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RTI International created a geospatial grid-based sampling methodology that: achieved a probability-based household sample and unbiased estimates; was comparable across countries; used simple frame data available from the in-country census; and was efficient with minimal training to local field staff. The methodology used 1 km[superscript 2] LandScan world population data, which was further subdivided to smaller grid cells ranging from 50 to 150 m[superscript 2]. To guide fieldwork, georeferenced maps were generated and viewed without an Internet connection using a third-party app and the tablet's internal global positioning systems receiver. The field staff's location was displayed in real time as a blue dot enabling field staff to determine household inclusivity. This methodology was successfully implemented for household surveys Latin America, Africa, and Asia. The design is transferable, adaptable, scalable, and can be used anywhere in the world.

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously derived growth and survival responses for 94 tree species, representing >90% of the contiguous US forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found averaged across temperature scenarios the relative abundance of 60 species were projected to decrease more than 5% and 20 species were projected to increase more than 5%; and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of US forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario. [ABSTRACT FROM AUTHOR]

Impact evaluation (IE) of large infrastructure presents numerous challenges, and investments in urban piped water and sanitation are no exception. Here we present methods for more systematic assessment of the implications of such interventions, discussing tradeoffs between validity, relevance and practicality that arise from alternative approaches. Then, to more clearly illustrate the many issues that typically arise in such IEs, we draw on an example application in Zarqa, Jordan, where the Millennium Challenge Corporation invested about US$275 million to upgrade and extend piped water and sewer networks, as well as increase the capacity of the country's largest wastewater treatment plant. The theory of change for the intervention took a systems view of impacts: the project aimed to improve water supply to urban areas while maintaining flows to irrigators through enhanced wastewater reuse. The case adds valuable evidence on the impacts of large infrastructure investments and illustrates well the challenges of capturing spillovers, mitigating study contamination, maintaining statistical power, and determining overall welfare effects, in situations involving diverse market and nonmarket impacts. These limitations notwithstanding, the application highlights the high value of conducting IEs, and why applied researchers should not give up on pragmatic and interdisciplinary collaborations to evaluation in the face of complex interventions. Key Points: Impact evaluation (IE) of water infrastructure presents numerous challenges that involve validity, relevance and practicality tradeoffsWe provide a guide for improved IE of water infrastructure and a demonstration case that highlights use of multiple methodsEvaluations of such investments must consider spillovers, contamination risk, statistical power, and overall welfare effects [ABSTRACT FROM AUTHOR]

Abstract: Atmospheric deposition of nitrogen (N) and sulfur (S) has increased dramatically over pre‐industrial levels, with many potential impacts on terrestrial and aquatic ecosystems. Quantitative thresholds, termed “critical loads” (CLs), have been developed to estimate the deposition rate above which damage is thought to occur. However, there remains no comprehensive comparison of when, where, and over what time periods individual CLs have been exceeded. We addressed this knowledge gap by combining several published data sources for historical and contemporary deposition, and overlaying these on six CL types from the National Critical Loads Database (NCLDv2.5; terrestrial acidification, aquatic acidification, lichen, nitrate leaching, plant community composition, and forest‐tree health) to examine exceedances from 1800 to 2011. We expressed CLs as the minimum, 10th, and 50th percentiles within 12‐km grid cells. Minimum CLs were relatively uniform across the country (200–400 eq·ha−1·yr−1), and have been exceeded for decades beginning in the early 20th century. The area exceeding minimum CLs peaked in the 1970s and 1980s, exposing 300,000 to 3 million km2 (depending on the CL type) to harmful levels of deposition, with a total area exceeded of 5.8 million km2 (~70% of the conterminous United States). Since then, deposition levels have dropped, especially for S, with modest reductions in exceedance by 2011 for all CL types, totaling 5.2 million km2 in exceedance. The 10th and 50th percentile CLs followed similar trends, but were not consistently available at the 12‐km grid scale. We also examined near‐term future deposition and exceedances in 2025 under current air quality regulations, and under various scenarios of climate change and additional nitrogen management controls. Current regulations were projected to reduce exceedances of any CL from 5.2 million km2 in 2011 to 4.8 million km2 in 2025. None of the additional N management or climate scenarios significantly affected areal exceedances, although exceedance severity declined. In total, it is clear that many CLs have been exceeded for decades, and are likely to remain so in the short term under current policies. Additionally, we suggest many areas for improvement to enhance our understanding of deposition and its effects to support informed decision making. [ABSTRACT FROM AUTHOR]

Because of concerns about Vibrio vulnificus, the U.S. Food and Drug Administration is considering requirements for postharvest processing (PHP) of oysters harvested from the Gulf of Mexico during warm-weather months and intended for raw consumption. As described in the paper, feasible PHP methods for warm-weather-harvested oysters include cool pasteurization, high hydrostatic pressure, and low-dose gamma-irradiation. We estimate that the costs of applying PHP are approximately 5 to 6 cents per half-shell oyster intended for raw consumption. However, most oyster processors have insufficient volumes to cost-effectively install PHP equipment. To assist these smaller operations, central PHP facilities operated by a 3rd party would be needed. A geographic information system analysis that minimized volume-weighted travel distances from each Gulf oyster operation identified 6 optimal PHP facility locations in the Gulf region. Even with the establishment of central PHP facilities, some oyster operations will become unprofitable and be at risk for closure. [ABSTRACT FROM AUTHOR]

The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth's terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly—and increasingly—followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States. [ABSTRACT FROM AUTHOR]

While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals (SDGs) in low- and middle-income countries (LMICs). Though some countries' statistical agencies maintain databases of persons or households for sampling, conducting household surveys in LMICs is complicated due to incomplete, outdated, or inaccurate sampling frames. As a means to develop or update household listings in LMICs, this paper explores the use of machine learning models to detect and enumerate building structures directly from satellite imagery in the Kaduna state of Nigeria. Specifically, an object detection model was used to identify and locate buildings in satellite images. In the test set, the model attained a mean average precision (mAP) of 0.48 for detecting structures, with relatively higher values in areas with lower building density (mAP = 0.65). Furthermore, when model predictions were compared against recent household listings from fieldwork in Nigeria, the predictions showed high correlation with household coverage (Pearson = 0.70; Spearman = 0.81). With the need to produce comparable, scalable SDG indicators, this case study explores the feasibility and challenges of using object detection models to help develop timely enumerated household lists in LMICs. [ABSTRACT FROM AUTHOR]

Forest composition and ecosystem services are sensitive to anthropogenic pressures like climate change and atmospheric deposition of nitrogen (N) and sulfur (S). Here we extend recent forest projections for the current cohort of trees in the contiguous US, characterizing potential changes in aboveground tree carbon at the county level in response to varying mean annual temperature, precipitation, and N and S deposition. We found that relative to a scenario with N and S deposition reduction and no climate change, greater climate change led generally to decreasing aboveground carbon (mean -7.5% under RCP4.5, -16% under RCP8.5). Keeping climate constant, reduced N deposition tended to lessen aboveground carbon (mean -7%), whereas reduced S deposition tended to increase aboveground carbon (+3%) by 2100. Through mid-century (2050), deposition was more important for predicting carbon responses except under the extreme climate scenarios (RCP 8.5 ); but, by 2100, climate drivers generally outweighed deposition. While more than 70% of counties showed reductions in aboveground carbon relative to the reference scenario, these were not evenly distributed across the US. Counties in the Northwest and Northern Great Plains, and the northern parts of New England and the Midwest, primarily showed positive responses, while counties in the Southeast showed negative responses. Counties with greater initial biomass showed less negative responses to climate change while those which exhibited the greatest change in composition (>15%) had a 95% chance of losing carbon relative to a no-climate change scenario. This analysis highlights that declines in forest growth and survival due to increases in mean temperature and reductions in atmospheric N deposition are likely to outweigh positive impacts of reduced S deposition and potential increases in precipitation. These effects vary at the regional and county level, however, so forest managers must consider local rather than national dynamics to maximize forest carbon sinks in the future.

The pervasive and potentially severe economic, social, and public health consequences of infectious disease in farmed animals require that plans be in place for a rapid response. Increasingly, agent-based models are being used to analyze the spread of animal-borne infectious disease outbreaks and derive policy alternatives to control future outbreaks. Although the locations, types, and sizes of animal farms are essential model inputs, no public domain nationwide geospatial database of actual farm locations and characteristics currently exists in the United States. This report describes a novel method to develop a synthetic dataset that replicates the spatial distribution of poultry farms, as well as the type and number of birds raised on them. It combines county-aggregated poultry farm counts, land use/land cover, transportation, business, and topographic data to generate locations in the conterminous United States where poultry farms are likely to be found. Simulation approaches used to evaluate the accuracy of this method when compared to that of a random placement alternative found this method to be superior. The results suggest the viability of adapting this method to simulate other livestock farms of interest to infectious disease researchers.

Communicable-disease transmission models are useful for the testing of prevention and intervention strategies. Agent-based models (ABMs) represent a new and important class of the many types of disease transmission models in use. Agent-based disease models benefit from their ability to assign disease transmission probabilities based on characteristics shared by individual agents. These shared characteristics allow ABMs to apply transmission probabilities when agents come together in geographic space. Modeling these types of social interactions requires data, and the results of the model largely depend on the quality of these input data. We initially generated a synthetic population for the United States, in support of the Models of Infectious Disease Agent Study. Subsequently, we created shared characteristics to use in ABMs. The specific goals for this task were to assign the appropriately aged populations to schools, workplaces, and public transit. Each goal presented its own challenges and problems; therefore, we used different techniques to create each type of shared characteristic. These shared characteristics have allowed disease models to more realistically predict the spread of disease, both spatially and temporally.

Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses.RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants.For this database, RTI developed the methods for generating synthesized households and personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes).In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models.Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.