Gomez-Losada T.,Environmental and Water Agency of Andalusia |
Gomez-Losada T.,University of Seville |
Lozano-Garcia A.,Environmental and Water Agency of Andalusia |
Pino-Mejias R.,University of Seville |
Contreras-Gonzalez J.,Environmental Council of the Junta de Andalucia
Science of the Total Environment | Year: 2014
Background: Existing air quality monitoring programs are, on occasion, not updated according to local, varying conditions and as such the monitoring programs become non-informative over time, under-detecting new sources of pollutants or duplicating information. Furthermore, inadequate maintenance may cause the monitoring equipment to be utterly deficient in providing information. To deal with these issues, a combination of formal statistical methods is used to optimize resources for monitoring and to characterize the monitoring networks, introducing new criteria for their refinement. Methods: Monitoring data were obtained on key pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM10) and sulfur dioxide (SO2) from 12 air quality monitoring sites in Seville (Spain) during 2012. A total of 49 data sets were fit to mixture models of Gaussian distribution using the expectation-maximization (EM) algorithm. To summarize these 49 models, the mean and coefficient of variation were calculated for each mixture and carried out a hierarchical clustering analysis (HCA) to study the grouping of the sites according to these statistics. To handle the lack of observational data from the sites with unmonitored pollutants, the missing statistical values were imputed by applying the random forests technique and then later, a principal component analysis (PCA) was carried out to better understand the relationship between the level of pollution and the classification of monitoring sites. All of the techniques were applied using free, open-source, statistical software. Results and conclusion: One example of source attribution and contribution is analyzed using mixture models and the potential for mixture models is posed in characterizing pollution trends. The mixture statistics have proven to be a fingerprint for every model and this work presents a novel use of them and represents a promising approach to characterizing mixture models in the air quality management discipline. The imputation technique used is allowed for estimating the missing information from key unmonitored pollutants to gather information about unknown pollution levels and to suggest new possible monitoring configurations for this network. Posterior PCA confirmed the misclassification of one site detected with HCA. The authors consider the stepwise approach used in this work to be promising and able to be applied to other air monitoring network studies. © 2014 Elsevier B.V.
Anaya-Romero M.,CSIC - Institute of Natural Resources and Agriculture Biology of Salamanca |
Abd-Elmabod S.K.,CSIC - Institute of Natural Resources and Agriculture Biology of Salamanca |
Abd-Elmabod S.K.,National Research Center of Egypt |
Munoz-Rojas M.,University of Western Australia |
And 5 more authors.
Land Degradation and Development | Year: 2015
European policies can be relevant to protect soils under climate change scenarios and therefore preserve the wide variety of functions and services provided by the soil. The European Thematic Strategy for Soil Protection will require member states to identify areas under risk from various soil threats and establish procedures to achieve sustainability. Five models Terraza, Cervatana, Sierra, Raizal, and Pantanal included in the Mediterranean Land Evaluation Information System decision support system packages were used to identify areas vulnerable to various soil threats under climate change scenarios in the Andalusia region. While Terraza and Cervatana forecast general land use capability for a broad series of possible agricultural uses, the Sierra model predicts forestry land suitability for the presence/absence of 22 typical Mediterranean forest species. Raizal and Pantanal models predict soil erosion vulnerability, contamination, and other processes. Interpretation of results in different scenarios allows quantifying the effects of climate change in terms of agricultural productivity, forestry land suitability, erosion, and contamination risks. The obtained results allow to identify detailed vulnerable areas and formulate site-specific management plans for soil protection. Climate change is expected to impact crop growth with a higher impact on summer crops (corn, sunflower, and cotton). The results show a potential opportunity for reforestation (Quercus spp.) in future climate scenarios, while other species such as Castanea sativa will not be suitable in the study area by 2070 and 2100. Soil contamination and erosion show only slight differences between the current and future scenario of climate change. © 2015 John Wiley & Sons, Ltd.