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.

Vehicular emissions contribute significantly to air pollution, and the number of vehicles in use is continuing to rise. Policymakers thus need to formulate vehicular emission reduction policies to improve urban air-quality. This study used different vehicle control scenarios to predict the associated potential of mitigating carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxide (NOₓ), particulate matter with an aerodynamic diameter less than 2.5 μm (PM₂.₅), and particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) in Xi’an China, in 2020 and 2025. One business-as-usual scenario and six control scenarios were established, and vehicular emission inventory was developed according to each scenario. The results revealed that eliminating high-emission vehicles and optimizing after-treatment devices would effectively reduce vehicular emissions. In addition, increasing the number of alternative fuel vehicles, restraining vehicle use, and restraining the growth of the vehicle population would all have certain effects on CO and VOCs emissions, but the effects would not be significant for NOx, PM₂.₅, and PM₁₀. The results also indicated that if all control measures were stringently applied together, emissions of CO, VOCs, NOₓ, PM₂.₅, and PM₁₀ would be reduced by 51.66%, 51.58%, 30.19%,71.12%, and 71.81% in 2020, and 53.55%, 51.44%, 19.09%, 54.88%, and 55.51%, in 2025, respectively.

The study of emissions and depositions of atmospheric reactive nitrogen species (Nᵣs) in a region is important to uncover the sources and sinks of atmospheric Nᵣs in the region. In this study, atmospheric total Nᵣs depositions including both wet-only and dry deposition were monitored simultaneously across major land-use types in a 105 km² catchment called Jinjing River Catchment (JRC) in subtropical central China from 2015 to 2016. Based on activity data and emission factors for the main Nᵣs emission sources, ammonia (NH₃) and nitrogen oxides (NOₓ) emission inventories for the catchment were also compiled. The estimated total Nᵣs deposition in JRC was 35.9 kg N ha⁻¹ yr⁻¹, with approximately 49.7 % attributed to reduced compounds (NHₓ), and 40.5 % attributed to oxidized (NOy). The total Nᵣs emission rate in JRC was 80.4 kg N ha⁻¹ yr⁻¹, with 61.5 and 18.9 kg N ha⁻¹ yr⁻¹ from NH₃ and NOₓ emissions, respectively. Livestock excretion and fertilization were the two main contributing emission sources for NH₃, while vehicle sources contributed the bulk of NOₓ emissions. The net atmospheric budgets of Nᵣs in paddy field, forest, and tea field were +3.7, −36.1, and +23.8 kg N ha⁻¹ yr⁻¹, respectively. At the catchment scale, the net atmospheric budget of Nᵣs was +47.7 kg N ha⁻¹ yr⁻¹, with +43.7 kg N ha⁻¹ yr⁻¹ of NHₓ and +4.0 kg N ha⁻¹ yr⁻¹ of NOy, indicating that the subtropical catchment was net sources of atmospheric Nᵣs. Considering that excessive atmospheric Nᵣ emissions and deposition may cause adverse effects on the environment, effects should be conducted to mitigate the Nᵣs emissions from agriculture and transportation, and increasing the area of forest is good for reducing the net positive budget of atmospheric Nᵣs in the subtropical catchments in China.

The active use of solid fossil fuels (coal) in the production of heat and electricity has led to significant pollution, climate change, environmental degradation, and an increase in morbidity and mortality. Many countries (in particular, European ones, China, Japan, the USA, Canada, etc.) have launched programs for using plant and agricultural raw materials to produce heat and electricity by burning them instead of or together with traditional fuels. It is a promising solution to produce slurry fuels, based on a mixture of coal processing, oil refining and agricultural waste. This paper presents the results of experimental research into the formation and assessment of the most hazardous emissions (sulfur and nitrogen oxides) from the combustion of promising coal slurry fuels with straw, sunflower and algae additives, i.e. the most common agricultural waste. A comparative analysis has been carried out to identify the differences in the concentrations of sulfur and nitrogen oxides from the combustion of typical coal, coal processing waste, as well as fuel slurries with and without plant additives. It has been shown that the concentration of sulfur and nitrogen oxides can be reduced by 62–87% and 12–57%, respectively, when using small masses of plant additives (no more than 10 wt%) and maintaining high combustion heat of the slurry fuel. However, the use of algae and straw in the slurry composition can increase the HCl emissions, which requires extra measures to fight corrosion. A generalizing criterion of slurry fuel vs. coal efficiency has been formulated to illustrate significant benefits of adding plant solid waste to coal-water slurries containing petrochemicals. Straw and sunflower waste (10 wt%) were found to be the best additives to reduce the air pollutant emissions.