Degradation of gaseous hydrocarbons in aerated stirred bioreactors inoculated with Rhodococcus erythropolis: Effect of the carbon source and SIFT-MS method development.

The study of microbial hydrocarbons removal is of great importance for the development of future bioremediation strategies. In this study, we evaluated the removal of a gaseous mixture containing toluene, m-xylene, ethylbenzene, cyclohexane, butane, pentane, hexane and heptane in aerated stirred bioreactors inoculated with Rhodococcus erythropolis and operated under non-sterile conditions. For the real-time measurement of hydrocarbons, a novel systematic approach was implemented using Selected-Ion Flow Tube Mass Spectrometry (SIFT-MS). The effect of the carbon source (∼9.5 ppmv) on (i) the bioreactors' performance (BR1: dosed with only cyclohexane as a single hydrocarbon versus BR2: dosed with a mixture of the 8 hydrocarbons) and (ii) the evolution of microbial communities over time were investigated. The results showed that cyclohexane reached a maximum removal efficiency (RE) of 53% ± 4% in BR1. In BR2, almost complete removal of toluene, m-xylene and ethylbenzene, being the most water-soluble and easy-to-degrade carbon sources, was observed. REs below 32% were obtained for the remaining compounds. By exposing the microbial consortium to only the five most recalcitrant hydrocarbons, REs between 45% ± 5% and 98% ± 1% were reached. In addition, we observed that airborne microorganisms populated the bioreactors and that the type of carbon source influenced the microbial communities developed. The abundance of species belonging to the genus Rhodococcus was below 10% in all bioreactors at the end of the experiments. This work provides fundamental insights to understand the complex behavior of gaseous hydrocarbon mixtures in bioreactors, along with a systematic approach for the development of SIFT-MS methods.

Profiles, exposure assessment and expanded screening of PAHs and their derivatives in one petroleum refinery facility of China.

This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons (PAHs) and their derivatives in one Chinese petroleum refinery facility. It was found that, following with high concentrations of 16 EPA PAHs (∑Parent-PAHs) in smelting subarea of studied petroleum refinery facility, total derivatives of PAHs [named as XPAHs, including nitro PAHs (NPAHs), chlorinated PAHs (Cl-PAHs), and brominated PAHs (Br-PAHs)] in gas (mean= 1.57 × 104 ng/m3), total suspended particulate (TSP) (mean= 4.33 × 103 ng/m3) and soil (mean= 4.37 × 103 ng/g) in this subarea had 1.76-6.19 times higher levels than those from other subareas of this facility, surrounding residential areas and reference areas, indicating that petroleum refining processes would lead apparent derivation of PAHs. Especially, compared with those in residential and reference areas, gas samples in the petrochemical areas had higher ∑NPAH/∑PAHs (mean=2.18), but lower ∑Cl-PAH/∑PAHs (mean=1.43 × 10-1) and ∑Br-PAH/∑PAHs ratios (mean=7.49 × 10-2), indicating the richer nitrification of PAHs than chlorination during petrochemical process. The occupational exposure to PAHs and XPAHs in this petroleum refinery facility were 24-343 times higher than non-occupational exposure, and the ILCR (1.04 × 10-4) for petrochemical workers was considered to be potential high risk. Furthermore, one expanded high-resolution screening through GC Orbitrap/MS was performed for soils from petrochemical area, and another 35 PAHs were found, including alkyl-PAHs, phenyl-PAHs and other species, indicating that profiles and risks of PAHs analogs in petrochemical areas deserve further expanded investigation.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Relative humidity-dependent evolution of molecular composition of α-pinene secondary organic aerosol upon heterogeneous oxidation by hydroxyl radicals.

Heterogeneous oxidation by gas-phase oxidants is an important chemical transformation pathway of secondary organic aerosol (SOA) and plays an important role in controlling the abundance, properties, as well as climate and health impacts of aerosols. However, our knowledge on this heterogeneous chemistry remains inadequate. In this study, the heterogeneous oxidation of α-pinene ozonolysis SOA by hydroxyl (OH) radicals was investigated under both low and high relative humidity (RH) conditions, with an emphasis on the evolution of molecular composition of SOA and its RH dependence. It is found that the heterogeneous oxidation of SOA at an OH exposure level equivalent to 12 hr of atmospheric aging leads to particle mass loss of 60% at 25% RH and 95% at 90% RH. The heterogeneous oxidation strongly changes the molecular composition of SOA. The dimer-to-monomer signal ratios increase dramatically with rising OH exposure, in particular under high RH conditions, suggesting that aerosol water stimulates the reaction of monomers with OH radicals more than that of dimers. In addition, the typical SOA tracer compounds such as pinic acid, pinonic acid, hydroxy pinonic acid and dimer esters (e.g., C17H26O8 and C19H28O7) have lifetimes of several hours against heterogeneous OH oxidation under typical atmospheric conditions, which highlights the need for the consideration of their heterogeneous loss in the estimation of monoterpene SOA concentrations using tracer-based methods. Our study sheds lights on the heterogeneous oxidation chemistry of monoterpene SOA and would help to understand their evolution and impacts in the atmosphere.

Unintentional emissions of polychlorinated naphthalenes in China: Sources, composition, and historical trends.

Polychlorinated naphthalenes (PCNs) are detrimental to human health and the environment. With the commercial production of PCNs banned, unintentional releases have emerged as a significant environmental source. However, relevant information is still scarce. In this study, provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database. The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron & steel industry as the biggest source. Low-chlorinated PCNs comprised 90% of emissions by mass, while highly chlorinated PCNs dominated in terms of toxicity, highlighting divergent priorities for mitigating emissions and safeguarding human health. The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron & steel industry in terms of source, and from North China and East China in terms of geographic area. Per-capita emissions followed an inverted U-shaped environmental Kuznets curve while emission intensities decreased with increasing per-capita Gross Domestic Product (GDP) following a nearly linear pattern when log-transformed.

Ultrasonic humidifier aerosols: Observed high heavy metal enrichment and a new emission control method.

Ultrasonic humidifiers are commonly used in households to maintain indoor humidity and generate a large number of droplets or spray aerosols. However, there have been various health concerns associated with humidifier use, largely due to aerosols generated during operation. Here, we investigated the size distribution, chemical composition, and charged fraction of aerosol particles emitted from commercial ultrasonic humidifiers. Heavy metals in water used for humidifiers were found to be highly enriched in the ultrasonic humidifier aerosols (UHA), with the enrichment factors ranging from 102 to 107. This enrichment may pose health concerns for the building occupants, as UHA concentrations of up to 106 particles/cm3 or 3 mg/m3 were observed. Furthermore, approximately 90% of UHA were observed to be electrically charged, for the first time according to our knowledge. Based on this discovery, we proposed and tested a new method to remove UHA by using a simple electrical field. The designed electrical field in this work can efficiently remove 81.4% of UHA. Therefore, applying this electrical field could be an effective method to significantly reduce the health risks by UHA.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Combustion aerosols and suspended dust with controlled processes in Lhasa: Elemental analysis and size distribution characteristics.

To explore air contamination resulting from special biomass combustion and suspended dust in Lhasa, the present study focused on the size distribution and chemical characteristics of particulate matter (PM) emission resulting from 7 types of non-fossil pollution sources. We investigated the concentration and size distribution of trace elements from 7 pollution sources collected in Lhasa. Combining Lhasa's atmospheric particulate matter data, enrichment factors (EFs) have been calculated to examine the potential impact of those pollution sources on the atmosphere quality of Lhasa. The highest mass concentration of total elements of biomass combustion appeared at PM0.4, and the second highest concentration existed in the size fraction 0.4-1 µm; the higher proportion (12 %) of toxic metals was produced by biomass combustion. The elemental composition of suspended dust and atmospheric particulate matter was close (except for As and Cd); the highest concentration of elements was all noted in PM2.5-10 (PM3-10). Potassium was found to be one of the main biomass markers. The proportion of Cu in suspended dust is significantly lower than that of atmospheric particulate matter (0.53 % and 3.75 %), which indicates that there are other anthropogenic sources. The EFs analysis showed that the Cr, Cu, Zn, and Pb produced by biomass combustion were highly enriched (EFs > 100) in all particle sizes. The EFs of most trace elements increased with decreasing particle size, indicating the greater influence of humanfactors on smaller particles.

Carbon emissions in China's steel industry from a life cycle perspective: Carbon footprint insights.

China is the most important steel producer in the world, and its steel industry is one of the most carbon-intensive industries in China. Consequently, research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals. We constructed a carbon dioxide (CO2) emission model for China's iron and steel industry from a life cycle perspective, conducted an empirical analysis based on data from 2019, and calculated the CO2 emissions of the industry throughout its life cycle. Key emission reduction factors were identified using sensitivity analysis. The results demonstrated that the CO2 emission intensity of the steel industry was 2.33 ton CO2/ton, and the production and manufacturing stages were the main sources of CO2 emissions, accounting for 89.84% of the total steel life-cycle emissions. Notably, fossil fuel combustion had the highest sensitivity to steel CO2 emissions, with a sensitivity coefficient of 0.68, reducing the amount of fossil fuel combustion by 20% and carbon emissions by 13.60%. The sensitivities of power structure optimization and scrap consumption were similar, while that of the transportation structure adjustment was the lowest, with a sensitivity coefficient of less than 0.1. Given the current strategic goals of peak carbon and carbon neutrality, it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies, increase the ratio of scrap steel to steelmaking, and build a new power system.

Emission characteristics of biogenic volatile organic compounds in a subtropical pristine forest of southern China.

Emission characteristics of biogenic volatile organic compounds (BVOCs) from dominant tree species in the subtropical pristine forests of China are extremely limited. Here we conducted in situ field measurements of BVOCs emissions from representative mature evergreen trees by using dynamic branch enclosures at four altitude gradients (600-1690 m a.s.l.) in the Nanling Mountains of southern China. Composition characteristics as well as seasonal and altitudinal variations were analyzed. Standardized emission rates and canopy-scale emission factors were then calculated. Results showed that BVOCs emission intensities in the wet season were generally higher than those in the dry season. Monoterpenes were the dominant BVOCs emitted from most broad-leaved trees, accounting for over 70% of the total. Schima superba, Yushania basihirsuta and Altingia chinensis had relatively high emission intensities and secondary pollutant formation potentials. The localized emission factors of isoprene were comparable to the defaults in the Model of Emissions of Gases and Aerosols from Nature (MEGAN), while emission factors of monoterpenes and sesquiterpenes were 2 to 58 times of those in the model. Our results can be used to update the current BVOCs emission inventory in MEGAN, thereby reducing the uncertainties of BVOCs emission estimations in forested regions of southern China.

Physicochemical characteristics of airborne microplastics of a typical coastal city in the Yangtze River Delta Region, China.

Airborne microplastics (MPs) are important pollutants that have been present in the environment for many years and are characterized by their universality, persistence, and potential toxicity. This study investigated the effects of terrestrial and marine transport of MPs in the atmosphere of a coastal city and compared the difference between daytime and nighttime. Laser direct infrared imaging (LDIR) and polarized light microscopy were used to characterize the physical and chemical properties of MPs, including number concentration, chemical types, shape, and size. Backward trajectories were used to distinguish the air masses from marine and terrestrial transport. Twenty chemical types were detected by LDIR, with rubber (16.7%) and phenol-formaldehyde resin (PFR; 14.8%) being major components. Three main morphological types of MPs were identified, and fragments (78.1%) are the dominant type. MPs in the atmosphere were concentrated in the small particle size segment (20-50 µm). The concentration of MPs in the air mass from marine transport was 14.7 items/m3 - lower than that from terrestrial transport (32.0 items/m3). The number concentration of airborne MPs was negatively correlated with relative humidity. MPs from terrestrial transport were mainly rubber (20.2%), while those from marine transport were mainly PFR (18%). MPs in the marine transport air mass were more aged and had a lower number concentration than those in the terrestrial transport air mass. The number concentration of airborne MPs is higher during the day than at night. These findings could contribute to the development of targeted control measures and methods to reduce MP pollution.

Emission characteristics and probabilistic health risk of volatile organic compounds from leather sofa.

Furniture is identified as a vital volatile organic compound (VOC) emission source in the indoor environment. Leather has become the most common raw and auxiliary fabric material for upholstered furniture, particularly with extensive consumption in sofas, due to its abundant resources and efficient functions. Despite being widely traded across the world, little research has been conducted on the VOCs released by leather materials and their health risk assessment in the indoor environment. Accordingly, this study investigated the VOC emissions of leather with different grades and the health risk of the inhalation exposure. Based on the ultra-fast gas phase electronic nose (EN) and GC-FID/Qtof, the substantial emissions of aliphatic aldehyde ketones (Aks), particularly hexanal, appear to be the cause of off-flavor in medium and low grade (MG and LG) sofa leathers. The health risk assessment indicated that leather materials barely pose non-carcinogenic and carcinogenic effects to residents. Given the abundance of VOC sources and the accumulation of health risks in the indoor environment, more stringent specifications concerning qualitative and quantitative content should be extended to provide VOC treatment basic for the manufacturing industry and obtain better indoor air quality.

Emission control status and future perspectives of diesel trucks in China.

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

Estimating the short-term effect of PM2.5 on the mortality of cardiovascular diseases based on instrumental variables.

BACKGROUND: PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method. METHODS: We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality. RESULTS: The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P < 0.05). However, no significant association was observed between PM2.5 and hypertension. CONCLUSION: PM2.5 was significantly associated with daily CVDs deaths (excluding hypertension). The estimates from the IVs method were slightly higher than those from the GAM. Previous studies based on GAM may have underestimated the impact of PM2.5 on CVDs.

Relationship between exposure to fine particulate matter and cardiovascular risk factors and the modifying effect of socioeconomic status: a cross-sectional study in Beijing, China.

Accumulating research suggested that long-term exposure to fine particulate matter (PM2.5) is related to cardiovascular disease (CVD). However, evidence regarding the relationship between PM2.5 and CVD risk factors remains inconsistent. We hypothesized that this association may be partially modified by socioeconomic status (SES). To investigate the relationships and to test the modifying effect of SES, we included baseline data for 21,018 adults from September 2017 to May 2018. PM2.5 concentrations were determined by employing an amalgamation of linear measurements obtained from monitoring stations located near the participants' residential and workplace addresses. We assessed SES across several domains, including income, education, and occupation levels, as well as through a composite SES index. The results indicated that for every 10 µg/m3 increase in PM2.5 exposure, the risk of hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and hyperhomocysteinemia (HHcy) increased by 7.7% [Odds ratio (OR) = 1.077, 95% Confidence Interval (CI) = 1.011, 1.146], 19.6% (OR = 1.196, 95% CI = 1.091, 1.312), 4.2% (OR = 1.042, 95% CI = 1.002, 1.084), and 17.1% (OR = 1.171, 95% CI = 1.133, 1.209), respectively. Compared to the high SES group, those with low SES are more prone to hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and HHcy. Notably, the disparities in SES appear significant in the relationship between PM2.5 exposure and hypercholesterolemia as well as hyperbetalipoproteinemia. But for diabetes and HHcy, the modification effect of SES on PM2.5 shows an inconsistent pattern. In conclusion, the results confirm the association between PM2.5 and cardiovascular risk factors and low SES significantly amplified the adverse PM2.5 effect on dyslipidemia. It is crucial to emphasize a need to improve the socioeconomic inequality among adults in Beijing and contribute to the understanding of the urgency in protecting the health of vulnerable groups.

Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models' forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models' test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

Adverse effects of meteorological factors and air pollutants on dry eye disease: a hospital-based retrospective cohort study.

Although previous studies have suggested that meteorological factors and air pollutants can cause dry eye disease (DED), few clinical cohort studies have determined the individual and combined effects of these factors on DED. We investigated the effects of meteorological factors (humidity and temperature) and air pollutants [particles with a diameter ≤ 2.5 µ m (PM2.5), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO)] on DED. A retrospective cohort study was conducted on 53 DED patients. DED was evaluated by Symptom Assessment in Dry Eye (SANDE), tear secretion, tear film break-up time (TBUT), ocular staining score (OSS), and tear osmolarity. To explore the individual, non-linear, and joint associations between meteorological factors, air pollutants, and DED parameters, we used generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR). After adjusting for all covariates, lower relative humidity or temperature was associated with a higher SANDE (p < 0.05). Higher PM2.5, O3, and NO2 levels were associated with higher SANDE and tear osmolarity (p < 0.05). Higher O3 levels were associated with lower tear secretion and TBUT, whereas higher NO2 levels were associated with higher OSS (p < 0.05). BKMR analyses indicated that a mixture of meteorological factors and air pollutants was significantly associated with increased SANDE, OSS, tear osmolarity, and decreased tear secretion.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China.

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

The Association between Exposure to Fine Particulate Air Pollution and the Trajectory of Internalizing and Externalizing Behaviors during Late Childhood and Early Adolescence: Evidence from the Adolescent Brain Cognitive Development (ABCD) Study.

BACKGROUND: Exposure to high levels of fine particulate matter (PM) with aerodynamic diameter ≤2.5µm (PM2.5) via air pollution may be a risk factor for psychiatric disorders during adulthood. Yet few studies have examined associations between exposure and the trajectory of symptoms across late childhood and early adolescence. OBJECTIVE: The current study evaluated whether PM2.5 exposure at 9-11 y of age affects both concurrent symptoms as well as the longitudinal trajectory of internalizing and externalizing behaviors across the following 3 y. This issue was examined using multiple measures of exposure and separate measures of symptoms of internalizing disorders (e.g., depression, anxiety) and externalizing disorders (e.g., conduct disorder), respectively. METHODS: In a sample of more than 10,000 youth from the Adolescent Brain Cognitive Development (ABCD) Study, we used a dataset of historical PM2.5 levels and growth curve modeling to evaluate associations of PM2.5 exposure with internalizing and externalizing symptom trajectories, as assessed by the Child Behavioral Check List. Three distinct measures of PM2.5 exposure were investigated: annual average concentration during 2016, number of days in 2016 above the US Environmental Protection Agency (US EPA) 24-h PM2.5 standards, and maximum 24-h concentration during 2016. RESULTS: At baseline, higher number of days with PM2.5 levels above US EPA standards was associated with higher parent-reported internalizing symptoms in the same year. This association remained significant up to a year following exposure and after controlling for PM2.5 annual average, maximum 24-h level, and informant psychopathology. There was also evidence of an association between PM2.5 annual average and externalizing symptom levels at baseline in females only. DISCUSSION: Results suggested PM2.5 exposure during childhood is associated with higher symptoms of internalizing and externalizing disorders at the time of exposure and 1 y later. In addition, effects of PM2.5 exposure on youth internalizing symptoms may be most impacted by the number of days of exposure above US EPA standards in comparison with annual average and maximum daily exposure. https://doi.org/10.1289/EHP13427.