Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch ...piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM2.5 removed natural dust (PM2.5_No Dust) concentrations and the corresponding population's PM2.5_No Dust exposure (PPM2.5E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM2.5_No Dust-polluted regions, whose spatial relative magnitude of PM2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM2.5_NoDust concentrations and PPM2.5E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM2.5_NoDust concentrations and PPM2.5E were greater in the medium stage than in the early and later stages. The local trends of PM2.5_NoDust concentration and PPM2.5E in the two higher PM2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM2.5_NoDust concentration and PPM2.5E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM2.5E to Europe. The PPM2.5E trend in Africa generally increased during the study period.
•First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions.•Firstly applies a developed Bayesian space-time model in investigating the global population’s exposure to PM2.5.•Deeply analyses the spatiotemporal trends of the global continental PM2.5 pollution removed natural dust.•Details the spatiotemporal trends of the global population’s exposure to anthropogenic PM2.5.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Autophagy was involved in vascular endothelial injury caused by PM2.5, which aggravated the pathogenesis of cardiovascular diseases. However, major toxic components and underlying mechanism ...responsible for PM2.5-induced autophagy remain unclear. In this study, the effects of water-extracted PM2.5 (WE-PM2.5) on autophagy in human umbilical vein endothelial cells (HUVEC) were studied. Our results showed WE-PM2.5 promoted autophagosome initiation and formation, meanwhile, lysosomal function was impaired, which further caused autophagic flux blockage in HUVEC cells. Furthermore, removal of metals alleviated WE-PM2.5-induced autophagic flux blockage, while the artificial metal mixture reproduced the WE-PM2.5 response. Mechanistically, ROS regulated autophagy-related proteins evidenced by BECN1, LC3B and p62 expression reversed by NAC pretreatment in WE-PM2.5-exposed cells. WE-PM2.5 also increased TXNIP expression mediated by ROS; moreover, knockdown of TXNIP in WE-PM2.5-exposed cells decreased BECN1 and LC3B expression, but had little effects on the expression of p62, CTSB, and CTSD, indicating WE-PM2.5-induced TXNIP was involved in autophagosome initiation and formation rather than autophagic degradation. Collectively, WE-PM2.5-induced ROS not only promoted autophagosome initiation and formation, but also inhibited autophagic degradation. However, as the downstream molecule of ROS, TXNIP was only involved in autophagosome initiation and formation. Importantly, WE-PM2.5-bound metals were largely responsible for autophagic flux blockage in HUVEC cells.
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•PM2.5 induced autophagosome initiation and inhibited autophagic degradation.•PM2.5-bound metals were largely responsible for abnormal autophagy in HUVECs.•ROS promoted autophagosome formation by positively regulating TXNIP.•It was ROS, not TXNIP, that inhibited autophagic degradation in PM2.5-exposed cells.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Airborne particulate matter (PM) emissions are mainly comprised of dust and biomass produced by ground-level combustion and fossil fuel emissions. PM retention by plant leaves can reduce PM pollution ...from atmosphere, but in urban areas winds can cause PM resuspension, thereby preventing retention and worsening airborne pollution. Unlike winds, rainfall events can cause PM to be washed off leaves and onto the ground, which represents a net removal of PM from the atmosphere. This systematic review examines previous studies of leaf-PM interaction events involved PM retention, PM resuspension, or PM wash-off from leaves. Publication frequency of studies on using plants for airborne PM reduction in urban areas had grown over the past decade and we focused on 65 published papers in this review. Most of these studies were performed in Europe and East Asia, and involved PM retention on the leaves of varied urban trees in different time and space. In general, these studies indicated that rough leaves showed higher degree of PM retention than glabrous leaves. However, only six out of the 65 papers considered PM wash-off from leaves; as shown in these studies, smooth leaves may have a higher PM wash-off level than rough leaves. We conclude by recommending that future researches should be focused on studying leaf-retained PM wash-off in greater detail. We also suggest that urban plant species associated with a higher PM wash-off efficiency should be identified. Moreover, we may be able to increase PM net removal mass and thereby reduce winter haze by adding more evergreen plants with a higher leaf-retained PM wash-off efficacy. Finally, establishing a standard evaluation system for airborne PM reduction based on leaf-retained PM wash-off mass may be highly useful for landscape planning and design in the future.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The health impact of airborne particulate matter (PM) has long been a concern to clinicians, biologists, and the general public. With many epidemiological studies confirming the association of PM ...with allergic respiratory diseases, an increasing number of follow-up empirical studies are being conducted to investigate the mechanisms underlying the toxic effects of PM on asthma and allergic rhinitis. In this review, we have briefly introduced the characteristics of PM and discussed its effects on public health. Subsequently, we have focused on recent studies to elucidate the association between PM and the allergic symptoms of human respiratory diseases. Specifically, we have discussed the mechanism of action of PM in allergic respiratory diseases according to different subtypes: coarse PM (PM2.5-10), fine PM (PM2.5), and ultrafine PM.
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FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This article consists of two important topics regarding variable flux memory motors (VFMMs). The first topic is magnetization characteristic analysis of VFMMs having a double-layer permanent magnet ...(PM). Two-dimensional simulations are executed to clarify relationship between magnetization characteristic and ratio of the double-layer PM. In addition, a prototype of a compact size VFMM is fabricated, and experiments are also carried out to investigate accuracy of magnetization characteristic analysis. The second topic is the proposed VFMM employing double-layer delta-type PM arrangements and extended flux barriers for traction applications. Conventional VFMMs have three critical issues, which are as follows: asymmetric positive and negative magnetizing current pulses, increase in the iron loss due to harmonics caused by demagnetized variable flux PMs (VPMs), and unintentional demagnetization of VPMs under load condition. The proposed VFMM can overcome the abovementioned problems by employing double-layer delta-type PM arrangements and extended flux barriers. In addition, the proposed VFMM achieves much higher efficiency than that of the target motor mounted in TOYOTA Prius fourth-generation over a wide operating range.
Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high ...dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM2.5; Particulate Matter 10, PM10 and lower atmospheric visibility, VIS), the atmospheric variables associated with increased respiratory induced mortality and recurrent health-care cost. Firstly, hourly air quality data series (January-2015 to December-2017) are demarcated into their respective intrinsic mode functions (IMFs) and a residual sub-series that reveal patterns and resolve data complexity characteristics, followed by partial autocorrelation function applied to each IMF and residual sub-series to unveil historical changes in air quality. To design the prescribed hybrid model, the data is partitioned into training (70%), validation (15%) and testing (15%) sub-sets. The online sequential-extreme learning machine (OS-ELM) algorithm integrated with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is designed as a data pre-processing system to robustly extract predictive patterns and fine-tune the model generalization to a near-optimal global solution, which represents modelled air quality at hourly forecast horizons. The resulting early warning AI-based framework denoted as ICEEMDAN-OS-ELM model, is individually constructed by forecasting each IMF and residual sub-series, with hourly PM2.5, PM10, and VIS obtained by the aggregated sum of forecasted IMFs and residual sub-series. The results are benchmarked with many competing predictive approaches; e.g., hybrid ICEEMDAN-multiple-linear regression (MLR), ICEEMDAN-M5 model tree and standalone versions: OS-ELM, MLR, M5 model tree. Statistical metrics including the root-mean-square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Legates & McCabe's Index (ELM) and Nash–Sutcliffe coefficients (ENS) are used to evaluate the model's accuracy. Both visual and statistical results show that the proposed ICEEMDAN-OS-ELM model registers superior results, outperforming alternative comparison approaches. For instance, for PM2.5,ELM values ranged from 0.65–0.82 vs. 0.59–0.77 for ICEEMDAN-M5 tree, 0.59–0.74 for ICEEMDAN-MLR, 0.28–0.54 for OS-ELM, 0.27–0.54 for M5 tree and 0.25–0.53 for the MLR model. For remaining air quality variables (i.e., PM10 & VIS), the objective model (ICEEMDAN-OS-ELM) outperformed the comparative models. In particular, ICEEMDAN-OS-ELM registered relatively low RMSE/MAE, ranging from approximately 0.7–1.03 μg/m3(MAE), 1.01–1.47 μg/m3(RMSE) for PM2.5 whereas for PM10, these metrics registered a value of 1.29–3.84 μg/m3(MAE), 3.01–7.04 μg/m3(RMSE) and for Visibility, they were 0.01–3.72 μg/m3 (MAE (Mm−1)), 0.04–5.98 μg/m3 (RMSE (Mm−1)). Visual analysis of forecasted and observed air quality through a Taylor diagram illustrates the objective model's preciseness, confirming the versatility of early warning AI-model in generating air quality forecasts. The excellent performance ascertains the hybrid model's potential utility for air quality monitoring and subsequent public health risk mitigation.
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•An artificial intelligence predictive framework devised for air quality prediction.•Efficient forecasting of air quality with 5 modelling approaches was recorded.•OS-ELM coupled with ICEEMDAN outperformed the other models.•AI models show potential in health informatics and Australia's environment sector.•AI models can empower public health risk mitigation to a create liveable society.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) is crucial to hazard assessment and epidemiology. This study integrated annual data ...from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 ± 27.1 μg/m3 (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 ± 13.9 μg/m3 (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.
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•The indoor PM2.5 in Shanghai was estimated to be 29.3 ± 27.1 μg/m3 for 2016–2017.•The annual weighted PM2.5 exposure was estimated to be 32.1 ± 13.9 μg/m3.•A novel model was developed to estimate PM2.5 exposure by mining big data.
A spatio-temporally weighted hybrid model was developed and attempted to improve PM2.5 exposure.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Estimation of hourly and continuous ground-level fine particulate matter (PM2.5) concentrations is essential for PM2.5 pollution sources identifications, targeted policy development and population ...exposure research. However, current PM2.5 estimation studies rely heavily on satellite-based aerosol optical depth (AOD) data, and the limited transit times of polar-orbiting satellites such as Terra and Aqua, nighttime gaps in data from geostationary satellites such as Himawari-8, and cloud contamination reported for both types of satellites challenge the estimation of spatiotemporally continuous PM2.5 concentrations. In this study, spatiotemporal PM2.5 characteristic was constructed by the spatiotemporal fusion method. Specifically, multi-source data, including spatiotemporal, periodic, meteorological, vegetation, anthropogenic and topological characteristics, were incorporated into an ensemble learning method that combined extreme gradient boosting (XGBoost), k-nearest neighbour (KNN) and back-propagation neural network (BPNN) algorithms in level 1 and used linear regression (LR) for integration in level 2. The optimized stacking strategy that considered PM2.5 spatiotemporal autocorrelation was called the ST-stacking model. The model was trained, validated and tested with data acquired for China in 2017. The ST-stacking model outperformed XGBoost, KNN and BPNN models by 9.27% on average, with an R2 = 0.9191. Using the model, the 24-h and continuous ground-level PM2.5 concentrations in mainland China on 11 May 2017 were mapped, and parts of Beijing and Chengdu were selected for more detailed analysis. The PM2.5 concentrations in Taklimakan Desert, North China Plain, Sichuan Basin and Yangtze Plain were much higher than those in other locations on this day, which was generally consistent with the long-term patterns reported in previous studies.
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•Spatiotemporal autocorrelation is considered for PM2.5 relationship reconstruction.•The ensemble learning method is established to improve robustness and accuracy.•The hourly and continuous PM2.5 concentrations in China on 11 May 2017 are mapped.•ST-stacking model outperforms the individual models, with R2 = 0.9191.•Spatiotemporal characteristic contributes the most to model performance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
From the past few decades, it has been observed that the urbanization and industrialization are expanding in the developed nations and are confronting the overwhelming air contamination issue. The ...citizens and governments have experienced and expressed the increasingly concerned regarding the impact of air pollution affecting human health and proposed sustainable development for overriding air pollution issues across the worldwide. The outcome of modern industrialization contains the liquid droplets, solid particles and gas molecules and is spreading in the atmospheric air. The heavy concentration of particulate matter of size PM10 and PM2.5 is seriously caused adverse health effect. Through the determination of particulate matter concentration in atmospheric air for the betterment of human being well in primary importance. In this paper machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017. These models were compared with the existing traditional models and perform better in predictive performance. The performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Source apportionment of PM2.5 was performed using positive matrix factorization (PMF) based on chemical speciation data from 24-h filters collected throughout 2015 at six sampling sites of varying ...urban influences in Hong Kong. The input data include major inorganic ions, organic and elemental carbon, elements, and organic tracers. Nine factors were resolved, including (1) secondary sulfate formation process, (2) secondary nitrate formation process, (3) industrial emissions, (4) biomass burning, (5) primary biogenic emissions, (6) vehicle emissions, (7) residual oil combustion, (8) dust, and (9) aged sea salt. The PMF-resolved factor contributions in conjunction with air mass back trajectories showed that the two major sources for PM2.5 mass, secondary sulfate (annual: 41%) and secondary nitrate (annual: 9.9%), were dominantly associated with regional and super-regional pollutant transport. Vehicular emissions are the most important local source, and its contributions exhibit a clear spatial variation pattern, with the highest (6.9 μg/m3, 24% of PM2.5) at a downtown roadside location and the lowest (0.4 μg/m3, 2.0% PM2.5) at two background sites away from city centers. The ability of producing a more reliable source separation and identifying new sources (e.g. primary biogenic source in this study) was a direct advantageous result of including organic tracers in the PMF analysis. PMF analysis conducted on the same dataset in this study but without including the organic tracers failed to separate the biomass burning emissions and industrial/coal combustion emissions. PMF analysis without the organic tracers would also over-apportion the contribution of vehicular emissions to PM2.5, which would bias the evaluation of the effectiveness of vehicle-related control measures. This work demonstrates the importance of organic markers in achieving more comprehensive and less biased source apportionment results.
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•One-year data without organic tracers was insufficient to resolve clean source factors.•Inclusion of saccharide tracers enables resolving primary biogenic source by PMF.•Organic tracers allow separation of biomass burning and industrial/coal combustion.•Alkane unresolved complex mixture (UCM) facilitates PMF-extraction of vehicular source.•PMF without organic tracers over-apportions contributions of vehicular source to PM2.5
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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