Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China

Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.

Covid-19 Policy Convergence in Response to Knightian Uncertainty

Domestic policy responses to COVID-19 were remarkably consistent during the early days of the pandemic. What explains this policy convergence? Our formal model suggests that the novel character of COVID-19 produced a period of maximum policy uncertainty, incentivizing political actors to converge on a common set of policies to minimize their exposure to electoral punishment. This convergence is likely to break down as policy feedback produces opinion divergence among experts and the public and as politicians recalculate the costs and benefits of various policy responses and under some conditions facing incentives to adopt extreme policies.

Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants

The implementation of short-term traffic restriction policies (TRPs) during major events positively influences the traffic emission reduction. However, few studies explore the impact of diesel vehicle emissions on air quality during short-term TRP. In particular, the intertwined influences of short-term TRP and Spring Festival remains unclear. Based on Beijing 2022 Olympic Games, this study analyzed the spatiotemporal changes in urban air quality and diesel vehicle emission during short-term TRP. The results showed that the TRPs and Spring Festival contributed equally to the improvement of air quality and reduction of diesel vehicle emissions. The "interruption-recovery" pattern of short-term TRPs is characterized by a longer duration and a slower decline/recovery rate. Additionally, the individual contribution rate of diesel vehicle emissions to urban air pollutants was 15-20 % higher than that of meteorological factors during short-term TRPs.

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Anthropogenic sources of fine particulate matter (PM_2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM_2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM_2.5 concentration in Shanghai. The PM_2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM_2.5 concentration in Shanghai. The PM_2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM_2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM_2.5 concentration predicting models were the PM_2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM_2.5 in the next day and the input influence factors, the PM_2.5 showed the closest relation with the PM_2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM_2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM_2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM_2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 μg/m³ increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO₂ concentration decreased by approximately 50%, while O₃ concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO₂ concentration decreased exponentially, whereas O₃ concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02351-7.

Temporal characteristics and spatial heterogeneity of air quality changes due to the COVID-19 lockdown in China

Previous studies have evaluated the impact of lockdown measures on air quality during the COVID-19 pandemic in China, but few have focused on the temporal characteristics and spatial heterogeneity of the impact across all 337 prefecture cities. In this study, we estimated the impact of the lockdown measures on air quality in each of 337 cities using the Regression Discontinuity in Time method. There was a short-term influence from January 24th to March 31th in 2020. The 337 cities could be divided into six categories showing different response and resilience patterns to the epidemic. Fine particulate matter (PM_2.5) in 89.5% of the cities was sensitive to the lockdown measures. The change of air pollutants showed high spatial heterogeneity. The provinces with a greater than 20% reduction in PM_2.5 and PM₁₀ and greater than 40% reduction in NO₂ during the impact period were mainly concentrated southeast of the "Hu Line". Compared to the no-pandemic scenario, the national annual average concentration of PM_2.5, NO₂, PM₁₀, SO₂, and CO in 2020 were decreased by 6.3%, 10.6%, 7.4%, 9.0%, and 12.5%, respectively, while that of O₃ increased by 1.1%.This result indicates that 2020 can still be used as a baseline for setting and allocating air improvement targets for the next five years.

The Impact of COVID-19 Strict Lockdown on the Air Quality of Smart Cities of Rajasthan, India

Aim:

The main focus of this study is to evaluate the air quality by comparing the concentration of particulate matter PM2.5, PM10, NO2, CO, SO2, and ozone of smart cities of Rajasthan before the lockdown and during the period of lockdown.

Background:

In India, the first case of the COVID-19 was reported on January 30th, 2020. Indian government declared strict lockdown, i.e., public health emergency in India on March 24th, 2020, which is implemented from March 25th, 2020, to April 14th, 2020, for 21 days.

Objective:

The objective of this study is to evaluate the air quality by comparing the levels of all parameters of air pollution during the COVID-19 lockdown period with values registered in the pre-lockdown period.

Methods:

Data were obtained from four automatic monitoring stations under the control of the Central Pollution Control Board, New Delhi (https://www.cpcb.nic.in/). Data regarding all the parameters were recorded as 24 hours average period.

Results:

CO levels showed the highest significant reduction in Udaipur (50.76%) followed by Jaipur (19.96%), Ajmer (17.11%), and Kota (5.51%) due to the ban on transport and driving. The levels of PM2.5, PM10, NO2, and SO2 were also decreased substantially for each smart city. Ozone concentrations were recorded greater than before due to decreased nitrogen oxides levels.

Conclusion:

This study can be useful considering our present role in environmental restoration or environmental destruction. It will also be helpful in updating our present plan toward the assurance and conservation of nature.

Airflow deflectors of external windowsto induce ventilation: Towards COVID-19 prevention and control

Since the Corona Virus Disease 2019 (COVID-19) outbreak, the normalization of the epidemic has posed great challenge to epidemic prevention and control in indoor environment. Ventilation systems are commonly used to prevent and control indoor transmission of disease. However, most naturally ventilated rooms are not efficient to prevent the spread of virus, i.e., classrooms. The goal of this work is to effectively adopt forced interference strategies (e.g., airflow deflector) applied to external windows to strengthen airflow diffusion performance (ADP) of natural ventilation. So far, no systematic study has been done to investigate the effectiveness of such airflow deflectors on its influence on natural ventilation and effectiveness of preventing the disease transmission in indoor environment. In this work, a case study was conducted based on cross-ventilated classrooms. Different settings of airflow deflectors (i.e., size and installation angle) were applied to the external windows. Air Diffusion Performance Index (ADPI) was utilized to evaluated airflow diffusion performance under different settings of the airflow deflectors. Then, the Wells-Riley model was applied to evaluate infection risk. According to the results, the infection risk can be reduced by 19.29% when infection source is located at the center of classroom and 17.47% when source is located near the side walls. This work would provide guidance for the design of classrooms ventilated with induced natural wind for epidemic prevention and control.