Journal
ATMOSPHERIC ENVIRONMENT
Volume 44, Issue 1, Pages 30-41Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2009.09.041
Keywords
Long-term trend; Air quality monitoring; Generalized least-squares (GLS) regression; Autoregressive moving average (ARMA) errors
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The trends in the atmospheric concentrations of the main gaseous and particulate pollutants in urban, industrial and rural environments across Finland were estimated for the period of 1994-2007. The statistical analysis was based on generalized least-squares regression with classical decomposition and autoregressive moving average (ARMA) errors, which was applied to monthly-averaged data. in addition, three alternative methods were tested. Altogether 102 pollutant time series from 42 sites were analyzed. During the study period, the concentrations of SO2, CO and NOx declined considerably and widely across Finland. The SO2 concentrations at urban and industrial sites were approaching background levels. The reductions in NOx and CO concentrations were comparable to those in national road traffic emissions. A downward trend was detected in half of the NO2 time series studied, but the reductions were not as large as would be expected on the basis of emission trends, or from NOx concentrations. For O-3, neither the mean nor peak values showed large changes in background areas, but were increasing in the urban data. For PM10, five of the 12 urban time series showed decreasing mean levels. However, the highest concentrations, typically attributable to the problematic springtime street dust, did not decrease as widely. The reduction of the long-range transported major ions, mainly driven by the large-scale reduction in sulphur emissions, possibly plays a significant part in the decreases in the mean PM10 concentrations. It was shown that the handling of the serially-correlated data with the ARMA processes improved the analysis of monthly values. The use of monthly rather than annually-averaged data helped to identify the weakest trends. (C) 2009 Elsevier Ltd. All rights reserved.
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