Exploring the interaction between O₃ and NOx pollution patterns in the atmosphere of Barcelona, Spain using the MCR-ALS method

Urban ozone variability using automated machine learning: inference from different feature importance schemes

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

Deposition-mediated phytoremediation of nitrogen oxide emissions

The growing global population and use of natural resources lead to significant air pollution. Nitrogen oxide emissions is a potential killer threatening human health requiring focus and remediation using vegetation being efficient and cheap. Here we review the mechanisms of removing nitrogen oxides by dry deposition of plants, discussing the principle of leaf absorption of pollutants and factors affecting the removal of nitrogen oxides providing a theoretical basis for the selection of urban greening vegetation.

An Evaluation of Risk Ratios on Physical and Mental Health Correlations due to Increases in Ambient Nitrogen Oxide (NOx) Concentrations

Nitrogen oxides (NOx) are gaseous pollutants contributing to pollution in their primary form and are also involved in reactions forming ground-level ozone and fine particulate matter. Thus, NOx is of great interest for targeted pollution reduction because of this cascade effect. Primary emissions originate from fossil fuel combustion making NOx a common outdoor and indoor air pollutant. Numerous studies documenting the observed physical health impacts of NOx were reviewed and, where available, were summarized using risk ratios. More recently, the literature has shifted to focus on the mental health implications of NOx exposure, and a review of the current literature found five main categories of mental health-related conditions with respect to NOx exposure: common mental health disorders, sleep, anxiety, depression, and suicide. All the physical and mental health effects with available risk ratios were organized in order of increasing risk. Mental health concerns emerged as those most influenced by NOx exposure, with physical health impacts, such as asthma, only beginning to surface as the fourth highest risk. Mental health conditions occupied seven of the top ten highest risk health ailments. The results summarized in this narrative review show that there are clear positive correlations between NOx and negative physical and mental health manifestations, thus strengthening the argument in support of the reduction in ambient NOx levels.

Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)

Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model–measurement comparisons for the monitoring sites of WOUDC for the period 2019–2020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018–2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815–0.889, root mean square errors (RMSEs) of 7.769–8.729 ppb, and mean absolute errors (MAEs) of 6.111–6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543–16.916 ppb, MAE = 11.130–12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas.

Temporal dynamics of SO2 and NOX pollution and contributions of driving forces in urban areas in China

SO₂ and NO_X pollution have significantly reduced the air quality in China in past decades. Haze and acid rain have negatively affected the health of animals, plants, and human beings. Documented studies have shown that air pollution is influenced by multiple socioeconomic driving forces. However, the relative contributions of these driving forces are not well understood. In this study, using the structural equation model (SEM), we quantified the contributing effects of various forces driving air pollution in 2015 in prefecture-level cities of China. Our results showed that there has been significant control of SO₂ pollution in the past 20 years. The annual average SO₂ concentration has dropped from 83 μg/m³ in 1996 to 21 μg/m³ in 2015, while the annual average NO_X concentration has increased from 47 μg/m³ in 1996 to 58 μg/m³ in 2015. We evaluated data on the annual average concentrations of SO₂, which in some cities may mask the differences of SO₂ concentrations between different months. Hence, SO₂ pollution should continue to be controlled in accordance with existing policies and regulations. However, we suggest that NO_X should become the new focus of air pollution prevention and treatment. The SEM results showed that industrial scale, city size, and residents' activities have a significant impact on NO_X pollution. Among these, industrial scale had the highest contribution. The findings from our study can provide a theoretical basis for the formulation of NO_X pollution control policy in China.

Indoor and outdoor air concentrations of volatile organic compounds and NO2 in schools of urban, industrial and rural areas in Central-Southern Spain

Thirty two VOCs including alkanes, aromatic hydrocarbons, terpenes and carbonyl compounds together with NO₂ were investigated in a kindergarten classroom, a primary classroom and the playground in 18 schools located in rural areas, an urban area (Ciudad Real) and an industrial area (Puertollano) in the province of Ciudad Real in central southern Spain. The most abundant pollutants at schools were the aldehydes formaldehyde and hexanal. After carbonyls, n-dodecane was the most abundant compound in the study areas. The NO₂ concentrations were higher in the urban area, followed by industrial area and rural areas. For benzene, its concentration in the industrial area was significantly higher than in the urban and rural areas which reflects the magnitude of the contribution to the indoor air by petrochemical plant during the sampling period. Principal component analysis, indoor/outdoor ratios, multiple linear regressions and Spearman correlation coefficients were used to investigate the origin, the indoor pollutant determinants and to establish common sources between VOCs and NO₂. Seven components were extracted from the application of PCA to the indoor measurements accounting for 77.5% of the total variance. The analysis of indoor/outdoor ratios and correlations demonstrated that sources in the indoor environment are prevailing for most of the investigated VOCs. Benzene and n-pentane have a major relevance as outdoor sources, while aldehydes, terpenes, alkanes and most aromatic hydrocarbons as indoor sources. For NO₂, ethylbenzene and toluene both indoor and outdoor sources probably contributed to the measured concentrations. Finally, the results reported in this paper demonstrate that during the measuring period there were not great differences in the indoor air quality of the schools of the three study areas.

Throughfall and bulk deposition of dissolved organic nitrogen to holm oak forests in the Iberian Peninsula: Flux estimation and identification of potential sources

Deposition of dissolved organic nitrogen (DON) in both bulk precipitation (BD) and canopy throughfall (TF) has been measured for the first time in the western Mediterranean. The study was carried out over a year from 2012 to 2013 at four evergreen holm oak forests located in the Iberian Peninsula: two sites in the Province of Barcelona (Northeastern Spain), one in the Province of Madrid (central Spain) and the fourth in the Province of Navarra (Northern Spain). In BD the annual volume weighted mean (VWM) concentration of DON ranged from 0.25 mg l(-1) in Madrid to 1.14 mg l(-1) in Navarra, whereas in TF it ranged from 0.93 mg l(-1) in Barcelona to 1.98 mg l(-1) in Madrid. The contribution of DON to total nitrogen deposition varied from 34% to 56% in BD in Barcelona and Navarra respectively, and from 38% in Barcelona to 72% in Madrid in TF. Agricultural activities and pollutants generated in metropolitan areas were identified as potential anthropogenic sources of DON at the study sites. Moreover, canopy uptake of DON in Navarra was found in spring and autumn, showing that organic nitrogen may be a supplementary nutrient for Mediterranean forests, assuming that a portion of the nitrogen taken up is assimilated during biologically active periods.