Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain.

Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 µg m-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.

Removal of fluoride from coke wastewater by aluminum doped chelating ion-exchange resins: a tertiary treatment.

Coke wastewater is one of the most problematic industrial wastewaters, due to its large volume and complex pollutant load. In this study, ion exchange technology was investigated with the objective of reducing the fluoride content of the effluent from a coke wastewater treatment plant (26.7 mg F-/L). Two Al-doped exchange resins with chelating aminomethyl-phosphonic acid and iminodiacetic groups were assessed: Al-doped TP260 and TP207 resins, respectively. The effect of resin dosage, varying from 5 to 25 g/L, was evaluated. F- removal was within the range 57.8-89.3% and 72.0-92.1% for Al-doped TP260 and TP207, respectively. A kinetic study based on a generalized integrated Langmuir kinetic equation fitted the experimental data (R2 > 0.98). The parameters of the said kinetics met the optimal conditions for the ion exchange process, which seemed to be more favorable with Al-doped TP260 resin than with Al-doped TP207 resin, using the same resin dosage. Furthermore, the experimental data were well described (R2 > 0.98) by Langmuir and Freundlich isotherm models, in agreement with the findings of the kinetic study: the maximum sorption capacity was obtained for the Al-doped TP260 resin.

Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm.

SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency.

Comparing feed-forward versus neural gas as estimators: application to coke wastewater treatment.

Numerous papers related to the estimation of wastewater parameters have used artificial neural networks. Although successful results have been reported, different problems have arisen such as overtraining, local minima and model instability. In this paper, two types of neural networks, feed-forward and neural gas, are trained to obtain a model that estimates the values of nitrates in the effluent stream of a three-step activated sludge system (two oxic and one anoxic). Placing the denitrification (anoxic) step at the head of the process can force denitrifying bacteria to use internal organic carbon. However, methanol has to be added to achieve high denitrification efficiencies in some industrial wastewaters. The aim of this paper is to compare the two networks in addition to suggesting a methodology to validate the models. The influence of the neighbourhood radius is important in the neural gas approach and must be selected correctly. Neural gas performs well due to its cooperation--competition procedure, with no problems of stability or overfitting arising in the experimental results. The neural gas model is also interesting for use as a direct plant model because of its robustness and deterministic behaviour.

Life cycle analysis of Municipal Solid Waste management possibilities in Asturias, Spain.

Directive 1994/62 concerning packaging and packaging waste and Directive 1999/31 related to waste disposal will substantially modify the management and treatment of Municipal Solid Waste (MSW) in Europe. In this study, a life cycle analysis has been carried out of the different possibilities of managing Municipal Solid Waste in Asturias. The "Integrated Waste Management" (IWM-1) model was employed, analysing the different alternatives for collection and treatment of MSW. This model predicts overall environmental burdens of MSW management systems and includes a parallel economical model. The sources of costs in the different systems of collection and treatment of MSW were considered in the economical analysis, as well as the sources of resource gathering that may be obtained via the sale of recovered materials. What emerges from this study is the soundness of management strategies based on biological treatment technologies in comparison with thermal treatments, together with the need to increase the level of collection at source.