Auditing the Assessment of the Environmental Impacts of Planning Projects

This paper presents the results from a post-auditing study-a comparison of the predictions of environmental impacts of projects made in environmental statements prior to developmentwith those that actually occurred upon or after development. The purpose of post-auditing is to provide an analysis of prediction performance in environmental impact assessment and thus to improve future practice by learning from experience. By means of a combination of site visits, interviews and document scrutiny, 865 predictions from 28 UK projects granted planning permission were examined. Of these, 488 (56%) were auditable, of which 383 (79%) were deemed 'accurate' or 'nearly accurate' and 105 (21%) 'inaccurate'. The remaining 377 (44%) predictions were not auditable, the main reasons being lack of data, vague or ambiguous predictions and time dependency.There were only six unpredicted impacts. The study reveals some encouraging findings, but also a number of discouraging ones, which indicate the need to increase both enthusiasm for, and implementationof, post-auditing activities. Several suggestions are made to help achieve these objectives.     

Analyses of phenolic compounds occurring in olive oil mill wastewaters by GC–MS

Phenolic compounds in olive oil mill wastewaters were analysed by HRGC–MS after extracting the acidified solution with ethyl acetate and derivatization with N,O-bis(trimethylsilyl)trifluoroacetamide. Both simple and complex phenols were detected with the latter being the most abundant. 1,2-dihydroxybenzene (catechol), p-hydroxyphenyl ethanol (tyrosol), 3,4-dihydroxyphenyl ethanol (hydroxytyrosol) and 4-hydroxy-3-methoxyphenyl ethanol (homovanillyl alcohol) predominated among the simple phenols using a gas chromatograph coupled with a mass selective detector.     

Polychlorinated biphenyl spatial patterns in San Francisco Bay forage fish

Industrialized waterways frequently contain nearshore hotspots of legacy polychlorinated biphenyl (PCB) contamination, with uncertain contribution to aquatic food web contamination. We evaluated the utility of estuarine forage fish as biosentinel indicators of local PCB contamination across multiple nearshore sites in San Francisco Bay. Topsmelt (Atherinops affinis) or Mississippi silverside (Menidia audens) contamination was compared between 12 targeted sites near historically polluted locations and 17 probabilistically chosen sites representative of ambient conditions. The average sum of 209 PCB congeners in fish from targeted stations (441 ± 432 ng g^?1 wet weight, mean ± SD) was significantly higher than probabilistic stations (138 ± 94 ng g^?1). Concentrations in both species were comparable to those of high lipid sport fish in the Bay, strongly correlated with spatial patterns in sediment contamination, and above selected literature thresholds for potential hazard to fish and wildlife. The highest concentrations were from targeted Central Bay locations, including Hunter’s Point Naval Shipyard (1347 ng g^?1; topsmelt) and Stege Marsh (1337 ng g^?1; silverside). Targeted sites exhibited increased abundance of lower chlorinated congeners, suggesting local source contributions, including Aroclor 1248. These findings indicate that current spatial patterns in PCB bioaccumulation correlate with historical sediment contamination due to industrial activity. They also demonstrate the utility of naturally occurring forage fish as biosentinels of localized PCB exposure.     

Analysis and prediction of $$\mathrm{PM}_{10}$$ concentration levels in Tunisia using statistical learning approaches

Over the past years, the health impact of airborne particulate matter \(\mathrm{PM}_{10}\) has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient \(\mathrm{PM}_{10}\) concentrations. In this paper, we are interested in the statistical classification of the daily mean \(\mathrm{PM}_{10}\) concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of \(\mathrm{PM}_{10}\) concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the \(\mathrm{PM}_{10}\) concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of \(\mathrm{PM}_{10}\) concentration may be crucial to get an accurate prediction.     

Pyrolysis of lemon peel waste in a fixed-bed reactor and characterization of innovative pyrolytic products

Journal of Material Cycles and Waste Management - Lemon peel waste (LPW) were pyrolysed in a laboratory fixed-bed reactor at final temperature of 300 °C, 400 °C and...     

Non-invasive fetal genotyping for maternal alleles with droplet digital PCR: A comparative study of analytical approaches

Objectives

To develop a flexible droplet digital PCR (ddPCR) workflow to perform non-invasive prenatal diagnosis via relative mutation dosage (RMD) for maternal pathogenic variants with a range of inheritance patterns, and to compare the accuracy of multiple analytical approaches.

Methods

Cell free DNA (cfDNA) was tested from 124 archived maternal plasma samples: 88 cases for sickle cell disease and 36 for rare Mendelian conditions. Three analytical methods were compared: sequential probability ratio testing (SPRT), Bayesian and z-score analyses.

Results

The SPRT, Bayesian and z-score analyses performed similarly well with correct prediction rates of 96%, 97% and 98%, respectively. However, there were high rates of inconclusive results for each cohort, particularly for z-score analysis which was 31% overall. Two samples were incorrectly classified by all three analytical methods; a false negative result predicted for a fetus affected with sickle cell disease and a false positive result predicting the presence of an X-linked IDS variant in an unaffected fetus.

Conclusions

ddPCR can be applied to RMD for diverse conditions and inheritance patterns, but all methods carry a small risk of erroneous results. Further evaluation is required both to reduce the rate of inconclusive results and explore discordant results in more detail.     

Atmospheric nitrogen deposition in south-east Scotland: Quantification of the organic nitrogen fraction in wet,dry and bulk deposition

Water soluble organic nitrogen (WSON) compounds are ubiquitous in precipitation and in the planetary boundary layer, and therefore are a potential source of bioavailable reactive nitrogen. This paper examines weekly rain data over a period of 22 months from June 2005 to March 2007 collected in 2 types of rain collector (bulk deposition and “dry + wet” deposition) located in a semi-rural area 15 km southwest of Edinburgh, UK (N55°51′44″, W3°12′19″). Bulk deposition collectors are denoted in this paper as “standard rain gauges”, and they are the design used in the UK national network for monitoring precipitation composition. “Dry + wet” deposition collectors are flushing rain gauges and they are equipped with a rain detector (conductivity array), a spray nozzle, a 2-way valve and two independent bottles to collect funnel washings (dry deposition) and true wet deposition. On average, for the 27 weekly samples with 3 valid replicates for the 2 types of collectors, dissolved organic nitrogen (DON) represented 23% of the total dissolved nitrogen (TDN) in bulk deposition. Dry deposition of particles and gas on the funnel surface, rather than rain, contributed over half of all N-containing species (inorganic and organic). Some discrepancies were found between bulk rain gauges and flushing rain gauges, for deposition of both TDN and DON, suggesting biological conversion and loss of inorganic N in the flushing samplers.     

Effect of spatial variation on salinity tolerance of macroinvertebrates in Eastern Australia and implications for ecosystem protection trigger values

Salinisation of freshwater has been identified as a serious environmental issue in Australia and around the world. Protective concentrations (trigger values) for salinity can be used to manage salinity impacts, though require locally relevant salinity tolerance information. 72-h acute salinity tolerance values were determined for 102 macroinvertebrates collected from 11 locations in four biologically distinct freshwater bio-regions in Northeast Australia and compared with sensitivities observed in Southeast Australia. The salinity tolerance of individual taxa was consistent across Northeast Australia and between Northeast and Southeast Australia. However, two distinct communities were identified in Northeast Australia using distributions of the acute tolerance values and a calculated index of salinity sensitivity. Salinity trigger values should therefore be representative of local or regionally relevant communities and may be adequately calculated using sensitivity values from throughout Eastern Australia. The results presented provide a basis for assessing salinity risk and determining trigger values for salinity in freshwater ecosystems at local and regional scales in Eastern Australia.