"Fortune amidst misfortune”: The impact of Covid-19 city lockdowns on air quality

Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.

Ground-Based MAX-DOAS Observation of Trace Gases from 2019 to 2021 in Huaibei, China

With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. © 2023 by the authors.

A Comparative Study of Pollution Levels in Major Cities of India During Covid-19 in India

This paper aims to study the major pollutants of the four metro cities of India before and after covid 19 first wave. The cities considered for the study are Bangalore, Delhi, Mumbai, and Kolkata. The major pollutants considered for the study are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The basic aim of the study is to find the effect of lockdown and covid restrictions on the level of pollutants across the four major cities of India. We used both parametric and non-parametric tests for the analysis using SPSS. From the study, it is clear that there is a significant decrease in all the major pollutants across India's major cities.6. © 2023, University of Wollongong. All rights reserved.

Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Comparison of ribavirin degradation in the UV/H2O2 and UV/PDS systems: Reaction mechanism, operational parameter and toxicity evaluation.

Residues in surface water of ribavirin, which used extensively during the COVID-19 pandemic, have become an emerging issue due to its adverse impact on the environment and human health. UV/H2O2 and UV/peroxydisulfate (PDS) have different degradation effects on ribavirin, and the same operational parameter have different effects on the two processes. In this study, the reaction mechanism and degradation efficiency for ribavirin were studied to compare the differences under UV/H2O2 and UV/PDS processes. We calculated the total rate constants of ribavirin with HO• and SO4 •- in the liquid phase as 2.73 × 108 and 9.39 × 105 M-1s-1. The density functional theory (DFT) calculation results showed that HO• and SO4 •- react more readily with ribavirin via H-abstraction (HAA). The nitrogen-containing heterocyclic ring is difficult to undergo ring-opening degradation. The UV/PDS process was more stable and performed better than the UV/H2O2 for the ribavirin degradation when the same molar oxidant dosage was applied. HO• plays an extremely important role in the degradation of ribavirin by UV/PDS. The reason for this phenomenon is the combination of the higher yield of HO• produced in the UV/PDS process and the faster reaction rate of ribavirin with HO•. The UV/H2O2 process is more sensitive to pH than UV/PDS. Alkaline condition can significantly inhibit the ribavirin degradation. The effects of natural organic matter (NOM) and ribavirin concentration were also compared. Eventually, the toxicity prediction of the product showed that the opening-ring products were more toxic than the parent compound.

Covering the Broad Spectrum of Intelligent Systems: From Neuromorphic Engineering to Robotics

Table 1 Highly cited articles published in Advanced Intelligent Systems in 2019/2020 with more than 25 citations in 2021 (Web of Science, 13 December 2022) Article Title (Article Type) Authors (Corresponding*) DOI 2021 Citations Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications (Review) Hamid Souri*, Hritwick Banerjee, Ardian Jusufi, Norbert Radacsi, Adam A. Stokes, Inkyu Park, Metin Sitti, Morteza Amjadi* https://doi.org/10.1002/aisy.202000039 61 Robotics, Smart Wearable Technologies, and Autonomous Intelligent Systems for Healthcare During the COVID-19 Pandemic: An Analysis of the State of the Art and Future Vision (Essay) Mahdi Tavakoli*, Jay Carriere, Ali Torabi https://doi.org/10.1002/aisy.202000071 52 Soft Actuators for Soft Robotic Applications: A Review (Review) Nazek El-Atab, Rishabh B. Mishra, Fhad Al-Modaf, Lana Joharji, Aljohara A. Alsharif, Haneen Alamoudi, Marlon Diaz,Nadeem Qaiser, Muhammad Mustafa Hussain* https://doi.org/10.1002/aisy.202000128 35 Magnetic Actuation Systems for Miniature Robots: A Review (Review) Zhengxin Yang, Li Zhang* https://doi.org/10.1002/aisy.202000082 29 Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction (Review) Ajay Vikram Singh*, Daniel Rosenkranz, Mohammad Hasan Dad Ansari, Rishabh Singh, Anurag Kanase, Shubham Pratap Singh, Blair Johnston, Jutta Tentschert, Peter Laux, Andreas Luch https://doi.org/10.1002/aisy.202000084 29 Complementary Metal-Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing (Progress Report) Mostafa Rahimi Azghadi*, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen,Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J. Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang* https://doi.org/10.1002/aisy.201900189 25 However, impact factor won't be the only metric reported by Wiley journals anymore. The scope of Advanced Intelligent Systems covers timely topics that are not only of interest to scientists and engineers but are also closely followed by the general public. Since the launch of the journal, several papers published in the journal have been highlighted by social media platforms and news outlets. Table 2 Papers published in 2022 with an Altmetric score above 40 (Altmetric, 13 Dec 2022) Article Title Authors (Corresponding*) DOI Altmetric Score An Autonomous Chemically Fueled Artificial Protein Muscle Matthias C. Huber, Uwe Jonas, Stefan M. Schiller* https://doi.org/10.1002/aisy.202100189 189 Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning Katherine S. Riley, Subhadeep Koner, Juan C. Osorio, Yongchao Yu, Harith Morgan, Janav P. Udani, Stephen A. Sarles*, Andres F. Arrieta* https://doi.org/10.1002/aisy.202200158 178 All-Electric Nonassociative Learning in Nickel Oxide Sandip Mondal*, Zhen Zhang, A. N. M. Nafiul Islam, Robert Andrawis, Sampath Gamage, Neda Alsadat Aghamiri, Qi Wang, Hua Zhou, Fanny Rodolakis, Richard Tran, Jasleen Kaur, Chi Chen, Shyue Ping Ong, Abhronil Sengupta, Yohannes Abate, Kaushik Roy, Shriram Ramanathan https://doi.org/10.1002/aisy.202200069 110 Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro-Processors Fazel Bateni, Robert W. Epps, Kameel Antami, Rokas Dargis, Jeffery A. Bennett, Kristofer G. Reyes, Milad Abolhasani* https://doi.org/10.1002/aisy.202200017 61 Overcoming the Force Limitations of Magnetic Robotic Surgery: Magnetic Pulse Actuated Collisions for Tissue-Penetrating-Needle for Tetherless Interventions Onder Erin*, Xiaolong Liu, Jiawei Ge, Justin Opfermann, Yotam Barnoy, Lamar O. Mair, Jin U. Kang, William Gensheimer, Irving N. Weinberg, Yancy Diaz-Mercado, Axel Krieger* https://doi.org/10.1002/aisy.202200072 43 Our promotional activities to enhance the visibility of the journal and its papers continued in 2022.

THE ROLE OF SOCIAL MEDIA IN RAISING HEALTH AWARENESS AMONG JORDANIAN UNIVERSITY STUDENTS DURING THE COVID-19 PANDEMIC

This research aims to find out how social media aided university students become more health conscious during the COVID-19 pandemic and the gratifications obtained from them. The researchers prepared a questionnaire that corresponds to the study's axis using a descriptive analytical approach. Its reliability, validity, and suitability for measuring the goals of this research were all verified. Students from Jordanian universities made up the study population, where the study sample was made up of 600 randomly selected individuals. The usage of social media helped increase students' awareness of their health during the COVID-19 pandemic. At the level of statistical significance (α = 0.05), differences in gender in favor of females, university type in favor of public universities, and level in favor of higher years were evident. The study made several key recommendations, some of which include working to develop mechanisms that restrict the spread of misleading information on each social media applications and platforms, the necessity of continuing to disseminate health awareness information in all of its forms, and the need to define the current situation and the most significant hazards of the coronavirus in the current period. © 2022 Science Press. All rights reserved.

The effects of aerator masks and medical masks on oxygen saturation (SO2) on mentally retarded athletes

The study purpose was investigated the effect of aerator masks on the oxygen saturation (SO2) of mentally retarded athletes compared to medical masks. The researcher used a comparative study. Parametric test was used to test the difference in SO2 percent and evaluate the questionnaire in the two groups. The results showed after under-going exercise, SO2 percent in participants was 94.60+/-0.55 for aerator masks and 96.60+/-0.55 for medical masks. The study also showed that there was no significant difference in SO2 percent of participants wearing aerator masks and medical masks. However, the decrease in oxygen saturation percent of participants wearing aerator masks experienced a lower decrease, 1.60+/-0.55 compared to medical masks of 2.00+/-1.00. On the other hand, wearing an aerator mask is better because it does not prevent maximum performance, better comfort, the mask material feels better on the skin, does not experience difficulties breathable, and suitable for exercise. An aerator mask is a mask with an adequate supply of oxygen so that every breath is maintained even when used during sports activities. Copyright © the Author(s),2022 Licensee PAGEPress, Italy.

A statistical analysis of air-pollution before COVID-19: experimental study of three years for intelligent environment conducted at North Indian Zone to extract knowledge

In populated and developing countries, governments consider the regulation and protection of the environment as a major task and should take into consideration the concept of smart environment monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, datasets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. So, forecasting air quality index using an intelligent environment system, which includes a machine learning model in order to predict air quality index for National Capital Region (NCR) was proposed. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3 were used. In recent years, machine learning in most emerging technology is used for prediction with 99.99% of accuracy by using historical data.

Handwashing Action Detection System for an Autonomous Social Robot

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics. © 2022 IEEE.

Design, Fabrication, and Testing of a Solar Powered Air Purifier with UV Sterilization Capability

Rising air pollution is a cause of concern throughout the world. With rapid industrialization, growth of transportation industry, increasing construction activities, all has taken a toll on the air quality. The air quality in most parts of our country remains poor to moderately pollute for maximum part of the year. P.M. 2.5, P.M. 10, NOx, and SOx are the primary pollutants. Along with the poor quality of air, COVID-19 has added to the misery by affecting the respiratory tract and further worsening the condition of a patient. Through this project, we aim to build a economical solar powered air purifier that can be installed in each and every household as well as outdoors, catering to the air quality indoors, and contributing in purification of the air in the surrounding environment. The air purifier would be capable of providing air filtration as well as sterilization be powered by solar energy and be available at an affordable price. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Cases Prediction Based on LSTM and SIR Model Using Social Media

The novel coronavirus (COVID-19) epidemic, which broke out in Wuhan, was spread worldwide along with India. Nowadays, social media platforms are one of the primary sources of conveying information. This paper presents a susceptible-infected-removed (SIR)-based model to simulate the COVID-19 epidemic. We also investigate the impacts of prevention and control measures and preventive awareness using COVID-19 data from social media platforms such as Twitter (TW), Reddit (RD), and Google News (GN). The infection rates are then updated using long short-term memory (LSTM), and a modified SIR epidemic spreading model is proposed. The proposed model reflects the effectiveness of information disseminated through multiple social media platforms for predicting infection cases, and furthermore, compared to other standard epidemiological models, integrating language processing elements from online textual data significantly reduces the prediction errors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Strength Characteristics and Failure Mode of Solidified Soil Reinforced by Waste Masks

COVID-19 prevention and control consumed a large number of disposable masks,which had a large amount of waste disposal and high environmental protection requirements. It is one of the important methods to solve the problems of difficult source and high price of high-quality subgrade fillers. This paper proposes the solution of used waste masks to reinforce sludge solidified soil with high moisture contents,which will be used as subgrade fillers,with cement as curing agents and waste masks as reinforcing materials,the unconfined compressive resistance of waste mask reinforced cement solidified soil under different waste mask contents,mask sizes and ages is measured,additionally,the influences of the mask sizes and ages on the reinforcement effect of cement solidified soil are discussed,and this study also analyze the influence of reinforcement of waste mask on the failure modes of cement solidified soil. The results show that,under the experimental conditions,the optimum content of waste mask is about 0.5%,and the unconfined compressive strength is increased by about 87.5%.The stress-strain curve of unconfined compressive strength of cement-solidified soil reinforced with waste masks shows a softening pattern,and the reinforcement of waste mask improves the deformation resistance of the sample. The cement-solidified soil reinforced with waste masks presents a certain plastic failure characteristic.

Detection of a Facemask Using Convolution Neural Network

Due to the COVID-19 pandemic, wearing the mask has become obligatory in public locations as it gives a most preventive impact in opposition to viral transmission. It has affected our day-to-day life to a greater extent. Though people had got vaccinated, mask wearing, social distance maintenance and sanitization need to be practiced probably till the pandemic gets vanished. Proposed work layout a real-time deep learning version to satisfy current demand for detection of facemask wearing position of someone earlier than he or she enters a public place. This paper provides a simplified method for achieving the intended goal in machine learning applications such as TensorFlow, Keras, OpenCV, and MobileNet. The proposed approach determines how the face mask is worn in real time;it leverages live image captures that provide accurate information about whether a person is wearing the mask appropriately. The parameters of the convolution neural network model are used to detect the presence of facial mask(s). The proposed approach attains the accuracy that is almost nearer to 99.75%. © 2022 IEEE.

Spatiotemporal impact of the COVID-19 pandemic lockdown on air quality pattern in Nanjing, China

In response to the COVID-19 outbreak, severe steps have been taken to control its rapid spread by countries globally. A nationwide lockdown was executed at the end of January 2020 in China, which resulted in a significant change and an improvement in air quality patterns. In this study, the objectives were to assess the spatiotemporal impact of the COVID-19 lockdown on air quality in Nanjing, China. The present study researched the six air pollutant parameters, namely, PM10, PM2.5, SO2, NO2, CO, and O-3. The data were divided into six periods, P1-P3: pre-lockdown, during lockdown, and after lockdown periods, P4-P6: 2017-19 (same dates of lockdown). The results reveal that during the COVID-19 control period, a significant drop and an improvement in air quality were observed. According to our findings, the PM10, PM2.5, SO2, NO2, and CO concentrations were reduced by -33.03%, -35.41%, -21.26%, -39.79%, and -20.65%, respectively, while the concentration of O-3 significantly increased by an average of 104.85% in Nanjing. From the previous 3 years to lockdown variations, PM10 (-40.60%), PM2.5 (-40.02%), SO2 (-54.19%), NO2 (-33.60%), and CO (23.16%) were also reduced, while O-3 increased (10.83%). Moreover, compared with those in the COVID-19 period, the levels of PM10, SO2, NO2, CO, and O-3 increased by 2.84%, 28.55%, 4.68%, 16.44%, and 37.36%, respectively, while PM2.5 reduced by up to -14.34% after the lockdown in Nanjing. The outcomes of our study provide a roadmap for the scientific community and local administration to make policies to control air pollution.

Assessment of Lockdown Effectiveness during COVID-19 Pandemic Using Air Pollution Data in Armenia in March-June 2019 and 2020: A Cross-Sectional Study

Various methods used by different countries' governments to control the spread of coronavirus disease 2019 (COVID-19), the cause of pandemic in 2020, affected air quality. The aim of this study was to evaluate the effects of lockdown in Armenia on the content of the main air pollutants-dust, SO2 and NO2. This was a cross-sectional study. We analyzed data on the concentrations of SO2, NO2 and dust from March to June, 2019 and the same period in 2020 as well as data on positive COVID-19 cases from Yerevan, Vanadzor and Hrazdan. In 2020, dust was found to be lower in Yerevan and in Hrazdan and higher in Vanadzor than in the same period in 2019. The same pattern was present for SO2 concentrations: in Yerevan and Hrazdan there was a decrease, and there was an increase in Vanadzor. The concentrations of NO2 increased in Yerevan and Hrazdan, with a slight decrease in Vanadzor. New cases of COVID-19 had a negative correlation with dust and a positive correlation with SO2. The strict quarantine measures were effective in containing the spread of COVID-19.

Post-genomic platform for development of oligonucleotide vaccines against RNA viruses: diamond cuts diamond.

The coronavirus pandemic has starkly demonstrated the need to create highly effective vaccines against various viral diseases. The emerging new platforms for vaccine creation (adenovirus vectors and mRNA vaccines) have shown their worth in the fight against the prevention of coronavirus infection. However, adenovirus vectors and mRNA vaccines have a serious disadvantage: as a rule, only the S protein of the coronavirus is presented as an antigen. This tactic for preventing infection allows the ever-mutating virus to escape quickly from the immunity protection provided by such vaccines. Today, viral genomic databases are well-developed, which makes it possible to create new vaccines on a fundamentally new post-genomic platform. In addition, the technology for the synthesis of nucleic acids is currently experiencing an upsurge in demand in various fields of molecular biology. The accumulated experience suggests that the unique genomic sequences of viruses can act as antigens that trigger powerful humoral and cellular immunity. To achieve this effect, the following conditions must be created: the structure of the nucleic acid must be single-stranded, have a permanent 3D nanostructure, and have a unique sequence absent in the vaccinated organism. Oligonucleotide vaccines are able to resist the rapidly changing genomic sequences of RNA viruses by using conserved regions of their genomes to generate a long-term immune response, acting according to the adage that a diamond cuts a diamond. In addition, oligonucleotide vaccines will not contribute to antibody-dependent enhanced infection, since the nucleic acid of the coronavirus is inside the viral particle. It is obvious that new epidemics and pandemics caused by RNA viruses will continue to arise periodically in the human population. The creation of new, safe, and effective platforms for the production of vaccines that can flexibly change and adapt to new subtypes of viruses is very urgent and at this moment should be considered as a strategically necessary task.

Role of air pollutants in dengue fever incidence: evidence from two southern cities in Taiwan.

Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 µm (PM10) or 2.5 µm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.

Impact of COVID-19 restrictive precautions on air quality: case of Zonguldak, Turkey

The purpose of this study was to investigate and assess how restrictive COVID-19 precautions affect air quality in Zonguldak, as well as to determine the relationship between air quality and meteorological variables under these conditions. Daily PM2.5, PM10, SO2, and NOx concentrations and meteorological data, from 1 March to 31 May 2018, 2019, and 2020 were collected for this research. During the 2020 restrictive COVID-19 precautions, it was determined that concentrations of air pollutants were different and low based on the 95% confidence interval by using paired t-test samples. Meteorological variables were found to be similar to previous years, and the correlation between them and air pollutants was found to be significant (P < 0.01) but low according to Pearson correlations. As a result, meteorological variables were determined to have no direct effect on the low concentrations of air quality emissions during the 2020 pandemic. The overall findings revealed that anthropogenic impact has a negative impact on air quality and the air quality had improved during the COVID pandemic. Furthermore, the restriction on the region's coalmines during the COVID-19 pandemic has significant impact on the improvement of air quality.

Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.