Environmental Science: Processes & Impacts blog

Richard Brown from the National Physical Laboratory, UK, discusses the causes and effects of data loss in environmental air sampling and proposes a modelling method for overcoming data loss for benzo(a)pyrene (BaP) concentrations.

If levels of a pollutant are not measured every day of every year, there will be inevitable gaps in the data. Often, it is not viable or necessary to sample every day and in the European Union there are minimum limits set on the time coverage (33% of the year) and data capture (90% of this time must result in valid data), and a maximum uncertainty of 50%. Such data should be obtained evenly over the year. Add to this the possibility for equipment breaking down, poor weather and quality control, this can mean significant gaps.

This is more of a problem when the concentration of the pollutant in question varies considerably with the season. BaP fits this category in urban and rural environments, but is more stable at industrial sites. The National Physical Laboratory is responsible for operating the UK PAH Monitoring network and Richard Brown here explores the effects of losing one month’s data for BaP and compares it against nickel in PM₁₀, which varies relatively little over the year.

He concludes that there could be a maximum underestimation of 13.5% in January and an underestimation of up to 7.1% in July with industrial stations included, which have consistent emission rates. Removing industrial sites gives -16.0% and +7.6%. The annual average is therefore biased. In contrast, losing 6 consecutive months of data for Ni still only gives discrepancies of around +/-5%.

Brown shows that for urban and rural BaP levels, the data fits a quadratic function very well and therefore this can be used to predict missing data fragments. He tests this on a data set with a month’s data removed, comparing the calculated annual average from the full data set and the data set with predictions filling in the missing month. This works well, with exceptions in months where the conditions are significantly different to the average (for example, being much colder than previous years). Therefore, he suggests taking measured ambient temperature data into consideration in future. This method is quicker and less complex than using dispersion modelling approaches and improves annual average result accuracy for highly seasonal pollutants with a block of missing data.

Read the interesting discussion of this intricate problem now, as this article is free to access for the next 4 weeks*:

Data loss from time series of pollutants in ambient air exhibiting seasonality: consequences and strategies for data prediction
Richard J. C. Brown
DOI: 10.1039/C3EM30918E

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The assessment and quantification of occupational exposure to volatile organic compounds is important, as such compounds are often carcinogenic and cause a range of chronic health problems.

In this HOT article, a research team at University of Minnesota calculates the health risk of the 8 VOCs most likely to be produced from swine production buildings. They calculate a probability distribution of the risk using Monte Carlo simulation.

The study tested the farrowing room, the office, the nursery and the gestation room. There were significantly higher levels of VOCs in the farrowing room. Notably, these concentrations did not exceed recommended exposure limits, but concentrations of p-cresol and benzene were above preliminary remediation goals (PRGs).

They also measured the emission rates of the VOCs from each room, with the highest rates coming from the gestation room. The nursery had the lowest emission rates for some compounds, with little difference between the nursery and farrowing rooms in terms of emissions. However, the farrowing room had the highest emission rates per head.

Monte Carlo simulations were used to calculate the health risks to workers in the different rooms. It was found that the cancer risk values for ethylbenzene of all workers exceeded the EPA’s target of one per million. The highest cumulative cancer risk was found for the worker who spent the day in the farrowing room. In terms of hazard risk, benzene had the highest risk and four of the VOCs had higher than acceptable risk values. Again, the worker in the farrowing barn had the highest risk.

The researchers conclude that around 1.13% of workers in farrowing barns are likely to develop chronic health problems; bearing in mind that this study only looked at 8 VOCs this figure could be higher. Rotating tasks may be a good way to reduce the risk.

Read this important occupational health study now, as this article is free to access for the next 4 weeks*:

Health risk assessment of occupational exposure to hazardous volatile organic compounds in swine gestation, farrowing and nursery barns
Neslihan Akdeniz, Larry D. Jacobson and Brian P. Hetchler
DOI: 10.1039/C2EM30722G

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The aerosol compositions generated by industrial processes such as welding have been found to contain metal fumes, including those of manganese. Such fumes are potentially harmful and have been known to cause neurological and psychological problems.

The bioaccessibility of the particles is a determinant of how much is taken up by the lungs into the bloodstreams of workers. The solubility of the particles that contain the metal may help to clarify the level of the health risk associated with exposure to manganese. This particle solubility may be different for different industrial processes as different formation mechanisms take place and particles develop differing chemical complexity and size. For example, higher fluorine content in welding fumes increases particle solubility.

Researchers from the National Institute of Occupational Health, Norway, and the Northwest Public Health Research Centre, Russia, have collaborated to test the bioavailability of manganese in welders. They cross-referenced results from blood, serum and urine samples with the results of solubility tests of personally collected aerosol samples. The researchers used a simulated lung lining fluid to test the pulmonary solubility of the aerosol samples.

They found statistically significant correlations between the manganese concentrations in the biological fluids of welders and their aerosol samples, whereas this was a non-significant relationship with the non-welder group and in former welders. They conclude that manganese in urine may be a better biomarker for immediate exposure than that in blood or serum. Manganese in the blood is associated with cumulative exposure over a number of years, but that it is not necessarily a good measure of such exposure as there may also be internal deposits in tissues.

Overall, this study shows that the fraction of manganese in welding fumes that is bioaccessible is low. Despite the correlation between soluble manganese and the amount in the biological samples, the background levels of manganese meant that the welders could not be distinguished from the non-welder group.

Read the full discussion of these interesting results in full, as this article is free to access for the next 4 weeks*, by clicking on the link below:

The bioavailability of manganese in welders in relation to its solubility in welding fumes
Dag G. Ellingsen, Evgenij Zibarev, Zarina Kusraeva, Balazs Berlinger, Maxim Chashchin, Rita Bast-Pettersen, Valery Chashchin and Yngvar Thomassen
DOI: 10.1039/C2EM30750B

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Research into how microbial organisms are adapting to the ever increasing levels of nanoparticles is important in assessing the impact of nanomaterials on various environmental systems and processes. However, the surprising ability of microorganisms to turn metals in the environment into nanoparticles is often overlooked and could be making a significant contribution to this rise of nanoparticles in the environment.

In this HOT article, Monika Mortimer et al. study the ability of Tetrahymena thermophile, a fresh-water inhabiting protozoa, to reduce silver ions to silver nanoparticles. It is the soluble extracellular fraction (SEF) of the protozoa in which this occurs. The team demonstrate that the protein fraction of this SEF is associated with the formation of silver nanoparticles.

The ability of some microorganisms to carry out this conversion is surprising due to the toxicity of many silver compounds and the well-known antibacterial properties of silver nanoparticles.

In this study, the protozoa do show a negative response to the silver compound (AgNO₃) within 2 hours of exposure, but then they convert AgNO₃ to less toxic nanoparticles, resulting in a recovery period over 24 hours. This supports the theory that it is not silver nanoparticles themselves that cause the toxicity but remaining dissolved silver ions.

This HOT article increases our knowledge of this surprising defence mechanism against toxic silver compounds and adds evidence to the ongoing debate surrounding the mechanism of toxicity of silver nanoparticles.

This article is free to access for 4 weeks*, read it by clicking the link below:

Extracellular conversion of silver ions into silver nanoparticles by protozoan Tetrahymena thermophila
Katre Juganson, Monika Mortimer, Angela Ivask, Kaja Kasemets and Anne Kahru
DOI: 10.1039/C2EM30731F

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