Developmental toxicity of PAH mixtures in fish early life stages. Part II: adverse effects in Japanese medaka

In aquatic environments, polycyclic aromatic hydrocarbons (PAHs) mostly occur as complex mixtures, for which risk assessment remains problematic. To better understand the effects of PAH mixture toxicity on fish early life stages, this study compared the developmental toxicity of three PAH complex mixtures. These mixtures were extracted from a PAH-contaminated sediment (Seine estuary, France) and two oils (Arabian Light and Erika). For each fraction, artificial sediment was spiked at three different environmental concentrations roughly equivalent to 0.5, 4, and 10 μg total PAH g^?1 dw. Japanese medaka embryos were incubated on these PAH-spiked sediments throughout their development, right up until hatching. Several endpoints were recorded at different developmental stages, including acute endpoints, morphological abnormalities, larvae locomotion, and genotoxicity (comet and micronucleus assays). The three PAH fractions delayed hatching, induced developmental abnormalities, disrupted larvae swimming activity, and damaged DNA at environmental concentrations. Differences in toxicity levels, likely related to differences in PAH proportions, were highlighted between fractions. The Arabian Light and Erika petrogenic fractions, containing a high proportion of alkylated PAHs and low molecular weight PAHs, were more toxic to Japanese medaka early life stages than the pyrolytic fraction. This was not supported by the toxic equivalency approach, which appeared unsuitable for assessing the toxicity of the three PAH fractions to fish early life stages. This study highlights the potential risks posed by environmental mixtures of alkylated and low molecular weight PAHs to early stages of fish development.     

Environmental effects of soil contamination by shale fuel oils

Estonia is currently one of the leading producers of shale oils in the world. Increased production, transportation and use of shale oils entail risks of environmental contamination. This paper studies the behaviour of two shale fuel oils (SFOs)—‘VKG D’ and ‘VKG sweet’—in different soil matrices under natural climatic conditions. Dynamics of SFOs’ hydrocarbons (C10–C40), 16 PAHs, and a number of soil heterotrophic bacteria in oil-spiked soils was investigated during the long-term (1 year) outdoor experiment. In parallel, toxicity of aqueous leachates of oil-spiked soils to aquatic organisms (crustaceans Daphnia magna and Thamnocephalus platyurus and marine bacteria Vibrio fischeri) and terrestrial plants (Sinapis alba and Hordeum vulgare) was evaluated. Our data showed that in temperate climate conditions, the degradation of SFOs in the oil-contaminated soils was very slow: after 1 year of treatment, the decrease of total hydrocarbons’ content in the soil did not exceed 25 %. In spite of the comparable chemical composition of the two studied SFOs, the VKG sweet posed higher hazard to the environment than the heavier fraction (VKG D) due to its higher mobility in the soil as well as higher toxicity to aquatic and terrestrial species. Our study demonstrated that the correlation between chemical parameters (such as total hydrocarbons or total PAHs) widely used for the evaluation of the soil pollution levels and corresponding toxicity to aquatic and terrestrial organisms was weak.     

Can ornamental potted plants remove volatile organic compounds from indoor air? — a review

Volatile organic compounds (VOCs) are found in indoor air, and many of these can affect human health (e.g. formaldehyde and benzene are carcinogenic). Plants affect the levels of VOCs in indoor environments, thus they represent a potential green solution for improving indoor air quality that at the same time can improve human health. This article reviews scientific studies of plants’ ability to remove VOCs from indoor air. The focus of the review is on pathways of VOC removal by the plants and factors affecting the efficiency and rate of VOC removal by plants. Laboratory based studies indicate that plant induced removal of VOCs is a combination of direct (e.g. absorption) and indirect (e.g. biotransformation by microorganisms) mechanisms. They also demonstrate that plants’ rate of reducing the level of VOCs is influenced by a number of factors such as plant species, light intensity and VOC concentration. For instance, an increase in light intensity has in some studies been shown to lead to an increase in removal of a pollutant. Studies conducted in real-life settings such as offices and homes are few and show mixed results.     

Evaluation of different isotherm models,kinetic, thermodynamic,and copper biosorption efficiency of Lobaria pulmonaria (L.) Hoffm.

The biosorption characteristics of Cu(II) ions from aqueous solution using Lobaria pulmonaria (L.) Hoffm. biomass were investigated. The biosorption efficiency of Cu(II) onto biomass was significantly influenced by the operating parameters. The maximum biosorption efficiency of L. pulmonaria was 65.3% at 10 mg/L initial metal concentration for 5 g/L lichen biomass dosage. The biosorption of Cu(II) ions onto biomass fits the Langmuir isotherm model and the pseudo-second-order kinetic model well. The thermodynamic parameters indicate the feasibility and exothermic and spontaneous nature of the biosorption. The effective desorption achieved with HCl was 96%. Information on the nature of possible interactions between the functional groups of the L. pulmonaria biomass and Cu(II) ions was obtained via Fourier transform infrared (FTIR) spectroscopy. The results indicated that the carboxyl (–COOH) and hydroxyl (–OH) groups of the biomass were mainly involved in the biosorption of Cu(II) onto L. pulmonaria biomass. The L. pulmonaria is a promising biosorbent for Cu(II) ions because of its availability, low cost, and high metal biosorption and desorption capacities.

Implications: Lobaria pulmonaria is a promising biosorbent for Cu(II) ions because of its availability, low cost, and high metal biosorption and desorption capacities. To the best of our knowledge, this is the first paper on the biosorption Cu by L. pulmonaria.     

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.     

Major ionic compositions of fine particulate matter in an animal feeding operation facility and its vicinity

Animal feeding operations (AFOs) produce particulate matter (PM) and gaseous pollutants. Investigation of the chemical composition of PM_2.5 inside and in the local vicinity of AFOs can help to understand the impact of the AFO emissions on ambient secondary PM formation. This study was conducted on a commercial egg production farm in North Carolina. Samples of PM_2.5 were collected from five stations, with one located in an egg production house and the other four located in the vicinity of the farm along four wind directions. The major ions of NH₄⁺, Na⁺, K⁺, SO₄^2?, Cl^?, and NO₃^? were analyzed using ion chromatography (IC). In the house, the mostly abundant ions were SO₄^2?, Cl^?, and K⁺. At ambient stations, SO₄^2?, and NH₄⁺ were the two most abundant ions. In the house, NH₄⁺, SO₄^2?, and NO₃^? accounted for only 10% of the PM_2.5 mass; at ambient locations, NH₄⁺, SO₄^2?, and NO₃^? accounted for 36–41% of the PM_2.5 mass. In the house, NH₄⁺ had small seasonal variations indicating that gas-phase NH₃ was not the only major force driving its gas–particle partitioning. At the ambient stations, NH₄⁺ had the highest concentrations in summer. In the house, K⁺, Na⁺, and Cl^? were highly correlated with each other. In ambient locations, SO₄^2? and NH₄⁺ had a strong correlation, whereas in the house, SO₄^2? and NH₄⁺ had a very weak correlation. Ambient temperature and solar radiation were positively correlated with NH₄⁺ and SO₄^2?. This study suggests that secondary PM formation inside the animal house was not an important source of PM_2.5. In the vicinity, NH₃ emissions had greater impact on PM_2.5 formation.

ImplicationsThe chemical composition of PM_2.5 inside and in the local vicinity of AFOs showed the impact of the AFO emissions on ambient secondary PM_2.5 formation, and the fate and transport of air pollutants associated with AFOs. The results may help to manage in-house animal facility air quality, and to develop regional air quality control strategies and policies, especially in animal agriculture-concentrated areas.     

Comparative statistical study of hourly precipitation determined by radar-based Stage IV and ground-based methods in the North Central United States

Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges.

Implications:

The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.     

Emission,speciation, and evaluation of impacts of non-methane volatile organic compounds from open dump site

Surface emission from Dhapa, the only garbage disposal ground in Kolkata, is a matter of concern to the local environment and also fuels the issues of occupational and environmental health. Surface emission of the Dhapa landfill site was studied using a flux chamber measurement for nonmethane volatile organic compounds (NMVOCs). Eighteen noncarbonyl volatile organic compounds (VOCs) and 14 carbonyl VOCs, including suspected and known carcinogens, were found in appreciable concentrations. The concentrations of the target species in the flux chamber were found to be significantly higher for most of the species in summer than winter. Surface emission rate of landfill gas was estimated by using two different approaches to assess the applicability for an open landfill site. It was found that the emissions predicted using the model Land GEM version 3.02 is one to two orders less than the emission rate calculated from flux chamber measurement for the target species. Tropospheric ozone formation has a serious impact for NMVOC emission. The total ozone-forming potential (OFP) of the Dhapa dumping ground considering all target NMVOCs was estimated to be 4.9E+04 and 1.2E+05 g/day in winter and summer, respectively. Also, it was found that carbonyl VOCs play a more important role than noncarbonyl VOCs for tropospheric ozone formation. Cumulative cancer risk estimated for all the carcinogenic species was found to be 2792 for 1 million population, while the total noncancer hazard index (HI) was estimated to be 246 for the occupational exposure to different compounds from surface emission to the dump-site workers at Dhapa.

Implications:This paper describes the real-time surface emission of NMVOCs from an open municipal solid waste (MSW) dump site studied using a flux chamber. Our study findings indicate that while planning for new landfill site in tropical meteorology, real-time emission data must be considered, rather than relying on modeled data. The formation of tropospheric ozone from emitted NMVOC has also been studied. Our result shows how an open landfill site acts as a source and adds to the tropospheric ozone for the airshed of a metropolitan city.     

U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM_2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM_2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM_2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM_2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.

ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM_2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM_2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.