Negative emissions technologies: A complementary solution for climate change mitigation.

Carbon dioxide (CO2) is the main greenhouse gas (GHG) and its atmospheric concentration is currently 50% higher than pre-industrial levels. The continuous GHGs emissions may lead to severe and irreversible consequences in the climate system. The reduction of GHG emissions may be not enough to mitigate climate change. Consequently, besides carbon capture from large emission sources, atmospheric CO2 capture may be also required. To meet the target defined for climate change mitigation, the removal of 10 Gt·yr-1 of CO2 globally by mid-century and 20 Gt·yr-1 of CO2 globally by the end of century. The technologies applied with this aim are known as negative emission technologies (NETs), as they lead to achieve a negative balance of carbon in atmosphere. This paper aims to present the recent research works regarding NETs, focusing the research findings achieved by academic groups and projects. Besides several advantages, NETs present high operational cost and its scale-up should be tested to know the real effect on climate change mitigation. With current knowledge, no single process should be seen as a solution. Research efforts should be performed to evaluate and reduce NETs costs and environmental impact.

Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

Wastewater treatment to enhance the economic viability of microalgae culture.

Microalgae culture is still not economically viable and it presents some negative environmental impacts, concerning water, nutrient and energy requirements. In this context, this study aims to review the recent advances on microalgal cultures in wastewaters to enhance their economic viability. We focused on three different culture concepts: (1) suspended cell systems, (2) cell immobilization, and (3) microalgae consortia. Cultures with suspended cells are the most studied. The nutrient removal efficiencies are usually high for wastewaters of different sources. However, biomass harvesting is difficult and a costly process due to the small cell size and lower culture density. On the other hand, the cell immobilization systems showed to be the solution for this problem, having as main limitation the nutrient diffusion from bulk to cells, which results in a reduced nutrient removal efficiency. The consortium between microalgae and bacteria enhances the growth of both microorganisms. This culture concept showed to be a promising technology to improve wastewater treatment, regarding not only nutrient removal but also biomass harvesting by bioflocculation. The aggregation mechanism must be studied in depth to find the process parameters that would lead to an effective and cheap harvesting process.

PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification.

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM10 and PM2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO2, PM10, NO2, CO, and O3; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ng m(-3), surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10(-6)) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98 × 10(-7) in PM10 and 1.06 × 10(-6) in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO2, NO2, PM10, CO, and solar radiation had positive correlation with PAHs concentrations, while O3, temperature, relative humidity, and wind speed were negatively correlated.

Short-term effects of air pollution on respiratory morbidity at Rio de Janeiro--PART I: Air pollution assessment.

Exposure to air pollution has been related with the most varied adverse health outcomes. This study aims to assess the impact of air pollution on the emergency hospitalization for respiratory disease in Rio de Janeiro, Brazil. The study was divided in two parts: Part I specifically addressing the air pollution assessment and Part II addressing the health assessment. Accordingly, this Part I aims to: i) evaluate the concentrations of PM(10), SO(2) and CO at two sites in Rio de Janeiro and compare them; ii) analyse the concentrations observed according to the national and international standards; and iii) analyse the air pollutants behaviour, namely, annually, seasonally, daily and considering weekdays/weekends variations. The pollutant concentrations were measured at two different sites in Rio de Janeiro and the analysis was performed for the period between September 2000 and December 2005. Results showed that PM(10) concentrations in Rio de Janeiro exceeded the daily and annual standards imposed by the European Union, the Brazilian legislation and WHO guidelines. Regarding SO(2) and CO, concentrations were, generally, below both European and Brazilian standards. Nevertheless, considering WHO guidelines, SO(2) threshold for daily concentrations (20 µg m(-3)) was exceeded around 150 times. Behaviour assessment showed that the influence of traffic is a major factor affecting the air pollution in Rio de Janeiro. Considering the results achieved and the proven health effects of air pollution, strategies should be defined for its reduction, particularly concerning particulate matter, and consequently contribute to the protection of public health.

Short-term effects of air pollution on respiratory morbidity at Rio de Janeiro--Part II: health assessment.

The effects of air pollution on health have been studied worldwide. Given that air pollution triggers oxidative stress and inflammation, it is plausible that high levels of air pollutants cause higher number of hospitalisations. This study aimed to assess the impact of air pollution on the emergency hospitalisation for respiratory disease in Rio de Janeiro, Brazil. The study was divided in two parts: Part I specifically addressing the air pollution assessment and Part II addressing the health assessment. Accordingly, this Part II aimed to estimate the association between the concentrations of PM10, SO2 and CO observed in Rio de Janeiro and the number of emergency hospitalisations at a central hospital due to respiratory diseases. The pollutant concentrations were measured at two different sites in Rio de Janeiro, but the excess relative risks were calculated based on the concentrations observed at one of the sites, where limits were generally exceeded more frequently, between September 2000 and December 2005. A time series analysis was performed using the number of hospitalisations, divided in three categories (children until 1 year old, children aged between 1 and 5 years old and elderly with 65 years old or more) as independent variable, the concentrations of pollutants as dependent variables and temperature, relative humidity, long term trend, and seasonality as confounders. Data were analysed using generalised additive models with smoothing for some of the dependent variables. Results showed an excess risk of hospitalisation for respiratory disease higher than 2% per 10 µg m⁻³ increase in PM10 concentrations for children under 5 years old, of 2% per 10 µg m⁻³ increase in SO2 for elderly above 65 years old and around 0.1% per 10 µg m⁻³ increase in CO for children under 1 year and elderly. Other studies have found associations that are in agreement with the results achieved in this study. The study suggests that the ambient levels of air pollutants experienced in Rio de Janeiro between 2000 and 2005 were linked to the number of hospitalisations for respiratory diseases among children and elderly.

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting.

INTRODUCTION: This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O(3)) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons. METHODS: Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O(3) concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO(2)), and O(3) (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004. RESULTS AND DISCUSSION: Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O(3) regimes were temperature, CO and NO(2) concentrations, due to their importance in O(3) chemistry in an urban atmosphere. CONCLUSION: In the prediction of O(3) concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.