Environmental Science Data Research Articles

Applications of R to evaluate environmental data science problems

Open source programming languages and software platforms play a vital role in the progress of research towards developing new methods for addressing data science problems. R is one such platform that the research community may adapt and make the required changes to the codes in accordance with the requisite needs, specifically when analyzing data in different forms (structured, semistructured, unstructured). This study demonstrated the applications of R for analyzing in‐bus carbon dioxide concentrations by: (i) importing the data into RStudio; (ii) performing an exploratory data analysis; (iii) developing statistical regression models; and (iv) developing tree models using machine learning methods. The readers may adopt the methods discussed in this paper to successfully address their own data science problems. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1358–1364, 2017

Wāhi, a discrete global grid gazetteer built using linked open data

ABSTRACTDiscrete global grid systems have become an important component of Digital Earth systems. However, previously there has not existed an easy way to map between named places (toponyms) and the cells of a discrete global grid system. The lack of such a tool has limited the opportunities to synthesize social place-based data with the more standard Earth and environmental science data currently being analyzed in Digital Earth applications. This paper introduces Wāhi, the first gazetteer to map entities from the GeoNames database to multiple discrete global grid systems. A gazetteer service is presented that exposes the grid system and the associated gazetteer data as Linked Data. A set of use cases for the discrete global grid gazetteer is discussed.

Laying a Community-Based Foundation for Data-Driven Semantic Standards in Environmental Health Sciences.

Background:Despite increasing availability of environmental health science (EHS) data, development, and implementation of relevant semantic standards, such as ontologies or hierarchical vocabularies, has lagged. Consequently, integration and analysis of information needed to better model environmental influences on human health remains a significant challenge.Objectives:We aimed to identify a committed community and mechanisms needed to develop EHS semantic standards that will advance understanding about the impacts of environmental exposures on human disease.Methods:The National Institute of Environmental Health Sciences sponsored the “Workshop for the Development of a Framework for Environmental Health Science Language” hosted at North Carolina State University on 15–16 September 2014. Through the assembly of data generators, users, publishers, and funders, we aimed to develop a foundation for enabling the development of community-based and data-driven standards that will ultimately improve standardization, sharing, and interoperability of EHS information.Discussion:Creating and maintaining an EHS common language is a continuous and iterative process, requiring community building around research interests and needs, enabling integration and reuse of existing data, and providing a low barrier of access for researchers needing to use or extend such a resource.Conclusions:Recommendations included developing a community-supported web-based toolkit that would enable a) collaborative development of EHS research questions and use cases, b) construction of user-friendly tools for searching and extending existing semantic resources, c) education and guidance about standards and their implementation, and d) creation of a plan for governance and sustainability.Citation:Mattingly CJ, Boyles R, Lawler CP, Haugen AC, Dearry A, Haendel M. 2016. Laying a community-based foundation for data-driven semantic standards in environmental health sciences. Environ Health Perspect 124:1136–1140; http://dx.doi.org/10.1289/ehp.1510438

Engaging Imperial County Communities In Participatory Hazard And Asset Mapping And Development Of Public Health Actions

INTRODUCTION: Imperial County, California, near the U.S.-Mexico border, is a low-income, primarily Latino community exposed to poor air quality, pesticides, and water contamination. A community-academic-state health agency partnership facilitated a participatory mapping process using mobile phones to document local hazards and assets. Participants used collected data in planning actions to improve community health. METHODS: A Community Steering Committee, representing diverse local stakeholders with experience in environmental health advocacy, guided the project and prioritized impacted neighborhoods. Residents of these neighborhoods were recruited and trained. An innovative crowd-sourcing mapping tool designed by and for local residents was adapted to allow data capture with cell phones. Using collected data and resident expertise, participants engaged in a process to prioritize issues and organize public health actions. RESULTS: 45 community residents received training and mapped sites over an 8-day period, collecting locations, photographs, and other details. Over 70 hazards were identified, including agricultural field burning, abandoned homes, and noise from the Naval Air Facility. Almost 70 assets were identified, including community parks, sports playing fields, and local organizations and clubs. Proposed community actions include advocating for local regulation enforcement, education/awareness campaigns, and enhancing school flag programs. CONCLUSIONS: Training of local residents in environmental science and geospatial data collection and adaptation of an existing community web tool are steps that 1) build capacity to address local environmental issues, 2) promote engagement with and use of data gathered, 3) strengthen relationships between local environmental health experts, and 4) foster collaboration with environmental regulatory agencies at all levels. Mobile phones can be a valuable tool in community-based participatory environmental health research.

Perceived discontinuities and continuities in transdisciplinary scientific working groups

We examine the DataONE (Data Observation Network for Earth) project, a transdisciplinary organization tasked with creating a cyberinfrastructure platform to ensure preservation of and access to environmental science and biological science data. Its objective was a difficult one to achieve, requiring innovative solutions. The DataONE project used a working group structure to organize its members. We use organizational discontinuity theory as our lens to understand the factors associated with success in such projects. Based on quantitative and qualitative data collected from DataONE members, we offer recommendations for the use of working groups in transdisciplinary synthesis. Recommendations include welcome diverse opinions and world views, establish shared communication practices, schedule periodic synchronous face-to-face meetings, and ensure the active participation of bridge builders or knowledge brokers such as librarians who know how to ask questions about disciplines not their own.

“Personas” to Support Development of Cyberinfrastructure for Scientific Data Sharing

Objective: To ensure that cyberinfrastructure for sharing scientific data is useful, system developers need to understand what scientists and other intended users do as well as the attitudes and beliefs that shape their behaviours. This paper introduces personas — detailed descriptions of an “archetypical user of a system” — as an approach for capturing and sharing knowledge about potential system users. Setting: Personas were developed to support development of the ‘DataONE’ (Data Observation Network for Earth) project, which has developed and deployed a sustainable long-term data preservation and access network to ensure the preservation and access to multi-scale, multi-discipline, and multi-national environmental and biological science data (https://www.dataone.org/what-dataone) (Michener et al. 2012). Methods: Personas for DataONE were developed based on data from surveys and interviews done by members of DataONE working groups along with sources such as usage scenarios for DataONE and the Data Conservancy project and the Purdue Data Curation Profiles (Witt et al. 2009). Results: A total of 11 personas were developed: five for various kinds of research scientists (e.g., at different career stages and using different types of data); a science data librarian; and five for secondary roles. Conclusion: Personas were found to be useful for helping developers and other project members to understand users and their needs. The developed DataONE personas may be useful for others trying to develop systems or programs for scientists involved in data sharing.

Estimation of Surface Longwave Radiation over the Tibetan Plateau Region Using MODIS Data for Cloud-Free Skies

This study addressed the estimation of downward surface longwave radiation (R-L,R-D) for the Tibetan Plateau region using the MODerate resolution Imaging Spectroradiometer (MODIS) measurements observed at the top of the atmosphere (TOA) for clear-sky conditions. To extend, the method developed by Tang and Li with only regard to MODIS/Terra data and with surface elevation only lower than 2.5 km, two separate look-up table (LUT) coefficients corresponding to different elevations and viewing zenith angles (VZAs) were developed for MODIS/Terra and MODIS/Aqua, respectively. The upward surface longwave radiation (R-L,R-U) was estimated using the Stefan-Boltzmann law with MODIS surface temperature/emissivity products (MOD11_L2/MYD11_L2). Combining the R-L,R-D and, the R-L,R-U net surface longwave radiation R-L,R-N can then be obtained. The R-L,R-D and R-L,R-N were validated using some in situ measurements from two sites of Asian Automatic Weather Station Network Project (AAN) (Dunhuang and Amdo) and three sites of Environmental and Ecological Science Data Center for West China (Huazhaizi, Yingke, and A'rou) in the Heihe River Basin. The results showed that the root mean square errors (RMSEs) between the estimated R-L,R-N and in situ measurements were about 30 W/m(2). In addition, the schemes developed, respectively, by Bisht et al. and Brutsaert were used to estimate the R-L,R-D and R-L,R-N for comparison with the proposed method. The results showed that the RMSE of the proposed method was the lowest one among the three methods, which indicated that the proposed method can estimate the R-L,R-N accurately for the Tibetan Plateau region.

Understanding plant invasions: An example of working with citizen scientists to collect environmental data

Citizen science programs are useful tools for collecting important environmental science data. To ensure data quality, however, it must be shown that data collected by volunteers can produce reliable results. We engaged 143 volunteers over four years to map and estimate abundance of invasive plants in New York and New Jersey parklands. We found that off trail abundance of only a few of our targeted invasive species were positively correlated with on trail abundance. Our results support that citizen science programs can be a useful and sometimes a much needed addition to environmental science protocols.

Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses.

Distribution of Permafrost in China: An Overview of Existing Permafrost Maps

ABSTRACTMany permafrost maps in China have been compiled since the early 1960s. The scales of these maps range from the local (1:600 000) to the regional scale (1:10 000 000). The permafrost classification systems differ among these maps. The indices for permafrost classification used in these mapping projects include spatial continuity (areal extent) and thickness of the permafrost, air and ground temperatures and ground ‐ice content. All of those data have been retrieved, digitised and published in the Environmental and Ecological Science Data Center for West China. These maps represent the best understanding at the time regarding the distribution of permafrost in China and its changes over the past century. An updated map of permafrost in China, including frozen ground area, is also provided. The total area of permafrost in China is estimated at ~ 1.59 × 106 km2 (glaciers and lakes excluded), and the area of seasonally frozen ground (excluding instantaneous frozen ground) is ~ 5.36 × 106 km2. The total area of high‐altitude (plateau and mountain) permafrost in China is ~ 1.35 × 106 km2, the area of mountain permafrost is ~ 0.30 × 106 km2 and the area of plateau permafrost is ~ 1.05 × 106 km2. The latitudinal permafrost is located in the northern part of northeastern China, and its area is ~ 0.24 × 106 km2. Additionally, some suggestions are proposed for future mapping of permafrost in China. Copyright © 2012 John Wiley & Sons, Ltd.

Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China

Static snow depth retrieval algorithms tend to underestimate the snow depth at the beginning of the snow season and overestimate the snow depth at the end of the snow season because the snow characteristics vary with the age of snow cover. A novel snow depth/water equivalent (SWE) data retrieval algorithm from passive microwave brightness temperature is proposed based on a priori snow characteristics, including the grain size, density and temperature of the layered snowpack. The layering scheme was established based on the brightness temperature difference (TBD) at two different frequencies, which indicates volume scattering, whereas the snow grain size and density of each layer were parameterized according to the age of the snow cover, and the snow temperature and temperature at the snow/soil interface were determined by the air temperature and snow depth. Furthermore, the microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperature at 10GHz, 18GHz and 36GHz based on the a priori snow characteristics including snow grain size, density, depth and snow layering. Finally, three look-up tables (one layer, two layers and three layers) were generated for each day, which represent the relationship between the brightness temperatures at 10GHz, 18GHz and 36GHz and snow depth. To avoid underestimation caused by the saturation of the microwave signal at 36GHz, the TBD1 (the difference of brightness temperature at 18 and 36GHz) was used to estimate the snow depth if TBD1 was less than 40K, and TBD2 (the difference of the brightness temperature at 10 and 18GHz) was used if TBD1 was greater than 40K. The snow depth and SWE determined by this new algorithm were validated by snow measurements at thirteen meteorological stations in Xinjiang, China from 2003 to 2010 and compared with existing SWE products from the National Snow and Ice Data Center (NSIDC), the Environmental and Ecological Science Data Center for West China (WESTDC), the European Space Agency (ESA) and measurements with a snow course. The results showed that the root mean squared error (RMSE) and the bias from this new algorithm were greatly reduced compared to NSIDC, moderately reduced compared to ESA and slightly reduced compared to WESTDC. The understanding of a priori local snow characteristics can improve the accuracy of snow depth and snow water equivalent estimation from passive microwave remote sensing data.

EVALUATING THE CONSISTENCY OF REMOTE SENSING BASED SNOW DEPTH PRODUCTS IN ARID ZONE OF WESTERN CHINA

Abstract. Snow cover is a sensitive indicator of global climate change. Among various snow cover parameters, snow depth which can indicate snow accumulation is essential for retrieving snow water equivalent. In arid zone of western China, based on different inversion models, snow depth products retrieved from passive microwave remote sensing sensors have been issued. However, none of them can promise a high accuracy due to the spatial heterogeneity of snow cover especially in mountain areas with complex terrain. This study aims to analyse the reliability of existing long-term snow depth products in arid zone of western China. Two datasets are compared including GlobSnow snow water equivalent (SWE) product and snow depth dataset provided by Environmental and Ecological Science Data Center for West China. Statistical techniques like regression and intra-class correlation coefficient (ICC) models are employed to examine the consistency of these two remote sensing based snow depth products in a selected sampling site. More than 260 samples during three years are tested covering from snow falling to snow melting periods. Result shows that there is a discrepancy between the two datasets. Accordingly, remote sensing based snow depth measurement is not reliable in mountain areas in arid zone of western China. This study gives an awareness of the stabilities of current snow depth detection models. A further study is expected to calibrate snow depth products based on in-situ observation and measurements from ground monitoring stations.

Toward an improved data stewardship and service for environmental and ecological science data in West China

Sharing of scientific data can help scientific research to flourish and facilitate more widespread use of scientific data for the benefit of society. The Environmental and Ecological Science Data Center for West China (WestDC), sponsored by the National Natural Science Foundation of China (NSFC), aims to collect, manage, integrate, and disseminate environmental and ecological data from western China. It also aims to provide a long-term data service for multidisciplinary research within NSFC's “Environment and Ecology of West China Research Plan” (NSFC West Plan). An integrated platform has been developed by the WestDC, and this has the function of data sharing, acting as a knowledge repository. Major data sets developed by the WestDC include basic geographic data, the regionalization of global data set for China, scientific data for cold and arid regions in China, scientific data for the cryosphere in countries that neighbor China, data relating to the inland river basins in northwestern China, and data submitted by the NSFC West Plan projects. In compliance with the “full and open” data sharing policy, most data in the WestDC can be accessed online. Highlights include detailed data documentation, the integration of data with bibliographic knowledge, data publishing, and data reference.

The man who wasn’t there: The problem of partially missing data

Existing commercial database management systems offer little or no functionality to handle the complexity of geoscience data—and other environmental science data—particularly in respect of missing and partially missing (incomplete or imprecise) data items. The emphasis of both the relational theorists (Codd, Date, and others) and the developers of database systems is on commercial applications where only rudimentary treatment of missing data is required, in the form of NULLs, and even these are not handled properly by the SQL language.

On the Extremal Behaviour of Generalised Periodic Sub-Sampled Moving Average Models with Regularly Varying Tails

Let {X k } be a stationary moving average sequence of the form X k = Σj = −∞∞ β j *Zk − j where {Z k } is an iid sequence of random variables with regularly varying tails, and the operator * denotes multiplication if Z k is continuous and binomial thinning if Z k is a non-negative integer-valued. Let \(g{\left( k \right)}:\mathbb{N}_{{\text{0}}} \to \mathbb{N}_{{\text{0}}} \) be a strictly increasing sequence with a periodic pattern of the form g(k + I) = g(k) + M for some fixed integers I and M verifying 1 ≤ I ≤ M. Define Y k = Xg(k) as the generalised periodic sub-sampled moving average sequence. In this work we look at the extremal properties of {Y k }. In particular, we investigate the limiting distribution of the sample maxima and the corresponding extremal index. Motivation comes from the comparison of schemes for monitoring a variety of medical, finance, environmental, and social science data sets.

Mosaic, HDF and EOSDIS: providing access to earth sciences data

The National Aeronautics and Space Administration is developing the Earth Observing System Data and Information System (EOSDIS) to receive, process, and distribute Earth and environmental science data from the EOS series of sensors to be launched beginning in 1997 as a part of the Mission to Planet Earth program. This paper describes how Mosaic, HDF, and NCSA's Collage can support access and sharing of Earth science data by users of this system. Additionally, the paper presents the relationship of WWW and EOSDIS development. “The U.S. Global Change Research Program requires massive quantities of highly diverse data and information to improve our understanding of global change processes.” — The U.S. Global Change Data and Information Management Program Plan, 1992.