emissions may be comparable in size to point sources in some regions and highlights the benefits of further top-down studies in other areas.

The expansion of an urban tree canopy is a commonly proposed nature-based solution to combat excess urban heat. The influence trees have on urban climates via shading is driven by the morphological characteristics of trees, whereas tree transpiration is predominantly a physiological process dependent on environmental conditions and the built environment. The heterogeneous nature of urban landscapes, unique tree species assemblages, and land management decisions make it difficult to predict the magnitude and direction of cooling by transpiration. In the present article, we synthesize the emerging literature on the mechanistic controls on urban tree transpiration. We present a case study that illustrates the relationship between transpiration (using sap flow data) and urban temperatures. We examine the potential feedbacks among urban canopy, the built environment, and climate with a focus on extreme heat events. Finally, we present modeled data demonstrating the influence of transpiration on temperatures with shifts in canopy extent and irrigation during a heat wave.

Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.

Cities are greenhouse gas emission hot spots, making them targets for emission reduction policies. Effective emission reduction policies must be supported by accurate and transparent emissions accounting. Top-down approaches to emissions estimation, based on atmospheric greenhouse gas measurements, are an important and complementary tool to assess, improve, and update the emission inventories on which policy decisions are based and assessed. In this study, we present results from 9 research flights measuring CO2 and CH4 around New York City during the nongrowing seasons of 2018–2020. We used an ensemble of dispersion model runs in a Bayesian inverse modeling framework to derive campaign-average posterior emission estimates for the New York–Newark, NJ, urban area of (125 ± 39) kmol CO2 s–1 and (0.62 ± 0.19) kmol CH4 s–1 (reported as mean ± 1σ variability across the nine flights). We also derived emission estimates of (45 ± 18) kmol CO2 s–1 and (0.20 ± 0.07) kmol CH4 s–1 for the 5 boroughs of New York City. These emission rates, among the first top-down estimates for New York City, are consistent with inventory estimates for CO2 but are 2.4 times larger than the gridded EPA CH4 inventory, consistent with previous work suggesting CH4 emissions from cities throughout the northeast United States are currently underestimated.

Extreme heat represents a growing threat to public health, especially across the densely populated, developed landscape of cities. Climate adaptation strategies that aim to manage urban microclimates through purposeful design can reduce the heat exposure of urban populations, however, it is unclear how the temperature impacts of urban green space and albedo vary across cities and background climate. This study quantifies the sensitivity of surface temperature to landcover characteristics tied to two widely used climate adaptation strategies, urban greening and albedo manipulation (e.g. white roofs), by combining long-term remote sensing observations of land surface temperature, albedo, and moisture with high-resolution landcover datasets in a spatial regression analysis at the census block scale across seven United States cities. We find tree cover to have an average cooling impact of -0.089 K per % cover, which is approximately four times stronger than the average grass cover cooling impact of -0.021 K per % cover. Variability in the magnitude of grass cover cooling impacts was primarily a function of vegetation moisture content, with the Land Surface Water Index (LSWI) explaining 89 % of the variability in grass cover cooling impacts across cities. Variability in tree cover cooling impacts was primarily a function of sunlight and vegetation moisture content, with solar irradiance and LSWI explaining 97 % of the cooling variability across cities. Albedo cooling impacts were consistent across cities with an average cooling impact of -0.187 K per increase of 0.01. While these interventions are broadly effective across cities, there are critical regional trade-offs between vegetation cooling efficiency, irrigation requirements, and the temporal duration and evolution of the cooling benefits. In warm, arid cities, high albedo surfaces offer multifaceted benefits such as cooling and water conservation, whereas temperate, mesic cities likely benefit from a combination of strategies, with greening efforts targeting highly paved neighborhoods.

Significance Methane emissions from distribution and end use of natural gas (NG) are not well known. We analyzed atmospheric methane measurements to quantify NG emissions in the Boston area over ∼8 y, finding NG emissions approximately three times larger than calculated by usage-based inventories. We observed no change in emissions over 8 y despite efforts from the state to address NG pipeline leaks. Seasonal emissions are directly related to consumption of NG, implying that sources other than pipelines, such as transmission and appliances, are important and may require future policy action. We estimate total supply chain losses of 3.3 to 4.7% for NG consumed in urban areas, which significantly increases the climate impacts of NG compared to Environmental Protection Agency estimates., Across many cities, estimates of methane emissions from natural gas (NG) distribution and end use based on atmospheric measurements have generally been more than double bottom-up estimates. We present a top-down study of NG methane emissions from the Boston urban region spanning 8 y (2012 to 2020) to assess total emissions, their seasonality, and trends. We used methane and ethane observations from five sites in and around Boston, combined with a high-resolution transport model, to calculate methane emissions of 76 ± 18 Gg/yr, with 49 ± 9 Gg/yr attributed to NG losses. We found no significant trend in the NG loss rate over 8 y, despite efforts from the city and state to increase the rate of repairing NG pipeline leaks. We estimate that 2.5 ± 0.5% of the gas entering the urban region is lost, approximately three times higher than bottom-up estimates. We saw a strong correlation between top-down NG emissions and NG consumed on a seasonal basis. This suggests that consumption-driven losses, such as in transmission or end-use, may be a large component of emissions that is missing from inventories, and require future policy action. We also compared top-down NG emission estimates from six US cities, all of which indicate significant missing sources in bottom-up inventories. Across these cities, we estimate NG losses from distribution and end use amount to 20 to 36% of all losses from the US NG supply chain, with a total loss rate of 3.3 to 4.7% of NG from well pad to urban consumer, notably larger than the current Environmental Protection Agency estimate of 1.4% [R. A. Alvarez et al., Science 361, 186–188 (2018)].

A fundamental challenge in verifying urban CO2 emissions reductions is estimating the biological influence that can confound emission source attribution across heterogeneous and diverse landscapes. Recent work using atmospheric radiocarbon revealed a substantial seasonal influence of the managed urban biosphere on regional carbon budgets in the Los Angeles megacity, but lacked spatially explicit attribution of the diverse biological influences needed for flux quantification and decision making. New high-resolution maps of land cover (0.6 m) and irrigation (30 m) derived from optical and thermal sensors can simultaneously resolve landscape influences related to vegetation type (tree, grass, shrub), land use, and fragmentation needed to accurately quantify biological influences on CO2 exchange in complex urban environments. We integrate these maps with the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to quantify spatial and seasonal variability in gross primary production (GPP) across urban and non-urban regions of Southern California Air Basin (SoCAB). Results show that land use and landscape fragmentation have a significant influence on urban GPP and canopy temperature within the water-limited Mediterranean SoCAB climate. Irrigated vegetation accounts for 31% of urban GPP, driven by turfgrass, and is more productive (1.7 vs 0.9 μmol m−2 s−1) and cooler (2.2 ± 0.5 K) than non-irrigated vegetation during hot dry summer months. Fragmented landscapes, representing mostly vegetated urban greenspaces, account for 50% of urban GPP. Cooling from irrigation alleviates strong warming along greenspace edges within 100 m of impervious surfaces, and increases GPP by a factor of two, compared to non-irrigated edges. Finally, we note that non-irrigated shrubs are typically more productive than non-irrigated trees and grass, and equally productive as irrigated vegetation. These results imply a potential water savings benefit of urban shrubs, but more work is needed to understand carbon vs water usage tradeoffs of managed vs unmanaged vegetation.

The Normalized Difference Vegetation Index (NDVI) is a measure of greenness widely used in environmental health research. High spatial resolution NDVI has become increasingly available; however, the implications of its use in exposure assessment are not well understood.To quantify the impact of NDVI spatial resolution on greenness exposure misclassification.Greenness exposure was assessed for 31,328 children in the Greater Boston Area in 2016 using NDVI from MODIS (250 mGreenness estimates were greater for coarser resolution NDVI, but exposure distributions were similar. Continuous estimates showed poor agreement and high consistency, while agreement in categorical estimates ranged from poor to strong. Exposure misclassification was higher with greater differences in resolution, smaller buffers, and greater number of exposure quantiles. The proportion of participants changing greenness quantiles was higher for MODIS (11-60%), followed by Landsat 8 (6-44%), and Sentinel-2 (5-33%).Greenness exposure assessment is sensitive to spatial resolution of NDVI, aggregation area, and number of exposure quantiles. Greenness exposure decisions should ponder relevant pathways for specific health outcomes and operational considerations.

The impacts of extreme heat events are amplified in cities due to unique urban thermal properties. Urban greenspace mitigates high temperatures through evapotranspiration and shading; however, quantification of vegetative cooling potential in cities is often limited to simple remote sensing greenness indices or sparse, in situ measurements. Here, we develop a spatially explicit, high-resolution model of urban latent heat flux from vegetation. The model iterates through three core equations that consider urban climatological and physiological characteristics, producing estimates of latent heat flux at 30-m spatial resolution and hourly temporal resolution. We find strong agreement between field observations and model estimates of latent heat flux across a range of ecosystem types, including cities. This model introduces a valuable tool to quantify the spatial heterogeneity of vegetation cooling benefits across the complex landscape of cities at an adequate resolution to inform policies addressing the effects of extreme heat events.

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state and national level. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop well-targeted emission reductions strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and better inform policy decisions.This presentation builds on previous work estimating methane emissions from New York City and the wider urban area based on measurements taken during nine research flights. We used an ensemble of dispersion model runs in a Bayesian inverse modelling framework to derive posterior emission estimates. Prior emissions were taken from three coarse-resolution inventories based on spatially disaggregated national totals. The most recent version of EDGAR (v5) and the gridded EPA inventory both required upscaling by more than a factor of two to be consistent with our measurements.Here, we construct a high-resolution methane emission prior using a combination of spatial proxies and reported emissions for various sectors. We present preliminary results evaluating the ability of this new prior to represent the magnitude and spatial distribution of emissions, through comparison with both the measured data and results obtained using coarser resolution inventories.

Assessing progress towards greenhouse gas mitigation targets in recent legislation requires reliable, precise methods for emissions quantification. Top-down approaches can provide a complementary assessment to the bottom-up inventories typically used by cities.In this work we have performed a series of 9 winter aircraft measurement flights downwind of New York City in 2018 – 2020. We use dispersion modeling driven by publicly available meteorological products to calculate footprints relevant to the flight data. To calculate modeled emissions, we combine these footprints with four CO2 inventories (ODIAC, EDGAR, ACES, and Vulcan) using a spatially explicit scaling factor approach. We show that we can isolate the emissions from two areas of interest, New York City and the New York-Newark urban area, by using the fraction of modeled enhancements originating in said areas of interest as weighting functions. We then calculate a scaling factor that optimizes agreement with measurements for each flight. Here we discuss this technique and the posterior emissions for both areas of interest as compared to inversion analyses for the same areas. We also quantify the variability across the ensemble including multiple meteorological products, scaling factor calculation methods, and mixing parameterizations across all inventories and flight days.

Background With a lack of United States federal policy to address climate change, cities, the private sector, and universities have shouldered much of the work to reduce carbon dioxide (CO2) and other greenhouse gas emissions. This study aims to determine how landcover characteristics influence the amount of carbon (C) sequestered and respired via biological processes, evaluating the role of land management on the overall C budget of an urban university. Boston University published a comprehensive Climate Action Plan in 2017 with the goal of achieving C neutrality by 2040. In this study, we digitized and discretized each of Boston University’s three urban campuses into landcover types, with C sequestration and respiration rates measured and scaled to provide a University-wide estimate of biogenic C fluxes within the broader context of total University emissions. Results Each of Boston University’s three highly urban campuses were net sources of biogenic C to the atmosphere. While trees were estimated to sequester 0.6 ± 0.2 kg C m−2 canopy cover year−1, mulch and lawn areas in 2018 emitted C at rates of 1.7 ± 0.4 kg C m−2 year−1 and 1.4 ± 0.4 kg C m−2 year−1, respectively. C uptake by tree canopy cover, which can spatially overlap lawn and mulched landcovers, was not large enough to offset biogenic emissions. The proportion of biogenic emissions to Scope 1 anthropogenic emissions on each campus varied from 0.5% to 2%, and depended primarily on the total anthropogenic emissions on each campus. Conclusions Our study quantifies the role of urban landcover in local C budgets, offering insights on how landscaping management strategies—such as decreasing mulch application rates and expanding tree canopy extent—can assist universities in minimizing biogenic C emissions and even potentially creating a small biogenic C sink. Although biogenic C fluxes represent a small fraction of overall anthropogenic emissions on urban university campuses, these biogenic fluxes are under active management by the university and should be included in climate action plans.

Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by research in tropical systems, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that, in contrast to tropical systems, temperate forest edges exhibit increased forest growth and biomass with no change in total mortality relative to the forest interior. We analyze >48,000 forest inventory plots across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. Inclusion of edge impacts increases estimates of forest productivity by up to 23% in agriculture-dominated areas, 15% in the metropolitan coast, and +2% in the least-fragmented regions. We also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. Our analyses upend the conventional wisdom of forest edges as less productive than intact forest and call for a reassessment of the conservation value of forest fragments., Studies from tropical regions indicate that fragmented forests are less productive. Here, the authors report higher growth and biomass in temperate forest edges in North America, and show that temperate forests are more fragmented than tropical forests globally.

International audience; Global fossil fuel carbon dioxide (FFCO 2) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO 2 emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO 2 emissions estimation through the atmospheric CO 2 inversion process. Whole-city FFCO 2 emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO 2 emissions, underconstrained despite the inclusion of coemitted species information.

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R2=0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha−1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

Land surface albedo is a key parameter controlling the local energy budget, and altering the albedo of built surfaces has been proposed as a tool to mitigate high near-surface temperatures in the urban heat island. However, most research on albedo in urban landscapes has used coarse-resolution data, and few studies have attempted to relate albedo to other urban land cover characteristics. This study provides an empirical description of urban summertime albedo using 30 m remote sensing measurements in the metropolitan area around Boston, Massachusetts, relating albedo to metrics of impervious cover fraction, tree canopy coverage, population density, and land surface temperature (LST). At 30 m spatial resolution, median albedo over the study area (excluding open water) was 0.152 (0.112–0.187). Trends of lower albedo with increasing urbanization metrics and temperature emerged only after aggregating data to 500 m or the boundaries of individual towns, at which scale a −0.01 change in albedo was associated with a 29 (25–35)% decrease in canopy cover, a 27 (24–30)% increase in impervious cover, and an increase in population from 11 to 386 km−2. The most intensively urbanized towns in the region showed albedo up to 0.035 lower than the least urbanized towns, and mean mid-morning LST 12.6°C higher. Trends in albedo derived from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were comparable, but indicated a strong contribution of open water at this coarser resolution. These results reveal linkages between albedo and urban land cover character, and offer empirical context for climate resilient planning and future landscape functional changes with urbanization.

On-road emissions vary widely on time scales as short as minutes and length scales as short as tens of meters. Detailed data on emissions at these scales are a prerequisite to accurately quantifying ambient pollution concentrations and identifying hotspots of human exposure within urban areas. We construct a highly resolved inventory of hourly fluxes of CO, NO2, NOx, PM2.5 and CO2 from road vehicles on 280,000 road segments in eastern Massachusetts for the year 2012. Our inventory integrates a large database of hourly vehicle speeds derived from mobile phone and vehicle GPS data with multiple regional datasets of vehicle flows, fleet characteristics, and local meteorology. We quantify the 'excess' emissions from traffic congestion, finding modest congestion enhancement (3-6%) at regional scales, but hundreds of local hotspots with highly elevated annual emissions (up to 75% for individual roadways in key corridors). Congestion-driven reductions in vehicle fuel economy necessitated 'excess' consumption of 113 million gallons of motor fuel, worth ∼ $415M, but this accounted for only 3.5% of the total fuel consumed in Massachusetts, as over 80% of vehicle travel occurs in uncongested conditions. Across our study domain, emissions are highly spatially concentrated, with 70% of pollution originating from only 10% of the roads. The 2011 EPA National Emissions Inventory (NEI) understates our aggregate emissions of NOx, PM2.5, and CO2 by 46%, 38%, and 18%, respectively. However, CO emissions agree within 5% for the two inventories, suggesting that the large biases in NOx and PM2.5 emissions arise from differences in estimates of diesel vehicle activity. By providing fine-scale information on local emission hotspots and regional emissions patterns, our inventory framework supports targeted traffic interventions, transparent benchmarking, and improvements in overall urban air quality.

Urban trees experience site-induced stress and this leads to reduced growth and health. A site assessment tool would be useful for urban forest managers to better match species tolerances and site qualities, and to assess the efficacy of soil management actions. Toward this goal, a rapid urban site index (RUSI) model was created and tested for its ability to predict urban tree performance. The RUSI model is field-based assessment tool that scores 15 parameters in approximately five minutes. This research was conducted in eight cities throughout the Midwest and Northeast USA to test the efficacy of the RUSI model. The RUSI model accurately predicted urban tree health and growth metrics (P

Many ecosystem models incorrectly treat urban areas as devoid of vegetation and biogenic carbon (C) fluxes. We sought to improve estimates of urban biomass and biogenic C fluxes using existing, nationally available data products. We characterized biogenic influence on urban C cycling throughout Massachusetts, USA using an ecosystem model that integrates improved representation of urban vegetation, growing conditions associated with urban heat island (UHI), and altered urban phenology. Boston's biomass density is 1/4 that of rural forests, however 87% of Massachusetts' urban landscape is vegetated. Model results suggest that, kilogram-for-kilogram, urban vegetation cycles C twice as fast as rural forests. Urban vegetation releases (RE) and absorbs (GEE) the equivalent of 11 and 14%, respectively, of anthropogenic emissions in the most urban portions of the state. While urban vegetation in Massachusetts fully sequesters anthropogenic emissions from smaller cities in the region, Boston's UHI reduces annual C storage by > 20% such that vegetation offsets only 2% of anthropogenic emissions. Asynchrony between temporal patterns of biogenic and anthropogenic C fluxes further constrains the emissions mitigation potential of urban vegetation. However, neglecting to account for biogenic C fluxes in cities can impair efforts to accurately monitor, report, verify, and reduce anthropogenic emissions.

Cities are home to the majority of humanity. Therefore, understanding the mechanisms that control urban climates has substantial societal importance to a variety of sectors, including public health and energy management. In this study, data from an urban sensor network (25 stations) and moderate-resolution remote sensing were used to explore how spatial variation in near-surface air temperature Ta, vapor pressure deficit (VPD), and land surface temperature (LST) depend on local variations in urban land use, both diurnally and seasonally, in the Boston, Massachusetts, metropolitan area. Positive correlations were observed between the amount of local impervious surface area (ISA) and both Ta and VPD. Heat-island effects peaked during the growing-season nighttime, when mean Ta and VPD increased by up to 0.02°C and 0.008 kPa, respectively, per unit ISA. Air temperature and VPD were strongly coupled, but their relationship exhibited significant diurnal hysteresis during the growing season, with changes in VPD generally preceding changes in Ta. Over 79% of the urban–rural difference in VPD was explained by differences in near-surface atmospheric water content, which the authors attribute to reduced evapotranspiration from lower canopy cover in Boston’s urban core. Changes in daytime heat-island intensity were mediated by seasonal feedbacks between vegetation transpiration and VPD forcing. Differences between LST and Ta showed weaker coupling in highly urbanized areas than in rural areas, with summertime surface-urban-heat-island intensity (based on LST) being up to 14°C higher than corresponding urban–rural differences in Ta.

Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called “Right Place, Right Tree - Boston” that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting.