ABSTRACT
Quantification of emission changes is a prerequisite for the assessment of control effectiveness in improving air quality. However, the traditional bottom-up method for characterizing emissions requires detailed investigation of emissions data (e.g., activity and other emission parameters) that usually takes months to perform and limits timely assessments. Here we propose a novel method to address this issue by using a response model that provides real-time estimation of emission changes based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. We applied the new method to quantify the emission changes on the North China Plain (NCP) due to the COVID-19 pandemic shutdown, which overlapped the Spring Festival (also known as Chinese New Year) holiday. Results suggest that the anthropogenic emissions of NO2,SO2, volatile organic compound (VOC) and primary PM2.5 on the NCP were reduced by 51 %, 28 %, 67 % and 63 %, respectively, due to the COVID-19 shutdown, indicating longer and stronger shutdown effects in 2020 compared to the previous Spring Festival holiday. The reductions of VOC and primary PM2.5 emissions are generally effective in reducing O3 and PM2.5 concentrations. However, such air quality improvements are largely offset by reductions inNOx emissions. NOx emission reductions lead to increases inO3 and PM2.5 concentrations on the NCP due to the strongly VOC-limited conditions in winter. A strong NH3-rich condition is also suggested from the air quality response to the substantial NOx emission reduction. Well-designed control strategies are recommended based on the air quality response associated with the unexpected emission changes during the COVID-19 period. In addition, our results demonstrate that the new response-based inversion model can well capture emission changes based on variations in ambient concentrations and thereby illustrate the great potential for improving the accuracy and efficiency of bottom-up emission inventory methods.
ABSTRACT
Background and aims: Calcium channel blockers (CCBs) and angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) are commonly used in the treatment of hypertension. However, it is still not clear whether there are differences among different anti-hypertensive drugs in the treatment of patients with coronavirus disease 2019 (COVID-19) and hypertension. Herein, we aimed to assess the relation between different anti-hypertensive medications and COVID-19 outcomes. Materials and methods: We conducted a retrospective analysis of 58 hypertensive patients with COVID-19 who were treated with different anti-hypertensive drugs and reviewed the clinical data obtained from electronic medical records, including epidemiological, clinical, laboratory, and the treatment and progression of the disease. Results: There was no obvious difference in clinical prognosis after using any anti-hypertensive drugs in patients with COVID-19 and hypertension, but the different anti-hypertensive drugs were associated with the use of non-invasive ventilator treatment at admission comparing two groups between ACEIs/ARBs and CCBs+ACEIs/ARBs. Conclusion: there is no evidence showing that the different use of anti-hypertensive drugs is related to outcomes of patients with COVID-19 and hypertension, even between single drug regimen and combined therapy (with at least two anti-hypertensive drugs as combined therapy).