ATA COLLECTED BY ENVIORNMENT DEPARTMENTABOUT EMISSION OF POLLUTION LEVEL IN VARIOUS METROPOLITIAN CITIES OF INDIA IS
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The present work reports the distribution of pollutants in the Madrid city and province from 22 monitoring stations during 2010 to 2017. Statistical tools were used to interpret and model air pollution data. The data include the annual average concentrations of nitrogen oxides, ozone, and particulate matter (PM10), collected in Madrid and its suburbs, which is one of the largest metropolitan places in Europe, and its air quality has not been studied sufficiently. A mapping of the distribution of these pollutants was done, in order to reveal the relationship between them and also with the demography of the region. The multivariate analysis employing correlation analysis, principal component analysis (PCA), and cluster analysis (CA) resulted in establishing a correlation between different pollutants. The results obtained allowed classification of different monitoring stations on the basis of each of the four pollutants, revealing information about their sources and mechanisms, visualizing their spatial distribution, and monitoring their levels according to the average annual limits established in the legislation. The elaboration of contour maps by the geostatistical method, ordinary kriging, also supported the interpretation derived from the multivariate analysis demonstrating the levels of NO2 exceeding the annual limit in the centre, south, and east of the Madrid province.
1. Introduction
During the last year, urban air pollution concentrations have increased globally. According to the World Health Organization (WHO), this increase can be estimated at 8% from 2008 to 2013 and more than 80% of people living in urban areas, where air pollution is monitored, are exposed to levels that exceed the limits given by WHO [1]. Urban air pollution is a serious environmental problem, and as urban air quality declines, the risk of stroke, heart diseases, lung cancer, and chronic and acute respiratory diseases, including asthma, increases. In addition, it contributes to damaging building materials and cultural objects [2]. Harmful effects of air pollution and its causes are widely studied [3–5] and, the urban quality declines are mainly related to the increase in traffic emissions, transport-related emissions being the main component of air pollution. A wide variety of air pollutants are emitted by vehicles with petrol-derivatives engines being the most important of them; nitrogen oxides, carbon monoxide, volatile organic compounds (VOCs), and particulate matter have an important impact on air quality in the urban areas [6–10]. Air pollution in big cities and close to the main roadways is dominated by road traffic but the pollution levels are very variable because air pollution is severely influenced by multiple environmental or meteorological factors as well as traffic patterns, size, and orientation of buildings or land use [11–13]. Consequently, determining population exposures is essential to study and understand the causes of these variations prior to the development of interventions and policy recommendation aiming at reduction exposures. In this sense, multivariate statistical techniques are an excellent tool to discover and analyse large dataset of environmental data. There are different methods of dealing with this extensive amount of data, being one of the most interesting to treat all data by means of the application of multivariate analysis methods (i.e., principal component analysis or cluster analysis). The main objective is aimed at grouping and classification of objects (in this case, measured parameters, stations, days, etc.), as well as modelling relationships between the different environmental data. The methods of multidimensional analysis have made it possible to establish some correlations between different parameters and at the same time finding correlations between the amounts of several pollutants [14].
Many multivariate methods can be used in environmental studies because they provide information about association, interpretation, and modelling from large environmental datasets. Correlation analysis is a very useful statistical tool to identify the relationship between pollutants or other variables that affect air quality, and it is very useful to understand or look for the most influential factors or sources of chemical components [15, 16]. This statistical tool has been applied in several studies on air pollution in Chicago [17], performs isotopic analyses in topsoil [16], and identifies sources and correlations between PAHs and heavy metals in Switzerland and Spain [18] and in the urban road dust of Xi’an (China) [19].
Principal component analysis (PCA) like many of the multivariate methods of analysis is based on data reduction, taking into account the correlation between the data.