Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Delivery and neonatal outcomes of pregnant women during the Shanghai lockdown: A retrospective analysis.

Objectives: Shanghai witnessed an unprecedented outbreak of COVID-19 and experienced a strict lockdown from March 28, 2022 to May 31, 2022. Most studies to date are on the first lockdown after the outbreak in December 2019. This study aimed to examine the impact of lockdown on delivery and neonatal outcomes among uninfected pregnant women in the new phase of the COVID-19 outbreak. Methods: A retrospective analysis was conducted in the Obstetrics and Gynecology Hospital of Fudan University. Pregnant women without COVID-19 who delivered from March 28, 2022 to May 31, 2022 (lockdown group) and the same period in 2021 (non-lockdown group) were recruited for this study. Logistic regression models and 1 : 1 propensity score matching (PSM) were used to assess the effect of lockdown on delivery outcomes. Results: A total of 2,962 patients were included in this study, 1,339 of whom were from the lockdown group. Compared with the non-lockdown group, pregnant women giving birth during lockdown had an increased risk of term prelabor rupture of membranes (TPROM) (aOR = 1.253, 95% CI: 1.026-1.530), and decreased risks of postpartum hemorrhage (PPH) (aOR = 0.362, 95% CI: 0.216-0.606) and fetal malformation (aOR = 0.309, 95% CI: 0.164-0.582). The risk of large for gestational age (LGA) (aOR = 0.802, 95% CI: 0.648-0.992) and rate of admission to the neonatal intensive care unit (NICU) (aOR = 0.722, 95% CI: 0.589-0.885) also significantly declined. After 1 : 1 PSM, the impact of lockdown on the risk of TPROM (aOR = 1.501, 95% CI: 1.083-2.080), PPH (aOR = 0.371, 95% CI: 0.211-0.654), fetal malformation (aOR = 0.332, 95% CI: 0.161-0.684), LGA (aOR = 0.749, 95% CI: 0.594-0.945) and rate of admission to the NICU (aOR = 0.700, 95% CI: 0.564-0.869) all remained. There were no other delivery or neonatal outcomes affected by the lockdown after the COVID-19 outbreak. Conclusion: This study indicated a significant increase in the risk of term PROM, significant decreases in the risk of PPH, fetal malformation and LGA, and a marked decline in the rate of admission to the NICU during Shanghai Lockdown.

Tracking long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China 2015-2021.

China has experienced severe air pollution in the past decade, especially PM2.5 and emerging ozone pollution recently. In this study, we comprehensively analyzed long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China during 2015-2021 regarding two-stage clean-air actions based on the Ministry of Ecology and the Environment (MEE) air monitoring network. Overall, the ratio of the population living in the regions exceeding the Chinese National Ambient Air Quality Standard (35 µg/m3) decreases by 29.9 % for PM2.5 from 2015 to 2021, driven by high proportions in the Middle Plain (MP, 42.3 %) and Lan-Xi (35.0 %) regions. However, this ratio almost remains unchanged for ozone and even increases by 1.5 % in the MP region. As expected, the improved air quality leads to 234.7 × 103 avoided premature mortality (ΔMort), mainly ascribed to the reduction in PM2.5 concentration. COVID-19 pandemic may influence the annual variation of PM2.5-related ΔMort as it affects the shape of the population exposure curve to become much steeper. Although all eleven urban agglomerations share stroke (43.6 %) and ischaemic heart disease (IHD, 30.1 %) as the two largest contributors to total ΔMort, cause-specific ΔMort is highly regional heterogeneous, in which ozone-related ΔMort is significantly higher (21 %) in the Tibet region than other urban agglomeration. Despite ozone-related ΔMort being one order of magnitude lower than PM2.5-related ΔMort from 2015 to 2021, ozone-related ΔMort is predicted to increase in major urban agglomerations initially along with a continuous decline for PM2.5-related ΔMort from 2020 to 2060, highlighting the importance of ozone control. Coordinated controls of PM2.5 and O3 are warranted for reducing health burdens in China during achieving carbon neutrality.

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) µg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 µg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.

Duration of post-COVID-19 symptoms is associated with sustained SARS-CoV-2-specific immune responses.

A subset of COVID-19 patients exhibit post-acute sequelae of COVID-19 (PASC), but little is known about the immune signatures associated with these syndromes. We investigated longitudinal peripheral blood samples in 50 individuals with previously confirmed SARS-CoV-2 infection, including 20 who experienced prolonged duration of COVID-19 symptoms (lasting more than 30 days; median = 74 days) compared with 30 who had symptom resolution within 20 days. Individuals with prolonged symptom duration maintained antigen-specific T cell response magnitudes to SARS-CoV-2 spike protein in CD4+ and circulating T follicular helper cell populations during late convalescence, while those without persistent symptoms demonstrated an expected decline. The prolonged group also displayed increased IgG avidity to SARS-CoV-2 spike protein. Significant correlations between symptom duration and both SARS-CoV-2-specific T cells and antibodies were observed. Activation and exhaustion markers were evaluated in multiple immune cell types, revealing few phenotypic differences between prolonged and recovered groups, suggesting that prolonged symptom duration is not due to persistent systemic inflammation. These findings demonstrate that SARS-CoV-2-specific immune responses are maintained in patients suffering from prolonged post-COVID-19 symptom duration in contrast to those with resolved symptoms and may suggest the persistence of viral antigens as an underlying etiology.

Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis.

The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.

SARS-CoV-2-specific circulating T follicular helper cells correlate with neutralizing antibodies and increase during early convalescence.

T-cell immunity is likely to play a role in protection against SARS-CoV-2 by helping generate neutralizing antibodies. We longitudinally studied CD4 T-cell responses to the M, N, and S structural proteins of SARS-CoV-2 in 26 convalescent individuals. Within the first two months following symptom onset, a majority of individuals (81%) mounted at least one CD4 T-cell response, and 48% of individuals mounted detectable SARS-CoV-2-specific circulating T follicular helper cells (cTfh, defined as CXCR5+PD1+ CD4 T cells). SARS-CoV-2-specific cTfh responses across all three protein specificities correlated with antibody neutralization with the strongest correlation observed for S protein-specific responses. When examined over time, cTfh responses, particularly to the M protein, increased in convalescence, and robust cTfh responses with magnitudes greater than 5% were detected at the second convalescent visit, a median of 38 days post-symptom onset. CD4 T-cell responses declined but persisted at low magnitudes three months and six months after symptom onset. These data deepen our understanding of antigen-specific cTfh responses in SARS-CoV-2 infection, suggesting that in addition to S protein, M and N protein-specific cTfh may also assist in the development of neutralizing antibodies and that cTfh response formation may be delayed in SARS-CoV-2 infection.

Effects of urban functional fragmentation on nitrogen dioxide (NO2) variation with anthropogenic-emission restriction in China.

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies.

Association between NO2 concentrations and spatial configuration: a study of the impacts of COVID-19 lockdowns in 54 US cities

The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO2 concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO2 difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO2 dispersion in different types of climates.

Sustained cellular immune dysregulation in individuals recovering from SARS-CoV-2 infection.

SARS-CoV-2 causes a wide spectrum of clinical manifestations and significant mortality. Studies investigating underlying immune characteristics are needed to understand disease pathogenesis and inform vaccine design. In this study, we examined immune cell subsets in hospitalized and nonhospitalized individuals. In hospitalized patients, many adaptive and innate immune cells were decreased in frequency compared with those of healthy and convalescent individuals, with the exception of an increase in B lymphocytes. Our findings show increased frequencies of T cell activation markers (CD69, OX40, HLA-DR, and CD154) in hospitalized patients, with other T cell activation/exhaustion markers (PD-L1 and TIGIT) remaining elevated in hospitalized and nonhospitalized individuals. B cells had a similar pattern of activation/exhaustion, with increased frequency of CD69 and CD95 during hospitalization followed by an increase in PD1 frequencies in nonhospitalized individuals. Interestingly, many of these changes were found to increase over time in nonhospitalized longitudinal samples, suggesting a prolonged period of immune dysregulation after SARS-CoV-2 infection. Changes in T cell activation/exhaustion in nonhospitalized patients were found to positively correlate with age. Severely infected individuals had increased expression of activation and exhaustion markers. These data suggest a prolonged period of immune dysregulation after SARS-CoV-2 infection, highlighting the need for additional studies investigating immune dysregulation in convalescent individuals.

Clinical characteristics of 17 patients with COVID-19 and systemic autoimmune diseases: a retrospective study.

OBJECTIVES: Increasing data about COVID-19 have been acquired from the general population. We aim to further evaluate the clinical characteristics of COVID-19 in patients with systemic autoimmune diseases (AIDs). METHODS: We included all confirmed inpatients with COVID-19 and systemic AIDs in Wuhan Tongji Hospital from 29 January to 8 March 2020. We retrospectively collected and analysed information on epidemiology of 1255 inpatients and additional clinical characteristics of patients with systemic AIDs. Outcomes were followed up until 16 April 2020. RESULTS: Of the 1255 patients with COVID-19, the median age was 64.0 years and 53.1% were male. More than half (63.0%) had chronic comorbidities. The proportions of elderly, male and patients with comorbidities were significantly higher in intensive care unit (ICU) than in the general ward (p<0.001). 17 (0.61%) patients with systemic AIDs were further screened and analysed from 2804 inpatients. The median age was 64.0 years and 82.4% were female. All patients were living in Wuhan and two family clusters were found. 1 (5.9%) patient was admitted to ICU and one died. 10 (62.5%) of 16 patients changed or stopped their anti-AIDs treatments during hospitalisation, and 5 of them felt that the disease had worsened after the quarantine. CONCLUSIONS: Older males with chronic comorbidities are more vulnerable to severe COVID-19. The lower proportion of COVID-19 in patients with systemic AIDs needs more high-quality human clinical trials and in-depth mechanism researches. Of note, the withdrawal of anti-AIDs treatments during hospitalisation can lead to flares of diseases.

Clinical characteristics of critically ill patients co-infected with SARS-CoV-2 and the influenza virus in Wuhan, China.

OBJECTIVE: To delineate the clinical characteristics of critically ill COVID-19 patients co-infected with influenza. METHODS: This study included adult patients with laboratory-confirmed COVID-19 form Tongji Hospital (Wuhan, China), with or without influenza, and compared their clinical characteristics. RESULTS: Among 93 patients, 44 died and 49 were discharged. Forty-four (47.3%) were infected with influenza virus A and two (2.2%) with influenza virus B. Twenty-two (50.0%) of the non-survivors and 24 (49.0%) of the survivors were infected with the influenza virus. Critically ill COVID-19 patients with influenza were more prone to cardiac injury than those without influenza. For the laboratory indicators at admission the following were higher in non-survivors with influenza than in those without influenza: white blood cell counts, neutrophil counts, levels of tumor necrosis factor-α, D-dimer value, and proportion of elevated creatinine. CONCLUSION: The results showed that a high proportion of COVID-19 patients were co-infected with influenza in Tongji Hospital, with no significant difference in the proportion of co-infection between survivors and non-survivors. The critically ill COVID-19 patients with influenza exhibited more severe inflammation and organ injury, indicating that co-infection with the influenza virus may induce an earlier and more frequently occurring cytokine storm.