Links between the concentrations of gaseous pollutants measured in different regions of Estonia.

The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R 2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R 2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R 2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.

Links between the concentrations of gaseous pollutants measured in different regions of Estonia

  The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.