Atmospheric environment characteristic of severe dust storms and its impact on sulfate formation in downstream city

This study comprehensively investigated the impact of dust storms (DSs) on downstream cities, by selecting representative DS events. In this paper, we discussed the characteristics of meteorological conditions, air pollutants, PM2.5 components, and their influence on sulfate formation mechanisms. During DSs, strong winds, reaching speeds of up to 10 m/s, led to significant increases in PM10 and PM2.5, with maximum concentrations of 2684.5 and 429 μg/m3, respectively. Primary gaseous pollutants experienced substantial reductions, with decline rates of 48.1, 34.9, 36.8, and 9.0 % for SO2, NO2, NH3, and CO, respectively. Despite a notable increase in PM2.5 concentrations, only 7.6 % of the total mass of PM2.5 was attributed to ionic and carbonaceous components, a much lower value than observed before the DSs (77.3 %). Concentrations of Fe, Ti, and Mn exhibited increases by factors of 6.5-14.1, 10.4-17.0, and 1.6-4.7, respectively. In contrast to the significant decrease of >76.2 % in nitrogen oxidation ratio (NOR), sulfur oxidation ratio (SOR) remained at a relatively high level, displaying a strong positive correlation with high concentrations of Fe, Mn, and Ti. Quantitative analysis revealed an average increase of 0.187 and 0.045 μg/m3 in sulfate from natural sources and heterogeneous generation, respectively. The heterogeneous reaction on mineral dust was closely linked to atmospheric humidity, radiation intensity, the form of metal existence, and concentrations of it. High concentrations of titanium dioxide and iron‑manganese oxides in mineral dust promoted heterogeneous oxidation of SO2 through photocatalysis during the daytime and metal ion catalysis during the nighttime. This study establishes that the metal components in mineral dust promote heterogeneous sulfate formation, quantifies the yield of sulfate generated as a result, and provides possible mechanisms for heterogeneous sulfate formation.

Exploring vehicle-centric strategies for sustainable urban mobility: A theoretical framework for saving energy and reducing noise in transportation

Adopting energy-saving and noise-reducing technologies in vehicle transportation has the potential to mitigate urban traffic pollution and promote sustainable urban mobility. However, a universal analytical framework for obtaining the combined energy savings and noise reduction patterns in vehicles is still lacking. This study addresses this gap by integrating a fundamental traffic noise model with a vehicle energy conservation equation. A theoretical framework was constructed that establishes the relationship between vehicle noise and energy consumption, with the theoretical origins of this framework explained. By summarizing a substantial body of classical literature, the typical model's properties are analyzed through the principle of optimality, and the noise interval for combined vehicle energy-saving and noise-reducing is determined. Subsequently, a rigorous vehicle experiment was conducted to validate the proposed framework's effectiveness, utilizing synchronized data on energy consumption and noise. The findings indicate that vehicles can achieve unconstrained combined energy-saving and noise-reducing in four driving states and conditional combined energy-saving and noise-reducing in five driving states. The Recall index demonstrates a verification rate exceeding 0.62 for the combined energy-saving and noise-reducing rules. This research provides valuable insights to support energy-saving and noise-reducing measures in urban traffic.

Secondary Organic Aerosol Formation Potential from Vehicular Non-tailpipe Emissions under Real-World Driving Conditions

Traffic emissions are a dominant source of secondary organic aerosol (SOA) in urban environments. Though tailpipe exhaust has drawn extensive attention, the impact of non-tailpipe emissions on atmospheric SOA has not been well studied. Here, a closure study was performed combining urban tunnel experiments and dynamometer tests using an oxidation flow reactor in situ photo-oxidation. Results show a significant gap between field and laboratory research; the average SOA formation potential from real-world fleet is 639 ± 156 mg kg fuel-1, higher than the reconstructed result (188 mg kg fuel-1) based on dynamometer tests coupled with fleet composition inside the tunnel. Considering the minimal variation of SOA/CO in emission standards, we also reconstruct CO and find the critical role of high-emitting events in the real-world SOA burden. Different profiles of organic gases are detected inside the tunnel than tailpipe exhaust, such as more abundant C6-C9 aromatics, C11-C16 species, and benzothiazoles, denoting contributions from non-tailpipe emissions to SOA formation. Using these surrogate chemical compounds, we roughly estimate that high-emitting, evaporative emission, and asphalt-related and tire sublimation share 14, 20, and 10% of the SOA budget, respectively, partially explaining the gap between field and laboratory research. These experimental results highlight the importance of non-tailpipe emissions to atmospheric SOA.

Investigating the impact of high-altitude on vehicle carbon emissions: A comprehensive on-road driving study

This study addresses the literature gap concerning accurately identifying vehicle carbon emission characteristics in high-altitude areas. Utilizing a portable emission measurement system (PEMS) for real-world testing, we quantified the influence of altitude on carbon emissions from light-duty gasoline (LDGV) and diesel vehicles (LDDV). The Random Forest (RF) algorithm was employed to analyze the complex nonlinear relationships between altitude, meteorological conditions, driving patterns, and carbon dioxide (CO2) emissions, enabling predictions across different altitudes. The results showed that CO2 emissions progressively increase with elevation. Furthermore, as altitude increases, combustion efficiency declines, and the overall impact of driving conditions on emission rates diminishes. Altitude and meteorological factors significantly contributed to CO2 emissions, whereas driving conditions and road grades contributed less. Compared with the COPERT model, the RF model demonstrates strong accuracy in predicting carbon emissions at different altitudes. Specifically, the CO2 emission rate nearly triples as altitude increases from 2.0 km to 4.5 km. This research bridges a critical gap in the understanding carbon emissions from high-altitude vehicles, offering insights into policy development for emission reduction strategies in such regions. Future studies should integrate diverse testing methodologies and comprehensive surveys to validate and extend the findings.

Regenerative braking system effectively reduces the formation of brake wear particles

Brake wear particles (BWPs) are considered one of the most significant non-exhaust particle emission sources from motor vehicles. Previous studies have primarily focused on BWPs from conventional fuel vehicles (CFVs), with limited research available on BWPs from new energy vehicles (NEVs). We developed an independent BWP emission testing system applicable to NEVs and conducted BWP emission tests on representative NEVs and CFVs under various testing cycles via a chassis dynamometer. The BWP emission characteristics of the NEVs equipped with regenerative braking system significantly differed from those of gasoline vehicles. For transient emission characteristics, gasoline vehicles exhibited higher peak concentrations during brake events than brake drag events, while those with regenerative braking exhibited the opposite feature. Under continuous braking, the concentration of ultrafine particles emitted by NEVs was reduced by more than 3 orders of magnitude compared to gasoline vehicles. In terms of single-particle morphology, BWPs could be mainly divided into three categories: carbonaceous particles, iron-rich particles, and mixed metal particles. We obtained realistic emission characteristics of BWPs from NEVs, which could provide data support and a scientific basis for the formulation of relevant emission standards and control measures in the future.

Evaluation indicators for road traffic energy consumption: a review and prospect

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

Transforming waste brake pads from automobiles into Nano-Catalyst: Synergistic Fe-C-Cu triple sites for efficient fenton-like oxidation of organic pollutants

The arbitrary disposal of used brake pads from motor vehicles has resulted in severe heavy metal pollution and resource wastage, highlighting the urgent need to explore the significant untapped potential of these discarded materials. In this study, The in-situ growth of highly dispersed Fe2O3 nanocrystals was achieved by simple oxidation annealing of brake pad debris(BPD). Interestingly, Cu remained unoxidized and acted as a "valence state transformation bridge of Fe2O3" to construct the "triple Fe-C-Cu sites". The Fenton degradation experiment of pollutants was conducted under constant temperature conditions at 40 °C, a stirring rate of 1300 rpm, a pH value of 3, a catalyst dosage of 0.5 g/L, pollutant dosage ranging from 50 to 400 mg/L, and H2O2 dosage of 0.25 g/L. Experimental results showed that BPD treated at 300 °C for 2 h exhibited optimal Fenton-like oxidation activity, achieving rapid degradation of over 90 % of refractory antibiotics, such as tetracycline and ciprofloxacin, in organic wastewater within 10 min. This remarkable performance was mainly attributed to the synergistic effect of "Fe-C-Cu triple sites", where the electron-donating role of C in the Fe-C and Cu-C interfaces facilitated the conversion of the Fe(III) to Fe(II) and Cu(II) to Cu(I). In addition, the ability of Cu2+ to accept electrons at the Fe-Cu interface promoted the transition from Fe (II) to Fe (III). This "balance of electron gain and loss" accelerated the interfacial electron transfer and the recycle of dual Fenton sites, Fe(II)/Fe(III) and Cu(I)/Cu(II), to generate more ·OH from H2O2. Therefore, this strategy of functionalizing BPD as Fenton-like catalysts without the addition of external Fe provides intriguing prospects for understanding the construction of Fe-based Fenton catalysts and resource utilization of Fe-containing solid waste materials.

Effects of heat waves and cold spells on blood parameters: a cohort study of blood donors in Tianjin, China

BACKGROUND

With the increasing occurrence of extreme temperature events due to climate change, the attention has been predominantly focused on the effects of heat waves and cold spells on morbidity and mortality. However, the influence of these temperature extremes on blood parameters has been overlooked.

METHODS

We conducted a cohort study involving 2,752 adult blood donors in Tianjin, China, between January 18, 2013, and June 25, 2021. The generalized additive mixed model was used to investigate the effects and lagged effects of heat waves and cold spells on six blood parameters of blood donors, including alanine aminotransferase (ALT), white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), and platelet count (PLT). Subgroup analyses were stratified by sex, age, and BMI.

RESULTS

Heat waves and cold spells are associated with changes in blood parameters, particularly HB and PLT. Heat waves increased HB and PLT, while cold spells increased HB and decreased PLT. The effect of heat waves is greater than that of cold spells. The largest effect of heat waves on HB and PLT occurred at lag1 with 2.6 g/L (95% CI: 1.76 to 3.45) and lag7 with 9.71 × 10^9/L (95% CI: 6.26 to 13.17), respectively, while the largest effect of cold spells on HB and PLT occurred at lag0 with 1.02 g/L (95% CI: 0.71 to 1.33) and lag2 with -3.85 × 10^9/L (95% CI: -5.00 to -2.70), respectively. In subgroup analysis, the effect of cold spells on ALT was greater in the 40-49 age group.

CONCLUSION

We indicated that heat waves and cold spells can impact hemoglobin and platelet counts in the human body. These findings provide evidence linking heat waves or cold spells to diseases and may reduce health risks caused by extreme temperature events.

Direct measurement of brake and tire wear particles based on real-world driving conditions

With implementing vehicle emission control policies, tailpipe particulate emissions have been gradually controlled, and the relative contribution of non-tailpipe particulate emissions, such as brake and tire wear, has further increased. A unified and scientific method for sampling non-tailpipe particulate matter (PM) emissions is essential to improve the accuracy of the emission characteristics and factors. This study proposes a novel sampling method based on real-world driving conditions to obtain information on emissions and extract characteristic conditions for tire and brake pad wear. We extracted 200 representative braking segments for simulation experiments based on road type, initial and final velocities, temperature, and deceleration rate. Two standard test cycles to simulate the tire wear conditions of the front and rear wheels were constructed based on velocity, lateral, and vertical forces. Under the real-world driving condition test cycle, the emission factors of PM2.5 and PM10 for brake wear particles of passenger vehicles were 2.66 mg/km and 11.65 mg/km, respectively. In contrast, the emission factors of PM2.5 and PM10 for tire wear particles were 0.21 mg/km and 1.27 mg/km, respectively. Moreover, this study provides insights and basic data for localizing and improving the emission model, which can enhance its applicability and accuracy.

Chemical characteristics of fine tire wear particles generated on a tire simulator

Tire wear is one of the major sources of traffic-related particle emissions, however, laboratory data on the components of tire wear particles (TWPs) is scarce. In this study, ten brands of tires, including two types and four-speed grades, were chosen for wear tests using a tire simulator in a closed chamber. The chemical components of PM2.5 were characterized in detail, including inorganic elements, water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), and polycyclic aromatic hydrocarbons (PAHs). Inorganic elements, WSIs, OC, and EC accounted for 8.7 ± 2.1%, 3.1 ± 0.7%, 44.0 ± 0.9%, and 9.6 ± 2.3% of the mass of PM2.5, respectively. The OC/EC ratio ranged from 2.8 to 7.6. The inorganic elements were dominated by Si and Zn. The primary ions were SO42- and NO3-, and TWPs were proven to be acidic by applying an ionic balance. The total PAHs content was 113 ± 45.0 μg g-1, with pyrene being dominant. In addition, the relationship between the chemical components and tire parameters was analyzed. Inorganic elements and WSIs in TWPs were more abundant in all-season tires than those in winter tires, whereas the content of PAHs was the opposite. The mass fractions of OC, Si, and Al in the TWPs all showed increasing trends with increasing tire speed grade, but the PAHs levels showed a decreasing trend. Ultimately, to provide more data for further research, a TWPs source profile was constructed considering the tire weighting factor.

Heavy vehicles' non-exhaust exhibits competitive contribution to PM2.5 compared with exhaust in port and nearby areas

Heavy port transportation networks are increasingly considered as significant contributors of PM2.5 pollution compared to vessels in recent decades. In addition, evidence points to the non-exhaust emission of port traffic as the real driver. This study linked PM2.5 concentrations to varied locations and traffic fleet characteristics in port area through filter sampling. The coupled emission ratio-positive matrix factorisation (ER-PMF) method resolves source factors by avoiding direct overlap from collinear sources. In the port central and entrance areas, freight delivery activity emissions including vehicle exhaust and non-exhaust particles, as well as induced road dust resuspension, accounted for nearly half of the total contribution (42.5%-49.9%). In particular, the contribution of non-exhaust from denser traffic with high proportion of trucks was competitive and equivalent to 52.3% of that from exhaust. Backward trajectory statistical models further interpreted the notably larger-scale coverage of non-exhaust emissions in the port's central area. The distribution of PM2.5 were interpolated within the scope of the port and nearby urban areas, displaying the potential contribution of non-exhaust within 1.15 μg/m3-4.68 μg/m3, slightly higher than the urban detections reported nearby. This study may provide useful insights into the increasing percentage of non-exhaust from trucks in ports and nearby urban areas and facilitate supplementary data collection on Euro-VII type-approval limit settings.

Evaluating vehicular exhaust and evaporative emissions via VOC measurement in an underground parking garage

Vehicular emissions, including both tailpipe exhaust and evaporative emissions, are major anthropogenic sources of volatile organic compounds (VOCs) in urban cities. Current knowledge on vehicle tailpipe and evaporative emissions was mainly obtained via laboratory tests on very few vehicles under experimental conditions. Information on fleet gasoline vehicles emission features under real-world conditions is lacking. Here, VOC measurement was conducted in a large residential underground parking garage in Tianjin, China, to reveal the feature of the exhaust and evaporative emissions from real-world gasoline vehicle fleets. The VOC concentration in the parking garage was on average 362.7 ± 87.7 μg m-3, significantly higher than that in the ambient atmosphere at the same period (63.2 μg m-3). Aromatics and alkanes were the mainly contributors on both weekdays and weekends. A positive correlation between VOCs and traffic flow was observed, especially in the daytime. Source apportionment through the positive matrix factorization model (PMF) revealed that the tailpipe and evaporative emissions accounted for 43.2% and 33.7% of VOCs, respectively. Evaporative emission contributed 69.3% to the VOCs at night due to diurnal breathing loss from numerous parked cars. In contrast, tailpipe emission was most remarkable during morning rush hours. Based on the PMF results, we reconstructed a vehicle-related VOCs profile representing the combination of the tailpipe exhaust and evaporative emission from fleet-average gasoline vehicles, which could benefit future source apportionment studies.

Effects of Air Pollution Exposure during Preconception and Pregnancy on Gestational Diabetes Mellitus

This study aimed to investigate the association between air pollution and gestational diabetes mellitus (GDM) in small- and medium-sized cities, identify sensitive periods and major pollutants, and explore the effects of air pollution on different populations. A total of 9820 women who delivered in Handan Maternal and Child Health Hospital in the Hebei Province from February 2018 to July 2020 were included in the study. Logistic regression and principal component logistic regression models were used to assess the effects of air pollution exposure during preconception and pregnancy on GDM risk and the differences in the effects across populations. The results suggested that each 20 μg/m3 increase in PM2.5 and PM10 exposure during preconception and pregnancy significantly increased the risk of GDM, and a 10 μg/m3 increase in NO2 exposure during pregnancy was also associated with the risk of GDM. In a subgroup analysis, pregnant women aged 30-35 years, nulliparous women, and those with less than a bachelor's education were the most sensitive groups. This study provides evidence for an association between air pollution and the prevalence of GDM, with PM2.5, PM10, and NO2 as risk factors for GDM.

Brake wear-derived particles: Single-particle mass spectral signatures and real-world emissions

Brake wear is an important but unregulated vehicle-related source of atmospheric particulate matter (PM). The single-particle spectral fingerprints of brake wear particles (BWPs) provide essential information for understanding their formation mechanism and atmospheric contributions. Herein, we obtained the single-particle mass spectra of BWPs by combining a brake dynamometer with an online single particle aerosol mass spectrometer and quantified real-world BWP emissions through a tunnel observation in Tianjin, China. The pure BWPs mainly include three distinct types of particles, namely, Ba-containing particles, mineral particles, and carbon-containing particles, accounting for 44.2%, 43.4%, and 10.3% of the total BWP number concentration, respectively. The diversified mass spectra indicate complex BWP formation pathways, such as mechanical, phase transition, and chemical processes. Notably, the mass spectra of Ba-containing particles are unique, which allows them to serve as an excellent indicator for estimating ambient BWP concentrations. By evaluating this indicator, we find that approximately 4.0% of the PM in the tunnel could be attributable to brake wear; the real-world fleet-average emission factor of 0.28 mg km^-1 veh^-1 is consistent with the estimation obtained using the receptor model. The results presented herein can be used to inform assessments of the environmental and health impacts of BWPs to formulate effective emissions control policies.

Characteristics, sources, and health risks of inorganic elements in PM2.5 and PM10 at Tianjin Binhai international airport

To study air pollution from aircraft activity at airport and its risks to human health, we conducted an experiment near Tianjin Binhai International Airport from November 11 to November 24, 2017. The characteristics, source apportionment, and health risk of inorganic elements in particles were determined in the airport environment. The mean mass concentrations of inorganic elements in PM₁₀ and PM_2.5 were 17.1 and 5.0 μg/m³, accounting for 19.0% of PM₁₀ mass and 12.3% of PM_2.5 mass, respectively. Inorganic elements, including arsenic, chromium, lead, zinc, sulphur, cadmium, potassium, sodium, and cobalt, were mainly concentrated in fine particulate matter. The particle number concentration within the 60-170 nm particle size range was significantly higher under polluted than non-polluted conditions. A principal component analysis revealed important contributions of Cr, Fe, K, Mn, Na, Pb, S, and Zn originating from airport activities, including aircraft exhaust, braking, tire wear, ground service equipment, and airport vehicles. Based on analyses of the non-carcinogenic and carcinogenic risks of heavy metal elements in PM₁₀ and PM_2.5, there were notable human health impacts, emphasising the importance of relevant research.

The optimal speed model based on minimum temporal and spatial energy consumption

Speed optimization of the vehicle is an efficient technique to address road traffic energy consumption issues. This paper constructed the energy conservation equation of the moving vehicle based on the energy flow principle and clarified the difference between it and the vehicle specific power model. The optimal speed models based on the minimum temporal and spatial energy consumption were built with the optimization principle, and the optimal speed was derived with the road, vehicle, and environment as constraints. By comparing the measured data of on-road experiment, the optimal speed models can increase the speed by 31.3%, decrease the delay by 21.4%, and reduce the vehicle energy consumption power by 42.9% and energy consumption by 36.7%. The power is minimum when the vehicle moves at time-optimal speed. The energy consumption is minimum when the vehicle moves at space-optimal speed. The energy-saving effect Recall of optimal speed is 0.78. Research can provide theoretical support for urban road traffic energy-saving strategies.

Optical properties of vehicular brown carbon emissions: Road tunnel and chassis dynamometer tests

Brown carbon (BrC), as an important light-absorbing aerosol, significantly impacts regional and global climate. Vehicle emission is a nonnegligible source of BrC, but the optical properties of BrC emitted from vehicles remain poorly understood. This study evaluates the absorption Ångström exponent (AAE) of traffic-related light-absorbing aerosols (i.e., AAE_Tr) and the absorption emission factor (EF_abs) of vehicular BrC via chassis dynamometer tests and a road tunnel measurement in Tianjin, China. AAE_Tr are estimated as 0.98-1.33 and 1.11 ± 0.001 for tested vehicles and on-road vehicle fleet, respectively. The AAE of vehicular BrC (AAE_BrC) is 3.83 ± 0.092 for on-road vehicle fleet. The vehicle technology updates effectively reduce the EF_abs of vehicular BrC. Among the four tested China 5 and China 6 gasoline vehicles in the chassis dynamometer tests, BrC EF_abs of China 5 gasoline direct injection vehicle is the highest, while China 6 mixing fuel injection vehicle exhibits the lowest EF_abs. The BrC EF_abs of on-road vehicle fleet at 370 nm wavelength are 0.081 ± 0.0058 m² kg^-1 for mixed fleet, 0.074 ± 0.018 m² kg^-1 for gasoline vehicles (GVs), and 1.66 ± 0.71 m² kg^-1 for diesel vehicles (DVs) in the tunnel measurement. EF_abs of GV fleet in the road tunnel is higher than China 5 and China 6 vehicles, as China 1-4 vehicles accounted for 26.8% of the total vehicle fleet in the tunnel. EF_abs of vehicular BrC are lower than those from biomass burning and coal combustion emissions. The light absorption of BrC from GVs and DVs accounts for 7.2 ± 2.1% and 1.5 ± 0.77% of total traffic-related absorption at 370 nm, respectively. Our study provides optical features of BrC from vehicle source and could contribute to estimating the impacts of vehicular aerosol emissions on global and regional climate change.

Evaluating traffic emission control policies based on large-scale and real-time data: A case study in central China

The traffic control policies, including "Odd and Even" (OAE) and "One Day Per Week" (ODPW), were adopted in Zhengzhou, China. In this study, we use the bottom-up policy evaluation framework to capture the temporal-spatial characteristics of traffic conditions and vehicle emissions under various traffic restriction scenarios. Moreover, we use the street-scale simulation model to evaluate the effectiveness of improving air quality. Results showed that the improvements in traffic conditions led to the emission decrease by about 28.3 % for carbon monoxide (CO), 16.2 % for nitrogen oxide (NOx), 21.3 % for particulate matter (PM_2.5), and 23.2 % for total hydrocarbon (THC) under OAE. During ODPW, total vehicle emissions decreased by 14.1 % for CO, 10.2 % for NOx, 13.7 % for PM_2.5, and 12.4 % for THC. However, the spatial analysis indicates traffic restrictions could not significantly reduce those emissions caused by high traffic volume; besides, buses, middle-duty trucks, and heavy-duty trucks have partly offset the reduction benefit from restrictions. The air quality simulation results reveal no significant concentration decrease of CO and nitrogen dioxide (NO₂) in most areas. With the update of vehicles, stricter management of high-emission vehicles, and limited coverage for implementation of policies, the traffic control policies were not as effective as before. The limitations of the restriction policies are gradually prominent, and upgrade policies are urgently needed to continuously improve urban air quality in the future.

Identifying risk factors to predict violent behaviour in community patients with severe mental disorders: A retrospective study of 5277 patients in China

BACKGROUND

Patients with Severe Mental Disorders (SMD) have a higher risk of violent behaviour than the general population. The study aimed to investigate the predictive factors for the occurrence of violent behaviour in community SMD patients.

METHODS

The cases and follow-up data were collected from SMD patient Information Management system in Jiangning District, Jiangsu Province. The incidence of violent behaviours was described and analyzed. Logistic regression model was used to examine the influencing factors for violent behaviours in those patients.

RESULTS

Among 5277 community patients with SMD in Jiangning District, 42.4% (2236/5277) had violent behaviours. The stepwise logistic regression analysis revealed that the disease-related factors (including disease type, disease course, times of hospitalization, medication adherence, past violent behaviours), the demographic factors (age, male sex, educational level, economic and social living status), and the policy-related factors (like free treatment, annual physical check, disability certificate, family physician services, and community interviews) were significantly related to the violent behaviours in community SMD patients. After gender stratification, we found that male patients with unmarried status and with a longer course of disease were more likely to violent. However, we found that female patients with lower economic status and educational experience were more likely to violent.

CONCLUSION

Our results suggest that community SMD patients had a high incidence of violent behaviour. The findings may provide valuable information for policymakers and mental health professionals worldwide taking a number of measures to reduce the incidence of violence in community SMD patients and to better maintain social security.

Impacts of heatwaves and cold spells on glaucoma in rural China: a national cross-sectional study

China faces increasing health risks from climate change. The structure and function of the eye and vision were affected by extreme heat and cold. The study aimed to evaluate the impacts of heatwaves and cold spells on glaucoma. A national cross-sectional study of the Rural Epidemiology for Glaucoma (REG-China) was conducted in ten provinces of China, and 36,081 adults aged 40 years or more were included. Glaucoma signs were assessed via a standard examination. A total of 15 heatwave definitions, based on intensity (95th to 99th percentiles of temperature distribution) and duration (≥2 days, 3 days, and 4 days), were used to quantify heatwave effects, and 6 cold spell definitions were defined based on threshold temperature percentile (5th and 10th) and duration (3 days, 5 days, and 9 days). Multivariable-adjusted logistic regression models paired with interaction analysis were performed to investigate the impacts of heatwaves and cold spells on glaucoma, and the dose-response relationships were assessed using a restricted cubic spline (RCS) model. Subgroup analysis was conducted stratified by gender, age, smoking status, occupation, and family history of glaucoma. The overall prevalence of glaucoma was 2.1% (95% CI 1.94-2.25%). Higher heatwaves were significantly correlated with higher OR of glaucoma, with the OR (95% CI) ranging from 1.014 (1.009, 1.018) to 1.090 (1.065, 1.115) by different definitions. Glaucoma was affected by heatwaves more strongly than by cold spells. The effects of both heatwaves and cold spells were higher in males than females and in smokers than nonsmokers. These results of the present study evoked the attention of prospective research to elucidate the relationship between extreme temperatures and eye diseases.

Comparison of CO2, NOx, and VOCs emissions between CNG and E10 fueled light-duty vehicles

In China, natural gas (NG) is the main vehicle fuel after gasoline and diesel, and the number of NG vehicles ranks first in the world. At present, there are many studies on the conventional gaseous pollutants and particulate matter of NG vehicles, but very few studies on their VOCs. In this study, the chassis dynamometer is used to test CNG/E10 bi-fuel light-duty vehicles, analyze the advantages of CNG in CO₂, fuel thermal efficiency, and cost, and discuss its disadvantages in NO_x emission. Most importantly, the emission characteristics and ozone formation potential of VOCs in the exhaust of CNG vehicles were analyzed in the study. Compared with E10, CNG fuel can reduce CO₂ emission by about 20 %, improve thermal efficiency by about 13 %, and save fuel costs by about 50 %. However, it will increase NO_x and NO₂ emissions by about 10 % and 13 % respectively. As for VOCs, the emission factor of VOCs from CNG fuel is about 54 % of E10 fuel. The VOCs group with the highest proportion in the exhaust of CNG-fueled vehicles is alkanes, >80 %. while the alkanes and alkenes with the highest proportion in E10 fuel are 30 % and 23 % respectively. C2 VOCs emitted by CNG account for >70 %, while C2 VOCs emitted by E10 are <60 %, followed by C4 VOCs, about 10 % - 30 %. The OFPs of VOCs in CNG exhaust is about 13.7 % of E10. Alkenes contribute the most to ozone, and the OFPs of alkenes in CNG and E10 vehicle exhaust accounts for about 55.3 % and 78.8 % of TVOCs respectively. The results of this study are helpful to improve people's understanding of the environmental value of using NG vehicles.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km^-1 veh^-1 and 0.057 ± 0.057 g (kg-fuel)^-1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km^-1 veh^-1 and 10.5 ± 2.52 mg km^-1 veh^-1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8-53.6 %, with 0.32 ± 0.45 mg km^-1 veh^-1 and 0.018 ± 0.020 g (kg-fuel)^-1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km^-1 veh^-1 and 3.06 ± 0.91 mg km^-1 veh^-1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Emissions of air pollutants from non-road construction machinery in Beijing from 2015 to 2019

Construction machinery (CM) is considered to be a significant source of air pollution. The estimation of emissions from CM is essential for policy-makers to control air pollution and reduce carbon emissions. In this study, air pollutant emissions from CM with four emissions standards (Beijing I-IV) in Beijing from 2015 to 2019 were estimated by combining data from the Non-road Mobile Source Emissions Inventory Compiled Technical Guidelines, Motor Vehicle Emission Simulator (MOVES) and actual measurements. Taking an example for 2019, emissions of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NO_x), particulate matter (PM) and carbon dioxide (CO₂) were estimated to be 4.80 Gg, 16.51 Gg, 27.77 Gg, 1.35 Gg and 5.09 Tg, respectively, representing annual mean decreases of 13.2%, 13.1%, 10.8%, 15.2% and 3.5%, respectively, over the five-year period. Tongzhou, Shunyi and Changping Districts contributed 53-67% of total CM emissions in 2019. Among the ten types of CM considered, loaders were the largest contributors to total emissions, accounting for 41-54% of total CM emissions in Beijing from 2015 to 2019. Machinery with a mean power above 75 kW accounted for the largest share (67-78%) of total CM emissions in Beijing from 2015 to 2019. Our results contribute to the limited data of estimated CM emissions and can help develop control strategies to improve air quality and alleviate climate change.

Joint effects of meteorological factors and PM2.5 on age-related macular degeneration: a national cross-sectional study in China

BACKGROUND

Weather conditions are a possible contributing factor to age-related macular degeneration (AMD), a leading cause of irreversible loss of vision. The present study evaluated the joint effects of meteorological factors and fine particulate matter (PM_2.5) on AMD.

METHODS

Data was extracted from a national cross-sectional survey conducted across 10 provinces in rural China. A total of 36,081 participants aged 40 and older were recruited. AMD was diagnosed clinically by slit-lamp ophthalmoscopy, fundus photography, and spectral domain optical coherence tomography (OCT). Meteorological data were calculated by European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and were matched to participants' home addresses by latitude and longitude. Participants' individual PM_2.5 exposure concentrations were calculated by a satellite-based model at a 1-km resolution level. Multivariable-adjusted logistic regression models paired with interaction analysis were performed to investigate the joint effects of meteorological factors and PM_2.5 on AMD.

RESULTS

The prevalence of AMD in the study population was 2.6% (95% CI 2.42-2.76%). The average annual PM_2.5 level during the study period was 63.1 ± 15.3 µg/m³. A significant positive association was detected between AMD and PM_2.5 level, temperature (T), and relative humidity (RH), in both the independent and the combined effect models. For PM_2.5, compared with the lowest quartile, the odds ratios (ORs) with 95% confidence intervals (CIs) across increasing quartiles were 0.828 (0.674,1.018), 1.105 (0.799,1.528), and 2.602 (1.516,4.468). Positive associations were observed between AMD and temperature, with ORs (95% CI) of 1.625 (1.059,2.494), 1.619 (1.026,2.553), and 3.276 (1.841,5.830), across increasing quartiles. In the interaction analysis, the estimated relative excess risk due to interaction (RERI) and the attributable proportion (AP) for combined atmospheric pressure and PM_2.5 was 0.864 (0.586,1.141) and 1.180 (0.768,1.592), respectively, indicating a synergistic effect between PM_2.5 and atmospheric pressure.

CONCLUSIONS

This study is among the first to characterize the coordinated effects of meteorological factors and PM_2.5 on AMD. The findings warrant further investigation to elucidate the relationship between ambient environment and AMD.

Review and prospect of research on road traffic flow energy model

Energy is a necessary prerequisite for the operation of road traffic flow. Describing the phenomena of traffic flow from an energy perspective will promote energy conservation and emission reduction technologies. To clarify the application status and trend of traffic flow energy, this paper studies and summarizes the literature on traffic flow energy, classifies the traffic flow energy models into four groups, discusses the benefits and drawbacks and practical scenarios of various models, and suggests future research topics. The research revealed that the ultra-microscopic traffic flow energy model, based on the principle of vehicle energy flow, has high calculation accuracy but is hard to forecast traffic flow energy, which is adequate for estimating the energy of a single vehicle; The microscopic traffic flow energy model, which can predict traffic flow energy but with low calculation accuracy, considers interactions between fleet vehicles and helps evaluate the energy consumption features of various traffic flow states; The average characteristic parameter of the traffic flow is the key to the macroscopic traffic flow energy model, which calculates energy precisely for the uniform traffic flow but lacks the macroscopic traffic flow theoretical underpinnings; The simplified traffic flow energy model of traffic flow has a straightforward structure but the low calculation accuracy, making it suitable for assessing the effectiveness of the traffic flow model. Large-scale vehicle testing, the thorough integration of macro and micro traffic flow theories, and the decrease of computing complexity should be the main focuses of future research on traffic flow energy models.

Alterations in Spontaneous Brain Activity in Drug-Naïve First-Episode Schizophrenia: An Anatomical/Activation Likelihood Estimation Meta-Analysis

OBJECTIVE

The etiology of schizophrenia is unknown and is associated with abnormal spontaneous brain activity. There are no consistent results regarding the change in spontaneous brain activity of people with schizophrenia. In this study, we determined the specific changes in the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) in patients with drug-naïve first-episode schizophrenia (Dn-FES).

METHODS

A comprehensive search of databases such as PubMed, Web of Science, and Embase was conducted to find articles on resting-state functional magnetic resonance imaging using ALFF/fALFF and ReHo in schizophrenia patients compared to healthy controls (HCs) and then, anatomical/activation likelihood estimation was performed.

RESULTS

Eighteen eligible studies were included in this meta-analysis. Compared to the spontaneous brain activity of HCs, we found changes in spontaneous brain activity in Dn-FES based on these two methods, mainly including the frontal lobe, putamen, lateral globus pallidus, insula, cerebellum, and posterior cingulate cortex.

CONCLUSION

We found that widespread abnormalities of spontaneous brain activity occur in the early stages of the onset of schizophrenia and may provide a reference for the early intervention of schizophrenia.

Applying machine learning to construct braking emission model for real-world road driving

Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM2.5 from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R2 and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furthermore, the CatBoost-based model was interpreted and visualized by an individual conditional expectation (ICE) plot to break the machine learning "black box", with BEI and MC showing nonlinear monotonic increasing relationships with braking emissions. ICE plot also provides viable technical solutions for controlling brake emissions in the future. Both avoiding aggressive braking driving behavior (e.g., the application of smart transportation technologies) and using brake pads with less metal content (e.g., using ceramic brake pads) can effectively reduce brake emissions. The construction of a machine learning-based brake emission model and the white-boxing of its model provide excellent insights for the future detailed assessment and control of brake emissions.

Determining factors and parameterization of brake wear particle emission

Brake wear emission contributes to an increasingly significant proportion of vehicle-related particulate matter, but knowledge of its emission features and determining factors is still highly insufficient. Here, brake dynamometer experiments were conducted under controlled variables tests and real-world driving conditions to systematically investigate brake wear particle (BWP) emission. Compared to the decelerating process, the separating of pads and disc releases more BWPs, accounting for 47-76% of the total PM_2.5 mass. Particle number and mass distributions exhibit bimodal (< 0.01 µm and 0.8-1.2 µm) and unimodal (2-5 µm) patterns, respectively. Larger speed reduction exponentially amplifies BWP emission, and the significant enhancement of nanoparticles is proved to be related to the evaporation of organic constituents in the pads with threshold ranging from 170 °C to 270 °C. Emissions from front and rear brake assemblies don't agree with braking torque distribution, mainly attributive to the different braking pressures. A parameterization scheme for BWP emission based on kinetic energy loss is further established and proved to sufficiently predict the variation of BWP under real-world driving conditions. Being corrected by 1.8th power of the initial speed, the scheme improves the prediction.

Integrated effects of SCR, velocity, and Air-fuel Ratio on gaseous pollutants and CO2 emissions from China V and VI heavy-duty diesel vehicles

Vehicle exhaust, an important source of air pollution, is affected by many factors, including driving conditions, combustion efficiencies, and the usage of emission control devices. In this study, the Portable Emission Measurement System (PEMS) was used to test the emissions from China V and China VI heavy-duty diesel vehicles to evaluate the integrated effects of Selective Catalytic Reduction (SCR), velocity, and air-fuel ratio on carbon dioxide (CO₂) and nitrogen oxide (NO_x) emissions. Our results reveal that the average distance-based CO₂ and CO emission factors at high velocities (50-90 km/h) are 25% and 61% lower than those at low velocities (less than 50 km/h). The use of SCR increases CO₂ emissions in the range of 70-90 km/h (an average increase of 10.9%). In addition, SCR leads to a 55% NO_x emission reduction at low velocities and 89% at high velocities, with an overall average reduction of 84%. We also find that SCR leads to a significant reduction in the correlation between NO_x emissions and air-fuel ratio (0.76 vs 0.47 for China V truck; 0.72 vs 0.05 for China VI truck), but it does not cause a drastic reduction in the correlation coefficients between CO₂ emissions and air-fuel ratio, which can be used to detect whether SCR is working effectively.

Real-world emission characteristics of an ocean-going vessel through long sailing measurement

To quantify the emission characteristics of large ocean-going ships, onboard measurements were carried out for a large ocean-going vessel using portable emission measurement system (PEMS). The emission factors (EFs) of conventional pollutants and volatile organic compounds (VOCs) were greatly influenced by real-world operating conditions and engine loads. The sulfur dioxide (SO₂) and particulate matter (PM) emissions were mainly influenced by fuel type. The particle size distribution basically showed a single peak pattern, with nucleation mode particles as the main particles and the peak particle sizes ranging between 30 nm and 50 nm. The EFs for particle number (PN) ranged from 2.82 × 10¹⁶ to 4.49 × 10¹⁶ #/kwh. Carbonaceous components accounted for approximately 31.8% to 41.6% of the PM. SO₄^2-, NH₄⁺, Ca²⁺, Na⁺, and NO₃^- were dominant in water-soluble ions, while V and Ni were high-concentration metal elements, with the ratio of V: Ni ranging from 0.17 to 0.33. Increase in driving speed can lead to the increase in VOCs emissions. Our study presented a comprehensive test method with PEMS, which provides a reference for acquiring future real-world EFs. However, only one representative ship in China using a specific fuel was selected for the test, so it is important to characterize a broader range of ships and fuels.

Emission inventory and control policy for non-road construction machinery in Tianjin

The establishment of a non-road construction machinery emission inventory forms the basis for the analysis of pollutant emission characteristics and for the formulation of control policy. We analyzed and investigated data on populations, emission factors, and activity levels for the construction machinery in Tianjin to estimate an emission inventory. Finally, a variety of emission reduction scenarios were used to simulate emission reductions and propose the most effective control policy. The results show that total emissions of CO, HC, NOx, PM₁₀, and PM_2.5 from non-road construction machinery in Tianjin of 2018 reached 4180.78, 951.44, 5833.85, 383.92, and 365.70 t, respectively. Forklifts, excavators, and loaders were the three most important emission sources in Tianjin. There are clear differences in the emissions of different districts. Large machinery emissions were mainly distributed across the Binhai New Area, which includes high volumes of port machinery and tractors in Tianjin Port. Based on various emission reduction scenarios, the effect of emission reductions is estimated. The IAD affected the reduction of CO and HC emissions with RR values of 17.6% and 17.3%, respectively, while EMO affected the mitigation of PM₁₀ and PM_2.5 emissions and RR values by 18.0% and 18.4%, respectively. The emission reduction control policy for non-road construction machinery is proposed, including the accelerated updating of non-road machinery emission standards; integrating diesel engine research and development institutions to accelerate the development of vehicle after-treatment technology; and establishing a cooperation mechanism for scientific research institutes, government departments, and enterprises in the control of non-road mobile machinery emissions.

Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: Model development, evaluation and interpretation

The transient simulation of CO₂ and NO_X from motor vehicles has essential applications in evaluating vehicular greenhouse gas emissions and pollutant emissions. However, accurately estimating vehicular transient emissions is challenging due to the heterogeneity between different vehicles and the continuous upgrading of vehicle exhaust purification technology. To accurately characterize the transient emissions of motor vehicles, a Super-learner model is used to build CO₂ and NOx transient emission models. The actual onboard test data of 9 China VI N₂ vehicles were used to train the model, and the test data of another China VI N₂ vehicle were selected for further robustness verification. There were significant differences in the emissions between the vehicles, but the constructed transient model could capture the common law of transient emissions from China VI N₂ vehicles. The R² values of CO₂ and NOx emission in the test data of the validation vehicle were 0.71 and 0.82, respectively. In addition, to further prove the model's robustness, the training data were synchronously modelled based on the Moves-method. The Super-learner model has a smaller RMSE on the validation set than the model based on the Moves-method, indicating that the Super-learner model has more transient simulation advantages. The marginal contributions of the model characteristics to the model results were analysed by SHapley Additive exPlanation (SHAP) value interpretation, and the marginal contributions of different pollutant characteristic parameters varied. Therefore, when establishing transient models of different pollutants, the selection of the model parameters demands considering the generation and purification process of different pollutants. The present work provides novel insights into the parameter selection, construction, and interpretation of the transient vehicle emission model.

Deletion in KRT75L4 linked to frizzle feather in Xiushui Yellow Chickens

Bird feathers are the product of interactions between natural and artificial selection. Feather-related traits are important for chicken selection and breeding. Frizzle feather is characterized by the abnormally development of feathers in chickens. In the current study, frizzle feather characteristics were observed in a local breed called Xiushui Yellow Chicken in Jiangxi, China. To determine the molecular mechanisms that underlie frizzle feather in Xiushui Yellow Chicken, four populations of three breeds (Xiushui Yellow Chicken with frizzle feathers, Xiushui Yellow Chicken with normal feathers, Guangfeng White-Ear Yellow Chicken, and Ningdu Yellow Chicken) were selected for whole-genome resequencing. Using a comparative genome strategy and genome-wide association study, a missense mutation (g.5281494A>G) and a 15-bp deletion (g.5285437-5285451delGATGCCGGCAGGACG) in KRT75L4 were identified as candidate mutations associated with frizzle feather in Xiushui Yellow Chicken. Based on genotyping performed in a large Xiushui Yellow Chicken population, the g.5285437-5285451delGATGCCGGCAGGACG mutation in KRT75L4 was confirmed as the putative causative mutation of frizzle feather. These results deepen the understanding of the molecular mechanisms responsible for frizzle feather, as well as facilitating the molecular detection and selection of the feather phenotype in Xiushui Yellow Chickens.

Refueling emission of volatile organic compounds from China 6 gasoline vehicles

Vehicular refueling emission is a potential source of urban atmospheric volatile organic compounds (VOCs) that is not well understood and controlled. China 6 vehicles have been equipped with the onboard refueling vapor recovery (ORVR) system to cut down refueling emissions, while the emission characteristics and reduction effectiveness are rarely reported. In this study, we conducted laboratory tests to measure the refueling emissions from ten China 6 vehicles and three China 5 vehicles (refueling-emission-uncontrolled, REU) and developed an inventory in a typical middle-sized Chinese city (Langfang) to explore the emission reduction resulted from relevant policies. Compared with headspace vapor and refueling vapor from REU vehicles, the emission profiles for China 6 vehicles are consist of considerably higher proportions of small alkanes and alkenes (C2-C3) and lower proportions of C6-C8 hydrocarbons. Such differences indicate that the headspace vapor profiles are incapable of representing the refueling emission for China 6 vehicles. The market-share-weighting emission factors (EFs) of total hydrocarbons (THCs) and total VOCs for China 6 vehicles are 11.2 mg/L and 6.4 mg/L, respectively, corresponding to control efficiency of approximately 98.8% compared with the REU vehicles. Based on the real-world EFs and the fuel consumption in Langfang, a refueling emission inventory with high spatiotemporal resolution is developed. The total refueling emission of THCs in Langfang is approximately 190.6 tons in 2018 and will likely decline to 25.0 tons in 2035. The implementation of the ORVR will contribute to 90% of the refueling emission reduction in 2035.

Quantitative analysis methods for studying fenestrations in liver sinusoidal endothelial cells. A comparative study

Liver Sinusoidal Endothelial Cells (LSEC) line the hepatic vasculature providing blood filtration via transmembrane nanopores called fenestrations. These structures are 50-300 nm in diameter, which is below the resolution limit of a conventional light microscopy. To date, there is no standardized method of fenestration image analysis. With this study, we provide and compare three different approaches: manual measurements, a semi-automatic (threshold-based) method, and an automatic method based on user-friendly open source machine learning software. Images were obtained using three super resolution techniques - atomic force microscopy (AFM), scanning electron microscopy (SEM), and structured illumination microscopy (SIM). Parameters describing fenestrations such as diameter, area, roundness, frequency, and porosity were measured. Finally, we studied the user bias by comparison of the data obtained by five different users applying provided analysis methods.

An AIS-based emission inventory and the impact on air quality in Tianjin port based on localized emission factors

Ship pollution has become a hot global issue. This study established a basic information database of Tianjin Port ship emissions and used it to screen representative ship types and perform real-world ship measurements by a portable emission measurement system (PEMS), which generated localized emission factors. The results show that the localized emission factors are significantly higher than those recommended in recommended in Chinese guidelines, which will lead to lower calculation results of the previous inventory. A high temporal-spatial ship emission inventory for Tianjin Port was developed using a "bottom-up" method based on automatic identification system (AIS) data by combining localized emission factors. The total estimated ship emissions for SO2, NOX, PM10, PM2.5, THC and CO in 2018 were 1.453 × 10⁴ t, 2.861 × 10⁴ t, 2.04 × 10³ t, 1.82 × 10³ t, 1.13 × 10³ t, and 2.21 × 10³ t, respectively. NOX was the primary pollutant, accounting for 56.9%, followed by SO2 (28.9%). The use of low-sulfur fuel in the port area has significantly reduced the discharge of SO₂ and primary particles. The main channel and anchorage are the areas with the highest emission intensity. The intermonth ship emissions varied according to the ship activity, lowest in February and highest in May. The contribution of cargo transportation vessels to various pollutant emissions is more than 60%. Main engines (MEs) were the largest source of emissions, followed by auxiliary engines (AEs). NOX and SOX from ships have the greatest impact on the air quality in the surrounding area, especially in summer and autumn, as analyzed by the atmospheric dispersion modeling system (ADMS) model. Our research will update localized emission factors and inventories and evaluate the impact of ship emissions on air quality.

Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test

The on-road remote sensing test was conducted in Zhengzhou to obtain a large dataset of ammonia emissions from in-use vehicles. The ammonia emission characteristics and high-emitter vehicles of different manufacture years, vehicles with different emission standards, and vehicles with different types of other fuel vehicles were analysed. The results show that the average ammonia emission concentration obtained through remote sensing tests fluctuated after the initial reduction. The ammonia emission factors generally range from 0.30 to 0.47 g/kg, 0.34-0.50 g/kg and 0.29-0.60 g/kg for gasoline vehicles, diesel vehicles and other fuel vehicles respectively. Improving the emission standards of new vehicles has a direct role in reducing exhaust pollution from in-use vehicles. However, after the China III emission standard, the ammonia emission level showed a stable trend and no obvious downward trend. The distributions of ammonia emission rates were highly skewed as the dirtiest 10% of vehicles emitted much higher emissions than other vehicles. In the group with the highest emissions, the emissions from other fuel vehicles were lower than those from gasoline and diesel vehicles. However, the percentage of high-emitters decreased with newer emission standards for vehicles. The results indicate that remote sensing test technology will be very effective in screening vehicles with high ammonia emissions. However, some clean vehicles can be exempted from annual inspection through remote sensing test technology. Finally, based on the comprehensive analysis of big data from remote sensing, the ammonia emissions of diesel vehicles and other fuel vehicles cannot be ignored.

Vehicular non-exhaust particulate emissions in Chinese megacities: Source profiles, real-world emission factors, and inventories

Vehicular non-exhaust emissions account for a significant share of atmospheric particulate matter (PM) pollution, but few studies have successfully quantified the contribution of non-exhaust emissions via real-world measurements. Here, we conduct a comprehensive study combining tunnel measurements, laboratory dynamometer and resuspension experiments, and chemical mass balance modeling to obtain source profiles, real-world emission factors (EFs), and inventories of vehicular non-exhaust PM emissions in Chinese megacities. The average vehicular PM_2.5 and PM₁₀ EFs measured in the four tunnels in four megacities (i.e., Beijing, Tianjin, Zhengzhou, and Qingdao) range from 8.8 to 16.0 mg km^-1 veh^-1 and from 37.4 to 63.9 mg km^-1 veh^-1, respectively. A two-step source apportionment is performed with the information of key tracers and localized profiles of each exhaust and non-exhaust source. Results show that the reconstructed PM₁₀ emissions embody 51-64% soil and cement dust, 26-40% tailpipe exhaust, 7-9% tire wear, and 1-3% brake wear, while PM_2.5 emissions are mainly composed of 59-80% tailpipe exhaust, 11-31% soil and cement dust, 4-10% tire wear, and 1-5% brake wear. Fleet composition, road gradient, and pavement roughness are essential factors in determining on-road non-exhaust emissions. Based on the EFs and the results of source apportionment, we estimate that the road dust, tire wear, and brake wear emit 8.1, 2.5, and 0.8 Gg year^-1 PM_2.5 in China, respectively. Our study highlights the importance of non-exhaust emissions in China, which is essential to assess their impacts on air quality, human health, and climate and formulating effective controlling measures.

Traffic-related metrics and adverse birth outcomes: A systematic review and meta-analysis

Given the inconsistency of epidemiologic evidence for associations between maternal exposures to traffic-related metrics and adverse birth outcomes, this manuscript aims to provide clarity on this topic. Pooled meta-estimates were calculated using random-effects analyses. Subgroup analyses were conducted by study area, study design, and Newcastle-Ottawa quality score (NOS). Funnel plots and Egger's test were conducted to evaluate the publication bias, and Fail-safe Numbers (Fail-safe N) were measured to evaluate the robustness of models. From the initial 740 studies (last search, July 11, 2019), 26 studies were included in our analysis. The pooled odds ratio for the change in small for gestational age associated with per 500 m decrease in the distance to roads was 1.016 (95% CI: 1.004, 1.029). Subgroup analyses revealed significant positive associations between term low birth weight and traffic density in higher-quality literatures with higher NOS [1.060 (95% CI: 1.002, 1.121)], cohort studies [1.020 (95% CI: 1.006, 1.033)], and studies in North America [1.018 (95% CI: 1.005, 1.131)]. The buffer of traffic density made no difference in the effect size. Traffic density seemed to be a better indicator of traffic pollution than the distance to roads.

Emission characteristics and temporal variation of PAHs and their derivatives from an ocean-going cargo vessel

Polycyclic aromatic hydrocarbons (PAHs), nitro- (NPAHs) and oxy-derivatives (OPAHs) are of considerable concern due to their toxicity and carcinogenic hazards. Ships are recognized as an important emission source of these compounds. Marine diesel oil (MDO) and heavy fuel oil (HFO) are the two most commonly used fuels. The emission characteristics and toxicities of PM_2.5-bound PAHs, NPAHs and OPAHs due to HFO and MDO combustion in atypical ocean-going vessel were investigated. The EF variability of polycyclic aromatic compounds (PACs) varied considerably with the fuel formulation (HFO and MDO) and engine loading (20%-100%). The concentration of ΣPACs was 0.63 mg/kWh for MDO and ranged from 2.14 to 9.80 mg/kWh for HFO. Compared to HFO-20%, the EFs of ΣPAHs, ΣNPAHs and ΣOPAHs from MDO-20% were reduced by 97%, 77% and 73%, respectively. As identified through the coefficient of divergence, the profile of HFO-20% was notably different from those under the other three engine loadings for HFO. In addition, the emissions of ΣPAHs and ΣOPAHs showed a significant correlation with PM_2.5, while they were relatively weak for ΣNPAHs. However, the CO and PAC emissions were not highly correlated. Furthermore, the BaP_eq-ΣPAHs values were 0.010 mg/g for MDO and ranged from 0.092 mg/g to 0.306 mg/g for HFO, and the reduction ranged from 89% to 97% by substituting MDO for HFO. These data highlight the importance of improving fuel quality in close proximity to port areas and are useful for enhancing relevant databases.

Validation of PM2.5 model particle through physicochemical evaluation and atherosclerotic plaque formation in ApoE-/- mice

PM_2.5 particles are regarded as prominent risk factors that contribute to the development of atherosclerosis. However, the composition of PM_2.5 is rather complicated. This study aimed to provide a model particle that simulates the behavior of actual PM_2.5, for subsequent use in exploring mechanisms and major complications arising from PM_2.5. To establish model particles of PM_2.5, a series of monodisperse SiO₂ microspheres with different average grain diameters were mixed according to the size distribution of actual PM_2.5. The organic carbon (OC) was removed from PM_2.5 and coated onto the SiO₂ model particle, to formulate simulant PM_2.5. Results showed that the size distribution of the model particle was highly approximate to that of the PM_2.5 core. The polycyclic aromatic hydrocarbon (PAHs) composition profile of the simulated PM_2.5 were approximate to PM_2.5, and loading efficiency was approximately 80%-120%. Furthermore, compared to the control, SiO₂-only model particle had negligible cytotoxicity on cell viability and oxidative stress of HUVECs, and marginal effect on the lipid metabolism and atherosclerotic plaque formation in ApoE^-/- mice. In contrast, simulated PM_2.5 exhibited similar cytotoxic and detrimental effects on lipid metabolism and atherosclerotic plaque formation with actual PM_2.5. Traffic-related PM_2.5 had negative effects on endothelial function and led to the formation of atherosclerosis via oxidative stress. The simulated PM_2.5 simulated the outcomes of actual PM_2.5 exposure. Here, we show that SiO₂ particle model cores coated with OC could significantly assist in the evaluation of the effects of specific organic compositions bound on PM_2.5, specifically in the context of environmental health and safety.

Vehicle emissions in a middle-sized city of China: Current status and future trends

Vehicle emissions are regarded as an important contributor to urban air pollution in China and most previous studies focused on megacities. However, the vehicle pollution in middle-sized cities becomes more severe due to the increasing vehicle population (VP) and lagged control policy. This study takes Langfang, a typical middle-sized city bordered by two megacities (Beijing and Tianjin), as the target domain to investigate vehicle emissions. The speed correction curves (SCC) are introduced to improve the vehicle emission factors (EF) simulation in official technical guidelines on emission inventory (GEI). A multi-year vehicle emission inventory (from 2011 to 2025) is developed in Langfang. From 2011 to 2017, the total vehicle emissions in Langfang decrease for carbon monoxide (CO), but increase for volatile organic compounds (VOCs), nitrogen oxides (NO_x), and inhalable particles (PM₁₀), respectively. From 2018 to 2025, the emissions would increase more rapidly in Langfang than in Beijing and Tianjin, indicating the middle-sized cities may become a significant contributor to air pollution in China. Four possible control policies, including VP constrained (VPC), public transportation promotion (PTP), new energy vehicles promotion (NEP), and freight transportation structure optimization (FTO) are evaluated. The most significant emissions reductions are observed under the FTO for CO, NO_x, and PM₁₀, and under the VPC for VOCs. The spatial distributions of vehicle emissions show a high order of heterogeneity, indicating that local conditions should be considered in policy formulation in addition to national consistency. More comprehensive policies should be implemented to mitigate the vehicle pollution in middle-sized cities.