Characterization of ambient air pollution for stochastic health models

This research is an analysis of various measures of ambient air pollution useful in cross-sectional epidemiological investigations and rick assessments. The Chestnut Ridge area health effects investigation, which includes a cross-sectional study of respiratory symptoms in young children, is used as a case study. Four large coal-fired electric generating power plants are the dominant pollution sources in this area of western Pennsylvania. The air pollution data base includes four years of sulfur dioxide and five years of total suspended particulate concentrations at seventeen monitors. Some 70 different characterizations of pollution are constructed and tested. These include pollutant concentrations at various percentiles and averaging times, exceedence measures which show the amount of time a specified threshold concentration is exceeded, and several dosage measures which transform non-linear dose-response relationships onto pollutant concentrations

Targeted acid rain strategies including uncertainty

Uncertainties of targeted emission control strategies aimed at the cost-effective control of sulfur deposition in Europe are evaluated using two procedures. The first accounts for parameter and data errors using a chance-constraint procedure. The second examines the effect of interannual meteorologic variability. Several techniques increase the accuracy and speed of the computations. Uncertainties are determined for scenarios using single, regional, and European-wide targets. For single and regional deposition targets, uncertainty decreases the specificity of prescriptive emission reductions and requires additional emission abatements. Cost and removal minimizing strategies have large differences. Providing a margin of safety is expensive for some deposition targets

Geographic range of plants drives long-term climate change

Long computation times in vegetation and climate models hamper our ability to evaluate the potentially powerful role of plants on weathering and carbon sequestration over the Phanerozoic Eon. Simulated vegetation over deep time is often homogenous, and disregards the spatial distribution of plants and the impact of local climatic variables on plant function. Here we couple a fast vegetation model (FLORA) to a spatially-resolved long-term climate-biogeochemical model (SCION), to assess links between plant geographical range, the long-term carbon cycle and climate. Model results show lower rates of carbon fixation and up to double the previously predicted atmospheric CO2 concentration due to a limited plant geographical range over the arid Pangea supercontinent. The Mesozoic dispersion of the continents increases modelled plant geographical range from 65% to > 90%, amplifying global CO₂ removal, consistent with geological data. We demonstrate that plant geographical range likely exerted a major, under-explored control on long-term climate change

Key role of symbiotic dinitrogen fixation in tropical forest secondary succession

Forests contribute a significant portion of the land carbon sink, but their ability to sequester CO2 may be constrained by nitrogen1, 2, 3, 4, 5, 6, a major plant-limiting nutrient. Many tropical forests possess tree species capable of fixing atmospheric dinitrogen (N2)7, but it is unclear whether this functional group can supply the nitrogen needed as forests recover from disturbance or previous land use1, or expand in response to rising CO2 (refs 6, 8). Here we identify a powerful feedback mechanism in which N2 fixation can overcome ecosystem-scale deficiencies in nitrogen that emerge during periods of rapid biomass accumulation in tropical forests. Over a 300-year chronosequence in Panama, N2-fixing tree species accumulated carbon up to nine times faster per individual than their non-fixing neighbours (greatest difference in youngest forests), and showed species-specific differences in the amount and timing of fixation. As a result of fast growth and high fixation, fixers provided a large fraction of the nitrogen needed to support net forest growth (50,000¿kg carbon per hectare) in the first 12¿years. A key element of ecosystem functional diversity was ensured by the presence of different N2-fixing tree species across the entire forest age sequence. These findings show that symbiotic N2 fixation can have a central role in nitrogen cycling during tropical forest stand development, with potentially important implications for the ability of tropical forests to sequester CO2

Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees

10.1111/ele.13129Ecology Letters21101486-149

Climate windows of opportunity for plant expansion during the Phanerozoic

Earth’s long-term climate may have profoundly influenced plant evolution. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth, and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic has not yet been established. Linking climate and dynamic vegetation modelling, we identify two key ‘windows of opportunity’ during the Ordovician and Jurassic-Paleogene capable of supporting dramatic expansions of potential plant biomass. These conditions are driven by continental dispersion, paleolatitude of continental area and a lack of glaciation, allowing for an intense hydrological cycle and greater water availability. These windows coincide with the initial expansion of land plants and the later angiosperm radiation. Our findings suggest that the timing and expansion of habitable space for plants played an important role in plant evolution and diversification

Air pollution Exposure Model for Individuals (EMI) in health studies: Evaluation for ambient PM_2.5 in Central North Carolina.

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies