Seasonal quantification of the inter-city transport of PM2.5 in the Yangtze River Delta region of China based on a source-oriented chemical transport model and the Michaelis-Menten equation.

Regional transport plays a crucial role in the pollution of fine particulate matter (PM2.5) over the Yangtze River Delta region (YRD). A practical joint regional emission control strategy requires quantitative assessment of the contribution of regional transport. In this study, the contribution of inter-city transport to PM2.5 among the 41 cities in the YRD region were quantitatively estimated using a source-oriented chemical transport model, and then the relationship between the cumulative contribution of regional transport and the distance was examined using the Michaelis-Menten equation. The results show that the Michaelis-Menten equation is suitable to represent the relationship between the cumulative contribution and transport distance. The coefficient of determination (r2) of the fittings is greater than 0.9 in 71 % of the cases in the six subregions and four seasons in YRD. Two key parameters in the Michaelis-Menten eq. K1, indicating the maximum contribution of regional transport, and K2, indicating the distance to which the regional transport contribution reach half the maximum contribution, show substantial regional and seasonal variations. The average K1 is 73.6 %, with lower values observed in the northern part of the YRD and higher values in central Jiangsu. K2 is larger in northern Jiangsu, as well as central and southern Zhejiang. The local contribution in autumn and winter is lower than that in spring and summer in the northern part of the YRD. Particularly in northern Jiangsu, the local contribution reaches 90.4 % in summer but drops to 53.0 % in autumn and winter, illustrating significant impacts of regional transport to PM2.5 in autumn and winter in this area. K2 is larger on polluted days, compared to clean days, indicating greater contributions from regional transport to PM2.5 in YRD. The results can serve as a scientific foundation for implementing regional joint prevention and control measures in the YRD region.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China.

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 µg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Modeling the molecular composition of secondary organic aerosol under highly polluted conditions: A case study in the Yangtze River Delta Region in China.

A near-explicit mechanism, the master chemical mechanism (MCMv3.3.1), coupled with the Community Multiscale Air Quality (CMAQ) model (CMAQ-MCM-SOA), was applied to investigate the characteristics of secondary organic aerosol (SOA) during a pollution event in the Yangtze River Delta (YRD) region in summer 2018. Model performances in predicting explicit volatile organic compounds (VOCs), organic aerosol (OA), secondary organic carbon (SOC), and other related pollutants in Taizhou, as well as ozone (O3) and fine particulate matter (PM2.5) in multiple cities in this region, were evaluated against observations and model predictions by the CMAQ model coupled with a lumped photochemical mechanism (SAPRC07tic, S07). MCM and S07 exhibited similar performances in predicting gaseous species, while MCM better captured the observed PM2.5 and inorganic aerosols. Both models underpredicted OA concentrations. When excluding data during biomass burning events, SOC concentrations were underpredicted by the CMAQ-MCM-SOA model (-28.4 %) and overpredicted by the CMAQ-S07 model (134.4 %), with better agreement with observations in the trend captured by the CMAQ-MCM-SOA model. Dicarbonyl SOA accounted for a significant fraction of total SOA in the YRD, while organic nitrates originating from aromatics were the most abundant species contributing to the SOA formation from gas-particle partitioning. The oxygen-to­carbon ratio (O/C) for SOA and OA were 0.68-0.75 and 0.20-0.65, respectively, indicating a higher oxidation state in the areas influenced by biogenic emissions. Finally, the phase state of SOA was examined by calculating the glass transition temperature (Tg) based on its molecular composition. It was found that semi-solid state characterized SOA in the YRD, which could potentially impact their chemical transformation and lifetimes along with those of their precursors.

Continued Rise in Health Burden from Ambient PM2.5 in India under SSP Scenarios Until 2100 despite Decreasing Concentrations.

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM2.5) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

Source-specified atmospheric age distribution of black carbon and its impact on optical properties over the Yangtze River Delta.

Black carbon (BC) exerts a profound and intricate impact on both air quality and climate due to its high light absorption. However, the uncertainty in representing the absorption enhancement of BC in climate models leads to an increased range in the modeled aerosol climate effects. Changes in BC optical properties could result either from atmospheric aging processes or from variations in its sources. In this study, a source-age model for identifying emission sources and aging states presented by University of California at Davis/California Institute of Technology (UCD/CIT) was used to simulate the atmospheric age distribution of BC from different sources and to quantify its impact on the optical properties of BC-containing particles. The results indicate that regions with greater aged BC concentrations do not correspond to regions with higher BC emissions due to atmospheric transport. High concentrations of aged BC are found in northern Yangtze River Delta (YRD) regions during summer. The chemical compositions of particles from different sources and with different atmospheric ages differ significantly. BC and primary organic aerosols (POA) are dominating in Traffic-dominated source while other components dominate in Industry-dominated source. As the atmospheric age increases, the mass fraction of secondary inorganic aerosols rises. Compared to the original model, the simulated mass absorption cross section of BC particles in the source-age model decreases while the single scattering albedo increases. This compensates for ~11 % of the overestimation of the simulated BC direct radiative forcing. Our study highlights that incorporating atmospheric age and source information into models can greatly improve the estimation of optical properties of BC-containing particles and deepen our understanding of their climate effects.

[Transport and Potential Sources Regions of Double High Pollution in Nanjing by Different Synoptic Situations].

Based on the hourly concentration data of fine particulate matter （PM2.5） and ozone （O3） in Nanjing from 2015 to 2019， the synoptic situation that occurred in Nanjing， in which high PM2.5 and high O3 coexisted （hereinafter referred to as double high pollution （DHP））， was typed using T-mode principal component analysis. Additionally， the backward trajectory clustering analysis method， potential source contribution method （PSCF）， and concentration weight trajectory analysis method （CWT） were used to study the transport paths and potential source region distribution of the DHP of Nanjing by different synoptic situations. The synoptic situations favorable to the DHP in Nanjing were the control of weak low-pressure type （Type1） and high-pressure center （Type2）. Synoptic situations could have had an effect on the directional origin of the backward trajectory. In Type1， the Nanjing area was affected by two low pressures in the northeast and southwest， and the clustering trajectories of the Nanjing air mass mainly came from the eastern and western directions. The average concentrations of PM2.5 and O3 in the trajectory were 83.48 µg·m-3 and 106.85 µg·m-3， respectively. In Type 2， Nanjing and its surroundings were at the edge of the high-pressure center， and the air mass cluster trajectories mainly came from the north and east. The average concentrations of PM2.5 and O3 in the trajectory were 94.47 µg·m-3 and 92.32 µg·m-3， respectively. Most of the two types of backward trajectories belonged to short and medium-distance regional transportation， indicating that the pollution of neighboring provinces was one of the main factors affecting the DHP in Nanjing. PSCF and CWT analysis showed that the distribution of the most important potential sources of PM2.5 and O3 in Type1 and Type2 were not completely consistent， which indicates that the two pollutants did not come from the same area in the DHP.

[Differences of Three Methods in Determining Ozone Sensitivity in Nanjing].

In recent years， ozone （O3） has become an increasingly important air pollutant in China. Identifying the sensitivity of O3 to the precursors volatile organic compounds （VOCs） and nitrogen oxides （NOx） can help make effective abatement strategies. This study compared three methods for determining O3-VOCs-NOx sensitivity： simulated photochemical indicator values and sensitivity coefficients derived from a three-dimensional air quality model and an observation-based model （OBM）， with a case study involving an O3 pollution event that occurred in Nanjing in late July 2017. The results showed that O3 sensitivity based on the photochemical indicator and sensitivity coefficients demonstrated similar spatial variations （over 50% of the grid cells of Nanjing exhibiting identical O3 sensitivity）. However， sensitivity coefficients identified a larger number of areas within a transitional O3 sensitivity regime， as opposed to the VOCs- or NOx-limited regime identified by the photochemical indicator. The determination of the latter was affected by the adopted threshold values. The OBM relied on the quality of the observational data. For example， positive biases in observed NO2 could lead to an underestimation of O3 sensitivity to NOx with the OBM. During the high pollution period， the three methods exhibited significant disparities. The photochemical indicator tended to suggest the VOCs-limited condition， whereas the OBM and sensitivity coefficients indicated the NOx-limited or transitional regimes.

China's carbon-neutral policies will reduce short-term PM2.5-associated excess incidence of cardiovascular diseases.

China's carbon-neutral target could have benefits for ambient fine particulate matter (PM2.5)-associated mortality. Although previous studies have researched such benefits, the potential impact on cardiovascular disease incidence burden is yet to be investigated thoroughly. Here, we first estimate the association between short-term PM2.5 exposure and the incidence of stroke and coronary heart disease (CHD) via a case-crossover study before projecting future changes in short-term PM2.5-associated excess incidence across China from 2025 to 2060 under three different emission scenarios. We find that, compared to the 2015-2020 baseline, average PM2.5 concentrations nationwide in 2060 under SSP119 (an approximation of a carbon-neutral scenario) are projected to decrease by 81.07%. The short-term PM2.5-related excess incidence of stroke and CHD is projected to be reduced to 3,352 cases (95% confidence interval: 939, 5,738)-compared with 34,485 cases under a medium-emissions scenario (SSP245)-and is expected to be accompanied by a 95% reduction in the related economic burden. China's carbon-neutral policies are likely to bring health benefits for cardiovascular disease by reducing short-term PM2.5-related incidence burden.

The joint effects of prenatal exposure to PM2.5 constituents and reduced fetal growth on children's accelerated growth in the first 3 years: a birth cohort study.

BACKGROUND: Prenatal fine particulate matter (PM2.5) constituents exposure and reduced fetal growth may be risk factors for accelerated growth in early childhood, an important indicator for lifelong health. OBJECTIVE: The study investigated whether the joint effects are present between PM2.5 constituents and reduced fetal growth. METHODS: The study was embedded in a birth cohort in China, including 5424 mother-child pairs. Prenatal PM2.5 and its constituents' [organic carbon (OC), elementary carbon (EC), ammonium (NH4+), nitrate (NO3-), and sulfate (SO42-)] concentrations were estimated based on maternal residential addresses. Fetal growth was evaluated by fetal growth trajectory in utero and preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA). Children's accelerated growth was defined as body mass index (BMI) Z-score change of >0.67 between birth and 3 years. Generalized logistic regression was used to analyze the effects of prenatal PM2.5 constituents exposure and fetal growth on children's accelerated growth. Joint effect was tested on multiplicative scale and additive scale with the relative excess risk due to interaction (RERI). RESULTS: Children with lower fetal growth trajectory, PTB, LBW, and SGA had increased odds of children's accelerated growth, with odds ratios (ORs) ranging from 1.704 to 11.605. Compared with lower exposure (≤median), higher exposure (>median) of PM2.5, OC, and SO42- were significantly associated with increased odds of children's accelerated growth, varying in ORs from 1.163 to 1.478. Prenatal exposure to OC had joint effects with lower fetal growth on children's accelerated growth. We observed that the interaction was statistically significant on an additive scale in OC and lower fetal growth trajectory (RERI: 0.497, 95% CI: 0.033,0.962). IMPACT: Fine particulate matter (PM2.5) is a huge threat to human health worldwide, causing 6.7 million death globally in 2019. According to the theory of DOHaD, prenatal PM2.5 exposure could influence early childhood growth, which is important for lifelong health. We found that prenatal exposure to PM2.5, OC, and SO42- was associated with higher risk of accelerated childhood growth in the first 3 years. More importantly, reduced fetal growth moderated these associations. Our findings highlight the need for policies and interventions on PM2.5 constituents to improve lifelong health, especially for those vulnerable populations with reduced fetal growth.

Trimethylamine from Subtropical Forests Rival Total Farmland Emissions in China.

Many types of living plants release gaseous trimethylamine (TMA), making it a potentially important contributor to new particle formation (NPF) in remote areas. However, a panoramic view of the importance of forest biogenic TMA at the regional scale is lacking. Here, we pioneered nationwide mobile measurements of TMA across a transect of contiguous farmland in eastern China and a transect of subtropical forests in southern China. In contrast to the farmland route, TMA concentrations measured during the subtropical forest route correlated significantly with isoprene, suggesting potential TMA emissions from leaves. Our high time-resolved concentrations obtained from a weak photo-oxidizing atmosphere reflected freshly emitted TMA, indicating the highest emission intensity from irrigated dryland (set as the baseline of 10), followed by paddy field (7.1), subtropical evergreen forests (5.9), and subtropical broadleaf and mixed forests (4.3). Extrapolating their proportions roughly to China, subtropical forests alone, which constitute half of the total forest area, account for nearly 70% of the TMA emissions from the nation's total farmland. Our estimates, despite the uncertainties, take the first step toward large-scale assessment of forest biogenic amines, highlighting the need for observational and modeling studies to consider this hitherto overlooked source of TMA.

[Impacts of Anthropogenic Emission Reduction on Urban Atmospheric Oxidizing Capacity During the COVID-19 Lockdown].

In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·ï¼‰ increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model.

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 µg/m3 in the north in all the seasons, while it reached ∼80 µg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 µg/m3), followed by industry (∼40 µg/m3), transportation (∼20 µg/m3), and agriculture (∼15 µg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

Dominant contribution of combustion-related ammonium during haze pollution in Beijing.

Aerosol ammonium (NH4+), mainly produced from the reactions of ammonia (NH3) with acids in the atmosphere, has significant impacts on air pollution, radiative forcing, and human health. Understanding the source and formation mechanism of NH4+ can provide scientific insights into air quality improvements. However, the sources of NH3 in urban areas are not well understood, and few studies focus on NH3/NH4+ at different heights within the atmospheric boundary layer, which hinders a comprehensive understanding of aerosol NH4+. In this study, we perform both field observation and modeling studies (the Community Multiscale Air Quality, CMAQ) to investigate regional NH3 emission sources and vertically resolved NH4+ formation mechanisms during the winter in Beijing. Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH3 emissions, including fossil fuel sources, NH3 slip, and biomass burning, are important sources of aerosol NH4+ with more than 60% contribution occurring on heavily polluted days. In contrast, volatilization-related NH3 sources (livestock breeding, N-fertilizer application, and human waste) are dominant on clean days. Combustion-related NH3 is mostly local from Beijing, and biomass burning is likely an important NH3 source (∼15%-20%) that was previously overlooked. More effective control strategies such as the two-product (e.g., reducing both SO2 and NH3) control policy should be considered to improve air quality.

Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model.

Accurate prediction of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) is important for environmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques. In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial correlation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant information is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 µg/m3. Additionally, to better understand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE). The deep learning model established in this study provide a valuable tool for air quality management and public health protection.

[Elucidating the Impacts of Meteorology and Emission Changes on Concentrations of Major Air Pollutants in Major Cities in the Yangtze River Delta Region Using a Machine Learning De-weather Method].

This study applied a de-weather method based on a machine learning technique to quantify the contribution of meteorology and emission changes to air quality from 2015 to 2021 in four cities in the Yangtze River Delta Region. The results showed that the significant reductions in PM2.5, NO2, and SO2 emissions(57.2%-68.2%, 80.7%-94.6%, and 81.6%-96.1%, respectively) offset the adverse effects of meteorological conditions, resulting in lower pollutant concentrations. The meteorological contribution of maximum daily 8-h average O3(MDA8_O3) showed a stronger effect than that of others(23.5%-42.1%), and meteorological factors promoted the increase in MDA8_O3 concentrations(4.7%); however, emission changes overall resulted in a decrease in MDA8_O3 concentrations(-3.2%). NO2 and MDA8_O3 decreased more rapidly from 2019 to 2021, mainly because the emissions played a stronger role in reducing pollutant concentrations than from 2015 to 2018. However, emissions changes had weaker reduction effects on PM2.5 and SO2 from 2019 to 2021 than from 2015 to 2018. De-weather methods could effectively seperate the effects of meteorology and emission changes on pollutant trends, which helps to evaluate the real effects of emission control policies on pollutant concentrations.

Performance Characteristics of Silicone Rubber for Use in Acidic Environments.

Silicone rubber insulators are widely used in power grids because of their excellent performance, but aging has been an inevitable problem of silicone rubber, especially in extreme conditions, such as acidic conditions. In order to clarify the performance changes in silicone rubber in an acidic environment, this paper uses the developed acid-resistant silicone rubber sheet and common silicone rubber samples as the research objects, and conducts an aging comparison test on them in a natural acidic environment. The electrical properties, physical properties, and chemical properties of the two types of silicone rubber specimens with different aging times are analyzed to obtain the performance characteristics of silicone rubber under a natural acidic environment. The research results show that the dry flash voltage and pollution flashover voltage of the acid-resistant silicone rubber after one year of aging are greater than those of the common type; the water repellency of both types of silicone rubber remains in good condition. The silicone rubber produced by our team according to the self-developed acid-resistant silicone rubber formula has indeed played a role in delaying aging in an acidic environment compared with the common-type silicone rubber.

[Response of PM2.5 and O3 to Emission Reductions in Nanjing Based on Random Forest Algorithm].

High levels of fine particulate matter (PM2.5) and ozone (O3) in ambient air affect climate change and also endanger human health and ecosystems. Air pollution in Nanjing has been improving since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013. However, Nanjing still faces PM2.5 and O3 pollution. Evaluating the response of pollutant concentrations to the reductions in precursor emissions is helpful to obtain effective strategies of emission reduction to improve pollution levels. The sensitive simulations of emission perturbation in atmospheric chemistry models directly demonstrate the response of pollution to the reductions in emissions. Nevertheless, these sensitive simulations are limited in computing time and resources. The random forest algorithm was trained by using the simulation results of the atmospheric chemical transport model (GEOS-Chem) in 2015. The changes in daily PM2.5 and daily maximum eight-hour O3 (MDA8 O3) concentrations in Nanjing in 2019 were efficiently predicted under different reduction scenarios of anthropogenic emissions. The simulations showed that the seasonal average of ρ(PM2.5) in Nanjing would decrease by 2-4 µg·m-3 with the reduction in anthropogenic emissions of 10% in 2019 in China. In the case of controlling only local emissions in Nanjing, the concentrations of PM2.5 in Nanjing decreased significantly without local anthropogenic emissions. Additionally, the simulations showed that the annual average of ρ(PM2.5) in Nanjing could be lower than the national secondary limit (35 µg·m-3) when the anthropogenic emission reduction in China was higher than 20% in 2019. For ozone, the equal proportional emission reductions in nitrogen oxides (NOx) and volatile organic pollutants (VOCs) of O3 precursors in China likely led to the increase in seasonal average concentrations of O3 in Nanjing. For the proportional reduction of anthropogenic emissions by 10%-50% in China, the seasonal average of ρ(MDA8 O3) in Nanjing in 2019 would increase by 1-3 µg·m-3 in spring, 1-4 µg·m-3 in autumn, and 3-11 µg·m-3 in winter, respectively, compared with that in the base simulation. With the reduction in anthropogenic NOx emission by 10% and VOCs by 20%, the seasonal average of ρ(MDA8 O3) in Nanjing would decrease by 3-6 µg·m-3. On this basis, further increasing the proportion (30%) of VOCs emission reduction could reduce the annual average of ρ(MDA8 O3) in Nanjing by 7 µg·m-3. However, the annual average of ρ(MDA8 O3) of Nanjing in 2019 increased by 1 µg·m-3, with the local emission reduction of NOx by 10% and VOCs by 30%. Therefore, this showed that the key to alleviate ozone pollution in Nanjing is a reasonable control ratio of ozone precursor emissions and the implementation of regional joint prevention and control. In order to effectively reduce the O3 pollution in Nanjing, the emission reduction ratio of NOx and VOCs in China should be less than 1:2. The response of pollutant concentrations to reductions in precursor emissions were efficiently obtained by the random forest algorithm and GEOS-Chem model. The simulations would provide the scientific basis for the emission control strategy to alleviate air pollution.

Insights on aerosol properties using two decades-long ground-based remote sensing datasets in Moldova, Eastern Europe.

Aerosol optical properties were studied over Chisinau in Moldova, one of the longest running AERONET sites in Eastern Europe. During two decades (September 1999-November 2018), the mean aerosol optical depth (AOD) and Angstrom exponent (AE) were observed as 0.21 ± 0.13 and 1.49 ± 0.29, respectively. The highest AOD (0.24 ± 0.13) and AE (1.60 ± 0.26) were observed during the summer. More than half (∼55%) of the share was occupied by clean continental aerosols with seasonal order of winter (74.8%) > autumn (62%) > spring (48.9%) > summer (44.8%) followed by mixed aerosols with a respective contribution of 30.7% (summer), 28.4% (spring), 22.5 (autumn) and 16.4% (winter). A clear dominance of volume size distribution in the fine mode indicated the stronger influence of anthropogenic activities resulting in fine aerosol load in the atmosphere. The peak in the fine mode was centered at 0.15 µm, whereas that of the coarse mode was centered either at 3.86 µm (summer and autumn) or 5.06 µm (spring and winter). 'Extreme' aerosol events were observed during 21 days with a mean AOD (AE) of 0.99 ± 0.32 (1.43 ± 0.43), whereas 'strong' events were observed during 123 days with a mean AOD (AE) of 0.57 ± 0.07 (1.44 ± 0.40), mainly influenced by anthropogenic aerosols (during 19 and 101 days of each event type) from urban/industrial and biomass burning indicated by high AE and fine mode fraction. During the whole period (excluding events days), the fine and coarse mode peaks were observed at the radius of 0.15 and 5.06 µm, which in the case of extreme (strong) events were at 0.19 (0.15) and 3.86 (2.24) µm respectively. The fine mode volume concentration was 4.78 and 3.32 times higher, whereas the coarse mode volume concentration was higher by a factor of 1.98 and 2.27 during extreme and strong events compared to the whole period.

[Composition Characteristics of Volatile Organic Compounds and Associated Contributions to Secondary Pollution in Shenyang Industrial Area in Summer].

Based on the observation data of volatile organic compounds (VOCs) in the industrial area of Shenyang during the summer of 2019 and 2020, the composition characteristics and sources of VOCs were preliminarily studied. The ozone formation potential (OFP) and aerosol formation potential (AFP) of VOCs were also estimated using the max incremental reactivity (MIR) and aerosol formation coefficient (FAC) methods, respectively. The results showed that the average concentration of VOCs was 41.66 µg·m-3, and the proportions of alkanes, olefins, aromatics, and acetylene were 48.50%, 14.08%, 15.37%, and 22.05%, respectively. The top ten species of VOCs were primarily C2-C5 alkanes, also including acetylene, ethylene, and some aromatics, accounting for 69.25% of the total VOCs. VOCs showed obvious diurnal variation characteristics with a high concentration in the morning and evening (at 06:00 and 22:00) and a low concentration in the afternoon (11:00-16:00). According to the value of toluene/benzene (T/B) and isopentane/n-pentane, the atmosphere of the industrial area was mainly affected by vehicle exhaust emissions, solvent use, combustion sources, and LPG/NG. The total AFP of VOCs was up to 41.43×10-2 µg·m-3, and aromatics were the largest contributor. The total OFP of VOCs reached 117.59 µg·m-3, in which the alkenes contributed the most.

Network analysis identifies a gene biomarker panel for sepsis-induced acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is characterized by non-cardiogenic pulmonary edema caused by inflammation, which can lead to serious respiratory complications. Due to the high mortality of ARDS caused by sepsis, biological markers that enable early diagnosis are urgently needed for clinical treatment. METHODS: In the present study, we used the public microarray data of whole blood from patients with sepsis-induced ARDS, patients with sepsis-alone and healthy controls to perform an integrated analysis based on differential expressed genes (DEGs) and co-expression network to identify the key genes and pathways related to the development of sepsis into ARDS that may be key targets for diagnosis and treatment. RESULTS: Compared with controls, we identified 180 DEGs in the sepsis-alone group and 152 DEGs in the sepsis-induced ARDS group. About 70% of these genes were unique to the two groups. Functional analysis of DEGs showed that neutrophil-mediated inflammation and mitochondrial dysfunction are the main features of ARDS induced by sepsis. Gene network analysis identified key modules and screened out key regulatory genes related to ARDS. The key genes and their upstream regulators comprised a gene panel, including EOMES, LTF, CSF1R, HLA-DRA, IRF8 and MPEG1. Compared with the healthy controls, the panel had an area under the curve (AUC) of 0.900 and 0.914 for sepsis-alone group and sepsis-induced ARDS group, respectively. The AUC was 0.746 between the sepsis-alone group and sepsis-induced ARDS group. Moreover, the panel of another independent blood transcriptional expression profile dataset showed the AUC was 0.769 in diagnosing sepsis-alone group and sepsis-induced ARDS group. CONCLUSIONS: Taken together, our method contributes to the diagnosis of sepsis and sepsis-induced ARDS. The biological pathway involved in this gene biomarker panel may also be a critical target in combating ARDS caused by sepsis.