ABSTRACT
The goal of this book is to improve the ability of enterprises to implement the green growth model and value chain reconstruction. China's environmental development strategies, such as carbon peak emission and carbon neutrality, have created new challenges and requirements for enterprises to "go green.” In addition, anti-globalization and the complex dynamic uncertainty caused by COVID-19 have changed the operational environment that enterprises face. The application of new technologies, including the new generation of information technologies and the whole process management technology, provides solutions for the implementation of enterprises' green growth model and value chain reconstruction. Based on China's enterprise management cases, this book reveals the connotative features of enterprises' green growth model and their evolutionary regularities, the overall framework and decision optimization of value chain reconstruction under the green growth model, and the approach to implementing the green growth model and value chain reconstruction. The theoretical framework of the green growth model and value chain reconstruction established in this book has enriched and developed the research results in this field. Cases of enterprises implementing the green growth model can provide references for the green transformation of enterprises and help enterprises appreciate the synergy between sustainability and growth. This book can also serve as a research reference for scholars engaged in the field of sustainable operations, as well as decision-makers and managers of relevant government departments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
ABSTRACT
Objectives: The public's stated preference for public health and social measures (PHSMs), and levels of pandemic fatigue are insufficiently fixed. We aim to quantify the public's preferences for varied PHSMs, and measure population's pandemic fatigue. Method(s): We conducted a cross-sectional, nationwide sampling, survey-based experiment to assess public preference for and attitudes towards PHSMs. A set of psychometric scales, specifically, the COVID-19 pandemic fatigue scale (CPFS), was used to screen fatigue levels in the respondents. The multinomial logit model (MNL) and latent class model (LCM) were utilized for choice tasks analysis, and Mann-Whitney tests were used for CPFS statistical analysis. Result(s): There were 689 respondents, who completed the survey, and were included in the study after quality control. The discrete choice experiments revealed that respondents attached the greatest importance to the risk of COVID-19 infection within three months (45.53%), followed by loss of income within three months (30.69%). Vulnerable populations (lower-income and older respondents) are more sensitive to the risk of infection, and younger respondents are more sensitive to income loss and prefer non-suspension of socialization and transportation. Migrants, and respondents with a higher level of fatigue, have less acceptance of the mandatory booster vaccination and suspension of transportation. Additionally, a higher fatigue level was observed in females, younger respondents, migrants, and relatively lower-income respondents. Conclusion(s): Fatigue and fear of COVID-19 infection contributed to the public's mental health problem. Hence, at the late-stage pandemic, policymakers should consider reducing people's mental burden via relieving people's fear of infection when PHSMs are being relaxed. And this also provides insights for the outbreaks' PHSMs implementation in the future.Copyright © 2023
ABSTRACT
Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
ABSTRACT
The COVID-19 pandemic has had a significant impact on both the financial market and the real economy, leading to widespread concern about the relationship between environmental, social, and governance (ESG) responsibilities and shareholders' wealth. This paper examines the relationship between ESG responsibility and stock returns in the context of the COVID-19 pandemic and investigates the impact of ESG responsibility on stock price resilience. The results indicate that corporate ESG scores have positive impacts on stock returns during and after the COVID-19 crisis, with the positive impacts of ESG being more significant in the post-crisis period. Among the different ESG dimensions, environmental responsibility (ESG_E) has a more significant impact on stock returns, while social responsibility (ESG_S) and governance responsibility (ESG_G) have mixed impacts during the crisis. Furthermore, during and after the outbreak of COVID-19, the positive impacts of ESG are more pronounced among firms located in low-trust regions and firms with lower analyst coverage. Additionally, the study finds that corporate ESG responsibility help restore the resilience of stock prices during the crisis, with better ESG performance leading to stronger stock price resilience. Overall, the results strongly support the conclusion that ESG has acted as an "equity vaccine” during the COVID-19 pandemic. © 2023 Elsevier B.V.
ABSTRACT
Anodic aluminium oxide-copper (AAO-Cu) coatings were prepared on the aluminium (Al) alloy substrates to attain excellent antibacterial performance and mechanical stability. The nanoporous AAO interlayer was ob-tained by anodic oxidation with an outer Cu layer deposited by electroplating. The intermediate zone (similar to 2 mu m thick) of the AAO-Cu coating plays a significant role in the coating properties. The interlocking effect in the AAO-Cu intermediate zone significantly enhances the coating adhesion and curbs the coating defoliation. The anti-bacterial tests show that the AAO-Cu zone provides excellent antibacterial ability even when the outer Cu coating was removed. The sustained antibacterial rate of the AAO-Cu intermediate zone against E. coli exceeded 95%. The Cu ions released from the embedded Cu in the nanoporous AAO structure ensure a long-term antibacterial capability. This coating system can be promoted in a large wide range of antibacterial products in public.
ABSTRACT
Inaccurate promotional information about tourist destinations may result in tourists' negative evaluations. This study proposes a new approach to measure the congruence between projected and received images of a destination's attractions. Based on online textual data, this study investigates how image congruence influences tourists' evaluations of their destination experiences. Using promotional messages and reviews of attractions in Hainan, China obtained from a leading Chinese online travel agency (Ctrip) and a three-way fixed-effects regression model, this study demonstrates that image congruence positively affects tourists' appraisal of their destination experiences. External crises (e.g., the COVID-19 pandemic), the readability of promotional messages, and tourists' expertise moderate this relationship, reducing the positive impact of image congruence on tourist experience evaluation. This study bridges theoretical and empirical gaps in destination image (in)congruence research, informing tourism marketing agencies of effective promotional strategies in different contexts. © 2023
ABSTRACT
With the emergence of the COVID-19, masks and protective clothing have been used in huge quantities. A large number of non-degradable materials have severely damaged the ecological environment. Now, people are increasingly pursuing the use of environmentally friendly materials to replace traditional chemical materials. Silk fibroin (SF) and Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) have received increasing attention because of their unique biodegradability and biocompatibility. In this paper, a series of biodegradable SF/PHBV nanofiber membranes with different PHBV content were fabricated by using electrospinning technology. The morphology of the electrospun SF/PHBV composite nanofiber was observed by scanning electron microscopy (SEM). The average diameters of the pure SF, SF/PHBV (4/1), SF/PHBV (3/1), and SF/PHBV (2/1) nanofibers were 55.16 ± 12.38 nm, 75.93 ± 21.83 nm, 69.35 ± 21.55 nm, and 61.40 ± 12.31 nm, respectively. Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) were used to explore the microstructure of the electrospun SF/PHBV composite nanofiber. The crystallization ability of the composite nanofiber was greatly improved with the addition of PHBV. The results of thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) indicated that the thermal stability of SF was better than PHBV obviously, so SF could improve the thermal stability of the composite materials within a certain range. The mechanical properties of the electrospun nanofiber membranes were evaluated by using a universal testing machine. In general, the elongation of the composite nanofiber membranes decreased, and the breaking strength increased with the addition of PHBV. The small pore size of the nanofiber membranes ensured that they had good application prospects in the field of filtration and protection. When the spinning time was 1 h, the filtration efficiency of SF/PHBV/PLA composite materials remained above 95%. © 2021 The Textile Institute.
ABSTRACT
Purpose: Uncertain times [e.g. coronavirus disease 2019 (COVID-19)] require service businesses to respond in creative, flexible and resilient ways. This paper aims to develop and test the theoretical relationship between digital transformation and organizational resilience (OR), and the consequences of OR on organizations and employees during turbulent times. Design/methodology/approach: A scale development was first conducted with an expert panel. Later, 474 participants who work as employees in small and medium-sized service enterprises were recruited for structural equation modeling (SEM). Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and path analysis were conducted to test the relationship between dimensions of digital maturity, dimensions of OR and two consequential variables: organizational performance and employees' state optimism. Findings: Strategic technology investment helps organizations to develop systematic control sustain operations in crises but may not directly contribute to employees' capabilities of accurately understanding external turmoil, actively seeking available resources and rapidly developing adaptive solutions. Transformation management intensity equips an organization with transformative vision, governance and culture, and such transformative built-in leadership enables the organization to embrace employees with talents and innovativeness and help employees grow their capabilities when facing crises. The dimensions of OR have different influences on the organization and employees. Originality/value: This research develops and tests the dimensions and measurement items of OR for the services domain and empirically tested how the dimensions of digital maturity influence the dimensions of OR, and how OR influences the organization's performance and employees' state optimism. © 2021, Emerald Publishing Limited.
ABSTRACT
Digital analysis is an effective single-molecule detection method and has attracted extensive attention in the field of bioassays. However, most digital assays require microchambers for signal compartmentalization. Herein, we developed a microchamber-free and enzyme-free digital assay by labeling paramagnetic beads directly with ultrabright fluorescent microspheres. In this assay, a DNA sandwich analysis was firstly performed on the bead to label a fluorescent microsphere. Then, the beads were loaded on the glass slide to form a monolayer film for signal readout. The whole analysis process does not require the participation of enzymes and the preparation of microchambers, which greatly simplifies the experimental steps and saves the costs. Furthermore, by introducing non-enzymatic hybridization chain reaction (HCR) and biotinylated DNA-conjugated gold nanoparticles (Au NPs-Bio), the capture efficiency and analytical sensitivity were improved. As a proof of concept, single-stranded DNA (ssDNA) of SARS-CoV-2 fragment was chosen as a model, and a detection limit of 1.5 fM was achieved. Spiked and recovery experiments on human serum and saliva samples validated the good performance of the proposed digital assay in real biological samples. The proposed assay provides a facile way of signal generation and readout for digital analysis. © 2023 Elsevier B.V.
ABSTRACT
Background:Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and has spread worldwide. Clinical risk factors associated with the severity in COVID-19 patients have not yet been well delineated. The aim of this study was to explore the risk factors related with the progression of severe COVID-19 and establish a prediction model for severity in COVID-19 patients.Methods:We retrospectively recruited patients with confirmed COVID-19 admitted in Enze Hospital, Taizhou Enze Medical Center (Group) and Nanjing Drum Tower Hospital between January 24 and March 12, 2020. Take the Taizhou cohort as the training set and the Nanjing cohort as the validation set. Severe case was defined based on the World Health Organization Interim Guidance Report criteria for severe pneumonia. The patients were divided into severe and non-severe groups. Epidemiological, laboratory, clinical, and imaging data were recorded with data collection forms from the electronic medical record. The predictive model of severe COVID-19 was constructed, and the efficacy of the predictive model in predicting the risk of severe COVID-19 was analyzed by the receiver operating characteristic curve (ROC).Results:A total of 402 COVID-19 patients were included in the study, including 98 patients in the training set (Nanjing cohort) and 304 patients in the validation set (Nanjing cohort). There were 54 cases (13.43%) in severe group and 348 cases (86.57%) in non-severe group. Logistic regression analysis showed that body mass index (BMI) and lymphocyte count were independent risk factors for severe COVID-19 (all P < 0.05). Logistic regression equation based on risk factors was established as follows: Logit (BL)=-5.552-5.473 ×L + 0.418 × BMI. The area under the ROC curve (AUC) of the training set and the validation set were 0.928 and 0.848, respectively (all P < 0.001). The model was simplified to get a new model (BMI and lymphocyte count ratio, BLR) for predicting severe COVID-19 patients, and the AUC in the training set and validation set were 0.926 and 0.828, respectively (all P < 0.001).Conclusions:Higher BMI and lower lymphocyte count are critical factors associated with severity of COVID-19 patients. The simplified BLR model has a good predictive value for the severe COVID-19 patients. Metabolic factors involved in the development of COVID-19 need to be further investigated. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.
ABSTRACT
The pandemic of the COVID-19 has caused many problems in the cross-border food supply chain during transportation, such as food safety is difficult to guarantee due to the infection of people or environmental epidemics, information asymmetry and difficult to share in the food supply chain, and food related information is tampered with and difficult to trace during transportation. The emergence of blockchain technology has brought new solutions to the above problems. Based on the relevant functions and characteristics of the blockchain, this paper constructs a cross-border food supply chain information sharing platform under the blockchain technology, such as using smart contracts to monitor the epidemic security risk in the transportation process of the supply chain, using hash functions and tamper proof features to deal with food information fraud, and using the distributed and decentralized characteristics of the blockchain to solve the sharing problem of information asymmetry;The time stamp in the blockchain is used to trace the information of each link node of food and the initial node for accountability. In this paper, the construction process of the platform is described in detail from the perspective of model, function and subject, and the specific process of the platform is described in detail from the perspective of information storage, information sharing and information traceability. In this paper, the emerging blockchain technology is applied to all links of the food supply chain, and considering the current epidemic problems, the platform can bring new ideas to solve the problems of cross-border food transportation during the epidemic, and has certain theoretical value. © 2022 Association for Computing Machinery.
ABSTRACT
Automatic diagnosis of COVID-19 using chest CT images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network (<inline-formula><tex-math notation="LaTeX">$\textrm {D}
ABSTRACT
In this article, we searched the research literatures related to clinical investigation of non-invasive positive pressure ventilation (NPPV) in acute respiratory failure(ARF)/chronic respiratory failure(CRF) between 1st October 2021 and 30th September 2022 through Medline, and reviewed the important advances. Three prospective randomized controlled studies related to the efficacy and safety of NPPV and/or high-flow nasal cannula oxygen therapy (HFNC) on patients with COVID-19 with ARF were reported, showing that NPPV (including continuous positive airway pressure and bilevel positive airway pressure) was able to reduce the intubation rate, but the efficacy of HFNC was contradictory. In addition, progress has been made in outcome prediction models for ARF treated with NPPV, NPPV-related cardiac arrest, and the impact of human-machine interface on NPPV treatment outcomes. The effects of NPPV as preoxygenation method before intubation was reported to be able to reduce severe desaturation during intubation, especially in obese population. The use of NPPV in extubated patients resulting in reduced reintubation rate was also studied. With regard to long-term home application of NPPV, five indicators of successful initiation were proposed, but the success rate was low in clinical practice. Some reports showed that psychological support could improve the adherence to NPPV. The results of these studies contributed to the rational selection and optimal application of NPPV in clinical practice.
Subject(s)
COVID-19 , Noninvasive Ventilation , Respiratory Insufficiency , Humans , Prospective Studies , COVID-19/therapy , Noninvasive Ventilation/methods , Continuous Positive Airway Pressure/adverse effects , Continuous Positive Airway Pressure/methods , Respiratory Insufficiency/therapy , Respiratory Insufficiency/etiology , Intubation, IntratrachealABSTRACT
PurposeThe Sendai framework for disaster risk reduction (DRR) 2015-2030 offers guidelines to reduce disaster losses and further delivers a wake-up call to be conscious of disasters. Its four priorities hinge on science, technology and innovations as critical elements necessary to support the understanding of disasters and the alternatives to countermeasures. However, the changing dynamics of current and new risks highlight the need for existing approaches to keep pace with these changes. This is further relevant as the timeline for the framework enters its mid-point since its inception. Hence, this study reflects on the aspirations of the Sendai framework for DRR through a review of activities conducted in the past years under science, technology and innovations. Design/methodology/approachMultidimensional secondary datasets are collected and reviewed to give a general insight into the DRR activities of governments and other related agencies over the past years with case examples. The results are then discussed in the context of new global risks and technological advancement. FindingsIt becomes evident that GIS and remote sensing embedded technologies are spearheading innovations for DRR across many countries. However, the severity of the Covid-19 pandemic has accelerated innovations that use artificial intelligence-based technologies in diverse ways and has thus become important to risk management. These notwithstanding, the incorporation of science, technology and innovations in DRR faces many challenges. To mitigate some of the challenges, the study proposes reforms to the scope and application of science and technology for DRR, as well as suggests a new framework for risk reduction that harnesses stakeholder collaborations and resource mobilizations. Research limitations/implicationsThe approach and proposals made in this study are made in reference to known workable processes and procedures with proven successes. However, contextual differences may affect the suggested approaches. Originality/valueThe study provides alternatives to risk reduction approaches that hinge on practically tested procedures that harness inclusivity attributes deemed significant to the Sendai framework for DRR 2015-2030.
ABSTRACT
In the context of increasing medical resource constraints and the global pandemic of COVID-19, the acquisition and automatic diagnosis of electrocardiogram (ECG) signal at home is becoming more and more important. In this paper, we propose a dual arrhythmia classification algorithm for edge-cloud collaboration. We first design a lightweight single-lead ECG signal binary classification model incorporating RR intervals that can be deployed at the edge, which achieves lightweight ECG feature extraction by using depthwise separable convolution and positional attention, and fuses RR interval features to the fully connected layer to achieve normal or abnormal classification of ECG heartbeats. For heartbeats classified as abnormal using the above model, we design a dual-branch arrhythmia multi-classification model with channel and spatial dual attention that integrates simple convolutional neural network (CNN) modules that can be deployed in a cloud artificial intelligence (AI) server to perform accurate classification of abnormal ECG heartbeats, where the input of one branch is a heartbeat signal and the input of the other branch is an ECG segment containing adjacent R-peaks. The experimental results based on the MIT-BIH arrhythmia database demonstrate that our binary classification model achieves an average accuracy of 99.80% and the multi-classification model achieves an average accuracy of 99.71%, and our method ensures a high enough accuracy while performing dual analysis to make the analysis results more reliable. © 2022 IEEE.
ABSTRACT
COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amide I and III characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKUl, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amide I and III characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amide I and III characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group;HCoV-HKUl, HCoV-NL63 form a group;HCoV-229E and HCoV-OC43 form a group independently. The frequency of amide I and III in the same group is relatively close,and it is difficult to distinguish spike proteins by the frequency of amide I and III ;the characteristic peaks of amide I and III in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2. © 2022 Science Press. All rights reserved.
ABSTRACT
The global exploration investment, new oil and gas discoveries, exploration business adjustment strategies of oil companies in 2021, and future favorable exploration domains are systematically analyzed using commercial databases such as IHS and public information of oil companies. It has been found that the world oil and gas exploration situation in 2021 has continued the downturn since the outbreak of COVID-19. The investment and drilling workload decreased slightly, but the success rate of exploration wells, especially deepwater exploration wells, increased significantly, and the newly discovered reserves increased slightly compared with last year. Deep waters of the passive continental margin basins are still the leading sites for discovering conventional large and medium-sized oil and gas fields. The conventional oil and gas exploration in deep formations of onshore petroliferous basins has been keeping a good state, with tight/shale oil and gas discoveries made in Saudi Arabia, Russia, and other countries. While strengthening the exploration and development of local resources, national, international, and independent oil companies have been focusing on major overseas frontiers using their advantages, including risk exploration in deep waters and natural gas. Future favorable exploration directions in the three major frontiers, the global deep waters, deep onshore formations, and unconventional resources, have been clarified. Four suggestions are put forward for the global exploration business of Chinese oil companies: first, a farm in global deepwater frontier basins in advance through bidding at a low cost and adopt the “dual exploration model” after making large-scale discoveries;second, enter new blocks of emerging hot basins in the world through farm-in and other ways, to find large oil and gas fields quickly;third, cooperate with national oil companies of the resource host countries in the form of joint research and actively participate exploration of deep onshore formations of petroliferous basins;fourth, track tight/shale oil and gas cooperation opportunities in a few countries such as Saudi Arabia and Russia, and take advantage of mature domestic theories and technologies to farm in at an appropriate time. © 2022 Research Institute of Petroleum Exploration & Development, PetroChina
ABSTRACT
COVID-19 pandemic and its repercussions came as a surprise to nations including China. However, the unique central governance system of the country enhanced its ability to promulgate laws and guidelines which caused rapid changes across all aspects of its development. The result from this was a swift implementation of initiatives that ensured strict safety protocols to reduce the spread of COVID-19, generated advanced analytical technological systems to control the virus, and created new markets for some new technologies. Hence, the enormous growth of 5G contributed a lot to the economic recovery of China. Given its potential shown during the pandemic, the 5G strategy was considered as the most important attempt to face the challenges in post-COVID-19 by the Chinese government. This chapter outlines some of the structures, policy outcomes, and results during the pandemic and makes recommendations for curtailing future challenges. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 mu g/m(3)), and root mean square error (6.6 mu g/m(3)), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.