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The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH3) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption. Four identical CF rooms were utilized, each accommodating 175 hens of 77 wk of age (WOA). A Latin Square Design method was employed, with 4 treatment lengths: T1 = control (0 m), T2 = 12 ft (3.7 m), T3 = 24 ft (7.3 m), and T4 = 36 ft (11.0 m), where room and WOA are considered as blocking factors. Daily PM concentrations, temperature, and humidity measurements were conducted over 24 h, while NH3 levels, litter moisture content (LMC), and ventilation were measured twice a week in each treatment room. Statistical analysis involved ANOVA, and mean comparisons were performed using the Tukey HSD method with a significance level of P ≤ 0.05. This study found that the EPI system with longer wires reduced PM2.5 concentrations (P ≤ 0.01). Treatment T2, T3, and T4 led to reductions in PM2.5 by 12.1%, 19.3%, and 31.7%, respectively, and in small particle concentrations (particle size >0.5 µm) by 18.0%, 21.1%, and 32.4%, respectively. However, no significant differences were observed for PM10 and large particles (particle size >2.5 µm) (P < 0.10), though the data suggests potential reductions in PM10 (32.7%) and large particles (33.3%) by the T4 treatment. Similarly, there was no significant impact of treatment on NH3 reduction (P = 0.712), possibly due to low NH3 concentration (<2 ppm) and low LMC (<13%) among treatment rooms. Electricity consumption was significantly related to the length of the EPI system (P ≤ 0.01), with longer lengths leading to higher consumption rates. Overall, a longer-length EPI corona pipe is recommended for better air pollutant reduction in CF housing. Further research should focus on enhancing EPI technology, assessing cost-effectiveness, and exploring combinations with other PM reduction strategies.

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As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection.

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Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.

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Particulate matter (PM) concentrations are affected by anthropogenic emissions and sand transport jointly; however, the relative contributions from those two aspects are usually unknown. In our work, statistical analysis and back trajectories model were used to identify the dominant source in such area, by taking Yumen City as an example. We come to the conclusion that local emissions dominate the concentration of airborne pollutants, while sand transport plays a significant role on PM concentration. The conclusions were supported by the following results. (1) PM monthly mean concentrations at the two air quality stations, which are 70 km far away from each other, have the similar levels and variation trend; furthermore, a regression analysis of PM2.5 and PM10 daily concentrations between both stations indicated a significant correlation, suggesting that PM at both locations was influenced by the same emission sources; (2) statistical analysis results revealed that PM concentration has a positive correlation with wind speed, indicating the wind-blown dust and sand contribute mainly on PM concentration; (3) back-trajectory clustering analysis indicates that long-distance transport particulates from dust sources and their pathways had a significant impact on local PM concentrations.

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The effect of air pollution on the environment, economic and health of the people in the affected countries cannot be overemphasized. This paper investigates large scale air pollution elimination to remove pollutants that are already in existence in the environment. This method involves the use of Environmental Drones (E-drones) to autonomously monitor the air quality at a specific location. The E-drone flies up to a predetermined height (Ealtitude) every hour, measures the air pollutants at that location, implements on-board pollution abatement solutions for pollutants above the recommended threshold, and then flies back down to its location on the ground. The advantages of this system is its ability to measure air pollution concentration of CO2, CO, NH3, SO2, PM, O3 and NO2, detect when they are too high, and implement on-board pollution abatement solutions as needed. This system's novelty lies in the fact that it not only detects when there is excessive pollution, but it also automatically deals with and abates the detected air pollution above earth. When multiple E-drones are used in different locations, a custom software generates an Air Quality Health Index (AQHI) map of the region that can be used for present and long-term environmental analysis.

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The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn't thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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Air pollution is a serious environmental problem that has drawn worldwide attention. Predicting air pollution in advance has great significance on people's daily health control and government decision-making. However, existing research methods have failed to effectively extract the spatiotemporal features of air pollutant concentration data, and exhibited low precision in long-term predictions and sudden changes in air quality. In the present study, a spatiotemporal convolutional long short-term memory neural network extended (C-LSTME) model for predicting air quality concentration was proposed. In order to encompass the spatiality and temporality of the data, the model involved the historical air pollutant concentration of the present station, as well as that of the adaptive k-nearest neighboring stations, into the model. High-level spatiotemporal features were extracted through the combination of the convolutional neural network (CNN) and long short-term memory neural network (LSTM-NN), and meteorological data and aerosol data were also integrated, in order to improve model prediction performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 mm) concentration data collected at 1233 air quality monitoring stations in Beijing and the whole China from January 1, 2016 to December 31, 2017 were used to validate the effectiveness of the proposed C-LSTME model. The results show that the present model has achieved better performance than current state-of-the-art models for different time predictions at different regional scales.

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In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush-Kuhn-Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

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Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 µm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%).

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PM10 is the main air pollutant in Taiyuan, as the city is a heavy industrial center with coal as its main energy source. Therefore, research on the prediction of this pollutant's variation and concentration is of great theoretical significance for air pollution prevention and emergency solutions. The source of PM10 is very complex, as it is affected by industrial emissions, vehicle exhaust, fugitive dust, and many other factors. The emission sources of PM10 are difficult to determine accurately. The goal of our research was to give accurate forecasting results efficiently when only time-series PM10 concentrations, and no other exogenous information, is available. A support vector machine (SVM) enjoys good generalization performance in the PM10 concentration forecasting area. Traditionally, an SVM chooses historical data as the input features in the process of dealing with the time-series data of air pollutant concentrations. However, data with simple structure and incomplete information have become the fetter of generalization ability improvement. In this study, the data for simulation experiments was the PM10 concentration dataset collected from four monitoring stations in Taiyuan. The PM10 concentration time-series one-dimension data was decomposed into high dimension, constructed by low frequency and high frequency series using a wavelet transform. The wavelet-SVM forecasting model can be established by introducing the high-dimension data as the input features. The experiment results indicate that, contrasted with the traditional SVM, the wavelet-SVM model boasts higher accuracy for PM10 concentration prediction. In particular, it captures the concentration mutational points more accurately and provides information support that is more effective for atmospheric pollution warning. In addition, with the wavelet-SVM model, prediction accuracy for the concentration variations was significantly improved and laws that were more inherent in the PM10 concentration time series were revealed.