The fate of nitrogen in the urban area - The case of Zielona Góra, Poland.

The anthropogenic change of the nitrogen (N) cycle is strongly triggered by urban demand (such as food and meat consumption, energy demand and transport). As a consequence of high population density, impacts on human health through water and air pollution also concentrate on a city environment. Thus, an urban perspective on a predominantly rural pollution becomes relevant. Urban N budgets may be considered less intrinsically connected, so that separation of an agri-food chain and an industry-combustion chain is warranted. Results have been obtained for Zielona Góra, Poland, a city of 140,000 inhabitants characterized by domestic and transport sources and forest-dominated surroundings. In addition to food imports in Zielona Gora amounting to about 30 %, in the suburban area a significant share of N amounting to 41 % is related to fertilizer imports. The remaining imports are in fuel, electronics, textiles, plastics and paper. Most of the agri-food N (45 %) is denitrified in wastewater treatment. N associated with combustion (mainly NOx emissions from vehicles) represents a much smaller share than N entering via the agri-food system, amounting to 22 % of the total N imports. This overall picture is maintained also when specifically addressing the city center, with the exception of mineral fertilizer that plays a much smaller role, with just 7 % of N imports to the city.

Estimation of Coal's Sorption Parameters Using Artificial Neural Networks.

This article presents research results into the application of an artificial neural network (ANN) to determine coal's sorption parameters, such as the maximal sorption capacity and effective diffusion coefficient. Determining these parameters is currently time-consuming, and requires specialized and expensive equipment. The work was conducted with the use of feed-forward back-propagation networks (FNNs); it was aimed at estimating the values of the aforementioned parameters from information obtained through technical and densitometric analyses, as well as knowledge of the petrographic composition of the examined coal samples. Analyses showed significant compatibility between the values of the analyzed sorption parameters obtained with regressive neural models and the values of parameters determined with the gravimetric method using a sorption analyzer (prediction error for the best match was 6.1% and 0.2% for the effective diffusion coefficient and maximal sorption capacity, respectively). The established determination coefficients (0.982, 0.999) and the values of standard deviation ratios (below 0.1 in each case) confirmed very high prediction capacities of the adopted neural models. The research showed the great potential of the proposed method to describe the sorption properties of coal as a material that is a natural sorbent for methane and carbon dioxide.