Drivers of carbon dioxide emissions: an empirical investigation using hierarchical and non-hierarchical clustering methods

The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the second step, both hierarchical and non-hierarchical clustering methods are considered to identify the optimal number of clusters. We identify two to four clusters with different member countries, and in particular establish that in most cases, a 2-cluster solution appears to be optimal. The contents of clusters vary slightly according to the clustering methods for each period. The clustering results from using only the overall CO2 emissions indicate that the countries we consider form three clusters, with China and the USA each within a single member cluster. The remaining 70 countries form the third cluster. Our findings reflect the prominent roles of China and the USA in overall CO2 emissions. Analyses with sub-period and largest emitters reflect a different clustering structure. Some policy recommendations in setting emission reductions are made, considering different clusters across countries.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Spatiotemporal Variability of Modeled Watershed Scale Surface‐Depression Storage and Runoff for the Conterminous United States

This study explores the viability of using simulated monthly runoff as a proxy for landscape‐scale surface‐depression storage processes simulated by the United States Geological Survey’s National Hydrologic Model (NHM) infrastructure across the conterminous United States (CONUS). Two different temporal resolution model codes (daily and monthly) were run in the NHM with the same spatial discretization. Simulated values of daily surface‐depression storage (treated as a decimal fraction of maximum volume) as computed by the daily Precipitation‐Runoff Modeling System (NHM‐PRMS) and normalized runoff (0 to 1) as computed by the Monthly Water Balance Model (NHM‐MWBM) were aggregated to monthly and annual values for each hydrologic response unit (HRU) in the CONUS geospatial fabric (HRU; n = 109,951) and analyzed using Spearman’s rank correlation test. Correlations between simulated runoff and surface‐depression storage aggregated to monthly and annual values were compared to identify where which time scale had relatively higher correlation values across the CONUS. Results show Spearman’s rank values >0.75 (highly correlated) for the monthly time scale in 28,279 HRUs (53.35%) compared to the annual time scale in 41,655 HRUs (78.58%). The geographic distribution of HRUs with highly correlated monthly values show areas where surface‐depression storage features are known to be common (e.g., Prairie Pothole Region, Florida).     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

Muskox status,recent variation,and uncertain future

Ambio - Muskoxen (Ovibos moschatus) are an integral component of Arctic biodiversity. Given low genetic diversity, their ability to respond to future and rapid Arctic change is unknown, although...     

Growth of Dehalococcoides spp. and increased abundance of reductive dehalogenase genes in anaerobic PCB-contaminated sediment microcosms

Polychlorinated biphenyls (PCBs) contaminate 19% of US Superfund sites and represent a serious risk to human and environmental health. One promising strategy to remediate PCB-contaminated sediments utilizes organohalide-respiring bacteria (OHRB) that dechlorinate PCBs.

However, functional genes that act as biomarkers for PCB dechlorination processes (i.e., reductive dehalogenase genes) are poorly understood. Here, we developed anaerobic sediment microcosms that harbor an OHRB community dominated by the genus Dehalococcoides. During the 430-day microcosm incubation, Dehalococcoides 16S rRNA sequences increased two orders of magnitude to 10⁷ copies/g of sediment, and at the same time, PCB118 decreased by as much as 70%. In addition, the OHRB community dechlorinated a range of penta- and tetra-chlorinated PCB congeners including PCBs 66, 70?+?74?+?76, 95, 90?+?101, and PCB110 without exogenous electron donor. We quantified candidate reductive dehalogenase (RDase) genes over a 430-day incubation period and found rd14, a reductive dehalogenase that belongs to Dehalococcoides mccartyi strain CG5, was enriched to 10⁷ copies/g of sediment. At the same time, pcbA5 was enriched to only 10⁵ copies/g of sediment. A survey for additional RDase genes revealed sequences similar to strain CG5’s rd4 and rd8. In addition to demonstrating the PCB dechlorination potential of native microbial communities in contaminated freshwater sediments, our results suggest candidate functional genes with previously unexplored potential could serve as biomarkers of PCB dechlorination processes.

     

The influence on carbon,nitrogen recycling,and greenhouse gas emissions under different C/N ratios by black soldier fly

Environmental Science and Pollution Research - Currently, sustainable utilization, including recycling and valorization, is becoming increasingly popular in waste management. Black soldier fly...     

Molecular,functional, and cellular alterations of oocytes and cumulus cells induced by heat stress and shock in animals

Environmental Science and Pollution Research - Global warming is considered as the main environmental stress affecting ecosystems as well as physiological and biochemical characteristics, and...     

Hurricane Harvey and Greater Houston households: comparing pre-event preparedness with post-event health effects,event exposures,and recovery

Most disaster studies rely on convenience sampling and ‘after-only’ designs to assess impacts. This paper, focusing on Hurricane Harvey (2017) and leveraging a pre-/post-event sample of Greater Houston households (n=71) in the United States, establishes baselines for disaster preparedness and home structure flood hazard mitigation, explores household-level ramifications, and examines how preparedness and mitigation relate to health effects, event exposures, and recovery. Between 70 and 80 per cent of participants instituted preparedness measures. Mitigation actions varied: six per cent had interior drainage systems and 83 per cent had elevated indoor heating/cooling components. Sixty per cent reported home damage. One-half highlighted allergies and two-thirds indicated some level of post-traumatic stress (PTS). Three-quarters worried about family members/friends. The results of generalised linear models revealed that greater pre- event mitigation was associated with fewer physical health problems and adverse experiences, lower PTS, and faster recovery. The study design exposed the broad benefits of home structure flood hazard mitigation for households after Harvey.     

Effect of Hunting on the Reproduction of the Sable Population

Russian Journal of Ecology - Analysis of hunting samples of the Kamchatka sable population for 2001–2013 has revealed changes in the reproductive parameters of females over the period from...