Search Results (66)

Surface ozone is a pollutant linked to higher risks of cardiopulmonary diseases with long-term exposure. Timely forecasting of ozone levels helps authorities implement preventive measures to protect public health and safety. However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. The model is trained and tested at the Wanshouxigong site in Beijing. The ICEEMDAN method decomposes the ozone time series data to extract trends and obtain intrinsic mode functions (IMFs) and a residual (Res). Fourier period analysis is employed to elucidate the periodicity of the IMFs, which serves as the basis for selecting the prediction model (BiLSTM or persistence model) for different IMFs. Extensive experiments have shown that a hybrid model of ICEEMDAN, BiLSTM, and persistence model is able to achieve a good performance, with a prediction accuracy of R² = 0.86 and RMSE = 18.70 µg/m³ for the 36th hour, outperforming other models. Full article

The Po Valley is depicted in the literature as a region with one of the most severe air pollution profiles in Europe, frequently exceeding the permitted statutory concentration limits for several air pollutants. The aim of this paper is to present an assessment of the air quality over the Po Valley for the year 2022 as reported by ground-based air quality monitoring stations of the region and assess chemical transport modeling simulations which can enhance the spatiotemporal reporting in air quality levels which cannot be achieved by the sparse in situ monitoring station coverage. To achieve this, the concentration levels of two significant chemical compounds, namely ozone (O₃) and nitrogen dioxide (NO₂), are studied here. Measurements include the surface concentrations of in situ measurements from 28 stations reporting to the European Environment Agency (EEA), while chemical transport simulations from the Long-Term Ozone Simulation—European Operational Smog (LOTOS-EUROS) are employed for a comparative analysis of the relative levels observed in each of the two monitoring methods for air quality. The analysis of the EEA stations reports that, for year 2022, all selected monitoring stations exceeded the EU O₃ level limit for a minimum of 33 days and the World Health Organization (WHO) limit for a minimum of 78 days. The concentrations of surface O₃ and NO₂ studied by both the measurements as well as the simulations exhibit a close correlation with the documented diurnal, monthly, and seasonal variability, as previously reported in the literature. The LOTOS-EUROS CTM ozone simulations demonstrate a strong correlation with the EEA measurements, with a monthly correlation coefficient of R > 0.98 and a diurnal correlation coefficient of R > 0.83, indicating that the model is highly effective at capturing the diverse spatiotemporal patterns. The co-variability between ozone and nitrogen dioxide surface levels reported by the EEA in situ measurements reports high R values from −0.76 to −0.88, while the CTM, due to the spatial resolution of the simulations which disables the identification of local effects, reports higher correlations of −0.96 to −0.99. The CTM simulations are hence shown to be able to close the spatial gaps of the in situ measurements and provide a dependable auxiliary tool for air quality monitoring across Europe. Full article

Satellite retrievals can capture the spatiotemporal variation of O₃ over a large area near the surface. However, due to the unstable functional relationships between variables across spatiotemporal scales, the outlier predictions will reduce the accuracy of the prediction model. Therefore, a validated residual constrained random forest model (RF-RVC) is proposed to estimate the monthly and annual O₃ concentration datasets of 0.1° in China from 2005 to 2020 using O₃ precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O₃ concentrations in China and the four urban agglomerations (Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan–Chongqing (SC)) were calculated. The results show that the annual R² and RMSE of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m³. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual R² increasing from 0.59 to 0.8, and the RMSE decreasing from 17.24 μg/m³ to 10.74 μg/m³, which is significantly better than that of the RF model. The North China Plain is the focus of ozone pollution. Summer is the season of a high incidence of ozone pollution in China, YRD, PYD, and SC, while pollution in the PRD is delayed to October due to the monsoon. In addition, the trend of the O₃ and its excess proportion in China and the four urban agglomerations is not satisfactory; targeted measures should be taken to reduce the risk of environmental ozone. The research findings confirm the effectiveness of the residual constraint approach in long-term time-series modeling. In the future, it can be further extended to the modeling of other pollutants, providing more accurate data support for health risk assessments. Full article

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article

Surface ozone (O₃) and nitrogen oxides (NO_x = NO + NO₂) measured at the rural station in Belsk (51.83° N, 20.79° E), Poland, over the period of 1995–2023, were examined for long-term variability of O₃ and its relationship to changes in the air temperature and NO_x. Negative and positive trends were found for the 95th and 5th percentile, respectively, in the O₃ data. A weak positive correlation (statistically significant) of 0.33 was calculated between O₃ and the temperature averaged from sunrise to sunset during the photoactive part of the year (April–September). Recently, O₃ maxima have become less sensitive to temperature changes, reducing the incidence of photochemical smog. The ozone–climate penalty factor decreased from 4.4 µg/m³/°C in the 1995–2004 period to 3.9 µg/m³/°C in the 2015–2023 period. The relationship between O_x (O₃ + NO₂) and NO_x concentrations averaged from sunrise to sunset determined the local and regional contribution to O_x variability. The seasonal local and regional contributions remained unchanged in the period of 1995–2023, stabilizing the average O₃ level at Belsk. “NO_x-limited” and “VOC-limited” photochemical regimes prevailed in the summer and autumn, respectively. For many winter and spring seasons between 1995 and 2023, the type of photochemical regime could not be accurately determined, making it difficult to build an effective O₃ mitigation policy. Full article

As the predominant pollutant in North China during the summer months, ozone (O₃) exhibits strong oxidizing capabilities. Long-term exposure of crops to ozone will cause a decrease in various physiological indicators, affect crop yields, and pose a serious threat to food security. The North China Plain, the primary region for summer maize production in China, is afflicted by ozone pollution. In order to explore the effects of increasing O₃ concentration on the physiological characteristics and photosynthetic characteristics of summer maize, this study took summer-sown maize as the research object and carried out the ozone exposure experiment with open-top chamber (OTCs). The response of maize to O₃ exposure was studied by measuring the damage, physiological indexes and photosynthetic indexes in the silking stage (late July to late August) and filling stage (late August to mid-September). The results indicated the following: (1) Prolonged exposure to high O₃ concentrations exacerbated leaf chlorosis and damage. (2) The increase in O₃ concentration caused lipid peroxidation. The content of malondialdehyde was significantly increased by 32.6%~122.56%. At the same time, chlorophyll was destroyed and decreased by 2.17% to 4.86%. Under ozone exposure, ascorbic acid content was significantly increased by 7.58%~35.69%. The antioxidant indexes of maize were more sensitive during the filling stage. (3) Under O₃ exposure, photosynthetic rate, stomatal conductance and intercellular carbon dioxide concentration decreased significantly, indicating that the influence of O₃ on maize was mainly due to stomatal limitation. Water use efficiency and transpiration rate decreased significantly. The water use efficiency decreased by 12.84%~35.62%, which led to the weakening of the carbon fixation ability of maize and affected the normal growth and development of maize. Full article

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and reanalysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, captures the nuanced dynamics of Cyprus’s atmospheric conditions. With a temporal resolution of 15 min and a spatial of 0.05° × 0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models’ accuracy but also highlights its proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), ranging from −2 to −4% per decade, have been found in autumn, especially marked in the island’s northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of −2 to −3% per decade, which were found in spring, do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the day light integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data. Full article

Severe ozone (O₃) pollution has been recorded in China in recent years. The key precursor, volatile organic compounds (VOCs), is still not well understood in Nanning, which is a less developed city compared to other megacities in China. In this study, a year-long measurement of VOCs was conducted from 1 October 2020 to 30 September 2021, to characterize the ambient variations and apportion the source contributions of VOCs. The daily-averaged concentration of VOCs was measured to be 26.4 ppb, ranging from 3.2 ppb to 136.2 ppb across the whole year. Alkanes and oxygenated VOCs (OVOCs) were major species, contributing 46.9% and 25.2% of total VOC concentrations, respectively. Propane, ethane, and ethanol were the most abundant in Nanning, which differed from the other significant species, such as toluene (3.7 ppb) in Guangzhou, ethylene (3.8 ppb) in Nanjing, and isopentane (5.5 ppb), in Chengdu. The positive matrix factorization (PMF) model resolved six source factors, including vehicular emission (contributing 33% of total VOCs), NG and LPG combustion (19%), fuel burning (17%), solvent use (16%), industry emission (10%), and biogenic emission (5%). This indicated that Nanning was less affected by industrial emission compared with other megacities of China, with industry contributing 12–50%. Ethylene, m/p-xylene, butane, propylene, and isoprene were key species determined by ozone formation potential (OFP) analysis, which should be priority-controlled. The variations in estimated OFP and observed O₃ concentrations were significantly different, suggesting that VOC reactivity-based strategies as well as meteorological and NO_x effects should be considered collectively in controlling O₃ pollution. This study presents a year-long dataset of VOC measurements in Nanning, which gives valuable implications for VOC control in terms of key sources and reactive species and is also beneficial to the formulation of effective ozone control strategies in other less developed regions of China. Full article

Aerosol samples (PM_2.5) were collected in Xi’an (XN) from 11 August to 11 September 2021 and in Qinling (QL) from 14 July to 24 August 2021, respectively. In addition, ozone (O₃) data were collected in order to investigate the characteristics and source areas of PM_2.5 and O₃ in the Guanzhong Basin (GB). The concentrations of PM_2.5, organic carbon (OC), and elemental carbon (EC) in XN (53.40 ± 17.42, 4.61 ± 2.41, and 0.78 ± 0.60 μg m⁻³, respectively) were higher than those in QL (27.57 ± 8.27, 4.23 ± 1.37, and 0.67 ± 0.53 μg m⁻³, respectively) in summer. Total water-soluble ions (TWSIIs) accounted for 19.40% and 39.37% of the PM_2.5 concentrations in XN and QL, respectively. O₃ concentrations in summer were 102.44 ± 35.08 μg m⁻³ and 47.95 ± 21.63 μg m⁻³ in XN and QL, respectively, and they showed a significant correlation with O_x. The positive matrix factorization (PMF) model identified three main sources in XN and QL, including coal combustion source (COB), secondary aerosol (SA), and dust sources (DUSs). The potential source contribution function (PSCF) and a concentration weight trajectory (CWT) model with back-trajectory analysis showed that Inner Mongolia, the interior of Shaanxi, and nearby areas to the southwest were the sources and source areas of carbonaceous matter in XN and QL. The results of this study can contribute to the development of prevention and control policies and guidelines for PM_2.5 and O₃ in the GB. Furthermore, long-term and sustainable measuring and monitoring of PM_2.5 and O₃ are necessary, which is of great significance for studying climate change and the sustainable development of the environment. Full article

In this research, long-term monitoring data from 2020 to 2023 were used to characterize O₃ pollution in a typical inland city in northwest China (34°21′ N 109°30′ E), which indicated that ozone pollution yielded typical regular fluctuations and high ozone concentrations from April to September were observed. Ozone varied in the range of 16–176 μg/m³, and maximum peaks were found usually at 14:00–17:00 in June and July. Correlation analysis showed a significant positive relationship between ozone and temperature, with correlation coefficients of 0.93. The wind speed exhibits a similar variation as ozone. Meanwhile, negative correlations were not so notably observed among ozone, humidity, VOCs, and NOx. Finally, the empirical kinetic model OZIPR (Ozone Isopleth Plotting Program for Research) was employed to analyze the sensitivity relationship among ozone and precursor compounds by calculating EMKA (Empirical Kinetics Modeling Approach) curves. The EKMA analysis results showed that during the whole ozone pollution period, ozone formation is mainly dominated by VOCs due to all the ratios of VOCs/NOx which fell in the VOCs control region. Therefore, VOCs should be priority controlled and more measures should be taken for better ozone pollution control abatement. Full article

A long-term vertical ozone observational dataset has been provided during 2001–2019 by ozonesonde measurements in Beijing on the North China Plain. Previous studies using this dataset primarily focused on the vertical characteristics of climatological ozone and its variation; however, the driving factors of ozone variation have not been well discussed. In this study, by applying the wavelet analysis method (including continuous wavelet transform and cross wavelet) and sliding correlation coefficients to ~20 years of ozonesonde measurements collected in Beijing, we analyzed the dominant modes of ozone column variability within three height ranges over Beijing (total column ozone: TOT; stratospheric column ozone: SCO; and tropospheric column ozone: TCO). Moreover, we also preliminarily discussed the relationship between these three ozone columns and the El Niño Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), and 11-year solar activity cycle. The results revealed that the ozone columns within the three height ranges predominantly adhered to interannual variability patterns, and the short-term variabilities in TOT and SCO may have been related to eruptive volcanic activity. In comparison to the TOT and SCO, the TCO was more susceptible to the forcing influences of high-frequency factors such as pollutant transport. Similar to the results in other mid-latitude regions, strong ENSO and QBO signals were revealed in the interannual ozone column variability over Beijing. The TOT and SCO showed positive anomalous responses to ENSO warm-phase events, and the peak of the ENSO warm phase led the winter peaks of the TOT and SCO by approximately 3–6 months. During the strong cold–warm transition phase in 2009–2012, the TOT and SCO showed a significant positive correlation with the ENSO index. The strong seasonality of the meridional circulation process driven by the QBO led to a significant positive correlation between the QBO index and the TOT and SCO in the interannual cycle, except for two periods of abnormal QBO fluctuations in 2010–2012 and 2015–2017, whereas the TCO showed a time-lagged correlation of approximately 3 months in the annual cycle relative to the QBO due to the influence of the thermodynamic tropopause. In addition, analysis of the F10.7 index and the ozone columns revealed that the ozone columns over Beijing exhibited lagged responses to the peaks of sunspot activity, and there was no obvious correlation between ozone columns and 11-year solar activity cycle. Given the complex driving mechanism of the climatic factors on local ozone variability, the preliminary results obtained in this study still require further validation using longer time series of observational data and the combination of chemical models and more auxiliary data. Full article

Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions. Deep learning, as a powerful tool for fitting data, offers an alternative approach. However, most deep learning-based ozone-prediction models only take into account temporality and have limited capacity. Existing spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU). STAGRU uses a double attention mechanism, which includes temporal and spatial attention layers. It takes historical sequences from a target monitoring station and its neighboring stations as input to capture temporal and spatial information, respectively. This approach enables the achievement of more accurate results. The novel model was evaluated by comparing it to ozone observations in five major cities, Nanjing, Chengdu, Beijing, Guangzhou and Wuhan. All of these cities experience severe ozone pollution. The comparison involved Seq2Seq models, Seq2Seq+Attention models and our models. The experimental results show that our algorithm performs 14% better than Seq2Seq models and 4% better than Seq2Seq+Attention models. We also discuss the interpretability of our method, which reveals that temporality involves short-term dependency and long-term periodicity, while spatiality is mainly reflected in the transportation of ozone with the wind. This study emphasizes the significant impact of transportation on the implementation of ozone-pollution-control measures by the Chinese government. Full article

Delhi is one of the most densely populated megacities in the world, and it is experiencing deteriorating air quality due to rapid industrialization and excessive use of transportation. The limited emission control measures in Delhi have led to worsening air quality problems, which have become a serious threat to human health and the environment. In the present study, we investigate the long-term (2011–2021) interrelationship between air pollutants and the vegetation index using satellite datasets. Air pollutant data viz. nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) were obtained from NASA’s Aura satellite called the Ozone Monitoring Instrument (OMI)Additionally, the data for carbon monoxide (CO) and particulate matter 2.5 (PM_2.5) were obtained from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) model. The vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation oxide (EVI), were collected from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The analysis of both datasets revealed higher concentrations of air pollutants in the summer months when the NDVI and EVI were minimal. Furthermore, a higher pollution load was observed in the months of October–January when the NDVI and EVI were lower. Furthermore, we also investigated the spatial patterns of PM_2.5 and other gaseous pollutants (viz. CO, SO₂, and NO₂) and observed that their levels were lower in the vegetated region in comparison to the sparsely vegetated area of Delhi. The present study indicates that vegetation could ameliorate various air pollutants; however, it needs to be validated with ground observed data. Full article

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru. Full article

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone are required to understand the limits of anthropogenic emissions controlling ozone reduction. Long-term estimates of background ozone assist in characterising changes in atmospheric composition and can help quantify the influence of human activity on the atmosphere. Background tropospheric ozone measurements representative of continental air masses are scarce in Australia. Here, we use k-means clustering to identify a cluster of measurements from the long-term air quality monitoring station at Oakdale, NSW, which are likely to be representative of background air. The cluster is associated with NO_x-limited air masses of continental origin. From this analysis, we estimate background ozone representative of Eastern Australia. We find recent (2017–2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α = 0.05) trend in the mean of +1.8 (1.0–2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring. Full article