Effect of COVID-19 pandemic on air quality: a study based on Air Quality Index

Health risks and sources of trace elements and black carbon in PM2.5 from 2019 to 2021 in Beijing

A comprehensive health risk assessment of PM2.5 is meaningful to understand the current status and directions regarding further improving air quality from the perspective of human health. In this study, we evaluated the health risks of PM2.5 as well as highly toxic inorganic components, including heavy metals (HMs) and black carbon (BC) based on long-term observations in Beijing from 2019 to 2021. Our results showed that the relative risks of chronic obstructive pulmonary disease, lung cancer, acute lower respiratory tract infection, ischemic heart disease, and stroke decreased by 4.07%-9.30% in 2020 and 2.12%-6.70% in 2021 compared with 2019. However, they were still at high levels ranging from 1.26 to 1.77, in particular, stroke showed the highest value in 2021. Mn had the highest hazard quotient (HQ, from 2.18 to 2.56) for adults from 2019 to 2021, while Ni, Cr, Pb, As, and BC showed high carcinogenic risks (CR > 1.0×10-6) for adults. The HQ values of Mn and As and the CR values of Pb and As showed constant or slight upwards trends during our observations, which is in contrast to the downward trends of other HMs and PM2.5. Mn, Cr, and BC are crucial toxicants in PM2.5. A significant shrink of southern region sourcesof HMs and BCshrank suggests the increased importance of local sources. Industry, dust, and biomass burning are the major contributors to the non-carcinogenic risks, while traffic emissions and industry are the dominant contributors to the carcinogenic risks in Beijing.

Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models

Objectives

The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM_2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic.

Methods

Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5).

Results

The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM_2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM_2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM_2.5 in 2019-2021 decreased.

Conclusion

Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM2.5, PM10, NOx, CO, benzene, and AQI), five (PM2.5, PM10, NOx, SO2 and benzene), five (PM10, NOx, CO, benzene and AQI) and three (PM2.5, PM10, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Correlation Between Air Quality Index and Tear Film Lipid Layer Thickness: Comparison Between Patients with Sjogren's Syndrome and with Meibomian Gland Dysfunction

Purpose: To evaluate the effect of air pollution on the tear film stability by analyzing the correlation between Air Quality Index (AQI) and Lipid Layer thickness (LLT) in Xuzhou.Methods: As a prospective descriptive observational study, 284 patients with meibomian gland dysfunction (MGD), 157 patients with Sjögren's syndrome (SS), and 264 healthy volunteers were included. The tear film lipid layer thickness of the three groups of subjects was measured weekly and compared with the air quality index to analyze the correlation between the two indicators. Logistic regression analysis and linear regression analysis were used to analyze the effect of AQI on the thickness of the tear film lipid layer. The change of LLT with air pollution at different AQI levels was also analyzed.Results: There are obvious seasonal differences in the changes of air pollution index in Xuzhou. Significant differences could be observed in the thickness of the lipid layer of the tear film among the three groups. LLT in the MGD group and SS group decreased with the aggravation of air pollution, while remained unchanged in the control group. There was strong evidence of correlation between LLT of the MGD group (F = 353.494, p < 0.01, adjusted R2 = 0.695) and the SS group (F = 502.404, p < 0.01, adjusted R2 = 0.764) with AQI, while there was minor correlation between LLT with AQI in control group (F = 8.525, p < 0.01, adjusted R2 = 0.046).Conclusions: Air pollution can cause a decrease in the thickness of the tear film lipid layer, thereby affecting tear film stability, leading to the occurrence of dry eye.

An overview and thematic analysis of research on cities and the COVID-19 pandemic: Toward just, resilient, and sustainable urban planning and design

Since early 2020, researchers have made efforts to study various issues related to cities and the pandemic. Despite the wealth of research on this topic, there are only a few review articles that explore multiple issues related to it. This is partly because of the rapid pace of publications that makes systematic literature review challenging. To address this issue, in the present study, we rely on bibliometric analysis techniques to gain an overview of the knowledge structure and map key themes and trends of research on cities and the pandemic. Results of the analysis of 2,799 articles show that research mainly focuses on six broad themes: air quality, meteorological factors, built environment factors, transportation, socio-economic disparities, and smart cities, with the first three being dominant. Based on the findings, we discuss major lessons that can be learned from the pandemic and highlight key areas that need further research.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models' predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the "new normal". An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020-compared to the same period in 2018 and 2019. No significant changes were observed for the "new normal" as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

Global power and energy scenario during COVID-19 pandemic: Lessons from lockdown

Technology is advancing progressively at a very sharp rate in every phase of the commercial and economic issues, where energy plays a vital role in hi-tech enrichment. After continuous and multi directive research, human is efficient enough to put their steps on the peak of all imaginable pitches. But a human cannot fight against the environment in all instances. COVID-19, or Corona Virus, is such a type of environmental pandemic. Due to a large spread rate of COVID-19, the human society of the entire Globe is getting affected within a very small period. During its first and second wave, it collapses maximum countries health, economic structures along with the electrical sector. Many countries have enforced the ‘lockdown’ to their citizen for minimizing the community spread rate, which directly affects the energy sector. Standing in this scenario, this study focuses on the significant effect of COVID-19 on the energy sector of the Globe, especially in India. This paper also demonstrates that how several countries are trying to secure their power sector in this pandemic situation.

The effects of short-term and long-term air pollution exposure on meibomian gland dysfunction

We aim to assess the effects of different air pollutants on meibomian gland dysfunction (MGD). As a prospective multicenter study, 864 patients were recruited from four different regions (i.e., coal, oil, steel, and living). The oil region had a significantly lower temperature and higher O₃ and SO₂ concentrations than other regions. Notably, participants in oil region presented with more frequent and serious MGD signs and higher cytokine levels (median interleukin 6 [IL-6] in oil: 2.66, steel: 0.96, coal: 0.38, living: 0.56; IL-8 in oil: 117.52, steel: 46.94, coal: 26.89, living: 33; vascular endothelial growth factor [VEGF] in oil: 25.09, steel: 14.02, coal: 14.02, living: 28.47). The short-term fluctuations of cytokine levels were associated with the changes in gas levels (PM_2.5 and IL-8: β = 0.016 [0.004–0.029]; O₃ and IL-6: β = 0.576 [0.386–0.702]; O₃ and IL-8: β = 0.479 [0.369–0.890]; SO₂ and VEGF: β = 0.021 [0.001–0.047]). After long-term exposure, lid margin neovascularization (r = 0.402), meibomian gland (MG) expression (r = 0.377), MG secretion (r = 0.303), MG loss (r = 0.404), and tear meniscus height (r = − 0.345) were moderately correlated with air quality index (AQI). Individuals in oil region had more serious MGD signs and higher cytokine levels. MGD is susceptible to long-term exposure to high AQI.

Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.

Screening Approach for Short-Term PM2.5 Health Co-Benefits: A Case Study from 15 Metropolitan Cities around the World during the COVID-19 Pandemic

Fifteen cities across the world have been selected to investigate the public health co-benefits of PM2.5 reduction, during a period when various non-pharmaceutical interventions (NPIs) were adopted in the COVID-19 pandemic. Through applying a public health model, AirQ+, substantial spatial variations of global public health co-benefits were identified. Differences in seasonal air quality and population baselines were key underlying factors. For cities in North America, NPIs were introduced during the low pollution season, generating no co-benefits. On the other hand, tremendous health co-benefits were observed for cities in India and China, due to the high PM2.5 background with a large population. Among all, New Delhi has received the largest co-benefits, which saved over 14,700 premature deaths. As the pollution level (i.e., 45 μg m−3) with NPIs still exceeded the air quality standard, more rigorous emission controls are urgently needed to protect the public′s health in India. At last, a novel and practical tool for co-benefit screening was developed using data from one of the global measurement networks (i.e., IQAir).

COVID-19 and COPD: lessons beyond the pandemic

Early in the COVID pandemic there were concerns about the outcomes for patients with COPD who developed COVID-19. Although the pandemic has made the diagnosis and routine management of COPD more difficult, the risk of patients developing COVID or of having poor outcomes is less than anticipated and there have been some unexpected findings that may lead to significant improvements in the management of COPD in future.

Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of NO 2 , PM 10 , PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

Synoptic analysis of the most durable pollution and clean waves during 2009-2019 in Tehran City (capital of Iran)

The present study was done to analyze and compare synoptic conditions during the most durable pollution and clean waves (MDPW and MDCW) in Tehran City (capital of Iran). For this purpose, air quality index (AQI) data were first obtained during 2009-2019 from the Tehran Air Quality Control Company in Tehran Province. Then, a threshold of (AQI ≤ 101) representing a polluted and unhealthy atmosphere was considered as MDPW, and a threshold of (AQI ≤ 50) representing a clean (good) air was selected as MDCW in Tehran City by identifying the polluted days. The results showed the presence of MDPW during 43 polluted days from November 21, 2010 to January 2, 2011. Intensity and frequency of the polluted days during statistical period showed a decreasing trend that coincided with an increasing trend of precipitation. Analysis of synoptic conditions occurred during 43 days of MDPW indicated penetration of Siberian high-pressure from the east at sea level and extension of sub-tropical anticyclone in mid-level from the west that led to development of an anticyclone circulation with descending and converging flow in Iran and surrounding region. As a result, a calm and sustainable climate and stable concentration of pollutants were observed throughout Tehran City. Backward trajectory of sources and entry route of the polluted air to Tehran City during peak days of pollution showed that desert regions of southern Iraq and central Iran along the southern and southwestern parts played a major role in increasing concentration of pollutants during occurrence of durable wave. Different routes were observed in entry of dusty and the polluted air to Tehran City according to hybrid single-particle Lagrangian integrated trajectory (HY SPLIT) model, but in most of peak days, the areas that were near and adjacent to Tehran City with height of less than 1000 m played a greater role in particle transport. Investigation of instability indices during MDPW showed that in most days of pollution, values of instability intensity were classified into weak class and lower troposphere (1000-850 hPa) compared to higher levels, and degree of stability and calmness of the atmosphere was more. So that, the highest frequency of temperature inversions occurred near the earth's surface.