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The global water cycle has experienced significant changes due to the interplay of climate shifts and human activities, resulting in more frequent and severe droughts and floods. These shifts have started to impact the operational efficiency of water treatment and delivery systems. This, in turn, has implications for the economic performance of these assets and the climate-impacted cost of their financing through the issuing of municipal bonds. Analyzing a decade of water bond data (2009-2019), this study offers empirical evidence for the impact of flood and drought risks on bond investor demand to offset water risks. The results reveal that bond markets factored in coastal flood risks between 2013 and 2019, adjusting by 3-6 basis points (bps) per risk score unit, and riverine flood risks from 2009 to 2013, with a 5-11 basis points increase per risk score unit. These effects were primarily driven by bonds issued in the Pacific Coast and Great Plains regions, respectively. In contrast, the pricing of drought risks in the bond market followed a more nuanced pattern. Additionally, we show the channeling effects of water consumption and investor perceptions of climate change on water risk pricing in the bond market. These findings have significant implications for water risk management in the public sector as regions with heightened water risk exposure are perceived as riskier by market participants, leading to a higher cost of capital for municipalities and water agencies.

Dioxins are a family of chemical compounds that has received considerable attention, both historically and currently. This article reviews scientific field studies that examine the relationship between living on soil contaminated with dioxins and the level of dioxins in people's serum, with an emphasis on the University of Michigan Dioxin Exposure Study (UMDES), the largest and most comprehensive study of its kind. These studies conclude that the levels of dioxins in serum are most strongly correlated with age, gender, body mass index, weight loss, breast feeding, and smoking. Levels of dioxins in soil are not significant predictors for dioxin concentrations in serum. The increase in serum dioxin levels that is seen with age results from historic exposure and does not represent ongoing exposure. Based on the scientific field studies conducted to date, it appears that, in the absence of the consumption of contaminated animal products, there is little evidence of ongoing exposure from contaminated soil.

Green roof technology is recognized for mitigating stormwater runoff and energy consumption. Methods to overcome the cost gap between green roofs and conventional roofs were recently quantified by incorporating air quality benefits. This study investigates the impact of scaling on these benefits at the city-wide scale using Washington, DC as a test bed because of the proposed targets in the 20-20-20 vision (20 million ft(2) by 2020) articulated by Casey Trees, a nonprofit organization. Building-specific stormwater benefits were analyzed assuming two proposed policy scenarios for stormwater fees ranging from 35 to 50% reduction for green roof implementation. Heat flux calculations were used to estimate building-specific energy savings for commercial buildings. To assess benefits at the city scale, stormwater infrastructure savings were based on operational savings and size reduction due to reduced stormwater volume generation. Scaled energy infrastructure benefits were calculated using two size reductions methods for air conditioners. Avoided carbon dioxide, nitrogen oxide (NO(x)), and sulfur dioxide emissions were based on reductions in electricity and natural gas consumption. Lastly, experimental and fugacity-based estimates were used to quantify the NO(x) uptake by green roofs, which was translated to health benefits using U.S. Environmental Protection Agency models. The results of the net present value (NPV) analysis showed that stormwater infrastructure benefits totaled $1.04 million (M), while fee-based stormwater benefits were $0.22-0.32 M/y. Energy savings were $0.87 M/y, while air conditioner resizing benefits were estimated at $0.02 to $0.04 M/y and avoided emissions benefits (based on current emission trading values) were $0.09 M-0.41 M/y. Over the lifetime of the green roof (40 years), the NPV is about 30-40% less than that of conventional roofs (not including green roof maintenance costs). These considerable benefits, in concert with current and emerging policy frameworks, may facilitate future adoption of this technology.

The University of Michigan dioxin exposure study was undertaken to address concerns that the industrial discharge of dioxin-like compounds in the Midland, MI area had resulted in contamination of soils in the Tittabawassee River floodplain and downwind of the incinerator. The study was designed in a rigorously statistical manner comprising soil measurements of 29 polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) from 766 residential properties, selected probabilistically, in the Midland area and in Jackson and Calhoun Counties (Michigan) as a background comparison. A statistical comparison determined that the geometric mean toxic equivalent (TEQ) levels in samples from the target populations were statistically significantly above background. In addition, the probabilities of being above the 75th and 95th percentiles of background were also greater. Congener contributions to the TEQ were dominated by 2,3,4,7,8-PeCDF and 2,3,7,8-TCDF in the floodplain and by 2,3,7,8-TCDD in the incinerator plume. However, PCB 126 was the top congener contributing to the background TEQ. On the basis of statistical inference to the total population, it was estimated that about 36% of the properties in the floodplain and incinerator plume have at least one soil sample over the Michigan Department of Environmental Quality's soil direct contact criterion of 90 pg/g TEQ.

Deposition of pollutants around point sources of contamination, such as incinerators, can display complex spatial patterns depending on prevailing weather conditions, the local topography, and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decision-making, such as delineation of areas targeted for additional sampling or remediation. This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual variability of field samples. The approach is used to delineate areas with high levels of dioxin TEQ(DF)-WHO98 (toxic equivalents) around an incinerator, accounting for 53 field data and the output of the EPA Industrial Source Complex (ISC3) dispersion model. The dispersion model explains 43.7% of the variance in the soil TEQ data, whereas the regression residuals are spatially correlated with a range of 776 m. One hundred realizations of soil TEQ values are simulated on a grid with a 50 m spacing. The benefit of stochastic simulation over spatial interpolation is 2-fold: (1) maps of simulated point TEQ values can easily be aggregated to the geography that is the most relevant for decision making (e.g., census block, ZIP codes); and (2) the uncertainty at the larger scale is simply modeled by the empirical distribution of block-averaged simulated values. Incorporating the output of the atmospheric deposition model as a spatial trend yields a more realistic prediction of the spatial distribution of TEQ values than log-normal kriging using only the field data, in particular, in sparsely sampled areas away from the incinerator. The geostatistical model provided guidance for the study design (census block-based population sampling) of the University of Michigan Dioxin Exposure Study (UMDES), focused on quantifying exposure pathways to dioxins from industrial sources, relative to background exposures.

A key component in any investigation of cause-effect relationships between point source pollution, such as an incinerator, and human health is the availability of measurements and/or accurate models of exposure at the same scale or geography as the health data. Geostatistics allows one to simulate the spatial distribution of pollutant concentrations over various spatial supports while incorporating both field data and predictions of deterministic dispersion models. This methodology was used in a companion paper to identify the census blocks that have a high probability of exceeding a given level of dioxin TEQ (toxic equivalents) around an incinerator in Midland, MI. This geostatistical model, along with population data, provided guidance for the collection of 51 new soil data, which permits the verification of the geostatistical predictions, and calibration of the model. Each new soil measurement was compared to the set of 100 TEQ values simulated at the closest grid node. The correlation between the measured concentration and the averaged simulated value is moderate (0.44), and the actual concentrations are clearly overestimated in the vicinity of the plant property line. Nevertheless, probability intervals computed from simulated TEQ values provide an accurate model of uncertainty: the proportion of observations that fall within these intervals exceeds what is expected from the model. Simulation-based probability intervals are also narrower than the intervals derived from the global histogram of the data, which demonstrates the greater precision of the geostatistical approach. Log-normal ordinary kriging provided fairly similar estimation results for the small and well-sampled area used in this validation study; however, the model of uncertainty was not always accurate. The regression analysis and geostatistical simulation were then conducted using the combined set of 53 original and 51 new soil samples, leading to an updated model for the spatial distribution of TEQ in Midland, MI.

Green (vegetated) roofs have gained global acceptance as a technologythat has the potential to help mitigate the multifaceted, complex environmental problems of urban centers. While policies that encourage green roofs exist atthe local and regional level, installation costs remain at a premium and deter investment in this technology. The objective of this paper is to quantitatively integrate the range of stormwater, energy, and air pollution benefits of green roofs into an economic model that captures the building-specific scale. Currently, green roofs are primarily valued on increased roof longevity, reduced stormwater runoff, and decreased building energy consumption. Proper valuation of these benefits can reduce the present value of a green roof if investors look beyond the upfront capital costs. Net present value (NPV) analysis comparing a conventional roof system to an extensive green roof system demonstrates that at the end of the green roof lifetime the NPV for the green roof is between 20.3 and 25.2% less than the NPV for the conventional roof over 40 years. The additional upfront investment is recovered at the time when a conventional roof would be replaced. Increasing evidence suggests that green roofs may play a significant role in urban air quality improvement For example, uptake of N0x is estimated to range from $1683 to $6383 per metric ton of NOx reduction. These benefits were included in this study, and results translate to an annual benefit of $895-3392 for a 2000 square meter vegetated roof. Improved air quality leads to a mean NPV for the green roof that is 24.5-40.2% less than the mean conventional roof NPV. Through innovative policies, the inclusion of air pollution mitigation and the reduction of municipal stormwater infrastructure costs in economic valuation of environmental benefits of green roofs can reduce the cost gap that currently hinders U.S. investment in green roof technology.

Risk-based sediment management decisions require the characterization of contamination sources and fate processes in the field. Polytopic vector analysis (PVA) is a multivariate technique based on a linear mixing model, used to resolve chemical fingerprints and suited for forensic investigations of environmental contamination. The traditional algorithm is constrained to positive fingerprint (end-member) components and cannot resolve fingerprints with both positive and negative values required for a reactive end-member. We developed a modified algorithm (M-PVA) to resolve a dioxin dechlorination fingerprint, indicative of biotic/abiotic transformations in field samples of sediments. The new procedure isolates from the dioxin pattern net compositional changes due to dechlorination in a separate end-member. Using two artificial data sets for which the composition and sample contribution of all end-members are known, the dechlorination fingerprint was reproduced with a root mean square error of 28-41%. The dechlorination end-member contribution to the total variability (set at 4.0 and 10.0%, respectively) was overestimated 1-5-fold. The ability of M-PVA to reproduce the dechlorination pattern and its variability contribution depends on the actual contribution of dechlorination to variability. At an actual contribution of 4.0%, the model outcome deviates more strongly from the original than is the case for a contribution of 10.0%. As such, application of M-PVA to environmental data should include an uncertainty analysis to distinguish variability due to dechlorination from variability due to error. The development of the modified PVA procedure is an important step toward the field characterization of fate processes in dioxin-impacted sediments.

Persistent contaminants such as dioxins have been documented to undergo dechlorination reactions in the laboratory; however, little is known about the importance of these reactions in the field. Polytopic vector analysis (PVA) is a statistical pattern recognition technique for multivariate data traditionally used to identify fingerprints of contaminant sources. A modified PVA algorithm with uncertainty analysis was used to model dechlorination fingerprints and sources. The technique was applied to 351 sediment core-derived dioxin samples from the lower reach of the Passaic River, New Jersey. A dechlorination fingerprint was identified with a highly positive 2,3,7,8-tetraCDD component and a highly negative heptaCDD component. The most important industrial source of 2,3,7,8-tetraCDD is a fingerprint related to 2,4,5-trichlorophenoxyacetic acid production. The dechlorination contribution to the data variance is 3.00 +/- 1.00%, corresponding to an average of 1.2 microg/kg of 2,3,7,8-tetraCDD per sample at the expense of heptaCDD. The possible occurrence of dechlorination was validated by comparing the local dechlorination contribution in the results to the value of the ratio 2,3,7,8-tetraCDD/total 2,3,7,8-PCDD, which indicates dechlorination in the laboratory. Bootstrap uncertainty analysis yielded the same dechlorination EM in 40% of the realizations. The results indicated that bootstrapping is an important statistical tool to quantify uncertainties with respect to the dechlorination EM and some of the source EMs.

Iron-reducing conditions in subsurface environments promote dechlorination reactions via both biotic and abiotic pathways, the latter often mediated via biologically activated minerals formed by dissimilatory iron-reducing bacteria (DIRB). Here we report the major products and pathways associated with the abiotic transformation of carbon tetrachloride (CT) by nanoscale biogenic magnetite/maghemite particles produced by the DIRB Geobacter metallireducens. Product formation and free radical/carbene trapping studies indicate that CT transformation occurs via three parallel pathways. The first pathway (hydrogenolysis) results in the formation of chloroform (45-50%) via a trichloromethyl free radical (*CCl3) and possibly a trichloromethyl carbanion (**CCl3-). The second and third pathways involve a dichlorocarbene intermediate (**CCl2), which either hydrolyzes to form CO (approximately 38%) (carbene hydrolysis), or undergoes further reduction to yield methane (8-10%) (carbene reduction). The mechanism of methane formation from **CCl2 is not known, but is speculated to involve a sequence of surface coordinated carbenoid and free radical complexes. The large fraction of relatively benign products formed by the carbene-mediated pathways suggests that magnetite/maghemite particles may have a beneficial application in the remediation of CT contaminated environments.

Contributions of biotic (cell-mediated) and abiotic (mineral-mediated) reactions to carbon tetrachloride (CT) transformation were studied in a model iron-reducing system that used hydrous ferric oxide (HFO) as the electron acceptor, acetate as the substrate, and Geobacter metallireducens as a representative dissimilative iron-reducing bacteria (DIRB). Over a period of 2-3 weeks, nanoscale magnetite particles, Fe3O4, were consistently formed as a product of iron respiration in this system. CT transformation rates were measured independently in resting cell suspensions of G. metallireducens or in suspensions of washed magnetite particles recovered from spent cultures. Protein and surface area-normalized expressions were derived for the biotic and abiotic reaction rates, respectively. Using the yield of total protein and magnetite surface area formed during growth in the model system as a basis for comparison, the mineral-mediated (abiotic) reaction was estimated to be 60-260-fold faster than the biotic reaction throughout the incubation period. We conclude that G. metallireducens induces CT transformation in this system primarily through the formation of reactive mineral surfaces rather than via co-metabolic mechanisms. The findings indicate that reactive biogenic minerals could play a significant role in the natural attenuation of chlorinated solvents in iron-reducing environments. A novel approach for stimulating reductive transformation of contaminants may be to enhance the formation of reactive biogenic minerals in situ.

Contaminated sediment management is an urgent environmental and regulatory issue worldwide. Because remediation is expensive, sound quantitative assessments of uncertainty aboutthe spatial distribution of contaminants are critical, butthey are hampered bythe physical complexity of sediment environments. This paper describes the use of geostatistical modeling approaches to quantify uncertainty of 2,3,7,8-tetrachlorodibenzo-p-dioxin concentrations in Passaic River (New Jersey) sediments and to incorporate this information in decision-making processes, such as delineation of contaminated areas and additional sampling needs. First, coordinate transformation and analysis of three-dimensional semivariograms were used to describe and modelthe directional variability accounting forthe meandering course of the river. Then, indicator kriging was employed to provide models of local uncertainty at unsampled locations without requiring a prior transform (e.g. log-normal) of concentrations. Cross-validation results show that the use of probability thresholds leads to more efficient delineation of contaminated areas than a classification based on the exceedence of regulatory thresholds by concentration estimates. Depending on whether additional sampling aims at reducing prediction errors or misclassification rates, the variance of local probability distributions or a measure of the expected closeness to the regulatory threshold can be used to locate candidate locations.