Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm.

Corporate financial risks not only endanger the financial stability of digital industry but also cause huge losses to the macro-economy and social wealth. In order to detect and warn digital industry financial risks in time, this paper proposes an early warning system of digital industry financial risks based on improved K-means clustering algorithm. Aiming to speed up the K-means calculation and find the optimal clustering subspace, a specific transformation matrix is used to project the data. The feature space is divided into clustering space and noise space. The former contains all spatial structure information; the latter does not contain any information. Each iteration of K-means is carried out in the clustering space, and the effect of dimensionality screening is achieved in the iteration process. At the same time, the retained dimensions are fed back to the next iteration. The dimensional information of the cluster space is discovered automatically, so no additional parameters are introduced. Experimental results show that the accuracy of the proposed algorithm is higher than other algorithms in financial risk detection.

[Non-carcinogenic Risk Assessment of Cadmium Exposure Through Drinking Water in Chinese Residents Based on Age-stratification Weight].

Drinking water is an important cadmium (Cd) exposure pathway for residents in China. In order to quantitatively characterize the risk of cadmium exposure through drinking water, the Cd concentrations of three main drinking water types in China were collected via systematic literature review. The probability distribution of the exposure parameters was estimated using regression models. The non-carcinogenic risk of cadmium exposure to residents through drinking water was evaluated by Monte Carlo simulation. The results showed that significant differences were found among Cd contents in the three water types. The geometric mean of ρ(Cd) in tap water, untreated underground water, and surface water were (0.48±0.78), (1.07±1.82), and (1.04±1.38) µg·L-1, respectively. The non-carcinogenic risk assessment showed that the median of hazard quotients in all age groups and in the three water types were found to be less than 1. Compared with the exposure dose threshold recommended by the US EPA, the probabilities exceeding the threshold of underground water, surface source water, and tap water were 2.4%, 1.3%, and 1.0%, respectively. Children aged 6-8 had the greatest daily average dose of Cd, and the least exposure dose was found in the 15-17 age group. The result of Monte Carlo simulation based on age-stratification weight showed that people 18-44 years old accounted for 50.8% of the total number of people who exceeded the exposure threshold. In summary, the probability of noncarcinogenic risk caused by Cd exposure through drinking water of Chinese residents was relatively low. However, people in some sub-groups still had a relatively high probability of exceeding the exposure dose threshold. It is necessary to further strengthen the control of cadmium pollution in drinking water in order to reduce the health risk caused by cadmium exposure and ensure drinking water safety.

[Probabilistic Risk Assessment of Arsenic Exposure Through Drinking Water Intake in Chinese Residents].

The total arsenic concentrations of the three main drinking water types in China were determined through a systematic literature review. The distribution models of drinking water exposure parameters for different age groups were obtained using the regression method. The carcinogenic and non-carcinogenic risks of different population groups caused by arsenic exposure through different drinking water types were evaluated by a probabilistic risk assessment. The results showed that the geometric mean of total arsenic content in all the drinking water samples in China was (13.0±38.1) µg·L-1. The highest arsenic content was found in the Inner Mongolia Autonomous Region, followed by Guangxi and Shanxi. Based on the relevant standards for drinking water quality, the probability exceeding the threshold value of groundwater and source water is 23.2% and 18.4%, respectively. According to the results of non-carcinogenic risk assessment, the probability of the residents drinking well water and surface water from the water source area exceeding the daily average exposure dose threshold was 24.0% and 19.5%, respectively. According to the carcinogenic risk assessment, the median of carcinogenic risk caused by arsenic in drinking water in China was 3.22×10-5, which is acceptable. The population group of 18-45 years old had the highest risk, and the median LCR was 1.37×10-5. There was still a certain probability that the LCR of drinking well water and surface water would exceed the acceptable risk level recommended by the US EPA. In conclusion, the potential health risks caused by arsenic exposure through drinking water intake exist among Chinese residents. Further control of the arsenic concentration in drinking water is required to reduce the health risk and improve the safety of drinking water. Meanwhile, it is suggested to strengthen the research on risk threshold to provide a scientific basis for the residents' health protection.

[Refined Assessment of Exposure and Health Risks of Heavy Metals in Water for the Children in Xigu District, Lanzhou].

Water is an important pathway for human exposure to heavy metals. Accurate assessment of the health risks that are related to exposure to heavy metals in drinking and household water are required for the preparation of targeted health risk prevention measures. This study explores and identifies the health risks related to exposure to heavy metals via drinking and household water pathways in Xigu District, Lanzhou, northwestern China, using household water samples and survey data obtained during July-September 2015 (wet season) and December 2015-January 2016 (dry season). During each period, drinking water and household water that were available for use by children aged 0-5 and 6-17 years were sampled and a questionnaire on water-related behavior patterns was completed for each household. Cd, Cr, Pb, and As concentrations were analyzed in all water samples, and were used along with water-related exposure factors from the questionnaires to estimate exposure doses and associated health risks using models recommended by the United States Environmental Protection Agency (US EPA). The results showed that the concentrations of Cd, Cr, Pb, and As in both drinking and household water samples did not exceed the relative thresholds defined in China's national water quality standards. The concentrations of heavy metals in household water were more affected by seasonal factors than of those of drinking water. The non-cancer and cancer risks were in the ranges of 2.82×10-8-2.43×10-2 and 7.55×10-9-3.62×10-5, respectively, which are within acceptable levels, although the non-cancer and cancer risks from drinking water were both higher than of those determined for household water. Furthermore, the non-cancer and cancer risks from household water for children aged 0-5 years were lower than of those for children aged 6-17 years in each period. However, the cancer risk from drinking water for children aged 0-5 years was lower than of that for children aged 6-17 years, whereas the reverse was found for non-cancer risks. This study indicates that Cd, Cr, Pb, and As in drinking and household water did not pose significant detrimental effects to human health, and that the refined exposure assessment used could reduce uncertainties in health risk assessments.

[Concentration Characteristics and Assessment of Model-Predicted Results of PM2.5 in the Beijing-Tianjin-Hebei Region in Autumn and Winter].

This paper discusses the concentration characteristics of PM2.5, as well as its relationship with meteorological factors in autumn and winter (from September to the following February), from 2013 to 2018 in the Beijing-Tianjin-Hebei (BTH) region. The accuracy and uncertainty of the air quality forecast models NAQPMS(nested air quality prediction modeling system), CMAQ(community multiscale air quality modeling system), and CAMx (comprehensive air quality model with extensions) were analyzed based on the model-predicted and measured PM2.5 concentration in autumn and winter from 2015 to 2018. The accuracy of NAQPMS, CMAQ, and CAMx during typical heavy air pollution was also tested. Moreover, methods to improve the accuracy of the model forecast were discussed. The results showed that the mean concentrations of PM2.5 in the BTH region were 122, 98, 82, 99, and 65 µg·m-3 in the five autumn and winter periods, respectively. When the air quality index (AQI) exceeded 150 during each autumn and winter, it reached 229, 198, 210, 204, and 180 µg·m-3, respectively. There were 64 occurrences of heavy regional PM2.5 air pollution in autumn and winter from 2013 to 2018. The average duration was longest in the 2013 to 2014 period, and shortest in the 2017 to 2018 period. The peak concentration and average concentration of PM2.5 decreased year on year, except for the period from 2016 to 2017. In autumn and winter, PM2.5 concentration had a relatively close relationship with relative humidity, wind and sunshine duration, compared with a weak relationship with temperature and air pressure. Regional heavy air pollution always happened under the condition of low wind speed(less than 2 m·s-1),higher relative humidity(greater than 65%),and southwest and northeast wind direction. In addition, the heavy air pollution of PM2.5 in BTH in autumn and winter can be effectively forecasted by NAQPMS, CMAQ, and CAMx. The predicted and measured PM2.5 concentration showed a close relationship. The models performed well in forecasting Zhangjiakou, Chengde, and Qinhuangdao, but by contrast overestimated in Tangshan, Shijiazhuang, Baoding, Beijing, and Tianjin. The uncertainty of emission sources, measured and predicted meteorological data, and the atmospheric chemical reaction mechanism may be the main reasons for the overestimate.

[Source and health risk assessment of heavy metals in ambient air PM10 from one coking plant].

To investigate the sources and health risk of heavy metals in ambient air PM10 from the coking plant, the PM10 in the air around one coking plant was collected in June 2012. Then the heavy metals concentrations in PM10 were tested by the microwave-ICP-MS method. Furthermore, the USEPA's human exposure assessment model was applied to preliminarily evaluate the human health risks of the heavy metals in air particulate matter. The results show that the concentrations of 10 kinds of heavy metals in PM10 from the coking plant vary significantly, ranging from 3.06 x 10(-5) mg x m(-3) to 1.77 x 10(-2) mg x m(-3), of which the concentration of Cr is the highest, while the concentration of Co is the lowest, and the concentration of the carcinogenic substances is higher than that of the non-carcinogenic substances. The coking plant is identified to be the major source of the heavy metals in ambient air PM10, and Ni is the main polluting heavy metal. Moreover, the heavy risk assessment results reveal that the carcinogenic risks for adults are higher than children, while the carcinogenic risks in industrial areas and school relatively large. The non-carcinogenic risks for children are the highest in all the population, and the non-carcinogenic risks in residential area can not be ignored. Among the carcinogenic substances, the potential carcinogenic risks of Cd, Cr and As are comparatively large, Ni and Co have certain potential risks. Whereas, among the non-carcinogenic substances, the non-carcinogenic risks of Mn is great. Consequently, relevant departments should pay close attention to this situation.

[Health risk assessment of heavy metals in drinking water based on field measurement of exposure factors of Chinese people].

This study was carried out in Biyang County, located in the junction of Yangtze river and Huaihe river. Drinking water samples of 20 sites in urban and rural areas in this county were collected to measure 14 heavy metals by ICP/MS. About 2 500 subjects with different age and sex were selected to measure exposure factors. Time-activity of drinking water by ingestion and dermal contact of each individual subject during the last three days were kept in dairy in detail by questionnaires. Intake of drinking water from direct and indirect consumption of water and time duration of dermal contact to water in each individual subject were kept in record based on real time measurements. Human health risks were assessed and sensitivity of exposure factors and uncertainty of risks were also analyzed. The results showed that the average drinking water intake rate of male and female are 2276 mL/d, 2265 mL/d in urban adults and 2464 mL/d, 2170 mL/d in rural adults respectively. Body surface area of male and female are 1.806 m2, 1.641 m2 in urban adults and 1.747 m2, 1.617 m2 in rural adults respectively. The contents of 14 heavy metals in this study area are all below the national drinking water standards. Cancer risks from exposure to As are ranged from 2.5 x 10(-6) to 5.2 x 10(-6) through ingestion and 1.1 x 10(-7) to 2.3 x 10(-7) through dermal exposure. Non-cancer risks are ranged from 2.1 x 10(-7) to 1.7 x 10(-6) through ingestion and 1.0 x 10(-8) to 6.0 x 10(-8) through dermal exposure. Non-cancer risks in rural population are 2.1 times to 5.6 times to the risks in urban populations. However all the risks are below the acceptable level. The sensitivity of various exposure factors including drinking water intake rate, dermal exposure time and body surface area are 71.5%. This indicates that exposure factors play a very important role in health risk assessment. Health risks in this research based on real measurement of exposure factors are about 0.94 time to 6.33 times higher than the risks based on the references of the data from the exposure factors handbooks in other countries. This study showed that the accuracy of health risk assessment could be improved a lot by the real measurements of exposure factors. Attentions should be attached to exposure factor investigation to decrease uncertainty of health risk assessment.