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● A single particle observation was conducted in a high traffic flow road environment. ● Major particle types were vehicle exhausts, coal burning, and biomass burning. ● Contribution of non-exhaust emissions was calculated via PMF. ● Proportion of non-exhaust emissions can reach 10.1 % at road environment. A single particle aerosol mass spectrometer (SPAMS) was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment. The PM 2.5, black carbon, meteorological parameters and traffic flow were recorded during the test period. The daily trend for traffic flow and speed on TEDA Street showed obvious \"M\" and \"W\" characteristics. 6.3 million particles were captured via the SPAMS, including 1.3 million particles with positive and negative spectral map information. Heavy Metal, High molecular Organic Carbon, Organic Carbon, Mixed Carbon, Elemental Carbon, Rich Potassium, Levo-rotation Glucose, Rich Na, SiO 3 and other categories were analyzed. The particle number concentration measured by SPAMS showed a good linear correlation with the mass concentrations of PM 2.5 and BC, which indicates that the particulate matter captured by the SPAMS reflects the pollution level of fine particulate matter. EC, ECOC, OC, HM and crustal dust components were found to show high values from 7:00-9:00 AM, showing that these chemical components are directly or indirectly related to vehicle emissions. Based on the PMF model, 7 major factors are resolved. The relative contributions of each factor were determined: vehicle exhaust emission (44.8 %), coal-fired source (14.5 %), biomass combustion (12.2 %), crustal dust (9.4 %), ship emission (9.0 %), tires wear (6.6 %) and brake pads wear (3.5 %). The results show that the contribution of vehicle non-exhaust to particulate matter at roadside environment is approximately 10.1 %. Vehicle non-exhaust emissions are the focus of future research in the vehicle pollutant emission control field.

For earthquake-damaged reinforced concrete structures, the static loading test method is adopted to carry out loading tests under cyclic loading on reinforced concrete beams with different reinforcement rates and columns with different axial compression ratios, and the effect of reinforcing and repairing the damaged reinforced concrete structure by using adhesive steel plates and carbon fiber cloth is investigated. Through comparative studies of structural hysteresis curves and skeleton curves under different reinforcement methods, it is concluded that reinforcement has significantly improved the hysteresis characteristics and ductility of members, and the seismic performance of the carbon fiber reinforcement method is better than that of the steel plate reinforcement. This study provides valuable references and suggestions for practical earthquake-damaged building reinforcement and repair works and seismic reinforcement works.

Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM 2.5 ) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM 2.5 with the highest performance from ML. The trends of PM 2.5 showed insignificant differences ( p  > 0.05) for both the emission-related ( PM 2.5 EMI ) and meteorology-related components between TSM, ML, and CMAQ modeling results. PM 2.5 EMI estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM 2.5 . Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM 2.5 .

Understanding aerosol–cloud–climate interactions in the Arctic is key to predicting the climate in this rapidly changing region. Whilst many studies have focused on submicrometer aerosol (diameter less than 1 µm), relatively little is known about the supermicrometer aerosol (diameter above 1 µm). Here, we present a cluster analysis of multiyear (2015–2019) aerodynamic volume size distributions, with diameter ranging from 0.5 to 20 µm, measured continuously at the Gruvebadet Observatory in the Svalbard archipelago. Together with aerosol chemical composition data from several online and offline measurements, we apportioned the occurrence of the coarse-mode aerosols during the study period (mainly from March to October) to anthropogenic (two sources, 27 %) and natural (three sources, 73 %) origins. Specifically, two clusters are related to Arctic haze with high levels of black carbon, sulfate and accumulation mode (0.1–1 µm) aerosol. The first cluster (9 %) is attributed to ammonium sulfate-rich Arctic haze particles, whereas the second one (18 %) is attributed to larger-mode aerosol mixed with sea salt. The three natural aerosol clusters were open-ocean sea spray aerosol (34 %), mineral dust (7 %) and an unidentified source of sea spray-related aerosol (32 %). The results suggest that sea-spray-related aerosol in polar regions may be more complex than previously thought due to short- and long-distance origins and mixtures with Arctic haze, biogenic and likely blowing snow aerosols. Studying supermicrometer natural aerosol in the Arctic is imperative for understanding the impacts of changing natural processes on Arctic aerosol.

Black carbon (BC) significantly influences climate, air quality, and public health, and long-term observations are essential for understanding its adverse effects. While previous studies have primarily focused on spatiotemporal variations, deeper insights from such datasets remain uncovered. Using 13 years (2008–2020) of continuous measurements of equivalent black carbon (eBC) in China, this study reported the spatial-temporal variations of eBC and its sources, including solid fuel (eBCsf) and liquid fuel combustion (eBClf). The results showed that eBC and its sources exhibited higher concentrations in eastern and northern China compared to western and southern China. Seasonal variations of eBC and eBCsf generally showed lower values during summer and higher values during winter at most stations. Long-term trends indicated that eBC and eBClf decreased most rapidly at urban stations, while eBCsf declined faster at rural stations. Comparisons of eBC concentrations and trends between this study and global observations revealed higher eBC levels but lower reduction rates in China. These long-term observations showed that the model simulations performed well in simulating spatial distribution but poorly in capturing inter-annual variations. The weather-normalized eBC concentrations showed potential for adjusting emission estimates. The normalized results also suggested that emission control was the dominant driver of the BC reduction. This decrease was primarily driven by reductions from solid fuel combustion at rural and background stations. This study provides insights for reducing uncertainties in black carbon emission inventories and improving model performance in simulating surface concentrations.

Although calcium is known to be enriched in sea spray aerosols (SSAs), the factors that affect its enrichment remain ambiguous. In this study, we examine how environmental factors affect the distribution of water-soluble calcium (Ca2+) distribution in SSAs. We obtained our dataset from observations taken during the R/V Xuelong research cruise in the Ross Sea, Antarctica, from December 2017 to February 2018. Our observations showed that the enrichment of Ca2+ in aerosol samples was enhanced under specific conditions, including lower temperatures (<-3.5 ∘C), lower wind speeds (<7 m s−1), and the presence of sea ice. Our analysis of individual particle mass spectra revealed that a significant portion of calcium in SSAs was likely bound with organic matter (in the form of a single-particle type, OC-Ca, internally mixed organics with calcium). Our findings suggest that current estimations of Ca2+ enrichment based solely on water-soluble Ca2+ may be inaccurate. Our study is the first to observe a single-particle type dominated by calcium in the Antarctic atmosphere. Our findings suggest that future Antarctic atmospheric modeling should take into account the environmental behavior of individual OC-Ca particles. With the ongoing global warming and retreat of sea ice, it is essential to understand the mechanisms of calcium enrichment and the mixing state of individual particles to better comprehend the interactions between aerosols, clouds, and climate during the Antarctic summer.

The Arctic is a rapidly changing ecosystem, with complex ice–ocean–atmosphere feedbacks. An important process is new particle formation (NPF), from gas-phase precursors, which provides a climate forcing effect. NPF has been studied comprehensively at different sites in the Arctic, ranging from those in the High Arctic and those at Svalbard to those in the continental Arctic, but no harmonised analysis has been performed on all sites simultaneously, with no calculations of key NPF parameters available for some sites. Here, we analyse the formation and growth of new particles from six long-term ground-based stations in the Arctic (Alert, Villum, Tiksi, Zeppelin Mountain, Gruvebadet, and Utqiaġvik). Our analysis of particle formation and growth rates in addition to back-trajectory analysis shows a summertime maxima in the frequency of NPF and particle formation rate at all sites, although the mean frequency and particle formation rates themselves vary greatly between sites, with the highest at Svalbard and lowest in the High Arctic. The summertime growth rate, condensational sinks, and vapour source rates show a slight bias towards the southernmost sites, with vapour source rates varying by around an order of magnitude between the northernmost and southernmost sites. Air masses back-trajectories during NPF at these northernmost sites are associated with large areas of sea ice and snow, whereas events at Svalbard are associated with more sea ice and ocean regions. Events at the southernmost sites are associated with large areas of land and sea ice. These results emphasise how understanding the geographical variation in surface type across the Arctic is key to understanding secondary aerosol sources and providing a harmonised analysis of NPF across the Arctic.

Icelandic dust can impact the radiative budget in high-latitude regions directly by affecting light absorption and scattering and indirectly by changing the surface albedo after dust deposition. This tends to produce a positive radiative forcing. However, the limited knowledge of the spectral optical properties of Icelandic dust prevents an accurate assessment of these radiative effects. Here, the spectral single scattering albedo (SSA) and the complex refractive index (m=n-ik) of Icelandic dust from five major emission hotspots were retrieved between 370–950 nm using online measurements of size distribution and spectral absorption (βabs) and scattering (βsca) coefficients of particles suspended in a large-scale atmospheric simulation chamber. The SSA(λ) estimated from the measured βabs and βsca increased from 0.90–0.94 at 370 nm to 0.94–0.96 at 950 nm in Icelandic dust from the different hotspots, which falls within the range of mineral dust from northern Africa and eastern Asia. The spectral complex refractive index was retrieved by minimizing the differences between the measured βabs and βsca and those computed using the Mie theory for spherical and internally homogeneous particles, using the size distribution data as input. The real part of the complex refractive index (n(λ)) was found to be 1.60–1.61 in the different samples and be independent of wavelength. The imaginary part (k(λ)) was almost constant with wavelength and was found to be around 0.004 at 370 nm and 0.002–0.003 at 950 nm. The estimated complex refractive index was close to the initial estimates based on the mineralogical composition, also suggesting that the high magnetite content observed in Icelandic dust may contribute to its high absorption capacity in the shortwave part of the spectrum. The k(λ) values retrieved for Icelandic dust are at the upper end of the reported range for low-latitude dust (e.g., from the Sahel). Furthermore, Icelandic dust tends to be more absorbing towards the near-infrared. In Icelandic dust, k(λ) between 660–950 nm was 2–8 times higher than most of the dust samples sourced in northern Africa and eastern Asia. This suggests that Icelandic dust may have a stronger positive direct radiative forcing on climate that has not been accounted for in climate predictions.

Water-soluble inorganic ions such as ammonium, nitrate and sulfate are major components of fine aerosols in the atmosphere and are widely used in the estimation of aerosol acidity. However, different experimental practices and instrumentation may lead to uncertainties in ion concentrations. Here, an intercomparison experiment was conducted in 10 different laboratories (labs) to investigate the consistency of inorganic ion concentrations and resultant aerosol acidity estimates using the same set of aerosol filter samples. The results mostly exhibited good agreement for major ions Cl−, SO42-, NO3-, NH4+ and K+. However, F−, Mg2+ and Ca2+ were observed with more variations across the different labs. The Aerosol Chemical Speciation Monitor (ACSM) data of nonrefractory SO42-, NO3- and NH4+ generally correlated very well with the filter-analysis-based data in our study, but the absolute concentrations differ by up to 42 %. Cl− from the two methods are correlated, but the concentration differ by more than a factor of 3. The analyses of certified reference materials (CRMs) generally showed a good detection accuracy (DA) of all ions in all the labs, the majority of which ranged between 90 % and 110 %. The DA was also used to correct the ion concentrations to showcase the importance of using CRMs for calibration check and quality control. Better agreements were found for Cl−, SO42-, NO3-, NH4+ and K+ across the labs after their concentrations were corrected with DA; the coefficient of variation (CV) of Cl−, SO42-, NO3-, NH4+ and K+ decreased by 1.7 %, 3.4 %, 3.4 %, 1.2 % and 2.6 %, respectively, after DA correction. We found that the ratio of anion to cation equivalent concentrations (AE / CE) and ion balance (anions–cations) are not good indicators for aerosol acidity estimates, as the results in different labs did not agree well with each other. In situ aerosol pH calculated from the ISORROPIA II thermodynamic equilibrium model with measured ion and ammonia concentrations showed a similar trend and good agreement across the 10 labs. Our results indicate that although there are important uncertainties in aerosol ion concentration measurements, the estimated aerosol pH from the ISORROPIA II model is more consistent.

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training ( n  = 410), internal validation ( n  = 298), and external validation cohorts ( n  = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) ( P  < 0.001), the proportion of positive SLNs ( P  = 0.029), lymph-vascular invasion ( P  = 0.029), perineural invasion ( P  = 0.023), and estrogen receptor (ER) status ( P  = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676–0.785) for the training, 0.701 (95% CI 0.630–0.773) for internal validation, and 0.813 (95% CI 0.734–0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.