Air quality

Identifier:  imarina:6021085

Handle: https://hdl.handle.net/20.500.11797/imarina6021085

Authors:  Rovira, Joaquim; Domingo, Jose L; Schuhmacher, Marta

Abstract:
© 2019 The purpose of this study was to assess the impact to human health of air pollutants, through the integration of different technics: data statistics (spatial and temporal trends), population attributable fraction using AIRQ+ model developed by the WHO, and burden of disease using Disability-Adjusted Life Years (DALYs). The levels of SO2, NO, NO2, O3, H2S, benzene, PM10, PM2.5, CO, benzo(a)pyrene and metals, obtained between 2005 and 2017 from the air quality monitoring network across Camp de Tarragona County, were temporally and spatially determined. Health impacts were evaluated using the AIRQ+ model. Finally, the burden of disease was assessed through the calculation of Years of Lost life (YLL) and Years Lost due to Disability (YLD). In general terms, air quality was good according to European quality standards, but it did not fulfil the WHO guidelines, especially for O3, PM10 and PM2.5. Several decreasing (NO, NO2, SO2, PM10 and benzene) and an increasing (O3) temporal trend were found. Correlation between unemployment rate and air pollutant levels was found, pointing that the economic crisis (2008–2014) was a factor influencing the air pollutant levels. Reduction of air pollutant levels (PM2.5) to WHO guidelines in the Camp de Tarragona County would decrease the adult mortality between 23 and 297 cases per year, which means between 0.5 and 7% of all mortality in the area. In this County, for lung cancer, ischemic heart disease, stroke, and chronic obstructive pulmonary disease due to levels of PM2.5 above the WHO threshold limits, DAYLs were 240 years. This means around 80 DALYs for 100,000 persons every year -between 2005 and 2017. Population attributable fraction (PAF) and burden of disease (DALYs) methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyse the cost-effectiveness of interventions.