A neural-fuzzy approach to classify the ecological status in surface waters

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive.     

The cost of floods in developing countries’ megacities: a hedonic price analysis of the Jakarta housing market,Indonesia

Environmental Economics and Policy Studies - Although many megacities in developing countries experience floods annually that affect a large number of people, relatively few empirical studies have...     

Socioecological transition in the Cauca river valley,Colombia (1943–2010): towards an energy–landscape integrated analysis

Regional Environmental Change - Agroecosystems are facing a global challenge amidst a socioecological transition that places them in a dilemma between increasing land-use intensity to meet the...     

Emission factors of atmospheric and climatic pollutants from crop residues burning

Biomass burning is a common agricultural practice, because it allows elimination of postharvesting residues; nevertheless, it involves an inefficient combustion process that generates atmospheric pollutants emission, which has implications on health and climate change. This work focuses on the estimation of emission factors (EFs) of PM_2.5, PM₁₀, organic carbon (OC), elemental carbon (EC), carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄) of residues from burning alfalfa, barley, beans, cotton, maize, rice, sorghum, and wheat in Mexico. Chemical characteristics of the residues were determined to establish their relationship with EFs, as well as with the modified combustion efficiency (MCE). Essays were carried out in an open combustion chamber with isokinetic sampling, following modified EPA 201-A method. EFs did not present statistical differences among different varieties of the same crop, but were statistically different among different crops, showing that generic values of EFs for all the agricultural residues can introduce significant uncertainties when used for climatic and atmospheric pollutant inventories. EFs of PM_2.5 ranged from 1.19 to 11.30 g kg^?1, and of PM₁₀ from 1.77 to 21.56 g kg^?1. EFs of EC correlated with lignin content, whereas EFs of OC correlated inversely with carbon content. EFs of EC and OC in PM_2.5 ranged from 0.15 to 0.41 g kg^?1 and from 0.33 to 5.29 g kg^?1, respectively, and in PM₁₀, from 0.17 to 0.43 g kg^?1 and from 0.54 to 11.06 g kg^?1. CO₂ represented the largest gaseous emissions volume with 1053.35–1850.82 g kg^?1, whereas the lowest was CH₄ with 1.61–5.59 g kg^?1. CO ranged from 28.85 to 155.71 g kg^?1, correlating inversely with carbon content and MCE. EFs were used to calculate emissions from eight agricultural residues burning in the country during 2016, to know the potential mitigation of climatic and atmospheric pollutants, provided this practice was banned.

Implications: The emission factors of particles, short-lived climatic pollutants, and atmospheric pollutants from the crop residues burning of eight agricultural wastes crops, determined in this study using a standardized method, provides better knowledge of the emissions of those species in Latin America and other developing countries, and can be used as inputs in air quality models and climatic studies. The EFs will allow the development of more accurate inventories of aerosols and gaseous pollutants, which will lead to the design of effective mitigation strategies and planning processes for sustainable agriculture.