Interactions between atmospheric deposition and nutrient status or soil condition were studied using correlation analysis of the principal factors values for the year 1989. In case of both species, especially sulphur and calcium (factor 1) input is related to high foliar content of sulphur and calcium. The same influence can be detected for nitrogen (factor 3). Nitrogen inputs on Scots pine stands influenced the magnesium sufficient in a negative way

As the underlying mechanisms of the adverse effects of cold spells on cardiac events are not well understood, we explored the effects of cold spells on plasma viscosity, a blood parameter linked to cardiovascular disease. This cross-sectional study involved 3622 participants from the KORA S1 Study (1984–1985), performed in Augsburg, Germany. Exposure data was obtained from the Bavarian State Office for the Environment. Cold spells were defined as two or more consecutive days with daily mean temperatures below the 3ʳᵈ, 5ᵗʰ, or 10ᵗʰ percentile of the distribution. The effects of cold spells on plasma viscosity were explored by generalized additive models with distributed lag nonlinear models (DLNM). We estimated cumulative effects at lags 0–1, 0–6, 0–13, 0–20, and 0–27 days separately. Cold spells (mean temperature <3ʳᵈ, <5ᵗʰ or <10ᵗʰ percentile) were significantly associated with an increase in plasma viscosity with a lag of 0–1 days [%change of geometric mean (95% confidence interval): 1.35 (0.06–2.68), 1.35 (0.06–2.68), and 2.49 (0.34–4.69), respectively], and a lag of 0–27 days [18.81 (8.97–29.54), 17.85 (8.29–28.25), and 7.41 (3.35–11.0), respectively]. For the analysis with mean temperature <3ʳᵈ or 10ᵗʰ percentile, we also observed significant associations at lag 0–20 days [8.34 (0.43–16.88), and 4.96 (1.68, 8.35), respectively]. We found that cold spells had significant immediate and longer lagged effects on plasma viscosity. This finding supports the complex interplay of multiple mechanisms of cold on adverse cardiac events and enriches the knowledge about how cold exposure acts on the human body.

Lichens serve as important bioindicators of air pollution in cities. Here, we studied the diversity of epiphytic lichens in the urban area of Munich, Bavaria, southern Germany, to determine which factors influence species composition and diversity. Lichen diversity was quantified in altogether 18 plots and within each, five deciduous trees were investigated belonging to on average three tree species (range 1–5). Of the 18 plots, two were sampled in control areas in remote areas of southern Germany. For each lichen species, frequency of occurrence was determined in 10 quadrats of 100 cm² on the tree trunk. Moreover, the cover percentage of bryophytes was determined and used as a variable to represent potential biotic competition. We related our diversity data (species richness, Shannon index, evenness, abundance) to various environmental variables including tree traits, i.e. bark pH levels and species affiliation and air pollution data, i.e. NO₂ and SO₂ concentrations measured in the study plots. The SO₂ levels measured in our study were generally very low, while NO₂ levels were rather high in some plots. We found that the species composition of the epiphytic lichen communities was driven mainly by NO₂ pollution levels and all of the most common species in our study were nitrophilous lichens. Low NO₂ but high SO₂ values were associated with high lichen evenness. Tree-level lichen diversity and abundance were mainly determined by tree traits, not air pollution. These results confirm that ongoing NO₂ air pollution within cities is a major threat to lichen diversity, with non-nitrophilous lichens likely experiencing the greatest risk of local extinctions in urban areas in the future. Our study moreover highlights the importance of large urban green spaces for species diversity. City planners need to include large green spaces when designing urban areas, both to improve biodiversity and to promote human health and wellbeing.

The present study evaluated the interactive effects of global change and heavy metals on the growth and development of three soybean [Glycine max (L.) Merrill] cultivars and the consequences on yield and food safety. Soybean cultivars (Alim 3.14 from Argentina, and ES Mentor and Sigalia, from Germany) were grown until maturity in heavy metals polluted soils from the Rhine Valley, Germany, at two CO₂ concentrations (400 and 550 ppm) and heat stress (HS) episodes (9 days with 10 °C higher than maximum regular temperature) during the critical growth period in controlled environmental chambers. Different morpho-physiological parameters, heavy metal concentration in aerial organs, seed quality parameters, and toxicological index were recorded. The results showed that no morphological differences were observed related to CO₂. Moreover, Alim 3.14 showed the highest yield under control conditions, but it was more sensitive to climatic conditions than the German cultivars, especially to heat stress which strongly reduces the biomass of the fruits. Heavy metals concentration in soil exceeds the legislation limits for agricultural soils for Cd and Pb, with 1.6 and 487 mg kg⁻¹ respectively. In all cultivars, soybeans accumulated Cd in its aerial organs, and it could be translocated to fruits. Cd concentration in seeds ranged between 0.6 and 2.4 mg kg⁻¹, which exceed legislation limits and with toxicological risk to potential Chinese consumers. Pb levels were lower than Cd in seeds (0.03–0.17 mg kg⁻¹), and the accumulation were concentrated in the vegetative organs, with 93% of the Pb incorporated. Moreover, pods accumulated 11 times more Pb than seeds, which suggests that they act as a barrier to the passage of Pb to their offspring. These results evidence that soybean can easily translocate Cd, but not Pb, to reproductive organs. No regular patterns were observed in relation to climatic influence on heavy metal uptake.

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).