Air quality across a European hotspot: Spatial gradients, seasonality, diurnal cycles and trends in the Veneto region, NE Italy

Abstract

The Veneto region (NE Italy) lies in the eastern part of the Po Valley, a European hotspot for air pollution. Data for key air pollutants (CO, NO, NO, O, SO, PM and PM) measured over 7years (2008/2014) across 43 sites in Veneto were processed to characterise their spatial and temporal patterns and assess the air quality. Nitrogen oxides, PM and ozone are critical pollutants frequently breaching the EC limit and target values. Intersite analysis demonstrates a widespread pollution across the region and shows that primary pollutants (nitrogen oxides, CO, PM) are significantly higher in cities and over the flat lands due to higher anthropogenic pressures. The spatial variation of air pollutants at rural sites was then mapped to depict the gradient of background pollution: nitrogen oxides are higher in the plain area due to the presence of strong diffuse anthropogenic sources, while ozone increases toward the mountains probably due to the higher levels of biogenic ozone-precursors and low NO emissions which are not sufficient to titrate out the photochemical O. Data-depth classification analysis revealed a poor categorization among urban, traffic and industrial sites: weather and urban planning factors may cause a general homogeneity of air pollution within cities driving this poor classification. Seasonal and diurnal cycles were investigated: the effect of primary sources in populated areas is evident throughout the region and drives similar patterns for most pollutants: road traffic appears the predominant potential source shaping the daily cycles. Trend analysis of experimental data reveals a general decrease of air pollution across the region, which agrees well with changes assessed by emission inventories. This study provides key information on air quality across NE Italy and highlights future research needs and possible developments of the regional monitoring network.

Technical Review : Performance of Existing Imputation Methods for Missing Data in SVM Ensemble Creation

“…Another common method to interfere into missing values is to remove the variable or subjects for which missing values are there [14]. However, deleting the subjects may result in loss of data and will produce biased results, because of the systematic difference between the collected and uncollected data [21]. On the other side if the missing values are presented in a group of variables, then if the variable(s) is/are of no such importance then the variable can be deleted.…”

“…On the other side if the missing values are presented in a group of variables, then if the variable(s) is/are of no such importance then the variable can be deleted. However, in case where the available variables are distributed, then deleting such variable(s) will be of serious loss of data [21]. Moreover, variables who have missing values are not distributed randomly, then deleting those variable data may result in skewness of the distribution [8].…”

Circadian toxicity of environmental pollution. Inhalation of polluted air to give a precedent

“…Hence, concentration and composition of air pollution show strong correlations to the time of the day exhibiting diurnal pattern. In urban environments, levels of traffic related air pollutant such as carbon monoxide (CO) peak during times of high traffic [41]. Diurnal variations have also been found for the concentration of traffic-related volatile organic compounds such as benzene and toluene that peak during the morning and evening rush hours [42].…”

“…Diurnal variations have also been found for the concentration of traffic-related volatile organic compounds such as benzene and toluene that peak during the morning and evening rush hours [42]. In contrast, ambient concentrations of ozone (O 3 ), formed by a photochemical reaction of ozone precursors such as nitrogen oxides (NOx) and carbon monoxide (CO) peak later in the noon hours [41,43]. Particulate matter (PM) air pollution is a mixture of different sized particles (coarse particles <10 μm in aerodynamic diameter, PM 10 , crustal material, source: farming, mining, construction work, volcanic ash, and wood burning; fine particles <2.5 μm in aerodynamic diameter, PM 2.5 ; ultrafine/nano particles <0.1 μm, PM 0.1 , primarily products of combustion-derived processes, for example, traffic and industrial sources, smoking) that contain organic (OC) and elemental (EC) carbon, sulfate, nitrate, and ammonium, along with many elements such as silica and metals (e.g.…”

“…The concentrations of coarse PM follow diurnal pattern as well, showing peak concentrations in the midday to afternoon due to daily metro-logical changes (i.e. wind speed) [41,46]. Diurnal changes were also found in the composition of PM.…”