How much urban air quality is affected by local emissions: A unique case study from a megacity in the Pearl River Delta, China

In March 2022, the resurgence of COVID-19 cases in Shenzhen, a megacity in the Pearl River Delta (PRD) region of China, led to unusual restrictions on anthropogenic activities within a single city, in contrast to the restrictions COVID-19 caused on a national scale at the beginning of 2020. In this unique event, we found that only under unfavorable meteorological conditions did substantial urban local emission reductions have an impact on air pollutant changes (−42.4%–6.6%), whereas the deweathered changes were very small (−8.3%–3.4%) under favorable meteorological conditions. Primary anthropogenic pollutants, such as NO2, toluene, BC, and primary organic aerosol (POA), responded most considerably to emission reductions from early morning to noon during unfavorable meteorological days;for secondary organic aerosol (SOA), regulating the daytime total oxidant (Ox = O3 + NO2) was found to be more effective than controlling its precursors within the city scale, whereas secondary nitrate displayed the opposite trend. Since Ox changed little during the urban lockdown despite the remarkable decrease in precursors, it is emphasized that regionally coordinated control of VOCs and NOx is necessary to effectively reduce Ox levels. In addition, Shenzhen's NOx emission reduction efforts should be sustained in order to control PM2.5 and O3 pollution synergistically for long-term attainment.

Evaluation of the Relationship Between Aquaporin-1, Hepcidin, Zinc, Copper, and Iron Levels and Oxidative Stress in the Serum of Critically Ill Patients with COVID-19.

Our study aims to determine the relationship between hepcidin, aquaporin (AQP-1), copper (Cu), zinc (Zn), iron (Fe) levels, and oxidative stress in the sera of seriously ill COVID-19 patients with invasive mechanical ventilation. Ninety persons with and without COVID-19 were taken up and separated into two groups. The first group included seriously COVID-19 inpatients having endotracheal intubation in the intensive care unit (n = 45). The second group included individuals who had negative PCR tests and had no chronic disease (the healthy control group n = 45). AQP-1, hepcidin, Zn, Cu, Fe, total antioxidant status (TAS), and total oxidant status (TOS) were studied in the sera of both groups, and the relations of these levels with oxidative stress were determined. When the COVID-19 patient and the control groups were compared, all studied parameters were found to be statistically significant (p < 0.01). Total oxidant status (TOS), oxidative stress index (OSI), and AQP-1, hepcidin, and Cu levels were increased in patients with COVID-19 compared to healthy people. Serum TAC, Zn, and Fe levels were found to be lower in the patient group than in the control group. Significant correlations were detected between the studied parameters in COVID-19 patients. Results indicated that oxidative stress may play an important role in viral infection due to SARS-CoV-2. We think that oxidative stress parameters as well as some trace elements at the onset of COVID-19 disease will provide a better triage in terms of disease severity.

Evaluation of oxidative stress level: total antioxidant capacity, total oxidant status and glutathione activity in patients with COVID-19.

Coronavirus disease 2019 (COVID-19), as a dangerous global pandemic, has led to high morbidity and mortality in all countries. There is a lot of evidence for the possible role of oxidative stress in COVID-19. In the present study, we aimed to measure the levels of glutathione (GSH), total antioxidant capacity (TAC) and total oxidant status (TOS) in the serum of patients with COVID-19. A total of 96 individuals with and without COVID-19 were enrolled and divided into four groups, including hospitalised group in non-intensive care units (non-ICU) (n = 35), hospitalised group in intensive care units with endotracheal intubation (EI) (ICU with EI) (n = 19), hospitalised group in intensive care units without endotracheal intubation (ICU without EI) (n = 24) and healthy people without COVID-19 disease as our control group (n = 18). The present study revealed that the TOS level was significantly lower in the group of control (p = 0.001), and level of GSH remarkably increased in the patients' groups (p < 0.001). TAC activity in non-ICU group of patients had no significant difference in comparison with the control group. However, in hospitalised patients' groups in the ICU with and without EI this activity was significantly different from the control group (p < 0.001). Moreover, there was a significant relationship between the levels of TOS, GSH and TAC with blood oxygen saturation (SpO2), fever, duration of hospitalisation and the prognosis of this disease (p < 0.001). Area under the curve (CI, 95%) of TOS, TAC and GSH-C to predict death among patients were, respectively, 0.907 (0.841, 0.973), 0.735 (0.626, 0.843) and 0.820 (0.725, 0.914). Receiver operating characteristic curve analysis showed that TOS, TAC and GSH-C have the potential specificity and sensitivity to distinguish between alive and dead patients. We found that elevated levels of oxidative stress and reduction of antioxidant indices can aggravate disease's severity in hospitalised patients with COVID-19. Therefore, it can be suggested to apply antioxidant agents as one of the effective therapeutic strategies in these groups.

Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning.

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10 (particulate matter), O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city's average concentration reductions for the lockdown period were: -36.9 to -41.6%, and -6.6 to -14.2% for NO2 and PM10, respectively. However, an increase of 11.6-33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to -43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.