Future challenges and their impact on respiratory health and lung cancer

Air pollution, climate and population health are closely related in terms of their impacts on respiratory health and lung cancer. Air pollutants contribute to the exacerbation of chronic respiratory problems such as COPD and asthma. Air pollutants are also toxic and carcinogenic, initiating and promoting lung cancer development. Climate change in relation to environmental pollution affects the geographical distribution of food supply and diseases such as pneumonia in adults and children. The threat of air pollution, and hence global warming and climate changes, and their effects on population and respiratory health, is an imminent threat to the world and deserves immediate and sustainable combating strategies and efforts. The goals are to increase public awareness and engagement in action, with alignment of international collaboration and policy, and with steering towards further research. Now is the prime time for international collaborative efforts on planning and actions to fight air pollution and climate change before it is too late.Copyright © ERS 2021.

The Air and Viruses We Breathe: Assessing the Effect the PM2.5 Air Pollutant Has on the Burden of COVID-19

Evidence suggests an association between air pollutant exposure and worse outcomes for respiratory viral diseases, like COVID-19. However, does breathing polluted air over many years affect the susceptibility to SARS-CoV-2 infection or severity of COVID-19 disease, and how intense are these effects? As climate change intensifies, air pollutant levels may rise, which might further affect the burden of respiratory viral diseases. We assessed the effect of increasing exposure to PM2.5 (particulate matter = 2.5 microns in diameter) on SARS-CoV-2 susceptibility or COVID-19 severity and projected the impact on infections and hospitalisations over two years. Simulations in a hypothetical, representative population show that if exposure affects severity, then hospital admissions are projected to increase by 5-10% for a one-unit exposure increase. However, if exposure affects susceptibility, then infections would increase with the potential for onward transmission and hospital admissions could increase by over 60%. Implications of this study highlight the importance of considering this potential additional health and health system burden as part of strategic planning to mitigate and respond to changing air pollution levels. It is also important to better understand at which point PM2.5 exposure affects SARS-CoV-2 infection through to COVID-19 disease progression, to enable improved protection and better support of those most vulnerable.

Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China

The additional impact of emission-reduction measures in North China (NC) during autumn and winter on the air quality of downwind regions is an interesting but less addressed topic. The mass concentrations of routine air pollutants, the chemical compositions, and sources of fine particles (PM2.5) for January 2018, 2019, and 2020 at a megacity of Central China were identified, and meteorology-isolated by a machine-learning technique. Their variations were classified according to air mass direction. An unexpectedly sharp increase in emission-related PM2.5 by 22.7% (18.0 mug m-3) and 25.7% (19.4 mug m-3) for air masses from local and NC in 2019 was observed compared to those of 2018. Organic materials exhibited the highest increase in PM2.5 compositions by 6.90 mug m-3 and 6.23 mug m-3 for the air masses from local and NC. PM2.5 source contributions related to emission showed an upsurge from 1.39 mug m-3 (biomass burning) to 24.9 mug m-3 (secondary inorganic aerosol) in 2019 except for industrial processes, while all reduced in 2020. From 2018 to 2020, the emission-related contribution of coal combustion to PM2.5 increased from 10.0% to 19.0% for air masses from the local area. To support the priority natural gas quotas in northern China, additional coal in cities of southern China was consumed, raising related emissions from transportation activities and road dust in urban regions, as well as additional biofuel consumption in suburban or rural regions. All these activities could explain the increased primary PM2.5 and related precursor NO2. This study gave substantial evidence of air pollution control measures impacting the downwind regions and promote the necessity of air pollution joint control across the administration.Copyright © 2023 Elsevier Ltd

Modeling fuel consumption and emissions by taxis in Tabriz, Iran: uncertainty and sensitivity analyses.

Taxis pose a higher threat to global climate change and human health through air emissions. However, the evidence on this topic is scarce, especially, in developing countries. Therefore, this study conducted estimation of fuel consumption (FC) and emission inventories on Tabriz taxi fleet (TTF), Iran. A structured questionnaire to obtain operational data of TTF, municipality organizations, and literature review were used as data sources. Then modeling was used to estimate fuel consumption ratio (FCR), emission factors (EFs), annual FC, and emissions of TTF using uncertainty analysis. Also, the impact of COVID-19 pandemic period was considered on the studied parameters. The results showed that TTF have high FCRs of 18.68 L/100 km (95% CI=17.67-19.69 L/100 km), which are not affected by age or mileage of taxis, significantly. The estimated EFs for TTF are higher than Euro standards, but the differences are not significant. However, it is critical as can be an indication of inefficiency of periodic regulatory technical inspection tests for TTF. COVID-19 pandemic caused significant decrease in annual total FC and emissions (9.03-15.6%), but significant increase in EFs of per-passenger-kilometer traveled (47.9-57.3%). Annual vehicle-kilometer-traveled by TTF and the estimated EFs for gasoline-compressed natural gas bi-fueled TTF are the main influential parameters in the variability of annual FC and emission levels. More studies on sustainable FC and emissions mitigation strategies are needed for TTF.

Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Modelling and Forecast of Air Pollution Concentrations during COVID Pandemic Emergency with ARIMA Techniques: the Case Study of Two Italian Cities

An efficient and punctual monitoring of air pollutants is very useful to evaluate and prevent possible threats to human beings' health. Especially in areas where such pollutants are highly concentrated, an accurate collection of data could suggest mitigation actions to be implemented. Moreover, a well-performed data collection could also permit the forecast of future scenarios, in relation to the seasonality of the phenomenon. With a particular focus on COVID pandemic period, several literature works demonstrated a decreasing of pollutant concentrations in air of urban areas, mainly for NOx, while CO and PM10, on the opposite, has been observed to remain still, mainly because of the intensive usage of heating systems by the people forced to stay home (on specific regions). With the present contribution the authors here present an application of Time Series analysis (TSA) approach to pollutants concentration data of two Italian cities during first lockdown (9 march – 18 may 2020), demonstrating the possibility to predict pollutants concentration over time. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

Changes of atmospheric metal(loid) deposition from 2017 to 2021 at Mount Emei under China's air pollution control strategy

Acid deposition and particulate matter (PM) pollution have declined considerably in China. Although metal(loid) and acid deposition and PM have many common sources, the changes of metal(loid) deposition in China in the recent decade have not been well explored by using long-term monitoring. Therefore, we analyzed the dry and wet deposition of eleven metal(loid)s (including Al, As, Ba, Cd, Cu, Cr, Fe, Mn, Pb, Sr, and Zn) from 2017 to 2021 at Mount Emei, which is adjacent to the most economic-developed region in western China (Sichuan Basin (SCB)). Anthropogenic emissions contributed to over 80% of the annual wet deposition fluxes of metal(loid)s and acids (SO4 2-, NO3 -, and NH4 +) at Mount Emei, and the major source regions were the SCB, the Yunnan-Guizhou Plateau, and Gansu Province. Metal(loid) and acid deposition had similar seasonal variations with higher wet deposition fluxes in summer but higher wet deposition concentrations and dry fluxes in winter. The seasonal variations were partially associated with higher precipitation but lower pH in summer (968 mm and 5.52, respectively) than in winter (47 mm and 4.73, respectively). From 2017 to 2021, metal(loid) deposition did not decline as substantially as acid deposition (5.6%-30.4%). Both the annual total deposition fluxes and concentrations of Cr, Cu, Sr, Ba, and Pb were even higher in 2020-2021 than in 2017-2018. The inter-annual and seasonal changes implied the responses of metal(loid) deposition to anthropogenic emission changes were buffered (e.g., transformation, dilution, and degradation) by precipitation rates, acidity, natural emissions, and chemical reactions in the atmosphere, among others.Copyright © 2023 Elsevier Ltd

Microbial Air Pollutant Control using Commercial UV-C Lamp for Preparing Re-opening Class Activities at Universitas Indonesia

Airborne microorganisms must be controlled, especially during the COVID-19 pandemic, to prevent infectious diseases. This research was conducted to prepare a clean room and eliminate infectious pathogens. This study studied a 36-watt UV C commercial lamp to examine its effectiveness in controlling airborne microorganisms in rooms at Universitas Indonesia. The germicide effect of lamp (100 mJ/cm2) predicted by the UV-C test card could be achieved at a distance of 2 to 3 meter after exposure for 60 minutes. UVC's effectiveness as a germicide was also tested on bacteria, yeast, and mold. No germicides were observed in A. parasiticus and C. lunata after being exposed to the UV-C light at 1 to 2 meters distance for 60 minutes. The germicides UV-C lamps were also applied in examined rooms. Active and passive sampling methods measured airborne microorganisms before and after the treatment of UV-C lamp. The lowest germicide effect of UV-C lamp was 37.66% in the collaboration laboratory, and the highest was 86.12% obtained in seminar room at Department of Biology. Many factors, such as the type of group of microorganisms, air circulation, and equipment in the room, influence the germicide effect of UV-C lamp. Based on existing microorganism populations, the examined indoor air has good quality under 1,000 CFU/m3 © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License

[Impacts of Emission and Meteorological Conditions on Air Pollutants at Various Sites Around the COVID-19 Lockdown in Wuhan].

The random forest algorithm was used to separate the mass concentrations of six air pollutants (SO2, NO2, CO, PM10, PM2.5, and O3) contributed by emissions and meteorological conditions. Their variations for five types of sites including Wuhan's central urban, suburb, industrial, the third ring road traffic, and urban background sites were investigated. The results showed that the values of PM2.5/CO, PM10/CO, and NO2/CO during the lockdown period decreased by 10.8-21.7, 9.34-24.7, and 14.4-22.1 times compared with the period before the lockdown, indicating that the contributions of emissions to PM2.5, PM10, and NO2 were reduced. O3/CO increased by 50.1-61.5 times, implying that the secondary formation increased obviously. The contributions of emissions to various types of pollutants all increased after the lockdown. During the lockdown period, affected by the operation of some uninterrupted industrial processes, PM2.5 concentrations in industrial areas dropped the least (20.5%). Compared with the lockdown period, residential activities, transportation, and industrial production were basically restored after the lockdown, resulting in the alleviation of the reduction in PM2.5 emission-related concentrations. The increase in emission-related O3 concentrations could be associated with the decreased NO and PM2.5 concentrations during the lockdown period. The elevated O3 partially offset the improved air quality brought by the reduced NO2and PM2.5 concentrations. After the lockdown, ρ(O3) related with meteorology at the suburban and urban background sites increased by 16.2 µg·m-3 and 16.1 µg·m-3, respectively, which could be attributed to the increased ambient temperature and decreased relative humidity. The decrease in PM2.5 and increase in O3 concentrations caused by reduced traffic and industrial emissions at the third ring road traffic and central urban regions can provide reference for the current coordinated and precise control of PM2.5 and O3 in subregions.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States.

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM2.5, O3, SO2, NO2, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO2 and SO2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017–2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m−3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m−3) in winter and minimum concentration (0.38 ± 0.04 μg m−3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March–10 May 2020 and third lockdown 17 March–3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October–15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well. © 2022 Elsevier B.V.

Evaluation Of Indoor Air Quality In Hospitals: With Respect To Green Building Rating Systems

The World Health Organization tightened the various indoor air quality parameters to improve the quality of the air globally during COVID-19. In light of this, research (Pollution and Health: a progress update, 2022)made public in India in 2022 shows that air pollution is a major problem worldwide, with a projected 66.7 lac people dying as a result. Similar issues are present in developing countries like India, where 16.7 million fatalities were expected in 2019. According to the study (Air Quality Life Index: India Fact Sheet, 2022), air pollution shortens Indians' life expectancy by an average of 5 years. It is crucial to check that these new standards after Covid are compatible with the Green Building Rating Systems. The study is also based on in-depth discussions with doctors, administrators, green building designers, and building tenants, as well as surveys using questionnaires and interviews. The existing Green Rating Systems were found to require revision, with the weighting of the elements linked to air quality requiring strengthening with the installation of appropriate air quality monitoring of different contaminants. The study's main objective is to examine the air quality parameters, their weightings, and the monitoring tools. The study was focused on the evaluation of Green Rated healthcare buildings in India based on different Air Quality parameters. The overall evaluation of the air quality is found to be very critical and, in the areas, where extreme care and precautions are required with respect to cleanliness and hygiene like the Intensive care unit and Operation Theatre the values of the air pollutants like Formaldehyde, Volatile Organic Compound, Carbon Monoxide and Carbon Di Oxide are indicating serious problems. Copyright © 2022 Ubiquity Press. All rights reserved.

A review on characteristics and mitigation strategies of indoor air quality in underground subway stations.

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO2 and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO2, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO2 concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.

The Association of Environmental Factors on Emergency Pediatric Asthma-Related Health Care Utilization

Study Objectives: Asthma is a multifactorial disease in which complex environmental exposures contribute to asthma exacerbations, often requiring acute medical attention. Although asthma exacerbations follow a seasonal pattern, our ability to precisely predict the timing and magnitude of increased asthma admissions remains limited. Pediatric asthma-related emergency department (ED) visits historically follow a bimodal distribution with peak admissions occurring in the spring and fall, while being lowest during the summer. We aim to identify how the rates of pediatric ED visits for asthma at a large urban medical center are impacted by changes in environmental conditions. Method(s): A time series analysis for pediatric ED visits was analyzed from 1/1/2015 to 2/28/2021 at a New York City ED in a large hospital system (n=8,206). We compared admissions longitudinally relative to the same period the previous years. Here, an ensemble model will be developed using a data assimilation technique to retrospectively parameterize the incidence of asthma admissions using local ED admissions data in addition to viral respiratory illness lab markers, air pollutants, pollens, and meteorological conditions. This retrospective ensemble model is a data assimilation technique commonly applied in numerical weather prediction and has recently been applied to parameterize infectious diseases. Here, we will use it to parameterize the burden of asthma attributable to different environmental factors. To estimate the attributable fraction that respiratory infections and climatic factors such as air pollution or humidity and temperature have on asthma exacerbations, we will use viral lab tests to confirm respiratory infections, meteorological and air pollution conditions will be assessed to identify the relationship between exposure and exacerbations. The retrospective ensemble model will then be used as the basis for understanding the statistical relationships between the attributable fraction of environmental conditions and pediatric asthma admissions. Result(s): The classic bimodal peak for asthma related hospitalizations was not observed during the first year following the COVID-19 pandemic;pediatric asthma-related ED visits decreased by 78% between 3/1/20 and 2/28/21. During the initial 12 weeks following NYC's PAUSE order (March 22nd, 2020), a 92% reduction in admissions was observed, relative to the same period the previous year. Of the 8,206 pediatric patients seen for asthma that came through the ED, 1,827 were admitted to the hospital and 6,379 were sent home, indicating an admission rate of 22%. 18% of patients admitted for asthma had a clinical lab marker sent that returned positive to indicate a respiratory pathogen (positive RVP, etc). Conclusion(s): It is important to develop an inference system to provide insight on how changes in behavioral patterns and environmental exposures affect asthma-related health care utilization in order to plan current and future public health interventions in a timely manner. This clinical inference system is augmented here with the additional of an observational system via confirmed respiratory illness, helping strengthen our understanding of the burden of asthma that is attributable to respiratory infections. No, authors do not have interests to disclose Copyright © 2022

The impact of air pollution on COVID-19 severity among infected individuals in a population of italian MS patients

Introduction: Studies have found associations between air pollution and pneumonia and air pollution is an established risk factors for common COVID-19 complications including pneumonia. Additionally, air pollutants have been identified as possible risk factors for MS onset and relapses. To our knowledge, only one study explored the impact of air pollution on Covid-19 severity specifically among MS patients but has only focused on PM2.5 exposures. Aim(s): We aim to evaluate the association between long-term exposure to air pollution and COVID-19 severity, described as developing pneumonia in a population of COVID-19-positive MS patients. Method(s): Data on COVID-19 infection among MS patients were extracted from an Italian web-based platform (Musc-19). A casecontrol study was designed including patients with and without pneumonia at a case-control ratio of 1:2 and 615 patients were included. The included patients were asked to provide information on the geographical area where they had spent most time in the previous 5 years. When this information was missing, the address of the MS center was used as a proxy and evaluated in sensitivity analysis. Air quality was assessed as annual average particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) ground-level concentrations derived from air quality model results as provided by the 'Copernicus Atmospheric Monitoring Service', and evaluated as categorical exposures (terciles). The association between pollutants and COVID-19 pneumonia was studied using logistic regression models, also adjusting for confounders (age, sex, BMI, comorbidities, EDSS, MS type, duration and treatments). Result(s): Detailed exposure was obtained for 491 patients, of whom 34% had pneumonia. Higher concentrations of air pollutants were associated with increased odds of developing COVID-19 pneumonia in both unadjusted and adjusted models (Adjusted models estimates: PM2.5: 2nd vs 1st tercile OR(95% CI)=2.09 (1.20;3.65), 3rd vs 1st tercile OR(95% CI)=2.26(1.29;3.96);PM10: 2nd vs 1st tercile OR(95% CI)=1.83(1.05;3.20), 3rd vs 1st tercile OR(95% CI)=2.12(1.22;3.68);NO2: 3rd vs 1st tercile OR(95% CI)=2.12(1.21;3.69)). Results remained consistent in the sensitivity analysis. Conclusion(s): Higher long-term concentrations of PM2.5, PM10 and NO2 were associated with COVID-19 pneumonia among MS patients. Urgent measures to reduce air pollution should be adopted especially to protect the most vulnerable population.

Effects of Air Pollution on Chemosensory Dysfunction in COVID-19 Patients.

BACKGROUND: In some patients, coronavirus disease 2019 (COVID-19) is accompanied by loss of smell and taste, and this has been reportedly associated with exposure to air pollutants. This study investigated the relationship between the occurrence of chemosensory dysfunction in COVID-19 patients and air pollutant concentrations in Korea. METHODS: Information on the clinical symptom of chemosensory dysfunction, the date of diagnosis, residential area, age, and sex of 60,194 confirmed COVID-19 cases reported to the Korea Disease Control and Prevention Agency from January 20 to December 31, 2020 was collected. In addition, the daily average concentration of air pollutants for a week in the patients' residential area was collected from the Ministry of Environment based on the date of diagnosis of COVID-19. A binomial logistic regression model, using age and gender, standardized smoking rate, number of outpatient visits, 24-hour mean temperature and relative humidity at the regional level as covariates, was used to determine the effect of air pollution on chemosensory dysfunction. RESULTS: Symptoms of chemosensory dysfunction were most frequent among patients in their 20s and 30s, and occurred more frequently in large cities. The logistic analysis showed that the concentration of particulate matter 10 (PM10) and 2.5 (PM2.5) up to 2 days before the diagnosis of COVID-19 and the concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at least 7 days before the diagnosis of COVID-19 affected the development of chemosensory dysfunction. In the logistic regression model adjusted for age, sex, standardized smoking rate, number of outpatient visits, and daily average temperature and relative humidity, it was found that an increase in the interquartile range of PM10, PM2.5, SO2, NO2, and CO on the day of diagnosis increased the incidence of chemosensory dysfunction 1.10, 1.10, 1.17, 1.31, and 1.19-fold, respectively. In contrast, the O3 concentration had a negative association with chemosensory dysfunction. CONCLUSION: High concentrations of air pollutants such as PM10, PM2.5, SO2, NO2, and CO on the day of diagnosis increased the risk of developing chemosensory dysfunction from COVID-19 infection. This result underscores the need to actively prevent exposure to air pollution and prevent COVID-19 infection. In addition, policies that regulate activities and products that create high amounts of harmful environmental wastes may help in promoting better health for all during COVID-19 pandemic.

IMPACT OF COVID-19 PANDEMIC ON AIR POLLUTION: THE CASE OF ATHENS, GREECE

Lockdown restrictions due to the COVID-19 pandemic led to air, road and marine traffic limitations as well as to limitations of economic activities causing thus considerable reductions of air pollutant emissions and air quality levels. This paper aims at studying the impact of these restrictions, due to pandemic, on air pollutant emissions and on atmospheric pollutant concentrations in the Greater Area of Athens, Greece. Air pollutant emission levels and emission reductions due to COVID-19 containment measures were calculated and related to air pollutant concentrations from six air quality monitoring stations. Findings showed significant road, marine and air traffic emission reductions, ranging from 20 to 90%. In the analysis conducted, the relation between air pollutant levels and the corresponding emissions was identified showing that the most important contributor to high air pollutant levels is road traffic. The conclusions drawn on air quality levels may provide policy makers with useful insights in order to plan and apply more efficient measures to reduce air pollution and comply with air quality standards and European Directives. © 2022 Gheorghe Asachi Technical University of Iasi, Romania. All rights reserved.

The impact of COVID-19 on NO2 and PM2.5 levels and their associations with human mobility patterns in Singapore

The decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, overlooking the meteorological changes that could simultaneously mediate air pollution levels. This pitfall could potentially lead to over- or under-estimation of the effect of COVID-19 on air pollution. This study, thus, aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant levels in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results show that the mean NO2 and PM2.5 declined by 12% and 19%, which were less than the observed drops (i.e. 54% and 29%, respectively) without considering the effect of meteorological parameters. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019. Two correlation analyses were conducted to investigate how human mobility influenced air pollutant levels: one between daily PM2.5 and mobility changes at a regional scale and the other between weekly NO2 and mobility changes at a spatial resolution of 0.01°. The NO2 variation was found to be more associated with the change in human mobility and a cluster of stronger correlations was found in the South and East Coast of Singapore. Contrarily, PM2.5 and mobility had a weak correlation, which could be due to the limit of a coarse spatial resolution. [ FROM AUTHOR] Copyright of Annals of GIS is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)