Tillage and field scale controls on greenhouse gas emissions

There is a lack of understanding of how associations among soil properties and management-induced changes control the variability of greenhouse gas (GHG) emissions from soil. We performed a laboratory investigation to quantify relationships between GHG emissions and soil indicators in an irrigated agricultural field under standard tillage (ST) and a field recently converted (2 yr) to no-tillage (NT). Soil cores (15-cm depth) were incubated at 25 degrees C at field moisture content and 75% water holding capacity. Principal component analysis (PCA) identified that most of the variation of the measured soil properties was related to differences in soil C and N and soil water conditions under ST, but soil texture and bulk density under NT. This trend became more apparent after irrigation. However, principal component regression (PCR) suggested that soil physical properties or total C and N were less important in controlling GHG emissions across tillage systems. The CO2 flux was more strongly determined by microbial biomass under ST and inorganic N content under NT than soil physical properties. Similarly, N2O and CH4 fluxes were predominantly controlled by NO3- content and labile C and N availability in both ST and NT soils at field moisture content, and NH4+ content after irrigation. Our study indicates that the field-scale variability of GHG emissions is controlled primarily by biochemical parameters rather than physical parameters. Differences in the availability and type of C and N sources for microbial activity as affected by tillage and irrigation develop different levels and combinations of field-scale controls on GHG emissions.     

Hydrogen peroxide stabilization in one-dimensional flow columns

Rapid hydrogen peroxide decomposition is the primary limitation of catalyzed H(2)O(2) propagations in situ chemical oxidation (CHP ISCO) remediation of the subsurface. Two stabilizers of hydrogen peroxide, citrate and phytate, were investigated for their effectiveness in one-dimensional columns of iron oxide-coated and manganese oxide-coated sand. Hydrogen peroxide (5%) with and without 25 mM citrate or phytate was applied to the columns and samples were collected at 8 ports spaced 13 cm apart. Citrate was not an effective stabilizer for hydrogen peroxide in iron-coated sand; however, phytate was highly effective, increasing hydrogen peroxide residuals two orders of magnitude over unstabilized hydrogen peroxide. Both citrate and phytate were effective stabilizers for manganese-coated sand, increasing hydrogen peroxide residuals by four-fold over unstabilized hydrogen peroxide. Phytate and citrate did not degrade and were not retarded in the sand columns; furthermore, the addition of the stabilizers increased column flow rates relative to unstabilized columns. These results demonstrate that citrate and phytate are effective stabilizers of hydrogen peroxide under the dynamic conditions of one-dimensional columns, and suggest that citrate and phytate can be added to hydrogen peroxide before injection to the subsurface as an effective means for increasing the radius of influence of CHP ISCO.     

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Environmental Science and Pollution Research - Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and...     

An Approach for Evaluating the Repeatability of Rapid Wetland Assessment Methods: The Effects of Training and Experience

We sampled 92 wetlands from four different basins in the United States to quantify observer repeatability in rapid wetland condition assessment using the Delaware Rapid Assessment Protocol (DERAP). In the Inland Bays basin of Delaware, 58 wetland sites were sampled by multiple observers with varying levels of experience (novice to expert) following a thorough training workshop. In the Nanticoke (Delaware/Maryland), Cuyahoga (Ohio), and John Day (Oregon) basins, 34 wetlands were sampled by two expert teams of observers with minimal protocol training. The variance in observer to observer scoring at each site was used to calculate pooled standard deviations (SD_pool), coefficients of variation, and signal-to-noise ratios for each survey. The results showed that the experience level of the observer had little impact on the repeatability of the final rapid assessment score. Training, however, had a large impact on observer to observer repeatability. The SD_pool in the Inland Bay survey with training (2.2 points out of a 0–30 score) was about half that observed in the other three basins where observers had minimal training (SD_pool = 4.2 points). Using the results from the survey with training, we would expect that two sites assessed by different, trained observers who obtain DERAP scores differing by more than 4 points are highly likely to differ in ecological condition, and that sites with scores that differ by 2 or fewer points are within variability that can be attributed to observer differences.     

Vulnerability of Rehabilitated Agricultural Production Systems to Invasion by Nontarget Plant Species

Vast areas of arable land have been retired from crop production and “rehabilitated” to improved system states through landowner incentive programs in the United States (e.g., Conservation and Wetland Reserve Programs), as well as Europe (i.e., Agri-Environment Schemes). Our review of studies conducted on invasion of rehabilitated agricultural production systems by nontarget species elucidates several factors that may increase the vulnerability of these systems to invasion. These systems often exist in highly fragmented and agriculturally dominated landscapes, where propagule sources of target species for colonization may be limited, and are established under conditions where legacies of past disturbance persist and prevent target species from persisting. Furthermore, rehabilitation approaches often do not include or successfully attain all target species or historical ecological processes (e.g., hydrology, grazing, and/or fire cycles) key to resisting invasion. Uncertainty surrounds ways in which nontarget species may compromise long term goals of improving biodiversity and ecosystem services through rehabilitation efforts on former agricultural production lands. This review demonstrates that more studies are needed on the extent and ecological impacts of nontarget species as related to the goals of rehabilitation efforts to secure current and future environmental benefits arising from this widespread conservation practice.     

Preferred modes of travel among older adults: What factors affect the choice to walk instead of drive?

Introduction

There are many factors that influence older adults' travel choices. This paper explores the associations between mode of travel choice for a short trip and older adults' personal characteristics.

Methods

This study included 406 drivers over the age of 64 who were enrolled in a large integrated health plan in the United States between 1991 and 2001. Bivariate analyses and generalized linear modeling were used to examine associations between choosing to walk or drive and respondents' self-reported general health, physical and functional abilities, and confidence in walking and driving.

Results

Having more confidence in their ability to walk versus drive increased an older adult's likelihood of walking to make a short trip by about 20% (PR = 1.22; 95% CI: 1.06-1.40), and walking for exercise increased the likelihood by about 50% (PR = 1.53; 95% CI = 1.22-1.91). Reporting fair or poor health decreased the likelihood of walking, as did cutting down on the amount of driving due to a physical problem.

Discussion

Factors affecting a person's decision to walk for exercise may not be the same as those that influence their decision to walk as a mode of travel. It is important to understand the barriers to walking for exercise and walking for travel to develop strategies to help older adults meet both their exercise and mobility needs. Impact on Industry: Increasing walking over driving among older adults may require programs that increase confidence in walking and encourage walking for exercise.     

Best Practices in Environmental Communication: A Case Study of Louisiana's Coastal Crisis

This mixed-method case study of environmental communication best practices in Louisiana, USA, identifies trends and approaches as informed by environmental psychology. The results provide key insights for environmental communicators, especially communicators in coastal regions. In-depth interviews with both environmental psychologists and environmental communicators showed that both groups emphasized knowing the audience, telling local stories, building relationships with target audiences and targeted messaging. Both psychologists and communicators also frequently mentioned general messaging concerns of source credibility, avoiding controversial terms and talking about issues, impacts and solutions to which the target audience can relate. A representative survey revealed that Louisiana residents are most interested in hearing about how environmental issues such as climate change, coastal land loss and flooding are affecting their own communities. This finding supports the idea that environmental communicators could do a better job tapping into strong place attachment and sense of community among coastal residents to promote action.     

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 statistical model for predicting PM2.5 for the western United States

ABSTRACT

A new statistical model for predicting daily ground level fine scale particulate matter (PM_2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day’s PM_2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM_2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration’s Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM_2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM_2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.

Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM_2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.