Accounting for carbon stocks in models of land-use change: an application to Southern Yucatan

To assess the impact of land-use change on carbon stocks, we apply a new methodology, linking ecological and economic modeling, to southern Yucatan, Mexico. A spatial econometric multinomial logit model of ten land-cover classes is estimated (four primary forest categories, three secondary growth categories, an invasive species, and two agricultural land-cover categories), using satellite data on land cover, linked with census socioeconomic data and other biophysical spatial data from 2000. The analysis is novel in that it is the first attempt to link detailed satellite data on land use, with on-the-ground estimates of carbon stocks in a spatial econometric model of land use. The estimated multinomial logit model is then used with two scenarios of future economic growth (“low growth” and “high growth” changes in population, agricultural land use, market access, and education levels) in the region to predict land-cover changes resulting from the economic growth. The per hectare carbon (C) stocks in each land-cover class are derived from previously published estimates of biomass from field sampling across the study region. We consider aboveground-only, aboveground plus soil, transient and non-transient pools of carbon. These estimates are scaled up to the total area in each class according to the predictions of the model baseline and the two development scenarios. Subsequently, the changes in carbon stocks resulting from the predicted land-cover changes are calculated. Under the low growth scenario, carbon stocks declined by 5%; under the high growth scenario, losses were 12%. Including soil C, the proportional losses were lower, but the absolute amount lost was more than double (to 6 Tg C under the low and almost 15 Tg C under the high-growth scenario). This methodology could be further developed for applications in global change policy, such as payments for environmental services (PES) or reduction in emissions from deforestation and degradation (REDD).     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

Acute effects of removing large fish from a near-pristine coral reef

Large animals are severely depleted in many ecosystems, yet we are only beginning to understand the ecological implications of their loss. To empirically measure the short-term effects of removing large animals from an ocean ecosystem, we used exclosures to remove large fish from a near-pristine coral reef at Palmyra Atoll, Central Pacific Ocean. We identified a range of effects that followed from the removal of these large fish. These effects were revealed within weeks of their removal. Removing large fish (1) altered the behavior of prey fish; (2) reduced rates of herbivory on certain species of reef algae; (3) had both direct positive (reduced mortality of coral recruits) and indirect negative (through reduced grazing pressure on competitive algae) impacts on recruiting corals; and (4) tended to decrease abundances of small mobile benthic invertebrates. Results of this kind help advance our understanding of the ecological importance of large animals in ecosystems.     

Impact of helmet use on traumatic brain injury from road traffic accidents in Cambodia

Objective: Rapid urbanization and motorization without corresponding increases in helmet usage have made traumatic brain injury due to road traffic accidents a major public health crisis in Cambodia. This analysis was conducted to quantify the impact of helmets on severity of injury, neurosurgical indication, and functional outcomes at discharge for motorcycle operators who required hospitalization for a traumatic brain injury following a road traffic accident in Cambodia.

Methods: The medical records of 491 motorcycle operators who presented to a major tertiary care center in Cambodia with traumatic brain injury were retrospectively analyzed using multivariate logistic regression.

Results: The most common injuries at presentation were contusions (47.0%), epidural hematomas (30.1%), subdural hematomas (27.9%), subarachnoid hemorrhages (12.4%), skull fractures (21.4%), and facial fractures (18.5%). Moderate-to-severe loss of consciousness was present in 36.3% of patients. Not wearing a helmet was associated with an odds ratio of 2.20 (95% confidence interval [CI], 1.15–4.22) for presenting with moderate to severe loss of consciousness compared to helmeted patients. Craniotomy or craniectomy was indicated for evacuation of hematoma in 20.0% of cases, and nonhelmeted patients had 3.21-fold higher odds of requiring neurosurgical intervention (95% CI, 1.25–8.27). Furthermore, lack of helmet usage was associated with 2.72-fold higher odds of discharge with functional deficits (95% CI, 1.14–6.49). In total, 30.1% of patients were discharged with severe functional deficits.

Conclusions: Helmets demonstrate a protective effect and may be an effective public health intervention to significantly reduce the burden of traumatic brain injury in Cambodia and other developing countries with increasing rates of motorization across the world.     

Right versus left: How does political ideology affect the workplace?

Underscored by perceived moral truths and at times punctuated by hypocrisy, political ideology is a deeply held conviction for many individuals, affecting their behavior. In an organizational setting, conflicting political ideologies may lead to a number of undesirable outcomes. This Incubator calls for further study into this important individual trait.     

Evaluating PFAS cross contamination issues

Avoiding cross contamination from per‐ and polyfluoroalkyl substances (PFAS) that may occur during sampling of environmental media is the key to ensure reliable analytical results during a PFAS sampling program. Due to the ubiquitous nature of PFAS in commonly used sampling materials and personal protective equipment, mitigating the risk of cross contamination is a challenge that requires a conservative approach when planning and executing a PFAS sampling program. This article describes a conservative approach to PFAS sampling and includes a case study that evaluated three insect repellent products to determine their suitability for use during PFAS investigation. The three products were verified to be PFAS‐free for the 17 PFAS included in the analysis and, therefore, these products are suitable for use during PFAS sampling activities without concern for cross contamination.     

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models. Our two research questions were: (1) are there systematic differences in lake area and land use/land cover (LULC) surrounding lakes monitored by state agencies when compared to the entire population of lakes? and (2) after controlling for the effects of lake size, does the probability of sampling vary depending on the surrounding LULC features? We examined the biases associated with surrounding LULC because of the established links between LULC and lake water quality. For all states, we found that larger lakes had a higher probability of being sampled compared to smaller lakes. Significant interactions between lake size and LULC prohibit us from drawing conclusions about the main effects of LULC; however, in general lakes that are most likely to be sampled have either high urban use, high agricultural use, high forest cover, or low wetland cover. Our analyses support the assertion that data derived from non-probability-based surveys must be used with caution when attempting to make generalizations to the entire population of interest, and that probability-based surveys are needed to ensure unbiased, accurate estimates of lake status and trends at regional to national scales.     

Using a relative health indicator (RHI) metric to estimate health risk reductions in drinking water

When a new drinking water regulation is being developed, the USEPA conducts a health risk reduction and cost analysis to, in part, estimate quantifiable and non-quantifiable cost and benefits of the various regulatory alternatives. Numerous methodologies are available for cumulative risk assessment ranging from primarily qualitative to primarily quantitative. This research developed a summary metric of relative cumulative health impacts resulting from drinking water, the relative health indicator (RHI). An intermediate level of quantification and modeling was chosen, one which retains the concept of an aggregated metric of public health impact and hence allows for comparisons to be made across “cups of water,” but avoids the need for development and use of complex models that are beyond the existing state of the science. Using the USEPA Six-Year Review data and available national occurrence surveys of drinking water contaminants, the metric is used to test risk reduction as it pertains to the implementation of the arsenic and uranium maximum contaminant levels and quantify “meaningful” risk reduction. Uranium represented the threshold risk reduction against which national non-compliance risk reduction was compared for arsenic, nitrate, and radium. Arsenic non-compliance is most significant and efforts focused on bringing those non-compliant utilities into compliance with the 10 μg/L maximum contaminant level would meet the threshold for meaningful risk reduction.