A scoping study of component-specific toxicity of mercury in urban road dusts from three international locations

This scoping study presents an investigation of the total and bioaccessible mercury concentrations in road dust (RD) from three international urban sites, where a one-off sampling campaign was conducted at each. This was done to address the hypothesis that the matrix in which mercury is found influences its ability to become accessible to the body once inhaled. For that purpose, the samples were analysed for total and pulmonary bioaccessible mercury and the data compared to the chemical structure of individual particles by SEM. The results obtained from this study suggest that a high mercury content does not necessarily equate to high bioaccessibility, a phenomenon which could be ascribed to the chemical character of the individual particles. It was found that the Manchester samples contained more pulmonary soluble mercury species (as determined by elemental associations of Hg and Cl) in comparison to the other two samples, Curitiba, Brazil, and Johannesburg, South Africa. This finding ultimately underlines the necessity to conduct a site-specific in-depth analysis of RD, to determine the concentration, chemical structure and molecular speciation of the materials within the complex matrix of RD. Therefore, rather than simply assuming that higher bulk concentrations equate to more significant potential human health concerns, the leaching potential of the metal/element in its specific form (for example as a mineral) should be ascertained. The importance of individual particle behaviour in the determination of human health risk is therefore highlighted

Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

Air pollution impacts all populations globally, indiscriminately and has site-specific variation and characteristics. Airborne particulate matter (PM) levels were monitored in a typical industrial Russian city, Chelyabinsk in three destinations, one characterized by high traffic volumes and two by industrial zone emissions. The mass concentration and trace metal content of PM2.5 and PM10 were obtained from samples collected during four distinct seasons of 2020. The mean 24-h PM10 ranged between 6 and 64 μg/m3. 24-h PM2.5 levels were reported from 5 to 56 μg/m3. About half of the 24-h PM10 and most of the PM2.5 values in Chelyabinsk were higher than the WHO recommendations. The mean PM2.5/PM10 ratio was measured at 0.85, indicative of anthropogenic input. To evaluate the Al, Fe, As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn concentration in PM2.5 and PM10, inductively coupled plasma mass spectrometry (ICP-MS) was used. Fe (337–732 ng/m3) was the most abundant component in PM2.5 and PM10 samples while Zn (77–206 ng/m3), Mn (10–96 ng/m3), and Pb (11–41 ng/m3) had the highest concentrations among trace elements. Total non-carcinogenic risks for children were found higher than 1, indicating possible health hazards. This study also presents that the carcinogenic risk for As, Cr, Co, Cd, Ni, and Pb were observed higher than the acceptable limit (1 × 10−6)

The influence that different urban development models has on PM2.5 elemental and bioaccessible profiles

Limited studies have reported on in-vitro analysis of PM2.5 but as far as the authors are aware, bioaccessibility of PM2.5 in artificial lysosomal fluid (ALF) has not been linked to urban development models before. The Brazilian cities Manaus (Amazon) and Curitiba (South region) have different geographical locations, climates, and urban development strategies. Manaus drives its industrialization using the free trade zone policy and Curitiba adopted a services centered economy driven by sustainability. Therefore, these two cities were used to illustrate the influence that these different models have on PM2.5 in vitro profile. We compared PM2.5 mass concentrations and the average total elemental and bioaccessible profiles for Cu, Cr, Mn, and Pb. The total average elemental concentrations followed Mn > Pb > Cu > Cr in Manaus and Pb > Mn > Cu > Cr in Curitiba. Mn had the lowest solubility while Cu showed the highest bioaccessibility (100%) and was significantly higher in Curitiba than Manaus. Cr and Pb had higher bioaccessibility in Manaus than Curitiba. Despite similar mass concentrations, the public health risk in Manaus was higher than in Curitiba indicating that the free trade zone had a profound effect on the emission levels and sources of airborne PM. These findings illustrate the importance of adopting sustainable air quality strategies in urban planning

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O3, NO2, NO, PM2.5, and PM10, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution

BTEX profile and health risk at the largest bulk port in Latin America, Paranaguá Port

Port-related activities have a detrimental impact on the air quality both at the point of source and for considerable distances beyond. These activities include, but are not limited to, heavy cargo traffic, onboard, and at-berth emissions. Due to differences in construction, operation, location, and policies at ports, the site-specific air pollution cocktail could result in different human health risks. Thus, monitoring and evaluating such emissions are essential to predict the risk to the community. Environmental agencies often monitor key pollutants (PM2.5, PM10, NO2, SO2), but the volatile organic carbons (VOCs) most often are not, due to its analytical challenging. This study intends to fill that gap and evaluate the VOC emissions caused by activities related to the port of Paranaguá — one of the largest bulk ports in Latin America — by characterizing BTEX concentrations at the port and its surroundings. At seven different sites, passive samplers were used to measure the dispersion of BTEX concentrations throughout the port and around the city at weekly intervals from November 2018 to January 2019. The average and uncertainty of BTEX concentrations (µg m−3) were 0.60 ± 0.43, 5.58 ± 3.80, 3.30 ± 2.41, 4.66 ± 3.67, and 2.82 ± 1.95 for benzene, toluene, ethylbenzene, m- and p-xylene, and o-xylene, respectively. Relationships between toluene and benzene and health risk analysis were used to establish the potential effects of BTEX emissions on the population of the city of Paranaguá. Ratio analysis (T/B, B/T, m,p X/Et, and m,p X/B) indicate that the BTEX levels are mainly from fresh emission sources and that photochemical ageing was at minimum. The cancer risk varied across the sampling trajectory, whereas ethylbenzene represented a moderate cancer risk development for the exposed population in some of the locations. This study provided the necessary baseline data to support policymakers on how to change the circumstances of those currently at risk, putting in place a sustainable operation

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM2.5 concentrations varied from 0.98 to 54.2 mu g m(-3), temperature ranged from 8 to 26 degrees C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p <.001) for I lag day between PM2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available. (C) 2017 Elsevier Ltd. All rights reserved.Coordination for the Improvement of Higher Level -or Education- Personnel (CAPES)Brazilian National Council for Scientific and Technological Development (CNPq)Araucaria Foundation for Scientific and Technological Development of ParanaUniv Fed Parana, Environm Engn Dept, 210 Francisco H dos Santos St, BR-81531980 Curitiba, Parana, BrazilFed Univ Technol, Math Dept, Ponta Grossa, Parana, BrazilFed Univ Technol, Elect Engn Dept, Ponta Grossa, Parana, BrazilUniv Fed Parana, Chem Engn Dept, Curitiba, Parana, BrazilUniv Sao Paulo, Fac Med, Dept Pathol, LPAE Air Pollut Lab, Sao Paulo, BrazilUniv Fed Sao Paulo, Dept Environm Sci, Diadema, BrazilUniv Sao Paulo, Inst Astron Geophys & Atmospher Sci, Dept Atmospher Sci, Sao Paulo, BrazilUniv Fed Parana, Chem Dept, Curitiba, Parana, BrazilDeakin Univ, Sch Life & Environm Sci, Geelong, Vic, AustraliaUniv Fed Sao Paulo, Dept Environm Sci, Diadema, BrazilAraucaria Foundation for Scientific and Technological Development of Parana: 435/2014Web of Scienc

Identification and quantification of giant bioaerosol particles over the Amazon rainforest

Abstract: Eukarya dominate the coarse primary biological aerosol (PBA) above the Amazon rainforest canopy, but their vertical profile and seasonality is currently unknown. In this study, the stratification of coarse and giant PBA >5 µm were analyzed from the canopy to 300 m height at the Amazon Tall Tower Observatory in Brazil during the wet and dry seasons. We show that >2/3 of the coarse PBA were canopy debris, fungal spores commonly found on decaying matter were second most abundant (ranging from 15 to 41%), followed by pollens (up to 5%). The atmospheric roughness layer right above the canopy had the greatest giant PBA abundance. Measurements over 5 years showed an increased abundance of PBA during a low-rainfall period. Giant particles, such as pollen, are reduced at 300 m, suggesting their limited dispersal. These results give insights into the giant PBA emissions of this tropical rainforest, and present a major step in understanding the type of emitted particles and their vertical distribution.</p