Host-plant genotypic diversity mediates the distribution of an ecosystem engineer

Ecosystem engineers affect ecological communities by physically modifying the environment. Understanding the factors determining the distribution of engineers offers a powerful predictive tool for community ecology. In this study, we examine whether the goldenrod bunch gall midge (Rhopalomyia solidaginis) functions as an ecosystem engineer in an old-field ecosystem by altering the composition of arthropod species associated with a dominant host plant, Solidago altissima. We also examine the suite of factors that could affect the distribution and abundance of this ecosystem engineer. The presence of bunch galls increased species richness and altered the structure of associated arthropod communities. The best predictors of gall abundance were host-plant genotype and plot-level genotypic diversity. We found positive, nonadditive effects of genotypic diversity on gall abundance. Our results indicate that incorporating a genetic component in studies of ecosystem engineers can help predict their distribution and abundance, and ultimately their effects on biodiversity.     

Alternative carbon dioxide modelling approaches accounting for high residual gases in LandGEM

High Canadian waste disposal rates necessitate landfill gas monitoring and accurate forecasting. CO₂ estimates in LandGEM version 3.02 currently rest on the assumptions that CO₂ is a function of CH₄, where the two gases make up nearly 100% of landfill gas content, leading to overestimated CO₂ collection estimates. A total of 25 cases (five formulas, five approaches) compared annual CO₂ collection at four western Canadian landfills. Despite common use in literature, the 1:1 ratio of CH₄ to CO₂ was not recommended to forecast landfill gas collection in cold climates. The existing modelling approach significantly overestimated CO₂ production in three of four sites, resulting in the highest residual sum of squares. Optimization resulted in the most accurate results for all formulas and approaches, which had the greatest reduction in residual sums of squares (RSS) over the default approach (60.1 to 97.7%). The 1.4 Ratio approach for L _o:L _o-CO2 yielded the second most accurate results for CO₂ flow (mean RSS reduction of 50.2% for all sites and subsection models). The annual k-modified LandGEM calculated k’s via two empirical formulas (based on precipitation) and yielded the lowest accuracy in 12 of 20 approaches. Unlike other studies, strong relationships between optimized annual k’s and precipitation were not observed.

     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Characterization of indoor air quality and resident health in an Arizona senior housing apartment building

A survey of key indoor air quality (IAQ) parameters and resident health was carried out in 72 apartments within a single low-income senior housing building in Phoenix, Arizona. Air sampling was carried out simultaneously with a questionnaire on personal habits and general health of residents. Mean PM₁₀ concentrations are 66±16, 58±13, and 24±3 μg/m³ and mean PM_2.5 concentrations are 62±16, 53±13, and 20±2 μg/m³ for the living room, kitchen, and outdoor balcony, respectively. Median PM₁₀ concentrations are 17, 18 and 17 μg/m³ and median PM_2.5 concentrations are 13, 14, and 13 μg/m³, respectively. The initial results indicate that increased indoor particle concentrations coincide with residents who report smoking cigarettes. Indoor formaldehyde concentrations revealed median levels of 36.9, 38.8, and 4.3 ppb in the living room, kitchen, and balcony, respectively. Results show that 36% of living room samples and 44% of kitchen samples exceeded the Health Canada REL for chronic exposure to formaldehyde (40 ppb). Associations between occupants’ behavior, self-reported health conditions, and IAQ are evaluated.

Implications:This study provides a characterization of indoor air quality (IAQ) of subsidized apartments for seniors in Phoenix, Arizona. It is important for policy makers to understand the environments in which low-income seniors live, as they are vulnerable to the health impacts from poor IAQ. Formaldehyde concentrations were found to exceed the Health Canada 8-hr reference exposure level (REL) for up to 44% of indoor samples. Particulate matter exposure was governed by resident behavior (i.e., smoking). Associations between occupants’ behavior, IAQ, and self-reported health conditions are evaluated. This work can provide a foundation for subsequent remediation of IAQ conditions.     

Effects of glyphosate and foliar amendments on activity of microorganisms in the soybean rhizosphere

A field study was conducted to determine the effects of glyphosate on microbial activity in the rhizosphere of glyphosate-resistant (GR) soybean and to evaluate interactions with foliar amendments. Glyphosate at 0.84 kg ae ha^{? 1} was applied GR soybean at the V4–V5 development stages. Check treatments included a conventional herbicide tank mix (2003 study only) and no herbicides (hand-weeded). Ten days after herbicide application, a commercially available biostimulant and a urea solution (21.0% N) were applied to soybean foliage at 33.5 mL ha^{? 1} and 9.2 kg ha^{? 1}, respectively. Soil and plant samples were taken 0, 5, 10, 15, 20 and 25 days after herbicide application then assayed for enzyme and respiration activities. Soil respiration and enzyme activity increased with glyphosate and foliar amendment applications during the 2002 growing season; however, similar increases were not observed in 2003. Contrasting cumulative rainfall between 2002 and 2003 likely accounted for differences in soil microbial activities. Increases in soil microbial activity in 2002 suggest that adequate soil water and glyphosate application acted together to increase microbial activity. Our study suggests that general soil microbial properties including those involving C and N transformations are not sensitive enough to detect effects of glyphosate on rhizosphere microbial activity. Measurements of soil-plant-microbe relationships including specific microbial groups (i.e., root-associated Fusarium spp.) are likely better indicators of impacts of glyphosate on soil microbial ecology.     

Methods for Quantifying Variability and Uncertainty in AP-42 Emission Factors: Case Studies for Natural Gas-Fueled Engines

Abstract

Quantitative methods for characterizing variability and uncertainty were applied to case studies of oxides of nitrogen and total organic carbon emission factors for lean-burn natural gas-fueled internal combustion engines. Parametric probability distributions were fit to represent inter-engine variability in specific emission factors. Bootstrap simulation was used to quantify uncertainty in the fitted cumulative distribution function and in the mean emission factor. Some methodological challenges were encountered in analyzing the data. For example, in one instance, five data points were available, with each data point representing a different market share. Therefore, an approach was developed in which parametric distributions were fitted to population-weighted data. The uncertainty in mean emission factors ranges from as little as ~±10% to as much as -90 to 21+180%. The wide range of uncertainty in some emission factors emphasizes the importance of recognizing and accounting for uncertainty in emissions estimates. The skewness in some uncertainty estimates illustrates the importance of using numerical simulation approaches that do not impose restrictive symmetry assumptions on the confidence interval for the mean. In this paper, the quantitative method, the analysis results, and key findings are presented.     

Gas-phase and semivolatile organic emissions from a modern nonroad diesel engine equipped with advanced aftertreatment

ABSTRACT

U.S. Tier 4 Final and Euro Stage IV and V regulations for nonroad compression-ignition engines have led to the development of exhaust aftertreatment technologies optimized for nonroad engines and duty cycles. In this study, several aftertreatment configurations consisting of state-of-the-art diesel oxidation catalysts (DOCs), diesel particulate filters (DPFs), copper (Cu) zeolite– and vanadium-based selective catalytic reduction (SCR) catalysts, and ammonia oxidation (AMOX) catalysts are evaluated using both nonroad transient (NRTC) and steady (8-mode NRSC) cycles in order to understand both component- and system-level effects of diesel aftertreatment on gas-phase, semivolatile, and particle-phase and particle-bound unregulated organic emissions. Organic emissions reported in this work include total hydrocarbon (THC), n-alkanes, branched alkanes, saturated cycloalkanes, aromatics, aldehydes, ketones, hopanes, steranes, and soluble organic fraction (SOF). Brake-specific emissions are reported for four configurations, including engine-out, DOC+CuZ-SCR+AMOX, V-SCR+AMOX, and DOC+DPF+CuZ-SCR+AMOX, and conversion of engine-out emissions is reported for the three aftertreatment configurations. Mechanisms responsible for the reduction of organic species are discussed in detail. This summary of emissions from a current nonroad diesel engine equipped with advanced aftertreatment can be used to more accurately model the impact of anthropogenic emissions on the atmosphere with tools such as the U.S. Environmental Protection Agency’s Motor Vehicle Emissions Simulator (MOVES2014a) model.

Implications: Anthropogenic emissions are a source of significant human health and environmental risk. This study, focused on the treatment of exhaust emissions from a modern nonroad diesel engine with a variety of aftertreatment configurations, examines the impact that human industrial activity can have on air pollution. In particular, we focus on the remediation of gas-phase and semivolatile organic emissions by emission reduction technologies. This detailed summary of emissions from a current nonroad diesel engine equipped with advanced aftertreatment can be used to more accurately model the impact of anthropogenic emissions on the atmosphere with tools such as the U.S. Environmental Protection Agency’s MOVES2014a model.     

Remote sampling of a CO2 point source in an urban setting

High frequency CO₂ and wind speed measurements were used to examine the urban baseline eddy covariance CO₂ flux and analyse the CO₂ rich plume from a local power station. A reliable relationship between high frequency CO₂ maxima and the rate of CO₂ emission at the power station was established. This relationship was shown to be highly dependant on wind speed. The ensemble mean plume was found to be Gaussian in horizontal profile with a width dependant on wind speed. The relationship between peak CO₂ mixing ratio and averaging time was shown to be a simple power law with a time exponent of approximately 0.5. The large, short pulses in CO₂ mixing ratio in the power plant plume were found to have an approximately Lorentzian shape. These pulses generated negative vertical eddy flux measurements so data from the plume sector were necessarily excluded from the flux baseline results. The plume-excluded flux had a similar magnitude and variability to those reported in other urban CO₂ flux studies despite this site not being ideal due to the proximity of roughness elements to the measurement point.     

Aerobic degradation of di- and trichlorobenzenes by two bacteria isolated from polluted tropical soils

Two polychlorinated biphenyl (PCBs)-degrading bacteria were isolated by traditional enrichment technique from electrical transformer fluid (Askarel)-contaminated soils in Lagos, Nigeria. They were classified and identified as Enterobacter sp. SA-2 and Pseudomonas sp. SA-6 on the basis of 16S rRNA gene analysis, in addition to standard cultural and biochemical techniques. The strains were able to grow extensively on dichloro- and trichlorobenzenes. Although they failed to grow on tetrachlorobenzenes, monochloro- and dichlorobenzoic acids, they were able to utilize all monochlorobiphenyls, and some dichlorobiphenyls as sole sources of carbon and energy. The effect of incubation with axenic cultures on the degradation of 0.9 mM 1,4-dichlorobenzene, 0.44 mM 1,2,3- and 0.43 mM 1,3,5-trichlorobenzene in mineral salts medium was studied. Approximately, 80-90% of these xenobiotics were degraded in 200 h, concomitant with cell increase of up to three orders of magnitude, while generation times ranged significantly (P<0.05) from 17-32 h. Catechol 1,2-dioxygenase and catechol 2,3-dioxygenase activities were detected in crude cell-free extracts of cultures pre-grown with benzoate, with the latter enzyme exhibiting a slightly higher activity (0.15-0.17 micromolmin(-1) mg of protein(-1)) with catechol, suggesting that the meta-cleavage pathway is the most readily available catabolic route in the SA strains. The wider substrate specificity of these tropical isolates may help in assessing natural detoxification processes and in designing bioremediation and bioaugmentation methods.