Forecasting of bioaerosol concentration by a Back Propagation neural network model

Intelligent Gas Detection: g-C3N4/Polypyrrole Decorated Alginate Paper as Smart Selective NH3/NO2 Sensors at Room Temperature

Chemiresistive NH3/NO2 sensors are attracting considerable attention for use in air-conditioning systems. However, the existing sensors suffer from cross-sensitivity, detection limit, and power consumption, owing to the inadequate charge-transfer ability of gas-sensing materials. Herein, we develop a flexible NH3/NO2 sensor based on graphitic carbon nitride/polypyrrole decorated alginate paper (AP@g-CN/PPy). The flexible sensor can work at room temperature and exhibits a positive response of 23-246% and a negative response of 37-262% toward 0.1-5 ppm of NH3 and NO2, which is ∼4.5 times and ∼7.0 times higher than a pristine PPy sensor. Moreover, the sensor exhibits flexibility, reproducibility, long-term stability, anti-interference, and high resilience to humidity, indicating its promising potential in real applications. Using the 9 feature parameters extracted from the transient response, a matched deep learning model was developed to achieve qualitative recognition of different types of gases with distinguished decision boundaries. This work not only provides an alternative gas-sensing material for dual NH3/NO2 sensing but also establishes an intelligent strategy to identify hazardous gases under an interfering atmosphere.

Reversible Fusion-Fission MXene Fiber-Based Microelectrodes for Target-Specific Gram-Positive and Gram-Negative Bacterium Discrimination

Inaccurate or cumbersome clinical pathogen diagnosis between Gram-positive bacteria (G+) and Gram-negative (G-) bacteria lead to delayed clinical therapeutic interventions. Microelectrode-based electrochemical sensors exhibit the significant advantages of rapid response and minimal sample consumption, but the loading capacity and discrimination precision are weak. Herein, we develop reversible fusion-fission MXene-based fiber microelectrodes for G+/G- bacteria analysis. During the fissuring process, the spatial utilization, loading capacity, sensitivity, and selectivity of microelectrodes were maximized, and polymyxin B and vancomycin were assembled for G+/G- identification. The surface-tension-driven reversible fusion facilitated its reusability. A deep learning model was further applied for the electrochemical impedance spectroscopy (EIS) identification in diverse ratio concentrations of G+ and G- of (1:100-100:1) with higher accuracy (>93%) and gave predictable detection results for unknown samples. Meanwhile, the as-proposed sensing platform reached higher sensitivity toward E. coli (24.3 CFU/mL) and S. aureus (37.2 CFU/mL) in 20 min. The as-proposed platform provides valuable insights for bacterium discrimination and quantification.

Seasonal variability of airborne mold concentrations as related to dust in a coastal urban area in the Eastern Mediterranean

Recent studies have demonstrated that the amount of specific airborne mold types and their concentrations increase during dust events. This study investigates the seasonal variation of airborne mold concentrations before, during, and after the dust transport in an eastern Mediterranean coastal area, Izmir city, Turkey. A total of 136 airborne mold samples were collected between September 2020 and May 2021. Two different culture media, namely Potato Dextrose Agar (PDA) and Malt-Extract Agar (MEA), were used for enumeration and genus-based identification of the airborne mold. In addition to culture media, the influences of air temperature, relative humidity, and particulate matter equal to or less than 10 µm (PM10) were also investigated seasonally. The HYSPLIT trajectory model and web-based simulation results were mainly used to determine dusty days. The mean total mold concentrations (TMC) on dusty days (543 Colony Forming Unit (CFU)/m3 on PDA and 668 CFU/m3 on MEA) were approximately 2-2.5 times higher than those on non-dusty days (288 CFU/m3 on PDA and 254 CFU/m3 on MEA) for both culture media. TMC levels showed seasonal variations (p < 0.001), indicating that meteorological parameters influenced mold concentrations and compositions. Some mold genera, including Cladosporium sp., Chrysosporium sp., Aspergillus sp., Bipolaris sp., Alternaria sp., and yeast, were found higher during dusty days than non-dusty days. Thus, dust event impacts levels and types of airborne molds and has implications for regions where long-range dust transport widely occurs.

Cadmium toxicity to and accumulation in a soil collembolan (Folsomia candida): major factors and prediction using a back-propagation neural network model

Accurate prediction of cadmium (Cd) ecotoxicity to and accumulation in soil biota is important in soil health. However, very limited information on Cd ecotoxicity on naturally contaminated soils. Herein, we investigated soil Cd ecotoxicity using Folsomia candida, a standard single-species test animal, in 28 naturally Cd-contaminated soils, and the back-propagation neural network (BPNN) model was used to predict Cd ecotoxicity to and accumulation in F. candida. Soil total Cd and pH were the primary soil properties affecting Cd toxicity. However, soil pH was the main factor when the total Cd concentration was < 3 mg kg-1. Interestingly, correlation analysis and the K-spiked test confirmed nutrient potassium (K) was essential for Cd accumulation, highlighting the significance of studying K in Cd accumulation. The BPNN model showed greater prediction accuracy of collembolan survival rate (R2 = 0.797), reproduction inhibitory rate (R2 = 0.827), body Cd concentration (R2 = 0.961), and Cd bioaccumulation factor (R2 = 0.964) than multiple linear regression models. Then the developed BPNN model was used to predict Cd ecological risks in 57 soils in southern China. Compared to multiple linear regression models, the BPNN models can better identify high-risk regions. This study highlights the potential of BPNN as a novel and rapid tool for the evaluation and monitoring of Cd ecotoxicity in naturally contaminated soils.

Portable, Intelligent Fluorescence Sensing Platform for Dense Convolutional Network-Capable Detection of Indophenol Sulfate and Methylmalonic Acid Using a Luminescent Eu@HOF Film

Indophenol sulfate (IS) and methylmalonic acid (MMA) are biomarkers of chronic kidney disease (CKD) and diabetes polyneuropathy (DPN), respectively. Portable and accurate monitoring of IS and MMA is very important to ensuring human health. The dense convolutional network (DenseNet) with image recognition has great potential in fluorescence sensing, but developing a platform with high precision and portability to diagnose the disease still faces huge challenges. Herein, we developed a high-sensitivity platform with a fluorescence material, a smartphone, and the DenseNet to monitor IS and MMA. A red-emitting Eu@PFC-13 (1) is prepared, and 1 shows high selectivity and low detection limits (DLs) to detect IS and MMA. The sensing mechanism of 1 toward IS and MMA is investigated by experiments and theoretical calculation. For detecting IS and MMA in serum and urine, 1 is fabricated into an Eu@PFC-13/AG (2) film with DLs of 1.4 and 1.6 μM, respectively. In addition, a portable smartphone platform is designed to monitor IS and MMA with high precision. Moreover, the DenseNet is constructed by Python, which can output the concentration of analytes by identifying fluorescence images and judge whether any is in a dangerous range. This work not only proposes a novel method that integrates a fluorescence material, a smartphone, and deep learning to detect analytes but also opens a new way for the diagnosis of CKD and DPN.

Prediction and balanced allocation of thermal power carbon emissions from a provincial perspective of China

Carbon control in the thermal power generation industry is crucial for achieving the overall carbon peak target. How to predict, evaluate, and balance the allocation of inter provincial carbon emissions has a significant impact on the decision-making of reasonable allocation of inter provincial carbon emissions in the target year. Therefore, this paper uses Monte Carlo-ARIMA-BP neural network and ZSG-DEA model to conduct temporal trend prediction and carbon emission quota allocation research. We propose the "intra provincial and inter provincial" framework for carbon emissions trading in thermal power plants, which aims to break through the barriers in carbon emission rights exchange among provinces. The conclusions are as follows: (1) the growth trend of carbon emissions from thermal power is gradually slowing down and is expected to peak before 2030. (2) Inner Mongolia, Jiangsu, and Shandong have high input-output efficiency, and are all the main output provinces for carbon emission quota allocation. After being adjusted using the ZSG-DEA model, they can still be at the forefront of efficiency. (3) The "intra provincial and inter provincial" framework for carbon emissions trading can effectively predict and allocate the carbon emission demand of each province from time and space dimensions, balance the carbon emission rights and interests of each province, and provide forward-looking planning suggestions for inter provincial carbon emission rights exchange.

Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data

Biological agents are important to detect and identify with respect to environmental contamination and public health. Noise contamination in fluorescent spectra is one of the contributors to the uncertainties of identification. In order to investigate the noise-tolerant capability provided by laboratory-measured excitation-emission matrix (EEM) fluorescence spectra that are used as a database, fluorescence properties of four proteinaceous biotoxin samples and ten harmless protein samples were characterized by EEM fluorescence spectra, and the predicting performance of models trained by laboratory-measured fluorescence data was tested and verified from validation data with noise-contaminated spectra. By means of peak signal of noise (PSNR) as an indicator of noise levels, the potential impact of noise contaminations on the characterization and discrimination of these samples was evaluated quantitatively. Different classification schemes utilizing multivariate analysis techniques of Principal Component Analysis (PCA), Random Forest (RF), and Multi-layer Perceptron (MPL) coupled with feature descriptors of differential transform (DT), Fourier transform (FT) and wavelet transform (WT) were conducted under different PSNR values. We systematically analyzed the performance of classification schemes by the case study at 20 PSNR and by statistical analysis from 1-100 PSNR. The results show that the spectral features with EEM-WT decreased the demanding number of input variables while retaining high performances in sample classification. The spectral features with EEM-FT presented the worst performance although having the largest number of features. The distributions of feature importance and contribution were found sensitive to noise contaminations. The classification scheme of PCA prior to MPL with EEM-WT as input presented an improvement in lower PSNR. These results indicate that robust features extracted by corresponding techniques are critical to enhancing the spectral differentiation capabilities among these samples and play an important role in eliminating the noise effect. The study of classification schemes for discriminating protein samples with noise-contaminated spectra presents tremendous potential for future developments in the rapid detection and identification of proteinaceous biotoxins based on three-dimensional fluorescence spectrometry.

Uncertainty quantification of CO2 emissions from China's civil aviation industry to 2050

To mitigate aviation's carbon emissions of the aviation industry, the following steps are vital: accurately quantifying the carbon emission path by considering uncertainty factors, including transportation demand in the post-COVID-19 pandemic period; identifying gaps between this path and emission reduction targets; and providing mitigation measures. Some mitigation measures that can be employed by China's civil aviation industry include the gradual realization of large-scale production of sustainable aviation fuels and transition to 100% sustainable and low-carbon sources of energy. This study identified the key driving factors of carbon emissions by using the Delphi Method and set scenarios that consider uncertainty, such as aviation development and emission reduction policies. A backpropagation neural network and Monte Carlo simulation were used to quantify the carbon emission path. The study results show that China's civil aviation industry can effectively help the country achieve its carbon peak and carbon neutrality goals. However, to achieve the net-zero carbon emissions goal of global aviation, China needs to reduce its emissions by approximately 82%-91% based on the optimal emission scenario. Thus, under the international net-zero target, China's civil aviation industry will face significant pressure to reduce its emissions. The use of sustainable aviation fuels is the best way to reduce aviation emissions by 2050. Moreover, in addition to the application of sustainable aviation fuel, it will be necessary to develop a new generation of aircraft introducing new materials and upgrading technology, implement additional carbon absorption measures, and make use of carbon trading markets to facilitate China's civil aviation industry's contribution to reduce climate change.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM_2.5 and PM₁₀) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %-80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.

Long-Term Studies of Biological Components of Atmospheric Aerosol: Trends and Variability

Background: Biological components of atmospheric aerosol affect the quality of atmospheric air. Long-term trends in changes of the concentrations of total protein (a universal marker of the biogenic component of atmospheric aerosol) and culturable microorganisms in the air are studied. Methods: Atmospheric air samples are taken at two locations in the south of Western Siberia and during airborne sounding of the atmosphere. Sample analysis is carried out in the laboratory using standard culture methods (culturable microorganisms) and the fluorescence method (total protein). Results: Negative trends in the average annual concentration of total protein and culturable microorganisms in the air are revealed over more than 20 years of observations. For the concentration of total protein and culturable microorganisms in the air, intra-annual dynamics is revealed. The ratio of the maximum and minimum values of these concentrations reaches an order of magnitude. The variability of concentrations does not exceed, as a rule, two times for total protein and three times for culturable microorganisms. At the same time, for the data obtained in the course of airborne sounding of the atmosphere, a high temporal stability of the vertical profiles of the studied concentrations was found. The detected biodiversity of culturable microorganisms in atmospheric air samples demonstrates a very high variability at all observation sites. Conclusions: The revealed long-term changes in the biological components of atmospheric aerosol result in a decrease in their contribution to the atmospheric air quality index.

PM2.5 drives bacterial functions for carbon, nitrogen, and sulfur cycles in the atmosphere

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM_2.5) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM_2.5 samples, all of which are the important components of PM_2.5. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM_2.5 concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM_2.5 concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM_2.5 and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM_2.5, offering a new perspective for atmospheric ecology and pollution control.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

A Novel NaCl Concentration Detection Method Based on Ultrasonic Impedance Method

In this article, we propose a novel method for real-time detection of NaCl concentration based on ultrasonic impedance method. Specifically, our method is based on the fact that different concentration of NaCl leads to differences in acoustic characteristics of ultrasonic waves. We conducted an experimental study on the relationship between the concentration and the amplitude of ultrasonic echo. Our evaluations show that our approach can push the linear fitting degree r² to 0.9967. Then we used the BP neural network to classify and identify the collected sample data and achieved 100% classification accuracy. We also established a concentration-amplitude prediction model based on the BP neural network, and the result shows that the error between the predicted value and the ground truth is less than 8.36%. This method realizes non-contact measurement and can be applied to real-time monitoring of NaCl concentration in large, closed containers.

Changes and Predictions of Vertical Distributions of Global Light-Absorbing Aerosols Based on CALIPSO Observation

Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of the vertical distribution of global absorbing aerosols based on satellite measurement from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and proposed a neural network (NN) model to predict the vertical distribution of global absorbing aerosols. Gaussian fitting was proposed to derive the maximum fitted particle number concentration (MFNC), altitude corresponding to MFNC (MFA), and standard deviation (MFASD) for vertical distribution of dust and smoke aerosols. Results showed that higher MFA values of dust and smoke aerosols mainly occurred over deserts and tropical savannas, respectively. For dust aerosol, the MFA is mainly observed at 0.5 to 6 km above deserts, and low MFNC values occur in boreal spring and winter while high values in summer and autumn. The MFA of smoke is systematically lower than that of dust, ranging from 0.5 to 3.5 km over tropical rainforest and grassland. Moreover, we found that the MFA of global dust and smoke had decreased by 2.7 m yr−1 (statistical significance p = 0.02) and 1.7 m yr−1 (p = 0.02) over 2007–2016, respectively. The MFNC of global dust has increased by 0.63 cm−3 yr−1 (p = 0.05), whereas that of smoke has decreased by 0.12 cm−3 yr−1 (p = 0.05). In addition, the determination coefficient (R2) of the established prediction models for vertical distributions of absorbing aerosols were larger than 0.76 with root mean square error (RMSE) less than 1.42 cm−3, which should be helpful for the radiative forcing evaluation and atmospheric correction of satellite remote sensing.

Atmospheric Biodetection Part I: Study of Airborne Bacterial Concentrations from January 2018 to May 2020 at Saclay, France

Background: The monitoring of bioaerosol concentrations in the air is a relevant endeavor due to potential health risks associated with exposure to such particles and in the understanding of their role in climate. In this context, the atmospheric concentrations of bacteria were measured from January 2018 to May 2020 at Saclay, France. The aim of the study was to understand the seasonality, the daily variability, and to identify the geographical origin of airborne bacteria. Methods: 880 samples were collected daily on polycarbonate filters, extracted with purified water, and analyzed using the cultivable method and flow cytometry. A source receptor model was used to identify the origin of bacteria. Results: A tri-modal seasonality was identified with the highest concentrations early in spring and over the summer season with the lowest during the winter season. Extreme changes occurred daily due to rapid changes in meteorological conditions and shifts from clean air masses to polluted ones. Conclusion: Our work points toward bacterial concentrations originating from specific seasonal-geographical ecosystems. During pollution events, bacteria appear to rise from dense urban areas or are transported long distances from their sources. This key finding should drive future actions to better control the dispersion of potential pathogens in the air, like persistent microorganisms originating from contaminated areas.

Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice

Boiler forced draft systems play a critical role in maintaining power plant safety and efficiency. However, their control is notoriously intractable in terms of modelling difficulty, multiple disturbances and severe noise. To this end, this paper develops a data-driven paradigm by combining some popular data analytics methods in both modelling and control. First, singular value decomposition (SVD) is utilized for data classification, which further cooperates with back propagation (BP) neural network to de-noise the measurements. Second, prediction error method (PEM) is used to analyze the historical data and identify the dynamic model, whose responses agree well with the actual plant data. Third, by estimating the lumped disturbances via the real-time data, active disturbance rejection control (ADRC) is employed to control the forced draft system, whose stability is analyzed in the frequency domain. Simulation results demonstrate the efficiency and superiority of the proposed method over proportional-integral-differential (PID) controller and model predictive controller, depicting a promising prospect in the future industry practice.