This study addresses the occurrences and natural fates of chemotherapeutics and controlled drugs when found together in hospital effluents and surface waters. The results revealed the presence of 11 out of 16 drugs in hospital effluents, and the maximum detected concentrations were at the μg L−1 level in the hospital effluents and the ng L−1 level in surface waters. The highest concentrations corresponded to meperidine, morphine, 5-fluorouracil and cyclophosphamide. The sunlight photolysis of the target compounds was investigated, and the results indicated that morphine and codeine can be significantly attenuated, with half-lives of 0.27 and 2.5 h, respectively, in natural waters. Photolysis can lower the detected environmental concentrations, also lowering the estimated environmental risks of the target drugs to human health. Nevertheless, 5-fluorouracil and codeine were found to have a high risk quotient (RQ), demonstrating the high risks of directly releasing hospital wastewater into the environment.

Understanding weathering processes plays a critical role in oil spill forensics, which is based on the comparison of the distributions of selected compounds assumed to be recalcitrant and/or have consistent weathering transformations. Yet, these assumptions are based on limited laboratory and oil-spill studies. With access to additional sites that have been oiled by different types of oils and exposures, there is a great opportunity to expand on our knowledge about these transformations. Here, we demonstrate the effects of photooxidation on the overall composition of spilled oils caused by natural and simulated sunlight, and particularly on the often used polycyclic aromatic hydrocarbons (PAHs) and the biomarker triaromatic steranes (TAS). Both laboratory and field data from oil released from the Macondo well oil following the Deepwater Horizon disaster (2010), and heavy fuel-oil from the Prestige tanker spill (2002) have been obtained to improve the data interpretation of the typical fingerprinting methodology.

Little attention is given to applying the artificial neural network (ANN) modeling technique to understand site–specific air pollution dispersion mechanisms, the order of importance of meteorological variables in determining concentrations as well as the important time scales that influence emission patterns. In this paper, we propose a methodology for extracting the key information from routinely–available meteorological parameters and the emission pattern of sources present throughout the year (e.g. traffic emissions) to build a reliable and physically–based ANN air pollution forecasting tool. The methodology is tested by modeling NO2 concentrations at a site near a major highway in Auckland, New Zealand. The basic model consists of an ANN model for predicting NO2 concentrations using eight predictor variables: wind speed, wind direction, solar radiation, temperature, relative humidity, as well as “hour of the day”, “day of the week” and “month of the year” representing the time variations in emissions according to their corresponding time scales. Of the three input optimization techniques explored in this study, namely a genetic algorithm, forward selection, and backward elimination, the genetic algorithm technique gave predictions resulting in the smallest mean absolute error. The nature of the internal nonlinear function of the trained genetically–optimized neural network model was then extracted based on the response of the model to perturbations to individual predictor variables through sensitivity analyses. A simplified model, based on the successive removal of the least significant meteorological predictor variables, was then developed until subsequent removal resulted in a significant decrease in model performance. The developed ANN model was found to outperform a linear regression model based on the same input parameters. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to adequately capture the temporal variability in air pollution concentrations for a specific scenario.

Based on observations at Heshan, a boundary area in the city agglomeration of the Pearl River Delta region in China, atmospheric pollutants such as PM2.5, O3, CO, SO2, NOZ, NO2 and NO were monitored between the 12th and 29th November, 2010. Meteorological parameters, including temperature, humidity, dew point, air pressure, ultraviolet light, wind direction, and wind speed were also measured. By combining the meteorological parameters with the atmospheric pollutant data, we performed Positive Matrix Factorization (PMF) and ozone production efficiency (OPE) analysis to objectively understand the interrelations among the pollutants, as well as between the pollutants and the meteorological factors. During the observation period, there were various meteorological changes such as rainfall, cold air transit, and sunshine that created conditions for the formation or dispersal of pollutants. The study period coincided with the 16th Asian Games, during which time the government adopted strict measures to reduce the discharge of pollutants around the Pearl River Delta area. However, we still observed serious pollution of PM2.5 and O3, of which the highest value of PM2.5 was 210 μg m−3 and the highest value of O3 reached 117 ppb. At the same time, the high concentrations of CO, NO, NO2, NOZ, and SO2 could not be cleared away with rainfall in such a short period of time. On the basis of PMF analysis, we found that three factors influence the air quality of this region: local biomass burning, secondary pollutants of regional transport, and high industrial pollutant emissions. According to OPE analysis, the O3 pollution was mostly found to be VOC–sensitive but occasionally NOX–sensitive for OPE values greater than 10.

Ultrafine particle size distribution data were collected in downtown Toronto and rural Egbert from May 2007 to May 2008. Particle formation events were observed in both locations and contributed to increased concentrations of particles less than 25nm in diameter. These events were more frequent in spring and fall and rarely occurred in winter. Stronger solar radiation and drier air were correlated with the occurrence of formation events at both locations. Nucleation events occurred simultaneously at both sites on 10% of the days, and these events involved a shared air mass. Half of these simultaneous events were associated with northern air masses and only a quarter with southerly air masses. The higher loading of aged particles in southerly air masses transported from upwind industrial sectors appeared to limit the occurrence of nucleation events. Formation events occurred less frequently in downtown Toronto than at the rural site, and the frequency was lower on weekdays. It is hypothesized that vehicular emissions were responsible for the suppression of nucleation events in downtown Toronto.

The purpose of the present work was to study the concentration variations in O3, NO, NO2, NOX over a 4–year period (2006–2009), using the Kolmogorov–Zurbenko filter. Data were decomposed into seasonal and trend components. Seasonal component of the time–series analysis (2006–2009) of NO and NOx in Canoas and Esteio showed values above average during the cold seasons, while O3 showed an opposite pattern. The trend component was marked by the decrease of NO2 at Canoas and the increase of NO at Esteio, thus revealing their variation (NO and NOX) due to local emissions. Furthermore, evaluations of the mean daily concentrations of NO, NOX, NO2, O3, PM10 and CO, and correlations of these pollutants with meteorological parameters (ambient temperature, wind velocity, solar radiation and relative humidity) allowed the confirmation of the influence of mobile sources in the study area.

People spend most of their daytime in indoor environments. Their activities influence the composition of the indoor air by emitting volatile organic compounds (VOCs). The increasing number of different VOCs became the focus of attention in recent years as the question arises from the relationship between exposure to air pollutants and diseases. The present study of flats in Leipzig (Germany) is based on measurements of 60 different VOCs and is unique in the field of indoor air quality due to its enormous size of samples (n=2 242) and questionnaire data. The main purpose of our analysis was to identify the sources and patterns that characterize airborne VOCs in occupied flats. We combined two methods, principal components analysis (PCA) and non–negative matrix factorization (NMF), to assign compounds to their origin and to understand the coinstantaneous existence of several VOCs. PCA clustering provided a source apportionment and yielded 10 principal components (PCs) with an explained variance of 72%. However, real indoor air quality is often affected by combined sources. NMF reveals characteristic compositions of VOCs in indoor environments and emphasizes that constantly recurring structures are not single sources, but rather fusions of them, so called patterns. Interpreting these sources, we realized that homes were strongly influenced by ventilation, human activities, furnishings, natural processes (such as solar radiation) or their combinations. The very large set of samples and the combination with questionnaires applied on this comprehensive assessment of VOCs allows generalizing the results to homes in middle–scale cities with minor industrial pollution. As a conclusion, single VOC–dose–response relationships are inopportune for situations when indoor sources occur in combination. Further studies are necessary to assess associated health risks.

In view of the fatal illnesses caused by methylene blue (MB) which is contained in the dye wastewater, the present study focused on the use of natural sunlight in heterogeneous photocatalysis to decolorize and degrade MB. The present study also investigated the effects of enhancers (hydrogen peroxide and persulfate ion) and inhibitors (chloride and carbonate ions) on photodecolorization of MB. Pseudo-first-order rate constants for each studied effect were determined through Langmuir-Hinshelwood model. The recommended conditions to photodecolorize 60 ppm of MB under natural sunlight were 1.0 g/L of titanium dioxide nanopowder at initial pH 10.5 in order to achieve 85.3 % decolorization (rate constant of 10.8 × 10⁻³ min⁻¹). The addition of 4,080 ppm of hydrogen peroxide and persulfate ion significantly enhanced the decolorization efficiency up to 96.6 and 99.3 %, respectively (rate constants of 66.2 and 91.0 × 10⁻³ min⁻¹, respectively). However, the addition of 2,000 ppm of chloride and carbonate ions reduced the decolorization efficiency of MB to 74.7 and 70.2 %, respectively (rate constants of 7.8 and 7.3 × 10⁻³ min⁻¹, respectively). The present study implied that it was possible to use natural sunlight as a light source for photocatalytic treatment of dye in tropical countries like Malaysia.

During an extended period from 2010–2012 ambient air quality researches, concentration of nitrogen dioxide (NO₂) in the air was measured applying the passive method. In order to evaluate the spatial distribution of pollutants and the major sources, 12 sampling sites across the region were chosen. Additionally, the seasonal changes of this pollutant under different meteorological conditions (air temperature, humidity, wind speed and direction) were investigated. The long-term study showed 3.8 times higher NO₂concentrations in the Mažeikiai urban area (24.2 μg m⁻³) as compared to other locations in the region (6.3 μg m⁻³). This confirms the assumption that the main source of NO₂in this area is motor vehicle exhaust fumes. The analysis of the results obtained in different seasons showed a significant difference (p < 0.05) in NO₂concentrations under different meteorological conditions. The increase in NO₂concentrations was recorded in the winter and late autumn seasons, due to reduced solar radiation and lower temperatures. Cluster analysis results showed that sampling sites can be grouped into different classes based on NO₂main source, motor vehicles and traffic intensity.

This study demonstrates the potential of a new BiOCl₀.₈₇₅Br₀.₁₂₅ photocatalyst to degrade pharmaceuticals in water (i.e., carbamazepine (CBZ), ibuprofen (IBF), bezafibrate (BZF), and propranolol (PPL)), under simulated solar irradiation. Different parameters were examined through their influence on CBZ degradation. Increasing the catalyst concentration up to 500 mg/L increased CBZ degradation rate; however, above 500 mg/L, CBZ degradation rate was slightly reduced, most likely due to the catalyst’s light-screening effect at high concentrations. Increasing the pH of the tested solution from 4 to 9 decreased the degree of CBZ adsorption to the catalyst and consequently its degradation rate. Quantum yield for CBZ degradation was found to be 0.75 ± 0.05 % using an integrating sphere for absorbance measurements to correctly account for scattering of light by the suspended catalyst. Degradation rates of all examined compounds (at pH 7) followed the order PPL > BZF > IBF > CBZ (highest rate for PPL). Interestingly, PPL was least adsorbed to the catalyst, implying that adsorption is not always mandatory for efficient degradation with BiOCl₀.₈₇₅Br₀.₁₂₅. Different adsorption mechanisms were hypothesized for the different pharmaceuticals, including hydrophobic attraction for the neutrally charged CBZ and ion exchange for the negatively charged IBF and BZF.