Abstract
This paper analyses air pollution levels in an Italian mountain province in order to understand how they can affect air quality and therefore have significant negative effects on health. The analysis considers intra e inter-annual variability in air quality in the province of Trento, after taking into account meteorological conditions. The main purpose is the proposal of an analytical procedure that, starting from the statistical properties of the observed time-series for each of the seven monitoring sites, controls for that part of air pollution that is explained by the meteorological variables using a panel data model, and then moves to analyse its unexplained part and how it is affected by the three main pollutants.
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Notes
- 1.
- 2.
http://www.who.int/mediacentre/factsheets/fs313/en/, key fact 2.
- 3.
- 4.
- 5.
- 6.
- 7.
O3 data are not available for Trento 2.
- 8.
The trend was obtained by applying the Hodrick and Prescott filter to each time series.
- 9.
Wind speed (km/h) has been excluded because it is strongly correlated with wind run.
- 10.
We could have followed the procedure used by EEA which takes measurements of up to five key pollutants supported by modelled data and determines the index level that describes the current air quality situation at each monitoring station. The single index level would correspond to the poorest index level for any of five pollutants.
- 11.
Principal component factor analysis (see Passamani and Masotti 2016; Fontanella et al. 2007; Forni et al. 2000 for some applications) and principal component regression models (see Kumar and Goyal 2011 for an application) are techniques that are used to summarise the available information into one or more factors.
- 12.
While Passamani and Masotti (2016) suggests a dynamic approach, for the purpose of this work we adopt a static principal component factor approach.
- 13.
The graphs are available upon request.
- 14.
Stata package xtdcce2, see Ditzen(2016).
- 15.
Estimations do not account for cross-sectional dependence, that emerges as a consequence of the common dynamics of the meteorological variables. When we attempted to deal with this it by implementing the correction allowed by the DCCE technique, residuals were still affected by cross-sectional dependence and, at the same time, results did not change much. As a consequence, the specification of the model should be improved in future analyses in order to cope with this problem. Nonetheless, it does not affect the analysis of the residuals (see the next section).
References
APPA Trento. (2014). Air quality report 2014. URL: http://www.appa.provincia.tn.it/binary/pat_appa_restyle/rapporti_annuali_aria/Rapporto_QA_2014.1475227470.pdf
APPA Trento. (2015). Air quality report 2015. URL: http://www.appa.provincia.tn.it/binary/pat_appa_restyle/rapporti_annuali_aria/Rapporto_QA_2015.1475227283.pdf
Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.
Burnett, R., Ma, R., Jerrett, M., Goldberg, M. S., Cakmak, S., Pope, C. A., 3rd, & Krewski, D. (2001). The spatial association between community air pollution and mortality: A new method of analyzing correlated geographic cohort data. Environmental Health Perspectives, 109(Suppl 3), 375.
Chelani, A. B., & Devotta, S. (2006). Air quality forecasting using a hybrid autoregressive and nonlinear model. Atmospheric Environment, 40(10), 1774–1780.
Chudik, A., & Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393–420.
Ditzen, J. (2016). xtdcce2: Estimating dynamic common correlated effects in stata. Heriot Watt University. URL: http://repec.org/usug2016/ditzen_uksug16.pdf
Dominici, F., Daniels, M., Zeger, S. L., & Samet, J. M. (2002). Air pollution and mortality: Estimating regional and national dose-response relationships. Journal of the American Statistical Association, 97(457), 100–111.
Dominici, F., Sheppard, L., & Clyde, M. (2003). Health effects of air pollution: A statistical review. International Statistical Review, 71(2), 243–276.
Fontanella, L., Ippoliti, L., & Valentini, P. (2007). Environmental pollution analysis by dynamic structural equation models. Environmetrics, 18, 265–283.
Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000). The generalized dynamic-factor model: Identification and estimation. The Review of Economics and Statistics, 82, 540–554.
Haines, A., Kovats, R. S., Campbell-Lendrum, D., & Corvalan, C. (2006). Climate change and human health: Impacts, vulnerability and public health. Public Health, 120(7), 585–596.
Hoek, G., Brunekreef, B., Goldbohm, S., et al. (2002). Association between mortality and indicators of traffic-related air pollution in The Netherlands: A cohort study. Lancet, 360, 1203–1209.
Janes, H., Sheppard, L., & Shepherd, K. (2008). Statistical analysis of air pollution panel studies: An illustration. Annals of Epidemiology, 18(10), 792–802.
Jerrett, M., Burnett, R. T., Ma, R., Arden Pop, C., Krewski, D., Newbold, K. B., Thurston, G., Shi, Y., Finkelstein, N., Calle, E. E., & Thun, M. J. (2005). Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology, 16, 727–736.
Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362–367.
Kumar, A., & Goyal, P. (2011). Forecasting of air quality in Delhi using principal component regression technique. Atmospheric Pollution Research, 2(4), 436–444.
Milionis, A. E., & Davies, T. D. (1994). Regression and stochastic models for air pollution. Review, comments and suggestions. Atmospheric Environment, 28(17), 2801–2810.
O’Neill, M. S., Jerrett, M., Kawachi, I., Levy, J. I., Cohen, A. J., Gouveia, N., et al. (2003). Health, wealth, and air pollution: Advancing theory and methods. Environmental Health Perspectives, 111(16), 1861–1870.
Passamani, G., & Masotti, P. (2016). Local atmospheric pollution evolution through time series analysis. Journal of Mathematics and Statistical Science, 2(12), 781–788.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels (CESifo Working Paper Series No. 1229).
Salcedo, R. L. R., Ferraz, M. A., Alves, C. A., & Martins, F. G. (1999). Time-series analysis of air pollution data. Atmospheric Environment, 33(15), 2361–2372.
Van der Wal, J. T., & Janssen, L. H. J. M. (2000). Analysis of spatial and temporal variations of PM 10 concentrations in the Netherlands using Kalman filtering. Atmospheric Environment, 34(22), 3675–3687.
WHO. (2013). Review of evidence on health aspects of air pollution REVIHAAP Project. URL: http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf?ua=1
Yu, O., Sheppard, L., Lumley, T., Koenig, J. Q., & Shapiro, G. G. (2000). Effects of ambient air pollution on symptoms of asthma in Seattle-area children enrolled in the CAMP study. Environmental Health Perspectives, 108(12), 1209.
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Passamani, G., Tomaselli, M. (2018). Air Pollution and Health Risks: A Statistical Analysis Aiming at Improving Air Quality in an Alpine Italian Province. In: Skiadas, C., Skiadas, C. (eds) Demography and Health Issues. The Springer Series on Demographic Methods and Population Analysis, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-76002-5_17
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