Characteristics and health risk assessment of indoor and outdoor PM2.5 in a rural village, in Northeast of China: impact of coal and biomass burning.

Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. In this study, characteristics of PM2.5 chemical composition and health risk assessment from Songyuan, China, were investigated during day and night in indoor and outdoor from February 4 to 19, 2021. Relative high concentrations of PM2.5 were obtained in indoor environment than outdoor, with 503.95 ± 209.62 µg/m3 during the day and 357.52 ± 232.81 µg/m3 at night for the indoor environment. Relatively high total carbon, organic carbons, elemental carbons, polycyclic aromatic hydrocarbons (PAHs), and oxygenated polycyclic aromatic hydrocarbons (OPAHs) were obtained in indoor environment. However, the average concentrations of PAHs were higher during night (73.57 ± 43.09 ng/m3) in indoor and OPAHs during day (6.027 ± 2.960 ng/m3) in outdoor. They had different I/O distributions of these compounds during day and night. Indeno(1,2,3-cd) pyrene was the dominant PAHs, and benzanthrone was the dominant OPAHs; this is different with the previous studies. The high indoor/outdoor ratios showed the indoor coal and biomass burning greatly affect the indoor pollutants. Average ILCR health risk assessment for PAHs was all higher than 10-6 for different age gender, suggesting there has potential cancer risk existed for populations living in the rural coal and biomass burning area Songyuan, China.

Sediment records of black carbon variations over the last two centuries in North China.

Reconstructing historical black carbon (BC) variations based on sedimentary records are significant for understanding long-term BC emissions, tracing sources, and establishing effective strategies for pollution control. By comparing BC profiles between four lake sediment cores, historical BC variations were reconstructed on the southeastern Mongolian Plateau in North China. Except one, the other three records show close fluxes and similar temporal trends of soot, indicating their repetitiveness on revealing historical variations at a regional scale. Unlike soot, char and BC in these records, derived mainly from local sources, reflected the occurrence of natural fires and human activities near the lakes. Before the ∼1940s, these records showed no well-established anthropogenic BC signals except some occasional natural-related increases. This was different from the global BC increased since the Industrial Revolution, indicating a negligible influence of transboundary BC on the region. Anthropogenic BC in the region had increased since the 1940s-1950s ascribed to emissions from Inner Mongolia and nearby provinces. The increases were moderate in the 1950s-1970s, corresponded with the initial development of industry after the founding of the P.R. China. The most pronounced BC increases occurred in 1980s-2016, commensurate with rapid socio-economic development after the Reform and Opening-up in 1978. Different from model estimations on Chinese BC emissions, our records show unexpected BC increases in recent two decades caused by pollutant emission rises in this undeveloped region. This suggests that BC emissions in relatively small cities and rural areas in China were likely underestimated and their role on national BC cycling needs to be reassessed.

Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight.

Food safety is important for sustainable social and economic development and people's health. The traditional single risk assessment model is one-sided to the weight distribution of food safety factors including physical-chemical and pollutant indexes, which cannot comprehensively assess food safety risks. Therefore, a novel food safety risk assessment model combining the coefficient of variation (CV) integrating the entropy weight (EWM) (CV-EWM) is proposed in this paper. The CV and the EWM are used to calculate the objective weight of each index with physical-chemical and pollutant indexes effecting food safety, respectively. Then, the weights determined by the EWM and the CV are coupled by the Lagrange multiplier method. The ratio of the square root of the product of two weights and the weighted sum of the square root of the product are regarded as the combined weight. Thus, the CV-EWM risk assessment model is constructed to comprehensively assess the food safety risk. Moreover, the Spearman rank correlation coefficient method is used to test the compatibility of the risk assessment model. Finally, the proposed risk assessment model is applied to evaluate the quality and safety risk of sterilized milk. By analyzing the attribute weight and comprehensive risk value of physical-chemical and pollutant indexes effecting the sterilized milk quality, the results show that this proposed model can scientifically obtain the weight of physical-chemical and pollutant indexes to objectively and reasonably evaluate the overall risk of food, which has certain practical value for discovering the influencing factors of risk occurrence to risk prevention and control of food quality and safety.

Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network.

Nowadays, the world has achieved tremendous economic development at the expense of the long-term habitability of the planet. With the rapid economic development, the global greenhouse effect caused by excessive carbon dioxide (CO2) emissions is also accumulating, which generates the negative impact of global warming on nature and human beings. Meanwhile, economy and CO2 emissions prediction methods based on traditional neural networks lead to gradient disappearance or gradient explosion, making the economy and CO2 emissions prediction inaccurate. Therefore, this paper proposes a novel economy and CO2 emissions prediction model based on a residual neural network (RESNET) to optimize and analyze energy structures of different countries or regions in the world. The skip links are used in the inner residual block of the RESNET to alleviate vanishing gradients due to increasing depth in deep neural networks. Consequently, the proposed RESNET can optimize this problem and protect the integrity of information by directly bypassing the input information to the output, which can increase the precision of the prediction model. The needs for natural gas, hydroelectricity, oil, coal, nuclear energy, and renewable energy in 24 different countries or regions from 2009 to 2020 are used as inputs, the CO2 emissions and the gross domestic product (GDP) per capita are respectively used as the undesired output and the desired output of the RESNET to build an economy and CO2 emissions prediction model. The experimental results show that the RESNET has higher correctness and functionality than the traditional convolutional neural network (CNN), the radial basis function (RBF), the extreme learning machine (ELM) and the back propagation (BP). Furthermore, the proposed model provides guidance and development plans for countries or regions with low energy efficiency, which can improve energy efficiency, economic development and reasonably control CO2 emissions.

Assessment of the inhalation exposure and incremental lifetime cancer risk of PM2.5 bounded polycyclic aromatic hydrocarbons (PAHs) by different toxic equivalent factors and occupancy probability, in the case of Xi'an.

Polycyclic aromatic hydrocarbons (PAHs) are widespread toxic pollutants in the atmosphere and have attracted much attention for decades. In this study, we compared the health risks of PAHs based on different toxic equivalent factors (TEFs) in a heavily polluted area during heating and non-heating periods. We also pay attention to occupancy probability (OP) in different polluted areas. The results showed that there were big differences for calculations by different TEFs, and also by OP or not. Age groups except adults were all lower calculated by OP than not. The sensitivity analysis results on the incremental lifetime cancer risks (ILCR) for population groups by Monte Carlo simulation identified that the cancer slope factor extremely affected the health risk assessment in heating periods, followed by daily inhalation exposure levels. However, daily inhalation exposure levels have dominated the effect on the inhalation ILCR and then followed by the cancer slope factor in non-heating periods. The big differences by different calculations investigated that it is important to set up the correlations between the pollution level and health risks, especially for the longtime health assessment.

Sediment Soot Radiocarbon Indicates that Recent Pollution Controls Slowed Fossil Fuel Emissions in Southeastern China.

Fossil fuel (FF) combustion emissions account for a large, but uncertain, amount of the soot in the atmosphere, play an important role in climate change, and adversely affect human health. However, historical estimates of FF contributions to air pollution are limited by uncertainties in fuel usage and emission factors. Here, we constrained FF soot emissions from southeastern China over the past 110 years, based on a novel radiocarbon method applied to sedimentary soot. The reconstructed soot accumulations reflect the integrated effects of increased FF use caused by economic development and reductions in emissions due to pollution controls. A sharp increase in FF soot started in 1950 as southeastern China industrialized and developed economically, but decreased FF soot fluxes in recent years suggest that pollution controls reduced soot emissions. We compare FF soot history to changes in CO2 emissions, industrial and economic activities, and pollution controls and show that FF soot fluxes are more readily controlled than atmospheric CO2. Our independent FF soot record provides insights into the effects of economic development and controls on air pollution and the environmental impacts from the changes in soot emissions.

Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network.

The combustion of fossil fuels produces a large amount of carbon dioxide (CO2), which leads to global warming in the world. How to rationally consume fossil energy and control CO2 emissions has become an unavoidable problem for human beings while vigorously developing economy. This paper proposes a novel economy and CO2 emissions prediction model using an improved Attention mechanism based long short term memory (LSTM) neural network (Attention-LSTM) to analyze and optimize the energy consumption structures in different countries or areas. The Attention mechanism can add the weight of different inputs in the previous information or related factors to realize the indirect correlation between output and related inputs of the LSTM. Therefore, the Attention-LSTM can allocate more computing resources to the parts with a higher relevance of correlation in the case of limited computing power. Through inputs with the consumption of oil, natural gas, coal, hydroelectricity and renewable energy, the desirable output with the per capita gross domestic product (GDP) and the undesirable output with CO2 emissions prediction model of different countries and areas is established based on the Attention-LSTM. The experimental results show that compared with the normal LSTM, the back propagation (BP), the radial basis function (RBF) and the extreme learning machine (ELM) neural networks, the Attention-LSTM is more accurate and practical. Meanwhile, the proposed model provides guidance for optimizing energy structures to develop economy and reasonably control CO2 emissions.

Economy and carbon dioxide emissions effects of energy structures in the world: Evidence based on SBM-DEA model.

Nowadays, the increasing global warming phenomenon caused by large carbon dioxide (CO2) emissions has a huge impact on the economic and social sustainable development in the world. And CO2 emissions come mainly from the burning of fossil energy, such as oil, natural gas and coal. Therefore, a novel economy and CO2 emissions evaluation model based on the slacks-based measure integrating the data envelopment analysis (SBM-DEA) is proposed to analyze and optimize energy structures of some countries and regions in the world. The consumption of oil, natural gas and coal are inputs of the proposed method. In addition, per capita gross domestic product (GDP) value is the desirable output and CO2 emission is the undesirable output. Then the economy and CO2 emissions evaluation model of some countries and regions in the world is built. The results show that the overall efficiency of developed countries and regions is higher than that of developing countries. Moreover, due to the optimal configuration of slack variables of inputs and the undesirable output, the efficiency values of some inefficient countries and regions can be improved greatly. Furthermore, whether in 2017 or 2018, the average efficiency values of Europe and Oceania are both relatively high, and these two years average efficiency values of Asia are all the lowest among the five continents.

Historical variation of black carbon and PAHs over the last ~200 years in central North China: Evidence from lake sediment records.

As the largest coal-producing province in China, the coal production of Shanxi Province accounts for one third of the country's total. Thus it is of great importance to study the pollution history of typical pollutants in Shanxi Province and their links with energy usage in North China. Sediment cores from two relatively remote lakes in central North China were retrieved to investigate historical evolutions of black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) in the last ~200 years. The two records show several-fold increases in both concentrations and depositional fluxes of BC, char, soot, and PAHs in recent five decades, which were associated with the influence of anthropogenic activities resulting from socio-economic development in Shanxi Province. However, after ~2000 their fluxes decreased sharply due to China's effort on environmental protection. These changes indicate that atmospheric BC and PAHs loads in the region were affected significantly by recent anthropogenic activities and environmental policies. Ratios of individual PAHs and char/soot indicate pyrogenic sources of these increased pollutants in recent decades, with coking industry and coal combustion as the two major sources. Significant positive correlations between BC and PAHs were observed in both cores of Lake Gonghai and Lake Mayinghai, indicating that they were likely co-transported by BC particles from similar sources. This study provides new and important understanding of the atmospheric pollution history of BC and PAHs in North China.

Spatial distributions and sequestrations of organic carbon and black carbon in soils from the Chinese Loess Plateau.

Concentrations of soil organic carbon (SOC), black carbon (BC), char, and soot in topsoils (0-20 cm) and vertical soil profiles (0-100 cm) from the Chinese Loess Plateau (CLP) were investigated. Objectives of the study were to establish the spatial distributions and estimate the sequestrations of these substances. The SOC, BC, char and soot concentrations were higher in the eastern and southeastern parts of the plateau and lower in the north, which is consistent with the patterns of economic development and energy consumption. The highest average SOC concentration was found in the clayey loess zone, followed by the loess and sandy loess zones. Similar trends were observed for BC, char and soot, suggesting interactions with clay and silt are potentially important influences on OC and BC. The SOC contents in topsoils varied from 0.31 to 51.81 g kg(-1), with a mean value of 6.54 g kg(-1), while BC and char concentrations were 0.02 to 5.5 g kg(-1) and 0.003 to 4.19 g kg(-1), respectively, and soot ranged from 0.01 to 1.32 g kg(-1). Unlike SOC, both BC and char decreased with soil depth, whereas soot showed little variation with depth. BC and char were correlated in the topsoils, and both correlated moderately well with SOC (R(2)=0.60) and soot (R(2)=0.53). The SOC pools sequestered in the 0 to 20 cm and 0 to 100 cm depths were estimated to be 0.741 and 3.63 Pg, respectively, and the BC pools sequestered in the 0 to 20 cm and 0 to 100 cm depths were 0.073 and 0.456 Pg, respectively. Therefore the quantity of carbon stored in the sediments of the CLP evidently exceeds 10(9) tons. The char contained in the upper 20 cm layer was 0.053 Pg, which amounted to 72.5% of the BC in that layer.