Cross-biome synthesis of source versus sink limits to tree growth.

Uncertainties surrounding tree carbon allocation to growth are a major limitation to projections of forest carbon sequestration and response to climate change. The prevalence and extent to which carbon assimilation (source) or cambial activity (sink) mediate wood production are fundamentally important and remain elusive. We quantified source-sink relations across biomes by combining eddy-covariance gross primary production with extensive on-site and regional tree ring observations. We found widespread temporal decoupling between carbon assimilation and tree growth, underpinned by contrasting climatic sensitivities of these two processes. Substantial differences in assimilation-growth decoupling between angiosperms and gymnosperms were determined, as well as stronger decoupling with canopy closure, aridity, and decreasing temperatures. Our results reveal pervasive sink control over tree growth that is likely to be increasingly prominent under global climate change.

The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region.

Tibetan permafrost largely formed during the late Pleistocene glacial period and shrank in the Holocene Thermal Maximum period. Quantifying the impacts of paleoclimatic extremes on soil carbon stock can shed light on the vulnerability of permafrost carbon in the future. Here, we synthesize data from 1114 sites across the Tibetan permafrost region to report that paleoclimate is more important than modern climate in shaping current permafrost carbon distribution, and its importance increases with soil depth, mainly through forming the soil's physiochemical properties. We derive a new estimate of modern soil carbon stock to 3 m depth by including the paleoclimate effects, and find that the stock ([Formula: see text] PgC) is triple that predicted by ecosystem models (11.5 ± 4.2 s.e.m PgC), which use pre-industrial climate to initialize the soil carbon pool. The discrepancy highlights the urgent need to incorporate paleoclimate information into model initialization for simulating permafrost soil carbon stocks.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping.

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables.

BACKGROUND: Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors. OBJECTIVES: We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality. SEARCH METHODS: We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016. SELECTION CRITERIA: We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender. DATA COLLECTION AND ANALYSIS: We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies. MAIN RESULTS: We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and respiratory diseases ( Figure 1 , we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution. CONCLUSIONS: Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention.

Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: Hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada.

Previous investigations have reported intake fraction (iF) for different environments, which include ambient concentrations (outdoor exposure) and microenvironments (indoor exposure). However, little is known about iF variations due to space-time factors, especially in microenvironments. In this paper, we performed a spatio-temporal analysis of particulate matter (PM2.5) intake fractions for vehicular emissions. Specifically, we investigated hourly variation (12:00am-11:00pm) by micro-environments (residences and workplaces) in the Greater Toronto and Hamilton Area (GTHA), Canada. We used GIS modeling to estimate air pollution data (ambient concentration, and traffic emission) and population data in each microenvironment. Our estimates showed that the total iF at residences and workplaces accounts for 85% and 15%, respectively. Workplaces presented the highest 24h average iF (1.06ppm), which accounted for 25% higher than residences. Observing the iF by hour at residences, our estimates showed the highest average iF at 2:00am (iF=3.72ppm). These estimates indicate that approximately 4g of PM2.5 emitted from motor vehicles are inhaled for every million grams of PM2.5 emitted. For the workplaces, the highest exposure was observed at 10:00am, with average iF equal to 2.04ppm. The period of the day with the lower average iF for residences was at 8:00am (average iF=0.11ppm), while for the workplaces was at 4:00am (average iF=0.47ppm). Our approach provides a new perspective on human exposure to air pollution. Our results showed significant hourly variation in iF across the GTHA. Our findings can be incorporated in future investigations to advance environmental health effects research and human health risk assessment.

How do disturbances and climate effects on carbon and water fluxes differ between multi-aged and even-aged coniferous forests?

Disturbances and climatic changes significantly affect forest ecosystem productivity, water use efficiency (WUE) and carbon (C) flux dynamics. A deep understanding of terrestrial feedbacks to such effects and recovery mechanisms in forests across contrasting climatic regimes is essential to predict future regional/global C and water budgets, which are also closely related to the potential forest management decisions. However, the resilience of multi-aged and even-aged forests to disturbances has been debated for >60years because of technical measurement constraints. Here we evaluated 62site-years of eddy covariance measurements of net ecosystem production (NEP), evapotranspiration (ET), the estimates of gross primary productivity (GPP), ecosystem respiration (Re) and ecosystem-level WUE, as well as the relationships with environmental controls in three chronosequences of multi- and even-aged coniferous forests covering the Mediterranean, temperate and boreal regions. Age-specific dynamics in multi-year mean annual NEP and WUE revealed that forest age is a key variable that determines the sign and magnitude of recovering forest C source-sink strength from disturbances. However, the trends of annual NEP and WUE across succession stages between two stand structures differed substantially. The successional patterns of NEP exhibited an inverted-U trend with age at the two even-aged chronosequences, whereas NEP of the multi-aged chronosequence increased steadily through time. Meanwhile, site-level WUE of even-aged forests decreased gradually from young to mature, whereas an apparent increase occurred for the same forest age in multi-aged stands. Compared with even-aged forests, multi-aged forests sequestered more CO2 with forest age and maintained a relatively higher WUE in the later succession periods. With regard to the available flux measurements in this study, these behaviors are independent of tree species, stand ages and climate conditions. We also found that distinctly different environmental factors controlled forest C and water fluxes under three climatic regimes. Typical weather events such as temperature anomalies or drying-wetting cycles severely affected forest functions. Particularly, a summer drought in the boreal forest resulted in an increased NEP owing to a considerable decrease in Re, but at the cost of greater water loss from deeper groundwater resources. These findings will provide important implications for forest management strategies to mitigate global climate change.

Joint control of terrestrial gross primary productivity by plant phenology and physiology.

Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO2 uptake period (CUP) and the seasonal maximal capacity of CO2 uptake (GPPmax). The product of CUP and GPPmax explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r(2) = 0.90) and GPP recovery after a fire disturbance in South Dakota (r(2) = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPPmax than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPPmax and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space.

Latent heat exchange in the boreal and arctic biomes.

In this study latent heat flux (λE) measurements made at 65 boreal and arctic eddy-covariance (EC) sites were analyses by using the Penman-Monteith equation. Sites were stratified into nine different ecosystem types: harvested and burnt forest areas, pine forests, spruce or fir forests, Douglas-fir forests, broadleaf deciduous forests, larch forests, wetlands, tundra and natural grasslands. The Penman-Monteith equation was calibrated with variable surface resistances against half-hourly eddy-covariance data and clear differences between ecosystem types were observed. Based on the modeled behavior of surface and aerodynamic resistances, surface resistance tightly control λE in most mature forests, while it had less importance in ecosystems having shorter vegetation like young or recently harvested forests, grasslands, wetlands and tundra. The parameters of the Penman-Monteith equation were clearly different for winter and summer conditions, indicating that phenological effects on surface resistance are important. We also compared the simulated λE of different ecosystem types under meteorological conditions at one site. Values of λE varied between 15% and 38% of the net radiation in the simulations with mean ecosystem parameters. In general, the simulations suggest that λE is higher from forested ecosystems than from grasslands, wetlands or tundra-type ecosystems. Forests showed usually a tighter stomatal control of λE as indicated by a pronounced sensitivity of surface resistance to atmospheric vapor pressure deficit. Nevertheless, the surface resistance of forests was lower than for open vegetation types including wetlands. Tundra and wetlands had higher surface resistances, which were less sensitive to vapor pressure deficits. The results indicate that the variation in surface resistance within and between different vegetation types might play a significant role in energy exchange between terrestrial ecosystems and atmosphere. These results suggest the need to take into account vegetation type and phenology in energy exchange modeling.

Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults.

RATIONALE: Little is known about the long-term effects of air pollution on pneumonia hospitalization in the elderly. OBJECTIVES: To assess the effect of long-term exposure to ambient nitrogen dioxide, sulfur dioxide, and fine particulate matter with diameter equal to or smaller than 2.5 microm (PM(2.5)) on hospitalization for community-acquired pneumonia in older adults. METHODS: We used a population-based case-control study in Hamilton, Ontario, Canada. We enrolled 345 hospitalized patients aged 65 years or more for community-acquired pneumonia and 494 control participants, aged 65 years and more, randomly selected from the same community as cases from July 2003 to April 2005. Health data were collected by personal interview. Annual average levels of nitrogen dioxide, sulfur dioxide, and PM(2.5) before the study period were estimated at the residential addresses of participants by inverse distance weighting, bicubic splined and land use regression methods and merged with participants' health data. MEASUREMENTS AND MAIN RESULTS: Long-term exposure to higher levels of nitrogen dioxide and PM(2.5) was significantly associated with hospitalization for community-acquired pneumonia (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.25 to 4.21; P = 0.007 and OR, 2.26; 95% CI, 1.20 to 4.24; P = 0.012, respectively, over the 5th-95th percentile range increase of exposure). Sulfur dioxide did not appear to have any association (OR, 0.97; 95% CI, 0.59 to 1.61; P = 0.918). Results were somewhat sensitive to the choice of methods used to estimate air pollutant levels at residential addresses, although all risks from nitrogen dioxide and PM(2.5) exposure were positive and generally significant. CONCLUSIONS: In older adults, exposure to ambient nitrogen dioxide and PM(2.5) was associated with hospitalization for community-acquired pneumonia.

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups.

BACKGROUND: Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures. METHODS: A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994-95 (N approximately 1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO2) surface based on a network of 100 passive NO2 monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking. RESULTS: There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO2LUR OR = 1.86 (95%CI, 1.59-2.16) in all girls and OR = 2.98 (95%CI, 0.98-9.06) for older girls, over an interquartile range increase and controlling for confounders. CONCLUSION: Our findings indicate that traffic-related pollutants, such as NO2, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada.

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.

A review and evaluation of intraurban air pollution exposure models.

The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.