Hybrid attention based deep learning for forecasting boundary layer ozone using satellite derived profiles.

Gandaki Province is located in the mid-region of Nepal, encompassing an area of 21773 km2, which is approximately 14.66% of Nepal's total land area. The province experiences a diversity of climate variability. A massive glacier in the Himalayas is a crucial part of the freshwater and water ecosystem. Over the past two decades, anthropogenic activities within and near the boundary regions have degraded the regional ecosystem of the province. We investigate the Spatio-temporal characteristics and long-term trends derived from satellite measurements of tropospheric NO2 from the Ozone Monitoring Instrument (OMI) for the period 2005-2020. Tropospheric NO2 over the Gandaki province varies from 0.4- 0.7 x 1015 molec. cm-2 with higher values > 0.7 x 1015 molec. cm-2 over the southern region of the province and comparatively small < 0.7 x 1015 molec. cm-2 over the high-altitude region. We use a linear regression model to find the trend. There is a significant increasing trend in NO2 up to 0.1 × 1015 molec. cm–2 yr–1, with slightly higher values in the southern region of the province. The trends are seasonally more prominent in autumn (0.1 × 1015 molec. cm–2 yr–1). The main sources of NO2 in the regions are road transport, followed by agriculture. This study reveals that the high mountainous, pristine areas of the province are becoming gradually polluted due to the high anthropogenic activities within the region and the influence of nearby regions in recent years, indicating the impact of socioeconomic changes in the province.

Abstract The study on solar ultraviolet radiation (UVR) is essential for understanding the solar status of any location, which enables the determination of the level of exposure to solar UV radiation and the necessary precautions to be taken at that location. The measurement of solar UV radiation and its validation are increasingly prevalent worldwide, using various ground-based observations and satellite estimates. This paper compares the Ozone Monitoring Instrument (OMI)/Aura satellite solar ultraviolet index (UVI) with the ground-based UVI measurements at Biratnagar, Pokhara, Kathmandu, and Lukla in Nepal using data from 2009 to 2012. Trend analysis of UVI using moving averages, a box plot of overpass UVI and Total Ozone Column (TOC) to analyze their trends, and a scatter plot for comparison of OMI overpass UVI with ground-based UVI. Statistical tools were used to compare the datasets for UVI in all-sky conditions. The results show that satellite estimates tended to overestimate ground-based UVI levels, with a mean bias, relative bias, MAPE, RMSE, correlation coefficient, and standard deviation of error corresponding to 0.92, 1.9, 28.44, 1.6, 0.69, and 1.44 for UVI, respectively. The result also shows that the altitude effect is found to be (6.5-8.8) %/km approximately.

Estimation of surface formaldehyde (HCHO) concentrations and HCHO-related cancer risk in a pair of agricultural zones situated in southern Mexico through the Ozone Monitoring Instrument

Global assessment of aerosol radiative effects: New insights from observations, reanalysis, and climate models.

Abstract. Tropospheric ozone (O3) is a harmful secondary atmospheric pollutant and an important greenhouse gas. Multiple satellite records have shown conflicting long-term O3 trends across regions of the globe, including Europe. Here, we investigate lower-tropospheric sub-column O3 (LTCO3, surface – 450 hPa) records from three ultraviolet (UV) sounders produced by the Rutherford Appleton Laboratory (RAL): the Global Ozone Monitoring Experiment (GOME, 1996–2010), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, 2003–2011) and the Ozone Monitoring Instrument (OMI, 2005–2017). GOME and SCIAMACHY detect negative trends of approximately −0.2 DU yr−1, while OMI indicates a negligible trend. The TOMCAT 3-D chemical transport model was used to investigate processes driving simulated trends and to identify possible reasons for satellite trend discrepancies. The simulated LTCO3 trends were negligible (consistent with ozonesonde trends), even when spatiotemporally co-located to the satellite level-2 swath data and convolved by averaging kernels (i.e. a measure of the satellite retrieval vertical sensitivity). Model sensitivity experiments with the emissions or meteorology fixed to 2008 also showed negligible LTCO3 trends between 1996 and 2018, indicating that changes in emissions and meteorology had a limited impact on LTCO3 temporal evolution. Given the substantial decrease in air pollutant emissions, this was unexpected, while year-to-year variability dominated the meteorological influence on LTCO3. Finally, we find a negligible trend in the long-term stratosphere O3 flux into the free troposphere over this period arriving over Europe. Overall, our observational and modelling analysis indicates that European LTCO3 trends have been stable between 1996 and 2018.

Solar ultraviolet (UV) radiation and atmospheric ozone are critical determinants of ecosystem dynamics and human health. This study aimed to assess the terrestrial profile of solar UV radiation and its genotoxic risk in the South American subtropical region (29° S 53° W). From 2005 to 2021, ground-based physical sensors showed an increase of approximately 50% in UVB (280-315 nm; +0.28 kJ/m2 per year), but no significant trend in UVA (315-400 nm). Despite the existence of four defined climatic seasons, simultaneous measurements using UVA, UVB, and DNA-based sensors revealed two distinct UV seasons: a high-UV season encompassing spring and summer, and a low-UV season encompassing winter and autumn. Notably, spring sunlight was found to be as genotoxic as summer sunlight, and even winter and autumn sunlight may pose a genotoxic risk on cloudless days, as indicated by measurements of cyclobutane pyrimidine dimers and oxidized bases. Given the rising UVB levels without an increase in UVA, we investigated satellite-derived ozone data from NASA's ozone monitoring instrument (OMI) and total ozone mapping spectrometer (TOMS) sensors across South America and Antarctica. Overall, analysis from 1979 to 2021 showed negative ozone trends at 2° S 54° W (Santarém), 23° S 46° W (São Paulo), and 29° S 53° W (Santa Maria) even after the onset of the Montreal Protocol, while positive trends were observed at 53° S 70° W (Punta Arenas) and 62° S 58° W (Brazilian Antarctic Station) following the protocol. Strikingly, the UVB and ozone trends observed across seasons suggest that ozone is being transported poleward persistently rather than seasonally, possibly driven by a climate change-induced acceleration of the Brewer-Dobson Circulation. This persistent pattern demonstrates that ozone depletion at low and mid-latitudes is not limited to springtime but persists throughout the year. Our findings indicate that low- and mid-latitudes in South America are experiencing climate changes, stratospheric ozone depletion, and increased UVB incidence, resulting in heightened genotoxic risks, highlighting the urgent need for monitoring and mitigation strategies.

Abstract. Biogenic isoprene emissions play an important role in air quality so it is important to quantify their response to extreme events such as drought. This study aims at constraining drought stress on biogenic isoprene emissions in South Korea using satellite formaldehyde (HCHO) column, the key product of isoprene oxidation, and a chemistry transport model (GEOS-Chem) with Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). It was found that the HCHO column from the Ozone Monitoring Instrument (OMI) increased by 5.4 % under the drought condition compared to the normal condition, but GEOS-Chem simulated a 20.23 % increase indicating an overestimation of isoprene emissions under drought. We implemented two existing drought stress algorithms in MEGAN2.1 and found they were not effective to reduce HCHO column biases in South Korea because those algorithms were proposed and developed for the Southeast United States (SE US). To improve this, we applied an iterative finite difference mass balance (IFDMB) method to estimate isoprene emissions using the OMI HCHO column. With this method, isoprene emissions were reduced by 60 % under the drought condition compared to those simulated by the standard MEGAN2.1 implemented in GEOS-Chem. The increase of HCHO column under the drought condition compared to the normal condition was also reduced to 10.71 %, which is comparable to that in the satellite retrievals. Based on isoprene emission difference between MEGAN2.1 and IFDMB, we developed the empirical equations to adjust isoprene emissions in South Korea that also improved model prediction of the secondary pollutant such as ozone.

Anthropogenic emissions dominate long-term trends of ozone production sensitivity in southeastern China derived from the ozone monitoring instrument.

Nonlinear ozone response to extreme high temperature in a subtropical megacity basin: Integrated observation and modeling analysis.

Triple-platform validation of TROPOMI v2.4 NO2 retrievals: Quantifying surface albedo-driven changes across monsoon-vegetation regimes.

Abstract Variability in Arctic stratospheric ozone (ASO) has significant implications for surface climate. Using observational reanalysis datasets and Ozone Monitoring Instrument data, we found that the springtime ASO variations since the 2000s can serve as a precursor to El Niño–Southern Oscillation in the subsequent winter. Springtime ASO variability has become pronounced, particularly over Eurasia, due to the asymmetric structure of the Arctic stratospheric polar vortex. With the return of solar radiation to the Arctic in spring, elevated ASO increases solar absorption over Eurasia, contributing to localized stratospheric heating. This heating induces an upper-tropospheric cyclonic circulation over Siberia, facilitating wave energy propagation toward the tropical Pacific. Consequently, upper-level easterly and low-level westerly wind anomalies emerge over the equatorial Pacific, favoring El Niño development (cf. La Niña for decreased ASO). These results highlight the importance of chemical–radiative–dynamical processes in the Arctic stratosphere for understanding tropospheric climate variability.

Abstract. Using co-located satellite observations from the Aqua Moderate Resolution Imaging Spectroradiometer, the Aqua Cloud and the Earth Radiant Energy System, the Special Sensor Microwave Imager/Sounder, and the Ozone Monitoring Instrument, we investigated changes in absorbing aerosol direct radiative forcing (ADRF) in the spring through fall Arctic from 2005–2020 through an observation-based method, assisted by a neural network for estimating aerosol-free sky top-of-atmosphere (TOA) radiative fluxes, and an innovative, Monte Carlo-based method for estimating uncertainties in derived ADRF values. This study suggests that Arctic ADRF is a strong function of observing conditions, and changes in Arctic sea ice concentration (SIC) and cloud properties introduce a complex scenario for estimating ADRF. For example, the TOA ADRF reverses sign from negative (cooling) to positive (warming) for SIC above 60 % for a region with a relatively cloud-free scene. ADRF trends over Arctic land surfaces are primarily negative. Strong negative ADRF trends of up to −4 W m−2 were found over northern Russia and northern Canada in the summer months. Both positive and negative ADRF trends were found over the Arctic Ocean in the boreal summer, though these trends are much weaker than the over-land trends. Positive ADRF trends in the Arctic Ocean north of northeastern Russia and northern Canada are as high as +1.0 W m−2 per study period. The trend results suggest that increasing amounts of absorbing aerosols in the Arctic have a cooling effect from TOA that could act to counter Arctic warming.

Anthropogenic aerosols play a crucial role in contributing to visibility reduction, degrading air quality, altering the hydrological cycle, and perturbing the Earth's atmospheric energy balance. This study investigates the anthropogenic particulate matter emissions and their associated impacts on air quality, with a particular emphasis on the rising problem of smog formation in Peshawar, Pakistan. Therefore, for the first time, this study assesses US Consulate NowCast data on fine particulate matter (PM2.5) with ozone monitoring instrument (OMI) data on trace gases over the period from 1 January 2020 to 31 December 2024. It was observed that the daily and annual PM2.5 concentrations of 90 and 99µg/m3, respectively, surpass the permissible National Environmental Quality Standards (NEQS) and World Health Organization (WHO) air quality guidelines. The daily concentrations of PM2.5 were found to exceed threefold NEQS and sixfold WHO limits, while the annual amount was 7 times NEQS and 20 times WHO. The results of Mann's Kendal (MK) test revealed a significant (p ≤ 0.05) increasing trend in daily, 30-day rolling averages, and annual concentrations of PM2.5 from 2020 to 2023. In 2024, it was found to decrease, but it was still higher in the winter season. In addition, PM2.5 showed a moderate to strong correlation with key trace gases, including nitrogen dioxide, ozone, and sulfur dioxide. Likewise, PM2.5 concentrations were evaluated using the air quality index, and the study area was classified as 51% unhealthy and 26% unhealthy for sensitive groups. This study concludes that the air quality of Peshawar is worsening, with PM2.5 levels consistently surpassing air quality guidelines, highlighting the need for immediate and sustainable strategies to combat smog pollution in the region.

Abstract. Ground-level ozone (O3) formation in urban areas is nonlinearly dependent on the relative availability of its precursors: oxides of nitrogen (NOx) and volatile organic compounds (VOCs). To mitigate O3 pollution, a crucial question is to identify the O3 formation regime (NOx-limited or VOC-limited). Here, we leverage ground-based O3 observations alongside space-based observations of O3 precursors, namely, nitrogen dioxide (NO2) and formaldehyde (HCHO), to study the long-term shifts in O3 chemical regimes across global source regions. We first derive the regime threshold values for the satellite-derived HCHO/NO2 ratio by examining its relationship with the O3 weekend effect. We find that a regime transition from VOC-limited to NOx-limited occurs around 3.1 [2.7–3.4] for HCHO/NO2 with slight regional variations. By integrating data from four satellite instruments, including Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), we built a 27-year (1996–2022) satellite HCHO/NO2 record from which we assess the long-term trends in O3 production regimes. A discernible global trend towards NOx-limited regimes is evident, particularly in developed regions such as North America, Europe, and Japan, with emerging trends in developing countries like China and India over the past 2 decades. This shift is supported by both increasing HCHO/NO2 ratios and a diminishing O3 weekend effect. Yet, urban areas still hover in the VOC-limited and transitional regime on the basis of annual averages. Our findings stress the importance of adaptive emission control strategies to mitigate O3 pollution.

Abstract. Launched in 2020, the Korean Geostationary Environmental Monitoring Spectrometer (GEMS) is the first geostationary satellite mission for observing trace gas concentrations in the Earth's atmosphere. Observations are made over Asia. Geostationary orbits allow for hourly measurements, which lead to a much higher temporal resolution compared to daily measurements taken from low-Earth orbits, such as by the TROPOspheric Monitoring Instrument (TROPOMI) or the Ozone Monitoring Instrument (OMI). This work estimates the hourly concentration of surface nitrogen dioxide (NO2) from GEMS tropospheric NO2 vertical column densities (VCDs) and additional meteorological features, which serve as inputs for random forests and linear regression models. With several measurements per day, machine learning models can use not only current observations but also those from previous hours as inputs. We demonstrate that using these time-contiguous inputs leads to reliable improvements regarding all considered performance measures, such as Pearson correlation or mean square error. For random forests, the average performance gains are between 4.5 % and 7.5 %, depending on the performance measure. For linear regression models, average performance gains are between 7 % and 15 %. For performance evaluation, spatial cross-validation with surface in situ measurements is used to measure how well the trained models perform at locations where they have not received any training data. In other words, we inspect the models' ability to generalize to unseen locations. Additionally, we investigate the influence of tropospheric NO2 VCDs on the performance. The region of our study is South Korea.

Abstract. Accurate global aerosol single-scattering albedo (SSA) data are critical for assessing aerosol radiative effects and identifying aerosol composition. However, current satellite-based SSA retrievals are both limited and highly uncertain, whereas the more accurate ground-based observations lack global coverage. In this study, we employ an ensemble Kalman filter (EnKF) data synergy technique to construct two monthly mean SSA datasets over land by synergizing the Ozone Monitoring Instrument (OMI) and Polarization and Directionality of the Earth's Reflectance (POLDER) instrument with Aerosol Robotic Network (AERONET) observations, namely, the Merged-OMI and Merged-POLDER datasets. The background ensemble is constructed with 231/106 members using all monthly mean OMI/POLDER SSA available to represent the variability of the SSA field. Then, AERONET measurements are assimilated into each satellite dataset using the EnKF approach. The merged datasets show substantial improvements against the original products, with the correlation coefficient increased by up to 100 % and the mean absolute bias (MAB) and root mean square error (RMSE) reduced by more than 30 % compared with the AERONET results. Cross-validation using independent AERONET observations shows an average increase of 64 % in correlation, an 11 % reduction in RMSE, and a 10 % reduction in MAB for the Merged-OMI dataset, as well as similar – although weaker – improvement for Merged-POLDER mainly due to the smaller sample size. This study confirms the effectiveness of the EnKF technique in extending the information obtained from ground stations to larger regions. The two merged datasets generated in this study, available at https://doi.org/10.5281/zenodo.14294462 (Dong, 2025), can offer more accurate SSA estimates for assessing aerosol radiative forcing and improving climate modeling, serving as an important resource for advancing global aerosol research.

Abstract. Quantifying changes in global and regional tropospheric ozone is critical for understanding global atmospheric chemistry and its impact on air quality and climate. Satellites now provide multi-decadal records of daily global ozone profiles, but previous studies have found large disagreements in satellite-based ozone trends, including in trends from different products based on the same spectral radiances. In light of these disagreements, it is critical to quantify to what degree the observed trend is attributable to measurement error for each product by comparing satellite-retrieved ozone to long-term measurements from ozonesondes. NASA's TRopospheric Ozone and its Precursors from Earth System Sounding (TROPESS) project provides satellite retrievals of ozone from a suite of instruments, including Cross-track Infrared Sounder (CrIS), Atmospheric Infrared Sounder (AIRS), and multispectral combinations such as AIRS and Ozone Monitoring Instrument (OMI) (joint AIRS+OMI) using a common algorithm. We compare these products to ozonesondes and find that the evolution of global tropospheric ozone satellite–sonde biases for TROPESS CrIS (0.21 ± 3.6 % decade−1, 2016–2021), AIRS (−0.41 ± 0.57 % decade−1, 2002–2022), and joint AIRS+OMI (1.1 ± 1.0 % decade−1, 2004–2022) are less than the magnitude of trends in global tropospheric ozone reported by the Tropospheric Ozone Assessment Report Phase 1 (TOAR-I). We further quantify the bias in regional trends, which tend to be higher but with a smaller number of sondes, which can impact the satellite–sonde bias and trend. Our work represents an important basis for the utility of using satellite data to quantify changes in atmospheric composition in future studies.

Abstract This study investigates the complex interactions between aerosol absorption properties and maximum temperature during pre‐monsoon seasons (2005–2023) across Indo‐Gangetic Plains and Central India (21.5°–28.5° N, 69.5°–88.5° E). Utilising satellite‐derived aerosol measurements from the Ozone Monitoring Instrument (OMI) and maximum surface temperature data from the India Meteorological Department (IMD), both spatial patterns and temporal evolution of aerosol‐temperature coupling were analysed through correlation studies and vector autoregression (VAR) modelling. Our findings reveal distinct spatial gradients in aerosol distribution, with Aerosol Optical Depth (AOD) increasing west‐to‐east (0.35–0.93) and stronger absorption patterns in the eastern sector (Aerosol Absorption Optical Depth (AAOD): 0.037–0.092). Post‐2018, the region experienced intensified Ultraviolet Aerosol Index (UVAI) and increased frequency of temperature extremes exceeding 40°C, with 2021 marking a peak of 162 high UVAI (>2) events. To investigate UVAI‐temperature interactions, relative humidity and wind components were included in the VAR model with a consistent 2‐day lag relationship. Our analysis revealed regionally distinct response patterns: the Primary Region (eastern Chhattisgarh‐western Odisha) exhibits oscillatory temperature responses to UVAI shocks with multiple fluctuations persisting through day 10, with relative humidity functioning as a critical mediating factor and wind patterns establishing feedback mechanisms with aerosols. In contrast, the Secondary Region (eastern Uttar Pradesh) shows a smoother, monotonic temperature response with a more straightforward pathway from aerosol forcing to temperature response. Fire events are associated with only 6.1% of UVAI events (1.0–5.0), with declining fire association as UVAI intensity increases indicating substantial contributions from other sources, including industrial emissions and desert dust. These findings highlight the region‐specific nature of aerosol‐temperature coupling and its recent intensification, with important implications for understanding regional climate dynamics and air quality management strategies in South Asia.

This study presents a comprehensive analysis of Tropospheric Columnar NO2 (TCN) concentrations spanning 17 years (2005-2022) in the Northeastern Region of India (NERI). Remote sensing data from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite was utilized in analyzing the spatiotemporal patterns of nitrogen dioxide (NO2) concentrations within the region. NO2 is a prominent atmospheric pollutant that emerges from diverse sources like industrial emissions, vehicle combustion, biomass burning, and natural processes such as lightning and soil emissions. The varying levels of NO2 pollution in the NERI, with its distinctive topography and meteorological behaviors, may be attributed to urbanization, population growth, and energy utilization. TCN concentrations peak during pre-monsoon and winter months, driven primarily by factors like biomass burning and anthropogenic activities. Long-term data reveals an overall TCN increase, reflecting growing influences from rising vehicles, industrial expansion, and population density. Monthly variations indicate the significance of the pre-monsoon season, characterized by elevated NO2 levels influenced by lightning and transported NO2. Forest fires, biomass burning, and combustion engines contribute as major sources of both natural and anthropogenic NO2. Frequency distribution analysis results exhibit varying air quality statuses across NERI states, emphasizing the need for targeted interventions in regions consistently experiencing high TCN levels. Furthermore, the study assesses the impact of the COVID-19 pandemic, identifying fluctuations in NO2 concentrations during lockdowns in pre-monsoon seasons. This research emphasizes the requirement for strong monitoring and mitigation strategies to combat increasing NO2 pollution in the NERI, addressing air quality and broader environmental health issues, necessitating well-informed measures for healthier living conditions.