Search

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions. [ABSTRACT FROM AUTHOR]

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We use sensitivity analysis and visualization technology to open the neural network black box data model, and quantitatively discuss the potential impact of the input data on the target variables. This technique provides ground-level PM2.5 concentrations with high spatial resolution (0.01°) and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality. [ABSTRACT FROM AUTHOR]

Accurate measurement of air gases concentration are crucial for effective air quality management and minimizing the harm caused by pollution events, as well as providing guidance for healthy travel. We developed develop a physical informed deep learning model that combines a traditional atmospheric chemical transport model and a data-based deep learning model. The model uses multisource observation data to constrain and optimize the results of atmospheric pollution gases concentrations. The accuracy and spatial resolution in central and eastern China (20–45°N, 100–125°E) have been improved compared to the original Weather Research and Forecasting model coupled to Chemistry (WRF-CHEM) results by incorporating constraints from ground-based and satellite observations. Compared with the WRF-CHEM results, our model increases the verification Pearson coefficient (R) of NO2 and CNEMC sites from 0.56 to 0.8, and the RMSE decreases from 25.33 to 15.54 μg cm−3. For O3 results, the model increased the verification R of the CNEMC from 0.57 to 0.76, and the RMSE decreased from 24.55 to 20.22 cm−3. We employed independent MAXDOAS monitoring to authenticate the HCHO results. The R was 0.78, while the validation results for satellite HCHO and MAXDOAS exhibited a R of 0.63 and the validation results for WRF-CHEM and MAXDOAS demonstrated a R of 0.11. Additionally, the RMSE was 6.97E15 mole cm−2, whereas the validation results for satellite HCHO and MAX-DOAS had an RMSE of 7.16E15 mole cm−2. By utilizing satellite remote sensing to effectively capture the spatial distribution of pollutants, and by combining satellite data with other sources of monitoring data and optimizing our models, we have improved the accuracy of our results and have overcome the limitation of cloud coverage. As a result, we are now able to better understand the space-time distribution of pollutants in different regions. • By integrating multi-source data and constructing a PINN model, comprehensive coverage of O3, HCHO, and NO2 is achieved. • Satellite monitoring results can effectively improve the accuracy of the model and the spatial distribution of the results. • The method effectively filled in the results of pollutant gas concentration in rural areas without national control stations. [ABSTRACT FROM AUTHOR]

Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations of vertical air pollutant and wind profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and Doppler wind lidar, respectively. The northwest and the northeast are the two privileged wind directions. The wind direction and total transport scenarios were dominantly the northwest during clear days, turning to the northeast during the polluted days. Increased transport flux intensities of aerosol were found at altitudes below 400 m on heavily polluted days from the northeast to the southwest over the city. Considering pollution dependence on wind directions and speeds, surface-dominated northeast transport may contribute to local haze events. Northwest winds transporting clean air masses were dominant during clean periods and flux profiles characterized by high altitudes between 200 and 600 m in Luoyang. During the COVID-19 lockdown period in late January and February, clear reductions in transport flux were found for NO2 from the northeast and for HCHO from the northwest, while the corresponding main transport altitude remained unchanged. Our findings provide better understandings of regional transport characteristics, especially at different altitudes. [ABSTRACT FROM AUTHOR]

Vertical profiles and stratospheric HNO3 and HCl columns are retrieved by ground-based high resolution Fourier transform infrared spectroscopy (FTIR) remote sensing measurements at the Hefei site in China. The time series of stratospheric HNO3 and HCl columns from January 2017 to December 2019 showed similar annual variation trends, with an annually decreasing rate of (−9.45 ± 1.20)% yr−1 and (−7.04 ± 0.81)% yr−1 for stratospheric HNO3 and HCl, respectively. The seasonal amplitudes of stratospheric HNO3 and HCl are 2.67 × 1015 molec cm−2 and 4.76 × 1014 molec cm−2 respectively, both reaching their maximum in March and their minimum in September, due to the tropopause height variation. Further, HNO3 and HCl data were used to compare with Microwave Limb Sounder (MLS) satellite data. MLS satellite data showed similar seasonal variations and annual rates with FTIR data, and the stratospheric HNO3 and HCl columns of the two datasets have correlation coefficients (r) of 0.87 and 0.88, respectively. The mean bias between satellite and FTIR data of stratospheric HNO3 and HCl columns are (−8.58 ± 12.22)% and (4.58 ± 13.09)%, respectively. [ABSTRACT FROM AUTHOR]

Traditional ground-based air sampling measurements of air quality have blind monitoring areas in the junctions between provinces, cities and urban and rural areas, and they lack the ability of vertical monitoring. Stereoscopic hyperspectral remote sensing techniques provide a promising strategy for improving our understanding of air pollution. Satellite and ground based hyperspectral remote sensing techniques have been demonstrated to have unparalleled technical advantages in monitoring the horizontal and vertical distributions of air pollutants compared to other monitoring techniques. However, to unveil the complex evolutions and processes of the atmospheric environment, the current stereoscopic hyperspectral remote sensing techniques still face several technical bottlenecks, such as a limited temporal resolution in horizontal space, a limited stereoscopic spatial resolution, the limited types of trace gases, the impact of cloud coverage, and the difficulty in nighttime monitoring. The new technical requirements mainly include the following changes: (1) from horizontal and vertical to grid-stereoscopic monitoring; (2) from kilometer to meter resolutions; and (3) from once a day to full-time monitoring with a high temporal resolution. In this article, we systematically review the recent advances in satellite- and ground-based hyperspectral remote sensing techniques, including China's first hyperspectral satellite GF-5, hardware, algorithms, and applications. Moreover, we discuss the broad application prospects of the unmanned aerial vehicle hyperspectral remote sensing monitoring system, the active hyperspectral remote sensing monitoring system, and machine learning in air pollution monitoring in the future. We recommend using the expected multi-means joint hyperspectral stereoscopic remote sensing monitoring mode to assist the effective monitoring and regulation of air pollution in the future. [ABSTRACT FROM AUTHOR]

Atmospheric formaldehyde (HCHO) is considered as one of significant oxidization intermediate of most volatile organic compounds (VOCs). Previous studies using HCHO/NO 2 ratio for ozone formation sensitivity are based on the hypothesis that HCHO is a reliable indicator for total VOC reactivity (VOCR), while few studies focused on evaluating this hypothesis. To reassess this assumption, three fitting methods have been proposed for correlation analysis between the HCHO and VOCR, showing the Pearson correlation coefficient increased in the following order: logarithmic fitting < linear fitting < exponential fitting. Near-surface HCHO VMRs and the exponent-transformed VOCR showed good agreement during the entire daytime, indicating that HCHO concentrations primarily depend on VOCR, especially at midday and afternoon. High correlation coefficients are not only found between the HCHO and VOCR, but also between the HCHO and ozone formation potential (OFP), implying that HCHO is strongly influenced by the photochemical oxidation of VOCs and closely linked to ozone formation. Furthermore, good consistencies were exhibited between TROPOMI HCHO VCDs and the calculated photochemical property parameters of NMHCs (e.g., Total NMHCs, VOCR, and OFP), suggesting that satellite-based HCHO columns can infer the VOCs emissions over large spatial areas. We also make an attempt to convert the concentration of HCHO to total Prop-equiv concentration of NMHCs based on the linear regression analysis method, which overestimates the measured values by about 16%. Our findings imply that HCHO has the feasibility being a proxy for VOCs, which also indirectly proves the reasonability of HCHO/NO 2 ratio to indicate ozone sensitivity. [Display omitted] • HCHO concentrations primarily depend on VOCR especially at midday and afternoon. • Good consistency is found between the HCHO and ozone formation potential. • Ambient HCHO has the ability being a proxy for total NMHCs. • TROPOMI HCHO VCDs has the ability to infer the VOCs emissions. [ABSTRACT FROM AUTHOR]

Abstract Carbon monoxide (CO) is regarded as a useful tracer of biomass burning and anthropogenic pollution, so CO measurements can provide valuable information about the intensity of various anthropogenic activities. However, the emission estimates of CO based on inventories are associated with high uncertainties, especially in China. As CO is co-emitted with CO 2 in the combustion of carbonaceous fuels, the relationship between CO and CO 2 is often used to estimate regional CO emissions. Hefei is located in the area of eastern central China, which is one of the most industrialized regions in China, with severe regional air pollution. The enhancement slopes of ∆CO to ∆CO 2 were calculated and compared from ground-based remote sensing observations, surface in-situ measurements, satellite and emission inventory data at the Hefei site during the period from September 2015 to August 2017. Both inventory based ratios of ΔCO to ΔCO 2 are significantly larger than the ratios based on the observation data, including Fourier Transform Spectrometer (FTS) data, in-situ data, and satellite data. Further the CO emissions in the central China were estimated from the enhancement slopes of ∆CO/∆CO 2 combined with the CO 2 emission inventory. The CO emission estimated from the ground-based FTS observations and the Peking University (PKU) inventory based CO 2 emission is about 10.96 ± 0.88 and 11.95 ± 0.71 Tg CO yr−1 during the 2015–2016 and the 2016–2017 period, respectively. The CO emission estimated from the ground-based FTS observations and the Emission Database for Global Atmospheric Research (EDGAR) inventory based CO 2 emission is about 11.27 ± 0.91 and 12.35 ± 0.74 Tg CO yr−1, respectively. So the CO emissions estimated from the ground-based FTS data and the different inventory based CO 2 emission show a good agreement. However, CO emissions derived from FTS data are substantially lower than those calculated directly from the inventories, i.e. there is a large difference between CO emissions derived from FTS and CO emissions directly derived from the two inventories. The phenomenon suggests that the emission inventories greatly overestimate the actual CO emission in the study area. This study estimates the regional CO emissions from ground-based remote sensing observations and investigates how much the difference is between the emissions from inventories and ground-based measurements. Highlights • The enhancement slopes of ∆CO to ∆CO2 were calculated and compared from ground-based remote sensing observations, surface in-situ measurements, satellite and emission inventory data at the Hefei sit. • The CO emissions estimated from the ground-based FTS data and the different inventory based CO 2 emission show a good agreement. • The CO emissions derived from ground-based remote sensing are substantially lower than those calculated directly from the inventories. [ABSTRACT FROM AUTHOR]

Abstract Beijing–Tianjin–Hebei area is suffering from atmospheric pollution from a long time. The understanding of the air pollution mechanism is of great importance for officials to design strategies for the environmental governance. Mixing layer height (MLH) is a key factor influencing the diffusion of air pollutants. It plays an important role on the evolution of heavy pollution events. Light detection and ranging (lidar), is an effective remote-sensing tool, which can retrieve high spatial and temporal evolution process within mixing layer (ML), especially the variation of MLH. There are many methods to retrieve MLH, but each method has its own applicable limitations. The Mie-lidar data in Beijing was firstly used to compare three different algorithms which are widely used under different pollution levels. We find that the multi-layer structure near surface may cause errors in the detection of mixing layer. The MLH retrieved based on image edge detection was better than another two methods especially under heavy polluted episode. Then we applied this method to investigate the evolution of the mixing layer height during a pollution episode in December 2016. MLH at Gucheng county showed the positive correlation with the concentration of particulate matters during the start of this pollution episode. The elevated pollution level in Gucheng was not associated with MLH's decrease, and the significantly increased particulate matters raised the boundary layer, which trapped the pollutants near the surface. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O 3) profiles, especially within the boundary layer, are essential for studying the vertical, temporal, spatial variations, as well as the formation sensitivity and regional transport of O 3. However, it is rare to find continuous tropospheric O 3 profiles with high temporal and spatial resolutions without blind areas using current remote sensing technologies, with issues such as low near-surface sensitivity or systematic blind areas from satellite and LiDAR observations, respectively, being encountered. In this study, multi-source data including stratospheric O 3 profiles from external datasets and local monthly dependent a priori profiles were fused in the retrieval algorithm, then vertical O 3 profiles from the near-surface to the free troposphere were retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. With the aim of deriving a set of best practice recommendations for applying a profile inversion algorithm to long-term observations, we comprehensively investigated the influence of various settings on profile retrieval, with emphasis on the selection of a Fraunhofer reference spectrum and appropriate a priori profiles in the upper troposphere. These recommendations are essential for applying the algorithm to real long-term measurements. In this study, tropospheric O 3 profiles were retrieved for operational MAX-DOAS observations in Beijing, and the results, especially for the boundary layer, were evaluated in detail with respect to well-established independent O 3 datasets, including one-year ozonesonde profiles and tower-based in-situ measurements at different altitudes. A good level of agreement was found for both near-surface and elevated-altitude results, and the MAX-DOAS O 3 profiles were able to reproduce the vertical distributions measured by ozonesonde. However, MAX-DOAS measurements are less sensitive to the upper troposphere and converge to the a priori profile, and the smoothing effect induced by the optimal estimation method makes it difficult to reproduce steep vertical gradients in real atmosphere. • Developing a novel remote sensing algorithm for O 3 profiles without blind area. • Capturing O 3 near-surface and vertical distributions as indicated by validations. • Deriving best practice recommendations for applying inversion algorithm. • Indicating stable algorithm performance between different instruments' measurements. [ABSTRACT FROM AUTHOR]