Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China.

The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.

Investigation in PO blending and compression ratio on engine performance and gas emissions including environmental health risk assessment and economic analysis.

Waste management and mitigation is the primary necessity across the globe. The daily use of plastic materials in different forms emergence the plastic pollutions, and it has been significantly increased during the COVID-19 pandemic. Thus, mitigation of waste plastics generation is one of the major challenges in the present situation. The present study addressed the conversion of waste plastics into value-added products such as liquid hydrocarbon fuels and their application in reducing greenhouse gas emissions. A comprehensive investigation has been performed on engine performance and combustion characteristics at various compression ratios and PO blending. The effect of liquid fuel blending with commercial diesel was investigated at three different compression ratios (15.1, 16.2, and 16.7) under various BMEP conditions. The results revealed that blending of liquid fuel produced from waste plastic can improve the BTE significantly, and the highest 35.77% of BTE was observed for 10% blending at 15.1 CR. While the lowest BSFC of 5.77 × 10-5 kg/kW-s was estimated for 20% PO blending at 16.7 CR under optimum BMEP (4.0 bar) conditions. The investigation of combustion parameters such as cylinder pressure, net heat release rate, rate of pressure rise, and cumulative heat release showed that it increases with the compression ratio from 15.1 to 16.7. At the same time, the emissions of CO, CO2, and unburnt hydrocarbon was decreased significantly. The economic analysis for the present lab-scale study estimated that approximately ₹12.17 ($0.15) profit per liter is possible in the 1st year, while the significant profit starts from the 2nd year onward, which is in the range of ₹59.78-₹84.48 ($0.75-$1.07) when the PO is blended with CD within the permissible limits as per the norms.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19.

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

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.

Changes in temporal pattern and spatial distribution of environmental pollutants in 8 Asian countries owing to COVID-19 pandemic.

This study investigated the changes in air pollutant's concentration, spatio-temporal distribution and sensitivity of changes in air pollutant's concentration during pre and post COVID-19 outbreak. We employed Google Earth Engine Platform to access remote sensing datasets of air pollutants across Asian continent. Air pollution and cumulative confirmed-COVID cases data of Asian countries (Afghanistan, Bangladesh, China, India, Iran, Iraq, Pakistan, and Saudi Arabia) have been collected and analyzed for 2019 and 2020. The results indicate that aerosol index (AI) and nitrogen dioxide (NO2) is significantly reduced during COVID outbreak i.e. in year 2020. In addition, we found significantly positive (P < 0.05, 95% confidence interval, two-tailed) correlation between changes in AI and NO2 concentration for net active-COVID case increment in almost each country. For other atmospheric gases i.e. carbon monoxide (CO), formaldehyde (HCHO), ozone (O3), and Sulfur dioxide (SO2), insignificant and/or significant negative correlation is also observed. These results suggest that the atmospheric concentration of AI and NO2 are good indicators of human activities. Furthermore, the changes in O3 shows significantly negative correlation for net active-COVID case increment. In conclusion, we observed significant positive environmental impact of COVID-19 restrictions in Asia. This study would help and assist environmentalist and policy makers in restraining air pollution by implementing efficient restrictions on human activities with minimal economic loss.

National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China.

Civil aviation is an important source of air pollutants, but this field has received insufficient attention in China. Based on the standard emissions model of the International Civil Aviation Organization (ICAO) and actual flight information from 241 airports, this study estimated a comprehensive emissions inventory for 2010-2020 by considering the impacts of mixing layer height. The results showed that annual pollutant emissions rapidly trended upward along with population and economic growth; however, the emissions decreased owing to the impacts of the COVID-19 pandemic. In 2020, the emissions of carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM), methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O) were 34.34, 65.73, 0.10, 0.34, 0.40, 14,706.26, and 5733.11 Gg, respectively. The emissions of total volatile organic compounds (VOCs) from China's civil airports in 2020 were estimated at 17.20 Gg; the major components were formic acid (1.70 Gg), acetic acid (1.62 Gg), 1-butylene (1.03 Gg), acetone (0.96 Gg), and acetaldehyde (0.93 Gg). The distribution of pollutant emissions was consistent with the level of economic development, mainly in Beijing, Guangzhou, and Shanghai. In addition, we estimated future pollution trends for the aviation industry under four scenarios. Under the comprehensive scenario, which considered the impacts of economic growth, passenger turnover, cargo turnover, COVID-19, and technological efficiency, the levels of typical pollutants were expected to increase by nearly 1.51-fold from 2010 to 2035.

Environmental spatial heterogeneity of the impacts of COVID-19 on the top-20 metropolitan cities of Asia-Pacific.

This study investigated the environmental spatial heterogeneity of novel coronavirus (COVID-19) and spatial and temporal changes among the top-20 metropolitan cities of the Asia-Pacific. Remote sensing-based assessment is performed to analyze before and during the lockdown amid COVID-19 lockdown in the cities. Air pollution and mobility data of each city (Bangkok, Beijing, Busan, Dhaka, Delhi, Ho Chi Minh, Hong Kong, Karachi, Mumbai, Seoul, Shanghai, Singapore, Tokyo, Wuhan, and few others) have been collected and analyzed for 2019 and 2020. Results indicated that almost every city was impacted positively regarding environmental emissions and visible reduction were found in Aerosol Optical Depth (AOD), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) concentrations before and during lockdown periods of 2020 as compared to those of 2019. The highest NO2 emission reduction (~ 50%) was recorded in Wuhan city during the lockdown of 2020. AOD was highest in Beijing and lowest in Colombo (< 10%). Overall, 90% movement was reduced till mid-April, 2020. A 98% reduction in mobility was recorded in Delhi, Seoul, and Wuhan. This analysis suggests that smart mobility and partial shutdown policies could be developed to reduce environmental pollutions in the region. Wuhan city is one of the benchmarks and can be replicated for the rest of the Asian cities wherever applicable.

Diurnal and weekly patterns of primary pollutants in Beijing under COVID-19 restrictions.

Restrictions on movement in Beijing to limit the COVID-19 epidemic tended to reduce the emissions of primary pollutants. However, changes in pollutant concentrations are also affected by chemical transformation and meteorology. Decreases in concentrations were also not as obvious in Beijing when compared with Hubei Province, where lockdown was especially strict. Declines in concentrations between 2019 and 2020 are evident for both NO2 (37.0 to 26.2 µg m-3) and SO2 (5.86 to 4.15 µg m-3), but there was little evidence of change for PM2.5 (50.0 to 53.8 µg m-3) and CO (0.71 to 0.74 mg m-3). Despite this, Fourier analysis revealed that the weekly cycle of PM2.5 evident in 2019 was not apparent in 2020. In 2019, CO showed both diurnal and weekly signals, but these were absent under the restrictions of 2020. This suggests that while concentrations may remain relatively constant, the temporal distribution of pollutants can show subtle changes under restrictions imposed in attempts to limit the spread of the coronavirus.

Ambient air pollution, meteorology, and COVID-19 infection in Korea.

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID-19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0-14; OR = 1.09; 95% CI = 1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO2 (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR = 1.10, 95% CI = 1.04-1.16). There was a positive association between per 0.001 ppm of SO2 concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively. There were significantly temporal associations between temperature, NO2 , CO, and SO2 concentrations and daily COVID-19 confirmed cases in Korea.

Short-term exposure to ambient air pollution in association with COVID-19 of two clusters in South Korea.

OBJECTIVES: This study aimed to examine the association between six air pollutants and COVID-19 infection in two main clusters, which accounted for 83% of total confirmed cases in Korea. METHODS: We collected the data on daily confirmed cases between February 24, 2020 and September 12, 2020. Data on six air pollutants (PM2.5 , PM10 , O3 , NO2 , CO and SO2 ) and four meteorological factors (temperature, wind speed, humidity and air pressure) were obtained on seven days prior to the research period. The generalised additive model and the distributed lag nonlinear model were applied to generate the relative risks (RRs) and 95% confidence intervals (CIs) for the associations. Pooled estimates for clusters were obtained by applying a random-effects model. RESULTS: We found that NO2 concentration was positively associated with daily confirmed cases in both Seoul-Gyeonggi and Daegu-Gyeongbuk clusters, with RRs (95% CIs) of 1.22 (1.03-1.44) and 1.66 (1.25-2.19), respectively. However, SO2 concentration was observed to be associated with daily confirmed cases in the Seoul-Gyeonggi cluster only (RR = 1.30, 95% CI = 1.10-1.54), whereas PM2.5 and CO concentrations were observed to be associated with daily confirmed cases in the Daegu-Gyeongbuk cluster only (RR = 1.14, 95% CI = 1.02-1.27 and RR = 1.30, 95% CI = 1.15-1.48, respectively). CONCLUSIONS: Our data found that NO2 concentration was positively associated with daily confirmed cases in both clusters, whereas the effect of PM2.5 , CO and SO2 on COVID-19 infection in two clusters was different.

Effect of environmental pollutants PM-2.5, carbon monoxide, and ozone on the incidence and mortality of SARS-COV-2 infection in ten wildfire affected counties in California.

Various regions of California have experienced a large number of wildfires this year, at the same time the state has been experiencing a large number of cases of and deaths from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The present study aimed to investigate the relationship of wildfire allied pollutants, including particulate matter (PM-2.5 µm), carbon monoxide (CO), and Ozone (O3) with the dynamics of new daily cases and deaths due to SARS-COV 2 infection in 10 counties, which were affected by wildfire in California. The data on COVID-19 pertaining to daily new cases and deaths was recorded from Worldometer Web. The daily PM-2.5 µm, CO, and O3 concentrations were recorded from three metrological websites: BAAQMD- Air Quality Data; California Air Quality Index-AQI; and Environmental Protection Agency- EPA. The data recorded from the date of the appearance of first case of (SARS-CoV-2) in California region to the onset of wildfire, and from the onset of wildfire to September 22, 2020. After the wildfire, the PM2.5 concentration increased by 220.71%; O3 by 19.56%; and the CO concentration increased by 151.05%. After the wildfire, the numbers of cases and deaths due to COVID-19 both increased respectively by 56.9% and 148.2%. The California wildfire caused an increase in ambient concentrations of toxic pollutants which were temporally associated with an increase in the incidence and mortality of COVID-19.

Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China.

Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R0) was calculated with the maximum likelihood "removal" method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R0 was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R0 and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R0, while nitrogen dioxide (NO2), 24-h average concentration of nitrogen dioxide (NO2_24 h) and daily maximum temperature were inversely correlated to R0, with coefficients of 0.644, 0.661, -0.636, -0.657, -0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R0, including mean/maximum/minimum air pressure and mean wind speed, with coefficients of -0.697, -0.697, -0.697 and -0.841, respectively. There was no correlation with R0 and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.

COVID-19's impact on the atmospheric environment in the Southeast Asia region.

Since its first appearance in Wuhan, China at the end of 2019, the new coronavirus (COVID-19) has evolved a global pandemic within three months, with more than 4.3 million confirmed cases worldwide until mid-May 2020. As many countries around the world, Malaysia and other southeast Asian (SEA) countries have also enforced lockdown at different degrees to contain the spread of the disease, which has brought some positive effects on natural environment. Therefore, evaluating the reduction in anthropogenic emissions due to COVID-19 and the related governmental measures to restrict its expansion is crucial to assess its impacts on air pollution and economic growth. In this study, we used aerosol optical depth (AOD) observations from Himawari-8 satellite, along with tropospheric NO2 column density from Aura-OMI over SEA, and ground-based pollution measurements at several stations across Malaysia, in order to quantify the changes in aerosol and air pollutants associated with the general shutdown of anthropogenic and industrial activities due to COVID-19. The lockdown has led to a notable decrease in AOD over SEA and in the pollution outflow over the oceanic regions, while a significant decrease (27% - 30%) in tropospheric NO2 was observed over areas not affected by seasonal biomass burning. Especially in Malaysia, PM10, PM2.5, NO2, SO2, and CO concentrations have been decreased by 26-31%, 23-32%, 63-64%, 9-20%, and 25-31%, respectively, in the urban areas during the lockdown phase, compared to the same periods in 2018 and 2019. Notable reductions are also seen at industrial, suburban and rural sites across the country. Quantifying the reductions in major and health harmful air pollutants is crucial for health-related research and for air-quality and climate-change studies.