Effect of soil on the mutagenic properties of waste water

The disposal of complex mixtures such as waste water on agricultural lands poses known and unknown environmental risks. Mutagens may be introduced into the eco-system and perhaps concentrated by crop plants or leached into ground water supplies. The purpose of this study was to determine the biological effect of a mutagenic waste water before and after application to soil. We used an XAD-8 methanol extract of waste water from the municipal sewage treatment facility at Sauget IL. This extract was a potent direct acting mutagen when assayed with the Salmonella typhimurium. 1 and 3 ml of extract were brought up to 10 ml volumes with water and added to 10 a of sterile or nonsterile , native clay loam. These mixtures were placed in a shaking water bath at room temperature for 0, 24, and 48 h. After separation of solid and liquid portions by filtration, dichloromethane was added to extract the organic fractions from each component. Solvent extractions were evaporated to dryness under vacuum and brought up in DMSO. Tests for mutagenic activity were conducted using strain TA98. After addition to the soil for greater periods of time the mutagenic activity decreased. The solid component exhibited greater mutagenic activity than the liquid. The XAD-8 extract was also assayed using the yg2 assay in Zea mays and the micronucleus assay in Tradescantia. The extract did not induce mutation or chromosome aberrations in these assays. The sludge from the Sauqet plant was chemically fractionated and assayed with S. typhimurium strains TA98 and TA100. The neutral fraction was the most mutagenic fraction followed by the weak acid,-strong acid and basic fractions. These chemical fractions uncovered more mutagenic potency than was predicted by assaying a crude organic extract of the sludge.U.S. Department of the InteriorU.S. Geological SurveyOpe

Advanced receptor modeling of near–real–time, ambient PM2.5 and its associated components collected at an urban–industrial site in Toronto, Ontario

AbstractPM2.5 and other atmospheric pollutants were continuously monitored at high time resolution for 1 year at an urban–industrial location in Toronto, ON, Canada's largest city. The data collected for these pollutants were examined to determine seasonal trends and potential sources. Advanced receptor models including residence time weighted concentration (RTWC) and simplified quantitative transport bias analysis (sQTBA) trajectory ensemble models (TEM) and conditional probability function (CPF) were applied to these data to identify potential local and regional sources of pollution impacting this receptor site. Seasonal trends showed that concentrations of PM2.5 were more frequently high in winter than in any other season. Median concentrations of lead and arsenic were highest in fall while median levels of chromium were not significantly different over the four seasons. The black carbon–derived measurement commonly known as Delta C (i.e., BC370nm–BC880nm) had its greatest abundance in winter and lowest levels in summer. The seasonality of Delta C is indicative of the impact of residential wood combustion near the receptor site. CPF indicated that lead and iron had the most unidirectional radial plots with sectors located west–southwest of the receptor being the most likely local source regions. Winter CPF for Delta C is almost of equal strengths in all directions suggestive of near–uniform isotropic local impacts. The sQTBA model provided the most satisfactory spatial representation of impacting sources. The strongest sources of PM2.5 identified by the sQTBA model were both local and transboundary in origin. More potential source regions were found in winter and summer than in spring and fall

Characterization of fine particulate sources at Ashaiman in Greater Accra, Ghana

AbstractThe sources of airborne fine particles in PM2.5 range influencing air quality at Ashaiman, a semi–urban town north of Tema in Ghana had been investigated. Nuclepore and quartz fiber filters were used for the air particulate loadings and analyzed for elemental and carbonaceous compound (EC and OC) concentrations in the 8 carbon fractions using X–Ray spectrometry system and IMPROVE/Thermal Optical Reflectance method respectively. Positive matrix factorization (PMF) was utilized to identify the following eight sources; industrial emissions (11.4%), fresh sea salt (15.5%), diesel emissions (18.4%), biomass burning (9.5%), two stroke engines (5.1%), gasoline emissions (15.8%), aged sea salt (6.2%), and soil dust (17.7%). Source locations were verified by means of Conditional Probability Function (CPF) plots that utilize wind directions. The source contributions revealed the high influence of fossil fuel and biomass combustion on the air quality in Ashaiman. The presence of the harbor and the industries located at Tema were seen to have substantial impacts on respirable air particulate matter (APM) concentrations in Ashaiman

Laboratory and Field Testing of an Automated Atmospheric Particle-Bound Reactive Oxygen Species Sampling-Analysis System

In this study, various laboratory and field tests were performed to develop an effective automated particle-bound ROS sampling-analysis system. The system uses 2′ 7′-dichlorofluorescin (DCFH) fluorescence method as a nonspecific, general indicator of the particle-bound ROS. A sharp-cut cyclone and a particle-into-liquid sampler (PILS) were used to collect PM2.5 atmospheric particles into slurry produced by a DCFH-HRP solution. The laboratory results show that the DCFH and H2O2 standard solutions could be kept at room temperature for at least three and eight days, respectively. The field test in Rochester, NY, shows that the average ROS concentration was 8.3 ± 2.2 nmol of equivalent H2O2 m−3 of air. The ROS concentrations were observed to be greater after foggy conditions. This study demonstrates the first practical automated sampling-analysis system to measure this ambient particle component

Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors

There is concern regarding the heterogeneity of exposure to airborne particulate matter (PM) across urban areas leading to negatively biased health effects models. New, low-cost sensors now permit continuous and simultaneous measurements to be made in multiple locations. Measurements of ambient PM were made from October to April 2015–2016 and 2016–2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA. In general, there was moderate spatial inhomogeneity, as indicated by multiple pairwise measures including coefficient of divergence and signed rank tests of the value distributions. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the area, likely driven by a combination of meteorological conditions (wind speed, direction, and mixed layer heights) and the concentration of PM2.5 being transported into the region. Although the accuracy of these PM sensors is limited, they are sufficiently precise relative to one another and to research grade instruments that they can be useful is assessing the spatial and temporal variations across an area and provide concentration estimates based on higher-quality central site monitoring data

A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide (NO$_2$))-specific exposures and birth outcomes for 2012 in Harris County, Texas, using several approaches, including the newly developed method.Comment: 34 pages, 8 figure