Evaluation of airborne particulate matter and metals data in personal,indoor and outdoor environments using ED-XRF and ICP-MS and co-located duplicate samples

Factors and sources affecting measurement uncertainty associated with monitoring metals in airborne particulate matter (PM) were investigated as part of the Windsor, Ontario Exposure Assessment Study (WOEAS). The assessment was made using co-located duplicate samples and a comparison of two analytical approaches: ED-XRF and ICP-MS. Sampling variability was estimated using relative percent difference (RPD) of co-located duplicate samples. The comparison of ICP-MS and ED-XRF results yields very good correlations (R² ≥ 0.7) for elements present at concentrations that pass both ICP-MS and ED-XRF detection limits (e.g. Fe, Mn, Zn, Pb and Cu). PM concentration ranges (median, sample number) of 24-h indoor PM₁₀ and personal PM₁₀ filters, and outdoor PM_2.5 filters were determined to be 2.2–40.7 (11.0, n = 48) μg m^?3, 8.0–48.3 (11.9, n = 48) μg m^?3, and 17.1–42.3 (21.6, n = 18) μg m^?3, respectively. The gravimetric analytical results reveal that the variations in PM mass measurements for same-day sampling are insignificant compared to temporal or spatial variations: 92%, 100% and 96% of indoor, outdoor and personal duplicate samples, respectively, pass the quality criteria (RPD ≤ 20%). Uncertainties associated with ED-XRF elemental measurements of S, Ca, Mn, Fe and Zn for 24-h filter samples are low: 78%–100% of the duplicate samples passed the quality criteria. In the case of 24-h filter samples using ICP-MS, more elements passed the quality criteria due to the lower detection limits. These were: Li, Na, K, Ca, Si, Al, V, Fe, Mn, Co, Cu, Mo, Ag, Zn, Pb, As, Mg, Sb, Sn, Sr, Th, Ti, Tl, and U. Low air concentrations of metals (near or below instrumental detection limits) and/or inadvertent introduction of metal contamination are the main causes for excluding elements based on the pass/fail criteria. Uncertainty associated with elemental measurements must be assessed on an element-by-element basis.     

Microbial mediated arsenic biotransformation in wetlands

Arsenic (As) is a pervasive environmental toxin and carcinogenic metalloid. It ranks at the top of the US priority List of Hazardous Substances and causes worldwide human health problems. Wetlands, including natural and artificial ecosystems (i.e. paddy soils) are highly susceptible to As enrichment; acting not only as repositories for water but a host of other elemental/chemical moieties. While macroscale processes (physical and geological) supply As to wetlands, it is the micro-scale biogeochemistry that regulates the fluxes of As and other trace elements from the semi-terrestrial to neighboring plant/aquatic/atmospheric compartments. Among these fine-scale events, microbial mediated As biotransformations contribute most to the element’s changing forms, acting as the ‘switch’ in defining a wetland as either a source or sink of As. Much of our understanding of these important microbial catalyzed reactions follows relatively recent scientific discoveries. Here we document some of these key advances, with focuses on the implications that wetlands and their microbial mediated transformation pathways have on the global As cycle, the chemistries of microbial mediated As oxidation, reduction and methylation, and future research priorities areas.

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Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

Nitrite and Freshwater Fish

Nitrite occurs naturally in fresh waters as a result of nitrification of ammonia and denitrification of nitrate, and its concentration can be enhanced by partial oxidation of ammoniacal discharges. Nitrite is toxic to vertebrates including fish and a principal effect is the conversion of haemoglobin to methaemoglobin which is incapable of oxygen transport although there are circulatory and tissue effects as well. The toxic species is the nitrite ion (NO₂) which is believed to enter the blood via the branchial chloride/bicarbonate exchange and fish such as salmonids with high chloride uptake rates are more susceptible than those with low chloride uptake rates, for example carp. Nitrite toxicity is strongly aleviated by chloride and the concentration ratio of these ions is of great importance in assessing toxicity. Short term and long term toxicity data for a variety of fish species are presented. There are no field data on fish populations in waters where nitrite was the only pollutant. However extensive field surveys indicated that, waters with a mean chloride concentration of 25 mg l^-1 in good salmon fisheries were associated with concentrations of nitrite below 50 μg l^-1 N · NO₂, good coarse fisheries below 100 μg l^-1 N · NO₂.     

VALIDATION OF THE SWAT MODEL ON A LARGE RWER BASIN WITH POINT AND NONPOINT SOURCES1

ABSTRACT: The State of Texas has initiated the development of a Total Maximum Daily Load program in the Bosque River Watershed, where point and nonpoint sources of pollution are a concern. Soil Water Assessment Tool (SWAT) was validated for flow, sediment, and nutrients in the watershed to evaluate alternative management scenarios and estimate their effects in controlling pollution. This paper discusses the calibration and validation at two locations, Hico and Valley Mills, along the North Bosque River. Calibration for flow was performed from 1960 through 1998. Sediment and nutrient calibration was done from 1993 through 1997 at Hico and from 1996 through 1997 at Valley Mills. Model validation was performed for 1998. Time series plots and statistical measures were used to verify model predictions. Predicted values generally matched well with the observed values during calibration and validation (R²≥ 0.6 and Nash‐Suttcliffe Efficiency ≥ 0.5, in most instances) except for some underprediction of nitrogen during calibration at both locations and sediment and organic nutrients during validation at Valley Mills. This study showed that SWAT was able to predict flow, sediment, and nutrients successfully and can be used to study the effects of alternative management scenarios.     

Social learning in a policy-mandated collaboration: community wildfire protection planning in the eastern United States

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and transformative learning in three case studies of Community Wildfire Protection Planning (CWPP), a policy-mandated collaboration under HFRA. Not all CWPP groups engaged in social learning. Those that did learned most about organisational priorities and values through communicative learning. Few participants gained new skills or knowledge through instrumental learning. CWPP groups had to commit to learning, but the design of the collaborative-mandate influenced the type of learning that was most likely to occur. This research suggests a potential role for top-down policy in setting the structural context for learning at the local level, but also confirms the importance of collaborative context and process in fostering social learning.     

Arsenism Clinical Stages and their Relation with Hair Arsenic Concentration of Residents of Bayinmaodao Rural District, Inner Mongolia, China

The clinical skin lesions of arsenism in men and women in Bayinmaodao rural district in Inner Mongolia, Peoples Republic of China, have been examined by doctors, and their hair and drinking water samples analysed for arsenic by hydride generation and ICP-AES. Altogether 311 arsenism patients with a 15.53% prevalence rate for the district were recorded. The disease prevalence rate was positively related to population, age, and their exposure to elevated arsenic concentrations in the drinking water from 1983 when new wells were dug and drinking of surface water was abandoned. Hyperkeratosis was the most serious skin lesion with the highest occurrence rate, then depigmentation and pigmentation in decreasing order. With increasing severity of the disease, ranging from skin lesion with single hyperkeratosis 1° to hyperkeratosis 3° with depigmentation 3° and pigmentation 3°, the results showed that arsenic concentrations in head hair had increased. Arsenic concentrations in hair were positively correlated with the arsenic concentrations in drinking water obtained from local wells.