Concentration Monitoring Data Research Articles

Accurate and efficient prediction of atmospheric PM1, PM2.5, PM10, and O3 concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O3) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O3 are secondary pollutants, and there remain significant challenges in accurately and efficiently predicting their concentrations in the atmosphere. In this study, one-year monitoring data of PM1, PM2.5, PM10, and O3 concentrations as well as meteorological parameters and concentrations of various precursors (i.e., nitrogen oxides, SO2, CO, alkanes, aldehydes, and ketones) are obtained at a monitoring site in central China's Hunan province. The eXtreme Gradient Boosting model is trained and tested to achieve efficient and accurate predictions of the concentrations of the four pollutants. The effects of different datasets, input features, and model parameters on the prediction accuracy are investigated. Principal component analysis is employed to further reduce the dimensions of features, increasing the prediction efficiency. Finally, all model training and prediction processes are incorporated in an executable application using the PyQt5 framework to build a user interface. The customized software supports user-defined modeling. The software can simultaneously predict PM1, PM2.5, PM10, and O3 concentrations, making the prediction process convenient.

Establishment and validation of a population pharmacokinetic model for apatinib in patients with tumors

ObjectiveThis study aimed to develop a population pharmacokinetic (PPK) model for oral apatinib in Chinese oncology patients and investigate the factors influencing the pharmacokinetics of apatinib.MethodsWe gathered 199 blood concentration monitoring data points from 91 inpatient oncology participants receiving oral apatinib at the Third Affiliated Hospital of Soochow University. Covariates, such as age, gender, body weight, and indices of liver and renal function, were examined to assess their influence on the pharmacokinetic parameters of apatinib. The PPK model was developed using the nonlinear mixed-effects modeling procedure (NONMEM), and model validation was conducted using the bootstrap method and normalized prediction distribution error (NPDE) method.ResultsThe structural model adopted a one-compartment structure with first-order elimination. Notably, aspartate aminotransferase (AST) and the co-administered drug type emerged as primary covariates affecting apatinib clearance (CL/F). The finalized model was expressed as CL/F (L/h) = 56.7 × (AST/26.6)−0.298 × θ, when apatinib was combined with monoclonal antibodies, θ was 1; when combined with paclitaxel, θ was 0.58; when combined with other drugs (e.g., platinum, capecitabine, or the combination of tegafur, gimeracil, and oteracil potassium), θ was 1.60; When used as monotherapy, θ was 1.38. V/F = 674 L, and the absorption rate constant (Ka) was fixed at 0.08 h−1. Bootstrap results affirmed the model’s reliability and stability, while NPDE outcomes attested to the model’s fit.ConclusionOur study successfully established a PPK model for apatinib in oncology patients, revealing that liver function status and co-administered drug types significantly impacted apatinib CL/F. This finding underscored the potential necessity for dose adjustments to optimize efficacy, particularly in patients undergoing different chemotherapy regimens involving apatinib.

Reliability Assessment of PM2.5 Concentration Monitoring Data: A Case Study of China

This study systematically evaluates the reliability of PM2.5 monitoring data across major urban areas, utilizing a comprehensive dataset covering 283 cities in China over a seven-year period. By using Benford’s Law, robust regression analysis, and various machine learning methods, such as Gradient Boosting Trees and Random Forests, the overall reliability of China’s PM2.5 monitoring data is high. These models effectively captured complex patterns and detected anomalies related to both natural environmental and socioeconomic factors, as well as potential data manipulation. Based on the integrated models, the proportion of anomalies in PM2.5 concentration monitoring data across 283 cities in China from 2015 to 2022 was less than 2%, which strongly indicates the overall reliability of China’s PM2.5 concentration monitoring data. Additionally, machine learning models provided a ranking of the importance of different variables affecting PM2.5 concentrations, offering a scientific basis for understanding the driving factors behind the data. The three variables that have the greatest impact on PM2.5 concentrations are population density, average temperature, and relative humidity. By comparing with other related studies, we further validated our findings. Overall, this study provides new methods and perspectives for understanding and evaluating the reliability of PM2.5 data in China, laying a solid foundation for future research.

The interaction between water quality and meteorological factors on chlorophyll-a concentration in Honghu Lake: based on PiecewiseSEM-generalized additive model coupling model.

In order to explore the interactive effects of environmental factors on the chlorophyll-a (Chl-a) concentration variation in Honghu Lake, this study was based on the monitoring data of Chl-a mass concentration and water quality factors (water temperature, pH, dissolved oxygen, permanganate index, total nitrogen, total phosphorus) and meteorological factors (evaporation, precipitation, sunshine hours, average wind speed) at three research sites (Dakou, Chatan Island, Lantian) in Honghu Lake from January 2010 to December 2019. Time series analysis, piecewise structural equation model (PiecewiseSEM), and generalized additive model (GAM) were used to quantitatively study the spatial and temporal changes of different environmental factors and their interaction with chlorophyll-a concentration in Honghu Lake. The results showed that the effects of TN and DO on Chl-a at Dakou and Chatan Island were more significant than other environmental meteorological factors, while the effects of DO and CODMn on Chl-a at Lantian were more obvious. At the same time, it was found that Chl-a had a non-linear relationship with TN and DO at Dakou and Chatan Island, a non-linear relationship with DO at Lantian, and a linear relationship with CODMn. The interaction effect of dominant environmental meteorological factors on Chl-a was significantly higher than that of a single factor, and the explanation rates were 80.6%, 72.8%, and 64.6%, respectively. In conclusion, based on the Piecewise SEM and GAM model, it not only can reveal the influence of the interaction of influencing factors on the change of Chl-a concentration, but also has important significance for the early warning and control of lake eutrophication.

Online estimation of inlet contaminant concentration using Eulerian-Lagrangian method of fundamental solutions and Bayesian inference

The advection-diffusion equation is fundamental to modeling various transport phenomena, including the distribution of chemical species in surface or groundwater flow. In cases where the concentration at the source is unknown, inverse problem formulations are required to estimate the desired states by assimilating concentration monitoring data from specific points along the watercourse using a Bayesian approach. This paper proposes a combination of the Eulerian-Lagrangian method and the meshless method of fundamental solutions to solve the advection-diffusion equation. Moreover, the method was used as an evolution model for the sequential importance resampling particle filter algorithm to reconstruct the time-dependent inlet pollutant concentration in inverse problems. Numerical smooth and discontinuous inlet function results show that the particle filter - Eulerian-Lagrangian method of fundamental solutions combination can reconstruct inlet concentration time series.

Spatiotemporal Changes in and Forces Driving Ozone Concentration in the Beijing–Tianjin–Hebei (Jing–Jin–Ji) Region from 2015 to 2022

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration.

Synergistic control potential of flue gas pollutants under Ultra-Low emission standards in waste incineration plants

As the dominant waste disposal process, incineration is regarded as the main incentive for the “not-in-my-backyard” syndrome, and faces an inescapable pressures of ultra-low emissions (ULE). Establishing precise response relationships between emission factors (EFs) and full-process influencing factors can provide guidance for the synergistic mitigation of flue gas pollutants (FGPs). In this work, the multi-dimensional EFs of FGPs were identified by initially integrating FGPs concentration monitoring data of existing 1,226 processing lines in China, technologies applied and operational experience (OE), local economic and political characteristics. Significant regional imbalance performance was observed, which EFs in the coastal regions were 3.55–92.39 % lower than those of the inland areas. NOx, SO2, HCl were identified as critical components requiring further reduction under the ULE standards, with exceedance rates recorded at 73.07 %, 38.90 %, and 56.69 %, respectively. An indicative value of 20 mg/m3 for PM is recommended for the control of heavy metals of Cd + Tl and Sb + As + Pb + Cr + Co + Cu + Mn + Ni based on the correlation coefficients of r = 0.28 (p < 0.001) and r = 0.20 (p = 0.002), respectively. Waste composition and OE were quantified as the main contributors of EFs’ disparities by the tree-branching controlled variable approach established in this study. Predictive models for FGPs control process and corresponding EFs were constructed. EFs of nine FGPs in 2030 would decrease by 0.97–65.42 %, due to more complex purification processes employed to meet ULE’s limitations, such as the application of five-stage processes growing from 45.60 % to 58.28 %. While regional imbalance in EFs-SO2 and EFs-HCl were extended with increases from 25.83 % to 33.07 % and 9.91 % to 32.32 %, respectively, due to the consistent disparities of OE and growing heterogeneity of control policies. Enhancing interregional empirical exchanges, reducing the regional market monopolies, and formulating technical guidelines would be beneficial to synergize the reduction of FGPs emissions and alleviate regional imbalance.

Transport of radioactive materials from terrestrial to marine environments in Fukushima over the past decade

Abstract While 20 % of radionuclides released from the Fukushima Daiichi Nuclear Power Plant accident had been deposited in the terrestrial environment, rivers remain the long-term source for 137Cs, primarily through particulate transfer, from terrestrial to marine ecosystems. In this study, we estimated the suspended 137Cs flux to the ocean at 11 sites in the coastal area between October 2012 and December 2020 to be 17 TBq, based on our long-term monitoring data of concentrations of suspended 137Cs and suspended solids. The cumulative loss of suspended 137Cs from each site to the ocean ranged from 0.1 % to 1.7 % of initial deposition throughout the observation period, depending on the effect of dam lakes and normalized river discharge. The higher loss is also thought to be the large outflow of runoff during the typhoon. The current level of the suspended 137Cs concentrations was lowered to 1/10–1/100 of those immediately after the accident. The average value of the decreasing trend for each site was approximated using the equation: y = a1 e −k 1 t + a2 e −k 2t. The rate constant k 1 is higher in areas with intensive land use, such as pastures, bare land, and water surfaces, and k 2 is lower in urban areas.

Estimation Method of Covariance Matrix in Atmospheric Inversion of CO2 Emissions

Atmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux. In atmospheric inversion studies, fixed covariance functions are commonly used to generate covariance matrices, and the hyperparameters in the covariance functions are empirically estimated. In this study, we design and implement an ideal experiment based on meteorological data from the central urban area of Zhengzhou, using WRF-STILT to generate sensitivity matrices and construct real carbon emission inventories and prior inventories. Based on the real carbon emission inventories and sensitivity matrices of monitoring stations, simulated observation concentration values are generated. Firstly, based on the observed concentration values, sensitivity matrices of monitoring stations, prior inventories, and constructed covariance matrices, the values of hyperparameters are determined based on maximum marginal likelihood estimation. Then, the influence of different prior covariance functions on the inversion results is tested, and it is found that the prior covariance matrix generated by the balgovind covariance function is most suitable for the experimental data.

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.

Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence

Henan Province's plain area is the granary of China, yet its regional aquifer is being polluted by industrial wastewater, agricultural pesticide, fertilizer and domestic wastewater. In order to safeguard the security of food and drinking water, and in response to the problem of low prediction accuracy caused by the lack of samples and unevenly distributed groundwater monitoring data, we propose a new way to predict the aquifer vulnerability in large areas by rich small-scale data, so as to identify the pollution risks and to address the issue of sample shortage. In small regions with abundant nitrate data, we employed a Random Forest model to screen key impact indicators, using them as features and nitrate-N concentration as the target variable. Consequently, we established six machine learning prediction models, and then selected the best bagging model (R2 = 0.86) to predict the vulnerability of aquifers in larger regions lacking nitrate data. The predicted results showed that highly vulnerable areas accounted for 20 %, which were mainly affected by aquifer thickness (65.91 %). High nitrate-N concentration implies serious aquifer contamination. Therefore, a long series of groundwater nitrate-N concentration monitoring data in a large scale, the trend and slope of nitrate-N concentration showed a significant correlation with the model prediction results (Spearman's correlation coefficients are 0.75 and 0.58). This study can help identify the risk of aquifer contamination, solve the problem of sample shortage in large areas, thus contributing to the security of food and drinking water.

Attributions of emission-reduction and meteorological conditions to typical heavy pollution episodes in a cold metropolis, northeast China

Heavy pollution episodes frequently occurred in winter in northeast China due to the multiple anthropogenic emissions coupled with adverse meteorological conditions, which increased the difficulty of environmental pollution control. To better enact strategies for mitigating air pollution in the post-pandemic era, daily pollutant concentration monitoring and meteorological data were used to evaluate the changes and meteorological factors of air pollutants before (2019) and during (2020) the lockdown in Harbin City, northeast China. Moreover, typical pollution episodes under COVID-19 lockdown were identified, and their emission sources, meteorology conditions, and regional pollution transportation were analyzed. The results showed significant decreases in NO2, PM10 and CO, while O3 increased, and no differences in PM2.5 and SO2 during the lockdown compared with non-lockdown periods. It indicated that reduced activities of transportation resulted in reductions of NO2 concentrations by 16%, and stationary emission sources were less affected. Correlation between PM2.5 and O3 tended to change from positive to negative as the threshold of PM2.5 = 90 μg m−3, with the main controlling factor changed from their common gaseous precursors to meteorological conditions (temperature &amp;lt;0°C and wind speed &amp;lt;2 m s−1). Pollution days were concentrated in the COVID-19 lockdown period with PM2.5 as the primary pollutant. SO2 dominant pollution and PM2.5 dominant pollution were distinguished from six sustained heavy pollution events. PM2.5 and SO2 played essential roles in SO2 dominant pollution, which derived from local emissions of coal combustion and firework discharge. PM2.5 dominant pollution might be chemical transformed from coal burning, vehicle exhaust, and other secondary precursors, which was affected and aggravated by CO, NO2, high relative humidity and low wind speed affected by local emission and long-distance transport.

An assessment of O3-related health risks and economic losses in typical regions of China.

As one of the key areas for air pollution prevention and control in China, the Fenwei Plain is experiencing serious near-surface O3 pollution, which is a key issue that needs to be solved urgently. Based on pollutant concentration monitoring data and meteorological and health data over the same period, this study analyzed the temporal and spatial characteristics, the relationships with meteorological factors of O3 pollution, and the health effects and economic losses caused by exposure to O3 pollution using environmental health risk and environmental value assessment methods in 11 cities on the Fenwei Plain in China from 2014 to 2020. The results showed that O3 pollution has become increasingly serious on the Fenwei Plain in recent years. The annual average concentration of O3_8h_max showed an overall upwards trend, with an increase of 32.39% in 2020 compared to 2014. The mean concentrations observed in summer were the highest, followed by spring and autumn, and the lowest was in winter. The O3 concentration had a significant positive correlation with air temperature and sunshine hours. The evaluation results of the impact of air pollution on population health showed that the number of premature deaths caused by O3 pollution fluctuated and increased during 2014-2020. In 2020, the numbers of total, cardiovascular and respiratory deaths attributable to O3 pollution on the Fenwei Plain were 6,867 (95% CI: 3,739-9,965), 3,652 (95% CI: 1,363-5,905), and 1,257 (95% CI: 747-2,365), respectively, and the total number of premature deaths related to O3 exposure increased by 48.05% compared with 2014. The health and economic losses attributed to O3 pollution on the Fenwei Plain during the study period were 44.22 (95% CI: 22.17-69.18), 47.16 (95% CI: 23.64-73.77), 68.28 (95% CI: 34.27-106.31), 114.44 (95% CI: 57.42-177.76), 110.85 (95% CI: 55.45-172.52), 116.41 (95% CI: 58.24-180.74), and 116.81 (95% CI: 58.00-180.88) billion yuan, respectively. In Linfen City, the increasing rate of the number of premature deaths reached 283.39% because the O3 concentration increased greatly. Due to high O3 concentrations and obvious population growth in Xi'an, the problems of premature death and health and economic losses attributed to O3 concentrations exceeding the standard value are prominent.

A New Hybrid Forecasting Model Based on Dual Series Decomposition with Long-Term Short-Term Memory

In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air quality. Accurate and efficient ozone prediction is of great significance to the prevention and control of ozone pollution. The air quality monitoring network provides multisource pollutant concentration monitoring data for ozone prediction, but ozone prediction based on multisource monitoring data still faces the challenges of each station’s series of data. Aiming at the problems of low prediction accuracy and low computational efficiency in traditional atmospheric ozone concentration prediction, ozone concentration prediction using dual series decomposition was proposed by variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory (LSTM). First, the historical data series of Nanjing air quality monitoring stations is decomposed by VMD, and then the EEMD algorithm is applied to the residual of VMD to obtain several characteristic intrinsic mode function (IMF) components; each characteristic IMF component is trained by LSTM to obtain the prediction result of each component, and then the final result can be obtained by linear superposition. The proposed method achieved the best results with R2 = 99%, MSE = 5.38, MAE = 4.54, and MAPE = 3.12. Because LSTM has strong adaptive learning ability and good memory function, it has the learning advantage of long-term memory for long-term data, and the prediction results are more accurate. According to the data, the proposed method is superior to the baseline models in terms of statistical metrics. As a result, the proposed hybrid method can serve as a reliable model for ozone forecasting.

Therapeutic drug monitoring and the therapeutic reference range of levetiracetam for Chinese patients: Problems and issues

BackgroundLevetiracetam (LEV) is widely used in the clinical monotherapy or multi-drug combination treatment of seizures due to its good tolerability and efficacy. Due to a lack of large-scale clinical studies, the relationship between levetiracetam concentrations, disease activity and adverse is unclear, limiting the usefulness of therapeutic drug monitoring (TDM) based LEV plasma levels. This study was intended to investigate factors influencing the pharmacokinetics of and the appropriate reference range of LEV concentration using available LEV TDM data. MethodsA rapid, accurate and sensitive high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS) method was established to determine LEV plasma concentrations. In this study, the levetiracetam plasma concentration monitoring data from 352 samples (taken from 248 patients) were used to explore the relationship between levetiracetam dose, age, combined administration with other antiseizure medications in patients with epilepsy. ResultsAge and combined administration emerged as important affecting factors for the correlation of LEV concentration and dose. The correlation between concentration and dose was better in monotherapy. Combined administration may affect LEV concentration, especially when LEV is combined with oxcarbazepine, which might decrease the LEV concentration. ConclusionThese findings emphasize the need to monitor LEV routinely LEV, especially among children and older adults when other antiseizure comedications are prescribed in the treatment regimen. LEV TDM is a well-established tool for the management of patients with epilepsy.

Potential Source Area and Transport Route of Atmospheric Particulates in Xi’an, China

Atmospheric particulate pollution is one of the most common pollution related issues and poses a serious threat to human health. PM2.5 and PM10 are important indicators of atmospheric particulate pollution currently. Based on the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the hourly 72 h backward trajectory of particulate matter in Xi’an from March 2019 to February 2022 was calculated, and the main path of air flow to Xi’an was studied by cluster analysis. Combined with hourly concentration monitoring data of PM2.5 and PM10 at each station, the potential source area of particles in Xi’an was calculated by potential source contribution factor analysis and concentration weighted trajectory analysis. The results show that Xi’an was most polluted in winter, followed by autumn and spring, and cleanest in the summer. The annual average mass concentrations of PM2.5 and PM10 are 48.5 ± 28.7 μg/m3 and 89.2 ± 39.2 μg/m3, respectively, both exceeding the national secondary standard for ambient air quality. On an annual basis, back-trajectory analysis showed that predominantly transport was rapid from the northwest (44%). Transport from the other sectors were 24%, 19%, and 14% from the northeast, southeast, and southwest, respectively, and featured lower windspeeds on average. The potential source areas of particulate matter in Xi’an in the spring are mainly located at the junction of Chongqing, Hunan, and Hubei, and parts of the southeast and north of Sichuan. This study provides context for air quality and atmospheric transport conditions in this region of China.

Analysis of dust monitoring results of dust exposure enterprises in Shenxian from 2017 to 2020

Objective: In order to understand the dust concentration in the workplace of dust exposure enterprises in Shenxian. To evaluate the degree of occupational hazard factors of dust exposure enterprises. And provide basis for the formulation of occupational protection standards and management system of dust exposure enterprises. Methods: In February 2022, the dust concentration monitoring data of 89 dust exposure enterprises from 2017 to 2020 by the Shenxian Center for Disease Control and Prevention were collected, and the qualified rates of dust concentration detection of dust exposure enterprises in different years, dust types and enterprise sizes were analyzed. Results: A total of 89 dust enterprises were monitored from 2017 to 2020, 2132 dust samples were collected, and 1818 qualified samples were taken, with a total qualified rate of 85.3%. From 2017 to 2020, the dust detection qualified rates showed a year-by-year increase trend, 78.7% (447/568), 84.1% (471/560), 88.6% (418/472) and 90.6% (482/532), respectively, with statistically significant differences (χ(2)=36.27, P=0.003). The differences in the qualified rates of dust detection samples of silicon dust (66.1%, 41/62), grain dust (86.7%, 1549/1786), cotton dust (84.1%, 106/126) and wood dust (77.2%, 122/158) were statistically significant (χ(2)=29.66, P=0.002). The qualified rate of dust samples in large and medium-sized enterprises (95.1%, 1194/1256) was higher than that of small-sized enterprises (71.2%, 624/876), and the difference was statistically significant (χ(2)=1584.40, P=0.001) . Conclusion: The qualified rate of dust concentration monitoring results of dust exposure enterprises in Shenxian showed an increase trend year by year, while the pualified rate of dust concentration monitoring in small-sized enterprises was low, and the occupational hazard of silica dust was still severe.

Reconstructed lung doses for the million person study cohort of 26,650 Tennessee Eastman corporation workers employed between 1942 and 1947

Tennessee Eastman Corporation workers were exposed to uranium dust resulting in high-linear energy transfer (LET) irradiation to lung tissue. In this work, radiation lung doses were reconstructed for 26 650 men and women working at the plant between 1942 and 1947. Site air monitoring data of uranium concentrations and payroll records were used to determine the daily inhaled activities and annualized lung doses. Variations in the activity median aerodynamic diameter of the uranium dust, the solubility of particulate matter in the lungs and the sex-specific breathing rate were investigated as part of a sensitivity analysis. Male and female mean lung doses of 18.9 and 32.7 mGy, respectively, from high-LET alpha irradiation, and there was general agreement with evaluations from previously published epidemiological studies. Annual lung dose estimates and sensitivity analysis for the 26 650 workers in the TEC cohort have been archived on the United States Department of Energy Comprehensive Epidemiologic Data Resource.

Spatiotemporal Distribution Characteristics of Fire Scars Further Prove the Correlation between Permafrost Swamp Wildfires and Methane Geological Emissions

Affected by global warming, methane gas released by permafrost degradation may increase the frequency of wildfires, and there are few studies on wildfires in permafrost regions and their correlation with climate and regional methane emissions. The northwestern section of the Xiaoxing’an Mountains in China was selected as the study area, and the spatial relationship between permafrost and spring wildfires was studied based on Landsat TM and Sentinel-2 data. Combined with monitoring data of air temperature, humidity, and methane concentration, the impact of methane emissions on spring wildfires was analyzed. The study shows that the spatial distribution of fire scars in spring is highly consistent with permafrost, and the change trend of fire scars is in line with the law of permafrost degradation. Wildfires occur intensively during the snow melting period in spring, and the temporal variation pattern is basically consistent with the methane concentration. The number of fire points was positively correlated with air temperature and methane concentration in March and April, and spring wildfires in permafrost regions are the result of a combination of rising seasonal temperatures, surface snow melting, and concentrated methane emissions. Larger areas of discontinuous permafrost are more prone to recurring wildfires.

Mining biomarkers from routine laboratory tests in clinical records associated with air pollution health risk assessment

Clinical laboratory in hospital can produce amounts of health data every day. The purpose of this study was to mine biomarkers from clinical laboratory big data associated with the air pollution health risk assessment using clinical records. 13, 045, 629 clinical records of all 27 routine laboratory tests in Changsha Central Hospital, including ALB, TBIL, ALT, DBIL, AST, TP, UREA, UA, CREA, GLU, CK, CKMB, LDL-C, TG, TC, HDL-C, CRP, WBC, Na, K, Ca, Cl, APTT, PT, FIB, TT, RBC and those daily air pollutants concentration monitoring data of Changsha, including PM2.5, PM10, SO2, NO2, CO, and O3 from 2014 to 2016, were retrieved. The moving average method was used to the biological reference interval was established. The tests results were converted into daily abnormal rate. After data cleaning, GAM statistical model construction and data analysis, a concentration-response relationship between air pollutants and daily abnormal rate of routine laboratory tests was observed. Our study found that PM2.5 had a stable association with TP (lag07), ALB (lag07), ALT (lag07), AST (lag07), TBIL (lag07), DBIL (lag07), UREA (lag07), CREA (lag07), UA (lag07), CK (lag 06), GLU (lag07), WBC (lag07), Cl (lag07) and Ca (lag07), (P < 0.05); O3 had a stable association with AST (lag01), CKMB (lag06), TG (lag07), TC (lag05), HDL-C (lag07), K (lag05) and RBC (lag07) (P < 0.05); CO had a stable association with UREA (lag07), Na (lag7) and PT (lag07) (P < 0.05); SO2 had a stable association with TP (lag07) and LDL-C (lag0) (P < 0.05); NO2 had a stable association with APTT (lag7) (P < 0.05). These results showed that different air pollutants affected different routine laboratory tests and presented different pedigrees. Therefore, biomarkers mined from routine laboratory tests may potentially be used to low-cost assess the health risks associated with air pollutants.