Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator

Animal Biomonitoring for the Surveillance of Environment Affected by the Presence of Slight Contamination by β-HCH

The aim of this study was to evaluate the influence of hidden environmental pollution on some blood parameters of sheep to detect susceptible biomarkers able to reveal slight contamination. Four dairy sheep farms, two with semi-extensive and two with intensive type systems were involved in this study. Two farms in different systems were chosen as properly located in a southern area of Latium (Italy), close to the Sacco River, in which contamination with β-hexachlorocyclohexane (β-HCH) occurred in the past due to industrial waste. A recent study established the presence of low but detectable residual contamination in these areas. The other two farms were outside the contaminated area. Erythrocyte glutathione transferase (e-GST) and oxidative stress parameters were monitored as well as some immune response and metabolic profile parameters throughout the investigated period of four months. The present study showed a relevant and significant increase in e-GST (+63%) in the extensive farming system of the contaminated area, whereas some immune response biomarkers, i.e., white blood cells, neutrophils, lymphocytes, and lysozyme resulted within the physiological range. In all farms, oxidative stress and acute phase response parameters were also within the physiological range. Our results suggest that e-GST is a very effective alarm signal to reveal “hidden” persistent contamination by β-HCH, and reasonably, by many other different dangerous pollutants.

Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania

This study focuses on the Vilnius (capital of Lithuania) agglomeration, which is facing the issue of air pollution resulting from the city’s physical expansion. The increased number of industries and vehicles caused an increase in the rate of fuel consumption and pollution in Vilnius, which has rendered air pollution control policies and air pollution management more significant. In this study, the differences in the pollutants’ means were tested using two-sided t-tests. Additionally, a 2-layer artificial neural network and a pollution data were both used as tools for predicting and warning air pollution after loop traffic has taken effect in Vilnius Old Town from July of 2020. Highly accurate data analysis methods provide reliable data for predicting air pollution. According to the validation, the multilayer perceptron network (MLPN1), with a hyperbolic tangent activation function with a 4-4-2 partition, produced valuable results and identified the main pollutants affecting and predicting air quality in the Old Town: maximum concentration of sulphur dioxide per 1 hour (SO2_1 h, normalized importance = 100%); carbon monoxide (CO) was the second pollutant with the highest indication of normalized importance, equalling 59.0%.

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.