Long-term variations of air pollutants and public exposure in China during 2000-2020.

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 µg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 µg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 µg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 µg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000-2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models.

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 µg/m3) for PM2.5, 0.77 (6.00 µg/m3) for PM10, 0.85 (9.71 µg/m3) for NO2, and 0.85 (9.39 µg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

Improved estimation of particulate matter in China based on multisource data fusion.

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM2.5 (<2.5 µm) and PM10 (<10 µm). The three-stage model performs well with cross-validation coefficient of determination (R2) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 µg/m3 and 27.2 µg/m3 for PM2.5 and PM10, respectively. After data fusion from multiple sources, the concentrations of PM2.5 and PM10 are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM10 show similar spatial patterns to PM2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

Is atmospheric oxidation capacity better in indicating tropospheric O3 formation?

Tropospheric ozone (O3) concentration is increasing in China along with dramatic changes in precursor emissions and meteorological conditions, adversely affecting human health and ecosystems. O3 is formed from the complex nonlinear photochemical reactions from nitrogen oxides (NO x = NO + NO2) and volatile organic compounds (VOCs). Although the mechanism of O3 formation is rather clear, describing and analyzing its changes and formation potential at fine spatial and temporal resolution is still a challenge today. In this study, we briefly summarized and evaluated different approaches that indicate O3 formation regimes. We identify that atmospheric oxidation capacity (AOC) is a better indicator of photochemical reactions leading to the formation of O3 and other secondary pollutants. Results show that AOC has a prominent positive relationship to O3 in the major city clusters in China, with a goodness of fit (R 2) up to 0.6. This outcome provides a novel perspective in characterizing O3 formation and has significant implications for formulating control strategies of secondary pollutants.

Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013-2019.

Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in epidemiological studies. The national models with high spatiotemporal resolutions to predict ground ozone concentrations are limited in China so far. In this study, we aimed to develop a random forest model by combining ground ozone measurements from fixed stations, ozone simulations from the Community Multiscale Air Quality (CMAQ) modeling system, meteorological parameters, population density, road length, and elevation to predict ground maximum daily 8-h average (MDA8) ozone concentrations at a daily level and 1 km × 1 km spatial resolution. The model cross-validation R2 and root mean squared error (RMSE) were 0.80 and 20.93 µg/m3 at daily level in 2013-2019, respectively. CMAQ ozone simulations and near-surface temperature played vital roles in predicting ozone concentrations among all predictors. The population-weighted median concentrations of predicted MDA8 ozone were 89.34 µg/m3 in mainland China in 2013, and reached 100.96 µg/m3 in 2019. However, the long-term temporal variations among regions were heterogeneous. Central and Eastern China, as well as the Southeast Coastal Area, suffered higher ozone pollution and higher increased rates of ozone concentrations from 2013 to 2019. The seasonal pattern of ozone pollution varied spatially. The peak-season ozone pollution with the highest 6-month ozone concentrations occurred in different months among regions, with more than half domain in April-September. The predictions showed that not only the annual mean concentrations but also the percentages of grid-days with MDA8 ozone concentrations higher than 100/160 µg/m3 have been increasing in the past few years in China; meanwhile, majority areas in mainland China suffered peak-season ozone concentrations higher than the air quality guidelines launched by the World Health Organization in September 2021. The proposed model and ozone predictions with high spatiotemporal resolution and full coverage could provide health studies with flexible choices to evaluate ozone exposure levels at multiple spatiotemporal scales in the future.

Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity.

Economic activities and the associated emissions have significantly declined during the 2019 novel coronavirus (COVID-19) pandemic, which has created a natural experiment to assess the impact of the emitted precursor control policy on ozone (O3) pollution. In this study, we utilized comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O3 variations during the COVID-19 pandemic in China. Here, we found that the significant elevated O3 in the North China Plain (40%) and Yangtze River Delta (35%) were mainly attributed to the enhanced atmospheric oxidation capacity (AOC) in these regions, associated with the meteorology and emission reduction during lockdown. Besides, O3 formation regimes shifted from VOC-limited regimes to NOx-limited and transition regimes with the decline of NOx during lockdown. We suggest that future O3 control policies should comprehensively consider the effects of AOC on the O3 elevation and coordinated regulations of the O3 precursor emissions.

Responses of fine particulate matter and ozone to local emission reductions in the Sichuan Basin, southwestern China.

The Sichuan Basin (SCB) in southwestern China is largely affected by air pollution. Understanding the responses of air pollutant concentrations to emission changes is critical for designing and evaluating effective control strategies. Thus, this study used the Community Multi-scale Air Quality (CMAQ) model to simulate PM2.5 (i.e., particulate matter with an aerodynamic diameter ≤ 2.5 µm) in winter (January 2015) and ozone (O3) in summer (July 2015) under nine emission reduction scenarios. For each scenario, the anthropogenic emissions of each air pollutant in each SCB grid cell were reduced by the same percentage, ranging from 10% to 90%. We found that approximately 30-70% emission reductions are required to reduce the January mean PM2.5 concentrations in all the SCB urban centers to a value that is less than the Chinese standard for daily mean PM2.5 (24-h PM2.5: 75 µg m-3). However, the January mean PM2.5 concentrations under 90% emission reduction still exceeded the World Health Organization (WHO) guideline (25 µg m-3) in 16 SCB urban centers. Moreover, reducing both SCB and non-SCB emissions were critical for achieving the PM2.5 level recommended by WHO. An 80% emission reduction was required to prevent the occurrence of 8-h O3 (i.e., daily maximum 8-h mean O3) non-attainment days in all SCB urban centers. Under 90% emission reduction, July mean 8-h O3 concentrations still exceeded the WHO guideline of 47 ppb in approximately 35% of the SCB areas. In conclusion, this study suggests that (1) compared with the governmental emission reduction targets for 2015-2020 (2-27%), more significant emission reductions are required to meet the Chinese and WHO pollution standards; and (2) both SCB and non-SCB emissions must significantly reduce to achieve the desired pollution targets.

The aggravated short-term PM2.5-related health risk due to atmospheric transport in the Yangtze River Delta.

Severe fine particulate matter (PM2.5) pollution and the associated health risks remain pressing issues in the Yangtze River Delta (YRD), although significant efforts have been made locally, such as the Clean Air Action since 2013. Regional transport is an important contributor to high PM2.5 levels during haze episodes in the YRD, but its impact on human health is rarely analyzed. In this study, we evaluate the short-term PM2.5-related health risks and associated economic losses due to different source regions by estimating daily mortality based on model results in the YRD. The results show that regional transport induces significant health risks in the YRD during haze days, contributing over 60% of daily premature mortality in Shanghai and Nanjing (major cities in the YRD). Moreover, in Hangzhou and Jiaxing, regional transport's contribution can be as high as 70%. The total daily mean economic loss in the YRD is estimated as 526.8 million Chinese Yuan (approximately 81.4 million U.S. dollar) in winter of 2015 and 2016, accounting for 1.4% of the daily averaged gross domestic product (GDP) of the YRD. Emission control (in accordance with the 13th Five-year Energy Conservation and Emission Reduction Plan) is an effective way to reduce health risks in the YRD, reducing premature deaths during haze days by 12-33%. More stringent emission control measures are suggested for further reduce PM2.5-related health risks.

Enhanced atmospheric oxidation capacity and associated ozone increases during COVID-19 lockdown in the Yangtze River Delta.

Aggressive air pollution control in China since 2013 has achieved sharp decreases in fine particulate matter (PM2.5), along with increased ozone (O3) concentrations. Due to the pandemic of coronavirus disease 2019 (COVID-19), China imposed nationwide restriction, leading to large reductions in economic activities and associated emissions. In particular, large decreases were found in nitrogen oxides (NOx) emissions (>50%) from transportation. However, O3 increased in the Yangtze River Delta (YRD), which cannot be fully explained by changes in NOx and volatile organic compound (VOCs) emissions. In this study, the Community Multi-scale Air Quality model was used to investigate O3 increase in the YRD. Our results show a significant increase of atmospheric oxidation capacity (AOC) indicated by enhanced oxidants levels (up to +25%) especially in southern Jiangsu, Shanghai and northern Zhejiang, inducing the elevated O3 during lockdown. Moreover, net P(HOx) of 0.4 to 1.6 ppb h-1 during lockdown (Case 2) was larger than the case without lockdown (Case 1), mainly resulting in the enhanced AOC and higher O3 production rate (+12%). This comprehensive analysis improves our understanding on AOC and associated O3 formation, which helps to design effective strategies to control O3.

Responses of decline in air pollution and recovery associated with COVID-19 lockdown in the Pearl River Delta.

The Guangdong government implemented lockdown measures on January 23, 2020, to ease the spread of the coronavirus disease 2019 (COVID-19). These measures prohibit a series of human activities and lead to a great reduction in anthropogenic emissions. Starting on February 20, all companies resumed work and production, and emissions gradually recovered. To investigate the response of air pollutants in the Pearl River Delta (PRD) to the emission reduction and recovery related to COVID-19 lockdown, we used the Community Multi-scale Air Quality (CMAQ) model to estimate the changes in air pollutants, including three periods: Period I (January 10 to January 22, 2020), Period II (January 23 to February 19, 2020), Period III (February 20 to March 9, 2020). During Period II, under the concurrent influence of emissions and meteorology, air quality improved significantly with PM2.5, NO2, and SO2 decreased by 52%, 67%, and 25%, respectively. O3 had no obvious changes in most cities, which mainly due to the synergetic effects of emissions and meteorology. In Period III, with the recovery of emissions and the changes in meteorology, the increase of secondary components was faster than that of primary PM2.5 (PPM), which indicated that changes in PPM concentration were more sensitive to emissions reduction. O3 concentration increased as emission and temperature rising. Our findings elucidate that more effective emission control strategies should be implemented in PRD to alleviate the increasingly serious pollution situation.

Model vs. observation discrepancy in aerosol characteristics during a half-year long campaign in Northeast China: The role of biomass burning.

Complex air pollutant sources and distinct meteorological conditions resulted in unique wintertime haze pollution in the Harbin-Changchun (HC) metropolitan area, China's only national-level city cluster located in the severe cold climate region. In this study, field observation and air quality modeling were combined to investigate fine particulate matter (PM2.5) pollution during a six-month long heating season in HC's central city (Harbin). The model significantly underpredicted PM2.5 and organic carbon (by up to ∼230 µg/m3 and 110 µgC/m3, respectively, in terms of daily average) when levoglucosan concentrations were above 0.5 µg/m3. Based on a synthesis of levoglucosan concentrations and fire counts, the large gaps were attributed to underestimation of open burning emissions by the model. However, the model tended to overpredict elemental carbon (more significantly at higher NO2), likely pointing to an overestimation of vehicle emissions. With increasing levoglucosan, the difference between observed and simulated nitrate (nitrateobs ‒ nitratemod, i.e., Δnitrate) showed a transition from negative to positive values. The positive Δnitrate were attributed to underprediction of the open-burning related nitrate, whereas the negative Δnitrate were likely caused by overprediction of nitrate from other sources (presumably vehicle emissions). The dependence of Δnitrate on levoglucosan indicated that with stronger impact of open burning, the overprediction effect was gradually offset and finally overwhelmed. Influence of open burning on sulfate formation was evident as well, but less apparent compared to nitrate. This study illustrates how the uncertainties in open burning emissions will influence PM2.5 simulation, on not only primary components but also secondary species.

Contribution of biogenic sources to secondary organic aerosol in the summertime in Shaanxi, China.

A revised Community Multi-scale Air Quality (CMAQ) model with updated secondary organic aerosol (SOA) yields and a more detailed description of SOA formation from isoprene (ISOP) oxidation was applied to study the spatial distribution of SOA, its components and precursors in Shaanxi in July of 2013. The emissions of biogenic volatile organic compounds (BVOCs) were generated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), of which ISOP and monoterpene (MONO) were the top two, with 1.73 × 109 mol and 1.82 × 108 mol, respectively. The spatial distribution of BVOCs emission was significantly correlated with the vegetation coverage distribution. ISOP and its intermediate semi-volatile gases were up to ∼7.0 and ∼1.4 ppb respectively in the ambient. SOA was generally 2-6 µg/m3, of which biogenic SOA (BSOA) accounted for as high as 84% on average. There were three main BVOCs Precursors including ISOP (58%) and MONO (8%) emit in the studied domain, and ISOP (9%) transported. The Guanzhong Plain had the highest BSOA concentrations of 3-5 µg/m3, and the North Shaanxi had the lowest of 2-3 µg/m3. More than half of BSOA was due to reactive surface uptake of ISOP epoxide (0.2-0.7 µg/m3, ∼19%), glyoxal (GLY) (0.2-0.5 µg/m3, ∼11%) and methylglyoxal (MGLY) (0.4-1.4 µg/m3, ∼32%), while the remaining was due to the traditional equilibrium partitioning of semi-volatile components (0.1-1.2 µg/m3, ∼25%) and oligomerization (0.2-0.4 µg/m3, ∼12%). Overall, SOA formed from ISOP contributed 1-3 µg/m3 (∼80%) to BSOA.

Characterization of particulate matter and its extinction ability during different seasons and weather conditions in Sinkiang, China: two case studies.

The Sinkiang Uygur Autonomous Region is located in western China and the centre of the Asian-European continent, which frequently suffers from sandstorm disasters and haze weathers. A 1-year measurement campaign in two selected points in Shihezi and Urumqi of Sinkiang was conducted to characterise the effects of particle matters and factors on particle extinction under different weather conditions. Results showed that the average concentration of PM1-2.5 was 19.83, 9.230, 28.93 and 122.4 µg/m3 in Shihezi and 67.25, 16.80, 59.19 and 324.0 µg/m3 in Urumqi for spring, summer, autumn and winter, respectively. PM5-10 concentrations can reach up to 500.0 and 160.0 µg/m3 during polluted weather conditions in Shihezi and Urumqi, respectively. PM5-10 also accounted for the largest fraction in Shihezi and Urumqi for all types of weather, reaching up to 40.0% under dusty weather conditions. PM1-2.5 significantly increased during winter possibly due to the increased demand for heating compared with non-heating periods. PM0.5-1 is possibly produced from motor vehicle exhaust. Particle size is concluded to be the dominant factor for particle extinction capability under fine weather based on calculations of aerosol optical properties. The refractive index for a particle with a diameter of 100 µm (3.10-3.11i) is substantially larger than that with a diameter of 0.5000 µm (1.600-0.07000i), but the extinction capability of the latter is 1.30 times that of the former. Comparatively, when the mass concentration of coarse particles is over 17.0 times that of the fine particles, and then mass concentration becomes the dominant factor. Therefore, visibility is negatively correlated to particle mass variations during dust storms and hazy days but not for fine days.

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak.

Due to the pandemic of coronavirus disease 2019 in China, almost all avoidable activities in China are prohibited since Wuhan announced lockdown on January 23, 2020. With reduced activities, severe air pollution events still occurred in the North China Plain, causing discussions regarding why severe air pollution was not avoided. The Community Multi-scale Air Quality model was applied during January 01 to February 12, 2020 to study PM2.5 changes under emission reduction scenarios. The estimated emission reduction case (Case 3) better reproduced PM2.5. Compared with the case without emission change (Case 1), Case 3 predicted that PM2.5 concentrations decreased by up to 20% with absolute decreases of 5.35, 6.37, 9.23, 10.25, 10.30, 12.14, 12.75, 14.41, 18.00 and 30.79 µg/m3 in Guangzhou, Shanghai, Beijing, Shijiazhuang, Tianjin, Jinan, Taiyuan, Xi'an, Zhengzhou, Wuhan, respectively. In high-pollution days with PM2.5 greater than 75 µg/m3, the reductions of PM2.5 in Case 3 were 7.78, 9.51, 11.38, 13.42, 13.64, 14.15, 14.42, 16.95 and 22.08 µg/m3 in Shanghai, Jinan, Shijiazhuang, Beijing, Taiyuan, Xi'an, Tianjin, Zhengzhou and Wuhan, respectively. The reductions in emissions of PM2.5 precursors were ~2 times of that in concentrations, indicating that meteorology was unfavorable during simulation episode. A further analysis shows that benefits of emission reductions were overwhelmed by adverse meteorology and severe air pollution events were not avoided. This study highlights that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable. More efforts should be made to completely avoid severe air pollution.

Effects of minimally invasive approaches on chronic subdural hematoma by novel YL-1 puncture needle and burr-hole methods.

The objective of this study is to investigate effects of minimally invasive approaches on outcome of chronic subdural hematoma (CSDH) by novel YL-1 puncture needle and burr-hole methods. A retrospective analysis was performed in 158 hospitalized CSDH patients from January, 2013 to December, 2017 in Kunshan Hospital of Traditional Chinese Medicine. Patients' gender, age, history of trauma, volume of hematoma, hematoma location, application of urokinase, surgical approach, the operation time, hospitalized time, and CT scans 3 months after discharge were recorded. Prognostic indicators including symptom relief and post-hospital neuro-imaging findings were extracted to evaluate surgical efficacy. Statistical methods were conducted to evaluate surgical efficacy. Both YL-1 puncture needle and burr-hole surgeries had a satisfying follow-up (93.67%). There was non-significant group difference in follow-up results (p > 0.05). While YL-1 needle group needs less operation time ((p < 0.001) and hospitalized time (p < 0.001), gender (p = 0.144), age (p = 0.394), history of head trauma (p = 0.445), volume of hematoma (p = 0.068), hematoma location (p = 0.281), and application of urokinase (p = 0.545) were shown non-significantly associated with these two minimally invasive approaches. Volume of hematoma was significantly associated with follow-up outcomes (p = 0.016). Novel YL-1 puncture needle and classic burr-hole craniotomy are both proved to be safe and effective minimally invasive surgeries, which can provide an early intervention and minimally invasive strategy for neurosurgeons.

MicroRNA-590-3p enhances the radioresistance in glioblastoma cells by targeting LRIG1.

microRNA (miR)-590 has been found to serve potential roles in cancer development; however, the expression and function of miR-590 in human gliomas remains to be elucidated. The present study aimed to investigate the expression of miR-590 in human glioma tissues and radioresistant human glioblastoma cells (U251R), and to determine the effect and related molecular mechanism of miR-590-3p on the radiosensitivity of U251R cells in vitro. The results from reverse transcription-quantitative polymerase chain reaction indicated that miR-590-3p was upregulated in human glioma tissues and radioresistant human glioblastoma cells, and that miR-590-3p expression was higher in high grade than in low grade gliomas. In vitro experiments revealed that the miR-590-3p inhibitor enhanced the radiosensitivity of U251R cells by suppressing cell viability, decreasing colony formation capacity and increasing cell apoptosis rate, as demonstrated by MTT, colony formation and flow cytometry analyses. A luciferase reporter assay demonstrated that leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) was a direct target of miR-590-3p. Furthermore, it was demonstrated that the effect of miR-590-3p suppression on cell viability, colony formation capacity and cell apoptosis rate was attenuated by the knockdown of LRIG1 in the U251R cells. In conclusion, the present study revealed that miR-590-3p was upregulated in human glioma tissues and radioresistant human glioblastoma cells, and miR-590-3p contributes to the radioresistance of human glioblastoma cells by directly targeting LRIG1. These findings may provide potential therapeutic strategies to prevent radioresistance in human gliomas.