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Ozone (O3) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O3 levels by directly changing physical and chemical processes of O3 and indirectly changing natural emissions of O3 precursors. In this study, near‐surface O3 concentrations in China in 2030 and 2060 are predicted using the process‐based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi‐source data. The results show that the climate‐driven O3 levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1‐1.9) compared with the high emission scenario (SSP5‐8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O3 related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions. Plain Language Summary: O3 pollution is a severe air quality issue in China that threatens human health and ecosystem. Under the background of climate change, O3 pollution will continue to evolve in the future. Here, we predict near‐surface O3 concentrations in China in 2030 and 2060 based on an interpretable machine learning method, integrated with physical and chemical processes of O3, natural emissions of O3 precursors, and other multi‐source data. The direct (via changing physical and chemical processes of O3) and indirect (via changing natural emissions of O3 precursors) impacts of future climate change on O3 concentrations are quantitatively analyzed. It demonstrates that the climate‐driven O3 levels are projected to decrease by more than 0.4 ppb in 2060 over eastern China under a carbon neutral scenario relative to a high emission scenario. The changes in physical and chemical processes under climate change play a more important role in regulating O3 concentrations in the future than the changes in natural emissions. Key Points: Climate change influences O3 pollution in China through changing physical and chemical processes and natural precursor emissions of O3Physical and chemical processes play a dominant role in regulating future near‐surface O3 concentrations over eastern ChinaCarbon neutral scenario is an ideal pathway for China to mitigate both climate change and O3 pollution in 2060 [ABSTRACT FROM AUTHOR]

Due to the lockdown during the COVID-19 pandemic in China from late January to early April in 2020, a significant reduction in primary air pollutants, as compared to the same time period in 2019, has been identified by satellite and ground observations. However, this reduction is in contrast with the increase of surface ozone (O3) concentration in many parts of China during the same period from 2019 to 2020. The reasons for this contrast are studied here from two perspectives: emission changes and inter-annual meteorological variations. Based on top-down constraints of nitrogen oxide (NOx) emissions from TROPOMI measurements and GEOS-Chem model simulations, our analysis reveals that NOx and volatile organic compound (VOC) emission reductions as well as meteorological variations lead to 8 %, - 3 % and 1 % changes in O3 over North China, respectively. In South China, however, we find that meteorological variations cause ∼ 30 % increases in O3 , which is much larger than - 1 % and 2 % changes due to VOC and NOx emission reductions, respectively, and the overall O3 increase in the simulations is consistent with the surface observations. The higher temperature associated with the increase in solar radiation and the decreased relative humidity are the main reasons that led to the surface O3 increase in South China. Collectively, inter-annual meteorological variations had a larger impact than emission reductions on the aggravated surface O3 pollution in China during the lockdown period of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO 2 and ground-level ozone (O 3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2>0.85 and R2>0.8 , respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr -1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of + 7 % in global GPP. Present-day O 3 concentrations simulated by GEOS-Chem and an O 3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O 3 is present and biases are reduced, especially for regions with high O 3 damage. An increase in atmospheric CO 2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O 3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions. [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]

Objective To evaluate potential improvements in the diagnosis of thyroid nodules when conventional ultrasound (US) is combined with contrast-enhanced US (CEUS). Methods We recruited 515 participants with 323 malignant and 192 benign nodules, who underwent both US and CEUS examinations at 8 different medical centers in China between October 2020 and October 2021. We assessed the malignancy of thyroid nodules in US using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS). Diagnostic criteria for US and US + CEUS were developed by investigators based on evaluations of sonographic features. Using multivariate logistic regression and receiver operating characteristic (ROC) analysis, we compared diagnostic performance between the 2 methods based on criteria identified by investigators and via statistical models. Results On the basis of diagnostic criteria identified by investigators, we measured statistically significant differences in area under the curve (AUC) values between ACR TIRADS (0.83) and CEUS TIRADS (0.87; P <.001). On the basis of diagnostic regression models, we found statistically significant differences in AUC values between US (0.76) and US + CEUS (0.84; P =.001). Models based on US + CEUS outperformed those based on US alone (Akaike information criterion of 347.7 and significant improvement in integrated discrimination). These results were confirmed by similar analyses applied to a validation cohort. Conclusion The accuracy of conventional US for differentiating between benign and malignant thyroid nodules can be improved by combining this approach with CEUS. [ABSTRACT FROM AUTHOR]

Non-Methane Volatile Organic Compounds (NMVOCs) generate ozone (O3) when they are oxidized in the presence of oxides of nitrogen, modulate the oxidative capacity of the atmosphere and can lead to the formation of aerosol. Here, we assess the capability of a chemical transport model (GEOS-Chem) to simulate NMVOC concentrations by comparing ethane, propane and higher alkane observations in remote regions from the NOAA Flask Network and the World Meteorological Organization's Global Atmosphere Watch (GAW) network. Using the Community Emissions Data System (CEDS) inventory we find a significant underestimate in the simulated concentration of both ethane (35 %) and propane (64 %), consistent with previous studies. We run a new simulation where the total mass of anthropogenic NMVOC emitted in a grid box is the same as that used in CEDS, but with the NMVOC speciation derived from regional inventories. For US emissions we use the National Emissions Inventory (NEI), for Europe we use the UK National Atmospheric Emissions Inventory (NAEI), and for China, the Multi-resolution Emission Inventory for China (MEIC). These changes lead to a large increase in the modelled concentrations of ethane, improving the mean model bias from -35 % to -3.8 %. Simulated propane also improves (from -64 % to -48.0 % mean model bias), but there remains a substantial model underestimate. There were relatively minor changes to other NMVOCs. The low bias in simulated global ethane concentration is essentially removed, resolving one long-term issue in global simulations. Propane concentrations are improved but remain significantly underestimated, suggesting the potential for a missing global propane source. The change in the NMVOC emission speciation results in only minor changes in tropospheric O3 and OH concentrations. [ABSTRACT FROM AUTHOR]

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimation of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) are evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide, and (2) aircraft in situ HCHO measurements from campaigns conducted over the U.S. in 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI = 0.651 Ωairc + 2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data givesΩOMI = 0.659 ΩFTIR + 2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (~445 Tg yr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over Southeastern U.S. remains imperfectly elucidated. [ABSTRACT FROM AUTHOR]

Methane (CH4) is an important greenhouse as well as a chemically active gas. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. Nowadays, satellite measurements are widely used for CH4 studies. Here, we use the CH4 products from four commonly used satellites (GOSAT, TROPOMI, ARIS, and IASI) during the period from 2018 to 2020 to investigate the spatiotemporal variations of CH4 in China. In spite of the same target (CH4) for the four satellites, differences among them exist in terms of the instrument, spectrum, and retrieval algorithm. The GOSAT and TROPOMI CH4 retrievals use shortwave infrared spectra, with a better sensitivity near the surface, while the IASI and AIRS CH4 retrievals use thermal infrared spectra, showing a good sensitivity in the mid–upper troposphere but a weak sensitivity in the lower troposphere. The GOSAT and TROPOMI observe high CH4 concentrations in the east and south and low concentrations in the west and north, which is highly related to the CH4 emissions. The IASI and AIRS show a more uniform CH4 distribution over China, which reflects the variation of CH4 at a high altitude. However, a large discrepancy is observed between the IASI and AIRS despite using a similar retrieval band, e.g., significant differences in the seasonal variations of CH4 are observed between the IASI and AIRS across several regions in China. This study highlights the CH4 differences observed by the four satellites in China, and caution must be taken when using these satellite products. [ABSTRACT FROM AUTHOR]

A global chemical transport model GEOS‐Chem was applied to examine the concentrations of PM2.5 and O3 in China, related health burdens and the relative importance of changes in air pollutants and population aging over 2015–2060 under carbon neutral pathway (Shared Socioeconomic Pathway limiting end‐of‐century radiative forcing to 1.9 W m−2, SSP1‐1.9). Results showed synergistic reductions of 100.0%, 72.0%, and 24.4% in net CO2 emission, concentration of annual mean PM2.5, and the 90th maximum daily average 8‐hr (MDA8) O3 over China, respectively, during 2015–2060. Pollutants‐related deaths owing to changes in pollutants and population were simulated to increase by 38.1% over 2015–2060 because of population aging. Over 2015–2060, sensitivity studies showed that the decline in pollutants alone would mitigate deaths by 5,172.7 thousand, while the population aging alone would increase deaths by 2,633.3 thousand. Carbon neutrality will avoid 3,520.0 thousand deaths in 2060 relative to a higher‐carbon pathway (SSP5‐8.5). Plain Language Summary: The achievement of carbon neutrality by 2060, a goal proposed by Chinese government, will influence air quality and associated health burden in the future. Meanwhile, population aging is going to be a big challenge for the country to face. In this study, we assessed the changes in concentrations of PM2.5 and O3 and associated health impacts of different age populations from 2015 to 2060 using GEOS‐Chem model and exposure‐response functions. Contributions to premature deaths due to decline in pollutants and population aging were further quantified, respectively. We also compared the results based on carbon neutrality (SSP1‐1.9) with those simulated from a pathway with higher CO2 emission (SSP5‐8.5) to figure out the environmental and health benefits of carbon neutrality. Our results suggested that the decline in PM2.5 and O3 benefited from carbon neutrality would mitigate the increase in mortality owing to population aging from 2015 to 2060. The increase in premature deaths caused by population aging alone under SSP1‐1.9 will be less than that under SSP5‐8.5. Our study highlighted the importance of implementing carbon neutral measures from the environmental and health perspectives. Key Points: Carbon neutralization was projected to lead to synergistic reductions in CO2, PM2.5 and O3 in China in the futureMortality caused by PM2.5 and O3 will increase by 38.1% in China from 2015 to 2060 under carbon neutrality because of population agingRelative to carbon neutrality pathway, deaths in 2060 in China under SSP5‐8.5 would be higher by about 3,520.0 thousand [ABSTRACT FROM AUTHOR]

Due to the rapid development of industrialization and a substantial economy, China has become one of the global hotspots of nitrogen (N) and sulfur (S) deposition following Europe and the USA. Here, we developed a dataset with full coverage of N and S deposition from 2005 to 2020, with multiple statistical models that combine ground-level observations, chemistry transport simulations, satellite-derived vertical columns, and meteorological and geographic variables. Based on the newly developed random forest method, the multi-year averages of dry deposition of oxidized nitrogen (OXN), reduced nitrogen (RDN), and S in China were estimated at 10.4, 14.4, and 16.7 kg N/S ha -1 yr -1 , and the analogous numbers for total deposition were respectively 15.2, 20.2, and 25.9 kg N/S ha -1 yr -1 when wet deposition estimated previously with a generalized additive model (GAM) was included. The dry to wet deposition ratio (Rdry/wet) of N stabilized in earlier years and then gradually increased especially for RDN, while that of S declined for over 10 years and then slightly increased. The RDN to OXN deposition ratio (RRDN/OXN) was estimated to be larger than 1 for the whole research period and clearly larger than that of the USA and Europe, with a continuous decline from 2005 to 2011 and a more prominent rebound afterwards. Compared with the USA and Europe, a more prominent lagging response of OXN and S deposition to precursor emission abatement was found in China. The OXN dry deposition presented a descending gradient from east to west, while the S dry deposition a descending gradient from north to south. After 2012, the OXN and S deposition in eastern China declined faster than the west, attributable to stricter emission controls. Positive correlation was found between regional deposition and emissions, while smaller deposition to emission ratios (D/E) existed in developed eastern China, attributed to more intensive human activities and thereby anthropogenic emissions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the impact of educational, aesthetic, entertainment and escapist experiences on the perceptions of functional, emotional and social value as well as homestay visitor satisfaction and behavioural intentions. A total of 472 respondents completed a survey conducted at 45 homestays on the Chinese mainland. Using structural equation modelling (SEM), it is found that aesthetic and escapist experiences are significant predictors of perceived functional, emotional and social value by visitors. Educational experience affects emotional value, but entertainment experience does not affect functional, emotional or social values. Functional, emotional and social values affect homestay tourist satisfaction, which in turn drives behavioural intention. [ABSTRACT FROM AUTHOR]

Integrating two theoretical frameworks, the product level theory and the experience economy model, this research analyzed and compared robotic technology applications and customer experiences in selected case robot restaurants in the United States and China. Guided by the product level theory, we first analyzed in which product/service levels were robots applied in each case restaurant in Study 1. Then in study 2, guided by the experience economy model, we further explored customers' dining experiences and compared if customers' experience differs due to variations in product/service levels that robot applied. The study first contributes to the product level theory by extending its application to the context of robotic restaurants. It also contributes to the experience economy literature, and in particularly, whether applications of robotic technologies at different product levels matter in customers' dining experience. The study included case restaurants both from the United States and China, presenting findings with cultural implications. Given the challenges presented by COVID-19 and the industry is exploring alternative ways for service delivery and food production, such a study is particularly meaningful. [ABSTRACT FROM AUTHOR]

We explore the sensitivity of modeled tropospheric hydroxyl (OH) concentration trends to meteorology and near-term climate forcers (NTCFs), namely methane (CH 4) nitrogen oxides (NOx=NO2+NO) carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs) and ozone-depleting substances (ODSs), using the Geophysical Fluid Dynamics Laboratory (GFDL)'s atmospheric chemistry–climate model, the Atmospheric Model version 4.1 (AM4.1), driven by emissions inventories developed for the Sixth Coupled Model Intercomparison Project (CMIP6) and forced by observed sea surface temperatures and sea ice prepared in support of the CMIP6 Atmospheric Model Intercomparison Project (AMIP) simulations. We find that the modeled tropospheric air-mass-weighted mean [OH] has increased by ∼5 % globally from 1980 to 2014. We find that NOx emissions and CH 4 concentrations dominate the modeled global trend, while CO emissions and meteorology were also important in driving regional trends. Modeled tropospheric NO 2 column trends are largely consistent with those retrieved from the Ozone Monitoring Instrument (OMI) satellite, but simulated CO column trends generally overestimate those retrieved from the Measurements of Pollution in The Troposphere (MOPITT) satellite, possibly reflecting biases in input anthropogenic emission inventories, especially over China and South Asia. [ABSTRACT FROM AUTHOR]

The cross-regional transfer of food safety risks has become more prominent, bringing new challenges to food safety regulation. This study used a social network analysis to delve into the nuanced features and determinants of the cross-regional transfer of food safety risks based on the food safety inspection data of five provinces in East China from 2016 to 2020, thus contributing to the establishment of effective cross-regional cooperation in food safety regulation. The main findings are as follows: First, the cross-regional transfer of unqualified products accounts for 36.09% of all unqualified products. Second, the food safety risk transfer network presents a typical complex network—a relatively low but increasing network density, heterogeneous nodes, numerous subgroups, and a dynamic structure—bringing more difficulties to food safety cross-regional cooperation. Third, territorial regulation and intelligent supervision both contribute to restricting cross-regional transfers. However, the advantages of intelligent supervision have not yet been brought into play due to low data utilization. Fourth, the development of the food industry helps to mitigate the cross-regional transfer of food safety risks. To achieve effective cross-regional cooperation in food safety risks, it is essential to use food safety big data as a guide and to maintain synchronization between the development of the food industry and the improvement of regulations. [ABSTRACT FROM AUTHOR]

Based on the Technology Acceptance Model (TAM) and supplemented by social cognitive theory and gender schema theory, this study investigated the interactive mechanism of TAM by incorporating self-efficacy and gender as two factors in students' usage of sports bracelets. Data were collected from 682 Chinese college freshmen. Results indicated that perceived usefulness (PU), perceived ease of use (PEU) and attitude towards technology (ATT) significantly influenced students' intention to use sports bracelets. Self-efficacy (SE) not only was positively associated with PU and PEU, but, more importantly, self-efficacy moderated the relationship between PEU and behavioural intention (BI), and also between ATT and BI marginally. The effects of SE towards BI were found to be more apparent for low PEU and ATT students. In the case of sports bracelets, gender was found to moderate the relationships between PU and BI, and BI increased with PU for males but remained unchanged for females. Implications were discussed. [ABSTRACT FROM AUTHOR]

Background: Stage IE primary thyroid lymphoma (PTL) has been diagnosed in approximately half of patients with PTL; however, the optimal treatment for stage IE PTL has not yet been established., Methods: Stage IE PTL patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 1998 and 2019. Thereafter, the disease-specific survival (DSS) and treatment modalities (surgery alone, surgery + radiotherapy (RT) and/or chemotherapy (CT), and RT and/or CT) of these patients were compared by Kaplan-Meier curves and log-rank test after propensity score matching (PSM). Additionally, patients with PTL from the Affiliated Sixth People's Hospital of the Shanghai Jiao Tong University and School of Medicine (Shanghai, China) between 2007 and 2022 were retrospectively analyzed as an external cohort., Results: Among the 1596 patients with PTL from the SEER database, 842 were identified as patients with stage IE PTL, with an average follow-up period of 7.8 years. Pairwise analysis after PSM revealed no significant difference between the DSS of the three treatment groups. A total of 38 patients with PTL were identified in the external cohort, with an average follow-up period of 3.4 years. Compared with the RT and/or CT group, the surgery-alone group showed no significant difference in the incidence of hypothyroidism (p = 0.161) but had significantly fewer treatment-related complications (p = 0.021), shorter treatment duration (p < 0.001), and lower treatment costs (p = 0.025)., Conclusions: The results of our study demonstrate that surgery is a viable treatment option for patients with stage IE PTL., (© 2023. Society of Surgical Oncology.)

Unit energy consumption per kg pollutant removed (kWh/kgCODremoved) is used for the first time in assessing and ranking the sustainability of main treatment technologies of 1215 wastewater treatment plants (WWTPs) in China. The metric better measures the sustainability of main treatment technologies in WWTPs than unit energy consumption per cubic meter treated (kWh/m3). The energy consumption data of these WWTPs were selected from the database of 1399 WWTPs to evaluate the energy efficiency of different treatment technologies. 80.3% of the WWTPs applied anaerobic-oxic plus anaerobic–anoxic–oxic, oxidation ditch, and sequencing batch reactor as main technologies. Statistical analysis shows that the unit energy consumption of WWTPs decreases with increasing design flow rate, operation loading rate, and influent COD concentration. For example, the average unit energy consumption of SBR decreases from 2.76 kWh/kgCODremoved to 0.83 kWh/kgCODremoved when the design flow rate increases from less than 10,000 m3/d to 100,000–200,000 m3/d. The mean unit energy consumption of SBR decreases from 1.71 kWh/kgCODremoved to 1.32 kWh/ kgCODremoved and 2.85 kWh/ kgCODremoved to 0.63 kWh/kgCODremoved as the operation loading rate and COD removal increase from 40% to 100% and from less than 150 mg/L to over 450 mg/L, respectively. SBR has the lowest unit energy consumption among all the technologies. Therefore, SBR might be the most appropriate technology in small and medium-scale WWTPs in China. Regression equations were developed to predict the unit energy consumption for sustainable design treatment trains by input variables such as design flow rate, operation loading rate, and influent COD concentration. [ABSTRACT FROM AUTHOR]

Ozone (O 3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O 3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O 3 pollution, near-surface O 3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O 3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O 3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O 3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O 3 increases are primarily owing to the favorable meteorological conditions for O 3 photochemical formation in most Asian regions. We also find that the summertime O 3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O 3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation. [ABSTRACT FROM AUTHOR]

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models). [ABSTRACT FROM AUTHOR]

To clarify the chemical characteristics, source contributions, and health risks of pollution events associated with high PM2.5 in typical industrial areas of North China, manual sampling and analysis of PM2.5 were conducted in the spring, summer, autumn, and winter of 2019 in Pingyin County, Jinan City, Shandong Province. The results showed that the total concentration of 29 components in PM2.5 was 53.4 ± 43.9 μg·m−3, including OC/EC, water-soluble ions, inorganic elements, and metal elements. The largest contribution was from the NO3− ion, at 14.6 ± 14.2 μg·m−3, followed by organic carbon (OC), SO42−, and NH4+, with concentrations of 9.3 ± 5.5, 9.1 ± 6.4, and 8.1 ± 6.8 μg·m−3, respectively. The concentrations of OC, NO3−, and SO42− were highest in winter and lowest in summer, whereas the NH4+ concentration was highest in winter and lowest in spring. Typical heavy metals had higher concentrations in autumn and winter, and lower concentrations in spring and summer. The annual average sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) were 0.30 ± 0.14 and 0.21 ± 0.12, respectively, with the highest SO2 emission and conversion rates in winter, resulting in the SO42− concentration being highest in winter. The average concentration of secondary organic carbon in 2019 was 2.8 ± 1.9 μg·m−3, and it comprised approximately 30% of total OC. The concentrations of 18 elements including Na, Mg, and Al were between 2.3 ± 1.6 and 888.1 ± 415.2 ng·m−3, with Ni having the lowest concentration and K the highest. The health risk assessment for typical heavy metals showed that Pb poses a potential carcinogenic risk for adults, whereas As may pose a carcinogenic risk for adults, children, and adolescents. The non-carcinogenic risk coefficients for all heavy metals were lower than 1.0, indicating that the non-carcinogenic risk was negligible. Positive matrix factorization analysis indicated that coal-burning emissions contributed the largest fraction of PM2.5, accounting for 35.9% of the total. The contribution of automotive emissions is similar to that of coal, at 32.1%. The third-largest contributor was industrial sources, which accounted for 17.2%. The contributions of dust and other emissions sources to PM2.5 were 8.4% and 6.4%, respectively. This study provides reference data for policymakers to improve the air quality in the NCP. [ABSTRACT FROM AUTHOR]

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM 2.5 and O3 , we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30 %–42 % using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches. [ABSTRACT FROM AUTHOR]

The seismic behavior of the independent type of a stone masonry loess cave (SMLC) is examined. A tri-directional shake table test was employed on 1:4 reduced-scale specimen of SMLC. The specimen represented a typical traditional dwelling of the loess region in China, consisting of unreinforced masonry walls and inner loess, without any seismic treatment. The dynamic parameters directly measured by the test include the acceleration response, displacement response of each key position of the stone masonry loess cave, and the detailed record of the damage form of the structure under each loading. The analysis results indicated that the simplified model design method not only satisfied the main similarity relationship of the reduced-scale structure shaking table test, but also achieved excellent test results. Through the calculation results to determine the torsion angle of the node and the deformation between the layers, corresponding strengthening measures could be proposed for the life extension protection of this traditional building. Finally, according to the analysis of the test results, the damage state and damage level of the prototype of the SMLC is established. [ABSTRACT FROM AUTHOR]

This study predicted three future land-use type scenarios in 2050 (including the Shared Socioeconomic Pathway SSP126, SSP585, and carbon scenario) based on the Land-Use Harmonization (LUH2) project and the future evolution of land-use types considering China's carbon neutrality background. The contribution of land-use changes to terrestrial natural source biogenic volatile organic compounds (BVOCs), as well as O3 and PM concentrations, were determined. Under the SSP126 pathway, meteorological changes would increase BVOC emissions in China by 1.0 TgC in 2050, compared with 2015, while land-use changes would increase them by 1.5–7.1 TgC. The impact of land-use changes on O3 and PM concentrations would be less than 3.6% in 2050 and greater in summer. Regional differences must be considered when calculating future environmental background concentrations of pollutants. Due to more afforestation measures under the SSP126 scenario, the impact of land-use change on pollutants was more obvious under the SSP126 pathway than under the SSP585 pathway. Under the carbon scenario, the increase in PM concentration caused by land-use changes would pose a risk to air quality compliance; thus, it is necessary to consider reducing or offsetting this potential risk through anthropogenic emission control measures. [ABSTRACT FROM AUTHOR]

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Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. [ABSTRACT FROM AUTHOR]

Air pollution over the Yungui Plateau (YGP) in southwestern China can be caused by the transport of biomass burning aerosols from Southeast Asia; however, the magnitude and mechanisms of such long‐range transport have not been fully investigated. Here, we studied the impacts of fire emissions on vertical PM2.5 over the YGP and the transport mechanisms of PM2.5 during the fire seasons (March–April) of the neighboring Indo‐China Peninsula (ICP) region in 2015–2019 using ground‐based monitoring data, reanalysis of meteorology, and GEOS‐Chem model simulations. Average daily PM2.5 concentrations of 36.06 ± 14.86 μg m−3 were measured at 16 ground stations in the YGP with the highest value of 53.77 μg m−3 at Xishuangbanna, the nearest station to the ICP. Model simulations showed that fire emissions contributed approximately 50%–60% of the vertical PM2.5 over the YGP at a height of 3–4 km, with larger contributions in meridional than zonal cross‐sections. Four weather patterns with low pressure over the YGP were identified as favorable conditions for smoke transport. The pattern with the lowest pressure over the northern YGP and the strongest vertical wind disturbance was the most favorable for the eastward transport of fire air pollution to the YGP. Another pattern, which had the strongest southerly wind, promoted smoke aerosols to climb from eastern Myanmar and northern Laos/Vietnam to the YGP. Through these typical pathways, ICP biomass burning significantly impacted PM2.5 pollution in southwestern China. Plain Language Summary: Based on the ground atmospheric monitoring data, reanalysis of meteorological data, and simulations of chemical transport model, we studied the impacts of fire emissions on vertical PM2.5 over the Yungui Plateau (YGP) and the transport mechanisms of PM2.5 during the fire season of the neighboring Indo‐China Peninsula (ICP) region in 2015–2019. We find that fire emissions contributed approximately 50%{plus minus}20% of the vertical PM2.5 over the YGP at a height of 3–4 km, with larger contributions in meridional than zonal cross‐sections. Four weather patterns with low pressure over the YGP were identified as favorable conditions for smoke transport and the weather pattern showed two typical pathways of smoke transport from the ICP to the YGP. This study reveals the large impacts of trans‐boundary air pollution from the ICP on the air quality in the YGP during biomass burning seasons. Key Points: Average PM2.5 concentrations at Yungui Plateau (YGP) stations were 36.06 ± 14.86 μg m−3 with the highest value at the nearest station to the Indo‐China PeninsulaFire emissions contributed 50% ± 20% of the vertical PM2.5 at a height of 3–4 km over the YGPFour weather patterns with low air pressure were favorable for the transport of PM2.5 to the YGP through two typical pathways [ABSTRACT FROM AUTHOR]

Tropospheric ozone, as a critical atmospheric component, plays an important role in influencing radiation equilibrium and ecological health. It is affected not only by anthropogenic activities but also by natural climate variabilities. Here we examine the tropospheric ozone changes in China associated with the eastern Pacific (EP) and central Pacific (CP) El Niño using satellite observations from 2007 to 2017 and GEOS-Chem simulations from 1980 to 2017. GEOS-Chem reasonably reproduced the satellite-retrieved lower tropospheric ozone (LTO) changes despite a slight underestimation. In general, both types of El Niño exert negative impacts on LTO concentration in China, except for southeastern China during the pre-CP El Niño autumn and post-EP El Niño summer. Ozone budget analysis further reveals that for both events, LTO changes are dominated by the transport processes controlled by circulation patterns and the chemical processes influenced by local meteorological anomalies associated with El Niño, especially the changes in solar radiation and relative humidity. The differences between EP- and CP-induced LTO changes mostly lie in southern China. The different strengths, positions, and duration of the western North Pacific anomalous anticyclone induced by tropical warming are likely responsible for the different EP and CP LTO changes. During the post-EP El Niño summer, the Indian Ocean capacitor effect also plays an important role in mediating LTO changes over southern China. [ABSTRACT FROM AUTHOR]

The warming climate increases the probability of hot days, which leads to a penalty effect of increasing ozone (O3)‐polluted days in polluted regions. Here, we established a random forest algorithm to predict future probabilities of O3 exceedance (P) during hot days and further examined the future co‐occurrences of O3‐polluted days and hot days under two different scenarios of Shared Socioeconomic Pathways (SSPs) 1‐2.6 and 5‐8.5 for 2030–2050s. Ground‐level observations, simulated O3‐temperature sensitivities in the GEOS‐Chem model, multimodel seasonal‐mean O3 concentrations and daily maximum temperature from the sixth Coupled Model Intercomparison Project (CMIP6), leaf area index from reanalyzed data, and local geographical information were comprehensively utilized. Evaluations showed that the algorithm captured the spatial patterns of present‐day P values well with a correlation coefficient of 0.92 over China. Results showed that the strong reductions in anthropogenic emissions under SSP 1‐2.6 significantly reduced the risks of O3 exceedance during hot days nationwide from 3.7 days in the 2030s to 3.0 days in the 2050s. However, the SSP 5‐8.5 scenario witnessed more frequent co‐occurrences of O3‐polluted days and hot days over the 2030–2050s with nationally averaged values from 4.5 to 6.4 days. Our results highlight the co‐benefits of reducing anthropogenic emissions to alleviate the composite risks of extreme weather events and air‐polluted days in the future. Plain Language Summary: Temperature is generally considered as the most important meteorological factor that influences ground‐level ozone (O3) concentrations. A warmer climate is highly likely to lead to frequent occurrence of hot days, which could further increase ozone‐polluted days in regions with high anthropogenic emissions. The co‐occurrences of O3‐polluted days and hot days lead to composite risks to human health, but how such co‐occurrences will change in the future is rarely examined. Here, we established a novel machine‐learning approach by using a random forest algorithm to quantify the probability of O3 exceedance (P) during hot days and then predicted the future co‐occurrence of O3‐polluted days and hot days under two different future emission scenarios (Shared Socioeconomic Pathways [SSP]1‐2.6 and SSP5‐8.5). Multisource data, including ground‐level observations, reanalyzed data, GEOS‐Chem simulations, and multimodel outputs from Coupled Model Intercomparison Project, were comprehensively utilized. We found that the random forest algorithm was capable to derive a reasonable pattern of P values over China. Composite risks of O3 exceedance during hot days are projected to be reduced significantly in the future under the SSP 1‐2.6 scenario, whereas to keep increasing until the 2050s under the SSP 5‐8.5 scenario. Key Points: A machine‐learning approach was developed to quantify the climatological risk level of ozone (O3) pollution during hot days over ChinaCo‐occurrence of O3‐polluted days and hot days reduced from 3.7 days in 2030s to 3.0 days in 2050s over China under the Shared Socioeconomic Pathway (SSP) 1‐2.6 scenarioThe SSP 5‐8.5 scenario led to higher co‐occurrences of O3‐polluted days and hot days in 2050s, especially in North China and northwest China [ABSTRACT FROM AUTHOR]

Tea, the most common infusion worldwide, is usually sold in teabags due to the ease of usage. The increase in the use of plastic materials in the food packaging industry has led to an increase in released contaminants, such as microplastics (MPs) and phthalates (PAEs), in various food products including teabags. In this research, the abundance and features of MPs as well as PAEs concentration were investigated in 45 teabag samples of different Persian and German brands. The abundance of MPs in the Persian and German teabag samples was averagely 412.32 and 147.28 items/single teabag, respectively. Also, average PAEs levels in the Persian and German teabag samples were 2.87 and 2.37 mg/g, respectively. The predominant size category of MPs was related to 100-250 μm. Fibers and transparent were the dominant shape and color of detected MPs in teabags, respectively. Polyethylene (PE) and nylon were the most common MP polymer types. The most prominent PAEs congeners in teabag samples were diethylhexyl phthalate (DEHP) and diisobutyl phthalate (DiBP). Furthermore, MP exposure hazard through tea ingestion was analyzed for children and adults. DEHP showed the cancer risk (CR) for children and adults. The findings of this research indicated that high MPs and PAEs levels are released from teabags into tea drinks. Considering a daily drinking of a volume of 150 and 250 mL tea by children and adults, 486 and 810 MPs may enter their bodies, respectively. Thus, tea prepared with teabag-packed herbs may pose a significant health risk for consumers., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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]

Ethane (C2H6) is an important greenhouse gas and plays a significant role in tropospheric chemistry and climate change. This study first presents and then quantifies the variability, sources, and transport of C2H6 over densely populated and highly industrialized eastern China using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing along with atmospheric modeling techniques. We obtained a retrieval error of 6.21 ± 1.2 (1 σ)% and degrees of freedom (DOFS) of 1.47 ± 0.2 (1 σ) in the retrieval of C2H6 tropospheric column-averaged dry-air mole fraction (troDMF) over Hefei, eastern China (32 ∘ N, 117 ∘ E; 30 ma.s.l.). The observed C2H6 troDMF reached a minimum monthly mean value of 0.36 ± 0.26 ppbv in July and a maximum monthly mean value of 1.76 ± 0.35 ppbv in December, and showed a negative change rate of - 2.60 ± 1.34 %yr-1 from 2015 to 2020. The dependencies of C2H6 troDMF on meteorological and emission factors were analyzed using generalized additive models (GAMs). Generally, both meteorological and emission factors have positive influences on C2H6 troDMF in the cold season (December–January–February/March–April–May, DJF/MAM) and negative influences on C2H6 troDMF in the warm season (June–July–August/September–October–November, JJA/SON). GEOS-Chem chemical model simulation captured the observed C2H6 troDMF variability and was, thus, used for source attribution. GEOS-Chem model sensitivity simulations concluded that the anthropogenic emissions (fossil fuel plus biofuel emissions) and the natural emissions (biomass burning plus biogenic emissions) accounted for 48.1 % and 39.7 % of C2H6 troDMF variability over Hefei, respectively. The observed C2H6 troDMF variability mainly results from the emissions within China (74.1 %), where central, eastern, and northern China dominated the contribution (57.6 %). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the midlatitude westerlies and the Asian monsoon system. Reduction in C2H6 abundance from 2015 to 2020 mainly results from the decrease in local and transported C2H6 emissions, which points to air quality improvement in China in recent years. [ABSTRACT FROM AUTHOR]

The inaccuracy of anthropogenic emission inventories on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, which is a typical emission inventory estimation method, requires a lot of human labor and material resources, whereas a top-down approach focuses on individual pollutants that can be measured directly as well as relying heavily on traditional numerical modeling. Lately, the deep neural network approach has achieved rapid development due to its high efficiency and nonlinear expression ability. In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation. Specifically, we utilized a neural-network-based comprehensive chemical transport model (NN-CTM) to explore the complex correlation between emission and air pollution. We further updated the emission inventory based on back-propagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NNs) and achieved a relatively good representation of the CTM, with similarity reaching 95 %. To reduce the gap between the CTM and observations, the NN model suggests updated emissions of NO x , NH 3 , SO 2 , volatile organic compounds (VOCs) and primary PM 2.5 changing, on average, by - 1.34 %, - 2.65 %, - 11.66 %, - 19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NO x and PM 2.5 are even higher (∼ 10 %) in regions that suffer from large uncertainties in original emissions, such as Northwest China. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO 2 , SO 2 , O 3 and PM 2.5 concentrations are reduced significantly (by about 10 %–20 %), indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of the emission inventory and the performance of the air quality model. [ABSTRACT FROM AUTHOR]

Persistent ozone (O3) pollution episodes (OPEs, with regionally averaged maximum daily 8‐h average (MDA8) O3 concentration exceeding 80 ppbv and lasting for 5 days or longer) occurred frequently in megalopolis over eastern China in recent years. Here, we apply a newly developed chemistry‐biosphere model (GEOS‐Chem‐YIBs) to comprehensively quantify the vegetation contributions to OPEs in North China and Yangtze River Delta (YRD) over May to October in 2014–2017. The simulated MDA8 O3 concentrations increases 16.7 and 16.6 ppbv) during OPEs in North China and YRD, of which about 1.4 ppbv (8.4%) and 3.8 ppbv (22.9%) are caused by the processes including increased BVOC emissions and reduced stomatal dry deposition. Furthermore, the O3 damages to vegetation stomata slightly increase seasonal‐mean O3 concentrations by <1 ppbv, but such effects are not exacerbated during OPEs despite the high O3 exposure. Plain Language Summary: The summertime ozone (O3) in eastern China has kept increasing in recent years, accompanying with more frequent persistent O3 pollution episodes (OPEs) in North China Plain and Yangtze River Delta (YRD). Vegetation‐related processes, including biogenic volatile organic compounds emissions and stomatal O3 uptakes, significantly influence O3 concentrations and dynamically vary with meteorology. Warm and dry weather conditions during OPEs can lead vegetation emitting more biogenic volatile organic compounds and uptaking less O3, but the magnitudes of vegetation contributions to recent OPEs over China have not been quantified yet. Here, 10 and 5 OPEs in North China and YRD are identified over 2014–2017, respectively, which lasted for 5 or longer days with regionally averaged maximum daily 8‐h average O3 concentrations larger than 80 ppbv. By applying a newly developed chemistry‐biosphere coupled model, we quantified that about 1.4 ppbv (8.4%) and 3.8 ppbv (22.9%) of simulated O3 enhancements (16.7 and 16.6 ppbv) during OPEs were caused by vegetation‐related processes in North China and YRD. Meanwhile, the O3 damages to vegetation stomata led to positive feedbacks onto seasonal‐mean surface O3, but the feedbacks are not exacerbated during OPEs. Our results highlight the important contributions of vegetation to OPEs in eastern China. Key Points: Over May to October in 2014–2017, 10 and 5 ozone pollution episodes (OPEs) occurred in North China and Yangtze River Delta, respectivelyEnhanced BVOC emissions and reduced stomatal O3 uptakes contributed 1.4 ppbv (8.4%) and 3.8 ppbv (22.9%) O3 to OPEs in North China and YRDThe O3 vegetation damages lead to positive feedbacks onto seasonal‐mean surface O3, but the feedbacks are not exacerbated during OPEs [ABSTRACT FROM AUTHOR]

The contribution of meteorology and emissions to long-term PM 2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM 2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM 2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM 2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM 2.5 : a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM 2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM 2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM 2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM 2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM 2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM 2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM 2.5 and gas precursors in these years. Although emissions dominated the long-term PM 2.5 trends, the meteorology-driven anomalies also contributed -3.9 % to 2.8 % of the annual mean PM 2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., - 6.3 % to 4.9 % of the annual mean PM 2.5 concentrations in the Beijing-Tianjin-Hebei region, - 5.1 % to 4.3 % in the Fenwei Plain, - 4.8 % to 4.3 % in the Yangtze River Delta, and - 25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM 2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future. [ABSTRACT FROM AUTHOR]

We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM 2.5 in January 2015 (normalized mean bias of - 9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021). [ABSTRACT FROM AUTHOR]

The major air pollutant emissions have decreased, and the overall air quality has substantially improved across China in recent years as a consequence of active clean air policies for mitigating severe air pollution problems. As key precursors of formaldehyde (HCHO) and ozone (O 3), the volatile organic compounds (VOCs) in China are still increasing due to the lack of mitigation measures for VOCs. In this study, we investigated the drivers of HCHO variability from 2015 to 2019 over Hefei, eastern China, by using ground-based high-resolution Fourier transform infrared (FTIR) spectroscopy and GEOS-Chem model simulation. Seasonal and interannual variabilities of HCHO over Hefei were analyzed and hydroxyl (OH) radical production rates from HCHO photolysis were evaluated. The relative contributions of emitted and photochemical sources to the observed HCHO were analyzed by using ground-level carbon monoxide (CO) and O x (O 3 + nitrogen oxide (NO 2)) as tracers for emitted and photochemical HCHO, respectively. Contributions of emission sources from various categories and geographical regions to the observed HCHO summertime enhancements were determined by using a series of GEOS-Chem sensitivity simulations. The column-averaged dry air mole fractions of HCHO (XHCHO) reached a maximum monthly mean value of 1.1 ± 0.27 ppbv in July and a minimum monthly mean value of 0.4 ± 0.11 ppbv in January. The XHCHO time series from 2015 to 2019 over Hefei showed a positive change rate of 2.38 ± 0.71 % per year. The photochemical HCHO is the dominant source of atmospheric HCHO over Hefei for most of the year (68.1 %). In the studied years, the HCHO photolysis was an important source of OH radicals over Hefei during all sunlight hours of both summer and winter days. The oxidations of both methane (CH 4) and nonmethane VOCs (NMVOCs) dominate the HCHO production over Hefei and constitute the main driver of its summertime enhancements. The NMVOC-related HCHO summertime enhancements were dominated by the emissions within eastern China. The observed increasing change rate of HCHO from 2015 to 2019 over Hefei was attributed to the increase in photochemical HCHO resulting from increasing change rates of both CH 4 and NMVOC oxidations, which overwhelmed the decrease in emitted HCHO. This study provides a valuable evaluation of recent VOC emissions and regional photochemical capacity in China. In addition, understanding the sources of HCHO is a necessary step for tackling air pollution in eastern China and mitigating the emissions of pollutants. [ABSTRACT FROM AUTHOR]

Surface ozone is a major pollutant in eastern China, especially during the summer season. The formation of surface ozone pollution highly depends on meteorological conditions largely controlled by regional circulation patterns which can modulate ozone concentrations by influencing the emission of the precursors, the chemical production rates, and regional transport. Here we show that summertime ozone pollution over eastern China is distinctly modulated by the variability of the western Pacific subtropical high (WPSH), a major synoptic system that controls the summertime weather conditions of East Asia. Composite and regression analyses indicate that a positive WPSH anomaly is associated with higher than normal surface ozone concentration over northern China but lower ozone over southern China. Stronger than normal WPSH leads to higher temperatures, stronger solar radiation at the land surface, lower relative humidity, and less precipitation in northern China, favoring the production and accumulation of surface ozone. In contrast, all meteorological variables show reverse changes in southern China under a stronger WPSH. GEOS-Chem simulations reasonably reproduce the observed ozone changes associated with the WPSH and support the statistical analyses. We further conduct a budget diagnosis to quantify the detailed contributions of chemistry, transport, mixing, and convection processes. The result shows that chemistry plays a decisive role in leading the ozone changes among these processes. Results show that the changes in ozone are primarily attributed to chemical processes. Moreover, the natural emission of precursors from biogenic and soil sources, a major component influencing the chemical production, accounts for ∼ 30 % of the total surface ozone changes. [ABSTRACT FROM AUTHOR]

The top-level emergency response to the COVID-19 pandemic involved exhaustive quarantine measures in China. The impacts of the COVID-19 quarantine on the decline in fine particulate matter (PM 2.5) were quantitatively assessed based on numerical simulations and observations in February. Relative to both February 2017 and the climate mean, anomalous southerlies and moister air occurred in the east of China in February 2020, which caused considerable PM 2.5 anomalies. Thus, it is a must to disentangle the contributions of stable meteorology from the effects of the COVID-19 lockdown. The contributions of routine emission reductions were also quantitatively extrapolated. The top-level emergency response substantially alleviated the level of haze pollution in the east of China. Although climate variability elevated the PM 2.5 by 29 % (relative to 2020 observations), a 59 % decline related to the COVID-19 pandemic and a 20 % decline from the expected pollution regulation dramatically exceeded the former in North China. The COVID-19 quarantine measures decreased the PM 2.5 in the Yangtze River Delta by 72 %. In Hubei Province where most pneumonia cases were confirmed, the impact of total emission reduction (72 %) evidently exceeded the rising percentage of PM 2.5 driven by meteorology (13 %). [ABSTRACT FROM AUTHOR]

Measurement‐model comparisons for various atmospheric species have been widely used for evaluating the reliability of model simulations, yet such comparisons are quite limited for atmospheric isoprene due to the lack of systematic observations over a large scale. Here, we collected ambient air samples concurrently at 20 sites in China during 2012–2014 and compared the observed isoprene mixing ratios with those simulated by coupling MEGAN v2.1 with the 3‐D Regional chEmical trAnsport Model. The observed average isoprene concentration for all 1,731 samples is 0.35 ± 0.03 ppbv, 13% higher than the model simulated average of 0.31 ± 0.02 ppbv; while during the growing season, the simulated average (0.53 ± 0.05 ppbv) is almost the same as the observed average (0.52 ± 0.04 ppbv). The observed isoprene average level in the north (0.35 ppbv) is similar to that in the south (0.37 ppbv). However, the model simulated average in the south (0.53 ppbv) is ∼4 times that in the north (0.14 ppbv). The model overestimated the observations at most southern sites but underestimated at most northern sites in the growing season, which might be resulted from inadequate characterization of isoprene‐emitting land vegetation with fast afforestation in the north and rapid urbanization in the south. Compared to the model simulation, the observations revealed abnormally high isoprene levels in winter at some northern sites, largely due to nonbiogenic emissions, as evidenced by the enhancements of measured combustion tracers concurrent with the abnormally elevated isoprene levels. Plain Language Summary: Isoprene alone accounts for approximately one‐third of global emission of volatile organic compounds from both nature and human activities, yet in many parts of the world biogenic isoprene emission estimates have large uncertainties (reported isoprene emission estimates span 4–29 TgC y−1 in China), and narrowing these uncertainties is a challenging task. Here we collected ambient air samples concurrently at 20 sites over China during 2012–2014, and made measurement‐model comparison to check the emission estimates for isoprene, and found that although overall observed and predicted average isoprene levels matched well particularly during growing seasons, there exists a south‐north discrepancy that model simulated averages in the south were much higher than the north while their observed averages were quite near each other. Modeled values overestimated at most southern sites but underestimated at most northern sites, probably the fast afforestation in the north and rapid urbanization in the southeast were inadequately characterized in the model. Abnormally higher isoprene levels were observed in winter at some northern sites probably due to contribution from nonbiogenic sources. Our results highlight that high‐resolution PFT/LAI, field campaigns with higher spatial and temporal resolutions, as well as biomass burning activity data, are necessary for better observation‐model comparison. Key Points: Ambient isoprene mixing ratios observed at 20 sites across China during 2012–2014 are compared with the MEGAN‐REAM model simulationThe model simulated averages in the south were much higher than those in north, while the observed averages were much more similarAbnormally high isoprene levels were observed in winter at some northern sites probably due to contributions from nonbiogenic sources [ABSTRACT FROM AUTHOR]

Meteorological conditions play important roles in the formation of ozone (O3) and fine particulate matter (PM2.5). China has been suffering from serious regional air pollution problems, characterized by high concentrations of surface O3 and PM2.5. In this study, the Community Multiscale Air Quality (CMAQ) model was used to quantify the sensitivity of surface O3 and PM2.5 to key meteorological parameters in different regions of China. Six meteorological parameters were perturbed to create different meteorological conditions, including temperature (T), wind speed (WS), absolute humidity (AH), planetary boundary layer height (PBLH), cloud liquid water content (CLW) and precipitation (PCP). Air quality simulations under the perturbed meteorological conditions were conducted in China in January and July of 2013. The changes in O3 and PM2.5 concentrations due to individual meteorological parameters were then quantified. T has a great influence on the daily maximum 8 h average O3 (O3 -8 h) concentrations, which leads to O3 -8 h increases by 1.7 in January in Chongqing and 1.1 ppb K -1 in July in Beijing. WS, AH, and PBLH have a smaller but notable influence on O3 -8 h with maximum change rates of 0.3 ppb % -1 , -0.15 ppb % -1 , and 0.14 ppb % -1 , respectively. T , WS, AH, and PBLH have important effects on PM2.5 formation of both in January and July. In general, PM2.5 sensitivities are negative to T , WS, and PBLH and positive to AH in most regions of China. The sensitivities in January are much larger than in July. PM2.5 sensitivity to T , WS, PBLH, and AH in January can be up to -5 µ g m -3 K -1 , -3 µ g m -3 % -1 , -1 µ g m -3 % -1 , and +0.6 µ g m -3 % -1 , respectively, and in July it can be up to -2 µ g m -3 K -1 , -0.4 µ g m -3 % -1 , -0.14 µ g m -3 % -1 , and +0.3 µ g m -3 % -1 , respectively. Other meteorological factors (CLW and PCP) have negligible effects on O3 -8 h (less than 0.01 ppb % -1) and PM2.5 (less than 0.01 µ g m -3 % -1). The results suggest that surface O3 and PM2.5 concentrations can change significantly due to changes in meteorological parameters, and it is necessary to consider these effects when developing emission control strategies in different regions of China. [ABSTRACT FROM AUTHOR]

North China experiences severe haze pollution in early winter, resulting in many premature deaths and considerable economic losses. The number of haze days in early winter (December and January) in North China (HDNC) increased rapidly after 2010 but declined slowly before 2010, reflecting a trend reversal. Global warming and emissions were two fundamental drivers of the long-term increasing trend of haze, but no studies have focused on this trend reversal. The autumn sea surface temperature (SST) in the Pacific and Atlantic, Eurasian snow cover and central Siberian soil moisture, which exhibited completely opposite trends before and after 2010, might have close relationships with identical trends of meteorological conditions related to haze pollution in North China. Numerical experiments with a fixed emission level confirmed the physical relationships between the climate drivers and HDNC during both decreasing and increasing periods. These external drivers induced a larger decreasing trend of HDNC than the observations, and combined with the persistently increasing trend of anthropogenic emissions, resulted in a realistic, slowly decreasing trend. However, after 2010, the increasing trends driven by these climate divers and human emissions jointly led to a rapid increase in HDNC. [ABSTRACT FROM AUTHOR]

Chemistry plays an indispensable role in investigations of the atmosphere; however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed tropospheric HOx – NOx –volatile organic compounds–ozone–bromine–aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a 3-year (2012–2014) simulation with anthropogenic emissions from the Community Emissions Data System (CEDS) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model captures well the spatial distributions and seasonal variations in tropospheric ozone, with seasonal mean biases of 0.4–2.2 ppbv at 700–400 hPa compared to satellite observations and within 10 ppbv at the surface to 500 hPa compared to global ozonesonde observations. The model has larger high-ozone biases over the tropics which we attribute to an overestimate of ozone chemical production. It underestimates ozone in the upper troposphere which is likely due either to the use of a simplified stratospheric ozone scheme or to biases in estimated stratosphere–troposphere exchange dynamics. The model diagnoses the global tropospheric ozone burden, OH concentration, and methane chemical lifetime to be 336 Tg, 1.16×106 molecule cm -3 , and 8.3 years, respectively, which is consistent with recent multimodel assessments. The spatiotemporal distributions of NO2 , CO, SO2 , CH2O , and aerosol optical depth are generally in agreement with satellite observations. The development of BCC-GEOS-Chem v1.0 represents an important step for the development of fully coupled earth system models (ESMs) in China. [ABSTRACT FROM AUTHOR]

Atmospheric nitrogen deposition in China has attracted public attention in recent years due to the increasing anthropogenic emission of reactive nitrogen (Nr) and its impacts on the terrestrial and aquatic ecosystems. However, limited long-term and multisite measurements have restrained the understanding of the mechanism of the Nr deposition and the chemical transport model (CTM) improvement. In this study, the performance of the simulated wet and dry deposition for different Nr species, i.e., particulate NO3- and NH4+ , gaseous NOx , HNO3 and NH3 have been conducted using the framework of Model Inter-Comparison Study for Asia (MICS-Asia) phase III. A total of nine models, including five Weather Research and Forecasting models coupled with the Community Multiscale Air Quality (WRF-CMAQ) models, two self-developed regional models, a global model and a Regional Atmospheric Modeling System coupled with the Community Multiscale Air Quality (RAMS-CMAQ) model have been selected for the comparison. For wet deposition, observation data from 83 measurement sites from the East Asia Acid Deposition Monitoring Network (EANET), Chinese Ecosystem Research Network (CERN), China Agricultural University Deposition Network (CAUDN), National Acid Deposition Monitoring Network (NADMN) and Department of Ecological Environment (DEE) of China have been collected and normalized for comparison with model results. In general, most models show the consistent spatial and temporal variation of both oxidized N (Nox) and reduced N (Nrd) wet deposition in China, with the normalized mean error (NME) at around 50 %, which is lower than the value of 70 % based on EANET observation over Asia. Both the ratio of wet or dry deposition to the total inorganic N (TIN) deposition and the ratios of TIN to their emissions have shown consistent results with the Nationwide Nitrogen Deposition Monitoring Network (NNDMN) estimates. The performance of ensemble results (ENMs) was further assessed with satellite measurements. In different regions of China, the results show that the simulated Nox wet deposition was overestimated in northeastern China (NE) but underestimated in the south of China, namely southeastern (SE) and southwestern (SW) China, while the Nrd wet deposition was underestimated in all regions by all models. The deposition of Nox has larger uncertainties than the Nrd , especially in northern China (NC), indicating the chemical reaction process is one of the most important factors affecting the model performance. Compared to the critical load (CL) value, the Nr deposition in NC, SE and SW reached or exceeded reported CL values and resulted in serious ecological impacts. The control of Nrd in NC and SW and Nox in SE would be an effective mitigation measure for TIN deposition in these regions. The Nr deposition in the Tibetan Plateau (TP) with a high ratio of TIN / emission (∼3.0), indicates a significant transmission from outside. Efforts to reduce these transmissions ought to be paramount due the climatic importance of the Tibetan region to the sensitive ecosystems throughout China. [ABSTRACT FROM AUTHOR]

We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models. WRF-GC offers regional modellers access to the latest GEOS-Chem chemical module, which is state of the science, well documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC uses unmodified copies of WRF and GEOS-Chem from their respective sources; the coupling structure allows future versions of either one of the two parent models to be integrated into WRF-GC with relative ease. Within WRF-GC, the physical and chemical state variables are managed in distributed memory and translated between WRF and GEOS-Chem by the WRF-GC coupler at runtime. We used the WRF-GC model to simulate surface PM 2.5 concentrations over China during 22 to 27 January 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem Classic nested-China simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM 2.5 , but the WRF-GC model (r=0.68 , bias =29 %) reproduced the observed daily PM 2.5 concentrations over eastern China better than the GEOS-Chem Classic model did (r=0.72 , bias =55 %). This was because the WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the pollution meteorology during the study period. The WRF-GC model is parallelized across computational cores and scales well on massively parallel architectures. In our tests where the two models were similarly configured, the WRF-GC simulation was 3 times more efficient than the GEOS-Chem Classic nested-grid simulation due to the efficient transport algorithm and the Message Passing Interface (MPI)-based parallelization provided by the WRF software framework. WRF-GC v1.0 supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no chemical feedbacks. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under way. The WRF-GC model is open source and freely available from http://wrf.geos-chem.org (last access: 10 July 2020). [ABSTRACT FROM AUTHOR]

Air quality in China has changed dramatically in response to rapid development of the economy and to policies. In this work, we investigate the changes in anthropogenic source contribution to ambient fine particulate matter (PM 2.5) air pollution and related health impacts in China during 1990–2015 and elucidate the drivers behind the decadal transition. We estimate the contribution of five anthropogenic emitting sectors to ambient PM 2.5 exposure and related premature mortality over China during 1990–2015 with 5-year intervals, by using an integrated model framework of a bottom-up emission inventory, a chemical transport model, and the Global Exposure Mortality Model (GEMM). The national anthropogenic PM 2.5 -related premature mortality estimated with the GEMM for nonaccidental deaths due to noncommunicable diseases and lower respiratory infections rose from 1.26 million (95 % confidence interval (CI) [1.05, 1.46]) in 1990 to 2.18 million (95 % CI [1.84, 2.50]) in 2005; then, it decreased to 2.10 million (95 % CI [1.76, 2.42]) in 2015. In 1990, the residential sector was the leading source of the PM 2.5 -related premature mortality (559 000, 95 % CI [467 000, 645 900], 44 % of total) in China, followed by industry (29 %), power (13 %), agriculture (9 %), and transportation (5 %). In 2015, the industrial sector became the largest contributor of PM 2.5 -related premature mortality (734 000, 95 % CI [615 500, 844 900], 35 % of total), followed by the residential sector (25 %), agriculture (23 %), transportation (10 %), and power (6 %). The decadal changes in source contribution to PM 2.5 -related premature mortality in China represent a combined impact of socioeconomic development and clean-air policy. For example, active control measures have successfully reduced pollution from the power sector, while contributions from the industrial and transportation sectors have continuously increased due to more prominent growth in activity rates. A transition in fuel consumption has dominated the decrease in the contribution from residential sector. Meanwhile, the contribution from the agriculture sector has continuously increased due to persistent NH3 emissions and enhanced formation of secondary inorganic aerosols under an NH3 -rich environment. [ABSTRACT FROM AUTHOR]

Ancient masonry towers are an important cultural heritage and particularly vulnerable to damage during earthquakes. To investigate the dynamic characterization and the seismic performances of historical masonry towers, a 1/8-scale masonry model representative of an ancient Chinese tower was studied by shaking table tests. The experimental model was subjected to four scaled earthquake ground motion in two directions, the dynamic characteristics, the seismic response as well as the failure mechanism were analyzed. In addition, a nonlinear finite element model was also established with macro-modeling approach to further study its seismic response, the results between the tests and the model were compared and agreed well. [ABSTRACT FROM AUTHOR]

China promulgated the Air Pollution Prevention and Control Action Plan (the Action Plan) in 2013 and developed stringent control measures to mitigate fine particulate matter (PM2.5) pollution. Here, we investigated the PM2.5 chemical composition changes over eastern China associated with the Action Plan during 2013–2017 using satellite-based PM2.5 chemical composition data derived using CMAQ simulations and satellite inputs. The PM2.5 concentrations decreased considerably during this time as a result of the reductions in all chemical species in PM2.5. The population-weighted mean concentrations over eastern China decreased from 11.1 to 6.7 μg m-3 for S O 4 2 − , 13.8–13.1 μg m-3 for N O 3 − , 7.4–5.8 μg m-3 for N H 4 + , 9.9–8.4 μg m-3 for OM, 4.6–3.8 μg m-3 for BC and 12.9–9.6 μg m-3 for other species in PM2.5. S O 4 2 − had the largest reduction of 40%, while N O 3 − had the lowest reduction of 5%, resulting in a greater fraction of N O 3 − and a smaller fraction of S O 4 2 − in PM2.5. Among the three key regions, Beijing-Tianjin-Hebei had the largest reduction in PM2.5 and its chemical compositions. The decrease in SO4 2 concentrations was in line with the reduction of SO2 emissions, and the major driver of the SO2 emission reductions was the industrial sector. The decrease in N O 3 − concentrations was limited because the decrease in SO2 emissions and the stable NH3 emissions facilitated the formation of N O 3 − from HNO3, which partially offset the reduction in NOx emissions driven by the power sector. To mitigate PM2.5 pollution more effectively, future efforts are needed to reduce NH3 emissions. [ABSTRACT FROM AUTHOR]

The United Nations has reported that 55% of the global population resides in urban areas, and 68% of the population is expected to be urban dwellers by 2050. Urbanization has critical implications for global land cover. Relevant literature has provided evidence attributing climatic effects to urban expansion; however, few studies have investigated the effect on public health and pollutant sensitivity to emissions. This study aimed to characterize the effect of urbanization‐induced changes in regional climate on ozone (O3), to evaluate ozone sensitivity to nitrogen oxide (NOx) and volatile organic compound (VOC) emissions, and to estimate premature mortalities due to O3 exposure. We employed atmospheric models with the higher‐order decoupled direct method to simulate effects of urbanization on O3 and to determine O3 sensitivity to NOx and VOC emissions. China‐specific concentration response functions were utilized to estimate cardiovascular and respiratory mortalities due to ozone exposure. Urbanization increased O3, which translated to a 39.6% increase in O3‐induced premature mortality (1,100 deaths). Moreover, O3 has become less/more sensitive to unit changes in NOx and VOC emissions in various cities. Urban greening may reduce urban temperature, but it may increase O3 in some cities due to the additional VOC emissions of greening. These findings highlight the strong interactions between land use policies, urban climate adaptation strategies, and air quality policies, suggesting the need of cobeneficial strategies and policies. We proposed a precision environmental management concept that emphasizes the importance of considering the specific atmospheric condition and composition of a city when formulating its environmental policies. Plain Language Summary: The United Nations has reported that 55% of the global population resides in urban areas, and 68% of the population is expected to be urban dwellers by 2050. Urbanization has critical implications for global land cover. Relevant literature has provided evidence attributing climatic effects to urban expansion; however, few studies have investigated the effect on public health and pollutant sensitivity to emissions. This study aimed to characterize the effect of urbanization‐induced changes in regional climate on ozone (O3) and to estimate premature mortalities due to O3 exposure. Urbanization increased O3, which translated to a 39.6% increase in O3‐induced premature mortality (1,100 deaths). Moreover, O3 has become less/more sensitive to unit changes in NOx and VOC emissions in various cities. Urban greening may reduce urban temperature, but it may increase O3 in some cities as a result of the VOC emissions of greening. These findings highlight the strong interactions between land use policies, urban climate adaptation strategies, and air quality policies, suggesting the need of cobeneficial strategies and policies. We proposed a precision environmental management concept that emphasizes the importance of considering the specific atmospheric condition and composition of a city when formulating its environmental policies. Key Points: Urbanization led to an increase in O3, increasing 39.6% premature mortality that was translated to ~1,100 deathsO3 became less/more sensitive to emissions, indicating that greening may reduce temperature but may increase O3 due to additional biogenic VOC emissionsWe show strong interactions between land use, urban climate, and air quality policies, suggesting the need of cobeneficial strategies [ABSTRACT FROM AUTHOR]

We applied the chemical transport model GEOS‐Chem to examine the changes in aerosols and tropospheric O3 in China from 2012–2017 and the associated radiative forcing and health impact. Simulated surface layer concentrations and column burdens of aerosols and O3 were evaluated by comparing with ground‐based and satellite‐retrieved measurements. Between 2012 and 2017, simulated annual mean concentrations of PM2.5 decreased by 21.0%, while O3 increased by 11.9% over eastern China (20–45°N, 105–122.5°E). Changes in aerosols and O3 over 2012–2017 jointly exerted a positive radiative forcing of 1.26 W/m2 over eastern China, which was dominated by the less cooling from PM2.5 reductions (1.18 W/m2). The Global Exposure Mortality Model predicted 268.3 (247.3–291.6) thousand (9.6%) fewer deaths in eastern China in 2017 relative to 2012. These results suggest an appreciable health benefit and a potential warming as a consequence of clean air actions. Plain Language Summary: Stringent clean air actions have been implemented in China since 2013, aiming to improve air quality. As a result, observed concentrations of aerosols decreased while those of O3 increased in eastern China. In this work we assess the radiative forcing and health impact resulted from the changes in aerosols and O3 in China for the period of 2012–2017 by using a chemical transport model. The model reproduces the observed changes in PM2.5 and O3 in recent years and estimates a net positive radiative forcing of 1.26 W/m2 in 2017 relative to 2012 over eastern China. Further estimates from the mortality model show that approximately 270,000 deaths are avoided. Results from our study suggest an appreciable health benefit as well as a potential warming as a consequence of clean air actions over 2012–2017. Key Points: Concentrations of PM2.5 decreased, and those of O3 increased as a result of air pollution control over 2012–2017Changes in PM2.5 and O3 led to a positive radiative forcing of 1.26 W/m2 over eastern China (20–45°N, 105–122.5°E)Changes in air pollution exposures are estimated to avoid 268.3 thousand deaths in eastern China [ABSTRACT FROM AUTHOR]

Ground-level observations, reanalyzed meteorological fields and a 3-D global chemical and transport model (GEOS-Chem) were applied in this study to investigate ozone (O3) pollution events (OPEs) in North China (36.5–40.5 ∘ N, 114.5–119.5 ∘ E) during 2014–2017. Ozone pollution days (OPDs) were defined as days with maximum daily averaged 8 h (MDA8) concentrations over North China larger than 160 µ g m -3 , and OPEs were defined as periods with 3 or more consecutive OPDs. Observations showed that there were 167 OPDs and 27 OPEs in North China during 2014–2017, in which 123 OPDs and 21 OPEs occurred from May to July. We found that OPEs in North China occurred under a typical weather pattern with high daily maximum temperature (Tmax), low relative humidity (RH), anomalous southerlies and divergence in the lower troposphere, an anomalous high-pressure system at 500 hPa, and an anomalous downward air flow from 500 hPa to the surface. Under such a weather pattern, chemical production of O3 was high between 800 and 900 hPa, which was then transported downward to enhance O3 pollution at the surface. A standardized index I_OPE was defined by applying four key meteorological parameters, including Tmax, RH, meridional winds at 850 hPa (V850) and zonal winds at 500 hPa (U500). I_OPE can capture approximately 80 % of the observed OPDs and OPEs, which has implications for forecasting OPEs in North China. [ABSTRACT FROM AUTHOR]