Inter-Urban Comparison of Ambient Volatile Organic Compound Concentrations in U.S. Cities

Airborne particulate matter from three ferrous foundries was sampled in granulometric fractions and analyzed by scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDXA), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) to determine morphology and qualitative bulk and surface chemical compositions. Cluster and principal components analyses (PCA) were used as an aid for interpretation of results. A clear pattern of composition as a function of size emerges; in particular, trace metals accumulate in fine particles and volatile species on the surface of these. Chemical composition changes are also related to daily schedules where applicable: trace metals appear during pouring operations, silicon is generated during mold making and unmolding. Cluster and PCA greatly ease examination of data and offer pictorial representations of results; in particular, the chemical composition versus particle size relationship is very neatly illustrated by PCA graphs.     

Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies

In this work the performance and theoretical background behind two of the most commonly used receptor modelling methods in aerosol science, principal components analysis (PCA) and positive matrix factorization (PMF), as well as multivariate curve resolution by alternating least squares (MCR-ALS) and weighted alternating least squares (MCR-WALS), are examined. The performance of the four methods was initially evaluated under standard operational conditions, and modifications regarding data pre-treatment were then included. The methods were applied using raw and scaled data, with and without uncertainty estimations. Strong similarities were found among the sources identified by PMF and MCR-WALS (weighted models), whereas discrepancies were obtained with MCR-ALS (unweighted model). Weighting of input data by means of uncertainty estimates was found to be essential to obtain robust and accurate factor identification. The use of scaled (as opposed to raw) data highlighted the contribution of trace elements to the compositional profiles, which was key to the correct interpretation of the nature of the sources. Our results validate the performance of MCR-WALS for aerosol pollution studies.     

Multivariate analysis of Almendares River waters.

The Almendares River is the most important surface water body of the Cuban capital, Havana. In the present work, the environmental quality of waters was studied as a function of the following 14 variables: content of calcium, cadmium, iron, potassium, magnesium, manganese, sodium, nickel, zinc, chlorine, bicarbonate, and sulfate; pH; and electric conductivity parameters, which were reduced to three new variables by means of principal component analysis (PCA). The content of metal increased in waters sampled at stations located near garbage dumps and decreased inside the Ejercito Rebelde dam. The variation of the river water environmental quality with rainy and dry seasons and the differentiation of samples in three groups along the river course were obtained by PCA and corroborated by discriminant analysis. Applied statistical techniques showed their ability for environmental interpretation of limited experimental data.     

Characterization of surface marine sediments from Ría de Arousa estuary according to extractable humic matter content

Surface marine sediments from Ría de Arousa estuary were analyzed for humic and fulvic acids by UV-visible spectrometry and have been characterized using elemental analysis (carbon, hydrogen and nitrogen elemental composition) and spectrometric data (A2/A4 ratio, absorbancies at 270 and 407 nm and E4/E6 ratio, absorbancies at 465 and 665 nm). These variables have been used as discriminating factors to distinguish of marine and terrestrial origin of humic and fulvic acids in Ría de Arousa surface marine sediments. Principal component analysis, PCA, and cluster analysis, CA, have been used as unsupervised pattern recognition procedures. The half-range central value transformation was used as data pre-treatment to homogenize data. After a Varimax rotation, PCA applied to humic acid data has reveled that spectrometric A2/A4 and E4/E6 ratios are the main dominating features in the first principal component (48.6% of total variance), the humic acid content is the feature with the highest weight in the second principal component (22.9% of the total variability) and the carbon elemental composition domain in the third principal component (13.3% of total variance). Results from PCA have revealed that surface sediments collected at inner-left part of the estuary and at the mouth of the river Ulla belong to the same group. Similarly, PCA has shown that surface sediments from the right mouth of the estuary form a compact group. Taking in account the water circulation in Ría de Arousa estuary, these findings mean that the organic matter in surface sediments from the inner-left part of the estuary derived mainly from terrestrial organic matter while the organic matter in surface sediments from the right mouth of the estuary is mainly derived from marine sources. Finally, it must be noticed that any classification of surface sediments was assessed when applying of PCA and CA from fulvic acids data.     

Electrostatic forces in wind-pollination—Part 2: Simulations of pollen capture

During fair-weather conditions, a 100 V m⁻¹ electric field exists between positive charge suspended in the air and negative charge distributed on the surfaces of plants and on the ground. The fields surrounding plants are highly complex reaching magnitudes up to 3×10⁶ V m⁻¹. These fields possibly influence the capture of charged wind-dispersed pollen grains. In this article, we model the electric fields around grounded conductive spherical “plants” and then estimate the forces and resulting trajectories of charged pollen grains approaching the plants. Pollen grain capture depends on many factors: the size, density, and charge of the pollen; the size and location of the plant reproductive structures; as well as wind speed, ambient electric field magnitude, and air viscosity. Electrostatic forces become increasingly important as pollen grain charge increases and pollen grain size (mass) decreases. A positively charged pollen grain is attracted to plants, while a negatively charged pollen grain is repelled. The model suggests that a pollen grain (10 μm radius, carrying a positive charge of 1 fC) is captured if passing within 2 mm of the plant. A similar negatively charged pollen grain is repelled and frequently uncapturable. The importance of electrostatic forces in pollen capture is limited by wind, becoming virtually irrelevant at high wind speeds (e.g. 10 m s⁻¹). However, during light wind conditions (e.g. 1 m s⁻¹), atmospheric electricity may be a significant factor in the capture of wind-dispersed pollen.     

Validation studies of turbulence closure schemes for high resolutions in mesoscale meteorological models – A case of gas dispersion at the local scale

This work is a contribution to a large project, aimed at the development of an advanced environmental assessment modelling system to be used in Japan. The modelling system here considered consisted of the RAMS and HYPACT coupled models. The RAMS code was modified to properly simulate local scale phenomena using a fine mesh size of 250 m. In this direction, the main aim here was to investigate the effect of the choice of the turbulence closure scheme on the dispersion of pollutants. Our modified version of the RAMS/HYPACT model chain was validated using field experiments which were carried out by the Japan Atomic Energy Research Institute (JAERI) in the area of Mt. Tsukuba (Japan). The mean flow, turbulence and concentration fields obtained using two alternative turbulence closure schemes are compared. A discussion on the different performances of the turbulence closures is presented and the influence of the closure schemes over the plume dispersion is investigated.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.     

人工快速渗滤系统堵塞-恢复实验研究

采用干湿交替间歇进水的运行方式,以不同粒径的河砂为填料构建3组人工快速渗滤(CRI)系统,研究CRI系统的堵塞-恢复规律,填料粒径与系统出水COD、水力负荷和纳污量之间的关系,以及环境温度对系统处理效率的影响。结果表明,CRI系统堵塞后经过一段时间的恢复期即可以自然恢复活性。细砂CRI系统的稳定性最好,出水COD最稳定,抗冲击能力和纳污量强于混砂和粗砂CRI系统。当温度在适宜微生物生长的范围内波动时,对系统处理效率的影响并不明显。实际工程应建造多级系统或并行系统解决堵塞问题,以保证系统的水力负荷和稳定运行。     

Predictive statistical modelling of cadmium content in durum wheat grain based on soil parameters

Regulatory limits on cadmium (Cd) content in food products are tending to become stricter, especially in cereals, which are a major contributor to dietary intake of Cd by humans. This is of particular importance for durum wheat, which accumulates more Cd than bread wheat. The contamination of durum wheat grain by Cd depends not only on the genotype but also to a large extent on soil Cd availability. Assessing the phytoavailability of Cd for durum wheat is thus crucial, and appropriate methods are required. For this purpose, we propose a statistical model to predict Cd accumulation in durum wheat grain based on soil geochemical properties related to Cd availability in French agricultural soils with low Cd contents and neutral to alkaline pH (soils commonly used to grow durum wheat). The best model is based on the concentration of total Cd in the soil solution, the pH of a soil CaCl₂ extract, the cation exchange capacity (CEC), and the content of manganese oxides (Tamm’s extraction) in the soil. The model variables suggest a major influence of cadmium buffering power of the soil and of Cd speciation in solution. The model successfully explains 88% of Cd variability in grains with, generally, below 0.02 mg Cd kg^?1 prediction error in wheat grain. Monte Carlo cross-validation indicated that model accuracy will suffice for the European Community project to reduce the regulatory limit from 0.2 to 0.15 mg Cd kg^?1 grain, but not for the intermediate step at 0.175 mg Cd kg^?1. The model will help farmers assess the risk that the Cd content of their durum wheat grain will exceed regulatory limits, and help food safety authorities test different regulatory thresholds to find a trade-off between food safety and the negative impact a too strict regulation could have on farmers.

     

Levels,sources, and health risk assessment of polycyclic aromatic hydrocarbons in Brno,Czech Republic: a 5-year study

This work aimed to determine the seasonal variations of polycyclic aromatic hydrocarbons (PAHs) in airborne PM₁₀ at two background sites (Masná—MS, Líšeň—LN) in Brno over a 5-year period (2009–2013). Samples were collected on quartz filters using a low-volume sampler by continual filtration. Concentrations of PAHs in collected PM₁₀ samples were determined using a gas chromatography with a mass spectrometer as a detector. A different number of PAHs were determined to be at each site, i.e., 11 PAHs at the MS site and six PAHs at the LN site, and similarities between them were identified using non-parametric analysis of variance. Potential sources were identified using principal component analysis (PCA) and PAHs diagnostic ratios. The work also focused on health risk assessment. This was estimated using toxic equivalent factors to calculate individual lifetime cancer risk, which quantifies risk of exposure to PAHs for specific age groups. The average 11-PAH concentrations in M|S site annually ranged from 19.28 ± 19.02 ng m⁻³ (2011) to 40.37 ± 21.35 ng m⁻³ (2013). With regard to the LN site, the average six-PAH concentrations annually ranged from 3.64 ± 3.87 ng m⁻³ (2009) and 5.27 ± 6.19 ng m⁻³ (2012). PCA and diagnostic ratios indicate the main sources to be traffic emissions and coal combustion. Health risk assessment showed carcinogenic risk under limit value in all cases.

     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Establishing ozone flux–response relationships for winter wheat: Analysis of uncertainties based on data for UK and Polish genotypes

The work outlined in this paper had three objectives. The first was to explore the effects of ozone pollution on grain yield and quality of commercially-grown winter wheat cultivars. The second was to derive a stomatal ozone flux model for winter wheat and compare with those already developed for spring wheat. The third was to evaluate exposure- versus flux–response approaches from a risk assessment perspective, and explore the implications of genetic variation in modelled ozone flux.Fifteen winter wheat cultivars were grown in open-top chambers where they were exposed to four levels of ozone. During fumigation, stomatal conductance measurements were made over the lifespan of the flag leaf across a range of environmental conditions. Although significant intra-specific variation in ‘ozone sensitivity’ (in terms of impacts on yield) was identified, yield was inversely related (R² = 0.63, P < 0.001) to the accumulated hourly averaged ozone exposure above 40 ppb during daylight hours (AOT40) across the dataset. The adverse effect of ozone on yield was principally due to a decline in seed weight. Algorithms defining the influence of environmental variables on stomatal uptake were subtly different from those currently in use, based on data for spring wheat, to map ozone impacts on pan-European cereal yield. Considerable intra-specific variation in phenological effects was identified. This meant that an ‘average behaviour’ had to be derived which reduced the predictive capability of the derived stomatal flux model (R² = 0.49, P < 0.001, 15 cultivars included). Indeed, given the intra-specific variability encountered, the flux model that was derived from the full dataset was no better in predicting O₃ impacts on wheat yield than was the AOT40 index. The study highlights the need to use ozone risk assessment tools appropriate to specific vegetation types when modelling and mapping ozone impacts at the regional level.     

Climate change impact assessment on Northeast China’s grain production

The international community is paying more attention to climate change because a consensus has been reached that climate change has an adverse effect not only on the environment but also on agriculture. Therefore, in this study, present and future climate datasets (obtained from general circulation models) including atmospheric carbon concentration were used to assess the impact of climate change on grain production for an important base of China (Northeast). An empirical model has been developed using climate and other additional variables (effective irrigation area, fertilizer, and labor force) to assess the effect of climate change on grain production. The results revealed that maximum temperature is a key climate determinant in grain production of the study area. Atmospheric carbon concentration showed a significant impact on grain outputs in most of the cases. During the analysis, it was observed that precipitation displayed a declining trend while an effective irrigation area showed positive non-significant contribution to grain production. Analysis based on different representative concentration pathways exhibited that maximum temperature may contribute negatively to grain production in the future. Overall, the analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.

     

Using Farmers’ Actions to Measure Crop Loss Due to Air Pollution

The effects of ozone on the profitability of Illinois cash grain farms is investigated by utilizing a profit function framework. This approach uses individual farms as the observational unit so the data are generated under field conditions. Hence, experimental plot data are not used although results from past biological experiments are utilized to frame initial hypotheses and for evaluation of the estimated model. This study demonstrates the benefits of combining economic data and biological science results to estimate the economic reactions of producers in terms of input and output (production) response to an ambient environmental characteristic.

A time series of cross sectional data on cash grain farms in Illinois is used to estimate two profit functions. Ozone data are measured on a growing season basis by county. The estimated model shows ozone has a negative effect on profits. Increases in ozone levels tend to depress output levels and lessen the demand for variable inputs. Additionally, a production function is derived from one of the profit functions. The impact of ozone is found to be in general agreement with dose-response data obtained in Illinois by plant scientists.