Traffic contributes to fine particulate matter (PM₂.₅) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM₂.₅ emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM₂.₅ over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM₂.₅, increasing at 6.1% year⁻¹, more than twice that of EC (2.9% year⁻¹). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM₂.₅ level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM₂.₅ concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM₂.₅ concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM₂.₅ concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 μ g/m³). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM₂.₅ concentration to the standard (35 μ g/m³). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM₂.₅, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.

Reliable long-horizon PM₂.₅ forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM₂.₅ forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM₂.₅ forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM₂.₅ simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM₂.₅ forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM₂.₅ forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM₂.₅) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002–1.006) and 0.3% (95% CI: 1.001–1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM₂.₅ and PM₁₀ were 2.2% (95% CI: 1.012–1.032) and 2.2% (95% CI: 1.012–1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Organic aerosol (OA) emitted from biomass burning (BB) impacts air quality and global radiation balance. However, the comprehensive characterization of OA remains poorly understood because of the complex evolutionary behavior of OA in atmospheric processes. In this work, smoke particles were generated from rice straw combustion. The effect of OH radicals photooxidation on size distribution, light absorption, and molecular compositions of smoke particles was systematically investigated. The results showed that the median diameters of smoke particles increased by a factor of approximately 1.2 after photooxidation. In the particle compositions, although both non-polar fractions (n-hexane-soluble organic carbon, HSOC) and polar fractions (water-soluble organic carbon, WSOC) underwent photobleaching after aging, the photobleaching properties of HSOC (1.87–2.19) was always higher than that of WSOC (1.52–1.33). Besides, the light-absorbing properties of HSOC were higher than that of WSOC, showing a factor of approximately 1.75 times for mass absorption efficiency at 365 nm (MAE₃₆₅). Consequently, the simple forcing efficiency (SFE) caused by absorption showed that HSOC has higher radiation effects than WSOC. After photooxidation, the concentration of 16 PAHs in HSOC fractions significantly decreased by 15.3%–72.5%. In WSOC fractions, the content of CHO, CHONS, and CHOS compounds decreased slightly, while the content of CHON compounds increased. Meantime, the variations in molecular properties supported the decrease in light absorption of WSOC fractions. These results reveal the aging behavior of smoke particles, then stress the importance of non-polar organic fractions in particles, providing new insights into understanding the atmospheric pollution caused by BB smoke particles.

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) – an episode with simultaneously high levels of air pollution and temperature – which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅) and ozone (O₃) for June to October over 20 years (2000–2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000–2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010–2019). The northern cities in the PRD region had higher levels of PM₂.₅ and O₃ than their southern counterparts. During HPEs, regional daily PM₂.₅ exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O₃ exceeded the WHO O₃ standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357–623,770) and 52,231 (95%CI: 26,116–78,346) excessive deaths were respectively attributable to exposure to PM₂.₅ and O₃ in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

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.

With the continued increase of global ammonia emission, the damage to human or animal caused by ammonia pollution has attracted wide attention. The noncoding RNAs have been reported to regulate a variety of biological processes under different environmental stimulation via ceRNA (competing endogenous RNA) networks. Autophagy is a hallmark of tissue damage from air pollution. However, the specific role of circular RNAs (circRNAs) in the injury of intestinal tissue caused by autophagy remains unclear. Here, we established 42-days old ammonia-exposed broiler models and observed that autophagy flux in broiler jejunum was activated under ammonia exposure. Meanwhile, a total of eight significantly dysregulated expressed circRNAs were obtained and a circRNAs-miRNAs-genes interaction networks were constructed by bioinformatics analysis. Furthermore, an axis named circRNA-IGLL1/miR-15a/RNF43 was predicted to participate in the excessive autophagy by targeting RNF43. The target relationship was proved by dual-luciferase reporter assay in vitro. Mechanistically, downregulated circRNA-IGLL1 could suppress the expression of RNF43 in ammonia-exposed jejunum and the Wnt/β-catenin pathway was activated. Inhibition of miR-15a reversed autophagy caused by downregulated circRNA-IGLL1. CircRNA-IGLL1 could competitively bind miR-15a to regulate RNF43 expression, thus modulating the occurrence of autophagy. Taken together, our results showed that circRNA-IGLL1/miR-15a/RNF43 axis is involved in ammonia-induced intestinal autophagy in broilers.

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.

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.