Environmental PCBs in Guánica Bay,Puerto Rico: implications for community health

Guánica Bay, located in southwestern Puerto Rico, has suffered oil spills and other pollution discharges since the 1960s. Previous research showed elevated concentrations of polychlorinated biphenyls (PCBs) in coral reef and sediment. This research examined PCB concentrations in sediment and fish. Sediment and fish sampling in the bay was facilitated by community members. This study identified the second highest reported PCB level (129,300 ng/g) in sediment in the USA. Fish samples also showed elevated concentrations (1623 to 3768 ng/g), which were higher than the thresholds of safe levels of PCBs in fish for human consumption. The alarmingly high concentration of PCBs calls for proactive community engagement to bring awareness about contamination of the bay and more extensive sampling to test for the concentration of PCBs in seafood and the people of Guánica. This study also underscores the value of the involvement of local communities during sampling design aimed at identifying hot spots of contaminants.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Revised algorithm for estimating light extinction from IMPROVE particle speciation data

The Interagency Monitoring of Protected Visual Environments (IMPROVE) particle monitoring network consists of approximately 160 sites at which fine particulate matter (PM2.5) mass and major species concentrations and course particulate matter (PM10) mass concentrations are determined by analysis of 24-hr duration sampling conducted on a 1-day-in-3 schedule A simple algorithm to estimate light extinction from the measured species concentrations was incorporated in the 1999 Regional Haze Rule as the basis for the haze metric used to track haze trends. A revised algorithm was developed that is more consistent with the recent atmospheric aerosol literature and reduces bias for high and low light extinction extremes. The revised algorithm differs from the original algorithm in having a term for estimating sea salt light scattering from Cl(-) ion data, using 1.8 instead of 1.4 for the mean ratio of organic mass to measured organic carbon, using site-specific Rayleigh scattering based on site elevation and mean temperature, employing a split component extinction efficiency associated with large and small size mode sulfate, nitrate and organic mass species, and adding a term for nitrogen dioxide (NO2) absorption for sites with NO2 concentration information. Light scattering estimates using the original and the revised algorithms are compared with nephelometer measurements at 21 IMPROVE monitoring sites. The revised algorithm reduces the underprediction of high haze periods and the overprediction of low haze periods compared with the performance of the original algorithm. This is most apparent at the hazier monitoring sites in the eastern United States. For each site, the PM10 composition for days selected as the best 20% and the worst 20% haze condition days are nearly identical regardless of whether the basis of selection was light scattering from the original or revised algorithms, or from nephelometer-measured light scattering.     

A spore-based miniaturized novel assay for rapid aflatoxin detection in milk

Environmental Chemistry Letters - Aflatoxin M1 contamination in milk and dairy products is a major issue worldwide. Aflatoxin is a carcinogen that enters into the milk through animals feed. Here,...     

Light scattering from sea-salt aerosols at Interagency Monitoring of Protected Visual Environments (IMPROVE) sites

A method is described to estimate light scattering (Bsp) by sea-salt aerosols at coastal locations in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. Dry mass scattering efficiencies for fine and coarse sea-salt particles were based on previously measured dry sea-salt size distributions. Enhancement of sea-salt particle scattering by hygroscopic growth was based on NaCl water activity data. Sea-salt aerosol mass at the IMPROVE site in the Virgin Islands (VIIS) was estimated from strontium (Sr) concentrations in IMPROVE aerosol samples. Estimated Bsp, including contributions from sea-salt mass based on Sr, agreed well with measured Bsp at the VIIS IMPROVE site. On average, sea salt accounted for 52% of estimated Bsp at this site. Sea-salt aerosol mass cannot be reliably estimated from Sr unless its crustal enrichment factor exceeds 10. Sodium (Na) concentrations are not accurately determined by X-ray fluorescence analysis in IMPROVE samples. It is recommended that Na be measured in the fine and coarse modes by a more appropriate method, such as atomic absorption spectroscopy or ion chromatography, to account for scattering by sea-salt particles at IMPROVE sites where such contributions may be significant.     

An optimal spatial sampling design for intra-urban population exposure assessment

This article offers an optimal spatial sampling design that captures maximum variance with the minimum sample size. The proposed sampling design addresses the weaknesses of the sampling design that Kanaroglou, P.S., M. Jerrett, J. Morrison, B. Beckerman, M.A. Arain, N.L. Gilbert, and J.R. Brook (2005. Establishing an air pollution monitoring network for intra-urban population exposure assessment: a location-allocation approach. Atmospheric Environment 39(13), 2399–409) used for identifying 100 sites for capturing population exposure to NO₂ in Toronto, Canada. Their sampling design suffers from a number of weaknesses and fails to capture the spatial variability in NO₂ effectively. The demand surface they used is spatially autocorrelated and weighted by the population size, which leads to the selection of redundant sites. The location-allocation model (LAM) available with the commercial software packages, which they used to identify their sample sites, is not designed to solve spatial sampling problems using spatially autocorrelated data. A computer application (written in C++) that utilizes spatial search algorithm was developed to implement the proposed sampling design. The proposed design has already been tested and implemented in three different urban environments – namely Cleveland, OH; Delhi, India; and Iowa City, IA – to identify optimal sample sites for monitoring airborne particulates.     

Variation of mass scattering efficiencies in IMPROVE

The Interagency Monitoring of Protected Visual Environments (IMPROVE) equation used to assess compliance under the U.S. Environmental Protection Agency (EPA) Haze Rule assumes that dry mass scattering efficiencies for aerosol chemical components are constant. However, examination of aerosol size distributions and chemical composition during the Big Bend Regional Aerosol and Visibility Observational Study and the Southeastern Aerosol and Visibility Study suggests that volume and mass scattering efficiencies vary directly with increasing particle light scattering and aerosol mass concentration. This is consistent with the observation that particle distributions were shifted to larger sizes under more polluted conditions and appears to be related to aging of the aerosol during transport to remote locations.     

Determination of the organic aerosol mass to organic carbon ratio in IMPROVE samples

The ratio of organic mass (OM) to organic carbon (OC) in PM(2.5) aerosols at US national parks in the IMPROVE network was estimated experimentally from solvent extraction of sample filters and from the difference between PM(2.5) mass and chemical constituents other than OC (mass balance) in IMPROVE samples from 1988 to 2003. Archived IMPROVE filters from five IMPROVE sites were extracted with dichloromethane (DCM), acetone and water. The extract residues were weighed to determine OM and analyzed for OC by thermal optical reflectance (TOR). On average, successive extracts of DCM, acetone, and water contained 64%, 21%, and 15%, respectively, of the extractable OC, respectively. On average, the non-blank-corrected recovery of the OC initially measured in these samples by TOR was 115+/-42%. OM/OC ratios from the combined DCM and acetone extracts averaged 1.92 and ranged from 1.58 at Indian Gardens, AZ in the Grand Canyon to 2.58 at Mount Rainier, WA. The average OM/OC ratio determined by mass balance was 2.07 across the IMPROVE network. The sensitivity of this ratio to assumptions concerning sulfate neutralization, water uptake by hygroscopic species, soil mass, and nitrate volatilization were evaluated. These results suggest that the value of 1.4 for the OM/OC ratio commonly used for mass and light extinction reconstruction in IMPROVE is too low.