Reduction of Baltic Sea Nutrient Inputs and Allocation of Abatement Costs Within the Baltic Sea Catchment

The Baltic Sea Action Plan (BSAP) requires tools to simulate effects and costs of various nutrient abatement strategies. Hierarchically connected databases and models of the entire catchment have been created to allow decision makers to view scenarios via the decision support system NEST. Increased intensity in agriculture in transient countries would result in increased nutrient loads to the Baltic Sea, particularly from Poland, the Baltic States, and Russia. Nutrient retentions are high, which means that the nutrient reduction goals of 135 000 tons N and 15 000 tons P, as formulated in the BSAP from 2007, correspond to a reduction in nutrient loadings to watersheds by 675 000 tons N and 158 000 tons P. A cost-minimization model was used to allocate nutrient reductions to measures and countries where the costs for reducing loads are low. The minimum annual cost to meet BSAP basin targets is estimated to 4.7 billion €.     

Marine organisms as heavy metal bioindicators in the Persian Gulf and the Gulf of Oman

In the present study, cadmium and lead concentrations were compared in barnacles, ghost shrimps, polychaetes, bivalves, and sediment from ten different locations along the intertidal zone of the Persian Gulf and the Gulf of Oman. The results revealed significant differences in the heavy metal concentrations between the organisms with barnacles showing, by far, the highest metal concentrations. The bioaccumulation factor of Cd in different animals follows this pattern with barnacles?>?bivalves?>?polychaetes?>?ghost shrimps, while the pattern for Pb was barnacles?>?polychaetes?>?bivalves?>?ghost shrimps. In most of the stations, sediments showed the lowest lead and cadmium concentrations. Therefore, it is concluded that barnacles with Pb concentrations between 0.17 and 2,016.1 μg/g and Cd concentrations ranging from 0.4 to 147.1 μg/g are the best organisms to be employed in monitoring programs designed to assess pollution with bioavailable metals in the Persian Gulf and the Gulf of Oman.     

Biomass smoke in Burkina Faso: what is the relationship between particulate matter,carbon monoxide,and kitchen characteristics?

In Burkina Faso where cooking with biomass is very common, little information exists regarding kitchen characteristics and their impact on air pollutant levels. The measurement of air pollutants such as respirable particulate matter (PM₁₀), an important component of biomass smoke that has been linked to adverse health outcomes, can also pose challenges in terms of cost and the type of equipment needed. Carbon monoxide could potentially be a more economical and simpler measure of air pollution. The focus of this study was to first assess the association of kitchen characteristics with measured PM₁₀ and CO levels and second, the relationship of PM₁₀ with CO concentrations, across these different kitchen characteristics in households in Nouna, Burkina Faso. Twenty-four-hour concentrations of PM₁₀ (area) were measured with portable monitors and CO (area and personal) estimated using color dosimeter tubes. Data on kitchen characteristics were collected through surveys. Most households used both wood and charcoal burned in three-stone and charcoal stoves. Mean outdoor kitchen PM₁₀ levels were relatively high (774 μg/m³, 95 % CI 329–1,218 μg/m³), but lower than indoor concentrations (Satterthwaite t value, ?6.14; p?<?0.0001). In multivariable analyses, outdoor kitchens were negatively associated with PM₁₀ (OR?=?0.06, 95 % CI 0.02–0.16, p value <0.0001) and CO (OR?=?0.03, 95 % CI 0.01–0.11, p value <0.0001) concentrations. Strong area PM₁₀ and area CO correlations were found with indoor kitchens (Spearman’s r?=?0.82, p?<?0.0001), indoor stove use (Spearman’s r?=?0.82, p?<?0.0001), and the presence of a smoker in the household (Spearman’s r?=?0.83, p?<?0.0001). Weak correlations between area PM₁₀ and personal CO levels were observed with three-stone (Spearman’s r?=?0.23, p?=?0.008) and improved stoves (Spearman’s r?=?0.34, p?=?0.003). This indicates that the extensive use of biomass fuels and multiple stove types for cooking still produce relatively high levels of exposure, even outdoors, suggesting that both fuel subsidies and stove improvement programs are likely necessary to address this problem. These findings also indicate that area CO color dosimeter tubes could be a useful measure of area PM₁₀ concentrations when levels are influenced by strong emission sources or when used in indoors. The weaker correlation observed between area PM₁₀ and personal CO levels suggests that area exposures are not as useful as proxies for personal exposures, which can vary widely from those recorded by stationary monitors.     

Release of vanadium from oxidized sediments: insights from different extraction and leaching procedures

Although the attention for vanadium (V) as a potentially harmful element is growing and some countries adopted threshold values for V in soils, sediments, groundwater, or surface water, V is generally of little importance in environmental legislation and the knowledge about the behavior of V in the environment is still limited. In the present study, the release of V from oxidized sediments, sediment-derived soils, and certified reference materials was investigated by means of several types of leaching tests and extractions that are frequently used for soil and sediment characterization. The pH_stat leaching tests and single and sequential extractions applied in this study show that V generally displays a very limited actual and potential mobility in sediment. “Mobile” V concentrations, as estimated by the amount of V released by a single extraction with CaCl₂ 0.01 mol L^?1, were low, even in the most contaminated sediment samples. Only under strongly acidifying conditions (pH 2), such as in the case of ingestion of soil or sediment or in accidental spills, a substantial release of V can be expected.     

Assessing and managing freshwater ecosystems vulnerable to environmental change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.     

Outdoor fine and ultrafine particle measurements at six bus stops with smoking on two California arterial highways—Results of a pilot study

As indoor smoking bans have become widely adopted, some U.S. communities are considering restricting smoking outdoors, creating a need for measurements of air pollution near smokers outdoors. Personal exposure experiments were conducted with four to five participants at six sidewalk bus stops located 1.5–3.3 m from the curb of two heavily traveled California arterial highways with 3300–5100 vehicles per hour. At each bus stop, a smoker in the group smoked a cigarette. Gravimetrically calibrated continuous monitors were used to measure fine particle concentrations (aerodynamic diameter ≤2.5 µm; PM_2.5) in the breathing zones (within 0.2 m from the nose and mouth) of each participant. At each bus stop, ultrafine particles (UFP), wind speed, temperature, relative humidity, and traffic counts were also measured. For 13 cigarette experiments, the mean PM_2.5 personal exposure of the nonsmoker seated 0.5 m from the smoker during a 5-min cigarette ranged from 15 to 153 µg/m³. Of four persons seated on the bench, the smoker received the highest PM_2.5 breathing-zone exposure of 192 µg/m³. There was a strong proximity effect: nonsmokers at distances 0.5, 1.0, and 1.5 m from the smoker received mean PM_2.5 personal exposures of 59, 40, and 28 µg/m³, respectively, compared with a background level of 1.7 µg/m³. Like the PM_2.5 concentrations, UFP concentrations measured 0.5 m from the smoker increased abruptly when a cigarette started and decreased when the cigarette ended, averaging 44,500 particles/cm³ compared with the background level of 7200 particles/cm³. During nonsmoking periods, the UFP background concentrations showed occasional peaks due to traffic, whereas PM_2.5 background concentrations were extremely low. The results indicate that a single cigarette smoked outdoors at a bus stop can cause PM_2.5 and UFP concentrations near the smoker that are 16–35 and 6.2 times, respectively, higher than the background concentrations due to cars and trucks on an adjacent arterial highway.

Implications: Rules banning smoking indoors have been widely adopted in the United States and in many countries. Some communities are considering smoking bans that would apply to outdoor locations. Although many measurements are available of pollutant concentrations from secondhand smoke at indoor locations, few measurements are available of exposure to secondhand smoke outdoors. This study provides new data on exposure to fine and ultrafine particles from secondhand smoke near a smoker outdoors. The levels are compared with the exposure measured next to a highway. The findings are important for policies that might be developed for reducing exposure to secondhand smoke outdoors.     

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.     

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.     

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.     

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.