LogD: lipophilicity for ionisable compounds

The octanol/water partition coefficient (Kow) for organic compounds is widely used in predictive environmental studies. A significant proportion of contaminants of surface and ground water are ionisable (e.g. many pesticides, pharmaceuticals, metabolites). Such compounds may be partially ionised dependent on the pH. Since the neutral and ionic species exhibit different polarities, the Kow value of ionisable pesticides is pH dependent. It is therefore essential to determine Kow values over the full range of pH that occurs in the environment in order to get appropriate predictors. Numerous methods are available to measure lipophilicity but only a few are appropriate for ionisable pesticides (e.g. pH metric and filter probe methods). Parameters such as pH and ionic strength need to be carefully controlled when working with ionisable compounds. Variation of these factors probably explains why literature can yield Kow values that differ by more than one order of magnitude for some compounds. In this article, Kow values obtained for six acidic pesticides with three different methods are compared as well (data from the literature, measured by pH metric method and calculated with five computer programs). The values used in predictive regression equations needs to be either measured with a suitable method or selected from the literature with great care.     

Halton iterative partitioning: spatially balanced sampling via partitioning

A new spatially balanced sampling design for environmental surveys is introduced, called Halton iterative partitioning (HIP). The design draws sample locations that are well spread over the study area. Spatially balanced designs are known to be efficient when surveying natural resources because nearby locations tend to be similar. The HIP design uses structural properties of the Halton sequence to partition a resource into nested boxes. Sample locations are then drawn from specific boxes in the partition to ensure spatial diversity. The method is conceptually simple and computationally efficient, draws spatially balanced samples in two or more dimensions and uses standard design-based estimators. Furthermore, HIP samples have an implicit ordering that can be used to define spatially balanced over-samples. This feature is particularly useful when sampling natural resources because we can dynamically add spatially balanced units from the over-sample to the sample as non-target or inaccessible units are discovered. We use several populations to show that HIP sampling draws spatially balanced samples and gives precise estimates of population totals.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

An evaluation of public participation information for land use decisions: public comment,surveys, and participatory mapping

ABSTRACT

Public participation in the form of public meetings and written submissions has been criticised as a democratic ritual that fails to give citizens a real voice in government decisions. Participatory mapping and community surveys are alternative public participation (PP) methods that can augment legally required processes for land use planning. To date, there has been little evaluation research comparing the information content generated by required PP processes and alternative PP sources with respect to local land use decisions. Using multiple development projects from a case study community, we analysed and compared information generated from three different sources of public participation: (1) formal public comment (written submissions), (2) responses to community survey questions, and (3) land use preferences generated from participatory mapping. We found public comment strongly supported development while results from survey questions and participatory mapping methods revealed community ambivalence. The differences in public opinion are attributed to two key factors: the representativeness of participants in the PP process and the specific methods used for measuring public opinion. Community surveys and participatory mapping generated more accurate and representative community information compared to the formal PP process which was characterised by lower participation and vulnerability to special interest manipulation. For local government decision makers, the political risk of broadening PP information appears high relative to the risk of inaccurately assessing public opinion thus limiting adoption of alternative PP methods such as participatory mapping.     

Use of CALPUFF for exposure assessment in a near-field,complex terrain setting

CALPUFF is an atmospheric source-receptor model recommended by the U.S. Environmental Protection Agency for use on a case-by-case basis in complex terrain and wind conditions. The ability of the model to provide useful information for exposure assessments in areas with those topographical and meteorological conditions has received little attention. This is an important knowledge gap for use of CALPUFF outside of regulatory applications, such as exposure analyses conducted in support of risk assessments and health studies. We compared deposition of cadmium (Cd), lead (Pb), and zinc (Zn) calculated with CALPUFF as a result of emissions from a zinc smelter with corresponding concentrations of the metals measured in attic dust and soil samples obtained from the surrounding area. On a point-by-point analysis, predictions from CALPUFF explained 11% (lead) to 53% (zinc) of the variability in concentrations measured in attic dust. Levels of heavy metals in soil interpolated to 100 residential addresses from the distribution of concentrations measured in soil samples also agreed well with deposition predicted with CALPUFF: R² of 0.46, 0.76, and 079 for Pb, Cd, and Zn, respectively. Community-average concentrations of Cd, Pb, and Zn measured in soil were significantly (p < 0.0001) and strongly correlated (R² ranged from 0.77 to 0.98) with predicted deposition rates. These findings demonstrate that CALPUFF can provide reasonably accurate predictions of the patterns of long-term air pollutant deposition in the near-field associated with emissions from a discrete source in complex terrain. Because deposition estimates are calculated as a linear function of air concentrations, CALPUFF is expected to be reliable model for prediction of long-term average, near-field ambient air concentrations in complex terrain as well.     

Making neighborhoods safer: Examining predictors of residents’ concerns about neighborhood safety

This study examined what factors best predict residents’ concerns about neighborhood safety. One-hundred and twenty-two participants were selected from a large, Midwestern metropolitan area. All participants lived in high crime areas. Participants completed a 22-item questionnaire that assessed their perceptions of neighborhood safety and vigilance. These items were clustered as: 1) Community care and vigilance, 2) neighborhood safety concerns, 3) physical incivilities, and 4) social incivilities. Police crime data were also used in the analyses. Our findings suggest that aspects of the broken window theory, collective efficacy, and place attachments/territoriality play a role in affecting residents’ concerns about neighborhood safety.     

Effects of economic growth on biodiversity in the United States

For many citizens and policymakers, the empirical relationship between economic growth and biodiversity conservation has not been sufficiently established for purposes of identifying the types of economic policies amenable to biodiversity conservation. Some think economic growth conflicts with biodiversity conservation; others think economic growth conduces biodiversity conservation. With panel data from 1997‐2011, encompassing US continental states, we developed a series of statistical models to investigate the relationships among species endangerment, human population, and economic growth as indicated by GDP and per capita GDP. Species endangerment is highly correlated with population and GDP, and per capita GDP is a significant regressor of species endangerment. Across US continental states, competitive exclusion of non‐human species occurs via human economic growth and population growth.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Coming to the table: collaborative governance and groundwater decision-making in coastal California

Collaborative governance is on the rise in the United States. This management approach brings together state and non-state actors for environmental decision-making, and it is frequently used in California for decisions regarding local groundwater management. This study examines groundwater decision-making groups and practices in a central California coastal community to understand whether groups meet specific collaborative governance criteria and whether and why certain subsets of the population are excluded from groundwater decision-making practices. It also identifies actions for better group inclusion. We find that small farmers, the Hispanic/Latino community, and the general public are often excluded from groundwater decision-making groups and practices due to unawareness, mistrust, and insufficient resources. Education and awareness as well as incentives could help increase inclusion. This study provides insights into more equitable groundwater decision-making groups and practices, and also calls for more critical examination of the current stakeholder approach to decision-making.     

Evaluation of give kids a boost: A school-based program to increase booster seat use among urban children in economically disadvantaged areas

Objective: This study evaluated the effectiveness of a series of 1-year multifaceted school-based programs aimed at increasing booster seat use among urban children 4–7 years of age in economically disadvantaged areas.

Methods: During 4 consecutive school years, 2011–2015, the Give Kids a Boost (GKB) program was implemented in a total of 8 schools with similar demographics in Dallas County. Observational surveys were conducted at project schools before project implementation (P₀), 1–4 weeks after the completion of project implementation (P₁), and 4–5 months later (P₂). Changes in booster seat use for the 3 time periods were compared for the 8 project and 14 comparison schools that received no intervention using a nonrandomized trial process.

The intervention included (1) train-the-trainer sessions with teachers and parents; (2) presentations about booster seat safety; (3) tailored communication to parents; (4) distribution of fact sheets/resources; (5) walk-around education; and (6) booster seat inspections.

The association between the GKB intervention and proper booster seat use was determined initially using univariate analysis. The association was also estimated using a generalized linear mixed model predicting a binomial outcome (booster seat use) for those aged 4 to 7 years, adjusted for child-level variables (age, sex, race/ethnicity) and car-level variables (vehicle type). The model incorporated the effects of clustering by site and by collection date to account for the possibility of repeated sampling.

Results: In the 8 project schools, booster seat use for children 4–7 years of age increased an average of 20.9 percentage points between P₀ and P₁ (P₀ = 4.8%, P₁ = 25.7%; odds ratio [OR] = 6.9; 95% confidence interval [CI], 5.5, 8.7; P < .001) and remained at that level in the P₂ time period (P₂ = 25.7%; P < .001, for P₀ vs. P₂) in the univariate analysis. The 14 comparison schools had minimal change in booster seat use. The multivariable model showed that children at the project schools were significantly more likely to be properly restrained in a booster seat after the intervention (OR = 2.7; 95% CI, 2.2, 3.3) compared to the P₀ time period and compared to the comparison schools.

Conclusion: Despite study limitations, the GKB program was positively associated with an increase in proper booster seat use for children 4–7 years of age in school settings among diverse populations in economically disadvantaged areas. These increases persisted into the following school year in a majority of the project schools. The GKB model may be a replicable strategy to increase booster seat use among school-age children in similar urban settings.