Probability analyses of combining background concentrations with model-predicted concentrations

In order to calculate total concentrations for comparison to ambient air quality standards, monitored background concentrations are often combined with model predicted concentrations. Models have low skill in predicting the locations or time series of observed concentrations. Further, adding fixed points on the probability distributions of monitored and predicted concentrations is very conservative and not mathematically correct. Simply adding the 99th percentile predicted to the 99th percentile background will not yield the 99th percentile of the combined distributions. Instead, an appropriate distribution can be created by calculating all possible pairwise combinations of the 1-hr daily maximum observed background and daily maximum predicted concentration, from which a 99th percentile total value can be obtained. This paper reviews some techniques commonly used for determining background concentrations and combining modeled and background concentrations. The paper proposes an approach to determine the joint probabilities of occurrence of modeled and background concentrations. The pairwise combinations approach yields a more realistic prediction of total concentrations than the U.S. Environmental Protection Agency's (EPA) guidance approach and agrees with the probabilistic form of the National Ambient Air Quality Standards.

Implications: EPA's current approaches to determining background concentrations for compliance modeling purposes often lead to “double counting” of background concentrations and actual plume impacts and thus lead to overpredictions of total impacts. Further, the current Tier 1 approach of simply adding the top ends of the background and model predicted concentrations (e.g., adding the 99th percentiles of these distributions together) results in design value concentrations at probabilities in excess of the form of the National Ambient Air Quality Standards.     

Misuse of null hypothesis significance testing: would estimation of positive and negative predictive values improve certainty of chemical risk assessment?

Although generally misunderstood, the p value is the probability of the test results or more extreme results given H₀ is true: it is not the probability of H₀ being true given the results. To obtain directly useful insight about H₀, the positive predictive value (PPV) and the negative predictive value (NPV) may be useful extensions of null hypothesis significance testing (NHST). They provide information about the probability of statistically significant and non-significant test outcomes being true based on an a priori defined biologically meaningful effect size. The present study explores the utility of PPV and NPV in an ecotoxicological context by using the frequently applied Daphnia magna reproduction test (OECD guideline 211) and the chemical stressor lindane as a model system. The results indicate that especially the NPV deviates meaningfully between a test design strictly following the guideline and an experimental procedure controlling for α and β at the level of 0.05. Consequently, PPV and NPV may be useful supplements to NHST that inform the researcher about the level of confidence warranted by both statistically significant and non-significant test results. This approach also reinforces the value of considering α, β, and a biologically meaningful effect size a priori.     

Estimating the Cumulative Ecological Effect of Local Scale Landscape Changes in South Florida

Ecosystem restoration in south Florida is a state and national priority centered on the Everglades wetlands. However, urban development pressures affect the restoration potential and remaining habitat functions of the natural undeveloped areas. Land use (LU) planning often focuses at the local level, but a better understanding of the cumulative effects of small projects at the landscape level is needed to support ecosystem restoration and preservation. The South Florida Ecosystem Portfolio Model (SFL EPM) is a regional LU planning tool developed to help stakeholders visualize LU scenario evaluation and improve communication about regional effects of LU decisions. One component of the SFL EPM is ecological value (EV), which is evaluated through modeled ecological criteria related to ecosystem services using metrics for (1) biodiversity potential, (2) threatened and endangered species, (3) rare and unique habitats, (4) landscape pattern and fragmentation, (5) water quality buffer potential, and (6) ecological restoration potential. In this article, we demonstrate the calculation of EV using two case studies: (1) assessing altered EV in the Biscayne Gateway area by comparing 2004 LU to potential LU in 2025 and 2050, and (2) the cumulative impact of adding limestone mines south of Miami. Our analyses spatially convey changing regional EV resulting from conversion of local natural and agricultural areas to urban, industrial, or extractive use. Different simulated local LU scenarios may result in different alterations in calculated regional EV. These case studies demonstrate methods that may facilitate evaluation of potential future LU patterns and incorporate EV into decision making.     

Long Range Cloud Diffusion in the Lower Atmosphere

Previous attempts to measure heavy metals in air are discussed with particular reference to lead. A polarographic method was developed for lead in air samples taken on glass fiber sheet. Using this method, the distribution of lead across the glass fiber sheet was investigated, and within experimental error, found to be uniform. Air samples from 18 Ontario cities, previously analyzed for benzo [a]pyrene content were analyzed.     

Enzyme-based resource allocated decomposition and landscape heterogeneity in the Florida Everglades

Enzyme catalyzed reactions are generally considered the rate-limiting step in organic matter degradation and may be significantly influenced by the structure and composition of plant communities. Changes in these rates have the potential to effect long-term peat accumulation and influence the topography of a wetland ecosystem. To determine habitat influences on enzyme activities, we examined slough and sawgrass plots within enriched and reference phosphorus (P) sites in the Everglades. Assays were performed for the enzymes involved in carbon (C), nitrogen (N), and P cycling and lignin depolymerization. Enzyme activities were normalized and analyzed in terms of a resource allocation strategy. Plant composition was found to significantly alter the allocation of enzymatic resources due to varying substrate complexities. Potential decomposition in the slough was less influenced by lignin than in the sawgrass habitats. Additionally, an index relating hydrolytic and oxidative enzymes was significantly greater in the slough habitats, whereas C/N ratios were significantly lower. These indices suggest more favorable decomposition conditions and thus slower peat accretion within the slough communities, which may contribute to the development of elevation differences within the sawgrass ridge and slough topography of the Everglades.     

Lead traceability along soil-melliferous flora-bee family-apiary products chain

Indirect measures were considered to study the lead transfer along the soil-melliferous flora-bee family-apiary products food chain. The concentrations of total and bioavailable lead in the soil were first investigated. Next, the lead level in different organs of melliferous flora and subsequently in the bee family and apiary products was measured. The results indicate a decrease in contamination as follows: honeybees > drones > propolis > wax > bee larvae > honey > royal jelly. The statistical analysis highlights the "lead pollution bioindicator" role of honeybees, honey and propolis. The risk factor parameter allows the prediction of the most impacted month and the least impacted of the melliferous season.     

Using the land transformation model to forecast vacant land

Growing or shrinking cities can experience increases in vacant land. As urban populations and boundaries fluctuate, holes can open in once tight urban areas. Many cities chase growth-oriented approaches to dealing with vacancies. It is critical to understand land-use alteration to accurately predict transformations of physical change in order to make better informed decisions about this phenomenon. This research utilizes the land transformation model (LTM), an artificial neural networking mechanism in Geographic Information Systems, to forecast vacant land. Variable influence on vacant land prediction and accuracy of the LTM is assessed by comparing input factors and patterns, using time-series data from 1990 to 2010 in Fort Worth, Texas, USA. Results indicate that the LTM can be useful in simulating vacant land-use changes but more precise mechanisms are necessary to increase accuracy. This will allow for more proactive decisions to better regulate the process of urban decline and regeneration.     

Ambient Air Monitoring for Sulfur Compounds

The role of sulfur compounds in our environment has been the subject of much speculation during the past two decades. An evaluation of the effect of man-made contributions to the biogeochemical sulfur cycle requires a comprehensive examination of source magnitudes, atmospheric concentrations and removal processes. Many voids exist in our present knowledge of these parameters despite the contributions of numerous researchers. Adequate information is needed concerning the various forms of sulfur as well as other critical constituents which exist in our biosphere so that their interrelationship and role in the mechanisms of the sulfur cycle may be more fully understood.