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As a global pandemic, Coronavirus disease 2019 (COVID-19) poses a serious threat to people's health. However, by comparing the deaths caused by COVID-19 and deaths from air pollution in 183 countries, our results show that air pollution was even more of a serious health problem than COVID-19 in 2020. The relative roles of air pollution and COVID-19-attributable deaths across countries were affected by PM2.5 exposure, population age structure, societal development and government policies. The countries with less strict policies during the early stages of COVID-19, to ensure stable economic development, paid more to control COVID-19 deaths in the subsequent stages, and hence showed large GDP reduction percentages in 2020. Our results indicate that the COVID-19 is a serious killer but also that the mortality caused by air pollution is high, which underline the concurrent need to control the dispersion of COVID-19 and improvement of air quality. ; As a global pandemic, Coronavirus disease 2019 (COVID-19) poses a serious threat to people's health. However, by comparing the deaths caused by COVID-19 and deaths from air pollution in 183 countries, our results show that air pollution was even more of a serious health problem than COVID-19 in 2020. The relative roles of air pollution and COVID-19-attributable deaths across countries were affected by PM 25 exposure, population age structure, societal development and govermnent policies. The countries with less strict policies during the early stages of COVID-19, to ensure stable economic development, paid more to control COVID-19 deaths in the subsequent stages, and hence showed large GDP reduction percentages in 2020. Our results indicate that the COVID-19 is a serious killer but also that the mortality caused by air pollution is high, which underline the concurrent need to control the dispersion of COVID-19 and improvement of air quality. ; Peer reviewed

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017-2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA Combining double low line 19.7 ± 11.3 μm2 cm-3) site and an urban background (UB, average LDSA Combining double low line 11.2 ± 7.1 μm2 cm-3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 μm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2Combining double low line0.80, MAE Combining double low line 3.7 μm2 cm-3) than at the UB site (R2Combining double low line0.77, MAE Combining double low line 2.3 μm2 cm-3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations. ; publishedVersion ; Peer reviewed

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µ m 2 cm −3 ) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µ m 2 cm −3 ) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination ( R 2 ), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µ m (PM 2.5 ), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM 2.5 , BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site ( R 2 =0.80 , MAE = 3.7 µ m 2 cm −3 ) than at the UB site ( R 2 =0.77 , MAE = 2.3 µ m 2 cm −3 ), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations. ; Peer reviewed

Lung deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we develop a novel statistical model, named input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 μm 2 cm −3 ) site and an urban background (UB, average LDSA = 11.2 ± 7.1 μm 2 cm −3 ) site in Helsinki, Finland. For the continuous estimation of LDSA, IAME model is automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 sub-models were generated and ranked by the coefficient of determination ( R 2 ), mean absolute error ( MAE ) and centred root-mean-square differences ( cRMSD ) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 μm (PM 2.5 ), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site the LDSA concentrations were found to be correlated with PM 2.5 , BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site ( R 2 = 0.80, MAE = 3.7 μm 2 cm −3 ) than at the UB site ( R 2 = 0.77, MAE = 2.3 μm 2 cm −3 ) plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrate that the additional adjustment by taking random effects into account improves the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

Long-term high-quality aerosol particulate matter (PM) concentration measurements have been conducted in southern Finland at Station for Measuring Ecosystem-Atmosphere Relations (SMEAR II, Hyytiälä) with different, yet comparable measurement equipment since 1995. In this paper, the mass concentrations measured between 2005 and 2017 using three different independent methods: i.e. 1. the cascade impactor, 2. Differential Mobility Particle Sizer (DMPS) and Aerosol Particle Sizer (APS) and 3. Synchronized Hybrid Ambient Real-time Particulate Monitor (SHARP) are compared and analysed. First, the mass concentrations of the different size classes, i.e. PM1 (PM within sub-micrometer particle diameter size range), PM2.5 (sub-2.5 µm) and PM10 (sub-10 µm), are reported. These data were further cross-compared through a bivariate fitting method. The comparison revealed an excellent equivalence among the three methods with slopes approaching unity and reasonable intercepts (≤ 1 µg m −3 ). An analysis of the seasonal variability of PM concentrations revealed that the mass concentrations were generally highest in summer in different size classes. The mean mass concentrations were 5.3, 5.4, and 6.5 µg m −3 for PM1, PM2.5 and PM10, respectively. The 2nd highest loadings were attained in spring, which were ca. 80–88 % of those in summer. The lowest loadings were measured in autumn and winter, when the mass concentrations were ca. 74–78 % of those in summer. Temperature had strong influence on the measured concentrations. While the high late spring and summertime temperatures promote secondary organic aerosol (SOA) formation and pollen emissions, the lowest wintertime temperatures enhance the need of residential heating processes yielding anthropogenic aerosol emissions (e.g. from traffic/industry/wood burning). The wintertime concentrations can also be expected to be influenced by boundary layer dynamics, which keep the PM emissions concentrated near Earth surface especially in winter. It is noteworthy that the mass concentrations were lower than those reported prior to 2005 (at SMEAR II). The descending trend (~−0.1–0.2 µg m 3 y −1 ) was clearly visible here for all PM size classes in spring, summer and winter, while the trend in autumn remained statistically insignificant. This might have resulted at least partly from more stringent EU air quality legislation.

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O-3 (MDA8 O-3) concentration was 122 +/- 11 mu g m(-3), with an increase rate of 7.88 lug mu g m(-3) yr(-1), and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 mu m(-3)), May (138 mu m(-3)) and July (132 mu g m(-3)). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb-Jenkinson method. The highly polluted weather categories included the S-W-N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 03 concentrations were 122, 126 and 128 mu g m(-3), respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O-3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %-63 % of the day-today variability in the MDA8 O-3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China. ; Peer reviewed

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O 3 (MDA8 O 3 ) concentration was 122±11 µ g m −3 , with an increase rate of 7.88 µ g m −3 yr −1 , and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 µ g m −3 ), May (138 µ g m −3 ) and July (132 µ g m −3 ). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb–Jenkinson method. The highly polluted weather categories included the S–W–N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 O 3 concentrations were 122, 126 and 128 µ g m −3 , respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O 3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %–63 % of the day-to-day variability in the MDA8 O 3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China.