COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China

Prenatal Exposure of PFAS in Cohorts of Pregnant Women: Identifying the Critical Windows of Vulnerability and Health Implications

Cohorts of pregnant women in 2018 and 2020 were selected to explore prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFAS). Maternal serum during the whole pregnancy (first to third trimesters) and matched cord serum were collected for the analysis of 50 PFAS. Perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and 6:2 fluorotelomer sulfonic acid (6:2 FTS) were the dominant PFAS in both the maternal and cord serum. The median ∑PFAS concentration was 14.18 ng/mL, and the ∑PFAS concentration was observed to decline from the first trimester to the third trimester. The transplacental transfer efficiencies (TTE) of 29 PFAS were comprehensively assessed, and a "U"-shaped trend in TTE values with increasing molecular chain length of perfluoroalkyl carboxylic acid (PFCA) was observed in this study. Moreover, the maternal concentrations of 9-chlorohexadecafluoro-3-oxanonane-1-sulfonic acid (6:2 Cl-PFESA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUdA), perfluorododecanoic acid (PFDoA), PFOS, and hexafluoropropylene oxide dimer acid (HFPO-DA) in the 2020 cohort were significantly lower than those in the 2018 cohort, declining by about 23.85-43.2% from 2018 to 2020 (p < 0.05). Higher proportions of emerging PFAS were observed in fetuses born in 2020. This birth cohort was collected during the COVID-19 epidemic period. The change in the PFAS exposure scene might be in response to the different exposure profiles of the 2018 and 2020 cohorts, which are attributed to the impact of COVID-19 on the social activities and environment of pregnant women. Finally, by application of a multiple informant model, the third trimester was identified as the critical window of vulnerability to PFAS exposure that affects birth weight and birth length.

Unraveling the O3-NOX-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai

The COVID-19 pandemic promoted strict restrictions to human activities in China, which led to an unexpected increase in ozone (O3) regarding to nitrogen oxides (NOx) and volatile organic compounds (VOCs) co-abatement in urban China. However, providing a quantitative assessment of the photochemistry that leads to O3 increase is still challenging. Here, we evaluated changes in O3 arising from photochemical production with precursors (NOX and VOCS) in industrial regions in Shanghai during the COVID-19 lockdowns by using machine learning models and box models. The changes of air pollutants (O3, NOX, VOCs) during the COVID-19 lockdowns were analyzed by deweathering and detrending machine learning models with regard to meteorological and emission effects. After accounting for effects of meteorological variability, we find increase in O3 concentration (49.5%). Except for meteorological effects, model results of detrending the business-as-usual changes indicate much smaller reduction (-0.6%), highlighting the O3 increase attributable to complex photochemistry mechanism and the upward trends of O3 due to clear air policy in Shanghai. We then used box models to assess the photochemistry mechanism and identify key factors that control O3 production during lockdowns. It was found that empirical evidence for a link between efficient radical propagation and the optimized O3 production efficiency of NOX under the VOC-limited conditions. Simulations with box models also indicate that priority should be given to controlling industrial emissions and vehicle exhaust while the VOCs and NOX should be managed at a proper ratio in order to control O3 in winter. While lockdown is not a condition that could ever be continued indefinitely, findings of this study offer theoretical support for formulating refined O3 management in industrial regions in Shanghai, especially in winter.

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China

The COVID-19 pandemic prompted several nations, including China, to enact unprecedented lockdown measures, leading to significant alterations in environmental conditions. Previous studies have solely analysed the impact of lockdown measures on air pollutants or carbon dioxide (CO2) emissions during the COVID-19 pandemic in China, but few have focused on the spatio-temporal change characteristics and synergistic effects between the two. In this study, we constructed a methodological framework to examine the spatiotemporal characteristics and co-effects of air quality (PM2.5, SO2, and NO2) and CO2 changes in 324 prefecture-level cities in China due to the COVID-19 blockade measures from January 24 to April 30, 2020, using the regression discontinuity in time method and co-effect control coordinate system. The results show that a significant improvement in air quality and CO2 emissions during the lockdown period, with notable north‒south heterogeneity. During the major lockdown period (January 24 to February 29), the measures resulted in respective reductions of 5.6%, 16.6%, and 25.1% in the concentrations of SO2, NO2, and CO2 nationwide. The proportions of cities with negative treatment effects on PM2.5, SO2, NO2, and CO2 were 39.20%, 70.99%, 84.6%, and 99.38%, respectively. Provinces where concentrations of CO2 and NO2 declined by over 30% were primarily concentrated in southern areas of the 'Yangtze River Defense Line'. Starting from March, the improvement effect of air quality and CO2 has weakened, and the concentration of air pollutants has rebounded. This study offers crucial insights into the causal effects of lockdown measures on air quality changes, and reveals the synergy between air quality and CO2, thereby providing a reference for devising effective air quality improvement and energy-saving emission reduction strategies.

The impact of the COVID-19 pandemic on global trade-embodied carbon emissions

We evaluate the response of global supply chains to carbon emissions through compiling multi-regional input-output (MRIO) models for import and export shocks in 14 countries/territories dominated by the COVID-19 crisis. Instead of traditional production-based inventories, we achieve CO2 emissions inventories based on intermediate inputs and final consumption to analyze the connected environmental impacts. In addition, we adopt the available data up to date to construct inventories of carbon emissions involved in imports and exports from different sectors. The results show that global carbon emissions could be decreased by 6.01% during the COVID-19, while export carbon emissions remained basically unchanged. As a result, imported carbon emissions fell by 5.2%, with the energy products sector most affected by the pandemic. Transport sector witnessed 18.42% carbon emission reduction. The impact of developing countries with a large proportion of resource-based industries is comparatively higher than that of developed countries with the technological advantage. International trade plays a crucial role in the choice of supply chain partners to control carbon emissions. Building a sustainable supply chain and reducing the "trade carbon deficit" between countries/regions requires the coordination of all departments of each country/region to promote the trade of energy-saving products, environmental protection services and environmental services.

How does COVID-19 lockdown affect air quality: Evidence from Lanzhou, a large city in Northwest China

Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.

Spatiotemporal variations and the ecological risks of organophosphate esters in Laizhou Bay waters between 2019 and 2021: Implying the impacts of the COVID-19 pandemic

Organophosphate esters (OPEs) are a group of synthetic chemicals used in numerous consumer products such as plastics and furniture. The Coronavirus Disease 2019 (COVID-19) pandemic significantly slowed anthropogenic activities and reduced the emissions of pollutants. Meanwhile, the mismanagement of large quantities of disposable plastic facemasks intensified the problems of plastic pollution and leachable pollutants in coastal waters. In this study, the joint effects of the COVID-19 outbreak on the occurrence of 12 targeted OPEs in the waters of Laizhou Bay (LZB) were investigated. The results showed that the median total OPE concentrations were 725, 363, and 109 ng L^-1 in the sewage treatment plant effluent, river water, and bay water in 2021, decreased significantly (p < 0.05) by 67%, 68%, and 70%, respectively, compared with those before the COVID-19 outbreak. The release potential of targeted OPEs from disposable surgical masks in the LZB area was ∼0.24 kg yr^-1, which was insufficient to increase the OPE concentration in the LZB waters. The concentrations of most individual OPEs significantly decreased in LZB waters from 2019 to 2021, except for TBOEP and TNBP. Spatially, a lower concentration of OPEs was found in the Yellow River estuary area in 2021 compared with that before the COVID-19 pandemic due to the high content of suspended particulate matter in the YR. A higher total OPE concentration was observed along the northeastern coast of LZB, mainly owing to the construction of an artificial island since 2020. The ecological risks of the OPE mixture in LZB waters were lower than those before the COVID-19 outbreak. However, TCEP, TNBP, and BDP should receive continuous attention because of their potential ecological risks to aquatic organisms.

Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China?

To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (-15%) and transportation (-90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.

The local-neighborhood effects of low-carbon city pilots program on PM2.5 in China: A spatial difference-in-differences analysis

Using the balanced panel of 260 cities in China from 2005 to 2018, this study explores the local-neighborhood effects of the low-carbon city pilots (LCCPs) program on PM_2.5 concentration by utilizing the spatial difference-in-differences (SDID) method. The results show that the LCCPs program can not only reduce the local PM_2.5 concentration but also effectively alleviate the smog pollution in neighboring cities. The reduction effect of LCCPs on PM_2.5 in local cities is more significant in central and western areas, second-tier and above cities and resource-based cities. Nevertheless, the spillover effect on neighboring cities is more significant in central and western areas, third-tier and below and non-resource-based cities. In addition, the impact of policy is mainly through green innovation, while the intermediary role of industrial structure upgrading is not significant. These findings can provide useful policy inspiration for scientifically implementing air pollution prevention and control actions and winning the battle to defend the blue sky.

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Hourly PM_2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM_2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM_2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMF_whole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMF_whole solution. After removing event data, PMF modeling was conducted for individual months (PMF_month) and the 4-year period (PMF_4-year), respectively. PMF_month solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF_4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO₂ and PM₁₀ at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017-2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO₂ (R² = 0.72) than PM₁₀ concentrations (R² = 0.35). The average decrease of NO₂ was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM₁₀ concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.

Changes in Ultrafine Particle Concentrations near a Major Airport Following Reduced Transportation Activity during the COVID-19 Pandemic

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017-9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020-6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)-51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

COVID-19 Pandemic, Air Quality, and PM2.5 Reduction-Induced Health Benefits: A Comparative Study for Three Significant Periods in Beijing

Previous studies have estimated the influence of control measures on air quality in the ecological environment during the COVID-19 pandemic. However, few have attached importance to the comparative study of several different periods and evaluated the health benefits of PM2.5 decrease caused by COVID-19. Therefore, we aimed to estimate the control measures' impact on air pollutants in 16 urban areas in Beijing and conducted a comparative study across three different periods by establishing the least squares dummy variable model and difference-in-differences model. We discovered that restriction measures did have an apparent impact on most air pollutants, but there were discrepancies in the three periods. The Air Quality Index (AQI) decreased by 7.8%, and SO2, NO2, PM10, PM2.5, and CO concentrations were lowered by 37.32, 46.76, 53.22, 34.07, and 19.97%, respectively, in the first period, while O3 increased by 36.27%. In addition, the air pollutant concentrations in the ecological environment, including O3, reduced significantly, of which O3 decreased by 7.26% in the second period. Furthermore, AQI and O3 concentrations slightly increased compared to the same period in 2019, while other pollutants dropped, with NO2 being the most apparent decrease in the third period. Lastly, we employed health effects and environmental value assessment methods to evaluate the additional public health benefits of PM2.5 reduction owing to the restriction measures in three periods. This research not only provides a natural experimental basis for governance actions of air pollution in the ecological environment, but also points out a significant direction for future control strategies.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021)

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19

China's efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM_2.5) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM_2.5 concentrations and questioned if the previous PM_2.5 declining trend was sustained or not. This study took a deep dive into the PM_2.5 trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM_2.5 data notably adjusted for a key measurement method change. We find that mean PM_2.5 concentrations in northern China increased by 5.16 µg/m³ in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m³ from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM_2.5 rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM_2.5 concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM_2.5 emission reductions, with an emphasis on key regions during key pollution periods.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown

Estimating the impacts on PM2.5 pollution and CO2 emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO2 emissions from the Carbon Monitor project. Our study shows that PM2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM2.5 concentration. Furthermore, CO2 concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO2 emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO2 emissions.

Evolution of organic carbon during COVID-19 lockdown period: Possible contribution of nocturnal chemistry

Carbonaceous aerosol is one of the main components of atmospheric particulate matter, which is of great significance due to its role in climate change, earth's radiation balance, visibility, and human health. In this work, carbonaceous aerosols were measured in Shijiazhuang and Beijing using the OC/EC analyzer from December 1, 2019 to March 15, 2020, which covered the Coronavirus Disease 2019 (COVID-19) pandemic. The observed results show that the gas-phase pollutants, such as NO, NO2, and aerosol-phase pollutants (Primary Organic Compounds, POC) from anthropogenic emissions, were significantly reduced during the lockdown period due to limited human activities in North China Plain (NCP). However, the atmospheric oxidation capacity (Ox/CO) shows a significantly increase during the lockdown period. Meanwhile, additional sources of nighttime Secondary Organic Carbon (SOC), Secondary Organic Aerosol (SOA), and babs, BrC(370 nm) are observed and ascribed to the nocturnal chemistry related to NO3 radical. The Potential Source Contribution Function (PSCF) analysis indicates that the southeast areas of the NCP region contributed more to the SOC during the lockdown period than the normal period. Our results highlight the importance of regional nocturnal chemistry in SOA formation.

New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing

During the SARS period in 2003 and COVID-19 pandemic period in 2020, unexpected severe particulate matter pollution occurred in northern China, although the anthropogenic activities and associated emissions have assumed to be reduced dramatically. This anomalistic increase in PM_2.5 pollution raises a question about how source emissions impact the air quality during these pandemic periods. In this study, we investigated the stable Cu and Si isotopic compositions and typical source-specific fingerprints of PM_2.5 and its sources. We show that the primary PM_2.5 emissions (PM_2.5 emitted directly from sources) actually had no reduction but redistribution during these pandemic periods, rather than the previous thought of being greatly reduced. This finding provided critical evidence to interpret the anomalistic PM_2.5 increase during the pandemic periods in north China. Our results also suggested that both the energy structure adjustment and stringent regulations on primary emissions should be synergistically implemented in a regional scale for clean air actions in China.

Changes in air quality during and after large-scale social restriction periods in Jakarta city, Indonesia

COVID-19 outbreak has constrained human activities in Jakarta, Indonesia during the large-scale social restriction (LSSR) period. The objective of this study was to evaluate the changes in the spatial variation of air pollutants over Jakarta during and after the LSSR periods. This study used satellite retrievals such as OMI, AIRS, and MERRA-2 satellite data to assess spatial variations of NO₂, CO, O₃, SO₂, and PM2.5 from May to June 2020 (during the LSSR period) and from July to August 2020 (after the LSSR period) over Jakarta. The satellite images were processed using GIS software to increase the clarity of the images. The relationship between air pollutants and meteorological data was analyzed using Pearson correlation. The results showed the levels of NO₂, PM2.5, O₃, and CO increased by 59.4%, 21.2%, 16.2%, and 1.0%, respectively, while SO₂ decreased by 19.1% after the LSSR period. The temperature value was inversely correlated with PM2.5, NO₂, and SO₂ concentrations. Furthermore, the backward trajectory analysis revealed that air pollutants from outland areas such as the east and southeast carried more particulate matter and gases pollutants, which contributed to the air pollution during and after the LSSR periods. As a whole, the COVID-19 outbreak had bad impacts on human health, but the increase in air pollutants levels after loosening the LSSR policy could also lead to a higher risk of severe respiratory diseases. This study provides new insight into air pollutant distribution during and after LSSR periods and recommends an effective method of mitigating the air pollution issues in Jakarta.

Biopsychosocial Processes of Health and Disease During the COVID-19 Pandemic

ABSTRACT

The biopsychosocial model provides a useful perspective for understanding the development and characteristics of the COVID-19 pandemic and its anticipated long-term consequences for society as well as individuals. This article provides a biopsychosocial perspective on the COVID pandemic and an editorial comment on the articles in this Special Issue of Psychosomatic Medicine. Based on analysis of the PubMed database, it is shown that the attention to psychological and social factors is 74% higher in COVID-19-related articles compared to all other health-related scientific articles published during the same time-period (between 1/1/2020 and 4/18/2021). Specifically, 18.6% of the ≈123,500 articles addressing COVID-19-related topics also included psychological or social factors in their content vs. 10.7% of articles that did not address COVID-19. The biopsychosocial model is relevant to understanding the interrelationships among risk factors and the multidimensional clinical and psychosocial COVID-19 outcomes. Clinical outcomes directly related to COVID-19 range from severe but rare events (mortality and intensive care treatment) to less severe common outcomes such as positive screening tests for COVID-19 with or without symptoms. In addition, psychosocial outcomes range in severity from frequently observed reduced psychological wellbeing to less common clinical mood and anxiety disorders and, in rare cases, suicidality. The COVID-19 pandemic is characterized by an unusually strong and short-term link between social factors and biological aspects of the disease, without mediating psychological factors. After a review of the articles presented in this Special Issue, this editorial concludes with suggestions for biopsychosocial models in research on COVID-19 and other large-scale health threats.