Identification and chemical characterization of industrial particulate matter sources in southwest Spain

A detailed physical and chemical characterization of coarse particulate matter (PM10) and fine particulate matter (PM2.5) in the city of Huelva (in Southwestern Spain) was carried out during 2001 and 2002. To identify the major emission sources with a significant influence on PM10 and PM2.5, a methodology was developed based on the combination of: (1) real-time measurements of levels of PM10, PM2.5, and very fine particulate matter (PM1); (2) chemical characterization and source apportionment analysis of PM10 and PM2.5; and (3) intensive measurements in field campaigns to characterize the emission plumes of several point sources. Annual means of 37, 19, and 16 microg/m3 were obtained for the study period for PM10, PM2.5, and PM1, respectively. High PM episodes, characterized by a very fine grain size distribution, are frequently detected in Huelva mainly in the winter as the result of the impact of the industrial emission plumes on the city. Chemical analysis showed that PM at Huelva is characterized by high PO4(3-) and As levels, as expected from the industrial activities. Source apportionment analyses identified a crustal source (36% of PM10 and 31% of PM2.5); a traffic-related source (33% of PM10 and 29% of PM2.5), and a marine aerosol contribution (only in PM10, 4%). In addition, two industrial emission sources were identified in PM10 and PM2.5: (1) a petrochemical source, 13% in PM10 and 8% in PM2.5; and (2) a mixed metallurgical-phosphate source, which accounts for 11-12% of PM10 and PM2.5. In PM2.5 a secondary source has been also identified, which contributed to 17% of the mass. A complete characterization of industrial emission plumes during their impact on the ground allowed for the identification of tracer species for specific point sources, such as petrochemical, metallurgic, and fertilizer and phosphate production industries.     

Occurrence of polybrominated diphenylethers, polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls in coastal sediments from Spain

Fifteen sediment samples were analysed in order to determine their content of polybrominated diphenylethers (PBDEs), polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs) and biphenyls (PCBs). Samples were collected from several hot spots on the Spanish coast, such as the harbours of Almeria and Tarragona, and the mouths of the Besos and Llobregat rivers in Barcelona. A generic analytical procedure based on Soxhlet extraction followed by an automated cleanup system and gas chromatography-ion trap-mass spectrometry was employed for determining the toxic congeners of PCDDs and PCDFs, as well as dioxin-like PCBs. As regards PBDE determinations, a rapid method based on the use of selective pressurized liquid extraction followed by gas chromatography-negative ion chemical ionization-mass spectrometry was applied. Total toxicity equivalent (WHO-TEQ) values were calculated using the toxicity equivalent factors proposed by WHO for dioxin-like PCBs, PCDDs and PCDFs. WHO-TEQ values ranged from 0.3 to 75 pg/g dry weight (dw), with PCB contribution on the toxicity of the samples between 1 and 84%. Total PBDE levels ranged from 2.7 to 134 ng/g dw, with BDE-209 contribution on the total PBDE contamination between 50 and 99%.     

Distribution of volatile organic compounds in Madrid (Spain)

From November 1995 to October 1996, airborne concentrations of VOCs were measured in the Madrid area to study the organic pollution in general, and the correlation between different pollutants in relation to such parameters as location and season. Mean concentrations for up to 90 compounds were measured at four test sites, including both urban and suburban areas. At the urban sites, maximum concentrations occurred in the autumn and winter, whereas minimum concentrations were reached in summer and spring. Similar changes were obtained for the lesscontaminated site located in the SE of the city, whereas a different pattern was found at the site in the NW of the city due to meteorological aspects. Mean levels of hydrocarbons in Madrid were quite similar to those found in other European cities. Chemometrical techniques were applied to the set of data in order to assess the influence of such factors as traffic, temperature and seasonal variations on the VOC levels.     

Umbilical artery velocity waveforms before and after chorionic villus sampling

Because a vascular aetiology has been suggested for the limb and oromandibular defects described after chorionic villus sampling (CVS), to determine whether transabdominal (TA) CVS causes noticeable changes in umbilical artery velocity waveforms in first-trimester pregnancies, the pulsatility index (PI) of the umbilical artery was evaluated before and after TA-CVS in 175 pregnancies sampled between 10·0 and 13·0 weeks' gestation. In 139 uncomplicated pregnancies, the mean PI values (with 95 per cent confidence interval) were before TA-CVS 2·751 (2·692–2·809), after 10 min 2·723 (2·697–2·809), and after 1 h 2·781 (2·722–2·840). There were no significant changes in PI relative to the CVS procedure either in pregnancies with an abnormal result or in those ending in spontaneous abortion. Our data do not support any statistically significant change in umbilical artery PI relative to TA-CVS in first-trimester pregnancies. This procedure, despite its invasive character, does not appear to affect the feto-placental circulation.     

Genetic Diversity Among Genogroup II Noroviruses and Progressive Emergence of GII.17 in Wastewaters in Italy (2011–2016) Revealed by Next-Generation and Sanger Sequencing

Noroviruses (NoV) are a major cause of gastroenteritis worldwide. Recently, a novel variant of NoV GII.17 (GII.P17_GII.17 NoV), termed Kawasaki 2014, has been increasingly reported in NoV outbreaks in Asia, and has also been described in Europe and North America. In this study, sewage samples were investigated to study the occurrence and genetic diversity of NoV genogroup II (GII) along a 6-year period. Moreover, the spread of GII.17 strains (first appearance and occurrence along time) was specifically assessed. A total of 122 sewage samples collected from 2011 to 2016 from four wastewater treatment plants in Rome (Italy) were initially tested using real-time RT-(q)PCR for GII NoV. Positive samples were subsequently subjected to genotypic characterization by RT-nested PCRs using broad-range primes targeting the region C of the capsid gene of GII NoV, and specific primers targeting the same region of GII.17 NoV. In total, eight different genotypes were detected with the broad-range assay: GII.1 (n = 6), GII.2 (n = 8), GII.3 (n = 3), GII.4 (n = 13), GII.6 (n = 3), GII.7 (n = 2), GII.13 (n = 2), and GII.17 (n = 3), with the latter two genotypes detected only in 2016. Specific amplification of GII.17 NoV was successful in 14 out of 110 positive samples, spanned over the years 2013–2016. The amplicons of the broad-range PCR, pooled per year, were further analyzed by next-generation sequencing (NGS) for a deeper analysis of the genotypes circulating in the study period. NGS confirmed the circulation of GII.17 NoV since 2013 and detected, beyond the eight genotypes identified by Sanger sequencing, three additional genotypes regarded as globally uncommon: GII.5, GII.16, and GII.21. This study provides evidence that GII.17 NoV Kawasaki has been circulating in the Italian population before its appearance and identification in clinical cases, and has become a major genotype in 2016. Our results confirm the usefulness of wastewater surveillance coupled with NGS to study the molecular epidemiology of NoV and to monitor the emergence of NoV strains.     

The business cycle and the incidence of workplace injuries: evidence from the U.S.A

Introduction

The current study explored the association between the business cycle and the incidence of workplace injuries to identify cyclically sensitive industries and the relative contribution of physical capital and labor utilization within industries.

Method

Bureau of Labor Statistics nonfatal injury rates from 1976 through 2007 were examined across five industry sectors with respect to several macroeconomic indicators. Within industries, injury associations with utilization of labor and physical capital over time were tested using time series regression methods.

Results

Pro-cyclical associations between business cycle indicators and injury incidence were observed in mining, construction, and manufacturing but not in agriculture or trade. Physical capital utilization was the highest potential contributor to injuries in mining while labor utilization was the highest potential contributor in construction. In manufacturing each effect had a similar association with injuries.

Conclusion

The incidence of workplace injury is associated with the business cycle. However, the degree of association and the mechanisms through with the business cycle affects the incidence of workplace injuries was not the same across industries.

Impact on Industry

The results suggest that firms in the construction, manufacturing, and mining industries should take additional precautionary safety measures during cyclical upturns. Potential differences among industries in the mechanisms through which the business cycle affects injury incidence suggest different protective strategies for those industries. For example, in construction, additional efforts might be undertaken to ensure workers are adequately trained and not excessively fatigued, while safety procedures continue to be followed even during boom times.     

Predictive model for areas with illegal landfills using logistic regression

The existence of illegal landfills is an environmental problem in most countries. However, research on this issue is scarce and limited by the availability and quality of data on the subject. Thus, most illegal landfill studies have only been conducted in a partial manner, focusing on geographical aspects or the causes of these landfills (lack of environmental awareness, inadequate waste management systems, and the role of local government). This research analyses a sample of 120 possible areas with illegal landfills in Andalusia using logistic regression in order to obtain a predictive model for the occurrence of these landfills, including both types of variables (geographical and behavioural) jointly. The results confirm that the variables that most influence the occurrence of illegal landfills are spatial (“Industrial Land”, “Plains” and “Rural Land”); whilst the variables that most reduce the likelihood of illegal landfills are those related to certain characteristics of the municipal waste management system and environmental awareness, such as “Availability of Recycling Facilities”, “Punitive Policies”, “Supervision” and “Awareness-raising Campaigns”. The model obtained shows that variables of very different nature and magnitude interact in the occurrence of illegal landfills, each of which contributes a series of features characteristic of its scale. It is advisable, therefore, to perform an analysis using a multi-scale approach in order to gain an overall understanding of the phenomenon.