Five permanent plots in pine and spruce stands were established at beginning of the 90s. The sites were situated in a gradient of air pollution level, from the south to the north of Poland. There are presented changes in SO2 and NO2 concentration in the air as well as loads of acidic compounds and the exceedance of critical loads in the period of 1993-2001. Response of the forest ecosystems was evaluated by changes in health status of trees and changes in biodiversity at forest permanent plots. The comparative study revealed an improvement in the health condition of trees and a tendency of ecosystems to regenerate due to greater than 30% decrease in emission in the last decade

Ozone (O₃) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O₃ concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O₃ concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O₃ observations on a site scale with much higher accuracy than existing O₃ models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O₃ reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O₃ events and the 1- and 24-h forecasts of hourly O₃ were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O₃, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME).With SHAP, we showed an increasing effect of temperature on O₃ concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O₃ concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O₃ concentrations during cold and wet periods.While recently developed MLAs for the spatial estimation of hourly O₃ concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m³, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m³. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O₃ concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m³, respectively).

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

In December 2019, a novel disease, coronavirus disease 19 (COVID-19), emerged in Wuhan, People’s Republic of China. COVID-19 is caused by a novel coronavirus (SARS-CoV-2) presumed to have jumped species from another mammal to humans. This virus has caused a rapidly spreading global pandemic. To date, over 300,000 cases of COVID-19 have been reported in England and over 40,000 patients have died. While progress has been achieved in managing this disease, the factors in addition to age that affect the severity and mortality of COVID-19 have not been clearly identified. Recent studies of COVID-19 in several countries identified links between air pollution and death rates. Here, we explored potential links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. We compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. After controlling for population density, age and median income, we show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. Using detailed UK Biobank data, we further show that PM₂.₅ was a major contributor to COVID-19 cases in England, as an increase of 1 m³ in the long-term average of PM₂.₅ was associated with a 12% increase in COVID-19 cases. The relationship between air pollution and COVID-19 withstands variations in the temporal scale of assessments (single-year vs 5-year average) and remains significant after adjusting for socioeconomic, demographic and health-related variables. We conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study provides a framework to guide both health and emissions policies in countries affected by this pandemic.

Fine particulate matter (PM₂.₅) pollution in Eastern China (EC) has raised concerns due to its adverse effects on air quality, climate, and human health. This study investigated the long-term variation trend in satellite-derived PM₂.₅ concentrations and how it was related to pollutant emissions and meteorological parameters over EC and seven regions of interest (ROIs) during 1998–2016. Over EC, the annual mean PM₂.₅ increased before 2006 due to the enhanced emissions of primary PM₂.₅, NOₓ and SO₂, but decreased with the reduced SO₂ emissions after 2006 evidently in response to China's clean air policies. In addition, results from statistical analyses indicated that in the North China Plain (NCP), Northeast China (NEC), Sichuan Basin (SCB) and Central China (CC) planetary boundary layer height (PBLH) was the dominant meteorological driver for the PM₂.₅ decadal changes, and in the Pearl River Delta (PRD) wind speed is the leading factor. Overall, the variation in meteorological parameters accounted for 48% of the variances in PM₂.₅ concentrations over EC. The population-weighted PM₂.₅ over EC increased from 36.4 μg/m³ in 1998–2004 (P1) to 49.4 μg/m³ in 2005–2010 (P2) then decreased to 46.5 μg/m³ in 2011–2016 (P3). In the NCP and NEC, the percentages of the population living above the World Health Organization (WHO) Interim Target-1 (IT-1, 35 μg/m³) have risen steadily over the past 20 yr, reaching maxima of 97.3% and 78.8% in P3, respectively, but decreases of ∼30% from P2 to P3 were found for the SCB and PRD.

Despite substantial mitigation of particulate matter (PM) pollution during the past decade in Beijing, the response of aerosol chemistry to clean air action and meteorology remains less understood. Here we characterized the changes in aerosol composition as responses to emission reductions by using two-year long-term measurements in 2011/2012 and 2017/2018, and WRF-Chem model. Our results showed substantial decreases for all aerosol species except nitrate from 2011/2012 to 2017/2018. Chloride exhibited the largest decrease by 65–89% followed by organics (37–70%), mainly due to reductions in coal combustion emissions in winter and agriculture burning in June. Primary and secondary organic aerosol (SOA) showed comparable decreases by 61–70% in fall and winter, and 34–63% in spring and summer, suggesting that reductions in primary emissions might also suppress SOA formation. The changes in nitrate were negligible and even showed increases due to less reductions in NOₓ emissions and increased formation potential from N₂O₅ heterogeneous reactions. As a result, nitrate exceeded sulfate and became the major secondary inorganic aerosol species in PM with the contribution increasing from 14–21% to 22–32%. Further analysis indicated that the reductions in aerosol species from 2011/2012 to 2017/2018 were mainly caused by the decreases of severely polluted events (PM₁ > 100 μg m⁻³). WRF-Chem simulations suggested that the decreases in OA and sulfate in fall and winter were mainly resulted from emission reductions (27–36% and 25–43%) and favorable meteorology (4–10% and 19–30%), while they were dominantly contributed by emission changes in spring and summer. Comparatively, the changes in nitrate were mainly associated with meteorological variations while the contributions of emissions changes were relatively small. Our results highlight different chemical responses of aerosol species to emission changes and meteorology, suggesting that future mitigation of air pollution in China needs species-targeted control policy.