Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method.

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 µgL-1 (carbamazepine) and higher than 61.56 µgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 µgL-1 (diazepam) and higher than 54.91 µgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Classifying micropollutants by environmental risk in groundwater using screening analysis associated to a hybrid multicriteria method combining (Q)SAR tools, fuzzy AHP and ELECTRE.

This study aimed to evaluate and monitor pesticides in groundwater of the Serra Geral aquifer, located in the Paraná Basin 3 (southern Brazil), using Liquid Chromatography coupled with a Quadrupole-Time-of-Flight Mass Spectrometer (LC-QTOF MS). A total of 117 samples, collected in three different moments, were analyzed over 36 months. Groundwater samples from 35 wells and four surface water points were monitored in each sampling campaign. A pesticide screening methodology was proposed with the tentative identification of 1607 pesticides and pesticide metabolites. The application of the proposed methodology resulted in the verification of 29 pesticides and pesticide metabolites, 7 as confirmed analytes and 22 as suspect compounds. (Q)SAR in silico predictions and GUS index calculation provided data on the potential environmental risk of the identified compounds, with eight endpoints considered. After in silico predictions, an alternative hybrid multicriteria method was applied, combining the weighting of endpoints of fuzzy AHP and micropollutants classification by environmental risk using ELECTRE. The fuzzy AHP results indicated the greatest importance of mutagenicity among the eight evaluated indicators, while the scarce influence of the physicochemical properties on the environmental risk suggested their exclusion from the model. Accordingly, the ELECTRE results highlighted the prevalence of thiamethoxam and carbendazim as the most dangerous for the environment. The application of the proposed method enabled the selection of the compounds that must be monitored, considering mutagenicity and toxicity predictions for environmental risk analysis.

Impacts of the COVID-19 pandemic on the profile and preferences of urban mobility in Brazil: Challenges and opportunities.

Daily commuting characteristics were highly affected by the COVID-19 pandemic, since restriction of the movement of people was one of the main preventive measures adopted. Understanding of the effects that the pandemic had on mobility is essential to help in mitigating the problems arising from this crisis, while also providing an opportunity for the implementation of sustainable policies in the post-pandemic period. Therefore, the aim of this study was to identify the impacts of the pandemic on the profile of travel behavior and mobility preferences in Brazil, using a case study of cities located in the state of Rio Grande do Sul. The data obtained from an online survey were modeled using exploratory factor analysis, resulting in the extraction of 15 main factors that explain behavioral changes in mobility due to the effects of the pandemic, as well as future perspectives. In the pandemic period, the use of private vehicles grew as the main mode of transport to the principal activity. Conversely, the use of public transport decreased drastically, due to compulsory measures taken by the health authorities to prevent the spread of the new virus. There was also greater receptivity to the adoption of active mobility, especially the bicycle, although it is necessary to provide better conditions for use of this transport mode. The findings support the development of public policies to reduce urban mobility problems and to provide guidelines for sustainable planning in the post-pandemic period.

Identification of contributing factors and hotspots of pedestrian-vehicle collisions in urban areas: An alternative hybrid method for small cities.

OBJECTIVE: Traffic safety numbers in developing countries show a serious global problem that requires studies and actions to promote safety for the most vulnerable users, such as pedestrians, as a way of contributing to sustainable urban mobility. Therefore, this research proposes a hybrid method to identify contributing factors and critical points according to the profile of crashes involving pedestrians being run over by motor vehicles in small cities. METHODS: The proposed method uses multivariate data analysis, specifically Exploratory Factor Analysis (EFA) and Cluster Analysis (CA), combined with geospatial tools. The study scenario corresponds to pedestrian-vehicle collisions that occurred in the urban area of Cachoeira do Sul, a small city in Rio Grande do Sul, Brazil, recorded in police reports between 2015 and 2018. Additionally, Geographic Information Tools (GIS) were used to collect characteristics of the road environment. RESULTS: According to EFA, a set of attributes configured as contributing factors are discussed according to the components of the traffic system: user, road, vehicle, and environment. Subsequently, a CA was applied with the road-environmental factors, and three pedestrian-vehicle collision profiles were extracted, with clustering according to road segments with homogeneous characteristics. Moreover, a Kernel Density Estimate (KDE) was used to obtain heat maps of the pedestrian collisions profiles. The results allowed interventions to be proposed according to the contributing factors of each profile. CONCLUSIONS: The multivariate statistical analysis supported the discussion of the factors contributing to the occurrence of pedestrian-vehicle collisions in small cities in developing countries. The spatial visualization of the results showed urban corridors with a high risk for pedestrians and the identification of hotspots in the city. In addition, the study demonstrated that the hybrid method tested is an alternative for the government of cities with few resources to promote safety mobility.

Qualitative evaluation of pharmaceuticals and metabolites in hospital effluent: Influence of sample preparation technique and outranking by environmental risk using the ELECTRE method.

The presence of pharmaceuticals and metabolites in effluents has become a serious environmental problem, so it is essential to be able to monitor these microcontaminants using qualitative approaches, as well as to assess the potential environmental risks that such compounds may present. Therefore, in this study, suspect screening analysis was performed of 2030 pharmaceuticals and metabolites in hospital effluent samples, applying different sample preparation techniques. Additionally, a pioneering association of (Q)SAR assessment of identified contaminants with the ELECTRE multi-criteria decision analysis technique made it possible to prioritize analytes according to their environmental risk, in order to enable their inclusion in environmental monitoring programs. The results showed that the most advantageous alternative sample preparation technique consisted of cleanup (100 mg of silica/alumina sorbent) + dispersive liquid-liquid microextraction (7.5 of aqueous matrix, 325 µL of chloroform (extracting solvent), and 500 µL of acetonitrile as dispersing solvent). This procedure resulted in the identification of 70 pharmaceuticals and metabolites in the composite sample tested. The suspect screening analysis found a total of 105 microcontaminants, 28 of them being "confirmed compounds" and 77 being "suspect compounds". Of the compounds identified, 87% were pharmaceuticals and 13% were metabolites. The compounds identified were subsequently evaluated using different open access software packages, considering eight endpoints: mobility, persistence, estrogen receptor binding, wastewater treatment plant total removal, biodegradability, PBT (persistent, bioaccumulation and toxic), mutagenicity, and carcinogenicity. The (Q)SAR prediction results were used as input data for the ELECTRE outranking method. Categorization of the identified compounds by ELECTRE resulted in the kernel (priority compounds) and a further 19 groups. ELECTRE sensitivity evaluation indicated that for all the cases, the kernel and the following two groups coincided. The categorization provided by the ELECTRE method constitutes a highly intuitive decision and choice tool, which can assist in the selection of compounds if subsequent quantitative analysis is to be carried out.

Trends in Fenton and photo-Fenton processes for degradation of antineoplastic agents in water matrices: current knowledge and future challenges evaluation using a bibliometric and systematic analysis.

Antineoplastic agents present potential hazards to human health and the environment. For this reason, these compounds have attracted a great deal of attention from researchers in the environmental sciences field. In order to help guide future research, it is important to understand the current state of investigation of the occurrence of these microcontaminants and methods for their removal, especially focusing on Fenton and photo-Fenton processes applied to various aqueous matrices in which this class of pharmaceuticals is present. For this purpose, a systematic review of these topics was performed by bibliometric analysis of articles published during the last decade and available in the Scopus and Web of Science databases. This study enables visualization of the current panorama and trends in this field, providing a guide for future collaborative research and exchange of knowledge. Various strategies have been suggested to improve the efficiency of Fenton and photo-Fenton processes, mainly by means of the application of multiples additions of iron, the use of heterogeneous catalysts, and/or the use of chelating agents. Some studies have evaluated different radiation sources employed for photo-Fenton processes, such as solar and/or artificial radiation. In turn, the identification of transformation products generated by Fenton and photo-Fenton treatments, together with their evaluation by in silico (Q)SAR predictions or experimental toxicological bioassays, are related subjects that have been less reported in published works and that should be studied in depth. These subjects can support treatment evaluations that are more realistic, considering their limitations or potentials.

Multi-criteria decision-making techniques associated with (Q)SAR risk assessment for ranking surface water microcontaminants identified using LC-QTOF MS.

Contaminants of emerging concern (CECs) have been a focus of study for years, with investigations revealing the contamination of different environmental matrices (surface water, soil, air, and sediment) by diverse classes of microcontaminants. Understanding the contamination profiles requires identification and risk assessment of the microcontaminants. In the present work, analysis was made of the presence of 3250 compounds in 27 samples from the Conceição River (Rio Grande do Sul State, Brazil), using an SPE-LC-QTOF MS method. In total, 150 microcontaminants (confirmed and suspected) of different classes, especially pesticides and pharmaceuticals, were identified by an initial qualitative analysis. Subsequently, in silico predictions of eight endpoints, using quantitative structure-activity relationship ((Q)SAR) models, were employed to determine the risk of each previously screened microcontaminant. This large amount of (Q)SAR data, frequently with conflicting information in relation to the responses of the different endpoints, makes it difficult to define which microcontaminants should be prioritized for analysis. Therefore, in order to rank the identified microcontaminants by risk assessment, two multi-criteria decision-making (MCDM) ranking techniques (ToxPi and TOPSIS), associated with a weighting method, were performed to establish the order of priority for further quantitative analysis of the most hazardous microcontaminants. The two rankings were statistically similar, especially for the 20 highest priority microcontaminants. Nonetheless, sensitivity tests carried out for the ToxPi and TOPSIS outputs showed higher performance robustness of TOPSIS, compared to ToxPi. This is the first time that such an approach (screening/(Q)SAR/MCDM methods) has been performed in the context of microcontaminant environmental risk evaluation and demonstrated to be an available strategy to help rank the most concern microcontaminants identified in aqueous environment samples.

Fuzzy analysis of students' perception of traffic safety in school environments: the case of a small Brazilian city.

Thousands of lives are lost every year due to traffic accidents worldwide, and youths are the most affected. The goal of this paper is to analyze the differences in young students' perceptions about traffic safety in school surroundings in order to help in the formulation of public policies and the development of infrastructure to make school travels safer. A questionnaire was used to obtain the perception of high school students from institutions with different urban characteristics. Data modelling with fuzzy logic and statistical analysis of variance indicated that students' perceptions are influenced by the different realities these youths are exposed to daily, such as school socioeconomic category, transport mode, urban environment and gender. Traffic engineering measures, public policies and road safety education action, inciting active mobility, can be validated and supported by these results. This road safety analysis may also be a participative alternative for locations with low data access.

Social choice functions: A tool for ranking variables involved in action plans against road noise.

Traffic noise is gaining importance in planning and operation of roads in developing countries, and particularly in Europe and Latin America. Many variables with different degrees of importance influence the perception of noise from roads. Thus, the problem of prioritizing road stretches for action against such noise is an important issue in environmental noise management. For example, it can be addressed using multicriteria methods. However, these methodologies require criteria or suitable variables to be ranked according to their relative importance. In the present study, for this ranking, a list of nine variables involved in the decision-making process (called "road stretch priority variables") was presented in the form of questionnaires to high-level experts from Andalusia, southern Spain. These experts ranked the variables by relevance. Using the same data, seven social choice functions (Plurality, Raynaud, Kemeny-Young, Copeland, Simpson, Schulze, and Borda) were used in order to rank the variables. The results indicate that the most important variables were those that take into account the parameters of greatest exposure for the citizens, followed by variables related to the intensity of the problem analyzed. The results show that a combination of the use of social choice functions on aggregated information from expert panels can provide a consensus for ranking priority variables related to road stretches.