Near-surface Concentrations Research Articles

Accuracy Verification of Satellite Products and Temporal and Spatial Distribution Analysis and Prediction of the CH4 Concentration in China

In this study, the spatiotemporal variations in CH4 concentrations in China from 2003 to 2021 are investigated, and their trends are forecasted over the next decade. Based on the seventh edition standard product retrieved by the atmospheric infrared detector (AIRS) at an altitude of 500 hPa, we verified monthly CH4 products using observational data provided by the World Data Center for Greenhouse Gases (WDCGG) from six ground stations in and around China. The correlation coefficients (R values) between the two data sets ranged from 0.68 to 0.92, signifying the ability of AIRS inversion data to represent temporal and spatial changes in surface CH4 concentrations. Additionally, China was classified into three regions (steps) based on terrain, and the changes in CH4 concentrations were assessed from three perspectives: spatial distribution, interannual variation, and seasonal variation. The results revealed that the CH4 concentration decreased with elevation along a topographic gradient, with high-value areas located in the first and second steps, corresponding to the eastern Qinghai–Tibet Plateau, northern Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region. Over 19 years, the average increase in CH4 concentration has ranged from 65 to 175 ppb. In addition, the CH4 concentrations were higher during summer and autumn and lower during spring and winter. Finally, a SARIMA model was used to predict the near-surface CH4 concentration trend in China over the next ten years, which indicated a continued seasonal increase.

Analyzing upper ocean variability of the Chlorofluorocarbons in the Indian Ocean using regional high-resolution ocean biogeochemical modeling

A regional coupled ocean-biogeochemical modeling experiment is carried out over the Indian Ocean region (covering the Arabian Sea and Bay of Bengal) to generate high-resolution gridded data of Chlorofluorocarbons (CFC11 and CFC12) for the period 1999–2017. The results show that model realistically simulates hydrography of the region. It is found that western part of the Arabian Sea (AS) shows higher CFC concentration as compared to the eastern part. In the Bay of Bengal (BoB) region, the CFC decreases from north to south. The highest (lowest) surface CFC concentration in the AS and BoB is found in the winter (monsoon) season. The results indicate that while the near-surface CFC concentration of the BoB remains higher as compared to the AS, in the sub-surface (e.g., below 70–80 m depth), reverse is true. The AS region exhibits higher interannual CFC variability as compared to the BoB region. The CFC concentration over these regions decreases with time, albeit at a faster rate in the BoB region. The quantitative evaluation of the local inventories of CFC11 concentration is carried out in terms of penetration depth variability. The results indicate significant influence of ENSO on the CFC concentration of AS and BoB.

Source attribution of near-surface ozone trends in the United States during 1995–2019

Abstract. Emissions of ozone (O3) precursors in the United States have decreased in recent decades, and near-surface O3 concentrations showed a significant decrease in summer but an increase in winter. In this study, an O3 source tagging technique is utilized in a chemistry–climate model to investigate the source contributions to O3 mixing ratios in the US from various emitting sectors and regions of nitrogen oxides (NOx) and reactive carbon species during 1995–2019. We show that domestic emission reductions from energy and surface transportation are primarily responsible for the decrease in summertime O3 during 1995–2019. However, in winter, the emission control also weakens the NOx titration process, resulting in considerable increases in O3 levels from natural sources. Additionally, increases in aviation and shipping emissions and transpacific transport of O3 from Asia largely contribute to the winter O3 increase. We also found that changes in large-scale circulation favoring O3 transport from the upper atmosphere and foreign transport from Asia also explain 15 % of the increase in the US near-surface O3 levels in winter.

Ion energy dependence of dry etch damage depth in Ga2O3 Schottky rectifiers

The β-polytype of Ga2O3 has a high critical electric field strength (∼8 MV.cm−1) and can be grown as large diameter (6 in.) single crystal boules by inexpensive melt-growth techniques. This makes it promising for next generation power electronics and solar-blind UV photodetectors. Since wet etching is not feasible for patterning such devices due to the chemical inertness of Ga2O3, attention is focused on dry etching, with the attendant issue of surface modification due to chemical or ion-induced changes. In this work, we demonstrate that dry etch damage in β-Ga2O3 is manifested by a reduction in near-surface carrier concentration, possibly due to the introduction of Gav acceptor states. The depth to which the carrier density is affected depends on the interplay between ion energy and etch rate, with faster etch rates reducing the depth of the remaining damaged region. The maximum damage depth observed experimentally from capacitance-voltage profiling on Schottky rectifiers was ∼ 110 nm, well beyond the range of the ions in the Inductively Coupled Plasmas and emphasizing that rapid diffusion of point defects occurs into the sample during the etch step. Using Schottky rectifier structures as a platform to understand the effect of dry etch damage on device parameters, we found that on-state resistance was more affected than ideality factor or breakdown voltage, with a large increase (5x) for ion energies ≥ 325 eV. Reverse current density characteristics showed the presence of trap-assisted space-charge-limited conduction in the damaged layers. The implications for high power rectifier fabrication are discussed.

Effects of Climate Events on Abundance and Distribution of Major Commercial Fishes in the Beibu Gulf, South China Sea

Improving prediction of ecological responses to climate variability requires understanding how local fish population dynamics are impacted by climate events. The present study was conducted in the Beibu Gulf of the northwestern South China Sea where the fisheries are characterized by high ecological and commercial value. We evaluated the relationship between major commercial fish population dynamics (abundance and distribution) and climate periods, using survey data from 2006–2020. The analysis using random forest and GAM models show that climate events are not the best predictors for the variations of fish abundance, because abundance of most fish stocks decreases significantly with the year, and the increasing fishing pressure over time can better explain the overall downward trend in fishery stocks. However, environmental variables that correlate significantly with interannual variation in ONI may impact fish abundance in short terms. Our research suggests that climate events leading to higher surface seawater salinity in winter favors pelagic fishes by improving habitat availability, and higher near-surface chlorophyll-α concentration during La Niña events provides better food condition for overwintering fish. In addition, there is no clear evidence that climatic events have a significant impact on gravity center of fish distribution, whereas climate change has caused most fishes to move to cooler coastal waters in the north.

Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation

Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM10 and PM2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data.

The impact of using assimilated Aeolus wind data on regional WRF-Chem dust simulations

Abstract. Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.

Rapid increase in tropospheric ozone over Southeast Asia attributed to changes in precursor emission source regions and sectors

Observations indicate that tropospheric ozone (O3) concentrations over Southeast Asia have been increasing rapidly since the 1990s. Here, we quantify source contributions from geographical regions and emission sectors of the two distinct types of O3 precursors, i.e., nitrogen oxides (NOx) and volatile organic compounds (VOCs), to the increase in tropospheric O3 in Southeast Asia during 1990–2019 using an O3 source tagging technique implemented in a global chemistry-climate model. The results show that although local anthropogenic emission of NOx in Southeast Asia only contributes 18% of the annual averaged near-surface O3 concentration, the increase in local NOx emission dominates the increasing trend of O3 concentration in Southeast Asia, accounting for 107% of the regional averaged trend of 1.07 ppb decade−1. Increases in NOx emissions from East Asia and South Asia explain 29% of the increasing trend, but 9% is offset by the emission reduction in North America. The vertical O3 trends in the troposphere contributed by individual source regions are consistent with those near the surface. Ground transportation is responsible for 79% of the rapid O3 increase, followed by 39% contribution from international shipping. Because an increase in anthropogenic NOx emissions enhances the O3 production efficiency by VOCs, the increase in O3 concentrations in Southeast Asia is thereby largely contributed by methane and biogenic VOCs.

Temporal Variation of NO2 and O3 in Rome (Italy) from Pandora and In Situ Measurements

To assess the best measures for the improvement of air quality, it is crucial to investigate in situ and columnar pollution levels. In this study, ground-based measurements of nitrogen dioxide (NO2) and ozone (O3) collected in Rome (Italy) between 2017 and 2022 are analyzed. Pandora sun-spectrometers provided the time series of the NO2 vertical column density (VC-NO2), tropospheric column density (TC-NO2), near-surface concentration (SC-NO2), and the O3 vertical column density (VC-O3). In situ concentrations of NO2 and O3 are provided by an urban background air quality station. The results show a clear reduction of NO2 over the years, thanks to the recent ecological transition policies, with marked seasonal variability, observable both by columnar and in situ data. Otherwise, O3 does not show inter-annual variations, although a clear seasonal cycle is detectable. The results suggest that the variation of in situ O3 is mainly imputable to photochemical reactions while, in the VC-O3, it is triggered by the predominant contribution of stratospheric O3. The outcomes highlight the importance of co-located in situ and columnar measurements in urban environments to investigate physical and chemical processes driving air pollution and to design tailored climate change adaptation strategies.

Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All

Soils comprise the largest pool of terrestrial carbon yet have lost significant stocks due to human activity. Changes to land management in cropland and grazing systems present opportunities to sequester carbon in soils at large scales. Uncertainty in the magnitude of this potential impact is largely driven by the difficulties and costs associated with measuring near-surface (0–30 cm) soil carbon concentrations; a key component of soil carbon stock assessments. Many techniques exist to optimize sampling, yet few studies have compared these techniques at varying sample intensities. In this study, we performed ex-ante, high-intensity sampling for soil carbon concentrations at four farms in the eastern United States. We used post hoc Monte-Carlo bootstrapping to investigate the most efficient sampling approaches for soil carbon inventory: K-means stratification, Conditioned Latin Hypercube Sampling (cLHS), simple random, and regular grid. No two study sites displayed similar patterns across all sampling techniques, although cLHS and grid emerged as the most efficient sampling schemes across all sites and strata sizes. The number of strata chosen when using K-means stratification can have a significant impact on sample efficiency, and we caution future inventories from using small strata n, while avoiding even allocation of sample between strata. Our findings reinforce the need for adaptive sampling methodologies where initial site inventory can inform primary, robust inventory with site-specific sampling techniques.

Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm

Methane (CH4), the second most significant greenhouse gas after carbon dioxide, contributes significantly to global warming. Owing to its wide monitoring range and long observation time, satellite remote sensing has emerged as a popular method for monitoring CH4. Although the existing algorithm for satellite retrievals of CH4 column amount is mature, it has some drawbacks: the retrieval calculation process is complicated, and there is still a discrepancy between the satellite-retried column amount of CH4 and surface CH4 concentrations. To obtain more accurate near-surface CH4 concentrations from satellite observations, this paper proposed a conversion method based on the Extreme Gradient Boosting (XGBoost) algorithm, taking column amount retrieved by the Greenhouse Gas Observation SATellite (GOSAT) satellite, meteorological factors and near-surface methane concentrations as predictor variables. Using this method, we analyzed the importance of the characteristic factors and predicted methane concentrations at four ground monitoring sites in World Center for Greenhouse Gases (WDCGG), and reached the following conclusions: (1) The model performed well on the test set, with a sample-based cross-validation coefficient of determination (R2) of 0.79, a root mean square error (RMSE) of 0.0251 ppm and a mean absolute percentage error (MAPE) of 0.88%. (2) Of the five meteorological input features, surface net solar radiation had a greater impact on the construction of the model than the other four. (3) The CH4 monthly average concentrations predicted by this model are generally consistent with the trend of the CH4 concentrations of ground monitoring stations.

Spatial and Temporal Distribution of Near-Surface Methane Concentration in Northwest China Based on GOSAT Satellite Data

Methane (CH4) is a common greenhouse gas that plays an important role in global warming and affects people’s production and life. In order to study the temporal and spatial distribution situation of methane concentration in northwest China (Shaanxi, Gansu, Ningxia, Xinjiang, Qinghai), this article used L4B level data products monitored by GOSAT satellite from 2009 to 2019 to study the temporal and spatial distribution characteristics of near-surface methane concentration in northwest China. The results of the analysis were as follows. Firstly, the annual average concentration of near-surface methane in northwest China was in a rising state, and the maximum annual average concentration increment was in 2016. Secondly, seasonal variation of methane concentration: Autumn>Summer>Spring>Winter. Finally, spatial variation of the annual average methane concentration: Shaanxi, Ningxia, and southern Gansu>northwest Xinjiang>other northwest China regions. The methane concentration decreased from the bottom to the top gradually.

Analysis of spatiotemporal distribution of methane column concentration and emission source in permafrost area of Northeast China based on AIRS data

Northeast China has the largest high-latitude permafrost distribution zone in China. With the intensification of global warming, the carbon stored in the permafrost will gradually thaw and release in the form of methane gas to air, thus increasing the methane column concentration in the near-surface troposphere. However, at present, the spatiotemporal distribution and emission source of methane column concentration in the near-surface troposphere are not clear. In this paper, using the AIRS (Atmospheric Infrared Sounder) remote sensing data from Aqua satellite, we analyzed the distribution and change trend of the methane column concentration in the near-surface troposphere in Northeast China from 2003 to 2021, and combined with the national meteorological and field monitoring data, we studied the emission mechanism and emission source of surface methane in the permafrost area of Northeast China. Study results show that: the permafrost zone in Northeast China has a high methane emission capacity in four seasons, and the methane column concentration shows a significant double-peak seasonal variation. The first peak appears in summer (June to August) and the maximum appears in August, the second peak appears in winter (December to February) and the maximum appears in December. However, the maximum average growth rate of near-surface methane column concentration in Northeast China appeared in spring (5.378ppbv/a), the reason is that the carbon stored under the permafrost is gradually exposed and released in the form of methane. The emission sources include microbial action, methane transported by wetland groundwater, geological methane (metastable methane hydrate, steady-state methane hydrate and thermogenic methane produced in the deep underground or coal seams) stored in frozen layer. The study provide data and technical support for the estimation of carbon emissions in permafrost areas in Northeast China.

Estimation of Chlorophyll-a, TSM and salinity in mangrove dominated tropical estuarine areas of Hooghly River, North East Coast of Bay of Bengal, India using sentinel-3 data

The study aims to assess variations in spatio-temporal characteristics of water quality parameters from three tropical estuaries, namely Muri-Ganga, Saptamukhi, and Hooghly, in the western portion of the Indian Sundarbans. Reliable retrieval of near-surface concentration of water quality parameters such as Chlorophyll-a, SST & TSM from diverse aquatic ecosystems with broad ranges of tropical requirements has always remained a complex issue. In this study, application of Case 2 Regional Colour Correction (C2RCC) processor has been tested for its accuracy across different bio-optical regimes in both inland and coastal waters. Satellite images for the same period were also collected and analysed using the C2RCC processing sequence to retrieve parameters like the depth of water, surface reflectance, water temperature, inherent optical properties (IOPs), chlorophyll-a, salinity, total suspended matter (TSM), etc., using the SNAP software. In situ sampling from specific locations within these estuaries and water quality analysis were conducted for the period 2017–2019. The OLCI retrieved datasets were compared and corroborated with field survey datasets. It was observed that the highest amount of TSM was recorded at Diamond Harbour during the 2018 pre-monsoon season (301.40 mg/L field-based value and 308.54 mg/L estimated value). Similarly, chlorophyll-a had higher concentrations throughout the monsoon season (3.03 mg m−3, (field survey), and 2.96 mg m−3, (estimated) at Fraserganj and Sagar south points. A very good correlation was observed for all seasons for Chl-a (r = 0.829) and TSM (r = 0.924) between the OLCI data and in situ measurements. Higher correlation and significant ‘r’ values highlight the importance of having both field-based as well as remotely-sensed information in understanding any dynamic system in a sustained manner. Results also confirm that the water quality model using OLCI Chl-a and TSM products outperforms conventional techniques. The study demonstrates the efficacy of using Sentinel 3 OCLI data for shallow marine and estuarine remote sensing applications, especially for monitoring TSM and Chl-a concentrations.

Dry air intrusions link Rossby wave breaking to large-scale dust storms in Northwest Africa: Four extreme cases

Large dust storms in the Saharan desert and the subsequent transport of airborne dust over large distances are a major meteorological hazard. Several mechanisms associated to dust emission, occurring on a range of scales, have been previously documented, notably involving Rossby wave breaking and a low-level jet. However, the mechanistic link between the different features and actual dust concentrations has not been coherently established. Here, using a Lagrangian approach, and the conceptual view of extratropical cyclone airstreams, the role of the dry intrusion airstream for translating the influence of the upper-tropospheric Rossby wave perturbation to near-surface flow conductive for the highest dust concentrations is examined. To this end, four large-scale dust storms that were accompanied by dry intrusions in west Africa are studied. Data from the Copernicus Atmospheric Monitoring Service (validated against AERONET station data) are combined with atmospheric data from reanalysis and objectively-identified Lagrangian trajectories of dry intrusions. Common, coherent structures highlighting the role of dry intrusions link the upper-tropospheric Rossby wave breaks with the lower-tropospheric dry and cold jets. These conditions favor dust uplift and transport along an arc-shaped cold front trailing from a Mediterranean cyclone. Consequently, the southwest side of the front is characterized by the highest near-surface dust concentrations ahead of the dry intrusion outflow, where the Intertropical Discontinuity shifts equatorward by 3 to 7°. The northeast part of the front is, however, accompanied by southerly, warm conveyor belt-like flow transporting the dust northward to the Mediterranean, Middle East and/or Europe at mid and upper-tropospheric levels. While case-to-case variations exists, the central role of dry intrusions in all cases calls for systematic investigation of its occurrence as a predictive tool for large dust transport events.

Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Abstract. Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.

7Be, 210Pb, airborne particulate matter and PM10 concentrations in relation to meteorological conditions in southern Poland in 1998–2016

An analysis of the concentration of 7Be in aerosol samples collected in one of the most polluted areas in Europe (Katowice and Krakow in southern Poland) indicated seasonal variability, with a maximum in the summer months. The average concentrations of 7Be were 4616.1 μBq m−3 in Katowice and 3259.4 μBq m−3 in Krakow, respectively, and they are among the highest values recorded in Poland in the studied period (1998–2016). These cities are also characterised by Poland's highest concentrations of 210Pb (547.8 μBq m−3 and 513.2 μBq m−3). The highest radioactive concentrations of 210Pb were observed in the winter and autumn, since in the case of these industrial areas, the combustion processes related to heating in the cold season of the year are an additional source of this isotope, next to its natural origin. The airborne particulate matter concentrations at both locations correlate with the concentrations of 210Pb. The average values of PM10 concentrations (71.1 μg m−3 in Krakow to 45.0 μg m−3 in Katowice), were 2–3 times higher than the average ones recorded in northern Poland. It has been proven that air temperature is the key parameter affecting the transport of isotopes, especially in the warm season of the year, when its increase causes increased thermal convection, leading to intense vertical mixing and exchange in the troposphere. Analyses using the machine learning method allowed for an indication of the correlation between relative humidity and atmospheric precipitation, as well as higher wind speed and concentrations of 7Be which is inversely proportional. Geographical factors (the latitude of the station and the land elevation) have no impact on near-surface concentrations of 7Be in Poland.

Contrasting Near-Surface Ozone Pollution in Wet and Dry Year over China.

The near-surface ozone concentration was evaluated in two typical years with contrasting climatic impacts over the China region induced by El Niño-Southern Oscillation, which had either dry conditions (drought) with intense solar radiation and higher temperatures or wet conditions with opposite meteorological conditions. Surface ozone was observed to aggravate notably by 30% over Northern China in summer and by 50% over Eastern China in autumn in the dry year compared to the wet year. The ozone aggravation was found to be mainly ascribed to the reduced precipitation (relative humidity), enhanced solar radiation and increased temperature rather than primary emission (indicated by carbon monoxide). The health impacts showed the mortality attributable to ozone sharply increased by ~55% in Guangdong while the number of cases dying from ozone-related respiratory diseases per 100,000 population at risk was elevated by ~41% and ~17% for Guangdong (in the Pearl River Delta) and Jiangsu (in the Yangtze River Delta) province (two regions that have been reported to be highly influenced by surface ozone in China), respectively, in the dry year relative to the wet year, indicative of the significant adverse health effects of ozone aggravation. These results highlight the essential contribution of climate anomalies to surface ozone pollution. Efforts to suppress ozone aggravation can be beneficial to public health if extreme drought is predicted, and reasonable policy is implemented.

Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and CO), we propose a robust estimation framework called Learning Air Pollutants from Satellite Observations (LAPSO). The principle of LAPSO is to derive a non-linear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyses based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models, lower hardware requirements as well as a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.8 in polluted areas and uncertainty ≪ 5 μg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOMI onboard the Sentinel-5P demonstrated marginally better performance for estimating PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> . Although the selection of satellite observations did not significantly affect the results of O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> estimation, the number and spatial sampling density of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> sites imposed large impacts on the O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

基于TROPOMI的近地面臭氧浓度估算研究

基于TROPOMI的近地面臭氧浓度估算研究