Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM₂.₅) in high-density cities, few studies have revealed the vertical differences in PM₂.₅ exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM₂.₅ exposure that combines a high-resolution LUR model with a vertical PM₂.₅ variation model to investigate the results of horizontal and vertical mobile PM₂.₅ monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM₂.₅ variation, respectively. Vertical measurements of PM₂.₅ from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM₂.₅ changes at a certain height relative to the ground. The vertical variation model for PM₂.₅ was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM₂.₅ exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM₂.₅, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Engine start-stop (S&S) technology has been substantially incorporated into modern vehicles to save fuel during idling in congested urban areas because fuel economy regulations have become more stringent. However, the potential for increasing particle emissions after engine restarts, especially in cold environments, is of great concern. To investigate the effects of S&S systems on fuel consumption and tailpipe emissions, a chassis dynamometer was employed to measure the fuel consumption, particulate matter (PM), solid particle number (PN), particle number size distribution and black carbon (BC) for a typical gasoline direct injection vehicle when the S&S was on (S&S-on) and when the S&S was off (S&S-off) according to the worldwide harmonized light-duty test cycle in both hot (28 °C) and cold (5 °C) environments. S&S operation resulted in 3.1–4.3% fuel-savings at 28 °C but had a tendency to increase particulate emissions, especially of BC (21.8–31.8%) and PM (19.2–32.8%). Although PN emissions with S&S-on over the entire cycle were slightly lower than those with S&S-off, more particles were emitted during the engine restart moments. In a cold environment, the fuel-savings advantage of the S&S system was weakened, and the negative impacts on the particle emissions during the restart moment worsened. The S&S system resulted in higher abundances of accumulation mode particles, especially under cold ambient conditions. The relationship between the PN reduction rates and idling segments was determining to be exponential. Our results indicate that the S&S system, which may increase particle emissions during restarts, does save fuel, and that a comprehensive evaluation of the system in cold environments is needed to determine the serviceability of new engine technologies and after-treatments.

To better evaluate the potential risks of aquatic polycyclic aromatic compounds (PACs), multiple approaches have been implemented in this study to assess the human health and ecological risks of parent, nitrated and oxygenated polycyclic aromatic hydrocarbons (PAHs, NPAHs and OPAHs) in the surface water of Jialing and Yangtze Rivers in downtown Chongqing in southwestern China. The concentrations of ∑PAHs (334 ± 125 ng L⁻¹) were much higher than those of ∑OPAHs (20.2 ± 7.49 ng L⁻¹) in the two rivers, while NPAHs were barely detected. Concentrations of detected PACs were higher in wet season than dry season, probably resulted from the elevated particle input due to heavy rainfall in wet season. Concentrations of PAHs were higher in the particulate phase than dissolved phase, while OPAHs levels showed a reverse pattern. The partition coefficients (Kₚ) of PACs in the water-SPM (suspended particulate matter) system were mainly affected by SPM concentrations and octanol/water partition coefficients of specific PACs. Human health risks calculated from non-probabilistic risk assessment model and probabilistic risk assessment model based on Monte Carlo simulation showed similar data pattern with slight difference in absolute values. Both models revealed potential or even severe human health risks contributed mainly by dermal exposure to aquatic PACs in this study. Furthermore, these models also manifested that infant stage was highly sensitive for PAC exposure. Sensitivity analysis indicated that health risk results was most sensitive to Benzo[a]pyrene equivalent toxic concentration (BaPₑq), followed by showering time and daily water intake volume. Levels of ecological risks and contributions of individual PACs differed from models based on different quality values. The adequacy of toxicity data was crucial for the reliability of ecological risk assessment.

Particulate-filtering respirators (PFRs) have been recommended as a practical personal-level intervention to protect individuals from the health effects of particulate matter exposure. However, the cardiovascular benefits of PFRs including improvements in key surrogate endpoints remain unclear. We performed a systematic review and meta-analysis of randomized studies (wearing versus not wearing PFRs) reporting the effects on blood pressure (BP) and heart rate variability (HRV). The search was performed on January 3, 2022 to identify published papers until this date. We queried three English databases, including PubMed, Web of Science Core Collection and Scopus. Of 527 articles identified, eight trials enrolling 312 participants (mean age ± standard deviation: 36 ± 19.8; 132 female) met our inclusion criteria for analyses. Study participants wore PFRs from 2 to 48 h during intervention periods. Wearing PFRs was associated with a non-significant pooled mean difference of −0.78 mmHg (95% confidence interval [CI]: −2.06, 0.50) and −0.49 mmHg (95%CI: −1.37, 0.38) in systolic and diastolic BP (SBP and DBP). There was a marginally significant reduction of mean arterial pressure (MAP) by nearly 1.1 mmHg (95%CI: −2.13, 0.01). The use of PFRs was associated with a significant increase of 38.92 ms² (95%CI: 1.07, 76.77) in pooled mean high frequency (power in the high frequency band (0.15–0.4 Hz)) and a reduction in the low (power in the low frequency band (0.04–0.15Hz))-to-high frequency ratio [−0.14 (95%CI: −0.27, 0.00)]. Other HRV indices were not significantly changed. Our meta-analysis demonstrates modest or non-significant improvements in BP and many HRV parameters from wearing PFRs over brief periods. However, these findings are limited by the small number of trials as well as variations in experimental designs and durations. Given the mounting global public health threat posed by air pollution, larger-scale trials are warranted to elucidate more conclusively the potential health benefits of PFRs.

Recently, increasing attention has been paid to antibiotic resistance genes (ARGs) in urban stormwater runoff. However, there were little data on the diversity and distribution of ARGs associated with road sediments transported in runoff. The investigation of ARGs diversity showed that sulfonamide resistance genes (sul2 and sul3) occupied 61.7%–82.3% of total ARGs in runoff. The analysis of ARGs distribution in particulate matter (PM) implied that both tetQ and trbC existed mainly in PM with size of 150–300 μm, but other ARGs and mobile genetic elements (MGEs) were dominant in PM with size <75 μm. The discussion of potential hosts indicated that target genes (ermF, blaOXA1/blaOXA30, ermC, qnrA, sul2, tnpA-01, intI2, tetW, intI1, sul3, trbC) had the strongest subordinate relationship with Proteobacteria at phylum level and Enterobacter at genus level. The effect evaluation of ARGs distribution suggested that 13 kinds of ARGs were positively correlated with Pr/PS and Zeta potential, resulting in the more ARGs in PM with smaller size (<75 μm).

In this study, the spatiotemporal variabilities and characteristics of ozone (O₃) and fine particulate matter (PM₂.₅) were reconstructed, and the interaction between meteorological conditions and the co-occurrence of O₃ and PM₂.₅ in Zhuhai, a city in the Pearl River Delta (China), was analysed. The vertical distributions of lower tropospheric O₃, aerosol extinction coefficient, and wind velocity were measured using a ground-based LiDAR system. The diurnal variations in air pollutant concentrations and meteorological conditions at ground level were examined from 28 November to December 8, 2020 considering the weather conditions in Zhuhai. Heavy pollution episodes with increased concentrations of O₃ and PM₂.₅ were observed from 6 to 7 December after a period of cold air invasion. The maximum hourly average concentrations of O₃ and PM₂.₅ at the ground level reached up to 190 μg/m³, 98 μg/m³, respectively. The horizontal wind speed rapidly decreased to less than 2 m/s during the heavy pollution episodes driven by O₃ and PM₂.₅, whereas the vertical wind velocity was dominated by the downdraught. When the large-scale synoptic winds were weak, a strengthening sea breeze in the afternoon could promote the landward propagation of warm marine air masses, and a lower surface wind speed was driven by the convergence of cold air from the north and warm air from the south. In turn, this increased the residence time of air pollutants and promoted their conversion to secondary pollutants. Regarding the pollution sources, the results indicated that the Pearl River Estuary represented a ‘pool’ of O₃ and PM₂.₅ pollution. In addition, the contribution of regional pollutant transport could not be ignored when considering the accumulative increase in air pollution. Overall, the relatively weak synoptic winds, low mixing height, and high generation of pollution around Zhuhai collectively resulted in high concentrations of O₃ and PM₂.₅.

As a widely used pure elemental carbon in colloidal particles, carbon black was listed as a group 2B carcinogen by IARC in 2010. The most available mechanism information about carbon black and carcinogenesis are from in vivo or in vitro studies. However, few studies concerned the nanoparticle's real-ambient exposure causing systemic change and further affecting the target organ. Herein, we used an ex vivo biosensor assay to investigate the transcriptome change of primary bronchial epithelial cells after treatment with the plasma from workers with long-term occupational carbon black exposure history. Based on ex vivo biosensor assay and transcriptome sequencing, we found the effect of internal systemic environment on epithelial cells after carbon black exposure was an inflammatory response, which mainly activates cell cycle-related pathways. After exposure to carbon black, the internal systemic environment could activate cancer-related pathways like epithelial-mesenchymal transition, hypoxia, TNF-α signaling via NF-κB. The hub genes in the carbon black group (CDC20 and PLK1) and their correlation with the systemic environment were uncovered by constructing the protein-protein interaction network. Inflammatory cytokines, especially CRP, were strongly correlated with the expression of CDC20 and PLK1. Besides, we also find a strong correlation between CDC20 and cytokinesis-block micronucleus endpoints in peripheral blood (rho = 0.591, P < 0.001). Our results show that long-term carbon black exposure might activate cell cycle-related pathways through circulating inflammation and increase the risk of cancer, while the oxidative stress caused by diesel exhaust particles are mainly related to PAHs exposure. After exposure to carbon black, the systemic environment could activate cancer-related pathways like diesel exhaust particles, increasing the risk of lung cancer. These attempts might provide a further understanding of the indirect effect of chronic occupational inhaled carbon black exposure on pulmonary carcinogenesis.

Particulate Matter (PM₂.₅) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM₂.₅ concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM₂.₅ concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM₂.₅ concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM₂.₅ concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM₂.₅ concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM₂.₅ pollution in China from a cross-provincial collaboration perspective.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

Airborne fine particles can affect climate change and human health; moreover, they can be transported over significant distances. However, studies on characteristics of individual particles and their morphology, elemental composition, aging processes, and spatial distribution after long-range transport over the Yellow Sea are limited. Therefore, in this study, we conducted shipborne measurements of fine particulate matter of less than 2.5 μm in diameter (PM₂.₅) over the Yellow Sea and classified the individual particles into seven types based on their morphology and composition. Overall, the percentage of organic-rich particles was the highest, followed by that of sea spray, sulfur-rich, dust, metals, fly ash, soot, and other particles. Near Shandong, China, the percentage of fly ash and sulfur-rich particles increased, while an increased percentage of only sulfur-rich particles was observed near the Korean Peninsula. In the open sea, the PM₂.₅ concentrations were the lowest, and sea spray particles predominated. During the cruises, three types (Types 1, 2, and 3) of events with substantially increased PM₂.₅ concentrations occurred, each with different dominant particles. Type 1 events frequently featured air masses from northern China and Mongolia with high wind speeds and increased dust particles. Type 2 events involved air masses from China with high wind speeds; fly ash, soot, organic-rich particles, and the sulfate percentage in PM₂.₅ increased. Type 3 events displayed stagnant conditions and local transport (from Korea); soot, dust particles, and the secondary sulfate and nitrate percentages in PM₂.₅ increased. Thus, different types of transport affected concentrations and dominant types of fine particles over the Yellow Sea during spring.