PM2.5-Bound Toxic Elements in an Urban City in East China: Concentrations, Sources, and Health Risks

Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks

Accurate prediction of fine particulate matter concentration in the future is important for human health due to the necessity of an early warning system. Generally, deep learning methods, when widely used, perform better in forecasting the concentration of PM2.5. However, the source information is limited, and the dynamic process is uncertain. The method of predicting short-term (3 h) and long-term trends has not been achieved. In order to deal with the issue, the research employed a novel mixed forecasting model by coupling the random forest (RF) variable selection and bidirectional long- and short-term memory (BiLSTM) neural net in order to forecast concentrations of PM2.5/0~12 h. Consequently, the average absolute percentage error of 1, 6, and 12 h shows that the PM2.5 concentration prediction is 3.73, 9.33, and 12.68 μg/m3 for Beijing, 1.33, 3.38, and 4.60 μg/m3 for Guangzhou, 1.37, 4.19, and 6.35 μg/m3 for Xi’an, and 2.20, 7.75, and 10.07 μg/m3 for Shenyang, respectively. Moreover, the results show that the suggested mixed model is an advanced method that can offer high accuracy of PM2.5 concentrations from 1 to 12 h post.

Sources of airborne particulate matter-bound metals and spatial-seasonal variability of health risk potentials in four large cities, South Korea

Fifteen airborne particulate matter-bound metals were analyzed at 14 sites in four large cities (Seoul, Incheon, Busan, Daegu) in South Korea, between August 2013 and June 2017. Among the seven sources resolved by positive matrix factorization, soil dust and marine aerosol accounted for the largest and second largest portions in the three cities; however, in Seoul, soil dust and traffic occupied the largest and the second largest, respectively. Non-carcinogenic risk assessed by inhalation of eight metals (Cd, Co, Ni, Pb, As, Al, Mn, and V) was greater than the hazard index (HI) of 1 at four sites located at or near the industrial complexes. Cumulative incremental lifetime cancer risk (ILCR) due to exposure to five metals (Cd, Co, Ni, Pb, and As) exceeded the 10^-6 cancer benchmark at 14 sites and 10^-5 at six sites, which includes four sites with HI greater than 1. The largest contributor to ILCR was coal combustion in Seoul, Incheon, and Daegu, and industry sources in Busan. Moreover, industry sources were the largest contributors to non-carcinogenic risk in Seoul, Busan, and Daegu, and soil dust was in Incheon. Incheon had the highest HI in spring because of the higher contribution of soil dust sources than in other seasons. The higher ILCR in Incheon in spring and winter and higher ILCR and HI in Daegu in autumn were mainly due to the influence of industry or coal combustion sources. Statistically significant differences in the ILCR and HI values among the sampling sites in Busan and Daegu resulted from the higher contribution of industry sources at a certain site in the respective city.

Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform

Accurate forecasting of air pollutant concentration is of great importance since it is an essential part of the early warning system. However, it still remains a challenge due to the limited information of emission source and high uncertainties of the dynamic processes. In order to improve the accuracy of air pollutant concentration forecast, this study proposes a novel hybrid model using clustering, feature selection, real-time decomposition by empirical wavelet transform, and deep learning neural network. First, all air pollutant time series are decomposed by empirical wavelet transform based on real-time decomposition, and subsets of output data are constructed by combining corresponding decomposed components. Second, each subset of output data is classified into several clusters by clustering algorithm, and then appropriate inputs are selected by feature selection method. Third, a deep learning-based predictor, which uses three dimensional convolutional neural network and bidirectional long short-term memory neural network, is applied to predict decomposition components of each cluster. Last, air pollutant concentration forecast for each monitoring station is obtained by reconstructing predicted values of all the decomposition components. PM_2.5 concentration data of Beijing, China is used to validate and test our model. Results show that the proposed model outperforms other models used in this study. In our model, mean absolute percentage error for 1, 6, 10 h ahead PM_2.5 concentration prediction is 4.03%, 6.87%, and 8.98%, respectively. These outcomes demonstrate that the proposed hybrid model is a powerful tool to provide highly accurate forecast for air pollutant concentration.

Short-Term Inhalation of Ultrafine Zinc Particles Could Alleviate Cardiac Dysfunctions in Rats of Myocardial Infarction

It is not clear for inhalation of ultrafine metal particles in air pollution to impair human health. In the study, we aimed to investigate whether short-term (4 weeks) inhalation of ultrafine zinc particles could deteriorate the cardiac and hemodynamic functions in rats of myocardial infarction (MI). MI was induced in Wistar rats through coronary artery ligation surgery and given an inhalation of ultrafine zinc particles for 4 weeks (post-MI 4 weeks, 4 days per week, and 4 h per day). Cardiac strain and strain rate were quantified by the speckle tracking echocardiography. The pressure and flow wave were recorded in the carotid artery and analyzed by using the Womersley model. Myocardial infarction resulted in the LV wall thinning, LV cavity dilation, remarkable decrease of ejection fraction, dp/dt Max, −dp/dt Min, myocardial strain and strain rates, and increased LV end-diastolic pressure, as well as impaired hemodynamic environment. The short-term inhalation of ultrafine zinc particles significantly alleviated cardiac and hemodynamic dysfunctions, which could protect from the MI-induced myocardial and hemodynamic impairments albeit it is unknown for the long-term inhalation.

Health Risk Assessment of Dietary Heavy Metals Intake from Fruits and Vegetables Grown in Selected Old Mining Areas-A Case Study: The Banat Area of Southern Carpathians

In this study, we conducted a noncarcinogenic risk assessment of consuming vegetables and fruits grown in two old mining areas from the Banat area of Southern Carpathians (Romania), Moldova Veche (M) and Rusca Montana (R) and in a nonpolluted reference area located near the village of Borlova (Ref). Concentrations of Fe, Mn, Zn, Cu, Ni, Cd and Pb in soils and commonly eaten vegetables and fruits were measured and used for calculating the weighted estimated daily intake of metals (WEDIM), the target hazard quotients (THQ) and the total target hazard quotients (TTHQ) for normal daily consumption in adults. Levels of certain metals in soils and plants from the R area (Pb) and the M area (Cu) were higher than those measured in the Ref area—and often exceeded normal or even alert-threshold levels. TTHQs for the R area (1.60; 6.03) and the M area (1.11; 2.54) were above one for leafy vegetables and root vegetables, respectively, suggesting a major risk of adverse health effects for diets, including these vegetal foodstuffs. Moreover, THQ and TTHQ indicated a higher population health risk for the R area than for the M area, with the Ref area being a safe zone.

Soluble Inorganic Arsenic Species in Atmospheric Submicron Particles in Two Polish Urban Background Sites

This paper presents results of the research on soluble inorganic As(III) and As(V) bound to submicron atmospheric particles (PM1) in two Polish urban background sites (Zabrze and Warsaw). The purpose of the research was to give some insight on the susceptibility to leaching of PM1-bound arsenic species from easily water-soluble compounds, i.e., considered potentially bioavailable based on its daily and seasonal changes. Quantitative analysis for 120 PM1 samples (collected from 24 June 2014 to 8 March 2015) was performed by using a high-performance liquid chromatography in combination with inductively coupled plasma mass spectrometry. The mean seasonal concentrations of dominant soluble As specie—As(V)—ranged from 0.27 ng/m3 in the summer season in Warsaw to 2.41 ng/m3 in the winter season in Zabrze. Its mean mass shares in total As were 44% in Warsaw and 75% in Zabrze in the winter and 18% and 48%, respectively, in the summer. Obtained results indicated fossil fuel combustion as the main source of PM1-bound As(V) and road traffic emission as its minor sources. In opposite to As(V), soluble As(III) was not clearly seasonally variable. In both seasons, its mean concentrations were higher in Zabrze than in Warsaw. As(III) concentrations were not preferentially shaped by an exact emission from road traffic in both cities.