Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models.

Ozone (O3) is a reactive oxidant that causes chronic effects on human health, vegetation, ecosystems and materials. This study aims to create O3 isopleths in urban and suburban environments, based on machine learning with air quality data collected from 2001 to 2017 at urban (EA) and suburban (CC) monitoring stations from Madrid (Spain). Artificial neural network (ANN) models have powerful fitting performance, describing correctly several complex and nonlinear relationships such as O3 and his precursors (VOC and NOx). Also, ANN learns from the experience provided by data, contrary to mechanistic models based on the fundamental laws of natural sciences. The determined isopleths showed a different behaviour of the VOC-NOx-O3 system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NOx concentrations, O3 concentrations decreased with VOC concentrations in the ANN model. Considering the difficulty to model all the phenomena (and acquired all the required data) that influences O3 concentrations, the statistical models may be a solution to describe this system correctly. The applied methodology is a valuable tool for defining mitigation strategies (control of precursors' emissions) to reduce O3 concentrations. However, as these models are obtained by air quality data, they are not geographical transferable.

Quality assessment of water intended for human consumption from Kwanza, Dande and Bengo rivers (Angola).

Angola is one of the countries with a high rate of waterborne diseases, due to the scarcity and poor quality of water for human consumption. The watercourses are receptors of many effluents, mainly domestic sewage, due to a precarious or inexistent sanitation system and a small number of wastewater treatment plants. Therefore, this study aims: (i) to evaluate the water quality (physicochemical and microbiological parameters) of three Angolan rivers (Kwanza, Bengo and Dande) in locations where water is used as drinking water or abstracted for human consumption; (ii) to develop a new water quality index able to quantitatively express the water quality in those sites; and (iii) to assess the spatial distribution of water pollution through principal component analysis (PCA). Water quality assessment was performed by conducting four field surveys (campaigns I to IV); the first two campaigns took place in the dry season, while the last two ones took place in the rainy season. In the first two campaigns, the water quality was suitable to be treated for the production of drinking water, while in the last two campaigns, the water was unsuitable for that purpose (high levels of faecal coliforms were detected). The water quality index allowed to classify the water as generally excellent (campaigns I and II) and poor (campaigns III and IV). The rudimentary disinfection usually performed by individual water suppliers may improve the water quality, but it was not enough to achieve the parametric values required for human consumption in the rainy season (campaigns III and IV) except for Bengo sites. PCA identified sampling sites with the same water quality patterns, grouping into four groups (Kwanza sites) and two groups (Dande and Bengo sites). Therefore, the results of this study may support decision-makers as regards water supply management in the river stretches under study. The new developed Water Quality Index can support decision-makers in terms of water supply management, especially in countries with a high rate of waterborne diseases (e.g. Angola).

Impact of the implementation of Lisbon low emission zone on air quality.

Air pollution is an increasing concern due to the negative impacts on human health, environment, and patrimony. The implementation of a Low Emission Zone (LEZ) is an important air quality policy action to reduce air pollutant emissions. This study aims to assess the air quality improvements in Lisbon with the LEZ implementation, analysing its impact on the air pollutant concentrations. The analysis performed from 2009 to 2016 showed an improvement in air quality. In the Zone 1, the reduction of PM10 and NO2 annual average concentrations were 29% and 12%, respectively, while, in the Zone 2, the reduction of PM10 and NO2 annual average concentrations were 23% and 22%, respectively. The background pollution analysis showed the LEZ effect on the lowest levels of ambient air pollution to which the population is chronically exposed. The achieved reductions of PM10 and NO2 levels were 30.5% and 9.4% in Zone 1, and 22.5% and 12.9% in the Zone 2, respectively. Concluding, this study evidenced an air quality improvement mainly for PM10 and NO2; however, insignificant reductions were observed for NOx and PM2.5. Therefore, stricter restriction standards should be defined, combining with other air quality policy decisions to reduce the population exposure to air pollutants.

Nutrient recovery from wastewaters by microalgae and its potential application as bio-char.

The intensive agricultural practices are increasing the demand for chemical fertilizers, being currently produced from a non-environmental friendly way. Besides the environmental impacts, the nutrient uptake efficiency by the crops is very low, representing huge losses into the fields. Therefore, it is crucial to study alternatives for the current chemical fertilizers, which simultaneous improve nutrient efficiency and minimize environmental impacts. A sustainable solution is to recover nutrients from wastewater streams with microalgal cultures and the biomass conversion into bio-char for soil amendment. Wastewaters are loaded with nitrogen and phosphorus and can be used as culture medium for microalgae. Thus, nutrients can be recycled, reducing the requirement of chemical fertilizers. This paper aims to review nutrient recovery from wastewater using microalgae and the biomass conversion into bio-char. This process promotes nutrient recycling and the bio-char (when added to soil) improves the nutrient uptake efficiency by crops.