Search

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]

A magnitude 6.8 strike-slip earthquake (EQ) struck Luding, Sichuan Province, China, on 5 September 2022, resulting in significant damage to nearby Ganzi Prefecture and the city of Ya'an. In this research, the near-surface atmospheric electric field (AEF) recorded at four sites 15 d before the Luding EQ was analyzed and differentiated, and multisource auxiliary data including precipitation, cloud base height, and low cloud cover were used at the same time. Nine possible seismic AEF anomalies at four sites were obtained preliminarily. Accordingly, microwave brightness temperature (MBT) data, which are very sensitive to the surface dielectrics and are closely related to the air ionization, together with surface soil moisture, lithology, and a 3D-simulated crustal stress field, were jointly analyzed to confirm the seismic relations of the obtained negative AEF anomalies. The geophysical environment for crustal high-stress concentration, positive charge carrier transfer, and surface accumulation was demonstrated to exist and to meet the conditions necessary to generate local negative AEF anomalies. Furthermore, to deal with the spatial disparities in sites and regions with potential atmospheric ionization, near-surface wind field data were employed to scrutinize the reliability of the AEF anomalies by comprehensively analyzing the spatial relationships among surface charges accumulation areas, wind direction and speed, and the AEF sites. Finally, four negative AEF anomalies were deemed to be closely related to the Luding EQ, and the remaining five possible anomalies were ruled out. A possible mechanism of negative AEF anomalies before the Luding EQ is proposed: positive charge carriers were generated from the underground high-stress concentration areas and then transferred to and accumulated on the ground surface to ionize the surface air, thus disturbing the AEF above the ground. This study presents a method for identifying and analyzing seismic AEF anomalies and is also beneficial for the examination of the pre-earthquake coupling process between the coversphere and the atmosphere. [ABSTRACT FROM AUTHOR]

Ozone (O 3) has become one of the most concerning air pollutants in China in recent decades. In this study, based on surface observations, reanalysis data, global atmospheric chemistry model simulations, and multi-model future predictions, meteorological characteristics conducive to extreme O 3 pollution in various regions of China are investigated, and their historical changes and future trends are analyzed. During the most severe O 3 polluted months, the chemical production of O 3 is enhanced under the hot and dry conditions over the North China Plain (NCP) in June 2018 and the Yangtze River Delta (YRD) in July 2017, while regional transport is the main reason for the severe O 3 pollution over the Sichuan Basin (SCB) in July 2015 and the Pearl River Delta (PRD) in September 2019. Over the last 4 decades, the frequencies of high-temperature and low-relative-humidity conditions increased in 2000–2019 relative to 1980–1999, indicating that O 3 pollution in both the NCP and YRD has become more frequent under historical climate change. In the SCB and PRD, the occurrence of atmospheric circulation patterns similar to those during the most polluted months increased, together with the more frequent hot and dry conditions, contributing to the increases in severe O 3 pollution in the SCB and PRD during 1980–2019. In the future (by 2100), the frequencies of months with anomalous high temperature show stronger increasing trends in the high-forcing scenario (Shared Socioeconomic Pathway (SSP5-8.5)) compared to the sustainable scenario (SSP1-2.6) in China. It suggests that high anthropogenic forcing will not only lead to slow economic growth and climate warming but also likely result in environmental pollution issues. [ABSTRACT FROM AUTHOR]

Surface ozone (O 3) is well known for posing significant threats to both human health and crop production worldwide. However, a multidecadal assessment of the impacts of O 3 on public health and crop yields in China is lacking due to insufficient long-term continuous O 3 observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O 3 concentrations simulated by a chemical transport model from 1981–2019 by integrating multi-source datasets. The ML-enabled bias correction offers improved performance in reproducing observed O 3 concentrations and thus further improves our estimates of the impacts of O 3 on human health and crop yields. The warm-season trends of increasing O 3 in Beijing–Tianjin–Hebei and its surroundings (BTHs) as well as in the Yangtze River Delta (YRD), Sichuan Basin (SCB), and Pearl River Delta (PRD) regions are 0.32, 0.63, 0.84, and 0.81 µ g m -3 yr -1 from 1981 to 2019, respectively. In more recent years, O 3 concentrations experienced more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 µ g m -3 yr -1 during 2013–2019. Using accumulated O 3 over a threshold of 40 ppb (AOT40-China) exposure–yield response relationships, the estimated relative yield losses (RYLs) for wheat, rice, soybean, and maize are 17.6 %, 13.8 %, 11.3 %, and 7.3 % in 1981, increasing to 24.2 %, 17.5 %, 16.3 %, and 9.8 % in 2019, with an increasing rate of +0.03 % yr -1 , +0.04 % yr -1 , +0.27 % yr -1 , and +0.13 % yr -1 , respectively. The estimated annual all-cause premature deaths induced by O 3 increased from ∼55900 in 1981 to ∼162000 in 2019 with an increasing trend of ∼2980 deaths per year. The annual premature deaths related to respiratory and cardiovascular disease are ∼34200 and ∼40300 in 1998 and ∼26500 and ∼79000 in 2019, having a rate of change of -546 and +1770 deaths per year during 1998–2019, respectively. Our study, for the first time, used ML to provide a robust dataset of O 3 concentrations over the past 4 decades in China, enabling a long-term evaluation of O 3 -induced crop losses and health impacts. These findings are expected to fill the gap of the long-term O 3 trend and impact assessment in China. [ABSTRACT FROM AUTHOR]

Elevated ground-level ozone concentrations have emerged as a major environmental issue in China. Nitrogen oxide (NO x) is a key precursor to ozone formation. Although control strategies aimed at reducing NO x emissions from conventional combustion sources are widely recognized, soil NO x emissions (mainly as NO) due to microbial processes have received little attention. The impact of soil NO emissions on ground-level ozone concentration is yet to be evaluated. This study estimated soil NO emissions in China using the Berkeley–Dalhousie Soil NO x Parameterization (BDSNP) algorithm. A typical modeling approach was used to quantify the contribution of soil NO emissions to surface ozone concentration. The brute-force method (BFM) and the Ozone Source Apportionment Technology (OSAT) implemented in the Comprehensive Air Quality Model with Extensions (CAMx) were used. The total soil NO emissions in China for 2018 were estimated to be 1157.9 Gg N, with an uncertainty range of 715.7–1902.6 Gg N. Spatially, soil NO emissions are mainly concentrated in Central China, North China, Northeast China, the northern Yangtze River Delta (YRD), and the eastern Sichuan Basin, with distinct diurnal and monthly variations that are mainly affected by the temperature and timing of fertilizer application. Both the BFM and OSAT results indicate a substantial contribution of soil NO emissions to the maximum daily 8 h (MDA8) ozone concentrations by 8.0–12.5 µ g m -3 on average for June 2018, with the OSAT results being consistently higher than the BFM results. The results also showed that soil NO emissions led to a relative increase in ozone exceedance days by 10.5 %–43.5 % for selected regions. Reducing the soil NO emissions resulted in a general decrease in monthly MDA8 ozone concentrations, and the magnitude of ozone reduction became more pronounced as reductions increased. However, even with complete reductions in soil NO emissions, approximately 450.3 million people are still exposed to unhealthy ozone levels, necessitating multiple control policies at the same time. This study highlights the importance of soil NO emissions for ground-level ozone concentrations and the potential for reducing NO emissions as a future control strategy for ozone mitigation in China. [ABSTRACT FROM AUTHOR]

The basal cavity of a rock block formed due to differential weathering is an important predisposing factor for rockfall in hard–soft interbedded rocks, which induces an eccentricity situation at the base of the rock block. Rock block falling due to the non-uniform distribution with the failure modes of toppling or sliding is defined as biased rockfall in this study. Taking into account the non-uniform stress distribution due to the eccentricity effect, a new analytical method is proposed for three-dimensional stability analysis of biased rockfall. The development of non-uniform stress distribution stress calculated by this analytical method was verified by numerical simulation. The biased rockfall progresses from partial damage of the soft underlying layer, caused by non-uniform distributed stress, to toppling and sliding of overhanging hard rock block due to overall unbalanced force. Therefore, a set of factors of safety (Fos's) against partial damage (compressive and tensile damage of the soft underlying layer) and overall failure (toppling and sliding of the hard rock block) are used to determine the rockfall susceptibility level. The analytical method is applied and validated using biased rockfalls on the northeastern edge of the Sichuan Basin in southwest China, where a significant number of rockfalls consisting of overhanging thick sandstone and underlying mudstone occur. The evolution process of biased rockfalls is divided into four stages, initial state, basal cavity formation, partially unstable and failure. The proposed method is validated by calculating Fos's of the typical unstable rock blocks in the study area. As the cavity continues to grow, the continuous retreat of mudstone causes stress redistribution between the hard and soft rock layers. This results in damage to the underlying soft rock layer due to the development of the non-uniform distribution, ultimately leading to the failure of the hard rock block. The critical retreat ratio is determined to be 0.33, which is used to classify the low and moderate rockfall susceptibility in the eastern Sichuan Basin. The proposed analytical method provides insights into the evolution of biased rockfall and a means for early identification and susceptibility assessment of rockfall. [ABSTRACT FROM AUTHOR]

Giving full play to the public's initiative for geohazard reduction is critical for sustainable disaster reduction under a government-led top-down disaster governance approach. According to the public's intention to participate in geohazard mitigation activities, this study introduces the analytical framework of the theory of planned behavior (TPB), with attitudes, subjective norms, and perceived behavioral control as the primary explanatory variables, with three added explanatory variables: risk perception, disaster experience, and participation perception. Survey data obtained from 260 respondents in Jinchuan County, Sichuan Province, China, are analyzed using structural equation modeling and combined with multivariate hierarchical regression to test the explanatory power of the model. The results indicate that attitude, subjective normative, perceived behavioral control, and participatory cognition are significant predictors of public intention to participate. Disaster experience is negatively associated with public intention to participate. In addition, the extended TPB model contributes 50.7 % to the explanation of the behavioral intention of public participation. Practical suggestions and theoretical guidance are provided for strengthening geohazard risk management and achieving sustainable disaster reduction. In particular, it is concluded that, while correctly guiding public awareness of disaster reduction activities, policymakers should continue developing participatory mechanisms, paying attention to two-way communication bridges between the public and the government, uniting social forces, and optimizing access to resources. [ABSTRACT FROM AUTHOR]

The volatilization of ammonia (NH 3) from fertilizer application and livestock wastes is an overwhelmingly important pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric NH 3. The volatilization of NH 3 highly depends on environmental and meteorological conditions, however, this phenomenon is poorly described in current emission inventory and atmospheric models. Here, we develop a dynamic NH 3 emission model capable of calculating the NH 3 emission rate interactively with temporal- and spatial-varying meteorological and soil conditions. The NH 3 flux parameterization relies on several meteorological factors and anthropogenic activity including fertilizer application, livestock waste, traffic, residential, and industrial sectors. The model is then embedded into a regional WRF–Chem model and is evaluated against field measurements of NH 3 concentrations, emission flux, and satellite retrievals of column loading. The evaluation shows a substantial improvement in the model performance of NH 3 flux and ambient concentration in China. The model represents the spatial and temporal variations of ambient NH 3 concentration well, indicating the highest emission in the North China Plain (NCP) and Sichuan Basin, especially during summertime. Compared with normal simulations using fixed emission inventory input, this model features superior capability in simulating NH 3 emission flux and concentration during drastic weather changes like frontal activities and precipitation. Such advances in emission quantification also improve the model performance of secondary inorganic aerosol on synoptic scales. While more laboratory and field measurements are still needed for better parameterization of NH 3 volatilization, the seamless coupling of soil emission with meteorology provides a better understanding of NH 3 emission evolution and its contribution to atmospheric chemistry. [ABSTRACT FROM AUTHOR]

Vehicle emissions are regarded as a primary contributor to air pollution and related adverse health impacts. Heavy traffic congestion increases traffic flow and thus produces more O3 precursor emissions, leading to more adverse air quality issues. Although the development of a vehicle emission inventory has received great concern and continuous efforts, limitations still exist. For example, real-time diurnal variations and increases in emission rates due to traffic congestion are not well understood. In this study, we developed a new temporal allocation approach in transportation emissions to investigate the impact on air quality and health burden due to traffic congestion in China in 2020. Both real-time congestion-level data and emission correction factors were considered in the approach. Results show that traffic congestion aggravates air pollution and health burden across China, especially in the urban clusters such as the North China Plain and Sichuan Basin. In these regions, the average annual increases in fine particulate matter (PM2.5) and ozone (O3) could be up to 3.5 µgm-3 and 1.1 ppb , respectively. The excess PM2.5 and O3 attributed to the traffic congestion also induce an additional 20 000 and 5000 premature deaths in China, respectively. In major cities, the increased rate of premature mortality caused by traffic congestion may reach 17.5 %. Therefore, more effective and comprehensive vehicle emission control policies or better planning of the road network should be established to reduce traffic congestion and improve air quality in China. [ABSTRACT FROM AUTHOR]

Haze pollution is a severe environmental problem, caused by elevation of fine particles (aerodynamic diameter <2.5 µm , PM 2.5), which is related to secondary aerosol formation, unfavourable synoptic conditions and regional transport, etc. The regional haze formation in basin areas, along with intensive emission of precursors, high relative humidity and poor dispersion conditions, is still limitedly understood. In this study, a field campaign was conducted to investigate the factors resulting in haze formation in the Sichuan Basin (SCB) during winter in 2021. The fine aerosol chemical composition was characterised using a time-of-flight aerosol chemical speciation monitor (ToF-ACSM), which also provided detailed information on the sources for organic aerosols (OAs). The average concentration of non-refractory fine particles (NR-PM 2.5) was 98.5±38.7 µgm-3 , and organics aerosols, nitrate, sulfate, ammonium and chloride took up 40.3 %, 28.8 %, 10.6 %, 15.3 % and 5.1 % of PM 2.5. Three factors, including a hydrocarbon-like OA (HOA), a biomass burning OA (BBOA) and an oxygenated OA (OOA), were identified by applying the positive matrix factorisation (PMF) analysis, and they constituted 24.2 %, 24.2 % and 51.6 % of OA on average, respectively. Nitrate formation was promoted by gas-phase and aqueous-phase oxidation, while sulfate was mainly formed through aqueous-phase process. OOA showed strong dependence on O x , demonstrating the contribution of photooxidation to OOA formation. OOA concentration increased as aerosol liquid water content (ALWC) increased within 200 µgm-3 and kept relatively constant when ALWC >200 µgm-3 , suggesting the insignificant effect of aqueous-phase reactions on OOA formation. Among the three haze episodes identified during the whole campaign, the driving factors were different: the first haze episode (H1) was driven by nitrate formation through photochemical and aqueous-phase reactions, and the second haze episode (H2) was mainly driven by the intense emission of primary organic aerosols from biomass burning and vehicle exhaust, while the third haze episode (H3) was mainly driven by reactions involving nitrate formation and biomass burning emission. HOA and BBOA were scavenged, while OOA, nitrate and sulfate formation was enhanced by aqueous-phase reactions during fog periods, which resulted in the increase of O:C from pre-fog to post-fog periods. This study revealed the factors driving severe haze formation in the SCB and implied the benefit of controlling nitrate as well as intense biomass burning and vehicle exhaust emission for the mitigation of heavy aerosol pollution in this region. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu "Qianxi" mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) is the most important alkaline gas in the atmosphere, which has negative effects on biodiversity, ecosystems, soil acidification and human health. China has the largest NH 3 emissions globally, mainly associated with agricultural sources including nitrogen fertilizer and livestock. However, there is still a limited number of ground monitoring sites in China, hindering our understanding of both surface NH 3 concentrations and emissions. In this study, using the polar-orbiting satellite (Infrared Atmospheric Sounding Interferometer – IASI) and Fengyun-4A Geostationary Interferometric Infrared Sounder (GIIRS), we analyzed the changes in hourly NH 3 concentrations and estimated surface NH 3 concentrations and NH 3 emissions in China. GIIRS-derived NH 3 concentrations in the daytime were generally higher than those at night, with high values during 10:00–16:00 local time. Satellite-derived surface NH 3 concentrations were generally consistent with the ground observations, with R -square at 0.72 and slope equal to 1.03. Satellite-based NH 3 emissions ranged from 12.17 to 17.77 Tg N yr -1 during 2008–2019. Spatially, high values of NH 3 emissions mainly occurred in the North China Plain, Northeast China and the Sichuan Basin, while low values were mainly distributed in West China (Qinghai–Tibet Plateau). Our study shows a high predictive power of using satellite data to estimate surface NH 3 concentrations and NH 3 emissions over multiple temporal and spatial scales, which provides an important reference for understanding NH 3 changes over China. [ABSTRACT FROM AUTHOR]

The heterogeneous reaction of N2O5 on Cl-containing aerosols (heterogeneous N2O5 + Cl chemistry) plays a key role in chlorine activation, NOx recycling, and consequently O3 and PM2.5 formation. In this study, we use the GEOS-Chem model with additional anthropogenic and biomass burning chlorine emissions combined with updated parameterizations for the heterogeneous N2O5 + Cl chemistry (i.e., the uptake coefficient of N2O5 (γN2O5) and the ClNO2 yield (φClNO2)) to investigate the impacts of chlorine chemistry on air quality in China, the role of the heterogeneous N2O5 + Cl chemistry, and the sensitivity of air pollution formation to chlorine emissions and parameterizations for γN2O5 and φClNO2. The model simulations are evaluated against multiple observational datasets across China and show significant improvement in reproducing observations of particulate chloride, N2O5 , and ClNO2 when including anthropogenic chlorine emissions and updates to the parameterization of the heterogeneous N2O5 + Cl chemistry relative to the default model. The simulations show that total tropospheric chlorine chemistry could increase annual mean maximum daily 8 h average (MDA8) O3 by up to 4.5 ppbv but decrease PM2.5 by up to 7.9 µgm-3 in China, 83 % and 90 % of which could be attributed to the effect of the heterogeneous N2O5 + Cl chemistry. The heterogeneous uptake of N2O5 on chloride-containing aerosol surfaces is an important loss pathway of N2O5 as well as an important source of O3 and hence is particularly useful in elucidating the commonly seen ozone underestimations relative to observations. The importance of chlorine chemistry largely depends on both chlorine emissions and the parameterizations for the heterogeneous N2O5 + Cl chemistry. With the additional chlorine emissions, the simulations show that annual MDA8 O3 in China could be increased by up to 3.5 ppbv. The corresponding effect on PM2.5 concentrations varies largely with regions, with an increase of up to 4.5 µgm-3 in the North China Plain but a decrease of up to 3.7 µgm-3 in the Sichuan Basin. On the other hand, even with the same chlorine emissions, the effects on MDA8 O3 and PM2.5 in China could differ by 48 % and 27 %, respectively, between different parameterizations. [ABSTRACT FROM AUTHOR]

Following a continuous increase in the surface ozone (O3) level from 2013 to 2019, the overall summertime O3 concentrations across China showed a significant reduction in 2020. In contrast to this overall reduction in surface O3 across China, unexpected surface O3 enhancements of 10.2 ± 0.8 ppbv (23.4 %) were observed in May–June 2020 (relative to 2019) over the Sichuan Basin (SCB), China. In this study, we use high-resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method, and the exposure–response relationship to determine the drivers and evaluate the health risks due to the unexpected surface O3 enhancements. We first use the XGBoost machine learning method to correct the GEOS-Chem model–measurement O3 discrepancy over the SCB. The relative contributions of meteorology and anthropogenic emission changes to the unexpected surface O3 enhancements are then quantified with a combination of GEOS-Chem and XGBoost models. In order to assess the health risks caused by the unexpected O3 enhancements over the SCB, total premature mortalities are estimated. The results show that changes in anthropogenic emissions caused a 0.9 ± 0.1 ppbv O3 reduction, whereas changes in meteorology caused an 11.1 ± 0.7 ppbv O3 increase in May–June 2020 relative to 2019. The meteorology-induced surface O3 increase is mainly attributed to an increase in temperature and decreases in precipitation, specific humidity, and cloud fractions over the SCB and surrounding regions in May–June 2020 relative to 2019. These changes in meteorology combined with the complex basin effect enhance biogenic emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), speed up O3 chemical production, and inhibit the ventilation of O3 and its precursors; therefore, they account for the surface O3 enhancements over the SCB. The total premature mortality due to the unexpected surface O3 enhancements over the SCB has increased by 89.8 % in May–June 2020 relative to 2019. [ABSTRACT FROM AUTHOR]

Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation. [ABSTRACT FROM AUTHOR]

High loadings of nitrate (NO3-) in the aerosol over China significantly exacerbate the air quality and pose a great threat to ecosystem safety through dry–wet deposition. Unfortunately, limited ground-level observation data make it challenging to fully reflect the spatial pattern of NO3- levels across China. Until now, long-term monthly particulate NO3- datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3- dataset at 0.25 ∘ resolution over China during 2005–2015 was developed by assimilating surface observations, satellite products, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient-boosting decision tree (GBDT), and extreme gradient-boosting (XGBoost) methods. The new developed product featured an excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 µgNm-3) and mean absolute error (MAE: 0.81 µgNm-3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scales. Moreover, the dataset displayed good agreement with (R2=0.85 , RMSE=0.74 µgNm-3 , and MAE=0.55 µgNm-3) some unlearned data collected from previous studies. The spatiotemporal variations in the developed product were also shown. The estimated NO3- concentration showed the highest value in the North China Plain (NCP) (3.55±1.25 µgNm-3); followed by the Yangtze River Delta (YRD) (2.56±1.12 µgNm-3), Pearl River Delta (PRD) (1.68±0.81 µgNm-3), and Sichuan Basin (1.53±0.63 µgNm-3), and the lowest one in the Tibetan Plateau (0.42±0.25 µgNm-3). The higher ambient NO3- concentrations in the NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3- pollution in the Sichuan Basin. The lowest ambient NO3- concentration in the Tibetan Plateau was contributed by the scarce anthropogenic emission and favourable meteorological factors (e.g. high wind speed). In addition, the ambient NO3- concentration showed a marked increasing tendency of 0.10 µgNm-3yr-1 during 2005–2014 (p<0.05), while it decreased sharply from 2014 to 2015 at a rate of -0.40 µgNm-3yr-1 (p<0.05). The ambient NO3- levels in Beijing–Tianjin–Hebei (BTH), YRD, and PRD displayed gradual increases at a rate of 0.20, 0.11, and 0.05 µgNm-3yr-1 (p<0.05) during 2005–2013, respectively. The gradual increases in NO3- concentrations in these regions from 2005 to 2013 were due to the fact that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase in energy consumption. Afterwards, the government further strengthened these emission reduction measures and thus caused the dramatic decreases in NO3- concentrations in these regions from 2013 to 2015 (p<0.05). The long-term NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005–2015 are open access at 10.5281/zenodo.3988307 (Li et al., 2020c). [ABSTRACT FROM AUTHOR]

The earthquake early warning systems (EEWSs) in China have achieved great progress, with warning alerts being successfully delivered to the public in some regions. We examined the performance of the EEWS in China's Sichuan Province during the 2019 Changning earthquake. Although its technical effectiveness was tested with the first alert released 10 s after the earthquake, we found that a big gap existed between the EEWS's message and the public's response. We highlight the importance of EEWS alert effectiveness and public participation for long-term resiliency, such as delivering useful alert messages through appropriate communication channels and training people to understand and properly respond. [ABSTRACT FROM AUTHOR]