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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

There has been controversy about the air pollutants emitted from sources closely related to people's daily life (such as cooking, fireworks burning, sacrificial incenses and joss paper burning, and barbecue, named as five missing sources, FMS) impacting the outdoor air quality to what extent. Till now, there is no emission estimation of air pollutants from FMS, as the missing of both activity dataset and emission factors. We attempted to combine the questionnaire data, various statistical data, and data of points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Then, the emission inventories of carbonaceous aerosol with the high spatial-temporal resolution for FMS were established firstly, and the spatial-variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) increased from 4268 t to 4919 t. The OC emission from FMS was 1.48-2.18 - of its total emission in China. The emissions of black carbon, element carbon (EC), and brown carbon absorption cross-section (ACSBrC) emissions from FMS were in the ranges of 22.6-43.9 t, 213-324 t, and 14.7-35.6 Gm2, respectively. Their emissions tended to concentrate in special periods and areas. The OC emission intensities in central urban areas were 3.85-50.5 times that of rural areas due to the high density of human activities. While the ACSBrC emissions in rural regions accounted for 63.0-79.5% of the total emission result from uncontrolled fireworks burning. A mass of fireworks burning led to extremely higher ACSBrC and EC emissions on Chinese New Year's eve, as 1444 and 262 times their corresponding yearly average values. Interestingly, significant (p<0.01) correlations between human incomes and pollutant emissions were found, but they were positive (r = 0.94) and negative (r = -0.94) for urban and rural regions, indicating the necessity of regulating human lifestyle and increasing income for urban and rural peoples, respectively. This study provided the first-hand data for identifying the emissions, variation trends and impacting factors of FMS, which is helpful for modeling works on air quality, climate effect, and human health risks at specific periods or regions and for modifying their emission control policies. The data in this work could be found at https://doi.org/10.6084/m9.figshare.19999991.v1 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn , last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and NMVOCs in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1% to 9.5% in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6% to 93.8%). The calculated RMSEs (0.3 mg/m³ for CO and 9.4-21.1 µg/m³ for other species, on the monthly scale) and correlation coefficients (0.76-0.94) were also improved from the a priori simulations, suggesting that CAQIEI can reasonably reproduce the magnitude and variation of emissions of different air pollutants in China. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the 6 species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions reduced by 54.1% for SO2, 44.4% for PM2.5, 33.6% for PM10, 35.7% for CO, and 15.1% for NOx, but increased by 21.0% for NMVOCs. Larger emission reductions were achieved during the 2018-2020 action plan than during the 2013-2017 action plan for most species. In particular, NOx and NMVOC emissions were shown to increase during the 2013-2017 action plain, and there were obvious emission increases in the Fengwei Plain area over the Central China region. However, NOx and NMVOC emissions declined during the 2018-2020 action plan, and the emissions over the Fengwei Plain area also decreased. This suggests that the emission control policies were improved in the 2018-2020 action plan. We also compared CAQIEI with previous inventories, which verified our inversion results in terms of total emissions of NOx, SO2 and NMVOCs, and more importantly identified the potential uncertainties in our current understanding of China's air pollutant emissions. Firstly, CO emissions in China may be substantially underestimated by current inventories, with the CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amount in previous inventories (120.7-237.7 Tg). Significant underestimations for other air pollutant emissions may also exist over western and northeastern China. In addition, the NMVOC emissions were shown to be substantially underestimated over northern China but overestimated in southern China. Secondly, the emission reduction rates during 2015-2018 estimated by CAQIEI are generally smaller than those estimated by previous inventories, especially for NOx, PM10 and NMVOCs, suggesting that the mitigation effects of the air pollution control may be overestimated currently. In particular, China's NMVOC emissions were shown to have increased by 26.6% from 2015 to 2018, especially over the North China Plain by 38.0%), Northeast China (by 38.3%), and Central China (60.0%). In contrast, the emissions reduction rate of CO may be underestimated. Overall, our emissions inventory sheds new light on the complex variations of air pollutant emissions in China during its two recent clean air action periods, which could significantly improve our understanding of air pollutant emissions and related changes in air quality in China. [ABSTRACT FROM AUTHOR]

The optimal choice of the planetary boundary layer (PBL) parameterization scheme is of particular interest and urgency to a wide range of scholars, especially for many works involving models. At present, there have been many works to evaluate the PBL schemes. However, little research has been conducted into a more comprehensive and systematic assessment of the performance capability of schemes in key regions of China, especially when it comes to the differences in the mechanisms of the schemes themselves, primarily because there is scarcely sufficient observational data, computer resources, and storage support to complete the work. In this companion paper (i.e., Part 1), four typical schemes (i.e., YSU, ACM2, BL, and MYJ) are selected to systematically analyze and evaluate near-surface meteorological parameters, PBL vertical structure, PBL height (PBLH), and turbulent diffusion coefficient (TDC) in five key regions of China (i.e., North China Plain, NCP; Yangtze River Delta, YRD; Sichuan Basin, SB; Pearl River Delta, PRD and Northwest Semi-arid region, NS) in different seasons (i.e., January, April, July, and October). The differences in the simulated 2 m temperatures between the nonlocal closure schemes are mainly affected by the downward shortwave radiation, but to compare the nonlocal closure schemes with the local closure schemes, the effect of sensible heat flux needs to be further considered. The 10 m wind speed is under the influence of factors like the momentum transfer coefficient and the integrated similarity functions at night. The wind speeds are more significantly overestimated in the plains and basin, while less overestimated or even underestimated in the mountains, as a result of the effect on topographic smoothing in the model. Moreover, the overestimation of small wind speeds at night is attributable to the inapplicability of the Monin–Obukhov similarity theory (MOST) at night. The model captures the vertical structure of temperature well, while the wind speed is outstandingly overestimated below 1000 m, largely because of the TDC. The difference between the MOST and the mixing length theory, PBLH, and Prandtl number is cited as the reason for the difference between the TDC of the YSU and ACM2 schemes. The TDCs of the BL and MYJ schemes are affected by the mixing length scale, which of BL is calculated on the basis of the effect of buoyancy, while MYJ calculates it with the consideration of the effect of the total turbulent kinetic energy. The PBLH of the BL scheme is better than the other schemes because of the better simulation results of temperature. In general, to select the optimal scheme, it is necessary to offer different options for different regions with different focuses (heat or momentum). The first focus is on the temperature field. The BL scheme is recommended for January in the NCP region, especially for Beijing, and the MYJ scheme is better for the other 3 months. The ACM2 scheme would be a good match for the YRD region, where the simulation differences between the four schemes are small. The topography of the SB region is more complex, but for most of the areas in the basin, the MYJ scheme is proposed, and if more stations outside the basin are involved, the BL scheme is recommended. The MYJ scheme is applied to the PRD region in January and April, and the BL scheme in July and October. The MYJ scheme is counseled for the NS region. The second focus is the wind field. The YSU scheme is recommended if the main concern is the near-surface layer, and the BL scheme is suggested if focusing on the variation in the vertical direction. The final evaluation of the parameterization scheme and uncertainties will lay the foundation for the improvement of the modules and forecasting of the GRAPES_CUACE regional model developed independently in China. [ABSTRACT FROM AUTHOR]

Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1,055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential, using present-day discharge from the CaMa-Flood model (2011-2015, 0.05 degree), and future discharge from the WaterGAP2 model used for ISIMIP2b (2021-2030, 0.5 degree). The dam count of 1,055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1,242 MW in the 1980s to 69,199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+16,854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+18,223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1,334,683 MW, where Laos is the highest at 514,887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2,000,000 MW. Laos and China are the highest at around 900,000 MW each, together forming over 80% of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better planning and management in the future hydropower hotspot Laos. The Mekong dam database is publicly available at https://doi.org/10.21979/N9/ACZIJN (Ang et al., 2023). [ABSTRACT FROM AUTHOR]

As earth system modeling develops ever finer grid resolutions, the inputting and outputting (I/O) of the increasingly large data fields becomes a processing bottleneck. Many models developed in China, as well as the community coupler (C-Coupler), do not fully benefit from existing parallel I/O supports. This paper reports the design and implementation of a common parallel input/output framework (CIOFC1.0) based on C-Coupler2.0. The CIOFC1.0 framework can accelerate the I/O of large data fields by parallelizing data read/write operations among processes. The framework also allows convenient specification by users of the I/O settings, e.g., the data fields for I/O, the time series of the data files for I/O, and the data grids in the files. The framework can also adaptively input data fields from a time series dataset during model integration, automatically interpolate data when necessary, and output fields either periodically or irregularly. CIOFC1.0 demonstrates the cooperative development of an I/O framework and coupler, and thus enables convenient and simultaneous use of a coupler and an I/O framework. [ABSTRACT FROM AUTHOR]

orest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting at 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R²) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.34 Mt C yr-1, of which more than 80% was from selective logging. Of the harvested carbon, 22%, 45%, 4%, and 29% entered the wood fuel, wood products, paper products, and residual pools, respectively. Direct combustion of wood fuel was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 90% of the harvested carbon during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023). [ABSTRACT FROM AUTHOR]

Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016–2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg , last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; 10.5281/zenodo.7500612). [ABSTRACT FROM AUTHOR]

The Sheshan Geomagnetic Observatory (International Association of Geomagnetism and Aeronomy (IAGA) code SSH), China was built in Xujiahui, Shanghai in 1874 and moved to Sheshan, Shanghai at the end of 1932. So far, the SSH has a history of nearly 150 years. It is one of the earliest geomagnetic observatories in China and one of the geomagnetic observatories with the longest history in the world. In this paper, we present the rescue and quality control (QC) of the historical data at the SSH from 1933 to 2019. The rescued data are the absolute hourly mean values (AHMVs) of declination (D), horizontal (H), and vertical (Z) components. Some of these data are paper-based records and some are stored in a floppy disk in BAS, DBF, MDB, and other file storage formats. After digitization and format transformation, we imported the data into the Toad database to achieve the unified data management. We performed statistics of completeness, visual analysis, outliers detects, and data correction on the stored data. We then conducted the consistency test of daily variation and secular variation (SV) by comparing the corrected data with the data of the reference observatory, and the computational data of the COV-OBS model, respectively. The consistency test reveals good agreement. However, the individual data should be used with caution because these data are suspicious values, but there is not any explanation or change registered in the available metadata and logbooks. Finally, we present examples of the datasets in discriminating geomagnetic jerks and study of storms. The digitized and quality-controlled AHMVs data are available at: 10.5281/zenodo.7005471 (Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Aviation emission accounting is the key to establishing market measures to control aviation pollutant emissions. Based on the fuel percentage method (FPM), this paper applies the improved BFFM2-FOA-FPM (Boeing Fuel Flow Method 2–First Order Approximation FPM) to calculate the emissions of six pollutants (CO 2 , CO, HC, NO x , SO 2 , and PM 2.5) between Chinese and foreign cities from 2014 to 2019, including CCD (climbing, cruising, and descending) emissions and LTO (landing and take-off) emissions. The error rate between the calculated results and the official data is about 2.75 %. The results show that the emissions of six pollutants changed before and after the proposal of the "Carbon Neutral Growth 2020" strategy (CNG2020 strategy). Although the total amount has increased, the average emission per tonne-kilometer of CO 2 , CO, HC, NO x , SO 2 , and PM 2.5 has decreased by 17.77 %, 17.26 %, 25.15 %, 14.32 %, 17.77 %, and 16.35 %, respectively. The results of this paper can provide a data basis and method reference for implementing the CNG2020 strategy and realizing global carbon emission reduction goals. The dataset is available from https://doi.org/10.6084/m9.figshare.20071751.v1 (Cui., 2022). [ABSTRACT FROM AUTHOR]

Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM 2.5 observations on the model chemical initial field PM 2.5 and visibility forecasts were investigated. The results show that assimilation of PM 2.5 reduces the uncertainty in the initial PM 2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM 2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µ g m -3 , and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM 2.5 improves PM 2.5 and visibility forecasts throughout the time window of 24 h. The PM 2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM 2.5 further contributes to the improvement of the visibility forecast. When the PM 2.5 increment is negative, it corresponds to an increase in visibility, and when the PM 2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM 2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface-subsurface hydrological model that employs a finite volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma Watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://shuddata.com), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

Water data is a crucial asset for sustainable water resource management. However, the availability of China's water datasets lags far behind modern expectations for open geoscientific data. This dataset is a part of the China Water Data Archive (CWDA), an upcoming national collection of water-related data covering all aspects of water data for boosting data sharing in China. The CWDA aims at providing free, clean, non-sensitive, coherent, and reliable water data within China for global researchers to support the national and global water resources management and the United Nations-Water Integrated Monitoring Initiative for Sustainable Development Goals 6 and 14. In this paper, we used Python and R language to collect, tidy, reorganize, and curate the publicly available inland and coastal/ocean surface water quality data in China, following a series of data quality dimensions (integrity, completeness, consistency, and accuracy). As the most comprehensive, publicly available, handy, and clean water quality dataset in China so far, it included water quality data for daily, weekly, and monthly in the period of 1980-2022, with 17 indicators for over 330,000 observations at 2384 sites from inland to coastal/ocean areas. This dataset will greatly support works relevant to the assessment, modelling, and projection of water quality, ocean biomass, and biodiversity in China. [ABSTRACT FROM AUTHOR]

The Sheshan geomagnetic observatory (IAGA code SSH), China was built in Xujiahui, Shanghai in 1874 and moved to Sheshan, Shanghai at the end of 1932. So far, SSH has a history of nearly 150 years. It is one of the earliest geomagnetic observatory in China and one of the geomagnetic stations with the longest history in the world. In this paper, we present the rescue and quality control of the historical data at Sheshan observatory (SSH) from 1933 to 2019. The rescued data are the absolute hourly mean values of D, H and Z components. Some of these data are paper-based records, and some are stored in a floppy disk in BAS, DBF, ACCESS and other file formats. After digitization and format transformation, we imported the data into the Toad database to achieve the unified data management. We performed statistics of continuity rate, visual analysis, outliers detects and data correction on the stored data. Then we conducted the consistency test of daily variation and secular variation by comparing the corrected data with these of the reference observatory data, and the computational data of the COV-OBS model, respectively. The consistency test revealed fairly good agreement. However, the individual data should be used with caution, because these data are suspicious values, but there is not any explanation or change registered in the available metadata and logbooks. Finally we present an example of the datasets in discriminating geomagnetic jerks. The digitized and quality-controlled AHMVs data are available at: https://doi.org/10.5281/zenodo.6584285 (Zhang et al, 2022). [ABSTRACT FROM AUTHOR]

The South China Sea Operational Oceanography Forecasting System (SCSOFS), constructed and operated by the National Marine Environmental Forecasting Center of China, has been providing daily updated hydrodynamic forecasting in the South China Sea (SCS) for the next 5 d since 2013. This paper presents recent comprehensive updates to the configurations of the physical model and data assimilation scheme in order to improve the forecasting skill of the SCSOFS. This paper highlights three of the most sensitive updates: the sea surface atmospheric forcing method, the discrete tracer advection scheme, and a modification of the data assimilation scheme. Intercomparison and accuracy assessment among the five sub-versions were performed during the entire upgrading process using the OceanPredict Intercomparison and Validation Task Team Class 4 metrics. The results indicate that remarkable improvements have been made to the SCSOFSv2 with respect to the original version (known as SCSOFSv1). The domain-averaged monthly mean root-mean-square errors of the sea surface temperature and sea level anomaly have decreased from 1.21 to 0.52 ∘ C and from 21.6 to 8.5 cm, respectively. [ABSTRACT FROM AUTHOR]

The South China Sea Operational Oceanography Forecasting System (SCSOFS), constructed and operated by the National Marine Environmental Forecasting Center of China, has been providing daily updated hydrodynamic forecasting in the South China Sea (SCS) for the next 5 d since 2013. This paper presents recent comprehensive updates to the configurations of the physical model and data assimilation scheme in order to improve the forecasting skill of the SCSOFS. This paper highlights three of the most sensitive updates: the sea surface atmospheric forcing method, the discrete tracer advection scheme, and a modification of the data assimilation scheme. Intercomparison and accuracy assessment among the five sub-versions were performed during the entire upgrading process using the OceanPredict Intercomparison and Validation Task Team Class 4 metrics. The results indicate that remarkable improvements have been made to the SCSOFSv2 with respect to the original version (known as SCSOFSv1). The domain-averaged monthly mean root-mean-square errors of the sea surface temperature and sea level anomaly have decreased from 1.21 to 0.52 ∘ C and from 21.6 to 8.5 cm, respectively. [ABSTRACT FROM AUTHOR]

With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is not a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 5548 river reaches globally. The indices time series in the dataset are available until 2021, the lengths of which vary from 30 to 215 years with an average of around 66 years. Restricted-access streamflow data of typical river basins in China are included in the dataset. Compared to existing global datasets, this global dataset covers more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective. [ABSTRACT FROM AUTHOR]

Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modelling to provide benefits to human well-being. However, it is still challenging to produce large-scale BRA due to the rather tiny size of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or sub-metric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatio-temporal scale. From the viewpoint of learning strategies, there is a non-trivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg) to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. With the designed training sample generation algorithms and the spatio-temporal aware learning strategies, the CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and the well agreement to other multi-annual impervious surface area datasets. The STSRSeg will enable low-cost, dynamic and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg). The CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; https://doi.org/10.5281/zenodo.7500612). [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing, geographic information (GI), artificial intelligence (AI) and other technologies, China is continuously strengthening emergency rescue innovation and numerous breakthroughs related to emergency rescue technologies and equipment which have been made through the National Key Research and Development Program. This paper first proposed and illustrated the concept, connotation and characteristics of integrated space-air-ground emergency rescue communication command from the perspective of mapping and geographic information, and then analysed the challenges faced by the integrated space-air-ground emergency rescue communication command in China. Subsequently, a space-air-ground emergency rescue command theoretical system is introduced by combining broadband emergency communication, accurate acquisition of real-time disaster information, disaster situation and scenario projection, emergency communication command services, and resilient emergency rescue command. The proposed integrated space-air-ground emergency communication and command design concept will be applied for China's disaster emergency rescue and command in the future to comprehensively improve the emergency rescue communication and command capability in China. [ABSTRACT FROM AUTHOR]

The data assimilation technique is one of the important ways to reduce the uncertainty of atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting the EnOI, such as localization length-scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field and PM2.5, visibility forecasts were investigated. The results show that assimilation of PM2.5 significantly reduces the uncertainty of the initial PM2.5 field. The mean error and root mean square error (RMSE) of initial PM2.5 for mainland China have all decreased by more than 75%, and the correlation coefficient could be improved to more than 0.95. Even greater improvements appear in North China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the lead time window of 24 h. The PM2.5 RMSE can be reduced by 10%-21% within 24 h, but the assimilation effect is most obvious in the first 12 h. The assimilation moment chosen at 1200 UTC is more effective than that at 0000 UTC for improving the forecast, because the discrepancy between simulation and observation at 1200 UTC is larger than that at 0000UTC, indicating the assimilation efficiency will be higher when the bias of the model is higher. Assimilation of PM2.5 also improves visibility forecast accuracy significantly. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light pollution period than in the heavy pollution period, since visibility is much more affected by humidity during the heavy pollution period accompanied by low or extreme low visibility. To get further visibility improvement, especially for extreme low visibility during severe haze pollution, not only PM2.5 but also relative humidity should be simultaneously assimilated as well. [ABSTRACT FROM AUTHOR]

The Yangtze Economic Belt (hereafter, the Belt) is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (land, food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Belt. By adopting systematic thinking and the methodology of system dynamics simulation, an integrated system-dynamics-based simulation model for the Belt, named ANEMI_Yangtze, has been developed based on the third version of ANEMI3. The nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper presents the ANEMI_Yangtze model description, which includes (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping the Belt's system behavior over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the description of a new fish sector and modifications to the population, food, energy, and water sectors, including the underlying theoretical basis for model equations. The validation and robustness tests confirm that the ANEMI_Yangtze model can be used to support scenario development, policy assessment, and decision-making. This study aims to improve the understanding of the complex interactions among coupled human–natural systems in the Belt to provide the foundation for science-based policies for the sustainable development of the Belt. [ABSTRACT FROM AUTHOR]

As Earth system modeling develops ever finer grid resolutions, the inputting and outputting (I/O) of the increasingly large data fields becomes a processing bottleneck. Many models developed in China, as well as the Community Coupler (C-Coupler), do not fully benefit from existing parallel I/O supports. This paper reports the design and implementation of a Common parallel Input/Output Framework based on C-Coupler2.0 (CIOFC1.0). Parallelization by CIOFC1.0 can accelerate the I/O of large data fields. The framework also allows convenient specification by users of the I/O settings; e.g., the data fields for I/O, the time series of the data files for I/O, and the data grids in the files. The framework can also adaptively input data fields from a time-series dataset during model integration, automatically interpolate data when necessary, and output fields either periodically or irregularly. CIOFC1.0 demonstrates the cooperative development of an I/O framework and coupler, and thus enables convenient and simultaneous use of a coupler and an I/O framework. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived AOT data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM 2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from 23 to 29 November 2018, during which a heavy air pollution event occurred in northern China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM 2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM 2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM 2.5 concentrations to be closer to the real distribution in northern China, which is of great value for studying heavy air pollution events. [ABSTRACT FROM AUTHOR]

In this paper, complex network theory has been applied to reveal the transport patterns and cooperative regions of fine particulate matter (PM 2.5) over China from 2015 to 2019. The results show that the degrees, weighted degrees, and edge lengths of PM 2.5 cities follow power law distributions. We find that the cities in the Beijing–Tianjin–Hebei–Henan–Shandong (BTHHS) region have a strong ability to export PM 2.5 pollution to other cities. By analyzing the transport routes, we show that a mass of links extends southward from the BTHHS to the Yangtze River Delta (YRD) regions with 1 or 2 d time lags. Hence, we conclude that earlier emission reduction in the BTHHS region and early warning measures in the YRD region will provide better air pollution mitigation in both regions. Moreover, significant links are concentrated in wintertime, suggesting the impact of the winter monsoon. In addition, all cities have been divided into nine clusters according to their spatial correlations. We suggest that the cities in the same clusters should be regarded as a whole to control the level of air pollution. This approach is able to characterize the transport and cluster for other air pollutants, such as ozone and NOx. [ABSTRACT FROM AUTHOR]

This paper presents the first development and evaluation of a reduced-complexity air quality model for China. In this study, the reduced-complexity Intervention Model for Air Pollution over China (InMAP-China) is developed by linking a regional air quality model, a reduced-complexity air quality model, an emission inventory database for China, and a health impact assessment model to rapidly estimate the air quality and health impacts of emission sources in China. The modeling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional Community Multiscale Air Quality (CMAQ) modeling system simulations and ground-based observations. We found that InMAP-China satisfactorily predicted total PM 2.5 concentrations in terms of statistical performance. Compared with the observed PM 2.5 concentrations, the mean bias (MB), normalized mean bias (NMB) and correlations of the total PM 2.5 concentrations are - 8.1 µ g m -3 , - 18 % and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the USA. The underestimation of total PM 2.5 concentrations was mainly caused by its composition, primary PM 2.5. In terms of the ability to quantify source contributions of PM 2.5 concentrations, InMAP-China presents similar results to those based on the CMAQ model, with variation mainly caused by the different treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM 2.5 -related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 250 000 deaths lower than estimated based on CMAQ simulations as a result of the underestimation of PM 2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China that provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy and to quantify the source contribution attributable to many emission sources. [ABSTRACT FROM AUTHOR]

This paper presents a three- dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical depth (AOD) retrievals and lidar- based aerosol profiles, which was developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived aerosol optical thickness (AOT) data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from November 23 to 29, 2018, during which a heavy air pollution event occurred in North China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in North China, which is of great value for studying heavy air pollution events [ABSTRACT FROM AUTHOR]

Climate warming has significantly changed the hydrological cycle in cold regions, especially in areas with distributed permafrost. Groundwater flow and its interactions with surface water are important components of the hydrological process; however, few studies or modeling works have been based on long-term groundwater level, temperature, hydrogeochemistry or isotopic field observations from boreholes due to obstacles such as remote locations, limited infrastructure, and harsh work conditions. In the Hulugou catchment located in the headwater region of the Heihe River on the northeastern Tibetan Plateau (TP), we drilled four sets of depth-specific wells and monitored the dynamic groundwater levels and temperatures at different depths. Surface water (including river water, glacier meltwater, and snow meltwater), precipitation, groundwater from boreholes, spring water, and soil water samples were collected to measure the hydrochemistry, dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and stable and radioactive isotopes at 64 sites. This study provides datasets of these groundwater parameters spanning six consecutive years of monitoring/measurements. These data can be used to investigate the groundwater flow process and the interactions between groundwater and surface water on the TP under global climate change. The datasets provided in this paper can be obtained at https://doi.org/10.5281/zenodo.5184470 (Ma et al., 2021b) and will be subject to further updates. [ABSTRACT FROM AUTHOR]

South China Sea Operational Oceanography Forecasting System (SCSOFS) had been constructed and operated in National Marine Environmental Forecasting Center of China to provide daily updated hydrodynamic forecasting in SCS for the future 5 days since 2013. This paper presents recent comprehensive updates of the configurations of the physical model and data assimilation scheme in order to improve SCSOFS forecasting skills. It highlights three of the most sensitive updates, including sea surface atmospheric forcing method, tracers advection discrete scheme, and modification of data assimilation scheme. Inter-comparison and accuracy assessment among five versions during the whole upgrading processes are performed by employing OceanPredict Inter-comparison and Validation Task Team Class4 metrics. The results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version known as SCSOFSv1. Domain averaged monthly mean root mean square errors decrease from 1.21 °C to 0.52 °C for sea surface temperature, from 21.6 cm to 8.5 cm for sea level anomaly, respectively. [ABSTRACT FROM AUTHOR]

Satellite remote sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 (the fine particulate matter with a diameter below 2.5 µm) in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD-LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, and the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported as 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 (determination coefficient) reached 0.84, and RMSE (root mean square error) and MAE (mean absolute error) were reported as 12.92 and 8.01 µgm-3 , respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites and RMSE and MAE were below 20 and 15 µgm-3 , respectively, in more than 70 % of the sites due to the correlation coefficient having a seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported as 62.10 µgm-3. However, there was only slight pollution in summer with an average concentration of PM2.5 being reported as 47.39 µgm-3. The period from 10:00 to 15:00 LT (Beijing time, UTC + 8 every day is the best time for model inversion; at this time the pollution is also high. The findings also indicate that North China and East China are more polluted than other areas, and their average annual concentration of PM2.5 was reported as 82.68 µgm-3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µgm-3. [ABSTRACT FROM AUTHOR]

Cadastral system plays a pivotal role in land administration, which has attracted notable attention from researchers and policy-makers worldwide. Despite China being the most populous country throughout the world, its cadastral system is rarely studied. This paper conducts a comprehensive survey on the evolution of the Chinese cadastral system by elaborating the historical and contemporary Chinese cadastre. Specifically, contemporary Chinese cadastre is articulated from the aspects including content and purpose, legal basis, organization, and technical approaches, and compared with Swiss and Hong Kong cadastre. It is concluded that the Chinese cadastre evolved in a different way from that of the western countries due to China's unique historical and economic background. This study sheds light on systematically understanding the development of the Chinese cadastre and further refining contemporary Chinese cadastral system. [ABSTRACT FROM AUTHOR]