Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown.

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe.

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.

Long-term exposure to ozone and children's respiratory health: Results from the RESPOZE study.

BACKGROUND: Although there is evidence on the effects of short-term ozone (O3) exposures on children's respiratory health, few studies have reported results on the effects of long-term exposures. We report the effects of long-term exposure to O3 on respiratory health outcomes in 10-11-year old children. METHODS: We conducted a panel study in a sample of the general population of school children in two cities with high average O3 concentrations, Athens and Thessaloniki, Greece. All 186 participating students were followed up intensively for 5 weeks spreading across a school year. Data was collected through questionnaires, weekly personal O3 measurements, spirometry, FeNO and time-activity diaries. Long-term O3 exposure was assessed using fixed site measurements and modeling, calibrated for personal exposures. The associations between measured lung function parameters and lung function growth over the study period, as well as FeNO and the occurrence of symptoms with long-term O3 exposure were assessed through the application of multiple mixed effects 2-level regression models, adjusting for confounders and for short-term exposures. RESULTS: A 10 µg/m3 increase in calibrated long-term O3exposure, using measurements from fixed site monitors was associated with lower FVC and FEV1 by 17 mL (95% Confidence Interval: 5-28) and 13 mL (3-21) respectively and small decreases in lung growth: 0.008% (0.002-0.014%) for FVC and 0.006% (0.000-0.012%) in FEV1 over the study period. No association was observed with PEF, FeNO or the occurrence of symptoms. A similar pattern was observed when the exposure estimates from the dispersion models were employed. CONCLUSIONS: Our study provides evidence that long-term O3 exposure is associated with reduced lung volumes and growth.

Top-Down NOX Emissions of European Cities Based on the Downwind Plume of Modelled and Space-Borne Tropospheric NO2 Columns.

Top-down estimates of surface NOX emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO2) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April⁻September) during 2013. Here we show that the top-down NOX emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NOX emissions from the MACC-III inventory data (R² = 0.88) driving the CTM demonstrating the potential of this method. OMI top-down NOX emissions over the 23 European cities are generally lower compared with the MACC-III emissions and their correlation is slightly lower (R² = 0.79). The uncertainty on the derived NO2 lifetimes and NOX emissions are on average ~55% for OMI and ~63% for LOTOS-EUROS data. The downwind NO2 plume method applied on both LOTOS-EUROS and OMI tropospheric NO2 columns allows to estimate NOX emissions from urban areas, demonstrating that this is a useful method for real-time updates of urban NOX emissions with reasonable accuracy.

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies.

BACKGROUND: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. OBJECTIVES: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. METHODS: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. RESULTS: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. CONCLUSIONS: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.